Feasibility of Stochastic Voltage/VAr Optimization Considering Renewable Energy Resources for Smart Grid

Science.gov (United States)

Momoh, James A.; Salkuti, Surender Reddy

2016-06-01

This paper proposes a stochastic optimization technique for solving the Voltage/VAr control problem including the load demand and Renewable Energy Resources (RERs) variation. The RERs often take along some inputs like stochastic behavior. One of the important challenges i. e., Voltage/VAr control is a prime source for handling power system complexity and reliability, hence it is the fundamental requirement for all the utility companies. There is a need for the robust and efficient Voltage/VAr optimization technique to meet the peak demand and reduction of system losses. The voltages beyond the limit may damage costly sub-station devices and equipments at consumer end as well. Especially, the RERs introduces more disturbances and some of the RERs are not even capable enough to meet the VAr demand. Therefore, there is a strong need for the Voltage/VAr control in RERs environment. This paper aims at the development of optimal scheme for Voltage/VAr control involving RERs. In this paper, Latin Hypercube Sampling (LHS) method is used to cover full range of variables by maximally satisfying the marginal distribution. Here, backward scenario reduction technique is used to reduce the number of scenarios effectively and maximally retain the fitting accuracy of samples. The developed optimization scheme is tested on IEEE 24 bus Reliability Test System (RTS) considering the load demand and RERs variation.

Developing a simulation framework for safe and optimal trajectories considering drivers’ driving style

DEFF Research Database (Denmark)

Gruber, Thierry; Larue, Grégoire S.; Rakotonirainy, Andry

2017-01-01

drivers with the optimal trajectory considering the motorist's driving style in real time. Travel duration and safety are the main parameters used to find the optimal trajectory. A simulation framework to determine the optimal trajectory was developed in which the ego car travels in a highway environment......Advanced driving assistance systems (ADAS) have huge potential for improving road safety and travel times. However, their take-up in the market is very slow; and these systems should consider driver's preferences to increase adoption rates. The aim of this study is to develop a model providing...

Co-occurrence of viruses and mosquitoes at the vectors' optimal climate range: An underestimated risk to temperate regions?

Science.gov (United States)

Blagrove, Marcus S C; Caminade, Cyril; Waldmann, Elisabeth; Sutton, Elizabeth R; Wardeh, Maya; Baylis, Matthew

2017-06-01

Mosquito-borne viruses have been estimated to cause over 100 million cases of human disease annually. Many methodologies have been developed to help identify areas most at risk from transmission of these viruses. However, generally, these methodologies focus predominantly on the effects of climate on either the vectors or the pathogens they spread, and do not consider the dynamic interaction between the optimal conditions for both vector and virus. Here, we use a new approach that considers the complex interplay between the optimal temperature for virus transmission, and the optimal climate for the mosquito vectors. Using published geolocated data we identified temperature and rainfall ranges in which a number of mosquito vectors have been observed to co-occur with West Nile virus, dengue virus or chikungunya virus. We then investigated whether the optimal climate for co-occurrence of vector and virus varies between "warmer" and "cooler" adapted vectors for the same virus. We found that different mosquito vectors co-occur with the same virus at different temperatures, despite significant overlap in vector temperature ranges. Specifically, we found that co-occurrence correlates with the optimal climatic conditions for the respective vector; cooler-adapted mosquitoes tend to co-occur with the same virus in cooler conditions than their warmer-adapted counterparts. We conclude that mosquitoes appear to be most able to transmit virus in the mosquitoes' optimal climate range, and hypothesise that this may be due to proportionally over-extended vector longevity, and other increased fitness attributes, within this optimal range. These results suggest that the threat posed by vector-competent mosquito species indigenous to temperate regions may have been underestimated, whilst the threat arising from invasive tropical vectors moving to cooler temperate regions may be overestimated.

Optimal hydro scheduling and offering strategies considering price uncertainty and risk management

International Nuclear Information System (INIS)

Catalão, J.P.S.; Pousinho, H.M.I.; Contreras, J.

2012-01-01

Hydro energy represents a priority in the energy policy of Portugal, with the aim of decreasing the dependence on fossil fuels. In this context, optimal hydro scheduling acquires added significance in moving towards a sustainable environment. A mixed-integer nonlinear programming approach is considered to enable optimal hydro scheduling for the short-term time horizon, including the effect of head on power production, start-up costs related to the units, multiple regions of operation, and constraints on discharge variation. As new contributions to the field, market uncertainty is introduced in the model via price scenarios and risk management is included using Conditional Value-at-Risk to limit profit volatility. Moreover, plant scheduling and pool offering by the hydro power producer are simultaneously considered to solve a realistic cascaded hydro system. -- Highlights: ► A mixed-integer nonlinear programming approach is considered for optimal hydro scheduling. ► Market uncertainty is introduced in the model via price scenarios. ► Risk management is included using conditional value-at-risk. ► Plant scheduling and pool offering by the hydro power producer are simultaneously considered. ► A realistic cascaded hydro system is solved.

An extended continuum model considering optimal velocity change with memory and numerical tests

Science.gov (United States)

Qingtao, Zhai; Hongxia, Ge; Rongjun, Cheng

2018-01-01

In this paper, an extended continuum model of traffic flow is proposed with the consideration of optimal velocity changes with memory. The new model's stability condition and KdV-Burgers equation considering the optimal velocities change with memory are deduced through linear stability theory and nonlinear analysis, respectively. Numerical simulation is carried out to study the extended continuum model, which explores how optimal velocity changes with memory affected velocity, density and energy consumption. Numerical results show that when considering the effects of optimal velocity changes with memory, the traffic jams can be suppressed efficiently. Both the memory step and sensitivity parameters of optimal velocity changes with memory will enhance the stability of traffic flow efficiently. Furthermore, numerical results demonstrates that the effect of optimal velocity changes with memory can avoid the disadvantage of historical information, which increases the stability of traffic flow on road, and so it improve the traffic flow stability and minimize cars' energy consumptions.

Multi-Objective Distribution Network Operation Based on Distributed Generation Optimal Placement Using New Antlion Optimizer Considering Reliability

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KHANBABAZADEH Javad

2016-10-01

Full Text Available Distribution network designers and operators are trying to deliver electrical energy with high reliability and quality to their subscribers. Due to high losses in the distribution systems, using distributed generation can improves reliability, reduces losses and improves voltage profile of distribution network. Therefore, the choice of the location of these resources and also determining the amount of their generated power to maximize the benefits of this type of resource is an important issue which is discussed from different points of view today. In this paper, a new multi-objective optimal location and sizing of distributed generation resources is performed to maximize its benefits on the 33 bus distribution test network considering reliability and using a new Antlion Optimizer (ALO. The benefits for DG are considered as system losses reduction, system reliability improvement and benefits from the sale electricity and voltage profile improvement. For each of the mentioned benefits, the ALO algorithm is used to optimize the location and sizing of distributed generation resources. In order to verify the proposed approach, the obtained results have been analyzed and compared with the results of particle swarm optimization (PSO algorithm. The results show that the ALO has shown better performance in optimization problem solution versus PSO.

An Improved Optimal Slip Ratio Prediction considering Tyre Inflation Pressure Changes

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Guoxing Li

2015-01-01

Full Text Available The prediction of optimal slip ratio is crucial to vehicle control systems. Many studies have verified there is a definitive impact of tyre pressure change on the optimal slip ratio. However, the existing method of optimal slip ratio prediction has not taken into account the influence of tyre pressure changes. By introducing a second-order factor, an improved optimal slip ratio prediction considering tyre inflation pressure is proposed in this paper. In order to verify and evaluate the performance of the improved prediction, a cosimulation platform is developed by using MATLAB/Simulink and CarSim software packages, achieving a comprehensive simulation study of vehicle braking performance cooperated with an ABS controller. The simulation results show that the braking distances and braking time under different tyre pressures and initial braking speeds are effectively shortened with the improved prediction of optimal slip ratio. When the tyre pressure is slightly lower than the nominal pressure, the difference of braking performances between original optimal slip ratio and improved optimal slip ratio is the most obvious.

Collective neurodynamic optimization for economic emission dispatch problem considering valve point effect in microgrid.

Science.gov (United States)

Wang, Tiancai; He, Xing; Huang, Tingwen; Li, Chuandong; Zhang, Wei

2017-09-01

The economic emission dispatch (EED) problem aims to control generation cost and reduce the impact of waste gas on the environment. It has multiple constraints and nonconvex objectives. To solve it, the collective neurodynamic optimization (CNO) method, which combines heuristic approach and projection neural network (PNN), is attempted to optimize scheduling of an electrical microgrid with ten thermal generators and minimize the plus of generation and emission cost. As the objective function has non-derivative points considering valve point effect (VPE), differential inclusion approach is employed in the PNN model introduced to deal with them. Under certain conditions, the local optimality and convergence of the dynamic model for the optimization problem is analyzed. The capability of the algorithm is verified in a complicated situation, where transmission loss and prohibited operating zones are considered. In addition, the dynamic variation of load power at demand side is considered and the optimal scheduling of generators within 24 h is described. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mechanical properties of two-way grid shells optimized considering roundness and elastic stiffness

International Nuclear Information System (INIS)

Ogawa, Toshiyuki; Yuta, Nishikawa; Rie, Tateishi; Ohsaki, Makoto

2002-01-01

A single-layer two-way grid shell defined by Bezier surface is optimized by coordinates of the control points as design variables. The purpose of this paper is to find optimal shapes considering roundness and elastic stiffness, and to investigate their mechanical properties. The distance of the center of curvature from the specified point is used for formulating the objective function for generating a round shape. Consider next a problem of minimizing the compliance as mechanical performance measure. The compliance is defined by the external work against the static loads applied to the nodes. The mechanically optimal shape is different from the round shape. Therefore, the multi objective optimization problem is formulated for optimizing the two objectives, which are roundness and the elastic stiffness defined by using the compliance. The constraint method is used for obtaining Pareto optimal solutions between the two objectives. We optimize single-layer two-way grid shells with square and rectangle plans. Mechanical properties of the optimal shapes are investigated by compliance and the distributions of axial force and bending moment. The round shape is significantly dominated by the bending moment and its compliance is large. The bending moment of the mechanically optimal shape is not very large, and the latticed shell has large stiffness through axial deformation. A trade-off shape is round enough, and the influence of the bending moment is smaller than that of the optimal round shape and the elastic stiffness is moderately large

Optimizing Operation Indices Considering Different Types of Distributed Generation in Microgrid Applications

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Niloofar Ghanbari

2018-04-01

Full Text Available The need for independent power generation has increased in recent years, especially with the growing demand in microgrid systems. In a microgrid with several generations of different types and with all kinds of loads of variable nature, an optimal power balance in the system has to be achieved. This optimal objective, which results in minimal energy losses over a specific period of time, requires an optimal location and sizing of the distributed generations (DGs in a microgrid. This paper proposes a new optimization method in which both optimal location of the DGs and their generation profile according to the load demand profile as well as the type of DG are determined during the life time of the DGs. The types of DGs that are considered in this paper are diesel generators and wind turbine. The method is based on simultaneously minimizing the cost of the investment and operation of the DGs, the cost of power delivered by the the external grid as well as the cost of power losses in the network. The proposed method is tested on the IEEE standard radial distribution network considering time-varying loads and the wind speed every hour of a day.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

Science.gov (United States)

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

Criteria for optimizing cortical hierarchies with continuous ranges

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Antje Krumnack

2010-03-01

Full Text Available In a recent paper (Reid et al.; 2009, NeuroImage we introduced a method to calculate optimal hierarchies in the visual network that utilizes continuous, rather than discrete, hierarchical levels, and permits a range of acceptable values rather than attempting to fit fixed hierarchical distances. There, to obtain a hierarchy, the sum of deviations from the constraints that define the hierarchy was minimized using linear optimization. In the short time since publication of that paper we noticed that many colleagues misinterpreted the meaning of the term optimal hierarchy. In particular, a majority of them were under the impression that there was perhaps only one optimal hierarchy, but a substantial difficulty in finding that one. However, there is not only more than one optimal hierarchy but also more than one option for defining optimality. Continuing the line of this work we look at additional options for optimizing the visual hierarchy: minimizing the number of violated constraints and minimizing the maximal size of a constraint violation using linear optimization and mixed integer programming. The implementation of both optimization criteria is explained in detail. In addition, using constraint sets based on the data from Felleman and Van Essen, optimal hierarchies for the visual network are calculated for both optimization methods.

Performance Optimization of Irreversible Air Heat Pumps Considering Size Effect

Science.gov (United States)

Bi, Yuehong; Chen, Lingen; Ding, Zemin; Sun, Fengrui

2018-06-01

Considering the size of an irreversible air heat pump (AHP), heating load density (HLD) is taken as thermodynamic optimization objective by using finite-time thermodynamics. Based on an irreversible AHP with infinite reservoir thermal-capacitance rate model, the expression of HLD of AHP is put forward. The HLD optimization processes are studied analytically and numerically, which consist of two aspects: (1) to choose pressure ratio; (2) to distribute heat-exchanger inventory. Heat reservoir temperatures, heat transfer performance of heat exchangers as well as irreversibility during compression and expansion processes are important factors influencing on the performance of an irreversible AHP, which are characterized with temperature ratio, heat exchanger inventory as well as isentropic efficiencies, respectively. Those impacts of parameters on the maximum HLD are thoroughly studied. The research results show that HLD optimization can make the size of the AHP system smaller and improve the compactness of system.

Reactive power dispatch considering voltage stability with seeker optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Dai, Chaohua; Chen, Weirong; Zhang, Xuexia [The School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031 (China); Zhu, Yunfang [Department of Computer and Communication Engineering, E' mei Campus, Southwest Jiaotong University, E' mei 614202 (China)

2009-10-15

Optimal reactive power dispatch (ORPD) has a growing impact on secure and economical operation of power systems. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In the last decades, computation intelligence-based techniques such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA) based method is proposed for ORPD considering static voltage stability and voltage deviation. The SOA is based on the concept of simulating the act of human searching where search direction is based on the empirical gradient by evaluating the response to the position changes and step length is based on uncertainty reasoning by using a simple Fuzzy rule. The algorithm's performance is studied with comparisons of two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms on the IEEE 57 and 118-bus power systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the ORPD problem. (author)

CALCULATION OF INITIALS OPTIMAL PRODUCTION CAPACITIES CONSIDERING UNCERTAINTY ELEMENTS

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Hilda Oquendo Ferrer

2016-04-01

Full Text Available In diversification, an attractive variant constitutes the projection of ethanol plants due to all the advantages that this represents and a crucial element for this to be effective is the existence of cane as a fundamental raw material for the sugar industry and therefore the derived productions. To project the initials optimal capacity of the plant, uncertainty in the raw material was considered. Mathematical models of capacity in time are obtained, choosing those that best fit, being the linear the simplest for future calculations. The initial capacity the plant should have is determined, also the time at which the first extension and the capacity of the plant should be done, which allows, considering other criteria, to make decisions about what should be the capacity of an ethanol plant in response to the current and future availability of sugar cane. It is presented a general method that can be used considering other tax sugar companies in a province or a region.

Behaviour - The keystone in optimizing free-ranging ungulate production

Science.gov (United States)

Free-ranging animal behaviour is a keystone to optimizing free-ranging domestic animal production. This chapter focuses on several aspects that emanate from foraging including defining terms, concepts and the complexity that underlie managing animals and landscapes. Behaviour is investigated in li...

Optimal Coordination of Directional Overcurrent Relays Using PSO-TVAC Considering Series Compensation

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Nabil Mancer

2015-01-01

Full Text Available The integration of system compensation such as Series Compensator (SC into the transmission line makes the coordination of directional overcurrent in a practical power system important and complex. This article presents an efficient variant of Particle Swarm Optimization (PSO algorithm based on Time-Varying Acceleration Coefficients (PSO-TVAC for optimal coordination of directional overcurrent relays (DOCRs considering the integration of series compensation. Simulation results are compared to other methods to confirm the efficiency of the proposed variant PSO in solving the optimal coordination of directional overcurrent relay in the presence of series compensation.

Joint optimization of LORA and spares stocks considering corrective maintenance time

Institute of Scientific and Technical Information of China (English)

Linhan Guo; Jiujiu Fan; Meilin Wen; Rui Kang

2015-01-01

Level of repair analysis (LORA) is an important method of maintenance decision for establishing systems of operation and maintenance in the equipment development period. Currently, the research on equipment of repair level focuses on economic analy-sis models which are used to optimize costs and rarely considers the maintenance time required by the implementation of the main-tenance program. In fact, as to the system requiring high mission complete success, the maintenance time is an important factor which has a great influence on the availability of equipment sys-tems. Considering the relationship between the maintenance time and the spares stocks level, it is obvious that there are contra-dictions between the maintenance time and the cost. In order to balance these two factors, it is necessary to build an optimization LORA model. To this end, the maintenance time representing per-formance characteristic is introduced, and on the basis of spares stocks which is traditional y regarded as a decision variable, a de-cision variable of repair level is added, and a multi-echelon multi-indenture (MEMI) optimization LORA model is built which takes the best cost-effectiveness ratio as the criterion, the expected num-ber of backorder (EBO) as the objective function and the cost as the constraint. Besides, the paper designs a convex programming algorithm of multi-variable for the optimization model, provides solutions to the non-convex objective function and methods for improving the efficiency of the algorithm. The method provided in this paper is proved to be credible and effective according to the numerical example and the simulation result.

Empirical study of long-range connections in a road network offers new ingredient for navigation optimization models

Science.gov (United States)

Wang, Pu; Liu, Like; Li, Xiamiao; Li, Guanliang; González, Marta C.

2014-01-01

Navigation problem in lattices with long-range connections has been widely studied to understand the design principles for optimal transport networks; however, the travel cost of long-range connections was not considered in previous models. We define long-range connection in a road network as the shortest path between a pair of nodes through highways and empirically analyze the travel cost properties of long-range connections. Based on the maximum speed allowed in each road segment, we observe that the time needed to travel through a long-range connection has a characteristic time Th ˜ 29 min, while the time required when using the alternative arterial road path has two different characteristic times Ta ˜ 13 and 41 min and follows a power law for times larger than 50 min. Using daily commuting origin-destination matrix data, we additionally find that the use of long-range connections helps people to save about half of the travel time in their daily commute. Based on the empirical results, we assign a more realistic travel cost to long-range connections in two-dimensional square lattices, observing dramatically different minimum average shortest path but similar optimal navigation conditions.

Thermo-economic optimization of heat recovery steam generator for a range of gas turbine exhaust temperatures

International Nuclear Information System (INIS)

Nadir, Mahmoud; Ghenaiet, Adel; Carcasci, Carlo

2016-01-01

Highlights: • Thermo-economic optimization of HRSG configurations. • The maximum value of the net present value was targeted for the economic optimization. • Three level HRSG is the best option in respect of power output and high priced medium. • Two level HRSG is the best for net benefit in low and intermediate priced mediums. - Abstract: This paper illustrates the effect of selling price on the optimum design parameters of a heat recovery steam generator (HRSG) and the selection of its ideal configuration for an outlet temperature range of 350–650 °C. The Particle Swarm Optimization (PSO) method was used, considering the steam cycle specific work as an objective to be maximized, the net present value as another objective to be maximized for the economic optimization and a combination of both. Three configurations of heat recovery steam generators are considered with one, two and three pressure levels and a reheat. The results show that, the three pressure level system is the best configuration from a thermodynamic point of view, but with respect to the economical aspect the two pressure levels is the best configuration for the low and medium selling prices (0.04 $/kW h, 0.08 $/kW h and 0.2 $/kW h), whereas the three pressure level configuration would only be interesting for a high selling price of 0.3 $/kW h and a temperature range 450–600 °C. For a temperature of 650 °C, the high cost of the three level system leads to a decrease in the net present value. As the selling price increases the optimized design parameters of the three pressure level HRSG based on economic or thermodynamic optimization are similar. The obtained results are used to elaborate a new correlation relating the net present value with the gas turbine outlet temperature, gas mass flow rate, number of levels of HRSG and selling price.

The Optimal Configuration Scheme of the Virtual Power Plant Considering Benefits and Risks of Investors

Directory of Open Access Journals (Sweden)

Jingmin Wang

2017-07-01

Full Text Available A virtual power plant (VPP is a special virtual unit that integrates various distributed energy resources (DERs distributed in the generation and consumption sides. The optimal configuration scheme of the VPP needs to break the geographical restrictions to make full use of DERs, considering the uncertainties. First, the components of the DERs and the structure of the VPP are briefly introduced. Next, the cubic exponential smoothing method is adopted to predict the VPP load requirement. Finally, the optimal configuration of the DER capacities inside the VPP is calculated by using portfolio theory and genetic algorithms (GA. The results show that the configuration scheme can optimize the DER capacities considering uncertainties, guaranteeing economic benefits of investors, and fully utilizing the DERs. Therefore, this paper provides a feasible reference for the optimal configuration scheme of the VPP from the perspective of investors.

Empirical study of long-range connections in a road network offers new ingredient for navigation optimization models

International Nuclear Information System (INIS)

Wang, Pu; Liu, Like; Li, Xiamiao; Li, Guanliang; González, Marta C

2014-01-01

Navigation problem in lattices with long-range connections has been widely studied to understand the design principles for optimal transport networks; however, the travel cost of long-range connections was not considered in previous models. We define long-range connection in a road network as the shortest path between a pair of nodes through highways and empirically analyze the travel cost properties of long-range connections. Based on the maximum speed allowed in each road segment, we observe that the time needed to travel through a long-range connection has a characteristic time T h  ∼ 29 min, while the time required when using the alternative arterial road path has two different characteristic times T a  ∼ 13 and 41 min and follows a power law for times larger than 50 min. Using daily commuting origin–destination matrix data, we additionally find that the use of long-range connections helps people to save about half of the travel time in their daily commute. Based on the empirical results, we assign a more realistic travel cost to long-range connections in two-dimensional square lattices, observing dramatically different minimum average shortest path 〈l〉 but similar optimal navigation conditions. (paper)

Single and multiple objective biomass-to-biofuel supply chain optimization considering environmental impacts

Science.gov (United States)

Valles Sosa, Claudia Evangelina

Bioenergy has become an important alternative source of energy to alleviate the reliance on petroleum energy. Bioenergy offers diminishing climate change by reducing Green House Gas Emissions, as well as providing energy security and enhancing rural development. The Energy Independence and Security Act mandate the use of 21 billion gallons of advanced biofuels including 16 billion gallons of cellulosic biofuels by the year 2022. It is clear that Biomass can make a substantial contribution to supply future energy demand in a sustainable way. However, the supply of sustainable energy is one of the main challenges that mankind will face over the coming decades. For instance, many logistical challenges will be faced in order to provide an efficient and reliable supply of quality feedstock to biorefineries. 700 million tons of biomass will be required to be sustainably delivered to biorefineries annually to meet the projected use of biofuels by the year of 2022. Approaching this complex logistic problem as a multi-commodity network flow structure, the present work proposes the use of a genetic algorithm as a single objective optimization problem that considers the maximization of profit and the present work also proposes the use of a Multiple Objective Evolutionary Algorithm to simultaneously maximize profit while minimizing global warming potential. Most transportation optimization problems available in the literature have mostly considered the maximization of profit or the minimization of total travel time as potential objectives to be optimized. However, on this research work, we take a more conscious and sustainable approach for this logistic problem. Planners are increasingly expected to adopt a multi-disciplinary approach, especially due to the rising importance of environmental stewardship. The role of a transportation planner and designer is shifting from simple economic analysis to promoting sustainability through the integration of environmental objectives. To

Multi-region optimal deployment of renewable energy considering different interregional transmission scenarios

International Nuclear Information System (INIS)

Wang, Ge; Zhang, Qi; Mclellan, Benjamin C.; Li, Hailong

2016-01-01

Renewable energy is expected to play much more important role in future low-carbon energy system, however, renewable energy has problems with regard to load-following and regional imbalance. This study aims to plan the deployment of intermittent renewable energy in multiple regions considering the impacts of regional natural conditions and generation capacity mix as well as interregional transmission capacity using a multi-region dynamic optimization model. The model was developed to find optimized development paths toward future smart electricity systems with high level penetration of intermittent renewable energy considering regional differences and interregional transmission at national scale. As a case study, the model was applied to plan power generation in nine interconnected regions in Japan out to 2030. Four scenarios were proposed with different supporting policies for the interregional power transmission infrastructures and different nuclear power phase-out scenarios. The analysis results show that (i) the government's support for power transmission infrastructures is vital important to develop more intermittent renewable energy in appropriate regions and utilize renewable energy more efficiently; (ii) nuclear and renewable can complement rather than replace each other if enough interregional transmission capacity is provided. - Highlights: • Plan the optimal deployment of intermittent renewable energy in multiple regions. • A multi-region dynamic optimization model was developed. • The impacts of natural conditions and interregional transmission are studied. • The government's support for transmission is vital important for renewable energy. • Nuclear and renewable can complement rather than replace each other.

On process optimization considering LCA methodology.

Science.gov (United States)

Pieragostini, Carla; Mussati, Miguel C; Aguirre, Pío

2012-04-15

The goal of this work is to research the state-of-the-art in process optimization techniques and tools based on LCA, focused in the process engineering field. A collection of methods, approaches, applications, specific software packages, and insights regarding experiences and progress made in applying the LCA methodology coupled to optimization frameworks is provided, and general trends are identified. The "cradle-to-gate" concept to define the system boundaries is the most used approach in practice, instead of the "cradle-to-grave" approach. Normally, the relationship between inventory data and impact category indicators is linearly expressed by the characterization factors; then, synergic effects of the contaminants are neglected. Among the LCIA methods, the eco-indicator 99, which is based on the endpoint category and the panel method, is the most used in practice. A single environmental impact function, resulting from the aggregation of environmental impacts, is formulated as the environmental objective in most analyzed cases. SimaPro is the most used software for LCA applications in literature analyzed. The multi-objective optimization is the most used approach for dealing with this kind of problems, where the ε-constraint method for generating the Pareto set is the most applied technique. However, a renewed interest in formulating a single economic objective function in optimization frameworks can be observed, favored by the development of life cycle cost software and progress made in assessing costs of environmental externalities. Finally, a trend to deal with multi-period scenarios into integrated LCA-optimization frameworks can be distinguished providing more accurate results upon data availability. Copyright © 2011 Elsevier Ltd. All rights reserved.

An Optimization Method for Condition Based Maintenance of Aircraft Fleet Considering Prognostics Uncertainty

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Qiang Feng

2014-01-01

Full Text Available An optimization method for condition based maintenance (CBM of aircraft fleet considering prognostics uncertainty is proposed. The CBM and dispatch process of aircraft fleet is analyzed first, and the alternative strategy sets for single aircraft are given. Then, the optimization problem of fleet CBM with lower maintenance cost and dispatch risk is translated to the combinatorial optimization problem of single aircraft strategy. Remain useful life (RUL distribution of the key line replaceable Module (LRM has been transformed into the failure probability of the aircraft and the fleet health status matrix is established. And the calculation method of the costs and risks for mission based on health status matrix and maintenance matrix is given. Further, an optimization method for fleet dispatch and CBM under acceptable risk is proposed based on an improved genetic algorithm. Finally, a fleet of 10 aircrafts is studied to verify the proposed method. The results shows that it could realize optimization and control of the aircraft fleet oriented to mission success.

An Optimized Control for LLC Resonant Converter with Wide Load Range

Science.gov (United States)

Xi, Xia; Qian, Qinsong

2017-05-01

This paper presents an optimized control which makes LLC resonant converters operate with a wider load range and provides good closed-loop performance. The proposed control employs two paralleled digital compensations to guarantee the good closed-loop performance in a wide load range during the steady state, an optimized trajectory control will take over to change the gate-driving signals immediately at the load transients. Finally, the proposed control has been implemented and tested on a 150W 200kHz 400V/24V LLC resonant converter and the result validates the proposed method.

Selection of magnetorheological brake types via optimal design considering maximum torque and constrained volume

International Nuclear Information System (INIS)

Nguyen, Q H; Choi, S B

2012-01-01

This research focuses on optimal design of different types of magnetorheological brakes (MRBs), from which an optimal selection of MRB types is identified. In the optimization, common types of MRB such as disc-type, drum-type, hybrid-types, and T-shaped type are considered. The optimization problem is to find the optimal value of significant geometric dimensions of the MRB that can produce a maximum braking torque. The MRB is constrained in a cylindrical volume of a specific radius and length. After a brief description of the configuration of MRB types, the braking torques of the MRBs are derived based on the Herschel–Bulkley model of the MR fluid. The optimal design of MRBs constrained in a specific cylindrical volume is then analysed. The objective of the optimization is to maximize the braking torque while the torque ratio (the ratio of maximum braking torque and the zero-field friction torque) is constrained to be greater than a certain value. A finite element analysis integrated with an optimization tool is employed to obtain optimal solutions of the MRBs. Optimal solutions of MRBs constrained in different volumes are obtained based on the proposed optimization procedure. From the results, discussions on the optimal selection of MRB types depending on constrained volumes are given. (paper)

Optimal Sequential Diagnostic Strategy Generation Considering Test Placement Cost for Multimode Systems

Directory of Open Access Journals (Sweden)

Shigang Zhang

2015-10-01

Full Text Available Sequential fault diagnosis is an approach that realizes fault isolation by executing the optimal test step by step. The strategy used, i.e., the sequential diagnostic strategy, has great influence on diagnostic accuracy and cost. Optimal sequential diagnostic strategy generation is an important step in the process of diagnosis system construction, which has been studied extensively in the literature. However, previous algorithms either are designed for single mode systems or do not consider test placement cost. They are not suitable to solve the sequential diagnostic strategy generation problem considering test placement cost for multimode systems. Therefore, this problem is studied in this paper. A formulation is presented. Two algorithms are proposed, one of which is realized by system transformation and the other is newly designed. Extensive simulations are carried out to test the effectiveness of the algorithms. A real-world system is also presented. All the results show that both of them have the ability to solve the diagnostic strategy generation problem, and they have different characteristics.

Optimal Sequential Diagnostic Strategy Generation Considering Test Placement Cost for Multimode Systems

Science.gov (United States)

Zhang, Shigang; Song, Lijun; Zhang, Wei; Hu, Zheng; Yang, Yongmin

2015-01-01

Sequential fault diagnosis is an approach that realizes fault isolation by executing the optimal test step by step. The strategy used, i.e., the sequential diagnostic strategy, has great influence on diagnostic accuracy and cost. Optimal sequential diagnostic strategy generation is an important step in the process of diagnosis system construction, which has been studied extensively in the literature. However, previous algorithms either are designed for single mode systems or do not consider test placement cost. They are not suitable to solve the sequential diagnostic strategy generation problem considering test placement cost for multimode systems. Therefore, this problem is studied in this paper. A formulation is presented. Two algorithms are proposed, one of which is realized by system transformation and the other is newly designed. Extensive simulations are carried out to test the effectiveness of the algorithms. A real-world system is also presented. All the results show that both of them have the ability to solve the diagnostic strategy generation problem, and they have different characteristics. PMID:26457709

An introduction to optimal satellite range scheduling

CERN Document Server

Vázquez Álvarez, Antonio José

2015-01-01

The satellite range scheduling (SRS) problem, an important operations research problem in the aerospace industry consisting of allocating tasks among satellites and Earth-bound objects, is examined in this book. SRS principles and solutions are applicable to many areas, including: Satellite communications, where tasks are communication intervals between sets of satellites and ground stations Earth observation, where tasks are observations of spots on the Earth by satellites Sensor scheduling, where tasks are observations of satellites by sensors on the Earth. This self-contained monograph begins with a structured compendium of the problem and moves on to explain the optimal approach to the solution, which includes aspects from graph theory, set theory, game theory and belief networks. This book is accessible to students, professionals and researchers in a variety of fields, including: operations research, optimization, scheduling theory, dynamic programming and game theory. Taking account of the distributed, ...

Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC

Directory of Open Access Journals (Sweden)

Yanzan Sun

2018-03-01

Full Text Available Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE. In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs and the pico Cell Range Expansion (CRE are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.

Optimal loading and protection of multi-state systems considering performance sharing mechanism

International Nuclear Information System (INIS)

Xiao, Hui; Shi, Daimin; Ding, Yi; Peng, Rui

2016-01-01

Engineering systems are designed to carry the load. The performance of the system largely depends on how much load it carries. On the other hand, the failure rate of the system is strongly affected by its load. Besides internal failures, such as fatigue and aging process, systems may also fail due to external impacts such as nature disasters and terrorism. In this paper, we integrate the effect of loading and protection of external impacts on multi-state systems with performance sharing mechanism. The objective of this research is to determine how to balance the load and protection on system elements. An availability evaluation algorithm of the proposed system is suggested and the corresponding optimization problem is solved utilizing genetic algorithms. - Highlights: • Performance sharing of multi-state systems is considered. • The effect of load on system elements is analyzed. • Joint optimization model of element loading and protection is formulated. • Genetic Algorithms are adapted to solve the reliability optimization problem.

Multi-objective optimization to improve the product range of baking systems

NARCIS (Netherlands)

Hadiyanto, M.; Boom, R.M.; Straten, van G.; Boxtel, van A.J.B.; Esveld, D.C.

2009-01-01

The operational range of a food production system can be used to obtain a variation in certain product characteristics. The range of product characteristics that can be simultaneously realized by an optimal choice of the process conditions is inherently limited. Knowledge of this feasible product

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

Directory of Open Access Journals (Sweden)

Bumin Meng

2018-03-01

Full Text Available This paper discusses a design of a Battery Management System (BMS solution for extending the life of Nickel-Metal Hydride (NI-MH battery. Combined with application of electric boat, a State of Charge (SoC optimal operation range control method based on high precision energy metering and online SoC correction is proposed. Firstly, a power metering scheme is introduced to reduce the original energy measurement error. Secondly, by establishing a model based parameter identification method and combining with Extended Kalman Filter (EKF method, the estimation accuracy of SoC is guaranteed. Finally, SoC optimal operation range control method is presented to make battery running in the optimal range. After two years of operation, the battery managed by proposed method has much better status, compared to batteries that use AH integral method and fixed SoC operating range. Considering the SoC estimation of NI-MH battery is more difficult becausing special electrical characteristics, proposed method also would have a very good reference value for other types of battery management.

Joint optimization of economic production quantity and preventive maintenance with considering multi-products and reserve time

International Nuclear Information System (INIS)

Liu, Xuejuan; Wang, Binrong

2017-01-01

Purpose: We deal with the problem of the joint determination of optimal economic production quantity (EPQ) and optimal preventive maintenance (PM) for a system that can produce multiple products alternately. The objective is to find the optimal number of production cycles and the PM policy simultaneously by minimizing the cost model. Design/methodology/approach: Considering the products go through the system in a sequence and a complete run of all products forms a production cycle. In each cycle, beyond production time we also consider some reserve time for maintenance and setup, shortage and overproduction may occur. We study the integrated problem based on two PM policies, and explain the situation with the other PM policies. The delay – time concept is used to model PM decisions. Findings: Using the integrated EPQ and PM model, we can calculate the optimal production planning and PM schedule simultaneously, especially we consider multiple products in each production cycle, which is more practical and economic than previous works. Originality/value: In modern companies, the production planning and maintenance schedule share the same system, and traditional research about two activities is separated, that always generate conflicts, such as inadequate or excessive maintenance, and shortages, etc., so we develop the integrated EPQ and PM model to avoid these undesirable effects.

Joint optimization of economic production quantity and preventive maintenance with considering multi-products and reserve time

Energy Technology Data Exchange (ETDEWEB)

Liu, Xuejuan; Wang, Binrong

2017-07-01

Purpose: We deal with the problem of the joint determination of optimal economic production quantity (EPQ) and optimal preventive maintenance (PM) for a system that can produce multiple products alternately. The objective is to find the optimal number of production cycles and the PM policy simultaneously by minimizing the cost model. Design/methodology/approach: Considering the products go through the system in a sequence and a complete run of all products forms a production cycle. In each cycle, beyond production time we also consider some reserve time for maintenance and setup, shortage and overproduction may occur. We study the integrated problem based on two PM policies, and explain the situation with the other PM policies. The delay – time concept is used to model PM decisions. Findings: Using the integrated EPQ and PM model, we can calculate the optimal production planning and PM schedule simultaneously, especially we consider multiple products in each production cycle, which is more practical and economic than previous works. Originality/value: In modern companies, the production planning and maintenance schedule share the same system, and traditional research about two activities is separated, that always generate conflicts, such as inadequate or excessive maintenance, and shortages, etc., so we develop the integrated EPQ and PM model to avoid these undesirable effects.

Optimal Frequency Ranges for Sub-Microsecond Precision Pulsar Timing

Science.gov (United States)

Lam, Michael Timothy; McLaughlin, Maura; Cordes, James; Chatterjee, Shami; Lazio, Joseph

2018-01-01

Precision pulsar timing requires optimization against measurement errors and astrophysical variance from the neutron stars themselves and the interstellar medium. We investigate optimization of arrival time precision as a function of radio frequency and bandwidth. We find that increases in bandwidth that reduce the contribution from receiver noise are countered by the strong chromatic dependence of interstellar effects and intrinsic pulse-profile evolution. The resulting optimal frequency range is therefore telescope and pulsar dependent. We demonstrate the results for five pulsars included in current pulsar timing arrays and determine that they are not optimally observed at current center frequencies. We also find that arrival-time precision can be improved by increases in total bandwidth. Wideband receivers centered at high frequencies can reduce required overall integration times and provide significant improvements in arrival time uncertainty by a factor of $\\sim$$\\sqrt{2}$ in most cases, assuming a fixed integration time. We also discuss how timing programs can be extended to pulsars with larger dispersion measures through the use of higher-frequency observations.

Multiobjective optimization model of intersection signal timing considering emissions based on field data: A case study of Beijing.

Science.gov (United States)

Kou, Weibin; Chen, Xumei; Yu, Lei; Gong, Huibo

2018-04-18

Most existing signal timing models are aimed to minimize the total delay and stops at intersections, without considering environmental factors. This paper analyzes the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. First, considering the different operating modes of cruising, acceleration, deceleration, and idling, field data of emissions and Global Positioning System (GPS) are collected to estimate emission rates for heavy-duty and light-duty vehicles. Second, multiobjective signal timing optimization model is established based on a genetic algorithm to minimize delay, stops, and emissions. Finally, a case study is conducted in Beijing. Nine scenarios are designed considering different weights of emission and traffic efficiency. The results compared with those using Highway Capacity Manual (HCM) 2010 show that signal timing optimized by the model proposed in this paper can decrease vehicles delay and emissions more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development. Vehicle emissions are heavily at signal intersections in urban area. The multiobjective signal timing optimization model is proposed considering the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. The results indicate that signal timing optimized by the model proposed in this paper can decrease vehicle emissions and delays more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.

Optimization and maintenance tests considering multiple failure modes, aging and imperfect maintenance

International Nuclear Information System (INIS)

Martorell, S.; Marton, I.; Sanchez, A.; Carlos, S.

2014-01-01

The paper focuses on the optimization of the test and maintenance intervals under the criteria of unavailability or cost including the effect of the aging of the components and models of imperfect maintenance. The results obtained in the case of application, which focuses on a system of safety of a nuclear power station, show differences, mainly in the outage when you consider the aging. (Author)

Valence electronic structure of cobalt phthalocyanine from an optimally tuned range-separated hybrid functional.

Science.gov (United States)

Brumboiu, Iulia Emilia; Prokopiou, Georgia; Kronik, Leeor; Brena, Barbara

2017-07-28

We analyse the valence electronic structure of cobalt phthalocyanine (CoPc) by means of optimally tuning a range-separated hybrid functional. The tuning is performed by modifying both the amount of short-range exact exchange (α) included in the hybrid functional and the range-separation parameter (γ), with two strategies employed for finding the optimal γ for each α. The influence of these two parameters on the structural, electronic, and magnetic properties of CoPc is thoroughly investigated. The electronic structure is found to be very sensitive to the amount and range in which the exact exchange is included. The electronic structure obtained using the optimal parameters is compared to gas-phase photo-electron data and GW calculations, with the unoccupied states additionally compared with inverse photo-electron spectroscopy measurements. The calculated spectrum with tuned γ, determined for the optimal value of α = 0.1, yields a very good agreement with both experimental results and with GW calculations that well-reproduce the experimental data.

Joint Optimization of Economic Production Quantity and Preventive Maintenance with Considering Multi-Products and Reserve Time

OpenAIRE

Liu, Xuejuan; Wang, Binrong

2017-01-01

Purpose: We deal with the problem of the joint determination of optimal economic production quantity (EPQ) and optimal preventive maintenance (PM) for a system that can produce multiple products alternately. The objective is to find the optimal number of production cycles and the PM policy simultaneously by minimizing the cost model. Design/methodology/approach: Considering the products go through the system in a sequence and a complete run of all products forms a production cy...

Optimization of a dual mode Rowland mount spectrometer used in the 120-950 nm wavelength range

Science.gov (United States)

McDowell, M. W.; Bouwer, H. K.

In a recent article, several configurations were described whereby a Rowland mount spectrometer could be modified to cover a wavelength range of 120-950 nm. In one of these configurations, large additional image aberration is introduced which severely limits the spectral resolving power. In the present article, the theoretical imaging properties of this configuration are considered and a simple method is proposed to reduce this aberration. The optimized system possesses an image quality similar to the conventional Rowland mount with the image surface slightly displaced from the Rowland circle but concentric to it.

Optimal Operation and Dispatch of Voltage Regulation Devices Considering High Penetrations of Distributed Photovoltaic Generation

Energy Technology Data Exchange (ETDEWEB)

Mather, Barry A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cho, Gyu-Jung [Sungkyunkwan University; Oh, Yun-Sik [Sungkyunkwan University; Kim, Min-Sung [Sungkyunkwan University; Kim, Ji-Soo [Sungkyunkwan University; Kim, Chul-Hwan [Sungkyunkwan University

2017-06-29

Voltage regulation devices have been traditionally installed and utilized to support distribution voltages. Installations of distributed energy resources (DERs) in distribution systems are rapidly increasing, and many of these generation resources have variable and uncertain power output. These generators can significantly change the voltage profile for a feeder; therefore, in the distribution system planning stage of the optimal operation and dispatch of voltage regulation devices, possible high penetrations of DERs should be considered. In this paper, we model the IEEE 34-bus test feeder, including all essential equipment. An optimization method is adopted to determine the optimal siting and operation of the voltage regulation devices in the presence of distributed solar power generation. Finally, we verify the optimal configuration of the entire system through the optimization and simulation results.

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty.

Science.gov (United States)

Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang

2015-01-01

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method.

Design of an optimal automatic regulator for regulating the power levels of a nuclear reactor in a wind power range

International Nuclear Information System (INIS)

Noori Khajavi, M.; Menhaj, M.B.; Ghofrani, M.B.

2000-01-01

Nuclear power reactors are, in nature nonlinear and time varying. These characteristics must be considered, if large power variations occur in their working regime. In this paper a robust optimal self-tuning regulator for regulating the power of a nuclear reactor has been designed and simulated. The proposed controller is capable of regulating power levels in a wide power range (10% to 100% power levels). The controller achieves a fast and good transient response. The simulation results show that the proposed controller outperforms the fixed optimal control recently cited in the literature for nuclear power plants

Day-ahead optimal dispatch for wind integrated power system considering zonal reserve requirements

International Nuclear Information System (INIS)

Liu, Fan; Bie, Zhaohong; Liu, Shiyu; Ding, Tao

2017-01-01

Highlights: • Analyzing zonal reserve requirements for wind integrated power system. • Modeling day-ahead optimal dispatch solved by chance constrained programming theory. • Determining optimal zonal reserve demand with minimum confidence interval. • Analyzing numerical results on test and large-scale real-life power systems. - Abstract: Large-scale integration of renewable power presents a great challenge for day-ahead dispatch to manage renewable resources while provide available reserve for system security. Considering zonal reserve is an effective way to ensure reserve deliverability when network congested, a random day-ahead dispatch optimization of wind integrated power system for a least operational cost is modeled including zonal reserve requirements and N − 1 security constraints. The random model is transformed into a deterministic one based on the theory of chance constrained programming and a determination method of optimal zonal reserve demand is proposed using the minimum confidence interval. After solving the deterministic model, the stochastic simulation is conducted to verify the validity of solution. Numerical tests and results on the IEEE 39 bus system and a large-scale real-life power system demonstrate the optimal day-ahead dispatch scheme is available and the proposed method is effective for improving reserve deliverability and reducing load shedding after large-capacity power outage.

An enhanced particle swarm optimization for dynamic economic dispatch problem considering valve-point loading

Energy Technology Data Exchange (ETDEWEB)

Sriyanyong, P. [King Mongkut' s Univ. of Technology, Bangkok (Thailand). Dept. of Teacher Training in Electrical Engineering

2008-07-01

This paper described the use of an enhanced particle swarm optimization (PSO) model to address the problem of dynamic economic dispatch (DED). A modified heuristic search method was incorporated into the PSO model. Both smooth and non-smooth cost functions were considered. The enhanced PSO model not only utilized the basic PSO algorithm in order to seek the optimal solution for the DED problem, but it also used a modified heuristic method to deal with constraints and increase the possibility of finding a feasible solution. In order to validate the enhanced PSO model, it was used and tested on 10-unit systems considering both smooth and non-smooth cost functions characteristics. The experimental results were also compared to other methods. The proposed technique was found to be better than other approaches. The enhanced PSO model outperformed others with respect to quality, stability and reliability. 23 refs., 1 tab., 8 figs.

Optimization of the Waterbus Operation Plan Considering Carbon Emissions: The Case of Zhoushan City

Directory of Open Access Journals (Sweden)

Juying Wang

2015-08-01

Full Text Available Recently, as more people are concerned with the issues around environment protection, research about how to reduce carbon emissions has drawn increasing attention. Encouraging public transportation is an effective measure to reduce carbon emissions. However, overland public transportation does less to lower carbon because of the gradually increasing pressure of the urban road traffic. Therefore, the waterbus along the coast becomes a new direction of the urban public transport development. In order to optimize the operation plan of the waterbus, a bi-level model considering carbon emissions is proposed in this paper. In the upper-level model, a multiple objective model is established, which considers both the interests of the passengers and the operator while considering the carbon emissions. The lower-level model is a traffic model split by using a Nested Logit model. A NSGA-II (Non-dominated Sorting Genetic Algorithm-II algorithm is proposed to solve the model. Finally, the city of Zhoushan is chosen as an example to prove the feasibility of the model and the algorithm. The result shows that the proposed model for waterbus operation optimization can efficiently reduce transportation carbon emissions and satisfy passenger demand at the same time.

Considering induction factor using BEM method in wind farm layout optimization

DEFF Research Database (Denmark)

Ghadirian, Amin; Dehghan, M.; Torabi, F.

2014-01-01

For wind farm layout optimization process, a simple linear model has been mostly used for considering the wake effect of a wind turbine on its downstream turbines. In this model, the wind velocity in the wake behind a turbine is obtained as a function of turbine induction factor which...... was considered to be 0.324 almost in all the previous studies. However, it is obviously evident that this factor is a strong function of turbine blade geometry and operational conditions. In the present study, a new method is introduced by which the induction factor for wind turbines can be calculated based...... on the method of Blade Element Momentum theory. By this method, the effect of blade profile, wind speed and angular velocity of wind turbine on the induction factor can be easily taken into account. The results show that for different blade profiles and operational conditions, the induction factor differs from...

Optimal bidding strategies in oligopoly markets considering bilateral contracts and transmission constraints

Energy Technology Data Exchange (ETDEWEB)

A.Badri; Jadid, S. [Department of Electrical Engineering, Iran University of Science and Technology (Iran); Rashidinejad, M. [Shahid Bahonar University, Kerman (Iran); Moghaddam, M.P. [Tarbiat Modarres University, Tehran (Iran)

2008-06-15

In electricity industry with transmission constraints and limited number of producers, Generation Companies (GenCos) are facing an oligopoly market rather than a perfect competition one. Under oligopoly market environment, each GenCo may increase its own profit through a favorable bidding strategy. This paper investigates the problem of developing optimal bidding strategies of GenCos, considering bilateral contracts and transmission constraints. The problem is modeled with a bi-level optimization algorithm, where in the first level each GenCo maximizes its payoff and in the second level a system dispatch will be accomplished through an OPF problem in which transmission constraints are taken into account. It is assumed that each GenCo has information about initial bidding strategies of other competitors. Impacts of exercising market power due to transmission constraints as well as irrational biddings of the some generators are studied and the interactions of different bidding strategies on participants' corresponding payoffs are presented. Furthermore, a risk management-based method to obtain GenCos' optimal bilateral contracts is proposed and the impacts of these contracts on GenCos' optimal biddings and obtained payoffs are investigated. At the end, IEEE 30-bus test system is used for the case study in order to demonstrate the simulation results and support the effectiveness of the proposed model. (author)

Optimal bidding strategies in oligopoly markets considering bilateral contracts and transmission constraints

International Nuclear Information System (INIS)

Badri, A.; Jadid, S.; Rashidinejad, M.; Moghaddam, M.P.

2008-01-01

In electricity industry with transmission constraints and limited number of producers, Generation Companies (GenCos) are facing an oligopoly market rather than a perfect competition one. Under oligopoly market environment, each GenCo may increase its own profit through a favorable bidding strategy. This paper investigates the problem of developing optimal bidding strategies of GenCos, considering bilateral contracts and transmission constraints. The problem is modeled with a bi-level optimization algorithm, where in the first level each GenCo maximizes its payoff and in the second level a system dispatch will be accomplished through an OPF problem in which transmission constraints are taken into account. It is assumed that each GenCo has information about initial bidding strategies of other competitors. Impacts of exercising market power due to transmission constraints as well as irrational biddings of the some generators are studied and the interactions of different bidding strategies on participants' corresponding payoffs are presented. Furthermore, a risk management-based method to obtain GenCos' optimal bilateral contracts is proposed and the impacts of these contracts on GenCos' optimal biddings and obtained payoffs are investigated. At the end, IEEE 30-bus test system is used for the case study in order to demonstrate the simulation results and support the effectiveness of the proposed model. (author)

Optimal design of an automotive magnetorheological brake considering geometric dimensions and zero-field friction heat

International Nuclear Information System (INIS)

Nguyen, Q H; Choi, S B

2010-01-01

This paper presents an optimal design of a magnetorheological (MR) brake for a middle-sized passenger car which can replace a conventional hydraulic disc-type brake. In the optimization, the required braking torque, the temperature due to zero-field friction of MR fluid, the mass of the brake system and all significant geometric dimensions are considered. After describing the configuration, the braking torque of the proposed MR brake is derived on the basis of the field-dependent Bingham and Herschel–Bulkley rheological model of the MR fluid. The optimal design of the MR brake is then analyzed taking into account available space, mass, braking torque and steady heat generated by zero-field friction torque of the MR brake. The optimization procedure based on the finite element analysis integrated with an optimization tool is proposed to obtain optimal geometric dimensions of the MR brake. Based on the proposed procedure, optimal solutions of single and multiple disc-type MR brakes featuring different types of MR fluid are achieved. From the results, the most effective MR brake for the middle-sized passenger car is identified and some discussions on the performance improvement of the optimized MR brake are described

Reliability- and performance-based robust design optimization of MEMS structures considering technological uncertainties

Science.gov (United States)

Martowicz, Adam; Uhl, Tadeusz

2012-10-01

The paper discusses the applicability of a reliability- and performance-based multi-criteria robust design optimization technique for micro-electromechanical systems, considering their technological uncertainties. Nowadays, micro-devices are commonly applied systems, especially in the automotive industry, taking advantage of utilizing both the mechanical structure and electronic control circuit on one board. Their frequent use motivates the elaboration of virtual prototyping tools that can be applied in design optimization with the introduction of technological uncertainties and reliability. The authors present a procedure for the optimization of micro-devices, which is based on the theory of reliability-based robust design optimization. This takes into consideration the performance of a micro-device and its reliability assessed by means of uncertainty analysis. The procedure assumes that, for each checked design configuration, the assessment of uncertainty propagation is performed with the meta-modeling technique. The described procedure is illustrated with an example of the optimization carried out for a finite element model of a micro-mirror. The multi-physics approach allowed the introduction of several physical phenomena to correctly model the electrostatic actuation and the squeezing effect present between electrodes. The optimization was preceded by sensitivity analysis to establish the design and uncertain domains. The genetic algorithms fulfilled the defined optimization task effectively. The best discovered individuals are characterized by a minimized value of the multi-criteria objective function, simultaneously satisfying the constraint on material strength. The restriction of the maximum equivalent stresses was introduced with the conditionally formulated objective function with a penalty component. The yielded results were successfully verified with a global uniform search through the input design domain.

Geometric optimal design of a magneto-rheological brake considering different shapes for the brake envelope

International Nuclear Information System (INIS)

Nguyen, Q H; Lang, V T; Nguyen, N D; Choi, S B

2014-01-01

When designing a magneto-rheological brake (MRB), it is well known that the shape of the brake envelope significantly affects the performance characteristics of the brake. In this study, different shapes for the MR brake envelope, such as rectangular, polygonal or spline shape, are considered and the most suitable shape identified. MRBs with different envelope shapes are introduced followed by the derivation of the braking torque based on Bingham-plastic behavior of the magneto-rheological fluid (MRF). Optimization of the design of the MRB with different envelope shapes is then done. The optimization problem is to find the optimal value for the significant geometric dimensions of the MRB that can produce a certain required braking torque while the brake mass is minimized. A finite element analysis integrated with an optimization tool is employed to obtain optimal solutions for the MRBs. From the results, the most suitable shape for the brake envelope is identified and discussed with the reduction of mass. In addition, the results of the analysis are compared with the experimental results to verify the proposed optimal design characteristics. (paper)

Geometric optimal design of a magneto-rheological brake considering different shapes for the brake envelope

Science.gov (United States)

Nguyen, Q. H.; Lang, V. T.; Nguyen, N. D.; Choi, S. B.

2014-01-01

When designing a magneto-rheological brake (MRB), it is well known that the shape of the brake envelope significantly affects the performance characteristics of the brake. In this study, different shapes for the MR brake envelope, such as rectangular, polygonal or spline shape, are considered and the most suitable shape identified. MRBs with different envelope shapes are introduced followed by the derivation of the braking torque based on Bingham-plastic behavior of the magneto-rheological fluid (MRF). Optimization of the design of the MRB with different envelope shapes is then done. The optimization problem is to find the optimal value for the significant geometric dimensions of the MRB that can produce a certain required braking torque while the brake mass is minimized. A finite element analysis integrated with an optimization tool is employed to obtain optimal solutions for the MRBs. From the results, the most suitable shape for the brake envelope is identified and discussed with the reduction of mass. In addition, the results of the analysis are compared with the experimental results to verify the proposed optimal design characteristics.

SU-F-J-133: Adaptive Radiation Therapy with a Four-Dimensional Dose Calculation Algorithm That Optimizes Dose Distribution Considering Breathing Motion

Energy Technology Data Exchange (ETDEWEB)

Ali, I; Algan, O; Ahmad, S [University of Oklahoma Health Sciences, Oklahoma City, OK (United States); Alsbou, N [University of Central Oklahoma, Edmond, OK (United States)

2016-06-15

Purpose: To model patient motion and produce four-dimensional (4D) optimized dose distributions that consider motion-artifacts in the dose calculation during the treatment planning process. Methods: An algorithm for dose calculation is developed where patient motion is considered in dose calculation at the stage of the treatment planning. First, optimal dose distributions are calculated for the stationary target volume where the dose distributions are optimized considering intensity-modulated radiation therapy (IMRT). Second, a convolution-kernel is produced from the best-fitting curve which matches the motion trajectory of the patient. Third, the motion kernel is deconvolved with the initial dose distribution optimized for the stationary target to produce a dose distribution that is optimized in four-dimensions. This algorithm is tested with measured doses using a mobile phantom that moves with controlled motion patterns. Results: A motion-optimized dose distribution is obtained from the initial dose distribution of the stationary target by deconvolution with the motion-kernel of the mobile target. This motion-optimized dose distribution is equivalent to that optimized for the stationary target using IMRT. The motion-optimized and measured dose distributions are tested with the gamma index with a passing rate of >95% considering 3% dose-difference and 3mm distance-to-agreement. If the dose delivery per beam takes place over several respiratory cycles, then the spread-out of the dose distributions is only dependent on the motion amplitude and not affected by motion frequency and phase. This algorithm is limited to motion amplitudes that are smaller than the length of the target along the direction of motion. Conclusion: An algorithm is developed to optimize dose in 4D. Besides IMRT that provides optimal dose coverage for a stationary target, it extends dose optimization to 4D considering target motion. This algorithm provides alternative to motion management

Discrete Material Buckling Optimization of Laminated Composite Structures considering "Worst" Shape Imperfections

DEFF Research Database (Denmark)

Henrichsen, Søren Randrup; Lindgaard, Esben; Lund, Erik

2015-01-01

Robust design of laminated composite structures is considered in this work. Because laminated composite structures are often thin walled, buckling failure can occur prior to material failure, making it desirable to maximize the buckling load. However, as a structure always contains imperfections...... and “worst” shape imperfection optimizations to design robust composite structures. The approach is demonstrated on an U-profile where the imperfection sensitivity is monitored, and based on the example it can be concluded that robust designs can be obtained....

Feature Optimization for Long-Range Visual Homing in Changing Environments

Directory of Open Access Journals (Sweden)

Qidan Zhu

2014-02-01

Full Text Available This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance. This paper presents a modified feature extraction algorithm to decrease the influence of anisotropic feature distribution. In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments. To verify the feasibility of the proposal, several comprehensive evaluations are conducted. The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

Multi-objective optimal power flow for active distribution network considering the stochastic characteristic of photovoltaic

Science.gov (United States)

Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming

2017-05-01

To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.

Reliability–redundancy allocation problem considering optimal redundancy strategy using parallel genetic algorithm

International Nuclear Information System (INIS)

Kim, Heungseob; Kim, Pansoo

2017-01-01

To maximize the reliability of a system, the traditional reliability–redundancy allocation problem (RRAP) determines the component reliability and level of redundancy for each subsystem. This paper proposes an advanced RRAP that also considers the optimal redundancy strategy, either active or cold standby. In addition, new examples are presented for it. Furthermore, the exact reliability function for a cold standby redundant subsystem with an imperfect detector/switch is suggested, and is expected to replace the previous approximating model that has been used in most related studies. A parallel genetic algorithm for solving the RRAP as a mixed-integer nonlinear programming model is presented, and its performance is compared with those of previous studies by using numerical examples on three benchmark problems. - Highlights: • Optimal strategy is proposed to solve reliability redundancy allocation problem. • The redundancy strategy uses parallel genetic algorithm. • Improved reliability function for a cold standby subsystem is suggested. • Proposed redundancy strategy enhances the system reliability.

Optimal design of a novel hybrid MR brake for motorcycles considering axial and radial magnetic flux

International Nuclear Information System (INIS)

Nguyen, Q H; Choi, S B

2012-01-01

This work presents an optimal solution of a new type of motorcycle brake featuring different smart magnetorheological (MR) fluids. In this study, typical types of commercial MR fluid are considered there for the design of a motorcycle MR brake; MRF-122-2ED (low yield stress), MRF-132-DG (medium yield stress) and MRF-140-CG (high yield stress). As a first step, a new configuration featuring a T-shaped drum MR brake is introduced and a hybrid concept of magnetic circuit (using both axial and radial magnetic flux) to generate braking force is analyzed based on the finite element method. An optimal design of the MR brake considering the required braking torque, the temperature due to friction of the MR fluid, the mass of the brake system and all significant geometric dimensions is then performed. For the optimization, the finite element analysis (FEA) is used to achieve principal geometric dimensions of the MR brake. In addition, the size, mass and power consumption of three different MR motorcycle brakes are quantitatively analyzed and compared. (paper)

Optimization of organic Rankine cycle power systems considering multistage axial turbine design

DEFF Research Database (Denmark)

Meroni, Andrea; Andreasen, Jesper Graa; Persico, Giacomo

2018-01-01

Organic Rankine cycle power systems represent a viable and efficient solution for the exploitation of medium-to-low temperature heat sources. Despite the large number of commissioned units, there is limited literature on the design and optimization of organic Rankine cycle power systems considering...... multistage turbine design. This work presents a preliminary design methodology and working fluid selection for organic Rankine cycle units featuring multistage axial turbines. The method is then applied to the case of waste heat recovery from a large marine diesel engine. A multistage axial turbine model...

Optimization of organic Rankine cycle power systems considering multistage axial turbine design

DEFF Research Database (Denmark)

Meroni, Andrea; Andreasen, Jesper Graa; Persico, Giacomo

2017-01-01

Organic Rankine cycle power systems represent a viable and efficient solution for the exploitation of medium-to-low temperature heat sources. Despite the large number of commissioned units, there is limited literature on the design and optimization of organic Rankine cycle power systems considering...... multistage turbine design. This work presents a preliminary design methodology and working fluid selection for organic Rankine cycle units featuring multistage axial turbines. The method is then applied to the case of waste heat recovery from a large marine diesel engine. A multistage axial turbine model...

Wolf, Canis lupus, visits to white-tailed deer, Odocoileus virginianus, summer ranges: Optimal foraging?

Science.gov (United States)

Demma, D.J.; Mech, L.D.

2009-01-01

We tested whether Wolf (Canis lupus) visits to individual female White-tailed Deer (Odocoileus virginianus) summer ranges during 2003 and 2004 in northeastern Minnesota were in accord with optimal-foraging theory. Using GPS collars with 10- to 30-minute location attempts on four Wolves and five female deer, plus eleven VHF-collared female deer in the Wolves' territory, provided new insights into the frequency of Wolf visits to summer ranges of female deer. Wolves made a mean 0.055 visits/day to summer ranges of deer three years and older, significantly more than their 0.032 mean visits/day to ranges of two-year-old deer, which generally produce fewer fawns, and most Wolf visits to ranges of older deer were much longer than those to ranges of younger deer. Because fawns comprise the major part of the Wolf's summer diet, this Wolf behavior accords with optimal-foraging theory.

Smart house-based optimal operation of thermal unit commitment for a smart grid considering transmission constraints

Science.gov (United States)

Howlader, Harun Or Rashid; Matayoshi, Hidehito; Noorzad, Ahmad Samim; Muarapaz, Cirio Celestino; Senjyu, Tomonobu

2018-05-01

This paper presents a smart house-based power system for thermal unit commitment programme. The proposed power system consists of smart houses, renewable energy plants and conventional thermal units. The transmission constraints are considered for the proposed system. The generated power of the large capacity renewable energy plant leads to the violated transmission constraints in the thermal unit commitment programme, therefore, the transmission constraint should be considered. This paper focuses on the optimal operation of the thermal units incorporated with controllable loads such as Electrical Vehicle and Heat Pump water heater of the smart houses. The proposed method is compared with the power flow in thermal units operation without controllable loads and the optimal operation without the transmission constraints. Simulation results show the validation of the proposed method.

Optimization of a pressure control valve for high power automatic transmission considering stability

Science.gov (United States)

Jian, Hongchao; Wei, Wei; Li, Hongcai; Yan, Qingdong

2018-02-01

The pilot-operated electrohydraulic clutch-actuator system is widely utilized by high power automatic transmission because of the demand of large flowrate and the excellent pressure regulating capability. However, a self-excited vibration induced by the inherent non-linear characteristics of valve spool motion coupled with the fluid dynamics can be generated during the working state of hydraulic systems due to inappropriate system parameters, which causes sustaining instability in the system and leads to unexpected performance deterioration and hardware damage. To ensure a stable and fast response performance of the clutch actuator system, an optimal design method for the pressure control valve considering stability is proposed in this paper. A non-linear dynamic model of the clutch actuator system is established based on the motion of the valve spool and coupling fluid dynamics in the system. The stability boundary in the parameter space is obtained by numerical stability analysis. Sensitivity of the stability boundary and output pressure response time corresponding to the valve parameters are identified using design of experiment (DOE) approach. The pressure control valve is optimized using particle swarm optimization (PSO) algorithm with the stability boundary as constraint. The simulation and experimental results reveal that the optimization method proposed in this paper helps in improving the response characteristics while ensuring the stability of the clutch actuator system during the entire gear shift process.

Optimal Allocation of Wind Turbines by Considering Transmission Security Constraints and Power System Stability

Directory of Open Access Journals (Sweden)

Rodrigo Palma-Behnke

2013-01-01

Full Text Available A novel optimization methodology consisting of finding the near optimal location of wind turbines (WTs on a planned transmission network in a secure and cost-effective way is presented on this paper. While minimizing the investment costs of WTs, the algorithm allocates the turbines so that a desired wind power energy-penetration level is reached. The optimization considers both transmission security and power system stability constraints. The results of the optimization provide regulators with a support instrument to give proper signals to WT investors, in order to achieve secure and cost effective wind power network integration. The proposal is especially aimed at countries in the initial stage of wind power development, where the WT network integration process can still be influenced by policy-makers. The proposed methodology is validated with a real power system. Obtained results are compared with those generated from a business-as-usual (BAU scenario, in which the WT network allocation is made according to existing WT projects. The proposed WT network allocation scheme not only reduces the total investment costs associated with a determined wind power energy target, but also improves power system stability.

Risk Based Optimal Fatigue Testing

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Faber, M.H.; Kroon, I.B.

1992-01-01

Optimal fatigue life testing of materials is considered. Based on minimization of the total expected costs of a mechanical component a strategy is suggested to determine the optimal stress range levels for which additional experiments are to be performed together with an optimal value...

Risk-informed optimal routing of ships considering different damage scenarios and operational conditions

International Nuclear Information System (INIS)

Decò, Alberto; Frangopol, Dan M.

2013-01-01

The aim of this paper is the development of a risk-informed decision tool for the optimal mission-oriented routing of ships. The strength of the hull is investigated by modeling the midship section with finite elements and by analyzing different damage levels depending on the propagation of plastification throughout the section. Vertical and horizontal flexural interaction is investigated. Uncertainties associated with geometry and material properties are accounted for by means of the implementation of the response surface method. Load effects are evaluated using strip theory. Reliability analysis is performed for several ship operational conditions and considering four different limit states. Then, risk is assessed by including the direct losses associated with five investigated damage states. The effects of corrosion on aged ships are included in the proposed approach. Polar representation of load effects, reliability, and direct risk are presented for a large spectrum of operational conditions. Finally, the optimal routing of ships is obtained by minimizing both the estimated time of arrival and the expected direct risk, which are clearly conflicting objectives. The optimization process provides feasible solutions belonging to the Pareto front. The proposed approach is applied to a Joint High Speed Sealift

A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers

Directory of Open Access Journals (Sweden)

Jun Yang

2015-08-01

Full Text Available The carbon emissions trading market and direct power purchases by large consumers are two promising directions of power system development. To trace the carbon emission flow in the power grid, the theory of carbon emission flow is improved by allocating power loss to the load side. Based on the improved carbon emission flow theory, an optimal dispatch model is proposed to optimize the cost of both large consumers and the power grid, which will benefit from the carbon emissions trading market. Moreover, to better simulate reality, the direct purchase of power by large consumers is also considered in this paper. The OPF (optimal power flow method is applied to solve the problem. To evaluate our proposed optimal dispatch strategy, an IEEE 30-bus system is used to test the performance. The effects of the price of carbon emissions and the price of electricity from normal generators and low-carbon generators with regards to the optimal dispatch are analyzed. The simulation results indicate that the proposed strategy can significantly reduce both the operation cost of the power grid and the power utilization cost of large consumers.

Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms

International Nuclear Information System (INIS)

Khorramdel, Benyamin; Raoofat, Mahdi

2012-01-01

Distributed Generators (DGs) in a microgrid may operate in three different reactive power control strategies, including PV, PQ and voltage droop schemes. This paper proposes a new stochastic programming approach for reactive power scheduling of a microgrid, considering the uncertainty of wind farms. The proposed algorithm firstly finds the expected optimal operating point of each DG in V-Q plane while the wind speed is a probabilistic variable. A multi-objective function with goals of loss minimization, reactive power reserve maximization and voltage security margin maximization is optimized using a four-stage multi-objective nonlinear programming. Then, using Monte Carlo simulation enhanced by scenario reduction technique, the proposed algorithm simulates actual condition and finds optimal operating strategy of DGs. Also, if any DGs are scheduled to operate in voltage droop scheme, the optimum droop is determined. Also, in the second part of the research, to enhance the optimality of the results, PSO algorithm is used for the multi-objective optimization problem. Numerical examples on IEEE 34-bus test system including two wind turbines are studied. The results show the benefits of voltage droop scheme for mitigating the impacts of the uncertainty of wind. Also, the results show preference of PSO method in the proposed approach. -- Highlights: ► Reactive power scheduling in a microgrid considering loss and voltage security. ► Stochastic nature of wind farms affects reactive power scheduling and is considered. ► Advantages of using the voltage droop characteristics of DGs in voltage security are shown. ► Power loss, voltage security and VAR reserve are three goals of a multi-objective optimization. ► Monte Carlo method with scenario reduction is used to determine optimal control strategy of DGs.

An improved lattice hydrodynamic model considering the influence of optimal flux for forward looking sites

Science.gov (United States)

Wang, Yunong; Ge, Hongxia; Cheng, Rongjun

2017-11-01

In this paper, a lattice hydrodynamic model is derived considering the delayed-feedback control influence of optimal flux for forward looking sites on a single-lane road which includes more comprehensive information. The control method is used to analyze the stability of the model. The critical condition for the linear steady traffic flow is deduced and the numerical simulation is carried out to investigate the advantage of the proposed model with and without the effect of optimal flux for forward looking sites. Moreover it indicates that the characteristic of the model can lead to a lower energy consumption in traffic system. The results are consistent with the theoretical analysis correspondingly.

Optimal coordinated scheduling of combined heat and power fuel cell, wind, and photovoltaic units in micro grids considering uncertainties

International Nuclear Information System (INIS)

Bornapour, Mosayeb; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin; Parastegari, Moein

2016-01-01

In this paper, a stochastic model is proposed for coordinated scheduling of combined heat and power units in micro grid considering wind turbine and photovoltaic units. Uncertainties of electrical market price; the speed of wind and solar radiation are considered using a scenario-based method. In the method, scenarios are generated using roulette wheel mechanism based on probability distribution functions of input random variables. Using this method, the probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective function, coordinated scheduling of combined heat and power, wind turbine, and photovoltaic units change this problem to a mixed integer nonlinear one. Therefore to solve this problem modified particle swarm optimization algorithm is employed. The mentioned uncertainties lead to an increase in profit. Moreover, the optimal coordinated scheduling of renewable energy resources and thermal units in micro grids increase the total profit. In order to evaluate the performance of the proposed method, its performance is executed on modified 33 bus distributed system as a micro grid. - Highlights: • Stochastic model is proposed for coordinated scheduling of renewable energy sources. • The effect of combined heat and power is considered. • Maximizing profits of micro grid is considered as objective function. • Considering the uncertainties of problem lead to profit increasing. • Optimal scheduling of renewable energy sources and thermal units increases profit.

Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system

International Nuclear Information System (INIS)

Wen, Shuli; Lan, Hai; Hong, Ying-Yi; Yu, David C.; Zhang, Lijun; Cheng, Peng

2016-01-01

Highlights: • An uncertainty model of PV generation on board is developed based on the experiments. • The moving and swinging of the ship are considered in the optimal ESS sizing problem. • Optimal sizing of ESS in a hybrid PV/diesel/ESS ship power system is gained by the interval optimization method. • Different cases were studied to show the significance of the proposed method considering the swinging effects on the cost. - Abstract: Owing to low efficiency of traditional ships and the serious environmental pollution that they cause, the use of solar energy and an energy storage system (ESS) in a ship’s power system is increasingly attracting attention. However, the swinging of a ship raises crucial challenges in designing an optimal system for a large oil tanker ship, which are associated with uncertainties in solar energy. In this study, a series of experiments are performed to investigate the characteristics of a photovoltaic (PV) system on a moving ship. Based on the experimental results, an interval uncertainty model of on-board PV generation is established, which considers the effect of the swinging of the ship. Due to the power balance equations, the outputs of the diesel generator and the ESS on a large oil tanker are also modeled using interval variables. An interval optimization method is developed to determine the optimal size of the ESS in this hybrid ship power system to reduce the fuel cost, capital cost of the ESS, and emissions of greenhouse gases. Variations of the ship load are analyzed using a new method, taking five operating conditions into account. Several cases are compared in detail to demonstrate the effectiveness of the proposed algorithm.

A Study on the Optimal Generation Mix Based on Portfolio Theory with Considering the Basic Condition for Power Supply

Science.gov (United States)

Kato, Moritoshi; Zhou, Yicheng

This paper presents a novel method to analyze the optimal generation mix based on portfolio theory with considering the basic condition for power supply, which means that electricity generation corresponds with load curve. The optimization of portfolio is integrated with the calculation of a capacity factor of each generation in order to satisfy the basic condition for power supply. Besides, each generation is considered to be an asset, and risks of the generation asset both in its operation period and construction period are considered. Environmental measures are evaluated through restriction of CO2 emissions, which are indicated by CO2 price. Numerical examples show the optimal generation mix according to risks such as the deviation of capacity factor of nuclear power or restriction of CO2 emissions, the possibility of introduction of clean coal technology (IGCC, CCS) or renewable energy, and so on. The results of this work will be possibly applied as setting the target of the generation mix for the future according to prospects of risks of each generation and restrictions of CO2 emissions.

Energy efficiency optimization in distribution transformers considering Spanish distribution regulation policy

International Nuclear Information System (INIS)

Pezzini, Paola; Gomis-Bellmunt, Oriol; Frau-Valenti, Joan; Sudria-Andreu, Antoni

2010-01-01

In transmission and distribution systems, the high number of installed transformers, a loss source in networks, suggests a good potential for energy savings. This paper presents how the Spanish Distribution regulation policy, Royal Decree 222/2008, affects the overall energy efficiency in distribution transformers. The objective of a utility is the maximization of the benefit, and in case of failures, to install a chosen transformer in order to maximize the profit. Here, a novel method to optimize energy efficiency, considering the constraints set by the Spanish Distribution regulation policy, is presented; its aim is to achieve the objectives of the utility when installing new transformers. The overall energy efficiency increase is a clear result that can help in meeting the requirements of European environmental plans, such as the '20-20-20' action plan.

Optimal stochastic scheduling of CHP-PEMFC, WT, PV units and hydrogen storage in reconfigurable micro grids considering reliability enhancement

International Nuclear Information System (INIS)

Bornapour, Mosayeb; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin; Parastegari, Moein

2017-01-01

Highlights: • Stochastic model is proposed for coordinated scheduling of renewable energy sources. • The effect of combined heat and power is considered. • Uncertainties of wind speed, solar radiation and electricity market price are considered. • Profit maximization, emission and AENS minimization are considered as objective functions. • Modified firefly algorithm is employed to solve the problem. - Abstract: Nowadays the operation of renewable energy sources and combined heat and power (CHP) units is increased in micro grids; therefore, to reach optimal performance, optimal scheduling of these units is required. In this regard, in this paper a micro grid consisting of proton exchange membrane fuel cell-combined heat and power (PEMFC-CHP), wind turbines (WT) and photovoltaic (PV) units, is modeled to determine the optimal scheduling state of these units by considering uncertain behavior of renewable energy resources. For this purpose, a scenario-based method is used for modeling the uncertainties of electrical market price, the wind speed, and solar irradiance. It should be noted that the hydrogen storage strategy is also applied in this study for PEMFC-CHP units. Market profit, total emission production, and average energy not supplied (AENS) are the objective functions considered in this paper simultaneously. Consideration of the above-mentioned objective functions converts the proposed problem to a mixed integer nonlinear programming. To solve this problem, a multi-objective firefly algorithm is used. The uncertainties of parameters convert the mixed integer nonlinear programming problem to a stochastic mixed integer nonlinear programming problem. Moreover, optimal coordinated scheduling of renewable energy resources and thermal units in micro-grids improve the value of the objective functions. Simulation results obtained from a modified 33-bus distributed network as a micro grid illustrates the effectiveness of the proposed method.

Wind Turbine Power Curve Design for Optimal Power Generation in Wind Farms Considering Wake Effect

Directory of Open Access Journals (Sweden)

Jie Tian

2017-03-01

Full Text Available In modern wind farms, maximum power point tracking (MPPT is widely implemented. Using the MPPT method, each individual wind turbine is controlled by its pitch angle and tip speed ratio to generate the maximum active power. In a wind farm, the upstream wind turbine may cause power loss to its downstream wind turbines due to the wake effect. According to the wake model, downstream power loss is also determined by the pitch angle and tip speed ratio of the upstream wind turbine. By optimizing the pitch angle and tip speed ratio of each wind turbine, the total active power of the wind farm can be increased. In this paper, the optimal pitch angle and tip speed ratio are selected for each wind turbine by the exhausted search. Considering the estimation error of the wake model, a solution to implement the optimized pitch angle and tip speed ratio is proposed, which is to generate the optimal control curves for each individual wind turbine off-line. In typical wind farms with regular layout, based on the detailed analysis of the influence of pitch angle and tip speed ratio on the total active power of the wind farm by the exhausted search, the optimization is simplified with the reduced computation complexity. By using the optimized control curves, the annual energy production (AEP is increased by 1.03% compared to using the MPPT method in a case-study of a typical eighty-turbine wind farm.

Optimization of the operating conditions of gas-turbine power stations considering the effect of equipment deterioration

Science.gov (United States)

Aminov, R. Z.; Kozhevnikov, A. I.

2017-10-01

In recent years in most power systems all over the world, a trend towards the growing nonuniformity of energy consumption and generation schedules has been observed. The increase in the portion of renewable energy sources is one of the important challenges for many countries. The ill-predictable character of such energy sources necessitates a search for practical solutions. Presently, the most efficient method for compensating for nonuniform generation of the electric power by the renewable energy sources—predominantly by the wind and solar energy—is generation of power at conventional fossil-fuel-fired power stations. In Russia, this problem is caused by the increasing portion in the generating capacity structure of the nuclear power stations, which are most efficient when operating under basic conditions. Introduction of hydropower and pumped storage hydroelectric power plants and other energy-storage technologies does not cover the demand for load-following power capacities. Owing to a simple design, low construction costs, and a sufficiently high economic efficiency, gas turbine plants (GTPs) prove to be the most suitable for covering the nonuniform electric-demand schedules. However, when the gas turbines are operated under varying duty conditions, the lifetime of the primary thermostressed components is considerably reduced and, consequently, the repair costs increase. A method is proposed for determination of the total operating costs considering the deterioration of the gas turbine equipment under varying duty and start-stop conditions. A methodology for optimization of the loading modes for the gas turbine equipment is developed. The consideration of the lifetime component allows varying the optimal operating conditions and, in some cases, rejecting short-time stops of the gas turbine plants. The calculations performed in a wide range of varying fuel prices and capital investments per gas turbine equipment unit show that the economic effectiveness can

Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Inoue, Tatsuya [Department of Radiology, Juntendo University Urayasu Hospital, Chiba (Japan); Widder, Joachim; Dijk, Lisanne V. van [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Takegawa, Hideki [Department of Radiation Oncology, Kansai Medical University Hirakata Hospital, Osaka (Japan); Koizumi, Masahiko; Takashina, Masaaki [Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka (Japan); Usui, Keisuke; Kurokawa, Chie; Sugimoto, Satoru [Department of Radiation Oncology, Juntendo University Graduate School of Medicine, Tokyo (Japan); Saito, Anneyuko I. [Department of Radiology, Juntendo University Urayasu Hospital, Chiba (Japan); Department of Radiation Oncology, Juntendo University Graduate School of Medicine, Tokyo (Japan); Sasai, Keisuke [Department of Radiation Oncology, Juntendo University Graduate School of Medicine, Tokyo (Japan); Veld, Aart A. van' t; Langendijk, Johannes A. [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Korevaar, Erik W., E-mail: e.w.korevaar@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

2016-11-01

Purpose: To investigate the impact of setup and range uncertainties, breathing motion, and interplay effects using scanning pencil beams in robustly optimized intensity modulated proton therapy (IMPT) for stage III non-small cell lung cancer (NSCLC). Methods and Materials: Three-field IMPT plans were created using a minimax robust optimization technique for 10 NSCLC patients. The plans accounted for 5- or 7-mm setup errors with ±3% range uncertainties. The robustness of the IMPT nominal plans was evaluated considering (1) isotropic 5-mm setup errors with ±3% range uncertainties; (2) breathing motion; (3) interplay effects; and (4) a combination of items 1 and 2. The plans were calculated using 4-dimensional and average intensity projection computed tomography images. The target coverage (TC, volume receiving 95% of prescribed dose) and homogeneity index (D{sub 2} − D{sub 98}, where D{sub 2} and D{sub 98} are the least doses received by 2% and 98% of the volume) for the internal clinical target volume, and dose indexes for lung, esophagus, heart and spinal cord were compared with that of clinical volumetric modulated arc therapy plans. Results: The TC and homogeneity index for all plans were within clinical limits when considering the breathing motion and interplay effects independently. The setup and range uncertainties had a larger effect when considering their combined effect. The TC decreased to <98% (clinical threshold) in 3 of 10 patients for robust 5-mm evaluations. However, the TC remained >98% for robust 7-mm evaluations for all patients. The organ at risk dose parameters did not significantly vary between the respective robust 5-mm and robust 7-mm evaluations for the 4 error types. Compared with the volumetric modulated arc therapy plans, the IMPT plans showed better target homogeneity and mean lung and heart dose parameters reduced by about 40% and 60%, respectively. Conclusions: In robustly optimized IMPT for stage III NSCLC, the setup and range

Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer

International Nuclear Information System (INIS)

Inoue, Tatsuya; Widder, Joachim; Dijk, Lisanne V. van; Takegawa, Hideki; Koizumi, Masahiko; Takashina, Masaaki; Usui, Keisuke; Kurokawa, Chie; Sugimoto, Satoru; Saito, Anneyuko I.; Sasai, Keisuke; Veld, Aart A. van't; Langendijk, Johannes A.; Korevaar, Erik W.

2016-01-01

Purpose: To investigate the impact of setup and range uncertainties, breathing motion, and interplay effects using scanning pencil beams in robustly optimized intensity modulated proton therapy (IMPT) for stage III non-small cell lung cancer (NSCLC). Methods and Materials: Three-field IMPT plans were created using a minimax robust optimization technique for 10 NSCLC patients. The plans accounted for 5- or 7-mm setup errors with ±3% range uncertainties. The robustness of the IMPT nominal plans was evaluated considering (1) isotropic 5-mm setup errors with ±3% range uncertainties; (2) breathing motion; (3) interplay effects; and (4) a combination of items 1 and 2. The plans were calculated using 4-dimensional and average intensity projection computed tomography images. The target coverage (TC, volume receiving 95% of prescribed dose) and homogeneity index (D_2 − D_9_8, where D_2 and D_9_8 are the least doses received by 2% and 98% of the volume) for the internal clinical target volume, and dose indexes for lung, esophagus, heart and spinal cord were compared with that of clinical volumetric modulated arc therapy plans. Results: The TC and homogeneity index for all plans were within clinical limits when considering the breathing motion and interplay effects independently. The setup and range uncertainties had a larger effect when considering their combined effect. The TC decreased to 98% for robust 7-mm evaluations for all patients. The organ at risk dose parameters did not significantly vary between the respective robust 5-mm and robust 7-mm evaluations for the 4 error types. Compared with the volumetric modulated arc therapy plans, the IMPT plans showed better target homogeneity and mean lung and heart dose parameters reduced by about 40% and 60%, respectively. Conclusions: In robustly optimized IMPT for stage III NSCLC, the setup and range uncertainties, breathing motion, and interplay effects have limited impact on target coverage, dose homogeneity, and

Energy efficiency optimization in distribution transformers considering Spanish distribution regulation policy

Energy Technology Data Exchange (ETDEWEB)

Pezzini, Paola [Centre d' Innovacio en Convertidors Estatics i Accionaments (CITCEA-UPC), E.T.S. Enginyeria Industrial Barcelona, Universitat Politecnica Catalunya, Diagonal, 647, Pl. 2, 08028 Barcelona (Spain); Gomis-Bellmunt, Oriol; Sudria-Andreu, Antoni [Centre d' Innovacio en Convertidors Estatics i Accionaments (CITCEA-UPC), E.T.S. Enginyeria Industrial Barcelona, Universitat Politecnica Catalunya, Diagonal, 647, Pl. 2, 08028 Barcelona (Spain); IREC Catalonia Institute for Energy Research, Josep Pla, B2, Pl. Baixa, 08019 Barcelona (Spain); Frau-Valenti, Joan [ENDESA, Carrer Joan Maragall, 16 07006 Palma (Spain)

2010-12-15

In transmission and distribution systems, the high number of installed transformers, a loss source in networks, suggests a good potential for energy savings. This paper presents how the Spanish Distribution regulation policy, Royal Decree 222/2008, affects the overall energy efficiency in distribution transformers. The objective of a utility is the maximization of the benefit, and in case of failures, to install a chosen transformer in order to maximize the profit. Here, a novel method to optimize energy efficiency, considering the constraints set by the Spanish Distribution regulation policy, is presented; its aim is to achieve the objectives of the utility when installing new transformers. The overall energy efficiency increase is a clear result that can help in meeting the requirements of European environmental plans, such as the '20-20-20' action plan. (author)

[Gestational weight gain and optimal ranges in Chinese mothers giving singleton and full-term births in 2013].

Science.gov (United States)

Wang, J; Duan, Y F; Pang, X H; Jiang, S; Yin, S A; Yang, Z Y; Lai, J Q

2018-01-06

Objective: To analyze the status of gestational weight gain (GWG) among Chinese mothers who gave singleton and full-term births, and to look at optimal GWG ranges. Methods: In 2013, using the multi-stage stratified and population proportional cluster sampling method, we investigated 8 323 mother-child pairs at their 0-24 months postpartum from 55 counties (cities/districts) of 30 provinces (except Tibet) in mainland China. Questionnaire was used to collect data on body weight before pregnancy and delivery, diseases during gestation, hemorrhage or not at postpartum, child birth weight and length, and other information about pregnant outcomes. We measured mother's body weight and height, and child's body weight and length. Based on 'Chinese Adult Body Weight Standard', we divided mothers into four groups according to their body weight before pregnancy: low weight (BMImothers and children, and according to P25-P75 of GWG among mothers who had good pregnant outcomes and good anthropometry, and whose children had good anthropometry. The status of GWG was assessed by the new optimal ranges. Results: P50 (P25-P75) of GWG among the 8 323 mothers was 15.0 (10.0-19.0) kg. According to the proposed optimal GWG ranges of IOM, the proportions of inadequate, optimal and excessive GWG accounted for 27.2% (2 263 mothers), 36.2% (3 016 mothers) and 36.6% (3 044 mothers). The optimal GWG ranges for low weight, normal weight, overweight and obesity were 11.5-18.0, 10.0-15.0, 8.0-14.0 and 5.0-11.5 kg. Based on these optimal GWG ranges established in this study, the rates of inadequate, optimal and excessive GWG were 15.7% (1 303 mothers), 45.0% (3 744 mothers) and 39.3% (3 276 mothers), and these rates were significantly different from that defined by the IOM standards (χ2=345.36, Pmothers is 15.0 kg, which is at a relatively higher level. This study suggests the optimal GWG ranges for Chinese women who give singleton and full-term babies, which appears lower than IOM's.

Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm

International Nuclear Information System (INIS)

Zhou, Jianzhong; Lu, Peng; Li, Yuanzheng; Wang, Chao; Yuan, Liu; Mo, Li

2016-01-01

Highlights: • HTWCS system is established while considering uncertainty of wind power. • An enhanced multi-objective bee colony optimization algorithm is proposed. • Some heuristic repairing strategies are designed to handle various constraints. • HTWCS problem with economic/environment objectives is solved by EMOBCO. - Abstract: This paper presents a short-term economic/environmental hydro-thermal-wind complementary scheduling (HTWCS) system considering uncertainty of wind power, as well as various complicated non-linear constraints. HTWCS system is formulated as a multi-objective optimization problem to optimize conflictive objectives, i.e., economic and environmental criteria. Then an enhanced multi-objective bee colony optimization algorithm (EMOBCO) is proposed to solve this problem, which adopts Elite archive set, adaptive mutation/selection mechanism and local searching strategy to improve global searching ability of standard bee colony optimization (BCO). Especially, a novel constraints-repairing strategy with compressing decision space and a violation-adjustment method are used to handle various hydraulic and electric constraints. Finally, a daily scheduling simulation case of hydro-thermal-wind system is conducted to verify feasibility and effectiveness of the proposed EMOBCO in solving HTWCS problem. The simulation results indicate that the proposed EMOBCO can provide lower economic cost and smaller pollutant emission than other method established recently while considering various complex constraints in HTWCS problem.

Optimized design of a low-resistance electrical conductor for the multimegahertz range

Science.gov (United States)

Kurs, André; Kesler, Morris; Johnson, Steven G.

2011-04-01

We propose a design for a conductive wire composed of several mutually insulated coaxial conducting shells. With the help of numerical optimization, it is possible to obtain electrical resistances significantly lower than those of a heavy-gauge copper wire or litz wire in the 2-20 MHz range. Moreover, much of the reduction in resistance can be achieved for just a few shells; in contrast, litz wire would need to contain ˜104 strands to perform comparably in this frequency range.

Dispatching Plan Based on Route Optimization Model Considering Random Wind for Aviation Emergency Rescue

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Ming Zhang

2016-01-01

Full Text Available Aviation emergency rescue is an effective means of nature disaster relief that is widely used in many countries. The dispatching plan of aviation emergency rescue guarantees the efficient implementation of this relief measure. The conventional dispatching plan that does not consider random wind factors leads to a nonprecise quick-responsive scheme and serious safety issues. In this study, an aviation emergency rescue framework that considers the influence of random wind at flight trajectory is proposed. In this framework, the predicted wind information for a disaster area is updated by using unscented Kalman filtering technology. Then, considering the practical scheduling problem of aircraft emergency rescue at present, a multiobjective model is established in this study. An optimization model aimed at maximizing the relief supply satisfaction, rescue priority satisfaction, and minimizing total rescue flight distance is formulated. Finally, the transport times of aircraft with and without the influence of random wind are analyzed on the basis of the data of an earthquake disaster area. Results show that the proposed dispatching plan that considers the constraints of updated wind speed and direction is highly applicable in real operations.

Optimized chord and twist angle distributions of wind turbine blade considering Reynolds number effects

Energy Technology Data Exchange (ETDEWEB)

Wang, L.; Tang, X. [Univ. of Central Lancashire. Engineering and Physical Sciences, Preston (United Kingdom); Liu, X. [Univ. of Cumbria. Sustainable Engineering, Workington (United Kingdom)

2012-07-01

The aerodynamic performance of a wind turbine depends very much on its blade geometric design, typically based on the blade element momentum (BEM) theory, which divides the blade into several blade elements. In current blade design practices based on Schmitz rotor design theory, the blade geometric parameters including chord and twist angle distributions are determined based on airfoil aerodynamic data at a specific Reynolds number. However, rotating wind turbine blade elements operate at different Reynolds numbers due to variable wind speed and different blade span locations. Therefore, the blade design through Schmitz rotor theory at a specific Reynolds number does not necessarily provide the best power performance under operational conditions. This paper aims to provide an optimal blade design strategy for horizontal-axis wind turbines operating at different Reynolds numbers. A fixed-pitch variable-speed (FPVS) wind turbine with S809 airfoil is chosen as a case study and a Matlab program which considers Reynolds number effects is developed to determine the optimized chord and twist angle distributions of the blade. The performance of the optimized blade is compared with that of the preliminary blade which is designed based on Schmitz rotor design theory at a specific Reynolds number. The results demonstrate that the proposed blade design optimization strategy can improve the power performance of the wind turbine. This approach can be further developed for any practice of horizontal axis wind turbine blade design. (Author)

Optimal stochastic management of renewable MG (micro-grids) considering electro-thermal model of PV (photovoltaic)

International Nuclear Information System (INIS)

Najibi, Fatemeh; Niknam, Taher; Kavousi-Fard, Abdollah

2016-01-01

This paper aims to report the results of the research conducted to one thermal and electrical model for photovoltaic. Moreover, one probabilistic framework is introduced for considering all uncertainties in the optimal energy management of Micro-Grid problem. It should be noted that one typical Micro-Grid is being studied as a case, including different renewable energy sources, such as Photovoltaic, Micro Turbine, Wind Turbine, and one battery as a storage device for storing energy. The uncertainties of market price variation, photovoltaic and wind turbine output power change and load demand error are covered by the suggested probabilistic framework. The Micro-Grid problem is of nonlinear nature because of the stochastic behavior of the renewable energy sources such as Photovoltaic and Wind Turbine units, and hence there is need for a powerful tool to solve the problem. Therefore, in addition to the simulated thermal model and suggested probabilistic framework, a new algorithm is also introduced. The Backtracking Search Optimization Algorithm is described as a useful method to optimize the MG (micro-grids) problem. This algorithm has the benefit of escaping from the local optima while converging fast, too. The proposed algorithm is also tested on the typical Micro-Grid. - Highlights: • Proposing an electro-thermal model for PV. • Proposing a new stochastic formulation for optimal operation of renewable MGs. • Introduction of a new optimization method based on BSO to explore the problem search space.

Offshore Wind Farm Layout Design Considering Optimized Power Dispatch Strategy

DEFF Research Database (Denmark)

Hou, Peng; Hu, Weihao; N. Soltani, Mohsen

2017-01-01

Offshore wind farm has drawn more and more attention recently due to its higher energy capacity and more freedom to occupy area. However, the investment is higher. In order to make a cost-effective wind farm, the wind farm layout should be optimized. The wake effect is one of the dominant factors...... leading to energy losses. It is expected that the optimized placement of wind turbines (WT) over a large sea area can lead to the best tradeoff between energy yields and capital investment. This paper proposes a novel way to position offshore WTs for a regular shaped wind farm. In addition to optimizing...... the direction of wind farm placement and the spacing between WTs, the control strategy’s impact on energy yields is also discussed. Since the problem is non-convex and lots of optimization variables are involved, an evolutionary algorithm, the particle swarm optimization algorithm (PSO), is adopted to find...

Optimal Operation of Micro-grids Considering the Uncertainties of Demand and Renewable Energy Resources Generation

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Malek Jasemi

2016-11-01

Full Text Available Nowadays, due to technical and economic reasons, the distributed generation (DG units are widely connected to the low and medium voltage network and created a new structure called micro-grid. Renewable energies (especially wind and solar based DGs are one of the most important generations units among DG units. Because of stochastic behavior of these resources, the optimum and safe management and operation of micro-grids has become one of the research priorities for researchers. So, in this study, the optimal operation of a typical micro-grid is investigated in order to maximize the penetration of renewable energy sources with the lowest operation cost with respect to the limitations for the load supply and the distributed generation resources. The understudy micro-grid consists of diesel generator, battery, wind turbines and photovoltaic panels. The objective function comprises of fuel cost, start-up cost, spinning reserve cost, power purchasing cost from the upstream grid and the sales revenue of the power to the upstream grid. In this paper, the uncertainties of demand, wind speed and solar radiation are considered and the optimization will be made by using the GAMS software and mixed integer planning method (MIP. Article History: Received May 21, 2016; Received in revised form July 11, 2016; Accepted October 15, 2016; Available online How to Cite This Article: Jasemi, M.,  Adabi, F., Mozafari, B., and Salahi, S. (2016 Optimal Operation of Micro-grids Considering the Uncertainties of Demand and Renewable Energy Resources Generation, Int. Journal of Renewable Energy Development, 5(3,233-248. http://dx.doi.org/10.14710/ijred.5.3.233-248

Considering Pilot Protection in the Optimal Coordination of Distance and Directional Overcurrent Relays

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Y. Damchi

2015-06-01

Full Text Available The aim of the relay coordination is that protection systems detect and isolate the faulted part as fast and selective as possible. On the other hand, in order to reduce the fault clearing time, distance protection relays are usually equipped with pilot protection schemes. Such schemes can be considered in the distance and directional overcurrent relays (D&DOCRs coordination to achieve faster protection systems, while the selectivity is maintained. Therefore, in this paper, a new formulation is presented for the relay coordination problem considering pilot protection. In the proposed formulation, the selectivity constraints for the primary distance and backup overcurrent relays are defined based on the fault at the end of the transmission lines, rather than those at the end of the first zone of the primary distance relay. To solve this nonlinear optimization problem, a combination of genetic algorithm (GA and linear programming (LP is used as a hybrid genetic algorithm (HGA. The proposed approach is tested on an 8-bus and the IEEE 14-bus test systems. Simulation results indicate that considering the pilot protection in the D&DOCRS coordination, not only obtains feasible and effective solutions for the relay settings, but also reduces the overall operating time of the protection system.

Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm

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Joon Heo

2009-06-01

Full Text Available Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.

Optimal Allocation of Smart Substations in a Distribution System Considering Interruption Costs of Customers

DEFF Research Database (Denmark)

Sun, Lei; You, Shi; Hu, Junjie

2016-01-01

number and allocation of smart substations in a given distribution system is presented, with the upgrade costs of substations and the interruption costs of customers taken into account. Besides, the reliability criterion is also properly considered in the model. By linearization strategies, the SSAM......One of the major functions of a smart substation (SS) is to restore power supply to interrupted customers as quickly as possible after an outage. The high cost of a smart substation limits its widespread utilization. In this paper, a smart substation allocation model (SSAM) to determine the optimal...

ATHENA optimized coating design

DEFF Research Database (Denmark)

Ferreira, Desiree Della Monica; Christensen, Finn Erland; Jakobsen, Anders Clemen

2012-01-01

The optimization of coating design for the ATHENA mission si described and the possibility of increasing the telescope effective area in the range between 0.1 and 10 keV is investigated. An independent computation of the on-axis effective area based on the mirror design of ATHENA is performed...... in order to review the current coating baseline. The performance of several material combinations, considering a simple bi-layer, simple multilayer and linear graded multilayer coatings are tested and simulation of the mirror performance considering both the optimized coating design and the coating...

Design of pressure-sensing diaphragm for MEMS capacitance diaphragm gauge considering size effect

Science.gov (United States)

Li, Gang; Li, Detian; Cheng, Yongjun; Sun, Wenjun; Han, Xiaodong; Wang, Chengxiang

2018-03-01

MEMS capacitance diaphragm gauge with a full range of (1˜1000) Pa is considered for its wide application prospect. The design of pressure-sensing diaphragm is the key to achieve balanced performance for this kind of gauges. The optimization process of the pressure-sensing diaphragm with island design of a capacitance diaphragm gauge based on MEMS technique has been reported in this work. For micro-components in micro scale range, mechanical properties are very different from that in the macro scale range, so the size effect should not be ignored. The modified strain gradient elasticity theory considering size effect has been applied to determine the bending rigidity of the pressure-sensing diaphragm, which is then used in the numerical model to calculate the deflection-pressure relation of the diaphragm. According to the deflection curves, capacitance variation can be determined by integrating over the radius of the diaphragm. At last, the design of the diaphragm has been optimized based on three parameters: sensitivity, linearity and ground capacitance. With this design, a full range of (1˜1000) Pa can be achieved, meanwhile, balanced sensitivity, resolution and linearity can be kept.

Topology optimization considering design-dependent Stokes flow loads

NARCIS (Netherlands)

Picelli, R.; Vicente, W.M.; Pavanello, R.; van Keulen, A.; Li, Qing; Steven, Grant P.; Zhang, Zhongpu

2015-01-01

This article presents an evolutionary topology optimization method for mean compliance minimization of structures under design-dependent viscous fluid flow loads. The structural domain is governed by the elasticity equation and the fluid by the incompressible Stokes flow equations. When the

Optimizing and Diversifying the Electric Range of Plug-in Hybrid Electric Vehicles for U.S. Drivers

International Nuclear Information System (INIS)

Lin, Zhenhong

2012-01-01

To provide useful information for automakers to design successful plug-in hybrid electric vehicle (PHEV) products and for energy and environmental analysts to understand the social impact of PHEVs, this paper addresses the question of how many of the U.S. consumers, if buying a PHEV, would prefer what electric ranges. The Market-oriented Optimal Range for PHEV (MOR-PHEV) model is developed to optimize the PHEV electric range for each of 36,664 sampled individuals representing U.S. new vehicle drivers. The optimization objective is the minimization of the sum of costs on battery, gasoline, electricity and refueling hassle. Assuming no battery subsidy, the empirical results suggest that: 1) the optimal PHEV electric range approximates two thirds of one s typical daily driving distance in the near term, defined as $450/kWh battery delivered price and $4/gallon gasoline price. 2) PHEVs are not ready to directly compete with HEVs at today s situation, defined by the $600/kWh battery delivered price and the $3-$4/gallon gasoline price, but can do so in the near term. 3) PHEV10s will be favored by the market over longer-range PHEVs in the near term, but longer-range PHEVs can dominate the PHEV market if gasoline prices reach as high as $5-$6 per gallon and/or battery delivered prices reach as low as $150-$300/kWh. 4) PHEVs can become much more attractive against HEVs in the near term if the electric range can be extended by only 10% with multiple charges per day, possible with improved charging infrastructure or adapted charging behavior. 5) the impact of a $100/kWh decrease in battery delivered prices on the competiveness of PHEVs against HEVs can be offset by about $1.25/gallon decrease in gasoline prices, or about 7/kWh increase in electricity prices. This also means that the impact of a $1/gallon decrease in gasoline prices can be offset by about 5/kWh decrease in electricity prices.

Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization

Science.gov (United States)

Soltani-Mohammadi, Saeed; Safa, Mohammad; Mokhtari, Hadi

2016-10-01

One of the most important stages in complementary exploration is optimal designing the additional drilling pattern or defining the optimum number and location of additional boreholes. Quite a lot research has been carried out in this regard in which for most of the proposed algorithms, kriging variance minimization as a criterion for uncertainty assessment is defined as objective function and the problem could be solved through optimization methods. Although kriging variance implementation is known to have many advantages in objective function definition, it is not sensitive to local variability. As a result, the only factors evaluated for locating the additional boreholes are initial data configuration and variogram model parameters and the effects of local variability are omitted. In this paper, with the goal of considering the local variability in boundaries uncertainty assessment, the application of combined variance is investigated to define the objective function. Thus in order to verify the applicability of the proposed objective function, it is used to locate the additional boreholes in Esfordi phosphate mine through the implementation of metaheuristic optimization methods such as simulated annealing and particle swarm optimization. Comparison of results from the proposed objective function and conventional methods indicates that the new changes imposed on the objective function has caused the algorithm output to be sensitive to the variations of grade, domain's boundaries and the thickness of mineralization domain. The comparison between the results of different optimization algorithms proved that for the presented case the application of particle swarm optimization is more appropriate than simulated annealing.

Optimization Scheduling Model for Wind-thermal Power System Considering the Dynamic penalty factor

Science.gov (United States)

PENG, Siyu; LUO, Jianchun; WANG, Yunyu; YANG, Jun; RAN, Hong; PENG, Xiaodong; HUANG, Ming; LIU, Wanyu

2018-03-01

In this paper, a new dynamic economic dispatch model for power system is presented.Objective function of the proposed model presents a major novelty in the dynamic economic dispatch including wind farm: introduced the “Dynamic penalty factor”, This factor could be computed by using fuzzy logic considering both the variable nature of active wind power and power demand, and it could change the wind curtailment cost according to the different state of the power system. Case studies were carried out on the IEEE30 system. Results show that the proposed optimization model could mitigate the wind curtailment and the total cost effectively, demonstrate the validity and effectiveness of the proposed model.

Dynamic optimization of the complex adaptive controlling by the structure of enterprise’s product range

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Andrey Fyodorovich Shorikov

2013-06-01

Full Text Available This paper reviews a methodical approach to solve multi-step dynamic problem of optimal integrated adaptive management of a product portfolio structure of the enterprise. For the organization of optimal adaptive terminal control of the system the recurrent algorithm, which reduces an initial multistage problem to the realization of the final sequence of problems of optimal program terminal control is offered. In turn, the decision of each problem of optimal program terminal control is reduced to the realization of the final sequence only single-step operations in the form of the problems solving of linear and convex mathematical programming. Thus, the offered approach allows to develop management solutions at current information support, which consider feedback, and which create the optimal structure of an enterprise’s product lines, contributing to optimising of profits, as well as maintenance of the desired level of profit for a long period of time

Operation optimization of a distributed energy system considering energy costs and exergy efficiency

International Nuclear Information System (INIS)

Di Somma, M.; Yan, B.; Bianco, N.; Graditi, G.; Luh, P.B.; Mongibello, L.; Naso, V.

2015-01-01

Highlights: • Operation optimization model of a Distributed Energy System (DES). • Multi-objective strategy to optimize energy cost and exergy efficiency. • Exergy analysis in building energy supply systems. - Abstract: With the growing demand of energy on a worldwide scale, improving the efficiency of energy resource use has become one of the key challenges. Application of exergy principles in the context of building energy supply systems can achieve rational use of energy resources by taking into account the different quality levels of energy resources as well as those of building demands. This paper is on the operation optimization of a Distributed Energy System (DES). The model involves multiple energy devices that convert a set of primary energy carriers with different energy quality levels to meet given time-varying user demands at different energy quality levels. By promoting the usage of low-temperature energy sources to satisfy low-quality thermal energy demands, the waste of high-quality energy resources can be reduced, thereby improving the overall exergy efficiency. To consider the economic factor as well, a multi-objective linear programming problem is formulated. The Pareto frontier, including the best possible trade-offs between the economic and exergetic objectives, is obtained by minimizing a weighted sum of the total energy cost and total primary exergy input using branch-and-cut. The operation strategies of the DES under different weights for the two objectives are discussed. The operators of DESs can choose the operation strategy from the Pareto frontier based on costs, essential in the short run, and sustainability, crucial in the long run. The contribution of each energy device in reducing energy costs and the total exergy input is also analyzed. In addition, results show that the energy cost can be much reduced and the overall exergy efficiency can be significantly improved by the optimized operation of the DES as compared with the

Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products

International Nuclear Information System (INIS)

Wang, Jianxiao; Zhong, Haiwang; Tang, Wenyuan; Rajagopal, Ram; Xia, Qing; Kang, Chongqing; Wang, Yi

2017-01-01

Highlights: •Flexible ramping products are modelled in the framework of a microgrid. •Microgrids’ optimal bidding model is proposed in energy and ancillary service markets. •A hybrid stochastic and robust optimization approach is adopted. •The effectiveness of the proposed bidding model is verified based on real-world data. -- Abstract: Due to the volatile nature of wind and photovoltaic power, wind farms and solar stations are generally thought of as the consumers of ramping services. However, a microgrid (MG) is able to strategically integrate various distributed energy resources (DERs) to provide both energy and ancillary services (ASs) for the bulk power system. To evaluate the ramping capabilities of an MG in the joint energy and AS markets, an optimal bidding strategy is developed in this paper considering flexible ramping products (FRPs). By aggregating and coordinating various DERs, including wind turbines (WTs), photovoltaic systems (PVs), micro-turbines (MTs) and energy storage systems (ESSs), the MG is able to optimally allocate the capacities for energy, spinning reserve and ramping. Taking advantage of the synergy among DERs, the MG can maximize its revenues from different markets. Moreover, the flexibility of the MG for the bulk power system can be fully explored. To address the uncertainties introduced by renewable generation and market prices, a hybrid stochastic/robust optimization (RO) approach is adopted. Case studies based on a real-world MG with various DERs demonstrate the market behavior of the MG using the proposed bidding model.

The decision optimization of product development by considering the customer demand saturation

Directory of Open Access Journals (Sweden)

Qing-song Xing

2015-05-01

Full Text Available Purpose: The purpose of this paper is to analyze the impacts of over meeting customer demands on the product development process, which is on the basis of the quantitative model of customer demands, development cost and time. Then propose the corresponding product development optimization decision. Design/methodology/approach: First of all, investigate to obtain the customer demand information, and then quantify customer demands weights by using variation coefficient method. Secondly, analyses the relationship between customer demands and product development time and cost based on the quality function deployment and establish corresponding mathematical model. On this basis, put forward the concept of customer demand saturation and optimization decision method of product development, and then apply it in the notebook development process of a company. Finally, when customer demand is saturated, it also needs to prove the consistency of strengthening satisfies customer demands and high attention degree customer demands, and the stability of customer demand saturation under different parameters. Findings: The development cost and the time will rise sharply when over meeting the customer demand. On the basis of considering the customer demand saturation, the relationship between customer demand and development time cost is quantified and balanced. And also there is basically consistent between the sequence of meeting customer demands and customer demands survey results. Originality/value: The paper proposes a model of customer demand saturation. It proves the correctness and effectiveness on the product development decision method.

An optimal frequency range for assessing the pressure reactivity index in patients with traumatic brain injury.

Science.gov (United States)

Howells, Tim; Johnson, Ulf; McKelvey, Tomas; Enblad, Per

2015-02-01

The objective of this study was to identify the optimal frequency range for computing the pressure reactivity index (PRx). PRx is a clinical method for assessing cerebral pressure autoregulation based on the correlation of spontaneous variations of arterial blood pressure (ABP) and intracranial pressure (ICP). Our hypothesis was that optimizing the methodology for computing PRx in this way could produce a more stable, reliable and clinically useful index of autoregulation status. The patients studied were a series of 131 traumatic brain injury patients. Pressure reactivity indices were computed in various frequency bands during the first 4 days following injury using bandpass filtering of the input ABP and ICP signals. Patient outcome was assessed using the extended Glasgow Outcome Scale (GOSe). The optimization criterion was the strength of the correlation with GOSe of the mean index value over the first 4 days following injury. Stability of the indices was measured as the mean absolute deviation of the minute by minute index value from 30-min moving averages. The optimal index frequency range for prediction of outcome was identified as 0.018-0.067 Hz (oscillations with periods from 55 to 15 s). The index based on this frequency range correlated with GOSe with ρ=-0.46 compared to -0.41 for standard PRx, and reduced the 30-min variation by 23%.

Optimal Pricing and Production Master Planning in a Multiperiod Horizon Considering Capacity and Inventory Constraints

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Neale R. Smith

2009-01-01

Full Text Available We formulate and solve a single-item joint pricing and master planning optimization problem with capacity and inventory constrains. The objective is to maximize profits over a discrete-time multiperiod horizon. The solution process consists of two steps. First, we solve the single-period problem exactly. Second, using the exact solution of the single-period problem, we solve the multiperiod problem using a dynamic programming approach. The solution process and the importance of considering both capacity and inventory constraints are illustrated with numerical examples.

Multi-Period Optimization Model for Electricity Generation Planning Considering Plug-in Hybrid Electric Vehicle Penetration

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Lena Ahmadi

2015-05-01

Full Text Available One of the main challenges for widespread penetration of plug-in hybrid electric vehicles (PHEVs is their impact on the electricity grid. The energy sector must anticipate and prepare for this extra demand and implement long-term planning for electricity production. In this paper, the additional electricity demand on the Ontario electricity grid from charging PHEVs is incorporated into an electricity production planning model. A case study pertaining to Ontario energy planning is considered to optimize the value of the cost of the electricity over sixteen years (2014–2030. The objective function consists of the fuel costs, fixed and variable operating and maintenance costs, capital costs for new power plants, and the retrofit costs of existing power plants. Five different case studies are performed with different PHEVs penetration rates, types of new power plants, and CO2 emission constraints. Among all the cases studied, the one requiring the most new capacity, (~8748 MW, is assuming the base case with 6% reduction in CO2 in year 2018 and high PHEV penetration. The next highest one is the base case, plus considering doubled NG prices, PHEV medium penetration rate and no CO2 emissions reduction target with an increase of 34.78% in the total installed capacity in 2030. Furthermore, optimization results indicate that by not utilizing coal power stations the CO2 emissions are the lowest: ~500 tonnes compared to ~900 tonnes when coal is permitted.

Chaotic particle swarm optimization for economic dispatch considering the generator constraints

International Nuclear Information System (INIS)

Cai, Jiejin; Ma, Xiaoqian; Li, Lixiang; Haipeng, Peng

2007-01-01

Chaotic particle swarm optimization (CPSO) methods are optimization approaches based on the proposed particle swarm optimization (PSO) with adaptive inertia weight factor (AIWF) and chaotic local search (CLS). In this paper, two CPSO methods based on the logistic equation and the Tent equation are presented to solve economic dispatch (ED) problems with generator constraints and applied in two power system cases. Compared with the traditional PSO method, the convergence iterative numbers of the CPSO methods are reduced, and the solutions generation costs decrease around 5 $/h in the six unit system and 24 $/h in the 15 unit system. The simulation results show that the CPSO methods have good convergence property. The generation costs of the CPSO methods are lower than those of the traditional particle swarm optimization algorithm, and hence, CPSO methods can result in great economic effect. For economic dispatch problems, the CPSO methods are more feasible and more effective alternative approaches than the traditional particle swarm optimization algorithm

Building of Reusable Reverse Logistics Model and its Optimization Considering the Decision of Backorder or Next Arrival of Goods

Science.gov (United States)

Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu; Lee, Hee-Hyol

This paper deals with the building of the reusable reverse logistics model considering the decision of the backorder or the next arrival of goods. The optimization method to minimize the transportation cost and to minimize the volume of the backorder or the next arrival of goods occurred by the Just in Time delivery of the final delivery stage between the manufacturer and the processing center is proposed. Through the optimization algorithms using the priority-based genetic algorithm and the hybrid genetic algorithm, the sub-optimal delivery routes are determined. Based on the case study of a distilling and sale company in Busan in Korea, the new model of the reusable reverse logistics of empty bottles is built and the effectiveness of the proposed method is verified.

Optimization of a dynamic supply portfolio considering risks and discount’s constraints

Directory of Open Access Journals (Sweden)

Masoud Rabbani

2014-01-01

Full Text Available Purpose: Nowadays finding reliable suppliers in the global supply chains has become so important for success, because reliable suppliers would lead to a reliable supply and besides that orders of customer are met effectively . Yet, there is little empirical evidence to support this view, hence the purpose of this paper is to fill this need by considering risk in order to find the optimum supply portfolio. Design/methodology/approach: This paper proposes a multi objective model for the supplier selection portfolio problem that uses conditional value at risk (CVaR criteria to control the risks of delayed, disrupted and defected supplies via scenario analysis. Also we consider discount’s constraints which are common assumptions in supplier selection problems. The proposed approach is capable of determining the optimal supply portfolio by calculating value-at-risk and minimizing conditional value-at-risk. In this study the Reservation Level driven Tchebycheff Procedure (RLTP which is one of the reference point methods, is used to solve small size of our model through coding in GAMS. As our model is NP-hard; a meta-heuristic approach, Non-dominated Sorting Genetic Algorithm (NSGA which is one of the most efficient methods for optimizing multi objective models, is applied to solve large scales of our model. Findings and Originality/value: In order to find a dynamic supply portfolio, we developed a Mixed Integer Linear Programming (MILP model which contains two objectives. One objective minimizes the cost and the other minimizes the risks of delayed, disrupted and defected supplies. CVaR is used as the risk controlling method which emphases on low-probability, high-consequence events. Discount option as a common offer from suppliers is also implanted in the proposed model. Our findings show that the proposed model can help in optimization of a dynamic supplier selection portfolio with controlling the corresponding risks for large scales of real word

Pair-Wise and Many-Body Dispersive Interactions Coupled to an Optimally Tuned Range-Separated Hybrid Functional.

Science.gov (United States)

Agrawal, Piyush; Tkatchenko, Alexandre; Kronik, Leeor

2013-08-13

We propose a nonempirical, pair-wise or many-body dispersion-corrected, optimally tuned range-separated hybrid functional. This functional retains the advantages of the optimal-tuning approach in the prediction of the electronic structure. At the same time, it gains accuracy in the prediction of binding energies for dispersively bound systems, as demonstrated on the S22 and S66 benchmark sets of weakly bound dimers.

Multidisciplinary design optimization of the belt drive system considering both structure and vibration characteristics based on improved genetic algorithm

Science.gov (United States)

Yuan, Yongliang; Song, Xueguan; Sun, Wei; Wang, Xiaobang

2018-05-01

The dynamic performance of a belt drive system is composed of many factors, such as the efficiency, the vibration, and the optimal parameters. The conventional design only considers the basic performance of the belt drive system, while ignoring its overall performance. To address all these challenges, the study on vibration characteristics and optimization strategies could be a feasible way. This paper proposes a new optimization strategy and takes a belt drive design optimization as a case study based on the multidisciplinary design optimization (MDO). The MDO of the belt drive system is established and the corresponding sub-systems are analyzed. The multidisciplinary optimization is performed by using an improved genetic algorithm. Based on the optimal results obtained from the MDO, the three-dimension (3D) model of the belt drive system is established for dynamics simulation by virtual prototyping. From the comparison of the results with respect to different velocities and loads, the MDO method can effectively reduce the transverse vibration amplitude. The law of the vibration displacement, the vibration frequency, and the influence of velocities on the transverse vibrations has been obtained. Results show that the MDO method is of great help to obtain the optimal structural parameters. Furthermore, the kinematics principle of the belt drive has been obtained. The belt drive design case indicates that the proposed method in this paper can also be used to solve other engineering optimization problems efficiently.

Optimal Design of a High Efficiency LLC Resonant Converter with a Narrow Frequency Range for Voltage Regulation

Directory of Open Access Journals (Sweden)

Junhao Luo

2018-05-01

Full Text Available As a key factor in the design of a voltage-adjustable LLC resonant converter, frequency regulation range is very important to the optimization of magnetic components and efficiency improvement. This paper presents a novel optimal design method for LLC resonant converters, which can narrow the frequency variation range and ensure high efficiency under the premise of a required gain achievement. A simplified gain model was utilized to simplify the calculation and the expected efficiency was initially set as 96.5%. The restricted area of parameter optimization design can be obtained by taking the intersection of the gain requirement, the efficiency requirement, and three restrictions of ZVS (Zero Voltage Switch. The proposed method was verified by simulation and experiments of a 150 W prototype. The results show that the proposed method can achieve ZVS from full-load to no-load conditions and can reach 1.6 times the normalized voltage gain in the frequency variation range of 18 kHz with a peak efficiency of up to 96.3%. Moreover, the expected efficiency is adjustable, which means a converter with a higher efficiency can be designed. The proposed method can also be used for the design of large-power LLC resonant converters to obtain a wide output voltage range and higher efficiency.

Optimal Scheduling of Residential Microgrids Considering Virtual Energy Storage System

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Weiliang Liu

2018-04-01

Full Text Available The increasingly complex residential microgrids (r-microgrid consisting of renewable generation, energy storage systems, and residential buildings require a more intelligent scheduling method. Firstly, aiming at the radiant floor heating/cooling system widely utilized in residential buildings, the mathematical relationship between the operative temperature and heating/cooling demand is established based on the equivalent thermodynamic parameters (ETP model, by which the thermal storage capacity is analyzed. Secondly, the radiant floor heating/cooling system is treated as virtual energy storage system (VESS, and an optimization model based on mixed-integer nonlinear programming (MINLP for r-microgrid scheduling is established which takes thermal comfort level and economy as the optimization objectives. Finally, the optimal scheduling results of two typical r-microgrids are analyzed. Case studies demonstrate that the proposed scheduling method can effectively employ the thermal storage capacity of radiant floor heating/cooling system, thus lowering the operating cost of the r-microgrid effectively while ensuring the thermal comfort level of users.

Optimal Scheduling of Integrated Energy Systems with Combined Heat and Power Generation, Photovoltaic and Energy Storage Considering Battery Lifetime Loss

Directory of Open Access Journals (Sweden)

Yongli Wang

2018-06-01

Full Text Available Integrated energy systems (IESs are considered a trending solution for the energy crisis and environmental problems. However, the diversity of energy sources and the complexity of the IES have brought challenges to the economic operation of IESs. Aiming at achieving optimal scheduling of components, an IES operation optimization model including photovoltaic, combined heat and power generation system (CHP and battery energy storage is developed in this paper. The goal of the optimization model is to minimize the operation cost under the system constraints. For the optimization process, an optimization principle is conducted, which achieves maximized utilization of photovoltaic by adjusting the controllable units such as energy storage and gas turbine, as well as taking into account the battery lifetime loss. In addition, an integrated energy system project is taken as a research case to validate the effectiveness of the model via the improved differential evolution algorithm (IDEA. The comparison between IDEA and a traditional differential evolution algorithm shows that IDEA could find the optimal solution faster, owing to the double variation differential strategy. The simulation results in three different battery states which show that the battery lifetime loss is an inevitable factor in the optimization model, and the optimized operation cost in 2016 drastically decreased compared with actual operation data.

A planning of exploitation to electric systems approach considering environmental criteria Description of a multicriteria optimization paradigm

International Nuclear Information System (INIS)

Schweickardt, Gustavo Alejandro; Gimenez Alvarez, Juan Manuel

2012-01-01

This work presents a context and a Model to approach the Planning of Exploitation of Electric Systems problem, in the medium term, considering environmental criteria. A decision making process from a Multicriteria Paradigm is introduced. In the past, environmental criteria just were considered or they were ignored. Due to the growing consciousness about environmental impacts of productive processes, a new orientation to the problem is required: a bigger integral quality of the planning process, instead of searching an optimal solution, based in a minimum investment cost. The Application Model considers the Total Cost of Energy Production and the Environmental Impact produced by emissions of CO 2 , SO 2 y NO x from Thermal Units, and is based in a Fuzzy Sets decision-making to represent the uncertainties in the system decision variables and satisfaction degree of solutions. The results obtained from the Traditional and Multicriteria Model, are finally presented.

Optimal production scheduling for energy efficiency improvement in biofuel feedstock preprocessing considering work-in-process particle separation

International Nuclear Information System (INIS)

Li, Lin; Sun, Zeyi; Yao, Xufeng; Wang, Donghai

2016-01-01

Biofuel is considered a promising alternative to traditional liquid transportation fuels. The large-scale substitution of biofuel can greatly enhance global energy security and mitigate greenhouse gas emissions. One major concern of the broad adoption of biofuel is the intensive energy consumption in biofuel manufacturing. This paper focuses on the energy efficiency improvement of biofuel feedstock preprocessing, a major process of cellulosic biofuel manufacturing. An improved scheme of the feedstock preprocessing considering work-in-process particle separation is introduced to reduce energy waste and improve energy efficiency. A scheduling model based on the improved scheme is also developed to identify an optimal production schedule that can minimize the energy consumption of the feedstock preprocessing under production target constraint. A numerical case study is used to illustrate the effectiveness of the proposed method. The research outcome is expected to improve the energy efficiency and enhance the environmental sustainability of biomass feedstock preprocessing. - Highlights: • A novel method to schedule production in biofuel feedstock preprocessing process. • Systems modeling approach is used. • Capable of optimize preprocessing to reduce energy waste and improve energy efficiency. • A numerical case is used to illustrate the effectiveness of the method. • Energy consumption per unit production can be significantly reduced.

Tuning Range Optimization of a Planar Inverted F Antenna for LTE Low Frequency Bands

DEFF Research Database (Denmark)

Barrio, Samantha Caporal Del; Pelosi, Mauro; Franek, Ondrej

2011-01-01

This paper presents a Planar Inverted F Antenna (PIFA) tuned with a fixed capacitor to the low frequency bands supported by the Long Term Evolution (LTE) technology. The tuning range is investigated and optimized with respect to the bandwidth and the efficiency of the resulting antenna. Simulatio...... and mock-ups are presented....

OPTIMIZATION OF THE RUSSIAN MACROECONOMIC POLICY FOR 2016-2020

Directory of Open Access Journals (Sweden)

Gilmundinov V. M.

2016-12-01

Full Text Available This paper is concerned with the methodological issues of economic policy elaboration and optimization of economic policy instruments’ parameters. Actuality of this research is provided by growing complexity of social and economic systems, important state role in their functioning as well as multi-targets of economic policy with limited number of instruments. Considering a big variety of internal and external restrictions of social and economic development of modern Russia it has wide range of applications. Extension of the dynamic econometric general equilibrium input-output model of the Russian economy with development of the sub-model of economic policy optimization is a key purpose of this study. The sub-model of economic policy optimization allows estimating impact of economic policy measures on target indicators as well as defining optimal values of their parameters. For this purpose, we extend Robert Mundell’s approach by considering dynamic optimization and wider range of economic policy targets and measures. Use of general equilibrium input-output model allows considering impact of economic policy on different aggregate markets and sectors. Approbation of suggested approach allows us to develop multi-variant forecast for the Russian economy for 2016-2020, define optimal values of monetary policy parameters and compare considered variants by values of social losses. The obtained results could be further used in theoretical as well as applied researches concerned with issues of economic policy elaboration and forecasting of social and economic development.

Optimization of solid state fermentation of sugar cane by Aspergillus niger considering particles size effect

Energy Technology Data Exchange (ETDEWEB)

Echevarria, J.; Rodriguez, L.J.A.; Delgado, G. (Instituto Cubano de Investigaciones de los Derivados de la Cana de Azucar (ICIDCA), La Habana (Cuba)); Espinosa, M.E. (Centro Nacional de Investigaciones Cientificas, La Habana (Cuba))

1991-01-01

The protein enrichment of sugar cane by solid state fermentation employing Aspergillus niger was optimized in a packed bed column using a two Factor Central Composit Design {alpha} = 2, considering as independent factors the particle diameter corresponding to different times of grinding for a sample and the air flow rate. It was significative for the air flow rate (optimum 4.34 VKgM) and the particle diameter (optimum 0.136 cm). The average particle size distribution, shape factor, specific surface, volume-surface mean diameter, number of particles, real and apparent density and holloweness for the different times of grinding were determined, in order to characterize the samples. (orig.).

Optimal residential smart appliances scheduling considering distribution network constraints

Directory of Open Access Journals (Sweden)

Yu-Ree Kim

2016-01-01

Full Text Available As smart appliances (SAs are more widely adopted within distribution networks, residential consumers can contribute to electricity market operations with demand response resources and reduce their electricity bill. However, if the schedules of demand response resources are determined only by the economic electricity rate signal, the schedule can be unfeasible due to the distribution network constraints. Furthermore, it is impossible for consumers to understand the complex physical characteristics and reflect them in their everyday behaviors. This paper introduces the concept of load coordinating retailer (LCR that deals with demand responsive appliances to reduce electrical consumption for the given distribution network constraints. The LCR can play the role of both conventional retailer and aggregated demand response provider for residential customers. It determines the optimal schedules for the aggregated neighboring SAs according to their types within each distribution feeder. The optimization algorithms are developed using Mixed Integer Linear Programming, and the distribution network is solved by the Newton–Raphson AC power flow.

Reliability-Based Robust Design Optimization of Structures Considering Uncertainty in Design Variables

Directory of Open Access Journals (Sweden)

Shujuan Wang

2015-01-01

Full Text Available This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method.

Design and Optimization of IPM Motor Considering Flux Weakening Capability and Vibration for Electric Vehicle Applications

Directory of Open Access Journals (Sweden)

Fangwu Ma

2018-05-01

Full Text Available As motor design is key to the development of electric vehicles (EVs and hybrid EVs (HEVs, it has recently become the subject of considerable interest. Interior permanent magnet (IPM motors offer advantages such as high torque density and high efficiency, benefiting from both permanent magnet (PM torque and reluctance torque. However an obvious disadvantage of IPM motors is that operation at high speed involves difficulties in achieving the required flux-weakening capability and low vibration. This study focuses on optimizing the flux-weakening performance and reducing the vibration of an IPM motor for EVs. Firstly, flux-weakening capability, cogging torque, torque ripple, and radical vibration force are analyzed based on the mathematical model. Secondly, three kinds of motors are optimized by the genetic algorithm and analyzed, providing visible insights into the contribution of different rotor structures to the torque characteristics, efficiency, and extended speed range. Thirdly, a slotted rotor configuration is proposed to reduce the torque ripple and radical vibration force. The flux density distributions are discussed, explaining the principle that motors with slotted rotors and stator skew slots have smaller torque ripple and radical vibration force. Lastly, the design and optimization results have been validated against experiments.

Optimal day-ahead wind-thermal unit commitment considering statistical and predicted features of wind speeds

International Nuclear Information System (INIS)

Sun, Yanan; Dong, Jizhe; Ding, Lijuan

2017-01-01

Highlights: • A day–ahead wind–thermal unit commitment model is presented. • Wind speed transfer matrix is formed to depict the sequential wind features. • Spinning reserve setting considering wind power accuracy and variation is proposed. • Verified study is performed to check the correctness of the program. - Abstract: The increasing penetration of intermittent wind power affects the secure operation of power systems and leads to a requirement of robust and economic generation scheduling. This paper presents an optimal day–ahead wind–thermal generation scheduling method that considers the statistical and predicted features of wind speeds. In this method, the statistical analysis of historical wind data, which represents the local wind regime, is first implemented. Then, according to the statistical results and the predicted wind power, the spinning reserve requirements for the scheduling period are calculated. Based on the calculated spinning reserve requirements, the wind–thermal generation scheduling is finally conducted. To validate the program, a verified study is performed on a test system. Then, numerical studies to demonstrate the effectiveness of the proposed method are conducted.

Two-layer optimization methodology for wind distributed generation planning considering plug-in electric vehicles uncertainty: A flexible active-reactive power approach

International Nuclear Information System (INIS)

Ahmadian, Ali; Sedghi, Mahdi; Aliakbar-Golkar, Masoud; Fowler, Michael; Elkamel, Ali

2016-01-01

Highlights: • Flexible active-reactive power control of WDGs is proposed for WDGs planning. • The uncertainty of PEVs effect is considered in WDGs planning. • The wind data is classified in four separate seasons to reach more accurate results. • The PSO algorithm is modified to overcome the complexity of problem. - Abstract: With increasing the penetration of wind power, the voltage regulation becomes a more important problem in active distribution networks. In addition, as an uncertain load Plug-in Electric Vehicles (PEVs) will introduce a new concern in voltage adjustment of future distribution networks. Hence, this paper presents a flexible active-reactive power based Wind Distributed Generation (WDG) planning procedure to address the mentioned challenges. The uncertainties related to WDGs, load demand as well as PEVs load have been handled using the Point Estimate Method (PEM). The distribution network under study is equipped to on-load tap-changer and, as a conventional voltage control component, the Capacitor Banks (CBs) will be planned simultaneously with WDGs. The planning procedure has been considered as a two-loop optimization problem that is solved using Particle Swarm Optimization (PSO) and Tabu Search (TS) algorithms. The tap position and power factor of WDGs are taken into account as stochastic variables with practical limitations. The proposed methodology is applied to a typical distribution network and several scenarios are considered and analyzed. Simulation results show that the standard deviation of power factor depends on PEVs penetration that highlights the capability curve of WDGs. The optimal penetration of wind power increases nonlinearly versus increasing of PEVs connected to the distribution network, however the fixed CBs are required to increase the optimal penetration of WDGs. The proposed Modified PSO (MPSO) is compared with the conventional PSO in numerical studies that show MPSO is more efficient than the conventional

Problem statement for optimal design of steel structures

Directory of Open Access Journals (Sweden)

Ginzburg Aleksandr Vital'evich

2014-07-01

Full Text Available The presented article considers the following complex of tasks. The main stages of the life cycle of a building construction with the indication of process entrance and process exit are described. Requirements imposed on steel constructions are considered. The optimum range of application for steel designs is specified, as well as merits and demerits of a design material. The nomenclature of metal designs is listed - the block diagram is constructed. Possible optimality criteria of steel designs, offered by various authors for various types of constructions are considered. It is established that most often the criterion of a minimum of design mass is accepted as criterion of optimality; more rarely - a minimum of the given expenses, a minimum of a design cost in business. In the present article special attention is paid to a type of objective function of optimization problem. It is also established that depending on the accepted optimality criterion, the use of different types of functions is possible. This complexity of objective function depends on completeness of optimality criterion application. In the work the authors consider the following objective functions: the mass of the main element of a design; objective function by criterion of factory cost; objective function by criterion of cost in business. According to these examples it can be seen that objective functions by the criteria of labor expenses for production of designs are generally non-linear, which complicates solving the optimization problem. Another important factor influencing the problem of optimal design solution for steel designs, which is analyzed, is account for operating restrictions. In the article 8 groups of restrictions are analyzed. Attempts to completely account for the parameters of objective function optimized by particular optimality criteria, taking into account all the operating restrictions, considerably complicates the problem of designing. For solving this

Optimal sizing and operation of energy storage systems considering long term assessment

Directory of Open Access Journals (Sweden)

Gerardo Guerra

2018-01-01

Full Text Available This paper proposes a procedure for estimating the optimal sizing of Photovoltaic Generators and Energy Storage units when they are operated from the utility’s perspective. The goal is to explore the potential improvement on the overall operating conditions of the distribution system to which the Generators and Storage units will be connected. Optimization is conducted by means of a General Parallel Genetic Algorithm that seeks to maximize the technical benefits for the distribution system. The paper proposes an operation strategy for Energy Storage units based on the daily variation of load and generation; the operation strategy is optimized for an evaluation period of one year using hourly power curves. The construction of the yearly Storage operation curve results in a high-dimension optimization problem; as a result, different day-classification methods are applied in order to reduce the dimension of the optimization. Results show that the proposed approach is capable of producing significant improvements in system operating conditions and that the best performance is obtained when the day-classification is based on the similarity among daily power curves.

Optimizing wind farm cable routing considering power losses

DEFF Research Database (Denmark)

Fischetti, Martina; Pisinger, David

2017-01-01

that must be spent immediately in cable and installation costs, and the future reduced revenues due to power losses. The latter goal has not been addressed in previous work. We present a Mixed-Integer Linear Programming approach to optimize the routing using both exact and math-heuristic methods....... In the power losses computation, wind scenarios are handled eciently as part of the preprocessing, resulting in a MIP model of only slightly larger size. A library of real-life instances is introduced and made publicly available for benchmarking. Computational results on this testbed show the viability of our...

Power Generation Expansion Optimization Model Considering Multi-Scenario Electricity Demand Constraints: A Case Study of Zhejiang Province, China

Directory of Open Access Journals (Sweden)

Peng Wang

2018-06-01

Full Text Available Reasonable and effective power planning contributes a lot to energy efficiency improvement, as well as the formulation of future economic and energy policies for a region. Since only a few provinces in China have nuclear power plants so far, nuclear power plants were not considered in many provincial-level power planning models. As an extremely important source of power generation in the future, the role of nuclear power plants can never be overlooked. In this paper, a comprehensive and detailed optimization model of provincial-level power generation expansion considering biomass and nuclear power plants is established from the perspective of electricity demand uncertainty. This model has been successfully applied to the case study of Zhejiang Province. The findings suggest that the nuclear power plants will contribute 9.56% of the total installed capacity, and it will become the second stable electricity source. The lowest total discounted cost is 1033.28 billion RMB and the fuel cost accounts for a large part of the total cost (about 69%. Different key performance indicators (KPI differentiate electricity demand in scenarios that are used to test the model. Low electricity demand in the development mode of the comprehensive adjustment scenario (COML produces the optimal power development path, as it provides the lowest discounted cost.

Considering FACTS in Optimal Transmission Expansion Planning

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K. Soleimani

2017-10-01

Full Text Available The expansion of power transmission systems is an important part of the expansion of power systems that requires enormous investment costs. Since the construction of new transmission lines is very expensive, it is necessary to choose the most efficient expansion plan that ensures system security with a minimal number of new lines. In this paper, the role of Flexible AC Transmission System (FACTS devices in the effective operation and expansion planning of transmission systems is examined. Effort was taken to implement a method based on sensitivity analysis to select the optimal number and location of FACTS devices, lines and other elements of the transmission system. Using this method, the transmission expansion plan for a 9 and a 39 bus power system was performed with and without the presence of FACTS with the use of DPL environment in Digsilent software 15.1. Results show that the use of these devices reduces the need for new transmission lines and minimizes the investment cost.

Optimal sizing of small wind/battery systems considering the DC bus voltage stability effect on energy capture, wind speed variability, and load uncertainty

International Nuclear Information System (INIS)

Lujano-Rojas, Juan M.; Dufo-López, Rodolfo; Bernal-Agustín, José L.

2012-01-01

Highlights: ► We propose a mathematical model for optimal sizing of small wind energy systems. ► No other previous work has considered all the aspects included in this paper. ► The model considers several parameters about batteries. ► Wind speed variability is considered by means of ARMA model. ► The results show how to minimize the expected energy that is not supplied. - Abstract: In this paper, a mathematical model for stochastic simulation and optimization of small wind energy systems is presented. This model is able to consider the operation of the charge controller, the coulombic efficiency during charge and discharge processes, the influence of temperature on the battery bank capacity, the wind speed variability, and load uncertainty. The joint effect of charge controller operation, ambient temperature, and coulombic efficiency is analyzed in a system installed in Zaragoza (Spain), concluding that if the analysis without considering these factors is carried out, the reliability level of the physical system could be lower than expected, and an increment of 25% in the battery bank capacity would be required to reach a reliability level of 90% in the analyzed case. Also, the effect of the wind speed variability and load uncertainty in the system reliability is analyzed. Finally, the uncertainty in the battery bank lifetime and its effect on the net present cost are discussed. The results showed that, considering uncertainty of 17.5% in the battery bank lifetime calculated using the Ah throughput model, about 12% of uncertainty in the net present cost is expected. The model presented in this research could be a useful stochastic simulation and optimization tool that allows the consideration of important uncertainty factors in techno-economic analysis.

Integrated Optimization of Long-Range Underwater Signal Detection, Feature Extraction, and Classification for Nuclear Treaty Monitoring

NARCIS (Netherlands)

Tuma, M.; Rorbech, V.; Prior, M.; Igel, C.

2016-01-01

We designed and jointly optimized an integrated signal processing chain for detection and classification of long-range passive-acoustic underwater signals recorded by the global geophysical monitoring network of the Comprehensive Nuclear-Test-Ban Treaty Organization. Starting at the level of raw

Retrieval interval mapping, a tool to optimize the spectral retrieval range in differential optical absorption spectroscopy

Science.gov (United States)

Vogel, L.; Sihler, H.; Lampel, J.; Wagner, T.; Platt, U.

2012-06-01

Remote sensing via differential optical absorption spectroscopy (DOAS) has become a standard technique to identify and quantify trace gases in the atmosphere. The technique is applied in a variety of configurations, commonly classified into active and passive instruments using artificial and natural light sources, respectively. Platforms range from ground based to satellite instruments and trace-gases are studied in all kinds of different environments. Due to the wide range of measurement conditions, atmospheric compositions and instruments used, a specific challenge of a DOAS retrieval is to optimize the parameters for each specific case and particular trace gas of interest. This becomes especially important when measuring close to the detection limit. A well chosen evaluation wavelength range is crucial to the DOAS technique. It should encompass strong absorption bands of the trace gas of interest in order to maximize the sensitivity of the retrieval, while at the same time minimizing absorption structures of other trace gases and thus potential interferences. Also, instrumental limitations and wavelength depending sources of errors (e.g. insufficient corrections for the Ring effect and cross correlations between trace gas cross sections) need to be taken into account. Most often, not all of these requirements can be fulfilled simultaneously and a compromise needs to be found depending on the conditions at hand. Although for many trace gases the overall dependence of common DOAS retrieval on the evaluation wavelength interval is known, a systematic approach to find the optimal retrieval wavelength range and qualitative assessment is missing. Here we present a novel tool to determine the optimal evaluation wavelength range. It is based on mapping retrieved values in the retrieval wavelength space and thus visualize the consequence of different choices of retrieval spectral ranges, e.g. caused by slightly erroneous absorption cross sections, cross correlations and

Design of a biomass-to-biorefinery logistics system through bio-inspired metaheuristic optimization considering multiple types of feedstocks

Science.gov (United States)

Trueba, Isidoro

fossil fuels to biofuels. In many ways biomass is a unique renewable resource. It can be stored and transported relatively easily in contrast to renewable options such as wind and solar, which create intermittent electrical power that requires immediate consumption and a connection to the grid. This thesis presents two different models for the design optimization of a biomass-to-biorefinery logistics system through bio-inspired metaheuristic optimization considering multiple types of feedstocks. This work compares the performance and solutions obtained by two types of metaheuristic approaches; genetic algorithm and ant colony optimization. Compared to rigorous mathematical optimization methods or iterative algorithms, metaheuristics do not guarantee that a global optimal solution can be found on some class of problems. Problems with similar characteristics to the one presented in this thesis have been previously solved using linear programming, integer programming and mixed integer programming methods. However, depending on the type of problem, these mathematical or complete methods might need exponential computation time in the worst-case. This often leads to computation times too high for practical purposes. Therefore, this thesis develops two types of metaheuristic approaches for the design optimization of a biomass-to-biorefinery logistics system considering multiple types of feedstocks and shows that metaheuristics are highly suitable to solve hard combinatorial optimization problems such as the one addressed in this research work.

The use of lifetime functions in the optimization of interventions on existing bridges considering maintenance and failure costs

Energy Technology Data Exchange (ETDEWEB)

Yang, Seung-Ie [Department of Civil, Enviromental, and Architectural Enginnering, University of Colorado, Campus Box 428, Boulder, CO 80309-0428 (United States)]. E-mail: yangsione@dreamwiz.com; Frangopol, Dan M. [Department of Civil, Enviromental, and Architectural Enginnering, University of Colorado, Campus Box 428, Boulder, CO 80309-0428 (United States)]. E-mail: dan.frangopol@colorado.edu; Kawakami, Yoriko [Hanshin Expressway Public Corporation, Kobe Maintenance Department, 16-1 Shinko-cho Chuo-ku Kobe City, Hyogo, 650-0041 (Japan)]. E-mail: yoriko-kawakami@hepc.go.jp; Neves, Luis C. [Department of Civil, Enviromental, and Architectural Enginnering, University of Colorado, Campus Box 428, Boulder, CO 80309-0428 (United States)]. E-mail: lneves@civil.uminho.pt

2006-06-15

In the last decade, it became clear that life-cycle cost analysis of existing civil infrastructure must be used to optimally manage the growing number of aging and deteriorating structures. The uncertainties associated with deteriorating structures require the use of probabilistic methods to properly evaluate their lifetime performance. In this paper, the deterioration and the effect of maintenance actions are analyzed considering the performance of existing structures characterized by lifetime functions. These functions allow, in a simple manner, the consideration of the effect of aging on the decrease of the probability of survival of a structure, as well as the effect of maintenance actions. Models for the effects of proactive and reactive preventive maintenance, and essential maintenance actions are presented. Since the probability of failure is different from zero during the entire service life of a deteriorating structure and depends strongly on the maintenance strategy, the cost of failure is included in this analysis. The failure of one component in a structure does not usually lead to failure of the structure and, as a result, the safety of existing structures must be analyzed using a system reliability framework. The optimization consists of minimizing the sum of the cumulative maintenance and expected failure cost during the prescribed time horizon. Two examples of application of the proposed methodology are presented. In the first example, the sum of the maintenance and failure costs of a bridge in Colorado is minimized considering essential maintenance only and a fixed minimum acceptable probability of failure. In the second example, the expected lifetime cost, including maintenance and expected failure costs, of a multi-girder bridge is minimized considering reactive preventive maintenance actions.

The use of lifetime functions in the optimization of interventions on existing bridges considering maintenance and failure costs

International Nuclear Information System (INIS)

Yang, Seung-Ie; Frangopol, Dan M.; Kawakami, Yoriko; Neves, Luis C.

2006-01-01

In the last decade, it became clear that life-cycle cost analysis of existing civil infrastructure must be used to optimally manage the growing number of aging and deteriorating structures. The uncertainties associated with deteriorating structures require the use of probabilistic methods to properly evaluate their lifetime performance. In this paper, the deterioration and the effect of maintenance actions are analyzed considering the performance of existing structures characterized by lifetime functions. These functions allow, in a simple manner, the consideration of the effect of aging on the decrease of the probability of survival of a structure, as well as the effect of maintenance actions. Models for the effects of proactive and reactive preventive maintenance, and essential maintenance actions are presented. Since the probability of failure is different from zero during the entire service life of a deteriorating structure and depends strongly on the maintenance strategy, the cost of failure is included in this analysis. The failure of one component in a structure does not usually lead to failure of the structure and, as a result, the safety of existing structures must be analyzed using a system reliability framework. The optimization consists of minimizing the sum of the cumulative maintenance and expected failure cost during the prescribed time horizon. Two examples of application of the proposed methodology are presented. In the first example, the sum of the maintenance and failure costs of a bridge in Colorado is minimized considering essential maintenance only and a fixed minimum acceptable probability of failure. In the second example, the expected lifetime cost, including maintenance and expected failure costs, of a multi-girder bridge is minimized considering reactive preventive maintenance actions

Optimal CO2 Enrichment Considering Emission from Soil for Cucumber Greenhouses

International Nuclear Information System (INIS)

Lee, D.H.; Lee, K.S.; Cho, Y.J.; Kim, H.J.; Choi, J.M.; Chung, S.O.

2012-01-01

Reducing carbon dioxide (CO2) exhaust has become a major issue for society in the last few years, especially since the initial release of the Kyoto Protocol in 1997 that strictly limited the emissions of greenhouse gas for each country. One of the primary sectors affecting the levels of atmospheric greenhouse gases is agriculture where CO2 is not only consumed by plants but also produced from various types of soil and agricultural ecosystems including greenhouses. In greenhouse cultivation, CO2 concentration plays an essential role in the photosynthesis process of crops. Optimum control of greenhouse CO2 enrichment based on accurate monitoring of the added CO2 can improve profitability through efficient crop production and reduce environmental impact, compared to traditional management practices. In this study, a sensor-based control system that could estimate the required CO2 concentration considering emission from soil for cucumber greenhouses was developed and evaluated. The relative profitability index (RPI) was defined by the ratio of growth rate to supplied CO2. RPI for a greenhouse controlled at lower set point of CO2 concentration (500 μmol * mol -1 ) was greater than that of greenhouse at higher set point (800 μmol * mol -1 ). Evaluation tests to optimize CO2 enrichment concluded that the developed control system would be applicable not only to minimize over-exhaust of CO2 but also to maintain the crop profitability

A Three-Stage Optimal Approach for Power System Economic Dispatch Considering Microgrids

Directory of Open Access Journals (Sweden)

Wei-Tzer Huang

2016-11-01

Full Text Available The inclusion of microgrids (MGs in power systems, especially distribution-substation-level MGs, significantly affects power systems because of the large volumes of import and export power flows. Consequently, power dispatch has become complicated, and finding an optimal solution is difficult. In this study, a three-stage optimal power dispatch model is proposed to solve such dispatch problems. In the proposed model, the entire power system is divided into two parts, namely, the main power grid and MGs. The optimal power dispatch problem is resolved on the basis of multi-area concepts. In stage I, the main power system economic dispatch (ED problem is solved by sensitive factors. In stage II, the optimal power dispatches of the local MGs are addressed via an improved direct search method. In stage III, the incremental linear models for the entire power system can be established on the basis of the solutions of the previous two stages and can be subjected to linear programming to determine the optimal reschedules from the original dispatch solutions. The proposed method is coded using Matlab and tested by utilizing an IEEE 14-bus test system to verify its feasibility and accuracy. Results demonstrated that the proposed approach can be used for the ED of power systems with MGs as virtual power plants.

Optimizing the supply chain of biomass and biogas for a single plant considering mass and energy losses

DEFF Research Database (Denmark)

Jensen, Ida Græsted; Münster, Marie; Pisinger, David

2017-01-01

plants. In this paper, a mixed integer programming (MIP) model for finding the optimal production and investment plan for a biogas supply chain is presented to ensure better economy for the full chain hopefully stimulating future investments in biogas. The model makes use of step-wise linear functions...... to represent capital and operational expenditures at the biogas plant; considers the chain from the farmer to the end market; and includes changes of mass and energy content along the chain by modeling the losses and gains for all processes in the chain. Biomass inputs are scheduled on a weekly basis whereas...... energy outputs are scheduled on an hourly basis to better capture the changes of energy prices and potentially take advantage of these changes. The model is tested on a case study with co-digestion of straw, sugar beet and manure, considering natural gas, heat, and electricity as end products. The model...

Component sizing optimization of plug-in hybrid electric vehicles

International Nuclear Information System (INIS)

Wu, Xiaolan; Cao, Binggang; Li, Xueyan; Xu, Jun; Ren, Xiaolong

2011-01-01

Plug-in hybrid electric vehicles (PHEVs) are considered as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This paper describes a methodology for the optimization of PHEVs component sizing using parallel chaos optimization algorithm (PCOA). In this approach, the objective function is defined so as to minimize the drivetrain cost. In addition, the driving performance requirements are considered as constraints. Finally, the optimization process is performed over three different all electric range (AER) and two types of batteries. The results from computer simulation show the effectiveness of the approach and the reduction in drivetrian cost while ensuring the vehicle performance.

National turnaround time survey: professional consensus standards for optimal performance and thresholds considered to compromise efficient and effective clinical management.

Science.gov (United States)

McKillop, Derek J; Auld, Peter

2017-01-01

Background Turnaround time can be defined as the time from receipt of a sample by the laboratory to the validation of the result. The Royal College of Pathologists recommends that a number of performance indicators for turnaround time should be agreed with stakeholders. The difficulty is in arriving at a goal which has some evidence base to support it other than what may simply be currently achievable technically. This survey sought to establish a professional consensus on the goals and meaning of targets for laboratory turnaround time. Methods A questionnaire was circulated by the National Audit Committee to 173 lead consultants for biochemistry in the UK. The survey asked each participant to state their current target turnaround time for core investigations in a broad group of clinical settings. Each participant was also asked to provide a professional opinion on what turnaround time would pose an unacceptable risk to patient safety for each departmental category. A super majority (2/3) was selected as the threshold for consensus. Results The overall response rate was 58% ( n = 100) with a range of 49-72% across the individual Association for Clinical Biochemistry and Laboratory Medicine regions. The consensus optimal turnaround time for the emergency department was 2 h considered unacceptable. The times for general practice and outpatient department were 48 h and for Wards 12 h, respectively. Conclusions We consider that the figures provide a useful benchmark of current opinion, but clearly more empirical standards will have to develop alongside other aspects of healthcare delivery.

Planning Target Volume D95 and Mean Dose Should Be Considered for Optimal Local Control for Stereotactic Ablative Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Zhao, Lina [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Zhou, Shouhao [Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Balter, Peter [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Shen, Chan [Department of Health Service Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Gomez, Daniel R.; Welsh, James D.; Lin, Steve H. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Chang, Joe Y., E-mail: jychang@mdanderson.org [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States)

2016-07-15

Purpose: To identify the optimal dose parameters predictive for local/lobar control after stereotactic ablative radiation therapy (SABR) in early-stage non-small cell lung cancer (NSCLC). Methods and Materials: This study encompassed a total of 1092 patients (1200 lesions) with NSCLC of clinical stage T1-T2 N0M0 who were treated with SABR of 50 Gy in 4 fractions or 70 Gy in 10 fractions, depending on tumor location/size, using computed tomography-based heterogeneity corrections and a convolution superposition calculation algorithm. Patients were monitored by chest CT or positron emission tomography/CT and/or biopsy after SABR. Factors predicting local/lobar recurrence (LR) were determined by competing risk multivariate analysis. Continuous variables were divided into 2 subgroups at cutoff values identified by receiver operating characteristic curves. Results: At a median follow-up time of 31.7 months (interquartile range, 14.8-51.3 months), the 5-year time to local recurrence within the same lobe and overall survival rates were 93.8% and 44.8%, respectively. Total cumulative number of patients experiencing LR was 40 (3.7%), occurring at a median time of 14.4 months (range, 4.8-46 months). Using multivariate competing risk analysis, independent predictive factors for LR after SABR were minimum biologically effective dose (BED{sub 10}) to 95% of planning target volume (PTVD95 BED{sub 10}) ≤86 Gy (corresponding to PTV D95 physics dose of 42 Gy in 4 fractions or 55 Gy in 10 fractions) and gross tumor volume ≥8.3 cm{sup 3}. The PTVmean BED{sub 10} was highly correlated with PTVD95 BED{sub 10.} In univariate analysis, a cutoff of 130 Gy for PTVmean BED{sub 10} (corresponding to PTVmean physics dose of 55 Gy in 4 fractions or 75 Gy in 10 fractions) was also significantly associated with LR. Conclusions: In addition to gross tumor volume, higher radiation dose delivered to the PTV predicts for better local/lobar control. We recommend that both PTVD95 BED

Optimal integrated sizing and planning of hubs with midsize/large CHP units considering reliability of supply

International Nuclear Information System (INIS)

Moradi, Saeed; Ghaffarpour, Reza; Ranjbar, Ali Mohammad; Mozaffari, Babak

2017-01-01

Highlights: • New hub planning formulation is proposed to exploit assets of midsize/large CHPs. • Linearization approaches are proposed for two-variable nonlinear CHP fuel function. • Efficient operation of addressed CHPs & hub devices at contingencies are considered. • Reliability-embedded integrated planning & sizing is formulated as one single MILP. • Noticeable results for costs & reliability-embedded planning due to mid/large CHPs. - Abstract: Use of multi-carrier energy systems and the energy hub concept has recently been a widespread trend worldwide. However, most of the related researches specialize in CHP systems with constant electricity/heat ratios and linear operating characteristics. In this paper, integrated energy hub planning and sizing is developed for the energy systems with mid-scale and large-scale CHP units, by taking their wide operating range into consideration. The proposed formulation is aimed at taking the best use of the beneficial degrees of freedom associated with these units for decreasing total costs and increasing reliability. High-accuracy piecewise linearization techniques with approximation errors of about 1% are introduced for the nonlinear two-dimensional CHP input-output function, making it possible to successfully integrate the CHP sizing. Efficient operation of CHP and the hub at contingencies is extracted via a new formulation, which is developed to be incorporated to the planning and sizing problem. Optimal operation, planning, sizing and contingency operation of hub components are integrated and formulated as a single comprehensive MILP problem. Results on a case study with midsize CHPs reveal a 33% reduction in total costs, and it is demonstrated that the proposed formulation ceases the need for additional components/capacities for increasing reliability of supply.

A multi-objective model for closed-loop supply chain optimization and efficient supplier selection in a competitive environment considering quantity discount policy

Science.gov (United States)

Jahangoshai Rezaee, Mustafa; Yousefi, Samuel; Hayati, Jamileh

2017-06-01

Supplier selection and allocation of optimal order quantity are two of the most important processes in closed-loop supply chain (CLSC) and reverse logistic (RL). So that providing high quality raw material is considered as a basic requirement for a manufacturer to produce popular products, as well as achieve more market shares. On the other hand, considering the existence of competitive environment, suppliers have to offer customers incentives like discounts and enhance the quality of their products in a competition with other manufacturers. Therefore, in this study, a model is presented for CLSC optimization, efficient supplier selection, as well as orders allocation considering quantity discount policy. It is modeled using multi-objective programming based on the integrated simultaneous data envelopment analysis-Nash bargaining game. In this study, maximizing profit and efficiency and minimizing defective and functions of delivery delay rate are taken into accounts. Beside supplier selection, the suggested model selects refurbishing sites, as well as determining the number of products and parts in each network's sector. The suggested model's solution is carried out using global criteria method. Furthermore, based on related studies, a numerical example is examined to validate it.

A two-step ionospheric modeling algorithm considering the impact of GLONASS pseudo-range inter-channel biases

Science.gov (United States)

Zhang, Rui; Yao, Yi-bin; Hu, Yue-ming; Song, Wei-wei

2017-12-01

The Global Navigation Satellite System presents a plausible and cost-effective way of computing the total electron content (TEC). But TEC estimated value could be seriously affected by the differential code biases (DCB) of frequency-dependent satellites and receivers. Unlike GPS and other satellite systems, GLONASS adopts a frequency-division multiplexing access mode to distinguish different satellites. This strategy leads to different wavelengths and inter-frequency biases (IFBs) for both pseudo-range and carrier phase observations, whose impacts are rarely considered in ionospheric modeling. We obtained observations from four groups of co-stations to analyze the characteristics of the GLONASS receiver P1P2 pseudo-range IFB with a double-difference method. The results showed that the GLONASS P1P2 pseudo-range IFB remained stable for a period of time and could catch up to several meters, which cannot be absorbed by the receiver DCB during ionospheric modeling. Given the characteristics of the GLONASS P1P2 pseudo-range IFB, we proposed a two-step ionosphere modeling method with the priori IFB information. The experimental analysis showed that the new algorithm can effectively eliminate the adverse effects on ionospheric model and hardware delay parameters estimation in different space environments. During high solar activity period, compared to the traditional GPS + GLONASS modeling algorithm, the absolute average deviation of TEC decreased from 2.17 to 2.07 TECu (TEC unit); simultaneously, the average RMS of GPS satellite DCB decreased from 0.225 to 0.219 ns, and the average deviation of GLONASS satellite DCB decreased from 0.253 to 0.113 ns with a great improvement in over 55%.

Component sizing optimization of plug-in hybrid electric vehicles

Energy Technology Data Exchange (ETDEWEB)

Wu, Xiaolan; Cao, Binggang; Li, Xueyan; Xu, Jun; Ren, Xiaolong [School of Mechanical Engineering, Xi' an Jiaotong University, Xi' an, 710049 (China)

2011-03-15

Plug-in hybrid electric vehicles (PHEVs) are considered as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This paper describes a methodology for the optimization of PHEVs component sizing using parallel chaos optimization algorithm (PCOA). In this approach, the objective function is defined so as to minimize the drivetrain cost. In addition, the driving performance requirements are considered as constraints. Finally, the optimization process is performed over three different all electric range (AER) and two types of batteries. The results from computer simulation show the effectiveness of the approach and the reduction in drivetrian cost while ensuring the vehicle performance. (author)

A Method for Determining Optimal Residential Energy Efficiency Packages

Energy Technology Data Exchange (ETDEWEB)

Polly, B. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Gestwick, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Bianchi, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Anderson, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Horowitz, S. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Christensen, C. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Judkoff, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States)

2011-04-01

This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location.

Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response

International Nuclear Information System (INIS)

Ju, Liwei; Li, Huanhuan; Zhao, Junwei; Chen, Kangting; Tan, Qingkun; Tan, Zhongfu

2016-01-01

Highlights: • Our research focuses on virtual power plant. • Electric vehicle group and demand response are integrated into virtual power plant. • Stochastic chance constraint planning is applied to overcome uncertainties. • A multi-objective stochastic scheduling model is proposed for virtual power plant. • A three-stage hybrid intelligent solution algorithm is proposed for solving the model. - Abstract: A stochastic chance-constrained planning method is applied to build a multi-objective optimization model for virtual power plant scheduling. Firstly, the implementation cost of demand response is calculated using the system income difference. Secondly, a wind power plant, photovoltaic power, an electric vehicle group and a conventional power plant are aggregated into a virtual power plant. A stochastic scheduling model is proposed for the virtual power plant, considering uncertainties under three objective functions. Thirdly, a three-stage hybrid intelligent solution algorithm is proposed, featuring the particle swarm optimization algorithm, the entropy weight method and the fuzzy satisfaction theory. Finally, the Yunnan distributed power demonstration project in China is utilized for example analysis. Simulation results demonstrate that when considering uncertainties, the system will reduce the grid connection of the wind power plant and photovoltaic power to decrease the power shortage punishment cost. The average reduction of the system power shortage punishment cost and the operation revenue of virtual power plant are 61.5% and 1.76%, respectively, while the average increase of the system abandoned energy cost is 40.4%. The output of the virtual power plant exhibits a reverse distribution with the confidence degree of the uncertainty variable. The proposed algorithm rapidly calculates a global optimal set. The electric vehicle group could provide spinning reserve to ensure stability of the output of the virtual power plant. Demand response could

Optimizing wind farm cable routing considering power losses

DEFF Research Database (Denmark)

Fischetti, Martina; Pisinger, David

2017-01-01

Wind energy is the fastest growing source of renewable energy, but as wind farms are getting larger and more remotely located, installation and infrastructure costs are rising. It is estimated that the expenses for electrical infrastructure account for 15-30% of the overall initial costs, hence...... that must be spent immediately in cable and installation costs, and the future reduced revenues due to power losses. The latter goal has not been addressed in previous work. We present a Mixed-Integer Linear Programming approach to optimize the routing using both exact and math-heuristic methods....... In the power losses computation, wind scenarios are handled eciently as part of the preprocessing, resulting in a MIP model of only slightly larger size. A library of real-life instances is introduced and made publicly available for benchmarking. Computational results on this testbed show the viability of our...

Optimizing wind farm cable routing considering power losses

DEFF Research Database (Denmark)

Fischetti, Martina; Pisinger, David

Wind energy is the fastest growing source of renewable energy, but as wind farms are getting larger and more remotely located, installation and infrastructure costs are rising. It is estimated that the expenses for electrical infrastructure account for 15-30% of the overall initial costs, hence...... that must be spent immediately in cable and installation costs, and the future reduced revenues due to power losses. The latter goal has not been addressed in previous work. We present a Mixed-Integer Linear Programming approach to optimize the routing using both exact and math-heuristic methods....... In the power losses computation, wind scenarios are handled eciently as part of the preprocessing, resulting in a MIP model of only slightly larger size. A library of real-life instances is introduced and made publicly available for benchmarking. Computational results on this testbed show the viability of our...

Optimization of a space based radiator

International Nuclear Information System (INIS)

Sam, Kien Fan Cesar Hung; Deng Zhongmin

2011-01-01

Nowadays there is an increased demand in satellite weight reduction for the reduction of costs. Thermal control system designers have to face the challenge of reducing both the weight of the system and required heater power while maintaining the components temperature within their design ranges. The main purpose of this paper is to present an optimization of a heat pipe radiator applied to a practical engineering design application. For this study, a communications satellite payload panel was considered. Four radiator areas were defined instead of a centralized one in order to improve the heat rejection into space; the radiator's dimensions were determined considering worst hot scenario, solar fluxes, heat dissipation and the component's design temperature upper limit. Dimensions, thermal properties of the structural panel, optical properties and degradation/contamination on thermal control coatings were also considered. A thermal model was constructed for thermal analysis and two heat pipe network designs were evaluated and compared. The model that allowed better radiator efficiency was selected for parametric thermal analysis and optimization. This pursues finding the minimum size of the heat pipe network while keeping complying with thermal control requirements without increasing power consumption. - Highlights: →Heat pipe radiator optimization applied to a practical engineering design application. →The heat pipe radiator of a communications satellite panel is optimized. →A thermal model was built for parametric thermal analysis and optimization. →Optimal heat pipe network size is determined for the optimal weight solution. →The thermal compliance was verified by transient thermal analysis.

Work space optimization of a r-r planar manipulator using particle ...

African Journals Online (AJOL)

A two link revolute planar robotic manipulator is optimized for maximization of work space covered by its end effector. A mathematical model for optimization is built considering singularities which control the range of design variables. Condition number which is the measure of change in output value (End effector position) ...

Optimization of Korean energy planning for sustainability considering uncertainties in learning rates and external factors

International Nuclear Information System (INIS)

Kim, Seunghyok; Koo, Jamin; Lee, Chang Jun; Yoon, En Sup

2012-01-01

During the last few decades, energy planning has focused on meeting domestic demand at lower total costs. However, global warming and the shared recognition of it have transformed the problem of energy planning into a more complex task with a greater number of issues to be considered. Since the key issue is to reduce greenhouse effects, governments around the world have begun to make investments in renewable energy systems (e.g., hydro, wind, solar, and/or biomass power). The relatively high costs of renewable energy systems and the uncertain outlook of their rate of diffusion in the market make it difficult to heavily rely on them. The uncertain variations in production cost over time are especially challenging. To handle uncertainties, the concept of the learning rate was adopted in this study so as to compute the costs of energy systems in the future and Monte Carlo simulation was performed. The aim of this study was to optimize plans of conventional and prospective renewable energy systems with respect to production cost. The production cost included capital, fixed, variable, and external costs. For the case study, the energy situation in South Korea was used. The results of the case study where the proposed methodology was applied could provide useful insights economically and strategies of sustainable energy management for ambiguous environments. -- Highlights: ► We propose energy planning method for sustainability. ► We consider uncertainties such as learning rate, fuel prices, and CO 2 prices. ► We consider the possibility of CO 2 trading. ► The proposed method is applied to South Korea case. ► The added capacities of energy systems depend on uncertainties.

The Optimization dispatching of Micro Grid Considering Load Control

Science.gov (United States)

Zhang, Pengfei; Xie, Jiqiang; Yang, Xiu; He, Hongli

2018-01-01

This paper proposes an optimization control of micro-grid system economy operation model. It coordinates the new energy and storage operation with diesel generator output, so as to achieve the economic operation purpose of micro-grid. In this paper, the micro-grid network economic operation model is transformed into mixed integer programming problem, which is solved by the mature commercial software, and the new model is proved to be economical, and the load control strategy can reduce the charge and discharge times of energy storage devices, and extend the service life of the energy storage device to a certain extent.

Range extender module. Enabler for electric mobility; Range-Extender-Modul. Wegbereiter fuer elektrische Mobilitaet

Energy Technology Data Exchange (ETDEWEB)

Fischer, Robert; Fraidl, Guenter Karl; Hubmann, Christian; Kapus, Paul Ernst; Kunzemann, Ralf; Sifferlinger, Bernhard; Beste, Frank [AVL List GmbH, Graz (Austria)

2009-10-15

The Range Extender as an auxiliary power supply for extended driving ranges is of significant importance in achieving a high level of customer acceptance for electric vehicles. The AVL concept is optimized for electric power generation in single-point operation and allows a compactly integrated, cost-efficient and weight-efficient module design. The internal combustion engine requirements of the Pure Range Extender from AVL permit not only the use of simplified four-stroke concepts but also the application of emission-optimized and fuel consumption-optimized two-stroke and rotary piston engines. (orig.)

A Optimal Dimension Parameters Design of Needle Roller Bearings Considering Multi Factors Affecting Life

Science.gov (United States)

Bai, XiaoBo; Li, Bo

2017-12-01

Taking into many influencing factors, a Needle Roller Bearings life calculation model was established using the Influence coefficient method and ISO formula. Take this model as the optimization objective function, some optimization variables were determined, and these variables were added constraint conditions. Using dynamic nonlinear strategy to improve the inertia weight, the arccosine strategy to adjust the learning factor, function constraints was solved by the methods of ensure particle legitimacy. Nonlinear optimization design of cylindrical roller bearings was realized by Improved Particle Swarm Algorithm. The proposed method of bearing design was verified effective by test of the optimization results.

Cordon Pricing Considering Air Pollutants Emission

Directory of Open Access Journals (Sweden)

Shahriar Afandizadeh

2016-04-01

Full Text Available This paper considers the issue of air pollutants emission for the optimal and sustainable determination of cordon location, toll level, and price of park and ride (P&R. Although air pollutants emission decreases within the cordon by the implementation of cordon pricing scheme, it may increase outside the cordon and the whole network. Hence, air pollutants emission may only transfer from inside of the cordon to its outside. Therefore, in this paper, a multi-objective bi-level optimization model is developed. A solution algorithm is also presented based on the second version of strength Pareto evolutionary algorithm (SPEA2. The results reveal that this multi-objective model can be a useful tool for the sustainable and optimal design of the cordon and P&R scheme. In addition, cordon pricing is a multi-objective problem. Therefore, it is necessary to consider air pollutants emission. By choosing another non-dominated result in the solution space, air pollutants emission outside the cordon and the whole network can be reduced without a significant reduction in social welfare.

Optimizing battery sizes of plug-in hybrid and extended range electric vehicles for different user types

International Nuclear Information System (INIS)

Redelbach, Martin; Özdemir, Enver Doruk; Friedrich, Horst E.

2014-01-01

There are ambitious greenhouse gas emission (GHG) targets for the manufacturers of light duty vehicles. To reduce the GHG emissions, plug-in hybrid electric vehicle (PHEV) and extended range electric vehicle (EREV) are promising powertrain technologies. However, the battery is still a very critical component due to the high production cost and heavy weight. This paper introduces a holistic approach for the optimization of the battery size of PHEVs and EREVs under German market conditions. The assessment focuses on the heterogeneity across drivers, by analyzing the impact of different driving profiles on the optimal battery setup from total cost of ownership (TCO) perspective. The results show that the battery size has a significant effect on the TCO. For an average German driver (15,000 km/a), battery capacities of 4 kWh (PHEV) and 6 kWh (EREV) would be cost optimal by 2020. However, these values vary strongly with the driving profile of the user. Moreover, the optimal battery size is also affected by external factors, e.g. electricity and fuel prices or battery production cost. Therefore, car manufacturers should develop a modular design for their batteries, which allows adapting the storage capacity to meet the individual customer requirements instead of “one size fits all”. - Highlights: • Optimization of the battery size of PHEVs and EREVs under German market conditions. • Focus on heterogeneity across drivers (e.g. mileage, trip distribution, speed). • Optimal battery size strongly depends on the driving profile and energy prices. • OEMs require a modular design for their batteries to meet individual requirements

Truss systems and shape optimization

Science.gov (United States)

Pricop, Mihai Victor; Bunea, Marian; Nedelcu, Roxana

2017-07-01

Structure optimization is an important topic because of its benefits and wide applicability range, from civil engineering to aerospace and automotive industries, contributing to a more green industry and life. Truss finite elements are still in use in many research/industrial codesfor their simple stiffness matrixand are naturally matching the requirements for cellular materials especially considering various 3D printing technologies. Optimality Criteria combined with Solid Isotropic Material with Penalization is the optimization method of choice, particularized for truss systems. Global locked structures areobtainedusinglocally locked lattice local organization, corresponding to structured or unstructured meshes. Post processing is important for downstream application of the method, to make a faster link to the CAD systems. To export the optimal structure in CATIA, a CATScript file is automatically generated. Results, findings and conclusions are given for two and three-dimensional cases.

An optimization of robust SMES with specified structure H∞ controller for power system stabilization considering superconducting magnetic coil size

International Nuclear Information System (INIS)

Ngamroo, Issarachai

2011-01-01

Even the superconducting magnetic energy storage (SMES) is the smart stabilizing device in electric power systems, the installation cost of SMES is very high. Especially, the superconducting magnetic coil size which is the critical part of SMES, must be well designed. On the contrary, various system operating conditions result in system uncertainties. The power controller of SMES designed without taking such uncertainties into account, may fail to stabilize the system. By considering both coil size and system uncertainties, this paper copes with the optimization of robust SMES controller. No need of exact mathematic equations, the normalized coprime factorization is applied to model system uncertainties. Based on the normalized integral square error index of inter-area rotor angle difference and specified structured H ∞ loop shaping optimization, the robust SMES controller with the smallest coil size, can be achieved by the genetic algorithm. The robustness of the proposed SMES with the smallest coil size can be confirmed by simulation study.

Application of a Continuous Particle Swarm Optimization (CPSO for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method

Directory of Open Access Journals (Sweden)

Abdul Wadood

2018-04-01

Full Text Available In an electrical power system, the coordination of the overcurrent relays plays an important role in protecting the electrical system by providing primary as well as backup protection. To reduce power outages, the coordination between these relays should be kept at the optimum value to minimize the total operating time and ensure that the least damage occurs under fault conditions. It is also imperative to ensure that the relay setting does not create an unintentional operation and consecutive sympathy trips. In a power system protection coordination problem, the objective function to be optimized is the sum of the total operating time of all main relays. In this paper, the coordination of overcurrent relays in a ring fed distribution system is formulated as an optimization problem. Coordination is performed using proposed continuous particle swarm optimization. In order to enhance and improve the quality of this solution a local search algorithm (LSA is implanted into the original particle swarm algorithm (PSO and, in addition to the constraints, these are amalgamated into the fitness function via the penalty method. The results achieved from the continuous particle swarm optimization algorithm (CPSO are compared with other evolutionary optimization algorithms (EA and this comparison showed that the proposed scheme is competent in dealing with the relevant problems. From further analyzing the obtained results, it was found that the continuous particle swarm approach provides the most globally optimum solution.

Optimal Referral Reward Considering Customer’s Budget Constraint

Directory of Open Access Journals (Sweden)

Dan Zhou

2015-12-01

Full Text Available Everyone likes Porsche but few can afford it. Budget constraints always play a critical role in a customer’s decision-making. The literature disproportionally focuses on how firms can induce customer valuations toward the product, but does not address how to assess the influence of budget constraints. We study these questions in the context of a referral reward program (RRP. RRP is a prominent marketing strategy that utilizes recommendations passed from existing customers to their friends and effectively stimulates word of mouth (WoM. We build a stylized game-theoretical model with a nested Stackelberg game involving three players: a firm, an existing customer, and a potential customer who is a friend of the existing customer. The budget is the friend’s private information. We show that RRPs might be optimal when the friend has either a low or a high valuation, but they work differently in each situation because of the budget. Furthermore, there are two budget thresholds, a fixed one and a variable one, which limit a firm’s ability to use rewards.

Topology optimization of the permanent magnet type MRI considering the magnetic field homogeneity

International Nuclear Information System (INIS)

Lee, Junghoon; Yoo, Jeonghoon

2010-01-01

This study is to suggest a concept design of the permanent magnet (PM) type magnetic resonance imaging (MRI) device based on the topology optimization method. The pulse currents in the gradient coils in the MRI device will introduce the effect of eddy currents in ferromagnetic material and it may worsen the quality of imaging. To equalize the magnetic flux in the PM type MRI device for good imaging, the eddy current effect in the ferromagnetic material must be reduced. This study attempts to use the topology optimization scheme for equalizing the magnetic flux in the measuring domain of the PM type MRI device using that the magnetic flux can be calculated directly by a commercial finite element analysis package. The density method is adopted for topology optimization and the sensitivity of the objective function is computed according to the density change of each finite element in the design domain. As a result, optimal shapes of the pole of the PM type MRI device can be obtained. The commercial package, ANSYS, is used for analyzing the magnetic field problem and obtaining the resultant magnetic flux.

Microgrid optimal scheduling considering impact of high penetration wind generation

Science.gov (United States)

Alanazi, Abdulaziz

The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.

Parametric optimization and range analysis of Organic Rankine Cycle for binary-cycle geothermal plant

International Nuclear Information System (INIS)

Wang, Xing; Liu, Xiaomin; Zhang, Chuhua

2014-01-01

Highlights: • Optimal level constitution of parameters for ORC system was obtained. • Order of system parameters’ sensitivity to the performance of ORC was revealed. • Evaporating temperature had significant effect on performance of ORC system. • Superheater had little effect on performance of ORC system. - Abstract: In this study, a thermodynamic model of Organic Rankine Cycle (ORC) system combined with orthogonal design is proposed. The comprehensive scoring method was adopted to obtain a comprehensive index to evaluate both of the thermodynamic performance and economic performance. The optimal level constitution of system parameters which improves the thermodynamic and economic performance of ORC system is provided by analyzing the result of orthogonal design. The range analysis based on orthogonal design is adopted to determine the sensitivity of system parameters to the net power output of ORC system, thermal efficiency, the SP factor of radial inflow turbine, the power decrease factor of the pump and the total heat transfer capacity. The results show that the optimal level constitution of system parameters is determined as the working fluid of R245fa, the super heating temperature of 10 °C, the pinch temperature difference in evaporator and condenser of 5 °C, the evaporating temperature of 65 °C, the isentropic efficiency for the pump of 0.75 and the isentropic efficiency of radial inflow turbine of 0.85. The order of system parameters’ sensitivity to the comprehensive index of orthogonal design is evaporating temperature > isentropic efficiency of radial inflow turbine > the working fluid > the pinch temperature difference of the evaporator and the condenser > isentropic efficiency of cycle pump > the super heating temperature. This study provides useful references for selecting main controlled parameters in the optimal design of ORC system

The optimization model for multi-type customers assisting wind power consumptive considering uncertainty and demand response based on robust stochastic theory

International Nuclear Information System (INIS)

Tan, Zhongfu; Ju, Liwei; Reed, Brent; Rao, Rao; Peng, Daoxin; Li, Huanhuan; Pan, Ge

2015-01-01

Highlights: • Our research focuses on demand response behaviors of multi-type customers. • A wind power simulation method is proposed based on the Brownian motion theory. • Demand response revenue functions are proposed for multi-type customers. • A robust stochastic optimization model is proposed for wind power consumptive. • Models are built to measure the impacts of demand response on wind power consumptive. - Abstract: In order to relieve the influence of wind power uncertainty on power system operation, demand response and robust stochastic theory are introduced to build a stochastic scheduling optimization model. Firstly, this paper presents a simulation method for wind power considering external environment based on Brownian motion theory. Secondly, price-based demand response and incentive-based demand response are introduced to build demand response model. Thirdly, the paper constructs the demand response revenue functions for electric vehicle customers, business customers, industry customers and residential customers. Furthermore, robust stochastic optimization theory is introduced to build a wind power consumption stochastic optimization model. Finally, simulation analysis is taken in the IEEE 36 nodes 10 units system connected with 650 MW wind farms. The results show the robust stochastic optimization theory is better to overcome wind power uncertainty. Demand response can improve system wind power consumption capability. Besides, price-based demand response could transform customers’ load demand distribution, but its load curtailment capacity is not as obvious as incentive-based demand response. Since price-based demand response cannot transfer customer’s load demand as the same as incentive-based demand response, the comprehensive optimization effect will reach best when incentive-based demand response and price-based demand response are both introduced.

Development of a model to optimize global use of nuclear energy considering competition of seawater uranium and reprocessing

International Nuclear Information System (INIS)

Undarmaa, Baatarkhuu; Horio, Kenta; Fujii, Yasumasa; Komiyama, Ryoichi

2017-01-01

In order to sustain long-term energy security and to mitigate the climate change, nuclear power remains an important baseload option for the global power generation mix. To utilize nuclear power in long-term, some important concerns such as economics, stability of fuel supply and spent fuel amount should be evaluated. Model developed in this study optimizes the global use nuclear power considering such issues. The Model is based on linear programming and calculates the best mix of nuclear reactor types by minimizing the current value of total power generation cost within the target period (next 100 years). Possibility of fuel cycle options such as reprocessing, seawater uranium and thorium utilization are also taken in to account, along with remaining spent fuel and plutonium stock. As result. reprocessing and uranium from seawater become essential part of nuclear fuel cycle in the long run. Amount of stored spent fuel is reduced following the deployment of Fast Breeder Reactor. Also, as an extension of current model, a baseload power generation mix model, which estimates the optimal mix of nuclear and coal-fired power generation will be introduced. (author)

Noise tolerant illumination optimization applied to display devices

Science.gov (United States)

Cassarly, William J.; Irving, Bruce

2005-02-01

Display devices have historically been designed through an iterative process using numerous hardware prototypes. This process is effective but the number of iterations is limited by the time and cost to make the prototypes. In recent years, virtual prototyping using illumination software modeling tools has replaced many of the hardware prototypes. Typically, the designer specifies the design parameters, builds the software model, predicts the performance using a Monte Carlo simulation, and uses the performance results to repeat this process until an acceptable design is obtained. What is highly desired, and now possible, is to use illumination optimization to automate the design process. Illumination optimization provides the ability to explore a wider range of design options while also providing improved performance. Since Monte Carlo simulations are often used to calculate the system performance but those predictions have statistical uncertainty, the use of noise tolerant optimization algorithms is important. The use of noise tolerant illumination optimization is demonstrated by considering display device designs that extract light using 2D paint patterns as well as 3D textured surfaces. A hybrid optimization approach that combines a mesh feedback optimization with a classical optimizer is demonstrated. Displays with LED sources and cold cathode fluorescent lamps are considered.

Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables

Directory of Open Access Journals (Sweden)

Ying-Yi Hong

2015-03-01

Full Text Available This paper explores real power generation planning, considering distributed generation resources and energy storage in a small standalone power system. On account of the Kyoto Protocol and Copenhagen Accord, wind and photovoltaic (PV powers are considered as clean and renewable energies. In this study, a genetic algorithm (GA was used to determine the optimal capacities of wind-turbine-generators, PV, diesel generators and energy storage in a small standalone power system. The investment costs (installation, unit and maintenance costs of the distributed generation resources and energy storage and the cost of fuel for the diesel generators were minimized while the reliability requirement and CO2 emission limit were fulfilled. The renewable sources and loads were modeled by random variables because of their uncertainties. The equality and inequality constraints in the genetic algorithms were treated by cumulant effects and cumulative probability of random variables, respectively. The IEEE reliability data for an 8760 h load profile with a 150 kW peak load were used to demonstrate the applicability of the proposed method.

Optimization design of toroidal core for magnetic energy harvesting near power line by considering saturation effect

Science.gov (United States)

Park, Bumjin; Kim, Dongwook; Park, Jaehyoung; Kim, Kibeom; Koo, Jay; Park, HyunHo; Ahn, Seungyoung

2018-05-01

Recently, magnetic energy harvesting technologies have been studied actively for self-sustainable operation of applications around power line. However, magnetic energy harvesting around power lines has the problem of magnetic saturation, which can cause power performance degradation of the harvester. In this paper, optimal design of a toroidal core for magnetic energy harvesters has been proposed with consideration of magnetic saturation near power lines. Using Permeability-H curve and Ampere's circuital law, the optimum dimensional parameters needed to generate induced voltage were analyzed via calculation and simulation. To reflect a real environment, we consider the nonlinear characteristic of the magnetic core material and supply current through a 3-phase distribution panel used in the industry. The effectiveness of the proposed design methodology is verified by experiments in a power distribution panel and takes 60.9 V from power line current of 60 A at 60 Hz.

Optimal Capacitor Placement in Wind Farms by Considering Harmonics Using Discrete Lightning Search Algorithm

Directory of Open Access Journals (Sweden)

Reza Sirjani

2017-09-01

Full Text Available Currently, many wind farms exist throughout the world and, in some cases, supply a significant portion of energy to networks. However, numerous uncertainties remain with respect to the amount of energy generated by wind turbines and other sophisticated operational aspects, such as voltage and reactive power management, which requires further development and consideration. To fix the problem of poor reactive power compensation in wind farms, optimal capacitor placement has been proposed in existing wind farms as a simple and relatively inexpensive method. However, the use of induction generators, transformers, and additional capacitors represent potential problems for the harmonics of a system and therefore must be taken into account at wind farms. The optimal location and size of capacitors at buses of an 80-MW wind farm were determined according to modelled wind speed, system equivalent circuits, and harmonics in order to minimize energy losses, optimize reactive power and reduce the management costs. The discrete version of the lightning search algorithm (DLSA is a powerful and flexible nature-inspired optimization technique that was developed and implemented herein for optimal capacitor placement in wind farms. The obtained results are compared with the results of the genetic algorithm (GA and the discrete harmony search algorithm (DHSA.

Role of metastructural matrixes in optimization ecotourism

Directory of Open Access Journals (Sweden)

A. N. Leuchin

2010-01-01

Full Text Available In the article possibilities anthropocentric and ecocentric developing paradigms ecotourism are shown. The updating role institutional functions ecotourism an expert by metastructural matrixes of optimization tourist-institutional space (TIS is specified. Long-range directions of socially-ecological interaction in system of ecotourism are designated, measures on optimisation of this interaction are considered.

A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China

International Nuclear Information System (INIS)

Yu, L.; Li, Y.P.; Huang, G.H.

2016-01-01

In this study, a FSSOM (fuzzy-stochastic simulation-optimization model) is developed for planning EPS (electric power systems) with considering peak demand under uncertainty. FSSOM integrates techniques of SVR (support vector regression), Monte Carlo simulation, and FICMP (fractile interval chance-constrained mixed-integer programming). In FSSOM, uncertainties expressed as fuzzy boundary intervals and random variables can be effectively tackled. In addition, SVR coupled Monte Carlo technique is used for predicting the peak-electricity demand. The FSSOM is applied to planning EPS for the City of Qingdao, China. Solutions of electricity generation pattern to satisfy the city's peak demand under different probability levels and p-necessity levels have been generated. Results reveal that the city's electricity supply from renewable energies would be low (only occupying 8.3% of the total electricity generation). Compared with the energy model without considering peak demand, the FSSOM can better guarantee the city's power supply and thus reduce the system failure risk. The findings can help decision makers not only adjust the existing electricity generation/supply pattern but also coordinate the conflict interaction among system cost, energy supply security, pollutant mitigation, as well as constraint-violation risk. - Highlights: • FSSOM (Fuzzy-stochastic simulation-optimization model) is developed for planning EPS. • It can address uncertainties as fuzzy-boundary intervals and random variables. • FSSOM can satisfy peak-electricity demand and optimize power allocation. • Solutions under different probability levels and p-necessity levels are analyzed. • Results create tradeoff among system cost and peak-electricity demand violation risk.

Optimal Tuning of Decentralized PI Controller of Nonlinear Multivariable Process Using Archival Based Multiobjective Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

R. Kotteeswaran

2014-01-01

Full Text Available A Multiobjective Particle Swarm Optimization (MOPSO algorithm is proposed to fine-tune the baseline PI controller parameters of Alstom gasifier. The existing baseline PI controller is not able to meet the performance requirements of Alstom gasifier for sinusoidal pressure disturbance at 0% load. This is considered the major drawback of controller design. A best optimal solution for Alstom gasifier is obtained from a set of nondominated solutions using MOPSO algorithm. Performance of gasifier is investigated at all load conditions. The controller with optimized controller parameters meets all the performance requirements at 0%, 50%, and 100% load conditions. The investigations are also extended for variations in coal quality, which shows an improved stability of the gasifier over a wide range of coal quality variations.

A Hybrid Optimization Method for Reactive Power and Voltage Control Considering Power Loss Minimization

DEFF Research Database (Denmark)

Liu, Chengxi; Qin, Nan; Bak, Claus Leth

2015-01-01

This paper proposes a hybrid optimization method to optimally control the voltage and reactive power with minimum power loss in transmission grid. This approach is used for the Danish automatic voltage control (AVC) system which is typically a non-linear non-convex problem mixed with both...

Optimal location selection for the installation of urban green roofs considering honeybee habitats along with socio-economic and environmental effects.

Science.gov (United States)

Gwak, Jae Ha; Lee, Bo Kyeong; Lee, Won Kyung; Sohn, So Young

2017-03-15

This study proposes a new framework for the selection of optimal locations for green roofs to achieve a sustainable urban ecosystem. The proposed framework selects building sites that can maximize the benefits of green roofs, based not only on the socio-economic and environmental benefits to urban residents, but also on the provision of urban foraging sites for honeybees. The framework comprises three steps. First, building candidates for green roofs are selected considering the building type. Second, the selected building candidates are ranked in terms of their expected socio-economic and environmental effects. The benefits of green roofs are improved energy efficiency and air quality, reduction of urban flood risk and infrastructure improvement costs, reuse of storm water, and creation of space for education and leisure. Furthermore, the estimated cost of installing green roofs is also considered. We employ spatial data to determine the expected effects of green roofs on each building unit, because the benefits and costs may vary depending on the location of the building. This is due to the heterogeneous spatial conditions. In the third step, the final building sites are proposed by solving the maximal covering location problem (MCLP) to determine the optimal locations for green roofs as urban honeybee foraging sites. As an illustrative example, we apply the proposed framework in Seoul, Korea. This new framework is expected to contribute to sustainable urban ecosystems. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimal design of microalgae-based biorefinery: Economics, opportunities and challenges

DEFF Research Database (Denmark)

Rizwan, Muhammad; Lee, Jay H.; Gani, Rafiqul

2015-01-01

Microalgae have great potential as a feedstock for the production of a wide range of end-products under the broad concept of biorefinery. In an earlier work, we proposed a superstructure based optimization model to find the optimal processing pathway for the production of biodiesel from microalgal...... biomass, and identified several challenges with the focus being on utilizing lipids extracted microalgal biomass for economic and environmentally friendly production of useful energy products. In this paper, we expand the previous optimization framework by considering the processing of microalgae residue...

Problem statement for optimal design of steel structures

OpenAIRE

Ginzburg Aleksandr Vital'evich; Vasil'kin Andrey Aleksandrovich

2014-01-01

The presented article considers the following complex of tasks. The main stages of the life cycle of a building construction with the indication of process entrance and process exit are described. Requirements imposed on steel constructions are considered. The optimum range of application for steel designs is specified, as well as merits and demerits of a design material. The nomenclature of metal designs is listed - the block diagram is constructed. Possible optimality criteria of steel desi...

Characterization and optimization of laser-driven electron and photon sources in keV and MeV energy ranges

International Nuclear Information System (INIS)

Bonnet, Thomas

2013-01-01

This work takes place in the framework of the characterization and the optimization of laser-driven electron and photon sources. With the goal of using these sources for nuclear physics experiments, we focused on 2 energy ranges: one around a few MeV and the other around a few tens of keV. The first part of this work is thus dedicated to the study of detectors routinely used for the characterization of laser-driven particle sources: Imaging Plates. A model has been developed and is fitted to experimental data. Response functions to electrons, photons, protons and alpha particles are established for SR, MS and TR Fuji Imaging Plates for energies ranging from a few keV to several MeV. The second part of this work present a study of ultrashort and intense electron and photon sources produced in the interaction of a laser with a solid or liquid target. An experiment was conducted at the ELFIE facility at LULI where beams of electrons and photons were accelerated up to several MeV. Energy and angular distributions of the electron and photons beams were characterized. The sources were optimized by varying the spatial extension of the plasma at both the front and the back end of the initial target position. In the optimal configuration of the laser-plasma coupling, more than 1011 electrons were accelerated. In the case of liquid target, a photon source was produced at a high repetition rate on an energy range of tens of keV by the interaction of the AURORE Laser at CELIA (10 16 W.cm -2 ) and a melted gallium target. It was shown that both the mean energy and the photon number can be increased by creating gallium jets at the surface of the liquid target with a pre-pulse. A physical interpretation supported by numerical simulations is proposed. (author)

Maintenance resources optimization applied to a manufacturing system

International Nuclear Information System (INIS)

Fiori de Castro, Helio; Lucchesi Cavalca, Katia

2006-01-01

This paper presents an availability optimization of an engineering system assembled in a series configuration, with redundancy of units and corrective maintenance resources as optimization parameters. The aim is to reach maximum availability, considering as constraints installation and corrective maintenance costs, weight and volume. The optimization method uses a Genetic Algorithm based on biological concepts of species evolution. It is a robust method, as it does not converge to a local optimum. It does not require the use of differential calculus, thus facilitating computational implementation. Results indicate that the methodology is suitable to solve a wide range of engineering design problems involving allocation of redundancies and maintenance resources

Optimization Formulations for the Maximum Nonlinear Buckling Load of Composite Structures

DEFF Research Database (Denmark)

Lindgaard, Esben; Lund, Erik

2011-01-01

This paper focuses on criterion functions for gradient based optimization of the buckling load of laminated composite structures considering different types of buckling behaviour. A local criterion is developed, and is, together with a range of local and global criterion functions from literature......, benchmarked on a number of numerical examples of laminated composite structures for the maximization of the buckling load considering fiber angle design variables. The optimization formulations are based on either linear or geometrically nonlinear analysis and formulated as mathematical programming problems...... solved using gradient based techniques. The developed local criterion is formulated such it captures nonlinear effects upon loading and proves useful for both analysis purposes and as a criterion for use in nonlinear buckling optimization. © 2010 Springer-Verlag....

Multi-Objective Optimization Considering Battery Degradation for a Multi-Mode Power-Split Electric Vehicle

Directory of Open Access Journals (Sweden)

Xuerui Ma

2017-07-01

Full Text Available A multi-mode power-split (MMPS hybrid electric vehicle (HEV has two planetary gearsets and clutches/grounds which results in several operation modes with enhanced electric drive capability and better fuel economy. Basically, the battery storage system is involved in different operation modes to satisfy the power demand and minimize the fuel consumption, whereas the complicated operation modes with frequent charging/discharging will absolutely influence the battery life because of degradation. In this paper, firstly, we introduce the solid electrolyte interface (SEI film growth model based on the previous study of the battery degradation principles and was verified according to the test data. We consider both the fuel economy and battery degradation as a multi-objective problem for MMPS HEV by normalization with a weighting factor. An instantaneous optimization is implemented based on the equivalent fuel consumption concept. Then the control strategy is implemented on a simulation framework integrating the MMPS powertrain model and the SEI film growth map model over some typical driving cycles, such as New European Driving Cycle (NEDC and Urban Dynamometer Driving Schedule (UDDS. Finally, the result demonstrates that these two objectives are conflicting and the trade-off reduces the battery degradation with fuel sacrifice. Additionally, the analysis reveals how the mode selection will reflect the battery degradation.

Optimization of European call options considering physical delivery network and reservoir operation rules

Science.gov (United States)

Cheng, Wei-Chen; Hsu, Nien-Sheng; Cheng, Wen-Ming; Yeh, William W.-G.

2011-10-01

This paper develops alternative strategies for European call options for water purchase under hydrological uncertainties that can be used by water resources managers for decision making. Each alternative strategy maximizes its own objective over a selected sequence of future hydrology that is characterized by exceedance probability. Water trade provides flexibility and enhances water distribution system reliability. However, water trade between two parties in a regional water distribution system involves many issues, such as delivery network, reservoir operation rules, storage space, demand, water availability, uncertainty, and any existing contracts. An option is a security giving the right to buy or sell an asset; in our case, the asset is water. We extend a flow path-based water distribution model to include reservoir operation rules. The model simultaneously considers both the physical distribution network as well as the relationships between water sellers and buyers. We first test the model extension. Then we apply the proposed optimization model for European call options to the Tainan water distribution system in southern Taiwan. The formulation lends itself to a mixed integer linear programming model. We use the weighing method to formulate a composite function for a multiobjective problem. The proposed methodology provides water resources managers with an overall picture of water trade strategies and the consequence of each strategy. The results from the case study indicate that the strategy associated with a streamflow exceedence probability of 50% or smaller should be adopted as the reference strategy for the Tainan water distribution system.

Optimization under uncertainty of parallel nonlinear energy sinks

Science.gov (United States)

Boroson, Ethan; Missoum, Samy; Mattei, Pierre-Olivier; Vergez, Christophe

2017-04-01

Nonlinear Energy Sinks (NESs) are a promising technique for passively reducing the amplitude of vibrations. Through nonlinear stiffness properties, a NES is able to passively and irreversibly absorb energy. Unlike the traditional Tuned Mass Damper (TMD), NESs do not require a specific tuning and absorb energy over a wider range of frequencies. Nevertheless, they are still only efficient over a limited range of excitations. In order to mitigate this limitation and maximize the efficiency range, this work investigates the optimization of multiple NESs configured in parallel. It is well known that the efficiency of a NES is extremely sensitive to small perturbations in loading conditions or design parameters. In fact, the efficiency of a NES has been shown to be nearly discontinuous in the neighborhood of its activation threshold. For this reason, uncertainties must be taken into account in the design optimization of NESs. In addition, the discontinuities require a specific treatment during the optimization process. In this work, the objective of the optimization is to maximize the expected value of the efficiency of NESs in parallel. The optimization algorithm is able to tackle design variables with uncertainty (e.g., nonlinear stiffness coefficients) as well as aleatory variables such as the initial velocity of the main system. The optimal design of several parallel NES configurations for maximum mean efficiency is investigated. Specifically, NES nonlinear stiffness properties, considered random design variables, are optimized for cases with 1, 2, 3, 4, 5, and 10 NESs in parallel. The distributions of efficiency for the optimal parallel configurations are compared to distributions of efficiencies of non-optimized NESs. It is observed that the optimization enables a sharp increase in the mean value of efficiency while reducing the corresponding variance, thus leading to more robust NES designs.

A constitutive equation for hot deformation range of 304 stainless steel considering grain sizes

International Nuclear Information System (INIS)

Parsa, M.H.; Ohadi, D.

2013-01-01

Highlights: • A hot deformation constitutive equation based on invariant theory is proposed. • Deformation variables are evaluated based on objectivity, entropy principle, etc. • Using hot compression tests, coefficients of equation have been found. • The ability of equation to show the variation of stress with strain is examined. - Abstract: A general constitutive equation based on the framework of invariant theory by consideration of hot deformation key variables and also the properties of the material such as initial grain size is presented in the current work. Soundness of the considered parameters to be used in the developed formula was initially verified based on the important axioms such as objectivity, entropy principle, and thermodynamics stability. To access the prediction ability of the method, the formula was simplified for the simple hot compression test. To evaluate the simplified formula, single-hit hot compression tests were carried out at the temperature range of 900–1100 °C under true strain rate of 0.01–1 s −1 on a AISI 304 stainless steel. The capability of proposed formula for reproducing the variation of flow stress with strain and the strain hardening rate with stress for the resultant flow stress data was examined. The good agreement between model predictions and actual results signified the applicability of this method as a general constitutive equation in hot deformation studies

An Environmental and Economic Assessment for Selecting the Optimal Ground Heat Exchanger by Considering the Entering Water Temperature

Directory of Open Access Journals (Sweden)

Jimin Kim

2015-07-01

Full Text Available In order to solve environmental problems such as global warming and resource depletion in the construction industry, interest in new renewable energy (NRE systems has increased. The ground source heat pump (GSHP system is the most efficient system among NRE systems. However, since the initial investment cost of the GSHP is quite expensive, a feasibility study needs to be conducted from the life-cycle perspective. Meanwhile, the efficiency of GSHP depends most significantly on the entering water temperature (EWT of the ground heat exchanger (GHE. Therefore, this study aims to assess the environmental and economic effects of the use of GHE for selecting the optimal GHE. This study was conducted in three steps: (i establishing the basic information and selecting key factors affecting GHE performances; (ii making possible alternatives of the GHE installation by considering EWT; and (iii using life-cycle assessment and life-cycle cost, as well as comprehensive evaluation of the environmental and economic effects on the GHE. These techniques allow for easy and accurate determination of the optimal design of the GHE from the environmental and economic effects in the early design phase. In future research, a multi-objective decision support model for the GSHP will be developed.

An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

Directory of Open Access Journals (Sweden)

Vivek Patel

2012-08-01

Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

Topology optimization of reinforced concrete structures considering control of shrinkage and strength failure

DEFF Research Database (Denmark)

Luo, Yangjun; Wang, Michael Yu; Zhou, Mingdong

2015-01-01

To take into account the shrinkage effect in the early stage of Reinforced Concrete (RC) design, an effective continuum topology optimization method is presented in this paper. Based on the power-law interpolation, shrinkage of concrete is numerically simulated by introducing an additional design......-dependent force. Under multi-axial stress conditions, the concrete failure surface is well fitted by two Drucker-Prager yield functions. The optimization problem aims at minimizing the cost function under yield strength constraints on concrete elements and a structural shrinkage volume constraint. In conjunction...... to ensure the structural safety under the combined action of external loads and shrinkage....

Comparing Consider-Covariance Analysis with Sigma-Point Consider Filter and Linear-Theory Consider Filter Formulations

Science.gov (United States)

Lisano, Michael E.

2007-01-01

Recent literature in applied estimation theory reflects growing interest in the sigma-point (also called unscented ) formulation for optimal sequential state estimation, often describing performance comparisons with extended Kalman filters as applied to specific dynamical problems [c.f. 1, 2, 3]. Favorable attributes of sigma-point filters are described as including a lower expected error for nonlinear even non-differentiable dynamical systems, and a straightforward formulation not requiring derivation or implementation of any partial derivative Jacobian matrices. These attributes are particularly attractive, e.g. in terms of enabling simplified code architecture and streamlined testing, in the formulation of estimators for nonlinear spaceflight mechanics systems, such as filter software onboard deep-space robotic spacecraft. As presented in [4], the Sigma-Point Consider Filter (SPCF) algorithm extends the sigma-point filter algorithm to the problem of consider covariance analysis. Considering parameters in a dynamical system, while estimating its state, provides an upper bound on the estimated state covariance, which is viewed as a conservative approach to designing estimators for problems of general guidance, navigation and control. This is because, whether a parameter in the system model is observable or not, error in the knowledge of the value of a non-estimated parameter will increase the actual uncertainty of the estimated state of the system beyond the level formally indicated by the covariance of an estimator that neglects errors or uncertainty in that parameter. The equations for SPCF covariance evolution are obtained in a fashion similar to the derivation approach taken with standard (i.e. linearized or extended) consider parameterized Kalman filters (c.f. [5]). While in [4] the SPCF and linear-theory consider filter (LTCF) were applied to an illustrative linear dynamics/linear measurement problem, in the present work examines the SPCF as applied to

Cluster analysis for portfolio optimization

OpenAIRE

Vincenzo Tola; Fabrizio Lillo; Mauro Gallegati; Rosario N. Mantegna

2005-01-01

We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio compositi...

Optimality Conditions in Vector Optimization

CERN Document Server

Jiménez, Manuel Arana; Lizana, Antonio Rufián

2011-01-01

Vector optimization is continuously needed in several science fields, particularly in economy, business, engineering, physics and mathematics. The evolution of these fields depends, in part, on the improvements in vector optimization in mathematical programming. The aim of this Ebook is to present the latest developments in vector optimization. The contributions have been written by some of the most eminent researchers in this field of mathematical programming. The Ebook is considered essential for researchers and students in this field.

Neural network for adapting nuclear power plant control for wide-range operation

International Nuclear Information System (INIS)

Ku, C.C.; Lee, K.Y.; Edwards, R.M.

1991-01-01

A new concept of using neural networks has been evaluated for optimal control of a nuclear reactor. The neural network uses the architecture of a standard backpropagation network; however, a new dynamic learning algorithm has been developed to capture the underlying system dynamics. The learning algorithm is based on parameter estimation for dynamic systems. The approach is demonstrated on an optimal reactor temperature controller by adjusting the feedback gains for wide-range operation. Application of optimal control to a reactor has been considered for improving temperature response using a robust fifth-order reactor power controller. Conventional gain scheduling can be employed to extend the range of good performance to accommodate large changes in power where nonlinear characteristics significantly modify the dynamics of the power plant. Gain scheduling is developed based on expected parameter variations, and it may be advantageous to further adapt feedback gains on-line to better match actual plant performance. A neural network approach is used here to adapt the gains to better accommodate plant uncertainties and thereby achieve improved robustness characteristics

Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization

Directory of Open Access Journals (Sweden)

R. Mukesh

2012-01-01

Full Text Available The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA, simulated annealing (SA, and particle swarm optimization (PSO, are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.

Dynamic supplier selection problem considering full truck load in probabilistic environment

Science.gov (United States)

Sutrisno, Wicaksono, Purnawan Adi

2017-11-01

In this paper, we propose a mathematical model in a probabilistic dynamic optimization to solve a dynamic supplier selection problem considering full truck load in probabilistic environment where some parameters are uncertain. We determine the optimal strategy for this problem by using stochastic dynamic programming. We give some numerical experiments to evaluate and analyze the model. From the results, the optimal supplier and the optimal product volume from the optimal supplier were determined for each time period.

Mono and multi-objective optimization techniques applied to a large range of industrial test cases using Metamodel assisted Evolutionary Algorithms

Science.gov (United States)

Fourment, Lionel; Ducloux, Richard; Marie, Stéphane; Ejday, Mohsen; Monnereau, Dominique; Massé, Thomas; Montmitonnet, Pierre

2010-06-01

The use of material processing numerical simulation allows a strategy of trial and error to improve virtual processes without incurring material costs or interrupting production and therefore save a lot of money, but it requires user time to analyze the results, adjust the operating conditions and restart the simulation. Automatic optimization is the perfect complement to simulation. Evolutionary Algorithm coupled with metamodelling makes it possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. Ten industrial partners have been selected to cover the different area of the mechanical forging industry and provide different examples of the forming simulation tools. It aims to demonstrate that it is possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. The large computational time is handled by a metamodel approach. It allows interpolating the objective function on the entire parameter space by only knowing the exact function values at a reduced number of "master points". Two algorithms are used: an evolution strategy combined with a Kriging metamodel and a genetic algorithm combined with a Meshless Finite Difference Method. The later approach is extended to multi-objective optimization. The set of solutions, which corresponds to the best possible compromises between the different objectives, is then computed in the same way. The population based approach allows using the parallel capabilities of the utilized computer with a high efficiency. An optimization module, fully embedded within the Forge2009 IHM, makes possible to cover all the defined examples, and the use of new multi-core hardware to compute several simulations at the same time reduces the needed time dramatically. The presented examples

Does the optimal position of the acetabular fragment should be within the radiological normal range for all developmental dysplasia of the hip? A patient-specific finite element analysis.

Science.gov (United States)

Wang, Xuyi; Peng, Jianping; Li, De; Zhang, Linlin; Wang, Hui; Jiang, Leisheng; Chen, Xiaodong

2016-10-04

The success of Bernese periacetabular osteotomy depends significantly on how extent the acetabular fragment can be corrected to its optimal position. This study was undertaken to investigate whether correcting the acetabular fragment into the so-called radiological "normal" range is the best choice for all developmental dysplasia of the hip with different severities of dysplasia from the biomechanical view? If not, is there any correlation between the biomechanically optimal position of the acetabular fragment and the severity of dysplasia? Four finite element models with different severities of dysplasia were developed. The virtual periacetabular osteotomy was performed with the acetabular fragment rotated anterolaterally to incremental center-edge angles; then, the contact area and pressure and von Mises stress in the cartilage were calculated at different correction angles. The optimal position of the acetabular fragment for patients 1, 2, and 3 was when the acetabular fragment rotated 17° laterally (with the lateral center-edge angle of 36° and anterior center-edge angle of 58°; both were slightly larger than the "normal" range), 25° laterally following further 5° anterior rotation (with the lateral center-edge angle of 31° and anterior center-edge angle of 51°; both were within the "normal" range), and 30° laterally following further 10° anterior rotation (with the lateral center-edge angle of 25° and anterior center-edge angle of 40°; both were less than the "normal" range), respectively. The optimal corrective position of the acetabular fragment is severity dependent rather than within the radiological "normal" range for developmental dysplasia of the hip. We prudently proposed that the optimal correction center-edge angle of mild, moderate, and severe developmental dysplasia of the hip is slightly larger than the "normal" range, within the "normal" range, and less than the lower limit of the "normal" range, respectively.

An ensemble-based method for constrained reservoir life-cycle optimization

NARCIS (Netherlands)

Leeuwenburgh, O.; Egberts, P.J.P.; Chitu, A.G.

2015-01-01

We consider the problem of finding optimal long-term (life-cycle) recovery strategies for hydrocarbon reservoirs by use of simulation models. In such problems the presence of operating constraints, such as for example a maximum rate limit for a group of wells, may strongly influence the range of

Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations

Directory of Open Access Journals (Sweden)

Akhtar Hussain

2017-06-01

Full Text Available In this paper, an optimal energy management strategy for a cooperative multi-microgrid system with combined cooling, heat and power (CCHP is proposed and has been verified for a test case of building microgrids (BMGs. Three different demand types of buildings are considered and the BMGs are assumed to be equipped with their own combined heat and power (CHP generators. In addition, the BMGs are also connected to an external energy network (EEN, which contains a large CHP, an adsorption chiller (ADC, a thermal storage tank, and an electric heat pump (EHP. By trading the excess electricity and heat energy with the utility grid and EEN, each BMG can fulfill its energy demands. Seasonal energy demand variations have been evaluated by selecting a representative day for the two extreme seasons (summer and winter of the year, among the real profiles of year-round data on electricity, heating, and cooling usage of all the three selected buildings. Especially, the thermal energy management aspect is emphasized where, bi-lateral heat trading between the energy supplier and the consumers, so-called energy prosumer concept, has been realized. An optimization model based on mixed integer linear programming has been developed for minimizing the daily operation cost of the EEN while fulfilling the energy demands of the BMGs. Simulation results have demonstrated the effectiveness of the proposed strategy.

Considering Decision Variable Diversity in Multi-Objective Optimization: Application in Hydrologic Model Calibration

Science.gov (United States)

Sahraei, S.; Asadzadeh, M.

2017-12-01

Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.

Range-extending Zinc-air battery for electric vehicle

Directory of Open Access Journals (Sweden)

Steven B. Sherman

2018-01-01

Full Text Available A vehicle model is used to evaluate a novel powertrain that is comprised of a dual energy storage system (Dual ESS. The system includes two battery packs with different chemistries and the necessary electronic controls to facilitate their coordination and optimization. Here, a lithium-ion battery pack is used as the primary pack and a Zinc-air battery as the secondary or range-extending pack. Zinc-air batteries are usually considered unsuitable for use in vehicles due to their poor cycle life, but the model demonstrates the feasibility of this technology with an appropriate control strategy, with limited cycling of the range extender pack. The battery pack sizes and the battery control strategy are configured to optimize range, cost and longevity. In simulation the vehicle performance compares favourably to a similar vehicle with a single energy storage system (Single ESS powertrain, travelling up to 75 km further under test conditions. The simulation demonstrates that the Zinc-air battery pack need only cycle 100 times to enjoy a ten-year lifespan. The Zinc-air battery model is based on leading Zinc-air battery research from literature, with some assumptions regarding achievable improvements. Having such a model clarifies the performance requirements of Zinc-air cells and improves the research community's ability to set performance targets for Zinc-air cells.

Optimized Quasi-Interpolators for Image Reconstruction.

Science.gov (United States)

Sacht, Leonardo; Nehab, Diego

2015-12-01

We propose new quasi-interpolators for the continuous reconstruction of sampled images, combining a narrowly supported piecewise-polynomial kernel and an efficient digital filter. In other words, our quasi-interpolators fit within the generalized sampling framework and are straightforward to use. We go against standard practice and optimize for approximation quality over the entire Nyquist range, rather than focusing exclusively on the asymptotic behavior as the sample spacing goes to zero. In contrast to previous work, we jointly optimize with respect to all degrees of freedom available in both the kernel and the digital filter. We consider linear, quadratic, and cubic schemes, offering different tradeoffs between quality and computational cost. Experiments with compounded rotations and translations over a range of input images confirm that, due to the additional degrees of freedom and the more realistic objective function, our new quasi-interpolators perform better than the state of the art, at a similar computational cost.

Optimal urban water conservation strategies considering embedded energy: coupling end-use and utility water-energy models.

Science.gov (United States)

Escriva-Bou, A.; Lund, J. R.; Pulido-Velazquez, M.; Spang, E. S.; Loge, F. J.

2014-12-01

Although most freshwater resources are used in agriculture, a greater amount of energy is consumed per unit of water supply for urban areas. Therefore, efforts to reduce the carbon footprint of water in cities, including the energy embedded within household uses, can be an order of magnitude larger than for other water uses. This characteristic of urban water systems creates a promising opportunity to reduce global greenhouse gas emissions, particularly given rapidly growing urbanization worldwide. Based on a previous Water-Energy-CO2 emissions model for household water end uses, this research introduces a probabilistic two-stage optimization model considering technical and behavioral decision variables to obtain the most economical strategies to minimize household water and water-related energy bills given both water and energy price shocks. Results show that adoption rates to reduce energy intensive appliances increase significantly, resulting in an overall 20% growth in indoor water conservation if household dwellers include the energy cost of their water use. To analyze the consequences on a utility-scale, we develop an hourly water-energy model based on data from East Bay Municipal Utility District in California, including the residential consumption, obtaining that water end uses accounts for roughly 90% of total water-related energy, but the 10% that is managed by the utility is worth over 12 million annually. Once the entire end-use + utility model is completed, several demand-side management conservation strategies were simulated for the city of San Ramon. In this smaller water district, roughly 5% of total EBMUD water use, we found that the optimal household strategies can reduce total GHG emissions by 4% and utility's energy cost over 70,000/yr. Especially interesting from the utility perspective could be the "smoothing" of water use peaks by avoiding daytime irrigation that among other benefits might reduce utility energy costs by 0.5% according to our

Optimization of Location-Routing Problem for Cold Chain Logistics Considering Carbon Footprint.

Science.gov (United States)

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-06

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location-routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network.

Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint

Science.gov (United States)

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-01

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location–routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network. PMID:29316639

Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint

Directory of Open Access Journals (Sweden)

Songyi Wang

2018-01-01

Full Text Available In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location–routing problem (LRP model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network.

Continuum topology optimization considering uncertainties in load locations based on the cloud model

Science.gov (United States)

Liu, Jie; Wen, Guilin

2018-06-01

Few researchers have paid attention to designing structures in consideration of uncertainties in the loading locations, which may significantly influence the structural performance. In this work, cloud models are employed to depict the uncertainties in the loading locations. A robust algorithm is developed in the context of minimizing the expectation of the structural compliance, while conforming to a material volume constraint. To guarantee optimal solutions, sufficient cloud drops are used, which in turn leads to low efficiency. An innovative strategy is then implemented to enormously improve the computational efficiency. A modified soft-kill bi-directional evolutionary structural optimization method using derived sensitivity numbers is used to output the robust novel configurations. Several numerical examples are presented to demonstrate the effectiveness and efficiency of the proposed algorithm.

Design Optimization of An Axial Flow Fan Blade Considering Airfoil Shape and Stacking Line

Energy Technology Data Exchange (ETDEWEB)

Lee, Ki Sang; Kim, Kwang Yong; Samad, Abdus [Inha Univ., Incheon (Korea, Republic of)

2007-07-01

This work presents a numerical optimization procedure for a low-speed axial flow fan blade with polynomial response surface approximation model. Reynolds-averaged Navier-Stokes equations with Shear Stress Turbulence (SST) model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. The airfoil shape as well as stacking line is modified to enhance blade total efficiency, i.e., the objective function. The design variables of blade lean, maximum thickness and location of maximum thickness are selected, and a design of experiments technique produces design points where flow analyses are performed to obtain values of the objective function. A gradient-based search algorithm is used to find the optimal design in the design space from the constructed response surface model for the objective function. As a main result, the efficiency is increased effectively by the present optimization procedure. And, it is also shown that the modification of blade lean is more effective to improve the efficiency rather than modifying blade profile.

Robust optimal self tuning regulator of nuclear reactors

International Nuclear Information System (INIS)

Nouri Khajavi, M.; Menhaj, M.B.; Ghofrani, M.B.

2000-01-01

Nuclear power reactors are, in nature nonlinear and time varying. These characteristics must be considered, if large power variations occur in their working regime. In this paper a robust optimal self-tuning regulator for regulating the power of a nuclear reactor has been designed and simulated. The proposed controller is capable of regulating power levels in a wide power range (10% to 100% power levels). The controller achieves a fast and good transient response. The simulation results show that the proposed controller outperforms the fixed optimal control recently cited in the literature for nuclear power plants

Robust Optimization Approach for Design for a Dynamic Cell Formation Considering Labor Utilization: Bi-objective Mathematical Mode

Directory of Open Access Journals (Sweden)

Hiwa Farughi

2016-05-01

Full Text Available In this paper, robust optimization of a bi-objective mathematical model in a dynamic cell formation problem considering labor utilization with uncertain data is carried out. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all the possible future scenarios. In this research, cost parameters of the cell formation and demand fluctuations are subject to uncertainty and a mixed-integer programming (MIP model is developed to formulate the related robust dynamic cell formation problem. Then the problem is transformed into a bi-objective linear one. The first objective function seeks to minimize relevant costs of the problem including machine procurement and relocation costs, machine variable cost, inter-cell movement and intra-cell movement costs, overtime cost and labor shifting cost between cells, machine maintenance cost, inventory, holding part cost. The second objective function seeks to minimize total man-hour deviations between cells or indeed labor utilization of the modeled.

Generation Expansion Planning Considering Integrating Large-scale Wind Generation

DEFF Research Database (Denmark)

Zhang, Chunyu; Ding, Yi; Østergaard, Jacob

2013-01-01

necessitated the inclusion of more innovative and sophisticated approaches in power system investment planning. A bi-level generation expansion planning approach considering large-scale wind generation was proposed in this paper. The first phase is investment decision, while the second phase is production...... optimization decision. A multi-objective PSO (MOPSO) algorithm was introduced to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of the proposed bi-level planning approach and the MOPSO...

A Multi-Point Method Considering the Maximum Power Point Tracking Dynamic Process for Aerodynamic Optimization of Variable-Speed Wind Turbine Blades

Directory of Open Access Journals (Sweden)

Zhiqiang Yang

2016-05-01

Full Text Available Due to the dynamic process of maximum power point tracking (MPPT caused by turbulence and large rotor inertia, variable-speed wind turbines (VSWTs cannot maintain the optimal tip speed ratio (TSR from cut-in wind speed up to the rated speed. Therefore, in order to increase the total captured wind energy, the existing aerodynamic design for VSWT blades, which only focuses on performance improvement at a single TSR, needs to be improved to a multi-point design. In this paper, based on a closed-loop system of VSWTs, including turbulent wind, rotor, drive train and MPPT controller, the distribution of operational TSR and its description based on inflow wind energy are investigated. Moreover, a multi-point method considering the MPPT dynamic process for the aerodynamic optimization of VSWT blades is proposed. In the proposed method, the distribution of operational TSR is obtained through a dynamic simulation of the closed-loop system under a specific turbulent wind, and accordingly the multiple design TSRs and the corresponding weighting coefficients in the objective function are determined. Finally, using the blade of a National Renewable Energy Laboratory (NREL 1.5 MW wind turbine as the baseline, the proposed method is compared with the conventional single-point optimization method using the commercial software Bladed. Simulation results verify the effectiveness of the proposed method.

Comparison between regenerative organic Rankine cycle (RORC) and basic organic Rankine cycle (BORC) based on thermoeconomic multi-objective optimization considering exergy efficiency and levelized energy cost (LEC)

International Nuclear Information System (INIS)

Feng, Yongqiang; Zhang, Yaning; Li, Bingxi; Yang, Jinfu; Shi, Yang

2015-01-01

Highlights: • The thermoeconomic comparison of regenerative RORC and BORC is investigated. • The Pareto frontier solution with bi-objective compares with the corresponding single-objective solutions. • The three-objective optimization of the RORC and BORC is studied. • The RORC owns 8.1% higher exergy efficiency and 21.1% more LEC than the BORC under the Pareto-optimal solution. - Abstract: Based on the thermoeconomic multi-objective optimization by using non-dominated sorting genetic algorithm (NSGA-II), considering both thermodynamic performance and economic factors, the thermoeconomic comparison of regenerative organic Rankine cycles (RORC) and basic organic Rankine cycles (BORC) are investigated. The effects of five key parameters including evaporator outlet temperature, condenser temperature, degree of superheat, pinch point temperature difference and degree of supercooling on the exergy efficiency and levelized energy cost (LEC) are examined. Meanwhile, the Pareto frontier solution with bi-objective for maximizing exergy efficiency and minimizing LEC is obtained and compared with the corresponding single-objective solutions. Research demonstrates that there is a significant negative correlation between thermodynamic performance and economic factors. And the optimum exergy efficiency and LEC for the Pareto-optimal solution of the RORC are 55.97% and 0.142 $/kW h, respectively, which are 8.1% higher exergy efficiency and 21.1% more LEC than that of the BORC under considered condition. Highest exergy and thermal efficiencies are accompanied with lowest net power output and worst economic performance. Furthermore, taking the net power output into account, detailed investigation on the three-objective optimization for maximizing exergy efficiency, maximizing net power output and minimizing LEC is discussed

Joint Optimization of Star P-hub Median Problem and Seat Inventory Control Decisions Considering a Hybrid Routing Transportation System

Directory of Open Access Journals (Sweden)

Hamid Tikani

2016-11-01

Full Text Available In this paper, we study the problem of integrated capacitated hub location problem and seat inventory control considering concept and techniques of revenue management. We consider an airline company maximizes its revenue by utilizing the best network topology and providing proper booking limits for all itineraries and fare classes. The transportation system arises in the form of a star/star network and includes both hub-stop and non-stop flights. This problem is formulated as a two-stage stochastic integer program with mixed-integer recourse. We solve various instances carried out from the Turkish network data set. Due to the NP-hardness of the problem, we propose a hybrid optimization method, consisting of an evolutionary algorithm based on genetic algorithm and exact solution. The quality of the solutions found by the proposed meta-heuristic is compared with the original version of GA and the mathematical programming model. The results obtained by the proposed model imply that integrating hub location and seat inventory control problem would help to increase the total revenue of airline companies. Also, in the case of serving non-stop flights, the model can provide more profit by employing less number of hubs.

Considering lost sale in inventory routing problems for perishable goods

DEFF Research Database (Denmark)

Mirzaei, Samira; Seifi, Abbas

2015-01-01

, the average optimality gaps are less than 10.9% and 13.4% using linear and exponential lost sale functions, respectively. Furthermore, we show that the optimality gaps found by CPLEX grow exponentially with the problem size while those obtained by the proposed meta-heuristic algorithm increase linearly....... is considered as lost sale. The proposed model balances the transportation cost, the cost of inventory holding and lost sale. In addition to the usual inventory routing constraints, we consider the cost of lost sale as a linear or an exponential function of the inventory age. The proposed model is solved...

Optimal Shape Design of Pyeongyeong Considering Structural and Acoustical Characteristics

Energy Technology Data Exchange (ETDEWEB)

Lee, Seungmok; Kang, Minseok [Gyeonggi Science High School, Suwon (Korea, Republic of); Lee, Jin Woo [Ajou Univ., Suwon (Korea, Republic of)

2014-03-15

An optimal shape design algorithm is suggested to systematically design a traditional Korean musical instrument, the Pyeongyeong. The Pyeongyeong consists of 16 different chime stones called Gyeongpyeons. The first natural vibration frequency of each Gyeongpyeon must be adjusted to its target frequency, which is determined by the traditional sound tuning method. The second and third natural frequencies must be proportional to the first natural frequency with a specific ratio (1:1.498:2.378). The key idea in our suggested design algorithm is to use the sensitivity of natural frequencies to the variation in the length of each side of a Gyeongpyeon. The dimensions of five different Gyeongpyeons are determined by following the suggested algorithm. Changes in natural frequencies with respect to local thickness variation are closely investigated to compensate for errors that may occur during manufacturing.

Optimal Shape Design of Pyeongyeong Considering Structural and Acoustical Characteristics

International Nuclear Information System (INIS)

Lee, Seungmok; Kang, Minseok; Lee, Jin Woo

2014-01-01

An optimal shape design algorithm is suggested to systematically design a traditional Korean musical instrument, the Pyeongyeong. The Pyeongyeong consists of 16 different chime stones called Gyeongpyeons. The first natural vibration frequency of each Gyeongpyeon must be adjusted to its target frequency, which is determined by the traditional sound tuning method. The second and third natural frequencies must be proportional to the first natural frequency with a specific ratio (1:1.498:2.378). The key idea in our suggested design algorithm is to use the sensitivity of natural frequencies to the variation in the length of each side of a Gyeongpyeon. The dimensions of five different Gyeongpyeons are determined by following the suggested algorithm. Changes in natural frequencies with respect to local thickness variation are closely investigated to compensate for errors that may occur during manufacturing

An Optimal Number-Dependent Preventive Maintenance Strategy for Offshore Wind Turbine Blades Considering Logistics

Directory of Open Access Journals (Sweden)

Mahmood Shafiee

2013-01-01

Full Text Available In offshore wind turbines, the blades are among the most critical and expensive components that suffer from different types of damage due to the harsh maritime environment and high load. The blade damages can be categorized into two types: the minor damage, which only causes a loss in wind capture without resulting in any turbine stoppage, and the major (catastrophic damage, which stops the wind turbine and can only be corrected by replacement. In this paper, we propose an optimal number-dependent preventive maintenance (NDPM strategy, in which a maintenance team is transported with an ordinary or expedited lead time to the offshore platform at the occurrence of the Nth minor damage or the first major damage, whichever comes first. The long-run expected cost of the maintenance strategy is derived, and the necessary conditions for an optimal solution are obtained. Finally, the proposed model is tested on real data collected from an offshore wind farm database. Also, a sensitivity analysis is conducted in order to evaluate the effect of changes in the model parameters on the optimal solution.

An evolutionary algorithm for port-of-entry security optimization considering sensor thresholds

International Nuclear Information System (INIS)

Concho, Ana Lisbeth; Ramirez-Marquez, Jose Emmanuel

2010-01-01

According to the US Customs and Border Protection (CBP), the number of offloaded ship cargo containers arriving at US seaports each year amounts to more than 11 million. The costs of locating an undetonated terrorist weapon at one US port, or even worst, the cost caused by a detonated weapon of mass destruction, would amount to billions of dollars. These costs do not yet account for the devastating consequences that it would cause in the ability to keep the supply chain operating and the sociological and psychological effects. As such, this paper is concerned with developing a container inspection strategy that minimizes the total cost of inspection while maintaining a user specified detection rate for 'suspicious' containers. In this respect and based on a general decision-tree model, this paper presents a holistic evolutionary algorithm for finding the following: (1) optimal threshold values for every sensor and (2) the optimal configuration of the inspection strategy. The algorithm is under the assumption that different sensors with different reliability and cost characteristics can be used. Testing and experimentation show the proposed approach consistently finds high quality solutions in a reduced computational time.

Robust Co-Optimization to Energy and Reserve Joint Dispatch Considering Wind Power Generation and Zonal Reserve Constraints in Real-Time Electricity Markets

Directory of Open Access Journals (Sweden)

Chunlai Li

2017-07-01

Full Text Available This paper proposes an energy and reserve joint dispatch model based on a robust optimization approach in real-time electricity markets, considering wind power generation uncertainties as well as zonal reserve constraints under both normal and N-1 contingency conditions. In the proposed model, the operating reserves are classified as regulating reserve and spinning reserve according to the response performance. More specifically, the regulating reserve is usually utilized to reduce the gap due to forecasting errors, while the spinning reserve is commonly adopted to enhance the ability for N-1 contingencies. Since the transmission bottlenecks may inhibit the deliverability of reserve, the zonal placement of spinning reserve is considered in this paper to improve the reserve deliverability under the contingencies. Numerical results on the IEEE 118-bus test system show the effectiveness of the proposed model.

Shape optimization of draft tubes for Agnew microhydro turbines

International Nuclear Information System (INIS)

Shojaeefard, Mohammad Hasan; Mirzaei, Ammar; Babaei, Ali

2014-01-01

Highlights: • The draft tube of Agnew microhydro turbine was optimized. • Pareto optimal solutions were determined by neural networks and NSGA-II algorithm. • The pressure recovery factor increases with height and angle over design ranges. • The loss coefficient reaches the minimum values at angles about 2 o . • Swirl of the incoming flow has great influence on the optimization results. - Abstract: In this study, the shape optimization of draft tubes utilized in Agnew type microhydro turbines has been discussed. The design parameters of the draft tube such as the cone angle and the height above the tailrace are considered in defining an optimization problem whose goal is to maximize the pressure recovery factor and minimize the energy loss coefficient of flow. The design space is determined by considering the experimental constraints and parameterized by the method of face-centered uniform ascertain distribution. The numerical simulations are performed using the boundary conditions found from laboratory tests and the obtained results are analyzed to create and validate a feed-forward neural network model, which is implemented as a surrogate model. The optimal Pareto solutions are finally determined using the NSGA-II evolutionary algorithm and compared for different inlet conditions. The results predict that the high swirl of the incoming flow drastically reduces the performance of the draft tube

Improving Range Estimation of a 3-Dimensional Flash Ladar via Blind Deconvolution

Science.gov (United States)

2010-09-01

limitation of the simplistic model and adaptation of the higher fidelity model is the catalyst of the material in Chapters V and VI. In order to...characterization has been done previously in fields such as heterodyne Light Detection and Ranging (LiDAR), RADAR, and positron emisson tomography (PET) [20...Figure 6.13(b). These values for pt are consistent with the values from the previous section. Considering all the above optimal pulse-width studies

Technical Note: A novel leaf sequencing optimization algorithm which considers previous underdose and overdose events for MLC tracking radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Wisotzky, Eric, E-mail: eric.wisotzky@charite.de, E-mail: eric.wisotzky@ipk.fraunhofer.de; O’Brien, Ricky; Keall, Paul J., E-mail: paul.keall@sydney.edu.au [Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006 (Australia)

2016-01-15

Purpose: Multileaf collimator (MLC) tracking radiotherapy is complex as the beam pattern needs to be modified due to the planned intensity modulation as well as the real-time target motion. The target motion cannot be planned; therefore, the modified beam pattern differs from the original plan and the MLC sequence needs to be recomputed online. Current MLC tracking algorithms use a greedy heuristic in that they optimize for a given time, but ignore past errors. To overcome this problem, the authors have developed and improved an algorithm that minimizes large underdose and overdose regions. Additionally, previous underdose and overdose events are taken into account to avoid regions with high quantity of dose events. Methods: The authors improved the existing MLC motion control algorithm by introducing a cumulative underdose/overdose map. This map represents the actual projection of the planned tumor shape and logs occurring dose events at each specific regions. These events have an impact on the dose cost calculation and reduce recurrence of dose events at each region. The authors studied the improvement of the new temporal optimization algorithm in terms of the L1-norm minimization of the sum of overdose and underdose compared to not accounting for previous dose events. For evaluation, the authors simulated the delivery of 5 conformal and 14 intensity-modulated radiotherapy (IMRT)-plans with 7 3D patient measured tumor motion traces. Results: Simulations with conformal shapes showed an improvement of L1-norm up to 8.5% after 100 MLC modification steps. Experiments showed comparable improvements with the same type of treatment plans. Conclusions: A novel leaf sequencing optimization algorithm which considers previous dose events for MLC tracking radiotherapy has been developed and investigated. Reductions in underdose/overdose are observed for conformal and IMRT delivery.

Optimally Controlled Flexible Fuel Powertrain System

Energy Technology Data Exchange (ETDEWEB)

Hakan Yilmaz; Mark Christie; Anna Stefanopoulou

2010-12-31

The primary objective of this project was to develop a true Flex Fuel Vehicle capable of running on any blend of ethanol from 0 to 85% with reduced penalty in usable vehicle range. A research and development program, targeting 10% improvement in fuel economy using a direct injection (DI) turbocharged spark ignition engine was conducted. In this project a gasoline-optimized high-technology engine was considered and the hardware and configuration modifications were defined for the engine, fueling system, and air path. Combined with a novel engine control strategy, control software, and calibration this resulted in a highly efficient and clean FFV concept. It was also intended to develop robust detection schemes of the ethanol content in the fuel integrated with adaptive control algorithms for optimized turbocharged direct injection engine combustion. The approach relies heavily on software-based adaptation and optimization striving for minimal modifications to the gasoline-optimized engine hardware system. Our ultimate objective was to develop a compact control methodology that takes advantage of any ethanol-based fuel mixture and not compromise the engine performance under gasoline operation.

A Multi-objective Optimization Application in Friction Stir Welding: Considering Thermo-mechanical Aspects

DEFF Research Database (Denmark)

Tutum, Cem Celal; Hattel, Jesper Henri

2010-01-01

speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2-dimensional sequentially coupled thermomechanical...

Efficiency Optimization by Considering the High Voltage Flyback Transformer Parasitics using an Automatic Winding Layout Technique

DEFF Research Database (Denmark)

Thummala, Prasanth; Schneider, Henrik; Zhang, Zhe

2015-01-01

.The energy efficiency is optimized using a proposed new automatic winding layout (AWL) technique and a comprehensive loss model.The AWL technique generates a large number of transformer winding layouts.The transformer parasitics such as dc resistance, leakage inductance and self-capacitance are calculated...... for each winding layout.An optimization technique is formulated to minimize the sum of energy losses during charge and discharge operations.The efficiency and energy loss distribution results from the optimization routine provide a deep insight into the high voltage transformer designand its impact...

Enhanced Particle Swarm Optimization-Based Feeder Reconfiguration Considering Uncertain Large Photovoltaic Powers and Demands

Directory of Open Access Journals (Sweden)

Ying-Yi Hong

2014-01-01

Full Text Available The Kyoto protocol recommended that industrialized countries limit their green gas emissions in 2012 to 5.2% below 1990 levels. Photovoltaic (PV arrays provide clear and sustainable renewable energy to electric power systems. Solar PV arrays can be installed in distribution systems of rural and urban areas, as opposed to wind-turbine generators, which cause noise in surrounding environments. However, a large PV array (several MW may incur several operation problems, for example, low power quality and reverse power. This work presents a novel method to reconfigure the distribution feeders in order to prevent the injection of reverse power into a substation connected to the transmission level. Moreover, a two-stage algorithm is developed, in which the uncertain bus loads and PV powers are clustered by fuzzy-c-means to gain representative scenarios; optimal reconfiguration is then achieved by a novel mean-variance-based particle swarm optimization. The system loss is minimized while the operational constraints, including reverse power and voltage variation, are satisfied due to the optimal feeder reconfiguration. Simulation results obtained from a 70-bus distribution system with 4 large PV arrays validate the proposed method.

Strategic wind power trading considering rival wind power production

DEFF Research Database (Denmark)

Exizidis, Lazaros; Kazempour, Jalal; Pinson, Pierre

2016-01-01

In an electricity market with high share of wind power, it is expected that wind power producers may exercise market power. However, wind producers have to cope with wind’s uncertain nature in order to optimally offer their generation, whereas in a market with more than one wind producers, uncert...... depending on the rival’s wind generation, given that its own expected generation is not high. Finally, as anticipated, expected system cost is higher when both wind power producers are expected to have low wind power generation......In an electricity market with high share of wind power, it is expected that wind power producers may exercise market power. However, wind producers have to cope with wind’s uncertain nature in order to optimally offer their generation, whereas in a market with more than one wind producers......, uncertainty of rival wind power generation should also be considered. Under this context, this paper addresses the impact of rival wind producers on the offering strategy and profits of a pricemaker wind producer. A stochastic day-ahead market setup is considered, which optimizes the day-ahead schedules...

FPGA based hardware optimized implementation of signal processing system for LFM pulsed radar

Science.gov (United States)

Azim, Noor ul; Jun, Wang

2016-11-01

Signal processing is one of the main parts of any radar system. Different signal processing algorithms are used to extract information about different parameters like range, speed, direction etc, of a target in the field of radar communication. This paper presents LFM (Linear Frequency Modulation) pulsed radar signal processing algorithms which are used to improve target detection, range resolution and to estimate the speed of a target. Firstly, these algorithms are simulated in MATLAB to verify the concept and theory. After the conceptual verification in MATLAB, the simulation is converted into implementation on hardware using Xilinx FPGA. Chosen FPGA is Xilinx Virtex-6 (XC6LVX75T). For hardware implementation pipeline optimization is adopted and also other factors are considered for resources optimization in the process of implementation. Focusing algorithms in this work for improving target detection, range resolution and speed estimation are hardware optimized fast convolution processing based pulse compression and pulse Doppler processing.

Design Optimization of Internal Flow Devices

DEFF Research Database (Denmark)

Madsen, Jens Ingemann

The power of computational fluid dynamics is boosted through the use of automated design optimization methodologies. The thesis considers both derivative-based search optimization and the use of response surface methodologies.......The power of computational fluid dynamics is boosted through the use of automated design optimization methodologies. The thesis considers both derivative-based search optimization and the use of response surface methodologies....

Maintenance Optimization Schedulingof Electric Power SystemsConsidering Renewable EnergySources

OpenAIRE

Yu, Jia

2015-01-01

Maintenance is crucial in any industry to keep components in a reasonable functionalcondition, especially in electric power system, where maintenance is done so that thefrequency and the duration of a fault can be shortened, thus increasing the availability of acertain component. And the reliability of the whole electric power system can also beimproved. In the many deregulated electricity markets, reliability and economic drivingforces are the two aspects that system operators mainly conside...

Optimization in liner shipping

DEFF Research Database (Denmark)

Brouer, Berit Dangaard; Karsten, Christian Vad; Pisinger, David

2017-01-01

Seaborne trade is the lynchpin in almost every international supply chain, and about 90% of non-bulk cargo worldwide is transported by container. In this survey we give an overview of data-driven optimization problems in liner shipping. Research in liner shipping is motivated by a need for handling...... still more complex decision problems, based on big data sets and going across several organizational entities. Moreover, liner shipping optimization problems are pushing the limits of optimization methods, creating a new breeding ground for advanced modelling and solution methods. Starting from liner...... shipping network design, we consider the problem of container routing and speed optimization. Next, we consider empty container repositioning and stowage planning as well as disruption management. In addition, the problem of bunker purchasing is considered in depth. In each section we give a clear problem...

A method of short range system analysis for nuclear utilities

International Nuclear Information System (INIS)

Eng, R.; Mason, E.A.; Benedict, M.

1976-01-01

An optimization procedure has been formulated and tested that is capable of solving for the optimal generation schedule of several nuclear power reactors in an electric power utility system, under short-range, resource-limited, conditions. The optimization procedure utilizes a new concept called the Opportunity Cost of Nuclear Power (OCNP) to optimally assign the resource-limited nuclear energy to the different weeks and hours in the short-range planning horizon. OCNP is defined as the cost of displaced energy when optimally distributed nuclear energy is marginally increased. Under resource-limited conditions, the short-range 'value' of nuclear power to a utility system is not its actual generation cost, but the cost of the next best alternative supply of energy, the OCNP. OCNP is a function of a week's system reserve capacity, the system's economic loading order, the customer demand function, and the nature of the available utility system generating units. The optimized OCNP value of the short-range planning period represents the utility's short-range energy replacement cost incurred when selling nuclear energy to a neighbouring utility. (author)

Opportunistic maintenance considering non-homogenous opportunity arrivals and stochastic opportunity durations

International Nuclear Information System (INIS)

Truong Ba, H.; Cholette, M.E.; Borghesani, P.; Zhou, Y.; Ma, L.

2017-01-01

Many systems and manufacturing processes undergo intermittent operation due to external factors (e.g. weather, low market prices), offering opportunities to conduct maintenance with reduced production losses. Making use of appropriate opportunities can thus lead to significant reduction in the total cost of maintenance and improvement in productivity. In this paper, an opportunistic maintenance (OM) model is developed considering two critical properties of real world opportunities: (i) non-homogeneous opportunity arrivals and (ii) stochastic opportunity duration. The model enables exploiting downtime cost savings from “partial” opportunities (stops shorter than the required maintenance time) thus extending the potential benefit of OM. The criteria for accepting maintenance opportunities are found by minimizing the single-cycle total cost. A closed form expression of the single-cycle total cost is derived for a given PM/OM policy and then a Genetic Algorithm is used to solve the optimization problem. Numerical results are presented to assess the benefit of opportunistic maintenance and the marginal benefit of considering partial opportunities. Results indicate that significant savings can be achieved by considering OM. Moreover, it is shown that the novel consideration of partial opportunities significantly increase the benefit of OM. - Highlights: • Opportunistic and time-based preventive maintenance jointly optimized. • Non-homogeneous opportunity arrivals and stochastic durations considered. • “Partial” opportunities considered for the first time. • Opportunity duration thresholds used as a decision criterion. • Numerical study conducted to evaluate benefit of optimized policy.

Welding Robot Collision-Free Path Optimization

Directory of Open Access Journals (Sweden)

Xuewu Wang

2017-02-01

Full Text Available Reasonable welding path has a significant impact on welding efficiency, and a collision-free path should be considered first in the process of welding robot path planning. The shortest path length is considered as an optimization objective, and obstacle avoidance is considered as the constraint condition in this paper. First, a grid method is used as a modeling method after the optimization objective is analyzed. For local collision-free path planning, an ant colony algorithm is selected as the search strategy. Then, to overcome the shortcomings of the ant colony algorithm, a secondary optimization is presented to improve the optimization performance. Finally, the particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the desired welding path can be obtained based on the optimization strategy.

Optimization of nonimaging focusing heliostat in dynamic correction of astigmatism for a wide range of incident angles.

Science.gov (United States)

Chong, Kok-Keong

2010-05-15

To overcome astigmatism has always been a great challenge in designing a heliostat capable of focusing the sunlight on a small receiver throughout the year. In this Letter, a nonimaging focusing heliostat with a dynamic adjustment of facet mirrors in a group manner has been analyzed for optimizing the astigmatic correction in a wide range of incident angles. This what is to the author's knowledge a new heliostat is not only designed to serve the purpose of concentrating sunlight to several hundreds of suns, but also to significantly reduce the variation of the solar flux distribution with the incident angle.

GIS-based approach for optimal siting and sizing of renewables considering techno-environmental constraints and the stochastic nature of meteorological inputs

Science.gov (United States)

Daskalou, Olympia; Karanastasi, Maria; Markonis, Yannis; Dimitriadis, Panayiotis; Koukouvinos, Antonis; Efstratiadis, Andreas; Koutsoyiannis, Demetris

2016-04-01

Following the legislative EU targets and taking advantage of its high renewable energy potential, Greece can obtain significant benefits from developing its water, solar and wind energy resources. In this context we present a GIS-based methodology for the optimal sizing and siting of solar and wind energy systems at the regional scale, which is tested in the Prefecture of Thessaly. First, we assess the wind and solar potential, taking into account the stochastic nature of the associated meteorological processes (i.e. wind speed and solar radiation, respectively), which is essential component for both planning (i.e., type selection and sizing of photovoltaic panels and wind turbines) and management purposes (i.e., real-time operation of the system). For the optimal siting, we assess the efficiency and economic performance of the energy system, also accounting for a number of constraints, associated with topographic limitations (e.g., terrain slope, proximity to road and electricity grid network, etc.), the environmental legislation and other land use constraints. Based on this analysis, we investigate favorable alternatives using technical, environmental as well as financial criteria. The final outcome is GIS maps that depict the available energy potential and the optimal layout for photovoltaic panels and wind turbines over the study area. We also consider a hypothetical scenario of future development of the study area, in which we assume the combined operation of the above renewables with major hydroelectric dams and pumped-storage facilities, thus providing a unique hybrid renewable system, extended at the regional scale.

A simulation-optimization model for Stone column-supported embankment stability considering rainfall effect

International Nuclear Information System (INIS)

Deb, Kousik; Dhar, Anirban; Purohit, Sandip

2016-01-01

Landslide due to rainfall has been and continues to be one of the most important concerns of geotechnical engineering. The paper presents the variation of factor of safety of stone column-supported embankment constructed over soft soil due to change in water level for an incessant period of rainfall. A combined simulation-optimization based methodology has been proposed to predict the critical surface of failure of the embankment and to optimize the corresponding factor of safety under rainfall conditions using an evolutionary genetic algorithm NSGA-II (Non-Dominated Sorted Genetic Algorithm-II). It has been observed that the position of water table can be reliably estimated with varying periods of infiltration using developed numerical method. The parametric study is presented to study the optimum factor of safety of the embankment and its corresponding critical failure surface under the steady-state infiltration condition. Results show that in case of floating stone columns, period of infiltration has no effect on factor of safety. Even critical failure surfaces for a particular floating column length remain same irrespective of rainfall duration

A simulation-optimization model for Stone column-supported embankment stability considering rainfall effect

Energy Technology Data Exchange (ETDEWEB)

Deb, Kousik, E-mail: kousik@civil.iitkgp.ernet.in [Associate Professor, Department of Civil Engineering, IIT Kharagpur, Kharagpur-721302 (India); Dhar, Anirban, E-mail: anirban@civil.iitkgp.ernet.in [Assistant Professor, Department of Civil Engineering, IIT Kharagpur, Kharagpur-721302 (India); Purohit, Sandip, E-mail: sandip.purohit91@gmail.com [Former B.Tech Student, Department of Civil Engineering, NIT Rourkela, Rourkela (India)

2016-02-01

Landslide due to rainfall has been and continues to be one of the most important concerns of geotechnical engineering. The paper presents the variation of factor of safety of stone column-supported embankment constructed over soft soil due to change in water level for an incessant period of rainfall. A combined simulation-optimization based methodology has been proposed to predict the critical surface of failure of the embankment and to optimize the corresponding factor of safety under rainfall conditions using an evolutionary genetic algorithm NSGA-II (Non-Dominated Sorted Genetic Algorithm-II). It has been observed that the position of water table can be reliably estimated with varying periods of infiltration using developed numerical method. The parametric study is presented to study the optimum factor of safety of the embankment and its corresponding critical failure surface under the steady-state infiltration condition. Results show that in case of floating stone columns, period of infiltration has no effect on factor of safety. Even critical failure surfaces for a particular floating column length remain same irrespective of rainfall duration.

Short-range fundamental forces

International Nuclear Information System (INIS)

Antoniadis, I.; Baessler, S.; Buchner, M.; Fedorov, V.V.; Hoedl, S.; Nesvizhevsky, V.V.; Pignol, G.; Protasov, K.V.; Lambrecht, A.; Reynaud, S.; Sobolev, Y.

2010-01-01

We consider theoretical motivations to search for extra short-range fundamental forces as well as experiments constraining their parameters. The forces could be of two types: 1) spin-independent forces; 2) spin-dependent axion-like forces. Different experimental techniques are sensitive in respective ranges of characteristic distances. The techniques include measurements of gravity at short distances, searches for extra interactions on top of the Casimir force, precision atomic and neutron experiments. We focus on neutron constraints, thus the range of characteristic distances considered here corresponds to the range accessible for neutron experiments

Profit and Risk Measures in Oil Production Optimization

DEFF Research Database (Denmark)

Capolei, Andrea; Foss, Bjarne; Jørgensen, John Bagterp

2015-01-01

In oil production optimization, we usually aim to maximize a deterministic scalar performance index such as the profit over the expected reservoir lifespan. However, when uncertainty in the parameters is considered, the profit results in a random variable that can assume a range of values dependi...... pro and cons for each of them. Finally, among the presented risk measures, we identify two of them as appropriate risk measures when minimizing the risk....

Method for Determining Optimal Residential Energy Efficiency Retrofit Packages

Energy Technology Data Exchange (ETDEWEB)

Polly, B.; Gestwick, M.; Bianchi, M.; Anderson, R.; Horowitz, S.; Christensen, C.; Judkoff, R.

2011-04-01

Businesses, government agencies, consumers, policy makers, and utilities currently have limited access to occupant-, building-, and location-specific recommendations for optimal energy retrofit packages, as defined by estimated costs and energy savings. This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location. Energy savings and incremental costs are calculated relative to a minimum upgrade reference scenario, which accounts for efficiency upgrades that would occur in the absence of a retrofit because of equipment wear-out and replacement with current minimum standards.

Topology optimization based on the harmony search method

International Nuclear Information System (INIS)

Lee, Seung-Min; Han, Seog-Young

2017-01-01

A new topology optimization scheme based on a Harmony search (HS) as a metaheuristic method was proposed and applied to static stiffness topology optimization problems. To apply the HS to topology optimization, the variables in HS were transformed to those in topology optimization. Compliance was used as an objective function, and harmony memory was defined as the set of the optimized topology. Also, a parametric study for Harmony memory considering rate (HMCR), Pitch adjusting rate (PAR), and Bandwidth (BW) was performed to find the appropriate range for topology optimization. Various techniques were employed such as a filtering scheme, simple average scheme and harmony rate. To provide a robust optimized topology, the concept of the harmony rate update rule was also implemented. Numerical examples are provided to verify the effectiveness of the HS by comparing the optimal layouts of the HS with those of Bidirectional evolutionary structural optimization (BESO) and Artificial bee colony algorithm (ABCA). The following conclu- sions could be made: (1) The proposed topology scheme is very effective for static stiffness topology optimization problems in terms of stability, robustness and convergence rate. (2) The suggested method provides a symmetric optimized topology despite the fact that the HS is a stochastic method like the ABCA. (3) The proposed scheme is applicable and practical in manufacturing since it produces a solid-void design of the optimized topology. (4) The suggested method appears to be very effective for large scale problems like topology optimization.

Topology optimization based on the harmony search method

Energy Technology Data Exchange (ETDEWEB)

Lee, Seung-Min; Han, Seog-Young [Hanyang University, Seoul (Korea, Republic of)

2017-06-15

A new topology optimization scheme based on a Harmony search (HS) as a metaheuristic method was proposed and applied to static stiffness topology optimization problems. To apply the HS to topology optimization, the variables in HS were transformed to those in topology optimization. Compliance was used as an objective function, and harmony memory was defined as the set of the optimized topology. Also, a parametric study for Harmony memory considering rate (HMCR), Pitch adjusting rate (PAR), and Bandwidth (BW) was performed to find the appropriate range for topology optimization. Various techniques were employed such as a filtering scheme, simple average scheme and harmony rate. To provide a robust optimized topology, the concept of the harmony rate update rule was also implemented. Numerical examples are provided to verify the effectiveness of the HS by comparing the optimal layouts of the HS with those of Bidirectional evolutionary structural optimization (BESO) and Artificial bee colony algorithm (ABCA). The following conclu- sions could be made: (1) The proposed topology scheme is very effective for static stiffness topology optimization problems in terms of stability, robustness and convergence rate. (2) The suggested method provides a symmetric optimized topology despite the fact that the HS is a stochastic method like the ABCA. (3) The proposed scheme is applicable and practical in manufacturing since it produces a solid-void design of the optimized topology. (4) The suggested method appears to be very effective for large scale problems like topology optimization.

Design of optimal input–output scaling factors based fuzzy PSS using bat algorithm

Directory of Open Access Journals (Sweden)

D.K. Sambariya

2016-06-01

Full Text Available In this article, a fuzzy logic based power system stabilizer (FPSS is designed by tuning its input–output scaling factors. Two input signals to FPSS are considered as change of speed and change in power, and the output signal is considered as a correcting voltage signal. The normalizing factors of these signals are considered as the optimization problem with minimization of integral of square error in single-machine and multi-machine power systems. These factors are optimally determined with bat algorithm (BA and considered as scaling factors of FPSS. The performance of power system with such a designed BA based FPSS (BA-FPSS is compared to that of response with FPSS, Harmony Search Algorithm based FPSS (HSA-FPSS and Particle Swarm Optimization based FPSS (PSO-FPSS. The systems considered are single-machine connected to infinite-bus, two-area 4-machine 10-bus and IEEE New England 10-machine 39-bus power systems for evaluating the performance of BA-FPSS. The comparison is carried out in terms of the integral of time-weighted absolute error (ITAE, integral of absolute error (IAE and integral of square error (ISE of speed response for systems with FPSS, HSA-FPSS and BA-FPSS. The superior performance of systems with BA-FPSS is established considering eight plant conditions of each system, which represents the wide range of operating conditions.

A Data-Driven Stochastic Reactive Power Optimization Considering Uncertainties in Active Distribution Networks and Decomposition Method

DEFF Research Database (Denmark)

Ding, Tao; Yang, Qingrun; Yang, Yongheng

2018-01-01

To address the uncertain output of distributed generators (DGs) for reactive power optimization in active distribution networks, the stochastic programming model is widely used. The model is employed to find an optimal control strategy with minimum expected network loss while satisfying all......, in this paper, a data-driven modeling approach is introduced to assume that the probability distribution from the historical data is uncertain within a confidence set. Furthermore, a data-driven stochastic programming model is formulated as a two-stage problem, where the first-stage variables find the optimal...... control for discrete reactive power compensation equipment under the worst probability distribution of the second stage recourse. The second-stage variables are adjusted to uncertain probability distribution. In particular, this two-stage problem has a special structure so that the second-stage problem...

Rational optimization of reliability and safety policies

International Nuclear Information System (INIS)

Melchers, Robert E.

2001-01-01

Optimization of structures for design has a long history, including optimization using numerical methods and optimality criteria. Much of this work has considered a subset of the complete design optimization problem--that of the technical issues alone. The more general problem must consider also non-technical issues and, importantly, the interplay between them and the parameters which influence them. Optimization involves optimal setting of design or acceptance criteria and, separately, optimal design within the criteria. In the modern context of probability based design codes this requires probabilistic acceptance criteria. The determination of such criteria involves more than the nominal code failure probability approach used for design code formulation. A more general view must be taken and a clear distinction must be made between those matters covered by technical reliability and non-technical reliability. The present paper considers this issue and outlines a framework for rational optimization of structural and other systems given the socio-economic and political systems within which optimization must be performed

SU-E-T-574: Novel Chance-Constrained Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

International Nuclear Information System (INIS)

An, Y; Liang, J; Liu, W

2015-01-01

Purpose: We propose to apply a probabilistic framework, namely chanceconstrained optimization, in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to hedge against the influence of uncertainties and improve robustness of treatment plans. Methods: IMPT plans were generated for a typical prostate patient. Nine dose distributions are computed — the nominal one and one each for ±5mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. These nine dose distributions are supplied to the solver CPLEX as chance constraints to explicitly control plan robustness under these representative uncertainty scenarios with certain probability. This probability is determined by the tolerance level. We make the chance-constrained model tractable by converting it to a mixed integer optimization problem. The quality of plans derived from this method is evaluated using dose-volume histogram (DVH) indices such as tumor dose homogeneity (D5% – D95%) and coverage (D95%) and normal tissue sparing like V70 of rectum, V65, and V40 of bladder. We also compare the results from this novel method with the conventional PTV-based method to further demonstrate its effectiveness Results: Our model can yield clinically acceptable plans within 50 seconds. The chance-constrained optimization produces IMPT plans with comparable target coverage, better target dose homogeneity, and better normal tissue sparing compared to the PTV-based optimization [D95% CTV: 67.9 vs 68.7 (Gy), D5% – D95% CTV: 11.9 vs 18 (Gy), V70 rectum: 0.0 % vs 0.33%, V65 bladder: 2.17% vs 9.33%, V40 bladder: 8.83% vs 21.83%]. It also simultaneously makes the plan more robust [Width of DVH band at D50%: 2.0 vs 10.0 (Gy)]. The tolerance level may be varied to control the tradeoff between plan robustness and quality. Conclusion: The chance-constrained optimization generates superior IMPT plan compared to the PTV-based optimization with

Development and application of a living probabilistic safety assessment tool: Multi-objective multi-dimensional optimization of surveillance requirements in NPPs considering their ageing

International Nuclear Information System (INIS)

Kančev, Duško; Čepin, Marko; Gjorgiev, Blaže

2014-01-01

. Substantial risk reduction, supplemented with reduction in costs and dose is implicated if the selected existing surveillance test intervals are replaced with the corresponding optimal ones. By this, the benefits of applying risk-informed decision making are once more emphasized. Also, the importance of the inclusion of safety equipment ageing in the plant long-term probabilistic safety assessment is recognized. - Highlights: • New model for assessing time-dependent component unavailability in NPP is developed. • Component ageing is considered within the model. • The model is coupled to PSA software used for system and plant level modelling. • Living PSA tool for time-dependent risk analysis on plant level is developed. • Plant level multi-objective optimization of surveillance requirements is performed

Multiobjective CVaR Optimization Model and Solving Method for Hydrothermal System Considering Uncertain Load Demand

Directory of Open Access Journals (Sweden)

Zhongfu Tan

2015-01-01

Full Text Available In order to solve the influence of load uncertainty on hydrothermal power system operation and achieve the optimal objectives of system power generation consumption, pollutant emissions, and first-stage hydropower station storage capacity, this paper introduced CVaR method and built a multiobjective optimization model and its solving method. In the optimization model, load demand’s actual values and deviation values are regarded as random variables, scheduling objective is redefined to meet confidence level requirement and system operation constraints and loss function constraints are taken into consideration. To solve the proposed model, this paper linearized nonlinear constraints, applied fuzzy satisfaction, fuzzy entropy, and weighted multiobjective function theories to build a fuzzy entropy multiobjective CVaR model. The model is a mixed integer linear programming problem. Then, six thermal power plants and three cascade hydropower stations are taken as the hydrothermal system for numerical simulation. The results verified that multiobjective CVaR method is applicable to solve hydrothermal scheduling problems. It can better reflect risk level of the scheduling result. The fuzzy entropy satisfaction degree solving algorithm can simplify solving difficulty and get the optimum operation scheduling scheme.

Optimization of a multilayer Laue lens system for a hard x-ray nanoprobe

International Nuclear Information System (INIS)

Jiang, Hui; Wang, Hua; Mao, Chengwen; Li, Aiguo; He, Yan; Dong, Zhaohui; Zheng, Yi

2014-01-01

Detailed designs of a multilayer Laue lens system for a hard x-ray nanoprobe, including flat and wedged types, are presented, to realize nanoscale point focus and high diffraction efficiency simultaneously. The difficulty of movement and alignment for lens, aperture and sample are considered in the optimization process. Considering the practical requirements of future experiments, the features of the beamline and the structural imperfections, the working energy range, the beam vibration and structural errors are estimated and discussed. (paper)

Research on Construction Optimization of Three-Connected-Arch Hydraulic Underground Cavities Considering Creep Property

Directory of Open Access Journals (Sweden)

Bao-yun Zhao

2014-01-01

Full Text Available In order to prevent the creep of surrounding rock in long-term construction, with consideration of different construction methods and other factors during the construction of large-scale underground cavity, three different construction schemes are designed for specific projects and a nonlinear viscoelastic-plastic creep model which can describe rock accelerated creeping is introduced and applied to construction optimization calculation of the large-scale three-connected-arch hydraulic underground cavity through secondary development of FLAC3D. The results show that the adoption of middle cavity construction method, the second construction method, enables the maximum vault displacement of 16.04 mm. This method results in less stress redistribution and plastic zone expansion to the cavity’s surrounding rock than the other two schemes, which is the safest construction scheme. The conclusion can provide essential reference and guidance to similar engineering for construction optimization.

Optimal Design and Related Areas in Optimization and Statistics

CERN Document Server

Pronzato, Luc

2009-01-01

This edited volume, dedicated to Henry P. Wynn, reflects his broad range of research interests, focusing in particular on the applications of optimal design theory in optimization and statistics. It covers algorithms for constructing optimal experimental designs, general gradient-type algorithms for convex optimization, majorization and stochastic ordering, algebraic statistics, Bayesian networks and nonlinear regression. Written by leading specialists in the field, each chapter contains a survey of the existing literature along with substantial new material. This work will appeal to both the

Optimal reactive power planning for distribution systems considering intermittent wind power using Markov model and genetic algorithm

Science.gov (United States)

Li, Cheng

Wind farms, photovoltaic arrays, fuel cells, and micro-turbines are all considered to be Distributed Generation (DG). DG is defined as the generation of power which is dispersed throughout a utility's service territory and either connected to the utility's distribution system or isolated in a small grid. This thesis addresses modeling and economic issues pertaining to the optimal reactive power planning for distribution system with wind power generation (WPG) units. Wind farms are inclined to cause reverse power flows and voltage variations due to the random-like outputs of wind turbines. To deal with this kind of problem caused by wide spread usage of wind power generation, this thesis investigates voltage and reactive power controls in such a distribution system. Consequently static capacitors (SC) and transformer taps are introduced into the system and treated as controllers. For the purpose of getting optimum voltage and realizing reactive power control, the research proposes a proper coordination among the controllers like on-load tap changer (OLTC), feeder-switched capacitors. What's more, in order to simulate its uncertainty, the wind power generation is modeled by the Markov model. In that way, calculating the probabilities for all the scenarios is possible. Some outputs with consecutive and discrete values have been used for transition between successive time states and within state wind speeds. The thesis will describe the method to generate the wind speed time series from the transition probability matrix. After that, utilizing genetic algorithm, the optimal locations of SCs, the sizes of SCs and transformer taps are determined so as to minimize the cost or minimize the power loss, and more importantly improve voltage profiles. The applicability of the proposed method is verified through simulation on a 9-bus system and a 30-bus system respectively. At last, the simulation results indicate that as long as the available capacitors are able to sufficiently

Pricing Decisions of a Dual-Channel Supply Chain considering Supply Disruption Risk

Directory of Open Access Journals (Sweden)

Yancong Zhou

2018-01-01

Full Text Available Supply disruption may cause strong complaints of customers, which is a cost loss for the firms in the supply chain. Obviously, if realizing that there is the disruption risk, the members in a supply chain will adjust their decisions. For analyzing the influence, we consider a popular supply chain mode with dual channels, where one manufacturer has its direct sales channel and one traditional retailer channel. The manufacturer may suffer a supply disruption so that all ordered products by the retailer or the direct retail channel will be lost, and the members in supply chain will bear the corresponding disruption penalty from the customers. By considering four structures with different market power relations, the closed-form optimal price decisions of the four models are given. We found that the disruption factor improves the sales prices for any member structure as compared to the supply chain without the disruption. And the direct retail prices in the different modes are the same as each other, but the price of the traditional channel is influenced by the market share. And the sorts of the sales prices under different structures are given. We also conduct some extensive numerical analysis and compare the results under different structures. We observe that the expected optimal profits of considering the external penalty are smaller than those of no external penalty, and we give a sort of the optimal expected profits. And we also provide the effects of some parameters on the optimal decisions and the optimal expected profits.

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features.

Science.gov (United States)

Han, Chang-Hee; Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan; Im, Chang-Hwan

2016-01-01

It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

Study on multi-objective flexible job-shop scheduling problem considering energy consumption

Directory of Open Access Journals (Sweden)

Zengqiang Jiang

2014-06-01

Full Text Available Purpose: Build a multi-objective Flexible Job-shop Scheduling Problem(FJSP optimization model, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered, then Design a Modified Non-dominated Sorting Genetic Algorithm (NSGA-II based on blood variation for above scheduling model.Design/methodology/approach: A multi-objective optimization theory based on Pareto optimal method is used in carrying out the optimization model. NSGA-II is used to solve the model.Findings: By analyzing the research status and insufficiency of multi-objective FJSP, Find that the difference in scheduling will also have an effect on energy consumption in machining process and environmental emissions. Therefore, job-shop scheduling requires not only guaranteeing the processing quality, time and cost, but also optimizing operation plan of machines and minimizing energy consumption.Originality/value: A multi-objective FJSP optimization model is put forward, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered. According to above model, Blood-Variation-based NSGA-II (BVNSGA-II is designed. In which, the chromosome mutation rate is determined after calculating the blood relationship between two cross chromosomes, crossover and mutation strategy of NSGA-II is optimized and the prematurity of population is overcome. Finally, the performance of the proposed model and algorithm is evaluated through a case study, and the results proved the efficiency and feasibility of the proposed model and algorithm.

Homogeneous Gaussian Profile P+-Type Emitters: Updated Parameters and Metal-Grid Optimization

Directory of Open Access Journals (Sweden)

M. Cid

2002-10-01

Full Text Available P+-type emitters were optimized keeping the base parameters constant. Updated internal parameters were considered. The surface recombination velocity was considered variable with the surface doping level. Passivated homogeneous emitters were found to have low emitter recombination density and high collection efficiency. A complete structure p+nn+ was analyzed, taking into account optimized shadowing and metal-contacted factors for laboratory cells as function of the surface doping level and the emitter thickness. The base parameters were kept constant to make the emitter characteristics evident. The most efficient P+-type passivated homogeneous emitters, provide efficiencies around 21% for a wide range of emitter sheet resistivity (50 -- 500 omega/ with the surface doping levels Ns=1×10(19 cm-3 and 5×10(19 cm-3. The output electrical parameters were evaluated considering the recently proposed value n i=9.65×10(9 (cm-3. A non-significant increase of 0.1% in the efficiency was obtained, validating all the conclusions obtained in this work, considering n i=1×10(10 cm-3.

Exploring quantum control landscapes: Topology, features, and optimization scaling

International Nuclear Information System (INIS)

Moore, Katharine W.; Rabitz, Herschel

2011-01-01

Quantum optimal control experiments and simulations have successfully manipulated the dynamics of systems ranging from atoms to biomolecules. Surprisingly, these collective works indicate that the effort (i.e., the number of algorithmic iterations) required to find an optimal control field appears to be essentially invariant to the complexity of the system. The present work explores this matter in a series of systematic optimizations of the state-to-state transition probability on model quantum systems with the number of states N ranging from 5 through 100. The optimizations occur over a landscape defined by the transition probability as a function of the control field. Previous theoretical studies on the topology of quantum control landscapes established that they should be free of suboptimal traps under reasonable physical conditions. The simulations in this work include nearly 5000 individual optimization test cases, all of which confirm this prediction by fully achieving optimal population transfer of at least 99.9% on careful attention to numerical procedures to ensure that the controls are free of constraints. Collectively, the simulation results additionally show invariance of required search effort to system dimension N. This behavior is rationalized in terms of the structural features of the underlying control landscape. The very attractive observed scaling with system complexity may be understood by considering the distance traveled on the control landscape during a search and the magnitude of the control landscape slope. Exceptions to this favorable scaling behavior can arise when the initial control field fluence is too large or when the target final state recedes from the initial state as N increases.

Multi-objective design optimization and control of magnetorheological fluid brakes for automotive applications

Science.gov (United States)

Shamieh, Hadi; Sedaghati, Ramin

2017-12-01

The magnetorheological brake (MRB) is an electromechanical device that generates a retarding torque through employing magnetorheological (MR) fluids. The objective of this paper is to design, optimize and control an MRB for automotive applications considering. The dynamic range of a disk-type MRB expressing the ratio of generated toque at on and off states has been formulated as a function of the rotational speed, geometrical and material properties, and applied electrical current. Analytical magnetic circuit analysis has been conducted to derive the relation between magnetic field intensity and the applied electrical current as a function of the MRB geometrical and material properties. A multidisciplinary design optimization problem has then been formulated to identify the optimal brake geometrical parameters to maximize the dynamic range and minimize the response time and weight of the MRB under weight, size and magnetic flux density constraints. The optimization problem has been solved using combined genetic and sequential quadratic programming algorithms. Finally, the performance of the optimally designed MRB has been investigated in a quarter vehicle model. A PID controller has been designed to regulate the applied current required by the MRB in order to improve vehicle’s slipping on different road conditions.

Optimization of wearable microwave antenna with simplified electromagnetic model of the human body

Science.gov (United States)

Januszkiewicz, Łukasz; Barba, Paolo Di; Hausman, Sławomir

2017-12-01

In this paper the problem of optimization design of a microwave wearable antenna is investigated. Reference is made to a specific antenna design that is a wideband Vee antenna the geometry of which is characterized by 6 parameters. These parameters were automatically adjusted with an evolution strategy based algorithm EStra to obtain the impedance matching of the antenna located in the proximity of the human body. The antenna was designed to operate in the ISM (industrial, scientific, medical) band which covers the frequency range of 2.4 GHz up to 2.5 GHz. The optimization procedure used the finite-difference time-domain method based full-wave simulator with a simplified human body model. In the optimization procedure small movements of antenna towards or away of the human body that are likely to happen during real use were considered. The stability of the antenna parameters irrespective of the movements of the user's body is an important factor in wearable antenna design. The optimization procedure allowed obtaining good impedance matching for a given range of antenna distances with respect to the human body.

Many-objective thermodynamic optimization of Stirling heat engine

International Nuclear Information System (INIS)

Patel, Vivek; Savsani, Vimal; Mudgal, Anurag

2017-01-01

This paper presents a rigorous investigation of many-objective (four-objective) thermodynamic optimization of a Stirling heat engine. Many-objective optimization problem is formed by considering maximization of thermal efficiency, power output, ecological function and exergy efficiency. Multi-objective heat transfer search (MOHTS) algorithm is proposed and applied to obtain a set of Pareto-optimal points. Many objective optimization results form a solution in a four dimensional hyper objective space and for visualization it is represented on a two dimension objective space. Thus, results of four-objective optimization are represented by six Pareto fronts in two dimension objective space. These six Pareto fronts are compared with their corresponding two-objective Pareto fronts. Quantitative assessment of the obtained Pareto solutions is reported in terms of spread and the spacing measures. Different decision making approaches such as LINMAP, TOPSIS and fuzzy are used to select a final optimal solution from Pareto optimal set of many-objective optimization. Finally, to reveal the level of conflict between these objectives, distribution of each decision variable in their allowable range is also shown in two dimensional objective spaces. - Highlights: • Many-objective (i.e. four objective) optimization of Stirling engine is investigated. • MOHTS algorithm is introduced and applied to obtain a set of Pareto points. • Comparative results of many-objective and multi-objectives are presented. • Relationship of design variables in many-objective optimization are obtained. • Optimum solution is selected by using decision making approaches.

Optimal control of greenhouse gas emissions and system cost for integrated municipal solid waste management with considering a hierarchical structure.

Science.gov (United States)

Li, Jing; He, Li; Fan, Xing; Chen, Yizhong; Lu, Hongwei

2017-08-01

This study presents a synergic optimization of control for greenhouse gas (GHG) emissions and system cost in integrated municipal solid waste (MSW) management on a basis of bi-level programming. The bi-level programming is formulated by integrating minimizations of GHG emissions at the leader level and system cost at the follower level into a general MSW framework. Different from traditional single- or multi-objective approaches, the proposed bi-level programming is capable of not only addressing the tradeoffs but also dealing with the leader-follower relationship between different decision makers, who have dissimilar perspectives interests. GHG emission control is placed at the leader level could emphasize the significant environmental concern in MSW management. A bi-level decision-making process based on satisfactory degree is then suitable for solving highly nonlinear problems with computationally effectiveness. The capabilities and effectiveness of the proposed bi-level programming are illustrated by an application of a MSW management problem in Canada. Results show that the obtained optimal management strategy can bring considerable revenues, approximately from 76 to 97 million dollars. Considering control of GHG emissions, it would give priority to the development of the recycling facility throughout the whole period, especially in latter periods. In terms of capacity, the existing landfill is enough in the future 30 years without development of new landfills, while expansion to the composting and recycling facilities should be paid more attention.

SOCIAL NETWORK OPTIMIZATION A NEW METHAHEURISTIC FOR GENERAL OPTIMIZATION PROBLEMS

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Hassan Sherafat

2017-12-01

Full Text Available In the recent years metaheuristics were studied and developed as powerful technics for hard optimization problems. Some of well-known technics in this field are: Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, and Swarm Intelligence, which are applied successfully to many complex optimization problems. In this paper, we introduce a new metaheuristic for solving such problems based on social networks concept, named as Social Network Optimization – SNO. We show that a wide range of np-hard optimization problems may be solved by SNO.

Optimal inspection and replacement periods of the safety system in Wolsung Nuclear Power Plant Unit 1 with an optimized cost perspective

International Nuclear Information System (INIS)

Jinil Mok; Poong Hyun Seong

1996-01-01

In this work, a model for determining the optimal inspection and replacement periods of the safety system in Wolsung Nuclear Power Plant Unit 1 is developed, which is to minimize economic loss caused by inadvertent trip and the system failure. This model uses cost benefit analysis method and the part for optimal inspection period considers the human error. The model is based on three factors as follows: (i) The cumulative failure distribution function of the safety system, (ii) The probability that the safety system does not operate due to failure of the system or human error when the safety system is needed at an emergency condition and (iii) The average probability that the reactor is tripped due to the failure of system components or human error. The model then is applied to evaluate the safety system in Wolsung Nuclear Power Plant Unit 1. The optimal replacement periods which are calculated with proposed model differ from those used in Wolsung NPP Unit 1 by about a few days or months, whereas the optimal inspection periods are in about the same range. (author)

Circuit model optimization of a nano split ring resonator dimer antenna operating in infrared spectral range

International Nuclear Information System (INIS)

Gneiding, N.; Zhuromskyy, O.; Peschel, U.; Shamonina, E.

2014-01-01

Metamaterials are comprised of metallic structures with a strong response to incident electromagnetic radiation, like, for example, split ring resonators. The interaction of resonator ensembles with electromagnetic waves can be simulated with finite difference or finite elements algorithms, however, above a certain ensemble size simulations become inadmissibly time or memory consuming. Alternatively a circuit description of metamaterials, a well developed modelling tool at radio and microwave frequencies, allows to significantly increase the simulated ensemble size. This approach can be extended to the IR spectral range with an appropriate set of circuit element parameters accounting for physical effects such as electron inertia and finite conductivity. The model is verified by comparing the coupling coefficients with the ones obtained from the full wave numerical simulations, and used to optimize the nano-antenna design with improved radiation characteristics.

Mechanical Design Optimization Using Advanced Optimization Techniques

CERN Document Server

Rao, R Venkata

2012-01-01

Mechanical design includes an optimization process in which designers always consider objectives such as strength, deflection, weight, wear, corrosion, etc. depending on the requirements. However, design optimization for a complete mechanical assembly leads to a complicated objective function with a large number of design variables. It is a good practice to apply optimization techniques for individual components or intermediate assemblies than a complete assembly. Analytical or numerical methods for calculating the extreme values of a function may perform well in many practical cases, but may fail in more complex design situations. In real design problems, the number of design parameters can be very large and their influence on the value to be optimized (the goal function) can be very complicated, having nonlinear character. In these complex cases, advanced optimization algorithms offer solutions to the problems, because they find a solution near to the global optimum within reasonable time and computational ...

Considering baseline factors and early response rates to optimize therapy for chronic myeloid leukemia in chronic phase.

Science.gov (United States)

Akard, Luke P; Bixby, Dale

2016-05-01

Multiple BCR-ABL tyrosine kinase inhibitors (TKIs) are available for the treatment of chronic myeloid leukemia in chronic phase (CML-CP), and several baseline and on-treatment predictive factors have been identified that can be used to help guide TKI selection for individual patients. In particular, early molecular response (EMR; BCR-ABL ≤10% on the International Scale at 3 months) has become an accepted benchmark for evaluating whether patients with CML-CP are responding optimally to frontline TKI therapy. Failure to achieve EMR is considered an inadequate initial response according to the National Comprehensive Cancer Network guidelines and a warning response according to the European LeukemiaNet recommendations. Here we review data supporting the importance of achieving EMR for improving patients' long-term outcomes and discuss key considerations for selecting a frontline TKI in light of these data. Because a higher proportion of patients achieve EMR with second-generation TKIs such as nilotinib and dasatinib than with imatinib, these TKIs may be preferable for many patients, particularly those with known negative prognostic factors at baseline. We also discuss other considerations for frontline TKI choice, including toxicities, cost-effectiveness, and the emerging goals of deep molecular response and treatment-free remission.

Development of Optimization method about Capital Structure and Senior-Sub Structure by considering Project-Risk

Science.gov (United States)

Kawamoto, Shigeru; Ikeda, Yuichi; Fukui, Chihiro; Tateshita, Fumihiko

Private finance initiative is a business scheme that materializes social infrastructure and public services by utilizing private-sector resources. In this paper we propose a new method to optimize capital structure, which is the ratio of capital to debt, and senior-sub structure, which is the ratio of senior loan to subordinated loan, for private finance initiative. We make the quantitative analysis of a private finance initiative's project using the proposed method. We analyze trade-off structure between risk and return in the project, and optimize capital structure and senior-sub structure. The method we propose helps to improve financial stability of the project, and to make a fund raising plan that is expected to be reasonable for project sponsor and moneylender.

Design and optimization of mixed flow pump impeller blades by varying semi-cone angle

Science.gov (United States)

Dash, Nehal; Roy, Apurba Kumar; Kumar, Kaushik

2018-03-01

The mixed flow pump is a cross between the axial and radial flow pump. These pumps are used in a large number of applications in modern fields. For the designing of these mixed flow pump impeller blades, a lot number of design parameters are needed to be considered which makes this a tedious task for which fundamentals of turbo-machinery and fluid mechanics are always prerequisites. The semi-cone angle of mixed flow pump impeller blade has a specified range of variations generally between 45o to 60o. From the literature review done related to this topic researchers have considered only a particular semi-cone angle and all the calculations are based on this very same semi-cone angle. By varying this semi-cone angle in the specified range, it can be verified if that affects the designing of the impeller blades for a mixed flow pump. Although a lot of methods are available for designing of mixed flow pump impeller blades like inverse time marching method, the pseudo-stream function method, Fourier expansion singularity method, free vortex method, mean stream line theory method etc. still the optimized design of the mixed flow pump impeller blade has been a cumbersome work. As stated above since all the available research works suggest or propose the blade designs with constant semi-cone angle, here the authors have designed the impeller blades by varying the semi-cone angle in a particular range with regular intervals for a Mixed-Flow pump. Henceforth several relevant impeller blade designs are obtained and optimization is carried out to obtain the optimized design (blade with optimal geometry) of impeller blade.

Optimal Ski Jump

Science.gov (United States)

Rebilas, Krzysztof

2013-02-01

Consider a skier who goes down a takeoff ramp, attains a speed V, and jumps, attempting to land as far as possible down the hill below (Fig. 1). At the moment of takeoff the angle between the skier's velocity and the horizontal is α. What is the optimal angle α that makes the jump the longest possible for the fixed magnitude of the velocity V? Of course, in practice, this is a very sophisticated problem; the skier's range depends on a variety of complex factors in addition to V and α. However, if we ignore these and assume the jumper is in free fall between the takeoff ramp and the landing point below, the problem becomes an exercise in kinematics that is suitable for introductory-level students. The solution is presented here.

Designing a Profit-Maximizing Critical Peak Pricing Scheme Considering the Payback Phenomenon

Directory of Open Access Journals (Sweden)

Sung Chan Park

2015-10-01

Full Text Available Critical peak pricing (CPP is a demand response program that can be used to maximize profits for a load serving entity in a deregulated market environment. Like other such programs, however, CPP is not free from the payback phenomenon: a rise in consumption after a critical event. This payback has a negative effect on profits and thus must be appropriately considered when designing a CPP scheme. However, few studies have examined CPP scheme design considering payback. This study thus characterizes payback using three parameters (duration, amount, and pattern and examines payback effects on the optimal schedule of critical events and on the optimal peak rate for two specific payback patterns. This analysis is verified through numerical simulations. The results demonstrate the need to properly consider payback parameters when designing a profit-maximizing CPP scheme.

Evaluating Maximum Photovoltaic Integration in District Distribution Systems Considering Optimal Inverter Dispatch and Cloud Shading Conditions

DEFF Research Database (Denmark)

Ding, Tao; Kou, Yu; Yang, Yongheng

2017-01-01

. However, the intermittency of solar PV energy (e.g., due to passing clouds) may affect the PV generation in the district distribution network. To address this issue, the voltage magnitude constraints under the cloud shading conditions should be taken into account in the optimization model, which can......As photovoltaic (PV) integration increases in distribution systems, to investigate the maximum allowable PV integration capacity for a district distribution system becomes necessary in the planning phase, an optimization model is thus proposed to evaluate the maximum PV integration capacity while...

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

Directory of Open Access Journals (Sweden)

Tobias Nüesch

2014-02-01

Full Text Available This paper presents a novel method to solve the energy management problem for hybrid electric vehicles (HEVs with engine start and gearshift costs. The method is based on a combination of deterministic dynamic programming (DP and convex optimization. As demonstrated in a case study, the method yields globally optimal results while returning the solution in much less time than the conventional DP method. In addition, the proposed method handles state constraints, which allows for the application to scenarios where the battery state of charge (SOC reaches its boundaries.

Optimal adaptive control for quantum metrology with time-dependent Hamiltonians

Science.gov (United States)

Pang, Shengshi; Jordan, Andrew N.

2017-01-01

Quantum metrology has been studied for a wide range of systems with time-independent Hamiltonians. For systems with time-dependent Hamiltonians, however, due to the complexity of dynamics, little has been known about quantum metrology. Here we investigate quantum metrology with time-dependent Hamiltonians to bridge this gap. We obtain the optimal quantum Fisher information for parameters in time-dependent Hamiltonians, and show proper Hamiltonian control is generally necessary to optimize the Fisher information. We derive the optimal Hamiltonian control, which is generally adaptive, and the measurement scheme to attain the optimal Fisher information. In a minimal example of a qubit in a rotating magnetic field, we find a surprising result that the fundamental limit of T2 time scaling of quantum Fisher information can be broken with time-dependent Hamiltonians, which reaches T4 in estimating the rotation frequency of the field. We conclude by considering level crossings in the derivatives of the Hamiltonians, and point out additional control is necessary for that case. PMID:28276428

Optimal adaptive control for quantum metrology with time-dependent Hamiltonians.

Science.gov (United States)

Pang, Shengshi; Jordan, Andrew N

2017-03-09

Quantum metrology has been studied for a wide range of systems with time-independent Hamiltonians. For systems with time-dependent Hamiltonians, however, due to the complexity of dynamics, little has been known about quantum metrology. Here we investigate quantum metrology with time-dependent Hamiltonians to bridge this gap. We obtain the optimal quantum Fisher information for parameters in time-dependent Hamiltonians, and show proper Hamiltonian control is generally necessary to optimize the Fisher information. We derive the optimal Hamiltonian control, which is generally adaptive, and the measurement scheme to attain the optimal Fisher information. In a minimal example of a qubit in a rotating magnetic field, we find a surprising result that the fundamental limit of T 2 time scaling of quantum Fisher information can be broken with time-dependent Hamiltonians, which reaches T 4 in estimating the rotation frequency of the field. We conclude by considering level crossings in the derivatives of the Hamiltonians, and point out additional control is necessary for that case.

Reactive Power Pricing Model Considering the Randomness of Wind Power Output

Science.gov (United States)

Dai, Zhong; Wu, Zhou

2018-01-01

With the increase of wind power capacity integrated into grid, the influence of the randomness of wind power output on the reactive power distribution of grid is gradually highlighted. Meanwhile, the power market reform puts forward higher requirements for reasonable pricing of reactive power service. Based on it, the article combined the optimal power flow model considering wind power randomness with integrated cost allocation method to price reactive power. Meanwhile, considering the advantages and disadvantages of the present cost allocation method and marginal cost pricing, an integrated cost allocation method based on optimal power flow tracing is proposed. The model realized the optimal power flow distribution of reactive power with the minimal integrated cost and wind power integration, under the premise of guaranteeing the balance of reactive power pricing. Finally, through the analysis of multi-scenario calculation examples and the stochastic simulation of wind power outputs, the article compared the results of the model pricing and the marginal cost pricing, which proved that the model is accurate and effective.

Joint market clearing in a stochastic framework considering power system security

International Nuclear Information System (INIS)

Aghaei, J.; Shayanfar, H.A.; Amjady, N.

2009-01-01

This paper presents a new stochastic framework for provision of reserve requirements (spinning and non-spinning reserves) as well as energy in day-ahead simultaneous auctions by pool-based aggregated market scheme. The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte-Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) and simulation studies are carried out to examine the effectiveness of the proposed method.

Optimization of single photon detection model based on GM-APD

Science.gov (United States)

Chen, Yu; Yang, Yi; Hao, Peiyu

2017-11-01

One hundred kilometers high precision laser ranging hopes the detector has very strong detection ability for very weak light. At present, Geiger-Mode of Avalanche Photodiode has more use. It has high sensitivity and high photoelectric conversion efficiency. Selecting and designing the detector parameters according to the system index is of great importance to the improvement of photon detection efficiency. Design optimization requires a good model. In this paper, we research the existing Poisson distribution model, and consider the important detector parameters of dark count rate, dead time, quantum efficiency and so on. We improve the optimization of detection model, select the appropriate parameters to achieve optimal photon detection efficiency. The simulation is carried out by using Matlab and compared with the actual test results. The rationality of the model is verified. It has certain reference value in engineering applications.

Multivariate constrained shape optimization: Application to extrusion bell shape for pasta production

Science.gov (United States)

Sarghini, Fabrizio; De Vivo, Angela; Marra, Francesco

2017-10-01

Computational science and engineering methods have allowed a major change in the way products and processes are designed, as validated virtual models - capable to simulate physical, chemical and bio changes occurring during production processes - can be realized and used in place of real prototypes and performing experiments, often time and money consuming. Among such techniques, Optimal Shape Design (OSD) (Mohammadi & Pironneau, 2004) represents an interesting approach. While most classical numerical simulations consider fixed geometrical configurations, in OSD a certain number of geometrical degrees of freedom is considered as a part of the unknowns: this implies that the geometry is not completely defined, but part of it is allowed to move dynamically in order to minimize or maximize the objective function. The applications of optimal shape design (OSD) are uncountable. For systems governed by partial differential equations, they range from structure mechanics to electromagnetism and fluid mechanics or to a combination of the three. This paper presents one of possible applications of OSD, particularly how extrusion bell shape, for past production, can be designed by applying a multivariate constrained shape optimization.

Optimized lighting method of applying shaped-function signal for increasing the dynamic range of LED-multispectral imaging system

Science.gov (United States)

Yang, Xue; Hu, Yajia; Li, Gang; Lin, Ling

2018-02-01

This paper proposes an optimized lighting method of applying a shaped-function signal for increasing the dynamic range of light emitting diode (LED)-multispectral imaging system. The optimized lighting method is based on the linear response zone of the analog-to-digital conversion (ADC) and the spectral response of the camera. The auxiliary light at a higher sensitivity-camera area is introduced to increase the A/D quantization levels that are within the linear response zone of ADC and improve the signal-to-noise ratio. The active light is modulated by the shaped-function signal to improve the gray-scale resolution of the image. And the auxiliary light is modulated by the constant intensity signal, which is easy to acquire the images under the active light irradiation. The least square method is employed to precisely extract the desired images. One wavelength in multispectral imaging based on LED illumination was taken as an example. It has been proven by experiments that the gray-scale resolution and the accuracy of information of the images acquired by the proposed method were both significantly improved. The optimum method opens up avenues for the hyperspectral imaging of biological tissue.

Optimizing the biocatalytic productivity of an engineered sialidase from Trypanosoma rangeli for 3′-sialyllactose production

DEFF Research Database (Denmark)

Zeuner, Birgitte; Luo, Jianquan; Nyffenegger, Christian

2014-01-01

An engineered sialidase, Tr6, from Trypanosoma rangeli was used for biosynthetic production of 3′-sialyllactose, a human milk oligosaccharide case compound, from casein glycomacropeptide (CGMP) and lactose, components abundantly present in industrial dairy side streams. Four different enzyme re......-use methods were compared to optimize the biocatalytic productivity, i.e. 3′-sialyllactose formation per amount of Tr6 employed: (i) His-tag immobilization on magnetic Cu2+-iminodiacetic acid-functionalized nanoparticles (MNPs), (ii) membrane immobilization, (iii) calcium alginate encapsulation of cross......-linked Tr6, and (iv) Tr6 catalysis in a membrane reactor. Tr6 immobilized on MNPs gave a biocatalytic productivity of 84mg 3′-sialyllactose/mg Tr6 after seven consecutive reaction runs. Calcium-alginate and membrane immobilization were inefficient. Using free Tr6 in a 10kDa membrane reactor produced a 9...

Optimization of whole-body simulator for photon emitters in the energy range 100 to 3000 KeV

International Nuclear Information System (INIS)

Dantas, Bernardo M.; Rosales, Geovana O.

1996-01-01

The calibration of the detection system for the in vivo determination of uniformly distributed radionuclides emitting photons in the energy range of 100 to 300 KeV requires the use of phantoms with dimensions close to the human body, in which known amounts of radionuclides are added. After the measurement of those phantoms, the calibration curves, channel x energy and energy x efficiency, are constructed. This type of phantom has been continuously optimized at the IRD-CNEN whole body counter with the objective of approximating its characteristics as close as possible to the standard man proposed in the ICRP 23. Furthermore, it has been tried to obtain a safe structure in terms of leakage and also of low cost. (author)

Optimal Sensor placement for acoustic range-based underwater robotic positioning

Digital Repository Service at National Institute of Oceanography (India)

Glotzbach, T.; Moreno-Salinas, D.; Aranda, J.; Pascoal, A.M.

by affording the reviewer an overview of relevant principles, methods, and results available in the literature in the area, as well as of the practical motivation for this challenging topic of research. After a brief literature survey, a method... position estimator. Naturally, the optimal placement solution is a function of the actual measurement setup, the measurement model, and the actual position of the target. At first inspection this problem may seem to have little practical relevance...

On-Board Real-Time Optimization Control for Turbo-Fan Engine Life Extending

Science.gov (United States)

Zheng, Qiangang; Zhang, Haibo; Miao, Lizhen; Sun, Fengyong

2017-11-01

A real-time optimization control method is proposed to extend turbo-fan engine service life. This real-time optimization control is based on an on-board engine mode, which is devised by a MRR-LSSVR (multi-input multi-output recursive reduced least squares support vector regression method). To solve the optimization problem, a FSQP (feasible sequential quadratic programming) algorithm is utilized. The thermal mechanical fatigue is taken into account during the optimization process. Furthermore, to describe the engine life decaying, a thermal mechanical fatigue model of engine acceleration process is established. The optimization objective function not only contains the sub-item which can get fast response of the engine, but also concludes the sub-item of the total mechanical strain range which has positive relationship to engine fatigue life. Finally, the simulations of the conventional optimization control which just consider engine acceleration performance or the proposed optimization method have been conducted. The simulations demonstrate that the time of the two control methods from idle to 99.5 % of the maximum power are equal. However, the engine life using the proposed optimization method could be surprisingly increased by 36.17 % compared with that using conventional optimization control.

Optimal Operation and Value Evaluation of Pumped Storage Power Plants Considering Spot Market Trading and Uncertainty of Bilateral Demand

Science.gov (United States)

Takahashi, Kenta; Hara, Ryoichi; Kita, Hiroyuki; Hasegawa, Jun

In recent years, as the deregulation in electric power industry has advanced in many countries, a spot market trading of electricity has been done. Generation companies are allowed to purchase the electricity through the electric power market and supply electric power for their bilateral customers. Under this circumstance, it is important for the generation companies to procure the required electricity with cheaper cost to increase their profit. The market price is volatile since it is determined by bidding between buyer and seller. The pumped storage power plant, one of the storage facilities is promising against such volatile market price since it can produce a profit by purchasing electricity with lower-price and selling it with higher-price. This paper discusses the optimal operation of the pumped storage power plants considering bidding strategy to an uncertain spot market. The volatilities in market price and demand are represented by the Vasicek model in our estimation. This paper also discusses the allocation of operational reserve to the pumped storage power plant.

A solution to the optimal power flow using multi-verse optimizer

Directory of Open Access Journals (Sweden)

Bachir Bentouati

2016-12-01

Full Text Available In this work, the most common problem of the modern power system named optimal power flow (OPF is optimized using the novel meta-heuristic optimization Multi-verse Optimizer(MVO algorithm. In order to solve the optimal power flow problem, the IEEE 30-bus and IEEE 57-bus systems are used. MVO is applied to solve the proposed problem. The problems considered in the OPF problem are fuel cost reduction, voltage profile improvement, voltage stability enhancement. The obtained results are compared with recently published meta-heuristics. Simulation results clearly reveal the effectiveness and the rapidity of the proposed algorithm for solving the OPF problem.

Optimization of the blade trailing edge geometric parameters for a small scale ORC turbine

Science.gov (United States)

Zhang, L.; Zhuge, W. L.; Peng, J.; Liu, S. J.; Zhang, Y. J.

2013-12-01

In general, the method proposed by Whitfield and Baines is adopted for the turbine preliminary design. In this design procedure for the turbine blade trailing edge geometry, two assumptions (ideal gas and zero discharge swirl) and two experience values (WR and γ) are used to get the three blade trailing edge geometric parameters: relative exit flow angle β6, the exit tip radius R6t and hub radius R6h for the purpose of maximizing the rotor total-to-static isentropic efficiency. The method above is established based on the experience and results of testing using air as working fluid, so it does not provide a mathematical optimal solution to instruct the optimization of geometry parameters and consider the real gas effects of the organic, working fluid which must be taken into consideration for the ORC turbine design procedure. In this paper, a new preliminary design and optimization method is established for the purpose of reducing the exit kinetic energy loss to improve the turbine efficiency ηts, and the blade trailing edge geometric parameters for a small scale ORC turbine with working fluid R123 are optimized based on this method. The mathematical optimal solution to minimize the exit kinetic energy is deduced, which can be used to design and optimize the exit shroud/hub radius and exit blade angle. And then, the influence of blade trailing edge geometric parameters on turbine efficiency ηts are analysed and the optimal working ranges of these parameters for the equations are recommended in consideration of working fluid R123. This method is used to modify an existing ORC turbine exit kinetic energy loss from 11.7% to 7%, which indicates the effectiveness of the method. However, the internal passage loss increases from 7.9% to 9.4%, so the only way to consider the influence of geometric parameters on internal passage loss is to give the empirical ranges of these parameters, such as the recommended ranges that the value of γ is at 0.3 to 0.4, and the value

Optimization of the blade trailing edge geometric parameters for a small scale ORC turbine

International Nuclear Information System (INIS)

Zhang, L; Zhuge, W L; Liu, S J; Zhang, Y J; Peng, J

2013-01-01

In general, the method proposed by Whitfield and Baines is adopted for the turbine preliminary design. In this design procedure for the turbine blade trailing edge geometry, two assumptions (ideal gas and zero discharge swirl) and two experience values (W R and γ) are used to get the three blade trailing edge geometric parameters: relative exit flow angle β 6 , the exit tip radius R 6t and hub radius R 6h for the purpose of maximizing the rotor total-to-static isentropic efficiency. The method above is established based on the experience and results of testing using air as working fluid, so it does not provide a mathematical optimal solution to instruct the optimization of geometry parameters and consider the real gas effects of the organic, working fluid which must be taken into consideration for the ORC turbine design procedure. In this paper, a new preliminary design and optimization method is established for the purpose of reducing the exit kinetic energy loss to improve the turbine efficiency η ts , and the blade trailing edge geometric parameters for a small scale ORC turbine with working fluid R123 are optimized based on this method. The mathematical optimal solution to minimize the exit kinetic energy is deduced, which can be used to design and optimize the exit shroud/hub radius and exit blade angle. And then, the influence of blade trailing edge geometric parameters on turbine efficiency η ts are analysed and the optimal working ranges of these parameters for the equations are recommended in consideration of working fluid R123. This method is used to modify an existing ORC turbine exit kinetic energy loss from 11.7% to 7%, which indicates the effectiveness of the method. However, the internal passage loss increases from 7.9% to 9.4%, so the only way to consider the influence of geometric parameters on internal passage loss is to give the empirical ranges of these parameters, such as the recommended ranges that the value of γ is at 0.3 to 0.4, and the

TH-CD-209-04: Fuzzy Robust Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

Energy Technology Data Exchange (ETDEWEB)

An, Y; Bues, M; Schild, S; Liu, W [Mayo Clinic Arizona, Phoenix, AZ (United States)

2016-06-15

Purpose: We propose to apply a robust optimization model based on fuzzy-logic constraints in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to ensure the plan robustness under uncertainty and obtain the best trade-off between tumor dose coverage and organ-at-risk(OAR) sparing. Methods: Two IMPT plans were generated for 3 head-and-neck cancer patients: one used the planning target volume(PTV) method; the other used the fuzzy robust optimization method. In the latter method, nine dose distributions were computed - the nominal one and one each for ±3mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. For tumors, these nine dose distributions were explicitly controlled by adding hard constraints with adjustable parameters. For OARs, fuzzy constraints that allow the dose to vary within a certain range were used so that the tumor dose distribution was guaranteed by minimum compromise of that of OARs. We rendered this model tractable by converting the fuzzy constraints to linear constraints. The plan quality was evaluated using dose-volume histogram(DVH) indices such as tumor dose coverage(D95%), homogeneity(D5%-D95%), plan robustness(DVH band at D95%), and OAR sparing like D1% of brain and D1% of brainstem. Results: Our model could yield clinically acceptable plans. The fuzzy-logic robust optimization method produced IMPT plans with comparable target dose coverage and homogeneity compared to the PTV method(unit: Gy[RBE]; average[min, max])(CTV D95%: 59 [52.7, 63.5] vs 53.5[46.4, 60.1], CTV D5% - D95%: 11.1[5.3, 18.6] vs 14.4[9.2, 21.5]). It also generated more robust plans(CTV DVH band at D95%: 3.8[1.2, 5.6] vs 11.5[6.2, 16.7]). The parameters of tumor constraints could be adjusted to control the tradeoff between tumor coverage and OAR sparing. Conclusion: The fuzzy-logic robust optimization generates superior IMPT with minimum compromise of OAR sparing. This research

TH-CD-209-04: Fuzzy Robust Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

International Nuclear Information System (INIS)

An, Y; Bues, M; Schild, S; Liu, W

2016-01-01

Purpose: We propose to apply a robust optimization model based on fuzzy-logic constraints in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to ensure the plan robustness under uncertainty and obtain the best trade-off between tumor dose coverage and organ-at-risk(OAR) sparing. Methods: Two IMPT plans were generated for 3 head-and-neck cancer patients: one used the planning target volume(PTV) method; the other used the fuzzy robust optimization method. In the latter method, nine dose distributions were computed - the nominal one and one each for ±3mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. For tumors, these nine dose distributions were explicitly controlled by adding hard constraints with adjustable parameters. For OARs, fuzzy constraints that allow the dose to vary within a certain range were used so that the tumor dose distribution was guaranteed by minimum compromise of that of OARs. We rendered this model tractable by converting the fuzzy constraints to linear constraints. The plan quality was evaluated using dose-volume histogram(DVH) indices such as tumor dose coverage(D95%), homogeneity(D5%-D95%), plan robustness(DVH band at D95%), and OAR sparing like D1% of brain and D1% of brainstem. Results: Our model could yield clinically acceptable plans. The fuzzy-logic robust optimization method produced IMPT plans with comparable target dose coverage and homogeneity compared to the PTV method(unit: Gy[RBE]; average[min, max])(CTV D95%: 59 [52.7, 63.5] vs 53.5[46.4, 60.1], CTV D5% - D95%: 11.1[5.3, 18.6] vs 14.4[9.2, 21.5]). It also generated more robust plans(CTV DVH band at D95%: 3.8[1.2, 5.6] vs 11.5[6.2, 16.7]). The parameters of tumor constraints could be adjusted to control the tradeoff between tumor coverage and OAR sparing. Conclusion: The fuzzy-logic robust optimization generates superior IMPT with minimum compromise of OAR sparing. This research

Range-Space Predictive Control for Optimal Robot Motion

Czech Academy of Sciences Publication Activity Database

Belda, Květoslav; Böhm, Josef

2008-01-01

Roč. 1, č. 1 (2008), s. 1-7 ISSN 1998-0140 R&D Projects: GA ČR GP102/06/P275 Institutional research plan: CEZ:AV0Z10750506 Keywords : Accurate manipulation * Industrial robotics * Predictive control * Range-space control Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/historie/belda-0305644.pdf

Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem

Directory of Open Access Journals (Sweden)

Ibidun Christiana Obagbuwa

2016-09-01

Full Text Available The Cockroach Swarm Optimization (CSO algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP, which is considered to be an NP-hard Combinatorial Optimization Problem (COP. A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO algorithm on TSP were compared to other meta-heuristic algorithms.

Optimizing mesoscopic two-band superconductors for observation of fractional vortex states

Energy Technology Data Exchange (ETDEWEB)

Piña, Juan C. [Departamento de Física, Universidade Federal de Pernambuco, Cidade Universitária, 50670-901 Recife, PE (Brazil); Núcleo de Tecnologia, CAA, Universidade Federal de Pernambuco, 55002-970 Caruaru, PE (Brazil); Souza Silva, Clécio C. de, E-mail: clecio@df.ufpe [Departamento de Física, Universidade Federal de Pernambuco, Cidade Universitária, 50670-901 Recife, PE (Brazil); Milošević, Milorad V. [Departamento de Física, Universidade Federal do Ceará, 60455-900 Fortaleza, Ceará (Brazil); Departement Fysica, Universiteit Antwerpen, Groenenborgerlaan 171, B-2020 Antwerpen (Belgium)

2014-08-15

Highlights: • Observation of fractional vortices in two-band superconductors of broad size range. • There is a minimal sample size for observing each particular fractional state. • Optimal value for stability of each fractional state is determined. • A suitable magnetic dot enhances stability even further. - Abstract: Using the two-component Ginzburg–Landau model, we investigate the effect of sample size and magnitude and homogeneity of external magnetic field on the stability of fractional vortex states in a mesoscopic two-band superconducting disk. We found that each fractional state has a preferable sample size, for which the range of applied field in which the state is stable is pronouncedly large. Vice versa, there exists an optimal magnitude of applied field for which a large range of possible sample radii will support the considered fractional state. Finally, we show that the stability of fractional states can be enhanced even further by magnetic nanostructuring of the sample, i.e. by suitably chosen geometrical parameters and magnetic moment of a ferromagnetic dot placed on top of the superconducting disk.

A Comprehensive Prediction Model of Hydraulic Extended-Reach Limit Considering the Allowable Range of Drilling Fluid Flow Rate in Horizontal Drilling.

Science.gov (United States)

Li, Xin; Gao, Deli; Chen, Xuyue

2017-06-08

Hydraulic extended-reach limit (HERL) model of horizontal extended-reach well (ERW) can predict the maximum measured depth (MMD) of the horizontal ERW. The HERL refers to the well's MMD when drilling fluid cannot be normally circulated by drilling pump. Previous model analyzed the following two constraint conditions, drilling pump rated pressure and rated power. However, effects of the allowable range of drilling fluid flow rate (Q min  ≤ Q ≤ Q max ) were not considered. In this study, three cases of HERL model are proposed according to the relationship between allowable range of drilling fluid flow rate and rated flow rate of drilling pump (Q r ). A horizontal ERW is analyzed to predict its HERL, especially its horizontal-section limit (L h ). Results show that when Q min  ≤ Q r  ≤ Q max (Case I), L h depends both on horizontal-section limit based on rated pump pressure (L h1 ) and horizontal-section limit based on rated pump power (L h2 ); when Q min  drilling fluid flow rate, while L h2 keeps decreasing as the drilling fluid flow rate increases. The comprehensive model provides a more accurate prediction on HERL.

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features

Directory of Open Access Journals (Sweden)

Chang-Hee Han

2016-01-01

Full Text Available It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network.

Science.gov (United States)

Wang, Yunpeng; Cheng, Long; Hou, Zeng-Guang; Yu, Junzhi; Tan, Min

2016-02-01

The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formation can also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.

Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method

Directory of Open Access Journals (Sweden)

Kyu-Hyung Jo

2018-05-01

Full Text Available In light of the dissemination of renewable energy connected to the power grid, it has become necessary to consider the uncertainty in the generation of renewable energy as a unit commitment (UC problem. A methodology for solving the UC problem is presented by considering various uncertainties, which are assumed to have a normal distribution, by using a Monte Carlo simulation. Based on the constructed scenarios for load, wind, solar, and generator outages, a combination of scenarios is found that meets the reserve requirement to secure the power balance of the power grid. In those scenarios, the uncertainty integration method (UIM identifies the best combination by minimizing the additional reserve requirements caused by the uncertainty of power sources. An integration process for uncertainties is formulated for stochastic unit commitment (SUC problems and optimized by the improved genetic algorithm (IGA. The IGA is composed of five procedures and finds the optimal combination of unit status at the scheduled time, based on the determined source data. According to the number of unit systems, the IGA demonstrates better performance than the other optimization methods by applying reserve repairing and an approximation process. To account for the result of the proposed method, various UC strategies are tested with a modified 24-h UC test system and compared.

Applications of combinatorial optimization

CERN Document Server

Paschos, Vangelis Th

2013-01-01

Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aims to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization. "Applications of Combinatorial Optimization" is presenting a certain number among the most common and well-known applications of Combinatorial Optimization.

Determinants of spatial behavior of a tropical forest seed predator: The roles of optimal foraging, dietary diversification, and home range defense.

Science.gov (United States)

Palminteri, Suzanne; Powell, George V N; Peres, Carlos A

2016-05-01

Specialized seed predators in tropical forests may avoid seasonal food scarcity and interspecific feeding competition but may need to diversify their daily diet to limit ingestion of any given toxin. Seed predators may, therefore, adopt foraging strategies that favor dietary diversity and resource monitoring, rather than efficient energy intake, as suggested by optimal foraging theory. We tested whether fine-scale space use by a small-group-living seed predator-the bald-faced saki monkey (Pithecia irrorata)-reflected optimization of short-term foraging efficiency, maximization of daily dietary diversity, and/or responses to the threat of territorial encroachment by neighboring groups. Food patches across home ranges of five adjacent saki groups were widely spread, but areas with higher densities of stems or food species were not allocated greater feeding time. Foraging patterns-specifically, relatively long daily travel paths that bypassed available fruiting trees and relatively short feeding bouts in undepleted food patches-suggest a strategy that maximizes dietary diversification, rather than "optimal" foraging. Travel distance was unrelated to the proportion of seeds in the diet. Moreover, while taxonomically diverse, the daily diets of our study groups were no more species-rich than randomly derived diets based on co-occurring available food species. Sakis preferentially used overlapping areas of their HRs, within which adjacent groups shared many food trees, yet the density of food plants or food species in these areas was no greater than in other HR areas. The high likelihood of depletion by neighboring groups of otherwise enduring food sources may encourage monitoring of peripheral food patches in overlap areas, even if at the expense of immediate energy intake, suggesting that between-group competition is a key driver of fine-scale home range use in sakis. © 2015 Wiley Periodicals, Inc.

Fractional order creep model for dam concrete considering degree of hydration

Science.gov (United States)

Huang, Yaoying; Xiao, Lei; Bao, Tengfei; Liu, Yu

2018-05-01

Concrete is a material that is an intermediate between an ideal solid and an ideal fluid. The creep of concrete is related not only to the loading age and duration, but also to its temperature and temperature history. Fractional order calculus is a powerful tool for solving physical mechanics modeling problems. Using a software element based on the generalized Kelvin model, a fractional order creep model of concrete considering the loading age and duration is established. Then, the hydration rate of cement is considered in terms of the degree of hydration, and the fractional order creep model of concrete considering the degree of hydration is established. Moreover, uniaxial tensile creep tests of dam concrete under different curing temperatures were conducted, and the results were combined with the creep test data and complex optimization method to optimize the parameters of a new creep model. The results show that the fractional tensile creep model based on hydration degree can better describe the tensile creep properties of concrete, and this model involves fewer parameters than the 8-parameter model.

Redo of Coil Spring Considering Transversal Direction Mode Tracking

International Nuclear Information System (INIS)

Lee, Jin Min; Jang, Junyong; Lee, Tae Hee

2013-01-01

When the values of design variables change, mode switching can often occur. If the mode of interest is not tracked, the frequencies and modes for design optimization may be miscalculated owing to modes that differ from the intended ones. Thus, mode tracking must be employed to identify the frequencies and modes of interest whenever the values of design variables change during optimization. Furthermore, reliability-based design optimization (Redo) must be performed for design problems with design variables containing uncertainty. In this research, we perform Redo considering the mode tracking of a compressive coil spring, i.e., a component of the joint spring that supports a compressor, with design variables containing uncertainty by using only kriging meta models based on multiple responses approach (MR A) without existing mode tracking methods. The reliability analyses for Redo are employed using kriging meta model-based Monte Carlo simulation

Model development and optimization of operating conditions to maximize PEMFC performance by response surface methodology

International Nuclear Information System (INIS)

Kanani, Homayoon; Shams, Mehrzad; Hasheminasab, Mohammadreza; Bozorgnezhad, Ali

2015-01-01

Highlights: • The optimization of the operating parameters in a serpentine PEMFC is done using RSM. • The RSM model can predict the cell power over the wide range of operating conditions. • St-An, St-Ca and RH-Ca have an optimum value to obtain the best performance. • The interactions of the operating conditions affect the output power significantly. • The cathode and anode stoichiometry are the most effective parameters on the power. - Abstract: Optimization of operating conditions to obtain maximum power in PEMFCs could have a significant role to reduce the costs of this emerging technology. In the present experimental study, a single serpentine PEMFC is used to investigate the effects of operating conditions on the electrical power production of the cell. Four significant parameters including cathode stoichiometry, anode stoichiometry, gases inlet temperature, and cathode relative humidity are studied using Design of Experiment (DOE) to obtain an optimal power. Central composite second order Response Surface Methodology (RSM) is used to model the relationship between goal function (power) and considered input parameters (operating conditions). Using this statistical–mathematical method leads to obtain a second-order equation for the cell power. This model considers interactions and quadratic effects of different operating conditions and predicts the maximum or minimum power production over the entire working range of the parameters. In this range, high stoichiometry of cathode and low stoichiometry of anode results in the minimum cell power and contrary the medium range of fuel and oxidant stoichiometry leads to the maximum power. Results show that there is an optimum value for the anode stoichiometry, cathode stoichiometry and relative humidity to reach the best performance. The predictions of the model are evaluated by experimental tests and they are in a good agreement for different ranges of the parameters

Optimization and optimal control in automotive systems

CERN Document Server

Kolmanovsky, Ilya; Steinbuch, Maarten; Re, Luigi

2014-01-01

This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier  approaches, based on some degree of heuristics, to the use of  more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization. Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applie...

Range Selection and Median

DEFF Research Database (Denmark)

Jørgensen, Allan Grønlund; Larsen, Kasper Green

2011-01-01

and several natural special cases thereof. The rst special case is known as range median, which arises when k is xed to b(j 􀀀 i + 1)=2c. The second case, denoted prex selection, arises when i is xed to 0. Finally, we also consider the bounded rank prex selection problem and the xed rank range......Range selection is the problem of preprocessing an input array A of n unique integers, such that given a query (i; j; k), one can report the k'th smallest integer in the subarray A[i];A[i+1]; : : : ;A[j]. In this paper we consider static data structures in the word-RAM for range selection...... selection problem. In the former, data structures must support prex selection queries under the assumption that k for some value n given at construction time, while in the latter, data structures must support range selection queries where k is xed beforehand for all queries. We prove cell probe lower bounds...

The effect of nanoparticle size on theranostic systems: the optimal particle size for imaging is not necessarily optimal for drug delivery

Science.gov (United States)

Dreifuss, Tamar; Betzer, Oshra; Barnoy, Eran; Motiei, Menachem; Popovtzer, Rachela

2018-02-01

Theranostics is an emerging field, defined as combination of therapeutic and diagnostic capabilities in the same material. Nanoparticles are considered as an efficient platform for theranostics, particularly in cancer treatment, as they offer substantial advantages over both common imaging contrast agents and chemotherapeutic drugs. However, the development of theranostic nanoplatforms raises an important question: Is the optimal particle for imaging also optimal for therapy? Are the specific parameters required for maximal drug delivery, similar to those required for imaging applications? Herein, we examined this issue by investigating the effect of nanoparticle size on tumor uptake and imaging. Anti-epidermal growth factor receptor (EGFR)-conjugated gold nanoparticles (GNPs) in different sizes (diameter range: 20-120 nm) were injected to tumor bearing mice and their uptake by tumors was measured, as well as their tumor visualization capabilities as tumor-targeted CT contrast agent. Interestingly, the results showed that different particles led to highest tumor uptake or highest contrast enhancement, meaning that the optimal particle size for drug delivery is not necessarily optimal for tumor imaging. These results have important implications on the design of theranostic nanoplatforms.

Truss topology optimization with discrete design variables — Guaranteed global optimality and benchmark examples

DEFF Research Database (Denmark)

Achtziger, Wolfgang; Stolpe, Mathias

2007-01-01

this problem is well-studied for continuous bar areas, we consider in this study the case of discrete areas. This problem is of major practical relevance if the truss must be built from pre-produced bars with given areas. As a special case, we consider the design problem for a single available bar area, i.......e., a 0/1 problem. In contrast to the heuristic methods considered in many other approaches, our goal is to compute guaranteed globally optimal structures. This is done by a branch-and-bound method for which convergence can be proven. In this branch-and-bound framework, lower bounds of the optimal......-integer problems. The main intention of this paper is to provide optimal solutions for single and multiple load benchmark examples, which can be used for testing and validating other methods or heuristics for the treatment of this discrete topology design problem....

Concepts of combinatorial optimization

CERN Document Server

Paschos, Vangelis Th

2014-01-01

Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management.  The three volumes of the Combinatorial Optimization series aim to cover a wide range  of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization.Concepts of Combinatorial Optimization, is divided into three parts:- On the complexity of combinatorial optimization problems, presenting basics about worst-case and randomi

An entropy flow optimization technique for helium liquefaction cycles

International Nuclear Information System (INIS)

Minta, M.; Smith, J.L.

1984-01-01

This chapter proposes a new method of analyzing thermodynamic cycles based on a continuous distribution of precooling over the temperature range of the cycle. The method gives the optimum distribution of precooling over the temperature range of the cycle by specifying the mass flow to be expanded at each temperature. The result is used to select a cycle configuration with discrete expansions and to initialize the independent variables for final optimization. Topics considered include the continuous precooling model, the results for ideal gas, the results for real gas, and the application to the design of a saturated vapor compression (SVC) cycle. The optimization technique for helium liquefaction cycles starts with the minimization of the generated entropy in a cycle model with continuous precooling. The pressure ratio, the pressure level and the distribution of the heat exchange are selected based on the results of the continuous precooling analysis. It is concluded that the technique incorporates the non-ideal behavior of helium in the procedure and allows the trade-off between heat exchange area and losses to be determined

Generalized Benders’ Decomposition for topology optimization problems

DEFF Research Database (Denmark)

Munoz Queupumil, Eduardo Javier; Stolpe, Mathias

2011-01-01

) problems with discrete design variables to global optimality. We present the theoretical aspects of the method, including a proof of finite convergence and conditions for obtaining global optimal solutions. The method is also linked to, and compared with, an Outer-Approximation approach and a mixed 0......–1 semi definite programming formulation of the considered problem. Several ways to accelerate the method are suggested and an implementation is described. Finally, a set of truss topology optimization problems are numerically solved to global optimality.......This article considers the non-linear mixed 0–1 optimization problems that appear in topology optimization of load carrying structures. The main objective is to present a Generalized Benders’ Decomposition (GBD) method for solving single and multiple load minimum compliance (maximum stiffness...

Optimal protocols and optimal transport in stochastic thermodynamics.

Science.gov (United States)

Aurell, Erik; Mejía-Monasterio, Carlos; Muratore-Ginanneschi, Paolo

2011-06-24

Thermodynamics of small systems has become an important field of statistical physics. Such systems are driven out of equilibrium by a control, and the question is naturally posed how such a control can be optimized. We show that optimization problems in small system thermodynamics are solved by (deterministic) optimal transport, for which very efficient numerical methods have been developed, and of which there are applications in cosmology, fluid mechanics, logistics, and many other fields. We show, in particular, that minimizing expected heat released or work done during a nonequilibrium transition in finite time is solved by the Burgers equation and mass transport by the Burgers velocity field. Our contribution hence considerably extends the range of solvable optimization problems in small system thermodynamics.

Optimal primitive reference frames

International Nuclear Information System (INIS)

Jennings, David

2011-01-01

We consider the smallest possible directional reference frames allowed and determine the best one can ever do in preserving quantum information in various scenarios. We find that for the preservation of a single spin state, two orthogonal spins are optimal primitive reference frames; and in a product state, they do approximately 22% as well as an infinite-sized classical frame. By adding a small amount of entanglement to the reference frame, this can be raised to 2(2/3) 5 =26%. Under the different criterion of entanglement preservation, a very similar optimal reference frame is found; however, this time it is for spins aligned at an optimal angle of 87 deg. In this case 24% of the negativity is preserved. The classical limit is considered numerically, and indicates under the criterion of entanglement preservation, that 90 deg. is selected out nonmonotonically, with a peak optimal angle of 96.5 deg. for L=3 spins.

On the complexity of determining tolerances for ->e--optimal solutions to min-max combinatorial optimization problems

NARCIS (Netherlands)

Ghosh, D.; Sierksma, G.

2000-01-01

Sensitivity analysis of e-optimal solutions is the problem of calculating the range within which a problem parameter may lie so that the given solution re-mains e-optimal. In this paper we study the sensitivity analysis problem for e-optimal solutions tocombinatorial optimization problems with

Topology optimization for nano-photonics

DEFF Research Database (Denmark)

Jensen, Jakob Søndergaard; Sigmund, Ole

2011-01-01

Topology optimization is a computational tool that can be used for the systematic design of photonic crystals, waveguides, resonators, filters and plasmonics. The method was originally developed for mechanical design problems but has within the last six years been applied to a range of photonics...... applications. Topology optimization may be based on finite element and finite difference type modeling methods in both frequency and time domain. The basic idea is that the material density of each element or grid point is a design variable, hence the geometry is parameterized in a pixel-like fashion....... The optimization problem is efficiently solved using mathematical programming-based optimization methods and analytical gradient calculations. The paper reviews the basic procedures behind topology optimization, a large number of applications ranging from photonic crystal design to surface plasmonic devices...

SU-E-T-452: Impact of Respiratory Motion On Robustly-Optimized Intensity-Modulated Proton Therapy to Treat Lung Cancers

International Nuclear Information System (INIS)

Liu, W; Schild, S; Bues, M; Liao, Z; Sahoo, N; Park, P; Li, H; Li, Y; Li, X; Shen, J; Anand, A; Dong, L; Zhu, X; Mohan, R

2014-01-01

Purpose: We compared conventionally optimized intensity-modulated proton therapy (IMPT) treatment plans against the worst-case robustly optimized treatment plans for lung cancer. The comparison of the two IMPT optimization strategies focused on the resulting plans' ability to retain dose objectives under the influence of patient set-up, inherent proton range uncertainty, and dose perturbation caused by respiratory motion. Methods: For each of the 9 lung cancer cases two treatment plans were created accounting for treatment uncertainties in two different ways: the first used the conventional Method: delivery of prescribed dose to the planning target volume (PTV) that is geometrically expanded from the internal target volume (ITV). The second employed the worst-case robust optimization scheme that addressed set-up and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of the changes in patient anatomy due to respiratory motion was investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the two groups were compared using two-sided paired t-tests. Results: Without respiratory motion considered, we affirmed that worst-case robust optimization is superior to PTV-based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, robust optimization still leads to more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality [D95% ITV: 96.6% versus 96.1% (p=0.26), D5% - D95% ITV: 10.0% versus 12.3% (p=0.082), D1% spinal cord: 31.8% versus 36.5% (p =0.035)]. Conclusion: Worst-case robust optimization led to superior solutions for lung IMPT. Despite of the fact that robust optimization did not explicitly

Enhancement of the range of electric-powered vehicles by means of an optimized thermal management; Steigerung der Reichweite von Elektrofahrzeugen durch optimiertes Thermomanagement

Energy Technology Data Exchange (ETDEWEB)

Aurich, Joerg; Baumgart, Rico; Unwerth, Thomas von [TU Chemnitz (Germany). Professur Alternative Fahrzeugantriebe

2012-07-01

Currently, the development of energy efficient air conditioning systems for electric-powered vehicles is one of the most difficult challenges because the reach of these vehicles significantly is influenced by the air-conditioning systems in part. For this reason, computational models have been developed at the Technical University of Chemnitz (Federal Republic of Germany) in order to simulate and optimize the processes in car air conditioning and passenger cabin. The passenger cabin model has now been extended by a simplified model of comfort which will be presented in more detail in the contribution under consideration. Under consideration of the occupants comfort, these models help to investigate the impact of the various optimization measures on the necessary amount of cooling capacity and the range of the vehicle. The calculations were made both for summerly and for winterly environmental conditions. As shown in detail, the required cooling capacity can be reduced by reducing the degree of transmittance of the windows for example in the summer. However, in contrast the heating power increases in the winter due to the lower solar heat input which in turn reduces the achievable range of electric-powered vehicles.

Feasibility and robustness of dose painting by numbers in proton therapy with contour-driven plan optimization

International Nuclear Information System (INIS)

Barragán, A. M.; Differding, S.; Lee, J. A.; Sterpin, E.; Janssens, G.

2015-01-01

Purpose: To prove the ability of protons to reproduce a dose gradient that matches a dose painting by numbers (DPBN) prescription in the presence of setup and range errors, by using contours and structure-based optimization in a commercial treatment planning system. Methods: For two patients with head and neck cancer, voxel-by-voxel prescription to the target volume (GTV PET ) was calculated from 18 FDG-PET images and approximated with several discrete prescription subcontours. Treatments were planned with proton pencil beam scanning. In order to determine the optimal plan parameters to approach the DPBN prescription, the effects of the scanning pattern, number of fields, number of subcontours, and use of range shifter were separately tested on each patient. Different constant scanning grids (i.e., spot spacing = Δx = Δy = 3.5, 4, and 5 mm) and uniform energy layer separation [4 and 5 mm WED (water equivalent distance)] were analyzed versus a dynamic and automatic selection of the spots grid. The number of subcontours was increased from 3 to 11 while the number of beams was set to 3, 5, or 7. Conventional PTV-based and robust clinical target volumes (CTV)-based optimization strategies were considered and their robustness against range and setup errors assessed. Because of the nonuniform prescription, ensuring robustness for coverage of GTV PET inevitably leads to overdosing, which was compared for both optimization schemes. Results: The optimal number of subcontours ranged from 5 to 7 for both patients. All considered scanning grids achieved accurate dose painting (1% average difference between the prescribed and planned doses). PTV-based plans led to nonrobust target coverage while robust-optimized plans improved it considerably (differences between worst-case CTV dose and the clinical constraint was up to 3 Gy for PTV-based plans and did not exceed 1 Gy for robust CTV-based plans). Also, only 15% of the points in the GTV PET (worst case) were above 5% of DPBN

Simulation-based optimization of thermal systems

International Nuclear Information System (INIS)

Jaluria, Yogesh

2009-01-01

This paper considers the design and optimization of thermal systems on the basis of the mathematical and numerical modeling of the system. Many complexities are often encountered in practical thermal processes and systems, making the modeling challenging and involved. These include property variations, complicated regions, combined transport mechanisms, chemical reactions, and intricate boundary conditions. The paper briefly presents approaches that may be used to accurately simulate these systems. Validation of the numerical model is a particularly critical aspect and is discussed. It is important to couple the modeling with the system performance, design, control and optimization. This aspect, which has often been ignored in the literature, is considered in this paper. Design of thermal systems based on concurrent simulation and experimentation is also discussed in terms of dynamic data-driven optimization methods. Optimization of the system and of the operating conditions is needed to minimize costs and improve product quality and system performance. Different optimization strategies that are currently used for thermal systems are outlined, focusing on new and emerging strategies. Of particular interest is multi-objective optimization, since most thermal systems involve several important objective functions, such as heat transfer rate and pressure in electronic cooling systems. A few practical thermal systems are considered in greater detail to illustrate these approaches and to present typical simulation, design and optimization results

Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

Science.gov (United States)

Zheng, Ling; Duan, Xuwei; Deng, Zhaoxue; Li, Yinong

2014-03-01

A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.

Modelling control of epidemics spreading by long-range interactions.

Science.gov (United States)

Dybiec, Bartłomiej; Kleczkowski, Adam; Gilligan, Christopher A

2009-10-06

We have studied the spread of epidemics characterized by a mixture of local and non-local interactions. The infection spreads on a two-dimensional lattice with the fixed nearest neighbour connections. In addition, long-range dynamical links are formed by moving agents (vectors). Vectors perform random walks, with step length distributed according to a thick-tail distribution. Two distributions are considered in this paper, an alpha-stable distribution describing self-similar vector movement, yet characterized by an infinite variance and an exponential power characterized by a large but finite variance. Such long-range interactions are hard to track and make control of epidemics very difficult. We also allowed for cryptic infection, whereby an infected individual on the lattice can be infectious prior to showing any symptoms of infection or disease. To account for such cryptic spread, we considered a control strategy in which not only detected, i.e. symptomatic, individuals but also all individuals within a certain control neighbourhood are treated upon the detection of disease. We show that it is possible to eradicate the disease by using such purely local control measures, even in the presence of long-range jumps. In particular, we show that the success of local control and the choice of the optimal strategy depend in a non-trivial way on the dispersal patterns of the vectors. By characterizing these patterns using the stability index of the alpha-stable distribution to change the power-law behaviour or the exponent characterizing the decay of an exponential power distribution, we show that infection can be successfully contained using relatively small control neighbourhoods for two limiting cases for long-distance dispersal and for vectors that are much more limited in their dispersal range.

Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Aristeidis Antonakis

2017-04-01

Full Text Available In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft–engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude–Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct interfaces between the optimizer and aircraft/engine performance codes. A two-level optimization scheme is employed and is shown to improve search performance compared to the basic PSO algorithm. The effectiveness of the proposed methodology is demonstrated in a hypothetic engine upgrade scenario for the F-4 aircraft considering the replacement of the aircraft’s J79 engine with the EJ200; a clear advantage of the EJ200-equipped configuration is unveiled, resulting, on average, in 15% faster climbs with 20% less fuel.

Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization

International Nuclear Information System (INIS)

Punnathanam, Varun; Kotecha, Prakash

2017-01-01

Highlights: • Efficient multi-objective optimization algorithm F-YYPO demonstrated. • Three Stirling engine applications with a total of eight cases. • Improvements in the objective function values of up to 30%. • Superior to the popularly used gamultiobj of MATLAB. • F-YYPO has extremely low time complexity. - Abstract: In this work, we demonstrate the performance of Front-based Yin-Yang-Pair Optimization (F-YYPO) to solve multi-objective problems related to Stirling engine systems. The performance of F-YYPO is compared with that of (i) a recently proposed multi-objective optimization algorithm (Multi-Objective Grey Wolf Optimizer) and (ii) an algorithm popularly employed in literature due to its easy accessibility (MATLAB’s inbuilt multi-objective Genetic Algorithm function: gamultiobj). We consider three Stirling engine based optimization problems: (i) the solar-dish Stirling engine system which considers objectives of output power, thermal efficiency and rate of entropy generation; (ii) Stirling engine thermal model which considers the associated irreversibility of the cycle with objectives of output power, thermal efficiency and pressure drop; and finally (iii) an experimentally validated polytropic finite speed thermodynamics based Stirling engine model also with objectives of output power and pressure drop. We observe F-YYPO to be significantly more effective as compared to its competitors in solving the problems, while requiring only a fraction of the computational time required by the other algorithms.

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

Directory of Open Access Journals (Sweden)

Tashkova Katerina

2011-10-01

Full Text Available Abstract Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA, particle-swarm optimization (PSO, and differential evolution (DE, as well as a local-search derivative-based algorithm 717 (A717 to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE clearly and significantly outperform the local derivative-based method (A717. Among the three meta-heuristics, differential evolution (DE performs best in terms of the objective function, i.e., reconstructing the output, and in terms of

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis.

Science.gov (United States)

Tashkova, Katerina; Korošec, Peter; Silc, Jurij; Todorovski, Ljupčo; Džeroski, Sašo

2011-10-11

We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and

Global Optimization Based on the Hybridization of Harmony Search and Particle Swarm Optimization Methods

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2014-01-01

Full Text Available We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS and swarm of particles (PSO, software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called PSO HS , present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.

Optimized positioning of autonomous surgical lamps

Science.gov (United States)

Teuber, Jörn; Weller, Rene; Kikinis, Ron; Oldhafer, Karl-Jürgen; Lipp, Michael J.; Zachmann, Gabriel

2017-03-01

We consider the problem of finding automatically optimal positions of surgical lamps throughout the whole surgical procedure, where we assume that future lamps could be robotized. We propose a two-tiered optimization technique for the real-time autonomous positioning of those robotized surgical lamps. Typically, finding optimal positions for surgical lamps is a multi-dimensional problem with several, in part conflicting, objectives, such as optimal lighting conditions at every point in time while minimizing the movement of the lamps in order to avoid distractions of the surgeon. Consequently, we use multi-objective optimization (MOO) to find optimal positions in real-time during the entire surgery. Due to the conflicting objectives, there is usually not a single optimal solution for such kinds of problems, but a set of solutions that realizes a Pareto-front. When our algorithm selects a solution from this set it additionally has to consider the individual preferences of the surgeon. This is a highly non-trivial task because the relationship between the solution and the parameters is not obvious. We have developed a novel meta-optimization that considers exactly this challenge. It delivers an easy to understand set of presets for the parameters and allows a balance between the lamp movement and lamp obstruction. This metaoptimization can be pre-computed for different kinds of operations and it then used by our online optimization for the selection of the appropriate Pareto solution. Both optimization approaches use data obtained by a depth camera that captures the surgical site but also the environment around the operating table. We have evaluated our algorithms with data recorded during a real open abdominal surgery. It is available for use for scientific purposes. The results show that our meta-optimization produces viable parameter sets for different parts of an intervention even when trained on a small portion of it.

Intelligent control for PMSM based on online PSO considering parameters change

Science.gov (United States)

Song, Zhengqiang; Yang, Huiling

2018-03-01

A novel online particle swarm optimization method is proposed to design speed and current controllers of vector controlled interior permanent magnet synchronous motor drives considering stator resistance variation. In the proposed drive system, the space vector modulation technique is employed to generate the switching signals for a two-level voltage-source inverter. The nonlinearity of the inverter is also taken into account due to the dead-time, threshold and voltage drop of the switching devices in order to simulate the system in the practical condition. Speed and PI current controller gains are optimized with PSO online, and the fitness function is changed according to the system dynamic and steady states. The proposed optimization algorithm is compared with conventional PI control method in the condition of step speed change and stator resistance variation, showing that the proposed online optimization method has better robustness and dynamic characteristics compared with conventional PI controller design.

A Novel Particle Swarm Optimization Algorithm for Global Optimization.

Science.gov (United States)

Wang, Chun-Feng; Liu, Kui

2016-01-01

Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.

Combined Solar Charging Stations and Energy Storage Units Allocation for Electric Vehicles by Considering Uncertainties

DEFF Research Database (Denmark)

Yousefi Khanghah, Babak; Anvari-Moghaddam, Amjad; Guerrero, Josep M.

2017-01-01

Electric vehicles (EVs) are becoming a key feature of smart grids. EVs will be embedded in the smart grids as a mobile load-storage with probabilistic behavior. In order to manage EVs as flexible loads, charging stations (CSs) have essential roles. In this paper, a new method for optimal sitting...... are considered based on time-of-use (TOU) demand response programs (DRPs). In order to solve the optimization problem considering uncertainty of load growth, electricity price, initial state of charge of batteries and solar power generation, genetic algorithm method using Monte-Carlo simulation is used...

Autonomous Target Ranging Techniques

DEFF Research Database (Denmark)

Jørgensen, Peter Siegbjørn; Jørgensen, John Leif; Denver, Troelz

2003-01-01

of this telescope, a fast determination of the range to and the motion of the detected targets are important. This is needed in order to prepare the future observation strategy for each target, i.e. when is the closest approach where imaging will be optimal. In order to quickly obtain such a determination two...... ranging strategies are presented. One is an improved laser ranger with an effective range with non-cooperative targets of at least 10,000 km, demonstrated in ground tests. The accuracy of the laser ranging will be approximately 1 m. The laser ranger may furthermore be used for trajectory determination...... of nano-gravity probes, which will perform direct mass measurements of selected targets. The other is triangulation from two spacecraft. For this method it is important to distinguish between detection and tracking range, which will be different for Bering since different instruments are used...

Ultrafast and Doppler-free femtosecondoptical ranging based on dispersivefrequency-modulated interferometry.

Science.gov (United States)

Xia, Haiyun; Zhang, Chunxi

2010-03-01

An ultrafast and Doppler-free optical ranging system based on dispersive frequency-modulated interferometry is demonstrated. The principle is similar to the conventional frequency-modulated continuous-wave interferometry where the range information is derived from the beat frequency between the object signal and the reference signal. However, a passive and static frequency scanning is performed based on the chromatic dispersion of a transform-limited femtosecond pulse in the time domain. We point out that the unbalanced dispersion introduced in the Mach-Zehnder interferometer can be optimized to eliminate the frequency chirp in the temporal interferograms pertaining to the third order dispersion of the all-fiber system, if the dynamic range being considered is small. Some negative factors, such as the polarization instability of the femtosecond pulse, the power fluctuation of the optical signal and the nonuniform gain spectrum of the erbium-doped fiber amplifier lead to an obvious envelope deformation of the temporal interferograms from the Gaussian shape. Thus a new data processing method is proposed to guarantee the range resolution. In the experiment, the vibration of a speaker is measured. A range resolution of 1.59 microm is achieved with an exposure time of 394 fs at a sampling rate of 48.6 MHz.

A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays

Directory of Open Access Journals (Sweden)

An Liu

2012-01-01

Full Text Available Coordination optimization of directional overcurrent relays (DOCRs is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS and pickup current (Ip values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO considers the optimal solution as the final solution, and an early convergence of PSO results in decreased overall performance and an increase in the risk of mistaking local optima for global optima. Therefore, this study proposes a new hybrid Nelder-Mead simplex search method and particle swarm optimization (proposed NM-PSO algorithm to solve the DOCR coordination optimization problem. PSO is the main optimizer, and the Nelder-Mead simplex search method is used to improve the efficiency of PSO due to its potential for rapid convergence. To validate the proposal, this study compared the performance of the proposed algorithm with that of PSO and original NM-PSO. The findings demonstrate the outstanding performance of the proposed NM-PSO in terms of computation speed, rate of convergence, and feasibility.

An Efficiency-Optimized Isolated Bidirectional DC-DC Converter with Extended Power Range for Energy Storage Systems in Microgrids

Directory of Open Access Journals (Sweden)

Xiaolong Shi

2012-12-01

Full Text Available This paper proposes a novel extended-single-phase shift (ESPS control strategy of isolated bidirectional full-bridge DC-DC converters (IBDCs which are a promising alternative as a power electronic interface in microgrids with an additional function of galvanic isolation. Based on the mathematical models of ESPS control under steady-state conditions, detailed theoretical and experimental analyses of IBDC under ESPS control are presented. Compared with conventional single-phase-shift (CSPS control, ESPS control can greatly improve the efficiency of IBDCs in microgrids through decreasing current stress and backflow power considerably over a wide input and output voltage range under light and medium loads. In addition, ESPS control only needs to adjust one single phase-shift angel to control transmission power, thus it retains implementation simplicity in comparison with dual-phase-shift (DPS control for microgrid applications. Furthermore, an efficiency-optimized modulation scheme based on ESPS and CSPS control is developed in the whole power range of IBDC for power distribution in microgrids. A 10 kW IBDC prototype is constructed and the experimental results validate the effectiveness of the proposed control strategy, showing that the proposed strategy can enhance the overall efficiency up to 30%.

Practical mathematical optimization basic optimization theory and gradient-based algorithms

CERN Document Server

Snyman, Jan A

2018-01-01

This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and dir...

Multiobjective Optimization Model for Wind Power Allocation

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Juan Alemany

2017-01-01

Full Text Available There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop a multiobjective optimization model that permits finding multiple trade-off solutions for the location of new wind power resources. It is based on the augmented ε-constrained methodology. Two competitive objectives are considered: maximization of preexisting energy injection and maximization of new wind energy injection, both embedded, in the maximization of load supply. The results show that the location of new renewable generation units affects considerably the transmission network flows, the load supply, and the preexisting energy injection. Moreover, there are diverse opportunities to benefit the preexisting generation, contrarily to the expected effect where renewable generation displaces conventional power. The proposed methodology produces a diverse range of equivalent solutions, expanding and enriching the horizon of options and giving flexibility to the decision-making process.

Reliability-Based Optimization of Structural Elements

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

In this paper structural elements from an optimization point of view are considered, i.e. only the geometry of a structural element is optimized. Reliability modelling of the structural element is discussed both from an element point of view and from a system point of view. The optimization...

Topology optimization of periodic microstructures for enhanced dynamic properties of viscoelastic composite materials

DEFF Research Database (Denmark)

Andreassen, Erik; Jensen, Jakob Søndergaard

2014-01-01

We present a topology optimization method for the design of periodic composites with dissipative materials for maximizing the loss/attenuation of propagating waves. The computational model is based on a finite element discretization of the periodic unit cell and a complex eigenvalue problem...... with a prescribed wave frequency. The attenuation in the material is described by its complex wavenumber, and we demonstrate in several examples optimized distributions of a stiff low loss and a soft lossy material in order to maximize the attenuation. In the examples we cover different frequency ranges and relate...... the results to previous studies on composites with high damping and stiffness based on quasi-static conditions for low frequencies and the bandgap phenomenon for high frequencies. Additionally, we consider the issues of stiffness and connectivity constraints and finally present optimized composites...

Topology optimized permanent magnet systems

DEFF Research Database (Denmark)

Bjørk, Rasmus; Bahl, Christian; Insinga, Andrea Roberto

2017-01-01

Topology optimization of permanent magnet systems consisting of permanent magnets, high permeability iron and air is presented. An implementation of topology optimization for magnetostatics is discussed and three examples are considered. The Halbach cylinder is topology optimized with iron...... and an increase of 15% in magnetic efficiency is shown. A topology optimized structure to concentrate a homogeneous field is shown to increase the magnitude of the field by 111%. Finally, a permanent magnet with alternating high and low field regions is topology optimized and a ΛcoolΛcool figure of merit of 0...

A comparison of the economic benefits of centralized and distributed model predictive control strategies for optimal and sub-optimal mine dewatering system designs

International Nuclear Information System (INIS)

Romero, Alberto; Millar, Dean; Carvalho, Monica; Maestre, José M.; Camacho, Eduardo F.

2015-01-01

conditions considered, total annualized cost savings in the range of 50%, in comparison with non-optimized designs, can be achieved through design optimization and the use of predictive control. - Highlights: • Optimal Mine Site Energy Supply (OMSES) applied to underground mine dewatering system. • Systems design and pump schedule, based on past operating conditions, were optimized. • Optimal system's design was simulated based on Model Predictive Control (MPC). • Centralized, distributed and decentralized controllers were compared. • Only centralized MPC showed robust and verified OMSES optimal design.

Competitive Supply Chain Network Design Considering Marketing Strategies: A Hybrid Metaheuristic Algorithm

Directory of Open Access Journals (Sweden)

Ali Akbar Hasani

2016-11-01

Full Text Available In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.

Totally optimal decision trees for Boolean functions

KAUST Repository

Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

2016-01-01

We study decision trees which are totally optimal relative to different sets of complexity parameters for Boolean functions. A totally optimal tree is an optimal tree relative to each parameter from the set simultaneously. We consider the parameters

Topology optimization of radio frequency and microwave structures

DEFF Research Database (Denmark)

Aage, Niels

in this thesis, concerns the optimization of devices for wireless energy transfer via strongly coupled magnetic resonators. A single design problem is considered to demonstrate proof of concept. The resulting design illustrates the possibilities of the optimization method, but also reveals its numerical...... of efficient antennas and power supplies. A topology optimization methodology is proposed based on a design parameterization which incorporates the skin effect. The numerical optimization procedure is implemented in Matlab, for 2D problems, and in a parallel C++ optimization framework, for 3D design problems...... formalism, a two step optimization procedure is presented. This scheme is applied to the design and optimization of a hemispherical sub-wavelength antenna. The optimized antenna configuration displayed a ratio of radiated power to input power in excess of 99 %. The third, and last, design problem considered...

Optimal reliability allocation for large software projects through soft computing techniques

DEFF Research Database (Denmark)

Madsen, Henrik; Albeanu, Grigore; Popentiu-Vladicescu, Florin

2012-01-01

or maximizing the system reliability subject to budget constraints. These kinds of optimization problems were considered both in deterministic and stochastic frameworks in literature. Recently, the intuitionistic-fuzzy optimization approach was considered as a soft computing successful modelling approach....... Firstly, a review on existing soft computing approaches to optimization is given. The main section extends the results considering self-organizing migrating algorithms for solving intuitionistic-fuzzy optimization problems attached to complex fault-tolerant software architectures which proved...

Study of CT-based positron range correction in high resolution 3D PET imaging

Energy Technology Data Exchange (ETDEWEB)

Cal-Gonzalez, J., E-mail: jacobo@nuclear.fis.ucm.es [Grupo de Fisica Nuclear, Dpto. Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain); Herraiz, J.L. [Grupo de Fisica Nuclear, Dpto. Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain); Espana, S. [Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA (United States); Vicente, E. [Grupo de Fisica Nuclear, Dpto. Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain); Instituto de Estructura de la Materia, Consejo Superior de Investigaciones Cientificas (CSIC), Madrid (Spain); Herranz, E. [Grupo de Fisica Nuclear, Dpto. Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain); Desco, M. [Unidad de Medicina y Cirugia Experimental, Hospital General Universitario Gregorio Maranon, Madrid (Spain); Vaquero, J.J. [Dpto. de Bioingenieria e Ingenieria Espacial, Universidad Carlos III, Madrid (Spain); Udias, J.M. [Grupo de Fisica Nuclear, Dpto. Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain)

2011-08-21

Positron range limits the spatial resolution of PET images and has a different effect for different isotopes and positron propagation materials. Therefore it is important to consider it during image reconstruction, in order to obtain optimal image quality. Positron range distributions for most common isotopes used in PET in different materials were computed using the Monte Carlo simulations with PeneloPET. The range profiles were introduced into the 3D OSEM image reconstruction software FIRST and employed to blur the image either in the forward projection or in the forward and backward projection. The blurring introduced takes into account the different materials in which the positron propagates. Information on these materials may be obtained, for instance, from a segmentation of a CT image. The results of introducing positron blurring in both forward and backward projection operations was compared to using it only during forward projection. Further, the effect of different shapes of positron range profile in the quality of the reconstructed images with positron range correction was studied. For high positron energy isotopes, the reconstructed images show significant improvement in spatial resolution when positron range is taken into account during reconstruction, compared to reconstructions without positron range modeling.

Study of CT-based positron range correction in high resolution 3D PET imaging

International Nuclear Information System (INIS)

Cal-Gonzalez, J.; Herraiz, J.L.; Espana, S.; Vicente, E.; Herranz, E.; Desco, M.; Vaquero, J.J.; Udias, J.M.

2011-01-01

Positron range limits the spatial resolution of PET images and has a different effect for different isotopes and positron propagation materials. Therefore it is important to consider it during image reconstruction, in order to obtain optimal image quality. Positron range distributions for most common isotopes used in PET in different materials were computed using the Monte Carlo simulations with PeneloPET. The range profiles were introduced into the 3D OSEM image reconstruction software FIRST and employed to blur the image either in the forward projection or in the forward and backward projection. The blurring introduced takes into account the different materials in which the positron propagates. Information on these materials may be obtained, for instance, from a segmentation of a CT image. The results of introducing positron blurring in both forward and backward projection operations was compared to using it only during forward projection. Further, the effect of different shapes of positron range profile in the quality of the reconstructed images with positron range correction was studied. For high positron energy isotopes, the reconstructed images show significant improvement in spatial resolution when positron range is taken into account during reconstruction, compared to reconstructions without positron range modeling.

Designing airport checked-baggage-screening strategies considering system capability and reliability

International Nuclear Information System (INIS)

Feng Qianmei; Sahin, Hande; Kapur, Kailash C.

2009-01-01

Emerging image-based technologies are critical components of airport security for screening checked baggage. Since these new technologies differ widely in cost and accuracy, a comprehensive mathematical framework should be developed for selecting technology or combination of technologies for efficient 100% baggage screening. This paper addresses the problem of setting threshold values of these screening technologies and determining the optimal combination of technologies in a two-level screening system by considering system capability and human reliability. Probability and optimization techniques are used to quantify and evaluate the cost- and risk-effectiveness of various deployment configurations, which is captured by using a system life-cycle cost model that incorporates the deployment cost, operating cost, and costs associated with system decisions. Two system decision rules are studied for a two-level screening system. For each decision rule, two different optimization approaches are formulated and investigated from practitioner's perspective. Numerical examples for different decision rules, optimization approaches and system arrangements are demonstrated

Development of GEM detector for plasma diagnostics application: simulations addressing optimization of its performance

Science.gov (United States)

Chernyshova, M.; Malinowski, K.; Kowalska-Strzęciwilk, E.; Czarski, T.; Linczuk, P.; Wojeński, A.; Krawczyk, R. D.

2017-12-01

The advanced Soft X-ray (SXR) diagnostics setup devoted to studies of the SXR plasma emissivity is at the moment a highly relevant and important for ITER/DEMO application. Especially focusing on the energy range of tungsten emission lines, as plasma contamination by W and its transport in the plasma must be understood and monitored for W plasma-facing material. The Gas Electron Multiplier, with a spatial and energy-resolved photon detecting chamber, based SXR radiation detection system under development by our group may become such a diagnostic setup considering and solving many physical, technical and technological aspects. This work presents the results of simulations aimed to optimize a design of the detector's internal chamber and its performance. The study of the effect of electrodes alignment allowed choosing the gap distances which maximizes electron transmission and choosing the optimal magnitudes of the applied electric fields. Finally, the optimal readout structure design was identified suitable to collect a total formed charge effectively, basing on the range of the simulated electron cloud at the readout plane which was in the order of ~ 2 mm.

Generation and Demand Scheduling for a Grid-Connected Hybrid Microgrid Considering Price-based Incentives

DEFF Research Database (Denmark)

Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Savaghebi, Mehdi

2017-01-01

Microgrids rely on energy management levels to optimally schedule their components. Conventionally, the research in this field has been focused on the optimal formulation of the generation or the demand side management separately without considering real case scenarios and validated only...... by simulation. This paper presents the power scheduling of a real site microgrid under a price-based demand response program defined in Shanghai, China managing generation and demand simultaneously. The proposed optimization problem aims to minimize operating cost by managing renewable energy sources as well...

Derivation of Optimal Operating Rules for Large-scale Reservoir Systems Considering Multiple Trade-off

Science.gov (United States)

Zhang, J.; Lei, X.; Liu, P.; Wang, H.; Li, Z.

2017-12-01

Flood control operation of multi-reservoir systems such as parallel reservoirs and hybrid reservoirs often suffer from complex interactions and trade-off among tributaries and the mainstream. The optimization of such systems is computationally intensive due to nonlinear storage curves, numerous constraints and complex hydraulic connections. This paper aims to derive the optimal flood control operating rules based on the trade-off among tributaries and the mainstream using a new algorithm known as weighted non-dominated sorting genetic algorithm II (WNSGA II). WNSGA II could locate the Pareto frontier in non-dominated region efficiently due to the directed searching by weighted crowding distance, and the results are compared with those of conventional operating rules (COR) and single objective genetic algorithm (GA). Xijiang river basin in China is selected as a case study, with eight reservoirs and five flood control sections within four tributaries and the mainstream. Furthermore, the effects of inflow uncertainty have been assessed. Results indicate that: (1) WNSGA II could locate the non-dominated solutions faster and provide better Pareto frontier than the traditional non-dominated sorting genetic algorithm II (NSGA II) due to the weighted crowding distance; (2) WNSGA II outperforms COR and GA on flood control in the whole basin; (3) The multi-objective operating rules from WNSGA II deal with the inflow uncertainties better than COR. Therefore, the WNSGA II can be used to derive stable operating rules for large-scale reservoir systems effectively and efficiently.

Structural Optimization of a High-Speed Press Considering Multi-Source Uncertainties Based on a New Heterogeneous TOPSIS

Directory of Open Access Journals (Sweden)

Jin Cheng

2018-01-01

Full Text Available In order to achieve high punching precision, good operational reliability and low manufacturing cost, the structural optimization of a high-speed press in the presence of a set of available alternatives comprises a heterogeneous multiple-attribute decision-making (HMADM problem involving deviation, fixation, cost and benefit attributes that can be described in various mathematical forms due to the existence of multi-source uncertainties. Such a HMADM problem cannot be easily resolved by existing methods. To overcome this difficulty, a new heterogeneous technique for order preference by similarity to an ideal solution (HTOPSIS is proposed. A new approach to normalization of heterogeneous attributes is proposed by integrating the possibility degree method, relative preference relation and the attribute transformation technique. Expressions for determining positive and negative ideal solutions corresponding to heterogeneous attributes are also developed. Finally, alternative structural configurations are ranked according to their relative closeness coefficients, and the optimal structural configuration can be determined. The validity and effectiveness of the proposed HTOPSIS are demonstrated by a numerical example. The proposed HTOPSIS can also be applied to structural optimization of other complex equipment, because there is no prerequisite of independency among various attributes for its application.

An adaptive robust optimization scheme for water-flooding optimization in oil reservoirs using residual analysis

NARCIS (Netherlands)

Siraj, M.M.; Van den Hof, P.M.J.; Jansen, J.D.

2017-01-01

Model-based dynamic optimization of the water-flooding process in oil reservoirs is a computationally complex problem and suffers from high levels of uncertainty. A traditional way of quantifying uncertainty in robust water-flooding optimization is by considering an ensemble of uncertain model

Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer

NARCIS (Netherlands)

Inoue, Tatsuya; Widder, Joachim; van Dijk, Lisanne V; Takegawa, Hideki; Koizumi, Masahiko; Takashina, Masaaki; Usui, Keisuke; Kurokawa, Chie; Sugimoto, Satoru; Saito, Anneyuko I; Sasai, Keisuke; Van't Veld, Aart A; Langendijk, Johannes A; Korevaar, Erik W

2016-01-01

Purpose: To investigate the impact of setup and range uncertainties, breathing motion, and interplay effects using scanning pencil beams in robustly optimized intensity modulated proton therapy (IMPT) for stage III non-small cell lung cancer (NSCLC). Methods and Materials: Three-field IMPT plans

Integrated Urban Flood Analysis considering Optimal Operation of Flood Control Facilities in Urban Drainage Networks

Science.gov (United States)

Moon, Y. I.; Kim, M. S.; Choi, J. H.; Yuk, G. M.

2017-12-01

eavy rainfall has become a recent major cause of urban area flooding due to the climate change and urbanization. To prevent property damage along with casualties, a system which can alert and forecast urban flooding must be developed. Optimal performance of reducing flood damage can be expected of urban drainage facilities when operated in smaller rainfall events over extreme ones. Thus, the purpose of this study is to execute: A) flood forecasting system using runoff analysis based on short term rainfall; and B) flood warning system which operates based on the data from pump stations and rainwater storage in urban basins. In result of the analysis, it is shown that urban drainage facilities using short term rainfall forecasting data by radar will be more effective to reduce urban flood damage than using only the inflow data of the facility. Keywords: Heavy Rainfall, Urban Flood, Short-term Rainfall Forecasting, Optimal operating of urban drainage facilities. AcknowledgmentsThis research was supported by a grant (17AWMP-B066744-05) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

Acoustical topology optimization of Zwicker's loudness with Padé approximation

DEFF Research Database (Denmark)

Kook, Junghwan; Jensen, Jakob Søndergaard; Wang, Semyung

2013-01-01

Zwicker's loudness is a conventional standard index for measuring human hearing annoyance and has been widely considered in many industrial fields for noise evaluations. The calculation of Zwicker's loudness, which is needed for a wide range of frequency responses with a fine frequency resolution......, this approach imposes prohibitively high computational costs. In this research, we propose a computationally-efficient approach to resolve the computational issue in the computation and optimization of Zwicker's loudness. We present an efficient approach which combines the finite element method (FEM......) with the Padé approximation (PA) procedure for obtaining Zwicker's loudness and for applying it in a gradient-based acoustical topology optimization procedure applied to the design of acoustic devices to minimize Zwicker's loudness. In this respect, the calculation of Zwicker's loudness is represented by the PA...

Optimal unit sizing for small-scale integrated energy systems using multi-objective interval optimization and evidential reasoning approach

International Nuclear Information System (INIS)

Wei, F.; Wu, Q.H.; Jing, Z.X.; Chen, J.J.; Zhou, X.X.

2016-01-01

This paper proposes a comprehensive framework including a multi-objective interval optimization model and evidential reasoning (ER) approach to solve the unit sizing problem of small-scale integrated energy systems, with uncertain wind and solar energies integrated. In the multi-objective interval optimization model, interval variables are introduced to tackle the uncertainties of the optimization problem. Aiming at simultaneously considering the cost and risk of a business investment, the average and deviation of life cycle cost (LCC) of the integrated energy system are formulated. In order to solve the problem, a novel multi-objective optimization algorithm, MGSOACC (multi-objective group search optimizer with adaptive covariance matrix and chaotic search), is developed, employing adaptive covariance matrix to make the search strategy adaptive and applying chaotic search to maintain the diversity of group. Furthermore, ER approach is applied to deal with multiple interests of an investor at the business decision making stage and to determine the final unit sizing solution from the Pareto-optimal solutions. This paper reports on the simulation results obtained using a small-scale direct district heating system (DH) and a small-scale district heating and cooling system (DHC) optimized by the proposed framework. The results demonstrate the superiority of the multi-objective interval optimization model and ER approach in tackling the unit sizing problem of integrated energy systems considering the integration of uncertian wind and solar energies. - Highlights: • Cost and risk of investment in small-scale integrated energy systems are considered. • A multi-objective interval optimization model is presented. • A novel multi-objective optimization algorithm (MGSOACC) is proposed. • The evidential reasoning (ER) approach is used to obtain the final optimal solution. • The MGSOACC and ER can tackle the unit sizing problem efficiently.

Optimal Responsible Investment

DEFF Research Database (Denmark)

Jessen, Pernille

Numerous institutions are now engaged in Socially Responsible Investment or have signed the "UN Principles for Responsible Investment". Retail investors, however, are still lacking behind. This is peculiar since the sector constitutes key stakeholders in environmental, social and governmental...... standards. This paper considers optimal responsible investment for a small retail investor. It extends conventional portfolio theory by allowing for a personal-value based investment decision. Preferences for responsibility are defined in the framework of mean-variance analysis and an optimal responsible...... investment model identified. Implications of the altered investment problem are investigated when the dynamics between portfolio risk, expected return and responsibility is considered. Relying on the definition of a responsible investor, it is shown how superior investment opportunities can emerge when...

Ranging Behaviour of Commercial Free-Range Broiler Chickens 1: Factors Related to Flock Variability.

Science.gov (United States)

Taylor, Peta S; Hemsworth, Paul H; Groves, Peter J; Gebhardt-Henrich, Sabine G; Rault, Jean-Loup

2017-07-20

Little is known about the ranging behaviour of chickens. Understanding ranging behaviour is required to improve management and shed and range design to ensure optimal ranging opportunities. Using Radio Frequency Identification technology, we tracked 300 individual broiler chickens in each of four mixed sex ROSS 308 flocks on one commercial farm across two seasons. Ranging behaviour was tracked from the first day of range access (21 days of age) until 35 days of age in winter and 44 days of age in summer. Range use was higher than previously reported from scan sampling studies. More chickens accessed the range in summer (81%) than winter (32%; p range use was greater in summer flocks (4.4 ± 0.1 visits for a total of 26.3 ± 0.8 min/day) than winter flocks (3.2 ± 0.2 visits for a total of 7.9 ± 1.0 min/day). Seasonal differences were only marginally explained by weather conditions and may reflect the reduction in range exposure between seasons (number of days, hours per day, and time of day). Specific times of the day ( p ranging and external factors that may explain ranging preferences.

Topology optimized permanent magnet systems

Science.gov (United States)

Bjørk, R.; Bahl, C. R. H.; Insinga, A. R.

2017-09-01

Topology optimization of permanent magnet systems consisting of permanent magnets, high permeability iron and air is presented. An implementation of topology optimization for magnetostatics is discussed and three examples are considered. The Halbach cylinder is topology optimized with iron and an increase of 15% in magnetic efficiency is shown. A topology optimized structure to concentrate a homogeneous field is shown to increase the magnitude of the field by 111%. Finally, a permanent magnet with alternating high and low field regions is topology optimized and a Λcool figure of merit of 0.472 is reached, which is an increase of 100% compared to a previous optimized design.

On generalized semi-infinite optimization and bilevel optimization

NARCIS (Netherlands)

Stein, O.; Still, Georg J.

2000-01-01

The paper studies the connections and differences between bilevel problems (BL) and generalized semi-infinite problems (GSIP). Under natural assumptions (GSIP) can be seen as a special case of a (BL). We consider the so-called reduction approach for (BL) and (GSIP) leading to optimality conditions

A novel optimization method, Gravitational Search Algorithm (GSA), for PWR core optimization

International Nuclear Information System (INIS)

Mahmoudi, S.M.; Aghaie, M.; Bahonar, M.; Poursalehi, N.

2016-01-01

Highlights: • The Gravitational Search Algorithm (GSA) is introduced. • The advantage of GSA is verified in Shekel’s Foxholes. • Reload optimizing in WWER-1000 and WWER-440 cases are performed. • Maximizing K eff , minimizing PPFs and flattening power density is considered. - Abstract: In-core fuel management optimization (ICFMO) is one of the most challenging concepts of nuclear engineering. In recent decades several meta-heuristic algorithms or computational intelligence methods have been expanded to optimize reactor core loading pattern. This paper presents a new method of using Gravitational Search Algorithm (GSA) for in-core fuel management optimization. The GSA is constructed based on the law of gravity and the notion of mass interactions. It uses the theory of Newtonian physics and searcher agents are the collection of masses. In this work, at the first step, GSA method is compared with other meta-heuristic algorithms on Shekel’s Foxholes problem. In the second step for finding the best core, the GSA algorithm has been performed for three PWR test cases including WWER-1000 and WWER-440 reactors. In these cases, Multi objective optimizations with the following goals are considered, increment of multiplication factor (K eff ), decrement of power peaking factor (PPF) and power density flattening. It is notable that for neutronic calculation, PARCS (Purdue Advanced Reactor Core Simulator) code is used. The results demonstrate that GSA algorithm have promising performance and could be proposed for other optimization problems of nuclear engineering field.

Optimizing signal recycling for detecting a stochastic gravitational-wave background

Science.gov (United States)

Tao, Duo; Christensen, Nelson

2018-06-01

Signal recycling is applied in laser interferometers such as the Advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO) to increase their sensitivity to gravitational waves. In this study, signal recycling configurations for detecting a stochastic gravitational wave background are optimized based on aLIGO parameters. Optimal transmission of the signal recycling mirror (SRM) and detuning phase of the signal recycling cavity under a fixed laser power and low-frequency cutoff are calculated. Based on the optimal configurations, the compatibility with a binary neutron star (BNS) search is discussed. Then, different laser powers and low-frequency cutoffs are considered. Two models for the dimensionless energy density of gravitational waves , the flat model and the model, are studied. For a stochastic background search, it is found that an interferometer using signal recycling has a better sensitivity than an interferometer not using it. The optimal stochastic search configurations are typically found when both the SRM transmission and the signal recycling detuning phase are low. In this region, the BNS range mostly lies between 160 and 180 Mpc. When a lower laser power is used the optimal signal recycling detuning phase increases, the optimal SRM transmission increases and the optimal sensitivity improves. A reduced low-frequency cutoff gives a better sensitivity limit. For both models of , a typical optimal sensitivity limit on the order of 10‑10 is achieved at a reference frequency of Hz.

Multiobjective Synthesis of Steerable UWB Circular Antenna Array considering Energy Patterns

Directory of Open Access Journals (Sweden)

Leopoldo A. Garza

2015-01-01

Full Text Available True-time delay antenna arrays have gained a prominent attention in ultrawideband (UWB applications such as directional communications and radar. This paper presents the design of steerable UWB circular array by using a multiobjective time-domain synthesis of energy pattern for circular antenna arrays. By this way we avoid individual beamforming for each frequency in UWB spectrum if the problem was addressed from the frequency domain. In order to obtain an energy pattern with low side lobe level and a desired main beam, the synthesis presented is performed by optimizing the true-time delays and amplitude coefficients for the antenna elements in a circular geometry. The method of Differential Evolution for Multiobjective Optimization (DEMO is used as the optimization algorithm in this work. This design of steerable UWB circular arrays considers the optimization of the true-time exciting delays and the amplitude coefficients across the antenna elements to operate with optimal performance in the whole azimuth plane (360°. A comparative analysis of the performance of the optimized design with the case of conventional progressive delay excitations is achieved. The provided results show a good performance for energy patterns and for their respective power patterns in the UWB spectrum.

Topology optimization for optical projection lithography with manufacturing uncertainties

DEFF Research Database (Denmark)

Zhou, Mingdong; Lazarov, Boyan Stefanov; Sigmund, Ole

2014-01-01

to manufacturing without additional optical proximity correction (OPC). The performance of the optimized device is robust toward the considered process variations. With the proposed unified approach, the design for photolithography is achieved by considering the optimal device performance and manufacturability......This article presents a topology optimization approach for micro-and nano-devices fabricated by optical projection lithography. Incorporating the photolithography process and the manufacturing uncertainties into the topology optimization process results in a binary mask that can be sent directly...

Multi-dimensional optimization of small wind turbine blades

DEFF Research Database (Denmark)

Sessarego, Matias; Wood, David

2015-01-01

used to reduce the rotor inertia to help minimize starting time. Two airfoils are considered: the 10% thick SG6043 which has excellent lift:drag performance at low Reynolds number and the SD7062 whose extra thickness (14%) has some structural advantages, particularly for the weaker material (c). All......This paper describes a computer method to allow the design of small wind turbine blades for the multiple objectives of rapid starting, efficient power extraction, low noise, and minimal mass. For the sake of brevity, only the first two and the last objectives are considered in this paper....... The optimization aimed to study a range of blade materials, from traditional fibreglass through sustainable alternatives to rapid prototyping plastic. Because starting performance depends on blade inertia, there is a complex interaction between the material properties and the aerodynamics. Example blades of 1.1 m...

Stochastic scheduling of renewable micro-grids considering photovoltaic source uncertainties

International Nuclear Information System (INIS)

Najibi, Fatemeh; Niknam, Taher

2015-01-01

Highlights: • Proposing a complete model for PV panels. • Suggesting a Scenario Based Method to see the uncertainties of problem. • Introduction of a new optimization algorithm for solving MG operation problem. • We propose one modification over the proposed algorithm to make it better working. - Abstract: This paper introduces a new electrical model of a PV array by simulating and tests it on one typical Micro-Grid (MG) to see its performance with regards of optimal energy management of Micro-Grids (MGS). In addition, it introduces a probabilistic framework based on a scenario-based method to overcome all the uncertainties in the optimal energy management of MGs with different renewable power sources, such as Photovoltaic (PV), Wind Turbine (WT), Micro Turbine (MT), and storage devices. Therefore, the uncertainty is considered for WT and PV output power variations, load demand forecasting error and grid bid changes at the same time. It is hard to solve MG problem with all its uncertainty for 24-h time intervals, and consider several equality and inequality at the same time. In fact, in order to resolve this issue, the problem needs one powerful technique that although it converges very fast, it escapes from the local optima. As a result, one modern Dolphin echolocation optimization algorithm (DEOA) is defined to explore all the search space globally. The DEO algorithm uses the ability of echolocation of the dolphins to find the best location. Additionally, the proposed modification method will be introduced in this paper. This method makes the algorithm work better and finds the locations faster. The proposed method is implemented on a test grid-connected MG and satisfying results can be seen after implementation

Optimal PMU Placement with Uncertainty Using Pareto Method

Directory of Open Access Journals (Sweden)

A. Ketabi

2012-01-01

Full Text Available This paper proposes a method for optimal placement of Phasor Measurement Units (PMUs in state estimation considering uncertainty. State estimation has first been turned into an optimization exercise in which the objective function is selected to be the number of unobservable buses which is determined based on Singular Value Decomposition (SVD. For the normal condition, Differential Evolution (DE algorithm is used to find the optimal placement of PMUs. By considering uncertainty, a multiobjective optimization exercise is hence formulated. To achieve this, DE algorithm based on Pareto optimum method has been proposed here. The suggested strategy is applied on the IEEE 30-bus test system in several case studies to evaluate the optimal PMUs placement.

Feature Selection via Chaotic Antlion Optimization.

Directory of Open Access Journals (Sweden)

Hossam M Zawbaa

Full Text Available Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting while minimizing the number of features used.We propose an optimization approach for the feature selection problem that considers a "chaotic" version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics.

An Optimization Model for Large–Scale Wind Power Grid Connection Considering Demand Response and Energy Storage Systems

Directory of Open Access Journals (Sweden)

Zhongfu Tan

2014-11-01

Full Text Available To reduce the influence of wind power output uncertainty on power system stability, demand response (DRPs and energy storage systems (ESSs are introduced while solving scheduling optimization problems. To simulate wind power scenarios, this paper uses Latin Hypercube Sampling (LHS to generate the initial scenario set and constructs a scenario reduction strategy based on Kantorovich distance. Since DRPs and ESSs can influence the distribution of demand load, this paper constructs a joint scheduling optimization model for wind power, ESSs and DRPs under the objective of minimizing total coal cost, and constraints of power demand and supply balance, users’ demand elasticity, thermal units’ startup-shutdown, thermal units’ output power climbing and wind power backup service. To analyze the influences of ESSs and DRPs on system wind power consumption capacity, example simulation is made in a 10 thermal units system with a 1000 MW wind farm and 400 MW energy storage systems under four simulation scenarios. The simulation results show that the introduction of DRPs and ESSs could promote system wind power consumption capacity with significantly economic and environment benefits, which include less coal consumption and less pollutant emission; and the optimization effect reaches the optimum when DRPs and ESSs are both introduced.

Particle Swarm Optimization

Science.gov (United States)

Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw

2002-01-01

The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

Slushy weightings for the optimal pilot model. [considering visual tracking task

Science.gov (United States)

Dillow, J. D.; Picha, D. G.; Anderson, R. O.

1975-01-01

A pilot model is described which accounts for the effect of motion cues in a well defined visual tracking task. The effect of visual and motion cues are accounted for in the model in two ways. First, the observation matrix in the pilot model is structured to account for the visual and motion inputs presented to the pilot. Secondly, the weightings in the quadratic cost function associated with the pilot model are modified to account for the pilot's perception of the variables he considers important in the task. Analytic results obtained using the pilot model are compared to experimental results and in general good agreement is demonstrated. The analytic model yields small improvements in tracking performance with the addition of motion cues for easily controlled task dynamics and large improvements in tracking performance with the addition of motion cues for difficult task dynamics.

Evaluating Maximum Photovoltaic Integration in District Distribution Systems Considering Optimal Inverter Dispatch and Cloud Shading Conditions

DEFF Research Database (Denmark)

Ding, Tao; Kou, Yu; Yang, Yongheng

2017-01-01

. However, the intermittency of solar PV energy (e.g., due to passing clouds) may affect the PV generation in the district distribution network. To address this issue, the voltage magnitude constraints under the cloud shading conditions should be taken into account in the optimization model, which can...

Design of pressure vessels using shape optimization: An integrated approach

Energy Technology Data Exchange (ETDEWEB)

Carbonari, R.C., E-mail: ronny@usp.br [Department of Mechatronic Engineering, Escola Politecnica da Universidade de Sao Paulo, Av. Prof. Mello Moraes, 2231 Sao Paulo, SP 05508-900 (Brazil); Munoz-Rojas, P.A., E-mail: pablo@joinville.udesc.br [Department of Mechanical Engineering, Universidade do Estado de Santa Catarina, Bom Retiro, Joinville, SC 89223-100 (Brazil); Andrade, E.Q., E-mail: edmundoq@petrobras.com.br [CENPES, PDP/Metodos Cientificos, Petrobras (Brazil); Paulino, G.H., E-mail: paulino@uiuc.edu [Newmark Laboratory, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 North Mathews Av., Urbana, IL 61801 (United States); Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, 158 Mechanical Engineering Building, 1206 West Green Street, Urbana, IL 61801-2906 (United States); Nishimoto, K., E-mail: knishimo@usp.br [Department of Naval Architecture and Ocean Engineering, Escola Politecnica da Universidade de Sao Paulo, Av. Prof. Mello Moraes, 2231 Sao Paulo, SP 05508-900 (Brazil); Silva, E.C.N., E-mail: ecnsilva@usp.br [Department of Mechatronic Engineering, Escola Politecnica da Universidade de Sao Paulo, Av. Prof. Mello Moraes, 2231 Sao Paulo, SP 05508-900 (Brazil)

2011-05-15

Previous papers related to the optimization of pressure vessels have considered the optimization of the nozzle independently from the dished end. This approach generates problems such as thickness variation from nozzle to dished end (coupling cylindrical region) and, as a consequence, it reduces the optimality of the final result which may also be influenced by the boundary conditions. Thus, this work discusses shape optimization of axisymmetric pressure vessels considering an integrated approach in which the entire pressure vessel model is used in conjunction with a multi-objective function that aims to minimize the von-Mises mechanical stress from nozzle to head. Representative examples are examined and solutions obtained for the entire vessel considering temperature and pressure loading. It is noteworthy that different shapes from the usual ones are obtained. Even though such different shapes may not be profitable considering present manufacturing processes, they may be competitive for future manufacturing technologies, and contribute to a better understanding of the actual influence of shape in the behavior of pressure vessels. - Highlights: > Shape optimization of entire pressure vessel considering an integrated approach. > By increasing the number of spline knots, the convergence stability is improved. > The null angle condition gives lower stress values resulting in a better design. > The cylinder stresses are very sensitive to the cylinder length. > The shape optimization of the entire vessel must be considered for cylinder length.

Considering only first-order effects? How simplifications lead to unrealistic technology optimism in climate change mitigation

Energy Technology Data Exchange (ETDEWEB)

Arvesen, Anders, E-mail: anders.arvesen@ntnu.no [Industrial Ecology Programme and Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim NO-7491 (Norway); Bright, Ryan M.; Hertwich, Edgar G. [Industrial Ecology Programme and Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim NO-7491 (Norway)

2011-11-15

This article challenges the notion that energy efficiency and 'clean' energy technologies can deliver sufficient degrees of climate change mitigation. By six arguments not widely recognized in the climate policy arena, we argue that unrealistic technology optimism exists in current climate change mitigation assessments, and, consequently, world energy and climate policy. The overarching theme of the arguments is that incomplete knowledge of indirect effects, and neglect of interactions between parts of physical and social sub-systems, systematically leads to overly optimistic assessments. Society must likely seek deeper changes in social and economic structures to preserve the climatic conditions to which the human civilization is adapted. We call for priority to be given to research evaluating aspects of mitigation in a broad, system-wide perspective. - Highlights: > We highlight some of the simplifying assumptions in climate change mitigation scenarios. > Mitigation assessments are the basis of unfounded technology optimism in climate policy. > Society must likely seek deeper changes in social and economic structures to stabilize climate.

Considering only first-order effects? How simplifications lead to unrealistic technology optimism in climate change mitigation

International Nuclear Information System (INIS)

Arvesen, Anders; Bright, Ryan M.; Hertwich, Edgar G.

2011-01-01

This article challenges the notion that energy efficiency and 'clean' energy technologies can deliver sufficient degrees of climate change mitigation. By six arguments not widely recognized in the climate policy arena, we argue that unrealistic technology optimism exists in current climate change mitigation assessments, and, consequently, world energy and climate policy. The overarching theme of the arguments is that incomplete knowledge of indirect effects, and neglect of interactions between parts of physical and social sub-systems, systematically leads to overly optimistic assessments. Society must likely seek deeper changes in social and economic structures to preserve the climatic conditions to which the human civilization is adapted. We call for priority to be given to research evaluating aspects of mitigation in a broad, system-wide perspective. - Highlights: → We highlight some of the simplifying assumptions in climate change mitigation scenarios. → Mitigation assessments are the basis of unfounded technology optimism in climate policy. → Society must likely seek deeper changes in social and economic structures to stabilize climate.

Thermodynamic performance analysis and optimization of DMC (Dual Miller Cycle) cogeneration system by considering exergetic performance coefficient and total exergy output criteria

International Nuclear Information System (INIS)

Ust, Yasin; Arslan, Feyyaz; Ozsari, Ibrahim; Cakir, Mehmet

2015-01-01

Miller cycle engines are one of the popular engine concepts that are available for improving performance, reducing fuel consumption and NO x emissions. There are many research studies that investigated the modification of existing conventional engines for operation on a Miller cycle. In this context, a comparative performance analysis and optimization based on exergetic performance criterion, total exergy output and exergy efficiency has been carried out for an irreversible Dual–Miller Cycle cogeneration system having finite-rate of heat transfer, heat leak and internal irreversibilities. The EPC (Exergetic Performance Coefficient) criterion defined as the ratio of total exergy output to the loss rate of availability. Performance analysis has been also extended to the Otto–Miller and Diesel-Miller cogeneration cycles which may be considered as two special cases of the Dual–Miller cycle. The effect of the design parameters such as compression ratio, pressure ratio, cut-off ratio, Miller cycle ratio, heat consumer temperature ratio, allocation ratio and the ratio of power to heat consumed have also been investigated. The results obtained from this paper will provide guidance for the design of Dual–Miller Cycle cogeneration system and can be used for selection of optimal design parameters. - Highlights: • A thermodynamic performance estimation tool for DM cogeneration cycle is presented. • Using the model two special cases OM and dM cogeneration cycles can be analyzed. • The effects of r M , ψ, χ 2 and R have been investigated. • The results evaluate exergy output and environmental aspects together.

Optimal Investment in Structured Bonds

DEFF Research Database (Denmark)

Jessen, Pernille; Jørgensen, Peter Løchte

The paper examines the role of structured bonds in the optimal portfolio of a small retail investor. We consider the typical structured bond essentially repacking an exotic option and a zero coupon bond, i.e. an investment with portfolio insurance. The optimal portfolio is found when the investment...

A new method of optimal capacitor switching based on minimum spanning tree theory in distribution systems

Science.gov (United States)

Li, H. W.; Pan, Z. Y.; Ren, Y. B.; Wang, J.; Gan, Y. L.; Zheng, Z. Z.; Wang, W.

2018-03-01

According to the radial operation characteristics in distribution systems, this paper proposes a new method based on minimum spanning trees method for optimal capacitor switching. Firstly, taking the minimal active power loss as objective function and not considering the capacity constraints of capacitors and source, this paper uses Prim algorithm among minimum spanning trees algorithms to get the power supply ranges of capacitors and source. Then with the capacity constraints of capacitors considered, capacitors are ranked by the method of breadth-first search. In term of the order from high to low of capacitor ranking, capacitor compensation capacity based on their power supply range is calculated. Finally, IEEE 69 bus system is adopted to test the accuracy and practicality of the proposed algorithm.

Optimal Laser Phototherapy Parameters for Pain Relief.

Science.gov (United States)

Kate, Rohit J; Rubatt, Sarah; Enwemeka, Chukuka S; Huddleston, Wendy E

2018-03-27

Studies on laser phototherapy for pain relief have used parameters that vary widely and have reported varying outcomes. The purpose of this study was to determine the optimal parameter ranges of laser phototherapy for pain relief by analyzing data aggregated from existing primary literature. Original studies were gathered from available sources and were screened to meet the pre-established inclusion criteria. The included articles were then subjected to meta-analysis using Cohen's d statistic for determining treatment effect size. From these studies, ranges of the reported parameters that always resulted into large effect sizes were determined. These optimal ranges were evaluated for their accuracy using leave-one-article-out cross-validation procedure. A total of 96 articles met the inclusion criteria for meta-analysis and yielded 232 effect sizes. The average effect size was highly significant: d = +1.36 (confidence interval [95% CI] = 1.04-1.68). Among all the parameters, total energy was found to have the greatest effect on pain relief and had the most prominent optimal ranges of 120-162 and 15.36-20.16 J, which always resulted in large effect sizes. The cross-validation accuracy of the optimal ranges for total energy was 68.57% (95% CI = 53.19-83.97). Fewer and less-prominent optimal ranges were obtained for the energy density and duration parameters. None of the remaining parameters was found to be independently related to pain relief outcomes. The findings of meta-analysis indicate that laser phototherapy is highly effective for pain relief. Based on the analysis of parameters, total energy can be optimized to yield the largest effect on pain relief.

Analysis and Optimization of Wireless Power Transfer Efficiency Considering the Tilt Angle of a Coil

Directory of Open Access Journals (Sweden)

Wei Huang

2018-01-01

Full Text Available Wireless power transfer (WPT based on magnetic resonant coupling is a promising technology in many industrial applications. Efficiency of the WPT system usually depends on the tilt angle of the transmitter or the receiver coil. This work analyzes the effect of the tilt angle on the efficiency of the WPT system with horizontal misalignment. The mutual inductance between two coils located at arbitrary positions with tilt angles is calculated using a numerical analysis based on the Neumann formula. The efficiency of the WPT system with a tilted coil is extracted using an equivalent circuit model with extracted mutual inductance. By analyzing the results, we propose an optimal tilt angle to maximize the efficiency of the WPT system. The best angle to maximize the efficiency depends on the radii of the two coils and their relative position. The calculated efficiencies versus the tilt angle for various WPT cases, which change the radius of RX (r2 = 0.075 m, 0.1 m, 0.15 m and the horizontal distance (y = 0 m, 0.05 m, 0.1 m, are compared with the experimental results. The analytically extracted efficiencies and the extracted optimal tilt angles agree well with those of the experimental results.

Nuclear-thermal-coupled optimization code for the fusion breeding blanket conceptual design

International Nuclear Information System (INIS)

Li, Jia; Jiang, Kecheng; Zhang, Xiaokang; Nie, Xingchen; Zhu, Qinjun; Liu, Songlin

2016-01-01

Highlights: • A nuclear-thermal-coupled predesign code has been developed for optimizing the radial build arrangement of fusion breeding blanket. • Coupling module aims at speeding up the efficiency of design progress by coupling the neutronics calculation code with the thermal-hydraulic analysis code. • Radial build optimization algorithm aims at optimal arrangement of breeding blanket considering one or multiple specified objectives subject to the design criteria such as material temperature limit and available TBR. - Abstract: Fusion breeding blanket as one of the key in-vessel components performs the functions of breeding the tritium, removing the nuclear heat and heat flux from plasma chamber as well as acting as part of shielding system. The radial build design which determines the arrangement of function zones and material properties on the radial direction is the basis of the detailed design of fusion breeding blanket. For facilitating the radial build design, this study aims for developing a pre-design code to optimize the radial build of blanket with considering the performance of nuclear and thermal-hydraulic simultaneously. Two main features of this code are: (1) Coupling of the neutronics analysis with the thermal-hydraulic analysis to speed up the analysis progress; (2) preliminary optimization algorithm using one or multiple specified objectives subject to the design criteria in the form of constrains imposed on design variables and performance parameters within the possible engineering ranges. This pre-design code has been applied to the conceptual design of water-cooled ceramic breeding blanket in project of China fusion engineering testing reactor (CFETR).

Nuclear-thermal-coupled optimization code for the fusion breeding blanket conceptual design

Energy Technology Data Exchange (ETDEWEB)

Li, Jia, E-mail: lijia@ustc.edu.cn [School of Nuclear Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui (China); Jiang, Kecheng; Zhang, Xiaokang [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, Anhui (China); Nie, Xingchen [School of Nuclear Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui (China); Zhu, Qinjun; Liu, Songlin [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, Anhui (China)

2016-12-15

Highlights: • A nuclear-thermal-coupled predesign code has been developed for optimizing the radial build arrangement of fusion breeding blanket. • Coupling module aims at speeding up the efficiency of design progress by coupling the neutronics calculation code with the thermal-hydraulic analysis code. • Radial build optimization algorithm aims at optimal arrangement of breeding blanket considering one or multiple specified objectives subject to the design criteria such as material temperature limit and available TBR. - Abstract: Fusion breeding blanket as one of the key in-vessel components performs the functions of breeding the tritium, removing the nuclear heat and heat flux from plasma chamber as well as acting as part of shielding system. The radial build design which determines the arrangement of function zones and material properties on the radial direction is the basis of the detailed design of fusion breeding blanket. For facilitating the radial build design, this study aims for developing a pre-design code to optimize the radial build of blanket with considering the performance of nuclear and thermal-hydraulic simultaneously. Two main features of this code are: (1) Coupling of the neutronics analysis with the thermal-hydraulic analysis to speed up the analysis progress; (2) preliminary optimization algorithm using one or multiple specified objectives subject to the design criteria in the form of constrains imposed on design variables and performance parameters within the possible engineering ranges. This pre-design code has been applied to the conceptual design of water-cooled ceramic breeding blanket in project of China fusion engineering testing reactor (CFETR).

Optimal design of MR shock absorber and application to vehicle suspension

International Nuclear Information System (INIS)

Nguyen, Quoc-Hung; Choi, Seung-Bok

2009-01-01

This paper presents an optimal design of a magnetorheological (MR) shock absorber based on finite element analysis. The MR shock absorber is constrained in a specific volume and the optimization problem identifies geometric dimensions of the shock absorber that minimize a multi-objective function. The objective function is proposed by considering the damping force, dynamic range and the inductive time constant of the shock absorber. After describing the configuration of the MR shock absorber, a quasi-static modeling of the shock absorber is performed based on the Bingham model of an MR fluid. The initial geometric dimensions of the shock absorber are then determined based on the assumption of constant magnetic flux density throughout the magnetic circuit. The objective function of the optimization problem is derived based on the solution of the initial shock absorber. An optimization procedure using a golden-section algorithm and a local quadratic fitting technique is constructed via a commercial finite element method parametric design language. Using the developed optimization tool, optimal solutions of the MR shock absorber, which is constrained in a specific cylindrical volume defined by its radius and height, are determined. Subsequently, a quarter-car suspension model with the optimized MR shock absorber is formulated and the vibration control performance of the suspension is evaluated under bump and sinusoidal road conditions

Optimization of NANOGrav's time allocation for maximum sensitivity to single sources

International Nuclear Information System (INIS)

Christy, Brian; Anella, Ryan; Lommen, Andrea; Camuccio, Richard; Handzo, Emma; Finn, Lee Samuel

2014-01-01

Pulsar timing arrays (PTAs) are a collection of precisely timed millisecond pulsars (MSPs) that can search for gravitational waves (GWs) in the nanohertz frequency range by observing characteristic signatures in the timing residuals. The sensitivity of a PTA depends on the direction of the propagating GW source, the timing accuracy of the pulsars, and the allocation of the available observing time. The goal of this paper is to determine the optimal time allocation strategy among the MSPs in the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) for a single source of GW under a particular set of assumptions. We consider both an isotropic distribution of sources across the sky and a specific source in the Virgo cluster. This work improves on previous efforts by modeling the effect of intrinsic spin noise for each pulsar. We find that, in general, the array is optimized by maximizing time spent on the best-timed pulsars, with sensitivity improvements typically ranging from a factor of 1.5 to 4.

Portfolio Optimization with Stochastic Dividends and Stochastic Volatility

Science.gov (United States)

Varga, Katherine Yvonne

2015-01-01

We consider an optimal investment-consumption portfolio optimization model in which an investor receives stochastic dividends. As a first problem, we allow the drift of stock price to be a bounded function. Next, we consider a stochastic volatility model. In each problem, we use the dynamic programming method to derive the Hamilton-Jacobi-Bellman…

Industrial cogeneration optimization program. Final report, September 1979

Energy Technology Data Exchange (ETDEWEB)

Davis, Jerry; McWhinney, Jr., Robert T.

1980-01-01

This study program is part of the DOE Integrated Industry Cogeneration Program to optimize, evaluate, and demonstrate cogeneration systems, with direct participation of the industries most affected. One objective is to characterize five major energy-intensive industries with respect to their energy-use profiles. The industries are: petroleum refining and related industries, textile mill products, paper and allied products, chemicals and allied products, and food and kindred products. Another objective is to select optimum cogeneration systems for site-specific reference case plants in terms of maximum energy savings subject to given return on investment hurdle rates. Analyses were made that define the range of optimal cogeneration systems for each reference-case plant considering technology applicability, economic factors, and energy savings by type of fuel. This study also provides guidance to other parts of the program through information developed with regard to component development requirements, institutional and regulatory barriers, as well as fuel use and environmental considerations. (MCW)

Visual prosthesis wireless energy transfer system optimal modeling.

Science.gov (United States)

Li, Xueping; Yang, Yuan; Gao, Yong

2014-01-16

Wireless energy transfer system is an effective way to solve the visual prosthesis energy supply problems, theoretical modeling of the system is the prerequisite to do optimal energy transfer system design. On the basis of the ideal model of the wireless energy transfer system, according to visual prosthesis application condition, the system modeling is optimized. During the optimal modeling, taking planar spiral coils as the coupling devices between energy transmitter and receiver, the effect of the parasitic capacitance of the transfer coil is considered, and especially the concept of biological capacitance is proposed to consider the influence of biological tissue on the energy transfer efficiency, resulting in the optimal modeling's more accuracy for the actual application. The simulation data of the optimal model in this paper is compared with that of the previous ideal model, the results show that under high frequency condition, the parasitic capacitance of inductance and biological capacitance considered in the optimal model could have great impact on the wireless energy transfer system. The further comparison with the experimental data verifies the validity and accuracy of the optimal model proposed in this paper. The optimal model proposed in this paper has a higher theoretical guiding significance for the wireless energy transfer system's further research, and provide a more precise model reference for solving the power supply problem in visual prosthesis clinical application.

An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties

International Nuclear Information System (INIS)

Bahmani-Firouzi, Bahman; Farjah, Ebrahim; Azizipanah-Abarghooee, Rasoul

2013-01-01

Renewable energy resources such as wind power plants are playing an ever-increasing role in power generation. This paper extends the dynamic economic emission dispatch problem by incorporating wind power plant. This problem is a multi-objective optimization approach in which total electrical power generation costs and combustion emissions are simultaneously minimized over a short-term time span. A stochastic approach based on scenarios is suggested to model the uncertainty associated with hourly load and wind power forecasts. A roulette wheel technique on the basis of probability distribution functions of load and wind power is implemented to generate scenarios. As a result, the stochastic nature of the suggested problem is emancipated by decomposing it into a set of equivalent deterministic problem. An improved multi-objective particle swarm optimization algorithm is applied to obtain the best expected solutions for the proposed stochastic programming framework. To enhance the overall performance and effectiveness of the particle swarm optimization, a fuzzy adaptive technique, θ-search and self-adaptive learning strategy for velocity updating are used to tune the inertia weight factor and to escape from local optima, respectively. The suggested algorithm goes through the search space in the polar coordinates instead of the Cartesian one; whereby the feasible space is more compact. In order to evaluate the efficiency and feasibility of the suggested framework, it is applied to two test systems with small and large scale characteristics. - Highlights: ► Formulates multi-objective DEED problem under a stochastic programming framework. ► Considers uncertainties related to forecasted values of load demand and wind power. ► Proposes an interactive fuzzy satisfying method based on the novel FSALPSO. ► Presents a new self-adaptive learning strategy to improve original PSO algorithm

Distributed generation incorporated with the thermal generation for optimum operation of a smart grid considering forecast error

International Nuclear Information System (INIS)

Howlader, Harun Or Rashid; Matayoshi, Hidehito; Senjyu, Tomonobu

2015-01-01

Highlights: • Optimal operation of the thermal generation for the smart grid system. • Different distributed generations are considered as the power generation sources. • Forecast error of the renewable energy systems is considered. • Controllable loads of the smart houses are considered to achieve the optimal operation. • Economical benefits can be achieved for the smart grid system. - Abstract: This paper concentrates on the optimal operation of the conventional thermal generators with distributed generations for a smart grid considering forecast error. The distributed generations are considered as wind generators, photovoltaic generators, battery energy storage systems in the supply side and a large number of smart houses in the demand side. A smart house consists of the electric vehicle, heat pump, photovoltaic generator and solar collector. The electric vehicle and heat pump are considered as the controllable loads which can compensate the power for the forecast error of renewable energy sources. As a result, power generation cost of the smart grid can reduce through coordinated with distributed generations and thermal units scheduling process. The electric vehicles of the smart house are considered as the spinning reserve in the scheduling process which lead to lessen the additional operation of thermal units. Finally, obtained results of the proposed system have been compared with the conventional method. The conventional method does not consider the electric vehicle in the smart houses. The acquired results demonstrate that total power generation cost of the smart grid has been reduced by the proposed method considering forecast error. Effectiveness of the proposed method has been verified by the extensive simulation results using MATLAB® software

An adaptive reentry guidance method considering the influence of blackout zone

Science.gov (United States)

Wu, Yu; Yao, Jianyao; Qu, Xiangju

2018-01-01

Reentry guidance has been researched as a popular topic because it is critical for a successful flight. In view that the existing guidance methods do not take into account the accumulated navigation error of Inertial Navigation System (INS) in the blackout zone, in this paper, an adaptive reentry guidance method is proposed to obtain the optimal reentry trajectory quickly with the target of minimum aerodynamic heating rate. The terminal error in position and attitude can be also reduced with the proposed method. In this method, the whole reentry guidance task is divided into two phases, i.e., the trajectory updating phase and the trajectory planning phase. In the first phase, the idea of model predictive control (MPC) is used, and the receding optimization procedure ensures the optimal trajectory in the next few seconds. In the trajectory planning phase, after the vehicle has flown out of the blackout zone, the optimal reentry trajectory is obtained by online planning to adapt to the navigation information. An effective swarm intelligence algorithm, i.e. pigeon inspired optimization (PIO) algorithm, is applied to obtain the optimal reentry trajectory in both of the two phases. Compared to the trajectory updating method, the proposed method can reduce the terminal error by about 30% considering both the position and attitude, especially, the terminal error of height has almost been eliminated. Besides, the PIO algorithm performs better than the particle swarm optimization (PSO) algorithm both in the trajectory updating phase and the trajectory planning phases.

Optimal control

CERN Document Server

Aschepkov, Leonid T; Kim, Taekyun; Agarwal, Ravi P

2016-01-01

This book is based on lectures from a one-year course at the Far Eastern Federal University (Vladivostok, Russia) as well as on workshops on optimal control offered to students at various mathematical departments at the university level. The main themes of the theory of linear and nonlinear systems are considered, including the basic problem of establishing the necessary and sufficient conditions of optimal processes. In the first part of the course, the theory of linear control systems is constructed on the basis of the separation theorem and the concept of a reachability set. The authors prove the closure of a reachability set in the class of piecewise continuous controls, and the problems of controllability, observability, identification, performance and terminal control are also considered. The second part of the course is devoted to nonlinear control systems. Using the method of variations and the Lagrange multipliers rule of nonlinear problems, the authors prove the Pontryagin maximum principle for prob...

Optimizing an experimental design for an electromagnetic experiment

Science.gov (United States)

Roux, Estelle; Garcia, Xavier

2013-04-01

Most of geophysical studies focus on data acquisition and analysis, but another aspect which is gaining importance is the discussion on acquisition of suitable datasets. This can be done through the design of an optimal experiment. Optimizing an experimental design implies a compromise between maximizing the information we get about the target and reducing the cost of the experiment, considering a wide range of constraints (logistical, financial, experimental …). We are currently developing a method to design an optimal controlled-source electromagnetic (CSEM) experiment to detect a potential CO2 reservoir and monitor this reservoir during and after CO2 injection. Our statistical algorithm combines the use of linearized inverse theory (to evaluate the quality of one given design via the objective function) and stochastic optimization methods like genetic algorithm (to examine a wide range of possible surveys). The particularity of our method is that it uses a multi-objective genetic algorithm that searches for designs that fit several objective functions simultaneously. One main advantage of this kind of technique to design an experiment is that it does not require the acquisition of any data and can thus be easily conducted before any geophysical survey. Our new experimental design algorithm has been tested with a realistic one-dimensional resistivity model of the Earth in the region of study (northern Spain CO2 sequestration test site). We show that a small number of well distributed observations have the potential to resolve the target. This simple test also points out the importance of a well chosen objective function. Finally, in the context of CO2 sequestration that motivates this study, we might be interested in maximizing the information we get about the reservoir layer. In that case, we show how the combination of two different objective functions considerably improve its resolution.

Optimal Control of Mechanical Systems

Directory of Open Access Journals (Sweden)

Vadim Azhmyakov

2007-01-01

Full Text Available In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some consistent numerical approximations of a multiobjective optimization problem to the initial optimal control problem. For solving the auxiliary problem, we propose an implementable numerical algorithm.

Optimal recombination in genetic algorithms for combinatorial optimization problems: Part I

Directory of Open Access Journals (Sweden)

Eremeev Anton V.

2014-01-01

Full Text Available This paper surveys results on complexity of the optimal recombination problem (ORP, which consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We consider efficient reductions of the ORPs, allowing to establish polynomial solvability or NP-hardness of the ORPs, as well as direct proofs of hardness results. Part I presents the basic principles of optimal recombination with a survey of results on Boolean Linear Programming Problems. Part II (to appear in a subsequent issue is devoted to the ORPs for problems which are naturally formulated in terms of search for an optimal permutation.

Impact of biological and economic variables on optimal parity for replacement in swine breed-to-wean herds.

Science.gov (United States)

Rodriguez-Zas, S L; Davis, C B; Ellinger, P N; Schnitkey, G D; Romine, N M; Connor, J F; Knox, R V; Southey, B R

2006-09-01

Voluntary and involuntary culling practices determine the average parity when sows are replaced in a herd. Underlying these practices is the economic effect of replacing a sow at different parities. A dynamic programming model was used to find the optimal parity and net present value in breed-to-wean swine herds. The model included income and costs per parity weighted by the discount rate and sow removal rate. Three scenarios that reflect a wide range of cases were considered: low removal rates per parity with no salvage value (LRNS), high removal rates per parity with no salvage value (HRNS), and high removal rates per parity with a percentage of the sows having a salvage value (HRYS). The optimal parity of replacement for the base biological and economic conditions was 4 and 5 parities in the high and low removal scenarios, respectively. Sensitivity analyses identified the variables influencing the optimal replacement parity. Optimal parity of replacement ranged from 3 to 7 parities in the low replacement scenario, compared with 1 to 5 parities in the high replacement scenarios. Sow replacement cost and salvage value had the greatest impact on optimal parity of replacement followed by revenues per piglet weaned. The discount rate and number of parities per year generally had little influence on optimal parity. For situations with high sow costs, low salvage values, and low revenues per piglet, the optimal parity at removal was as high as 6 to 10 parities, and for situations with low sow cost, high salvage values, and high revenues per piglet, the optimal parity at removal was as low as 1 to 2 parities depending on removal rates. The modified internal rate of return suggested that, for most LRNS and HRYS scenarios considered, investment in a swine breed-to-wean enterprise was favored over other investments involving a similar risk profile. Our results indicate that in US breeding herds, sows are culled on average near the optimal parity of 4. However, the

Energy Optimal Path Planning: Integrating Coastal Ocean Modelling with Optimal Control

Science.gov (United States)

Subramani, D. N.; Haley, P. J., Jr.; Lermusiaux, P. F. J.

2016-02-01

A stochastic optimization methodology is formulated for computing energy-optimal paths from among time-optimal paths of autonomous vehicles navigating in a dynamic flow field. To set up the energy optimization, the relative vehicle speed and headings are considered to be stochastic, and new stochastic Dynamically Orthogonal (DO) level-set equations that govern their stochastic time-optimal reachability fronts are derived. Their solution provides the distribution of time-optimal reachability fronts and corresponding distribution of time-optimal paths. An optimization is then performed on the vehicle's energy-time joint distribution to select the energy-optimal paths for each arrival time, among all stochastic time-optimal paths for that arrival time. The accuracy and efficiency of the DO level-set equations for solving the governing stochastic level-set reachability fronts are quantitatively assessed, including comparisons with independent semi-analytical solutions. Energy-optimal missions are studied in wind-driven barotropic quasi-geostrophic double-gyre circulations, and in realistic data-assimilative re-analyses of multiscale coastal ocean flows. The latter re-analyses are obtained from multi-resolution 2-way nested primitive-equation simulations of tidal-to-mesoscale dynamics in the Middle Atlantic Bight and Shelbreak Front region. The effects of tidal currents, strong wind events, coastal jets, and shelfbreak fronts on the energy-optimal paths are illustrated and quantified. Results showcase the opportunities for longer-duration missions that intelligently utilize the ocean environment to save energy, rigorously integrating ocean forecasting with optimal control of autonomous vehicles.

Dispositional optimism in adolescents with cancer : Differential associations of optimism and pessimism with positive and negative aspects of well-being

NARCIS (Netherlands)

Sulkers, Esther; Fleer, Joke; Brinksma, Aeltsje; Roodbol, Petrie F.; Kamps, Willem A.; Tissing, Wim J. E.; Sanderman, Robbert

ObjectivesDispositional optimism is often considered to be a unidimensional construct. Recent studies suggest, however, that optimism and pessimism are separate dimensions. In this study we investigated two issues. First, the levels of optimism and pessimism in adolescents with cancer compared with

Dispositional optimism in adolescents with cancer: Differential associations of optimism and pessimism with positive and negative aspects of well-being

NARCIS (Netherlands)

Sulkers, Esther; Fleer, Joke; Brinksma, Aeltsje; Roodbol, Petrie F.; Kamps, Willem A.; Tissing, Wim J.E.; Sanderman, Robbert

2013-01-01

Objectives Dispositional optimism is often considered to be a unidimensional construct. Recent studies suggest, however, that optimism and pessimism are separate dimensions. In this study we investigated two issues. First, the levels of optimism and pessimism in adolescents with cancer compared with

Global patterns of protection of elevational gradients in mountain ranges.

Science.gov (United States)

Elsen, Paul R; Monahan, William B; Merenlender, Adina M

2018-05-21

Protected areas (PAs) that span elevational gradients enhance protection for taxonomic and phylogenetic diversity and facilitate species range shifts under climate change. We quantified the global protection of elevational gradients by analyzing the elevational distributions of 44,155 PAs in 1,010 mountain ranges using the highest resolution digital elevation models available. We show that, on average, mountain ranges in Africa and Asia have the lowest elevational protection, ranges in Europe and South America have intermediate elevational protection, and ranges in North America and Oceania have the highest elevational protection. We use the Convention on Biological Diversity's Aichi Target 11 to assess the proportion of elevational gradients meeting the 17% suggested minimum target and examine how different protection categories contribute to elevational protection. When considering only strict PAs [International Union for Conservation of Nature (IUCN) categories I-IV, n = 24,706], nearly 40% of ranges do not contain any PAs, roughly half fail to meet the 17% target at any elevation, and ∼75% fail to meet the target throughout ≥50% of the elevational gradient. Observed elevational protection is well below optimal, and frequently below a null model of elevational protection. Including less stringent PAs (IUCN categories V-VI and nondesignated PAs, n = 19,449) significantly enhances elevational protection for most continents, but several highly biodiverse ranges require new or expanded PAs to increase elevational protection. Ensuring conservation outcomes for PAs with lower IUCN designations as well as strategically placing PAs to better represent and connect elevational gradients will enhance ecological representation and facilitate species range shifts under climate change. Copyright © 2018 the Author(s). Published by PNAS.

The Method of Optimization of Hydropower Plant Performance for Use in Group Active Power Controller

Directory of Open Access Journals (Sweden)

Glazyrin G.V.

2017-04-01

Full Text Available The problem of optimization of hydropower plant performance is considered in this paper. A new method of calculation of optimal load-sharing is proposed. The method is based on application of incremental water flow curves representing relationship between the per unit increase of water flow and active power. The optimal load-sharing is obtained by solving the nonlinear equation governing the balance of total active power and the station power set point with the same specific increase of water flow for all turbines. Unlike traditional optimization techniques, the solution of the equation is obtained without taking into account unit safe operating zones. Instead, if calculated active power of a unit violates the permissible power range, load-sharing is recalculated for the remaining generating units. Thus, optimal load-sharing algorithm suitable for digital control systems is developed. The proposed algorithm is implemented in group active power controller in Novosibirsk hydropower plant. An analysis of operation of group active power controller proves that the application of the proposed method allows obtaining optimal load-sharing at each control step with sufficient precision.

Design and Optimization of Permanent Magnet Brushless Machines for Electric Vehicle Applications

Directory of Open Access Journals (Sweden)

Weiwei Gu

2015-12-01

Full Text Available In this paper, by considering and establishing the relationship between the maximum operating speed and d-axis inductance, a new design and optimization method is proposed. Thus, a more extended constant power speed range, as well as reduced losses and increased efficiency, especially in the high-speed region, can be obtained, which is essential for electric vehicles (EVs. In the first step, the initial permanent magnet (PM brushless machine is designed based on the consideration of the maximum speed and performance specifications in the entire operation region. Then, on the basis of increasing d-axis inductance, and meanwhile maintaining constant permanent magnet flux linkage, the PM brushless machine is optimized. The corresponding performance of the initial and optimal PM brushless machines are analyzed and compared by the finite-element method (FEM. Several tests are carried out in an EV simulation model based on the urban dynamometer driving schedule (UDDS for evaluation. Both theoretical analysis and simulation results verify the validity of the proposed design and optimization method.

Genetic-evolution-based optimization methods for engineering design

Science.gov (United States)

Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.

1990-01-01

This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.

Isogeometric Shape Optimization of Vibrating Membranes

DEFF Research Database (Denmark)

Nguyen, Dang Manh; Evgrafov, Anton; Gersborg, Allan Roulund

2011-01-01

We consider a model problem of isogeometric shape optimization of vibrating membranes whose shapes are allowed to vary freely. The main obstacle we face is the need for robust and inexpensive extension of a B-spline parametrization from the boundary of a domain onto its interior, a task which has...... perform a number of numerical experiments with our isogeometric shape optimization algorithm and present smooth, optimized membrane shapes. Our conclusion is that isogeometric analysis fits well with shape optimization....

Determining an energy-optimal thermal management strategy for electric driven vehicles

Energy Technology Data Exchange (ETDEWEB)

Suchaneck, Andre; Probst, Tobias; Puente Leon, Fernando [Karlsruher Institut fuer Technology (KIT), Karlsruhe (Germany). Inst. of Industrial Information Technology (IIIT)

2012-11-01

In electric, hybrid electric and fuel cell vehicles, thermal management may have a significant impact on vehicle range. Therefore, optimal thermal management strategies are required. In this paper a method for determining an energy-optimal control strategy for thermal power generation in electric driven vehicles is presented considering all controlled devices (pumps, valves, fans, and the like) as well as influences like ambient temperature, vehicle speed, motor and battery and cooling cycle temperatures. The method is designed to be generic to increase the thermal management development process speed and to achieve the maximal energy reduction for any electric driven vehicle (e.g., by waste heat utilization). Based on simulations of a prototype electric vehicle with an advanced cooling cycle structure, the potential of the method is shown. (orig.)

Effects of ejector geometries on performance of ejector-expansion R410A air conditioner considering cooling seasonal performance factor

International Nuclear Information System (INIS)

Jeon, Yongseok; Jung, Jongho; Kim, Dongwoo; Kim, Sunjae; Kim, Yongchan

2017-01-01

Highlights: •The performance of an ejector-expansion R410A air conditioner is measured. •The effect of ejector geometries on the COP and CSPF is analyzed. •The mixing-section diameter of the ejector is optimized based on the CSPF. •The mixing-section diameter is optimized based on the climatic conditions. -- Abstract: The objective of this study was to investigate the effects of ejector geometries on the performance of an ejector-expansion air conditioner (EEAC) considering the cooling seasonal performance factor (CSPF). The performance of the EEAC using R410A was measured and analyzed by varying the compressor speed, outdoor-bin temperature, operating pressures, nozzle-throat diameter, and mixing-section diameter. The EEAC in the medium-capacity mode exhibited maximum coefficient of performance (COP) improvement, i.e., 7.5%, over the baseline (conventional) cycle. The optimum mixing-section diameter was determined to be 9 mm based on the CSPF. In addition, the optimum mixing-section diameter increased with an increase in the annual average outdoor temperature. The CSPF of the EEAC with the optimized mixing-section diameter improved in the range of 16.0–20.3% over the baseline cycle depending on the climatic conditions.

Weight optimization of plane truss using genetic algorithm

Science.gov (United States)

Neeraja, D.; Kamireddy, Thejesh; Santosh Kumar, Potnuru; Simha Reddy, Vijay

2017-11-01

Optimization of structure on basis of weight has many practical benefits in every engineering field. The efficiency is proportionally related to its weight and hence weight optimization gains prime importance. Considering the field of civil engineering, weight optimized structural elements are economical and easier to transport to the site. In this study, genetic optimization algorithm for weight optimization of steel truss considering its shape, size and topology aspects has been developed in MATLAB. Material strength and Buckling stability have been adopted from IS 800-2007 code of construction steel. The constraints considered in the present study are fabrication, basic nodes, displacements, and compatibility. Genetic programming is a natural selection search technique intended to combine good solutions to a problem from many generations to improve the results. All solutions are generated randomly and represented individually by a binary string with similarities of natural chromosomes, and hence it is termed as genetic programming. The outcome of the study is a MATLAB program, which can optimise a steel truss and display the optimised topology along with element shapes, deflections, and stress results.

Optimal waste heat recovery and reuse in industrial zones

International Nuclear Information System (INIS)

Stijepovic, Mirko Z.; Linke, Patrick

2011-01-01

Significant energy efficiency gains in zones with concentrated activity from energy intensive industries can often be achieved by recovering and reusing waste heat between processing plants. We present a systematic approach to target waste heat recovery potentials and design optimal reuse options across plants in industrial zones. The approach first establishes available waste heat qualities and reuse feasibilities considering distances between individual plants. A targeting optimization problem is solved to establish the maximum possible waste heat recovery for the industrial zone. Then, a design optimization problem is solved to identify concrete waste heat recovery options considering economic objectives. The paper describes the approach and illustrates its application with a case study. -- Highlights: → Developed a systematic approach to target waste heat recovery potentials and to design optimal recovery and reuse options across plants in industrial zones. → Five stage approach involving data acquisition, analysis, assessment, targeting and design. → Targeting optimization problem establishes the maximum possible waste heat recovery and reuse limit for the industrial zone. → Design optimization problem provides concrete waste heat recovery and reuse network design options considering economic objectives.

Optimized packings with applications

CERN Document Server

Pintér, János

2015-01-01

This volume presents a selection of case studies that address a substantial range of optimized object packings (OOP) and their applications. The contributing authors are well-recognized researchers and practitioners. The mathematical modelling and numerical solution aspects of each application case study are presented in sufficient detail. A broad range of OOP problems are discussed: these include various specific and non-standard container loading and object packing problems, as well as the stowing of hazardous and other materials on container ships, data centre resource management, automotive engineering design, space station logistic support, cutting and packing problems with placement constraints, the optimal design of LED street lighting, robust sensor deployment strategies, spatial scheduling problems, and graph coloring models and metaheuristics for packing applications. Novel points of view related to model development and to computational nonlinear, global, mixed integer optimization and heuristic st...

Reliability-based management of buried pipelines considering external corrosion defects

Science.gov (United States)

Miran, Seyedeh Azadeh

Corrosion is one of the main deteriorating mechanisms that degrade the energy pipeline integrity, due to transferring corrosive fluid or gas and interacting with corrosive environment. Corrosion defects are usually detected by periodical inspections using in-line inspection (ILI) methods. In order to ensure pipeline safety, this study develops a cost-effective maintenance strategy that consists of three aspects: corrosion growth model development using ILI data, time-dependent performance evaluation, and optimal inspection interval determination. In particular, the proposed study is applied to a cathodic protected buried steel pipeline located in Mexico. First, time-dependent power-law formulation is adopted to probabilistically characterize growth of the maximum depth and length of the external corrosion defects. Dependency between defect depth and length are considered in the model development and generation of the corrosion defects over time is characterized by the homogenous Poisson process. The growth models unknown parameters are evaluated based on the ILI data through the Bayesian updating method with Markov Chain Monte Carlo (MCMC) simulation technique. The proposed corrosion growth models can be used when either matched or non-matched defects are available, and have ability to consider newly generated defects since last inspection. Results of this part of study show that both depth and length growth models can predict damage quantities reasonably well and a strong correlation between defect depth and length is found. Next, time-dependent system failure probabilities are evaluated using developed corrosion growth models considering prevailing uncertainties where three failure modes, namely small leak, large leak and rupture are considered. Performance of the pipeline is evaluated through failure probability per km (or called a sub-system) where each subsystem is considered as a series system of detected and newly generated defects within that sub

Stiffened Composite Fuselage Barrel Optimization

Science.gov (United States)

Movva, R. G.; Mittal, A.; Agrawal, K.; Upadhyay, C. S.

2012-07-01

In a typical commercial transport aircraft, Stiffened skin panels and frames contribute around 40% of the fuselage weight. In the current study a stiffened composite fuselage skin panel optimization engine is developed for optimization of the layups of composite panels and stringers using Genetic Algorithm (GA). The skin and stringers of the fuselage section are optimized for the strength and the stability requirements. The selection of the GA parameters considered for the optimization is arrived by performing case studies on selected problems. The optimization engine facilitates in carrying out trade studies for selection of the optimum ply layup and material combination for the configuration being analyzed. The optimization process is applied on a sample model and the results are presented.

Optimization experiments on the study of giant resonance in nuclei

International Nuclear Information System (INIS)

Lyubarskij, G.Ya.; Savitskij, G.A.; Fartushnyj, V.A.; Khazhmuradov, M.A.; Levandovskij, S.P.

1988-01-01

Optimum choice of the target exposure to a beam in experiments on the study of giant resonances in nuclei is considered. Optimization is aimed at reducing mean square errors of defined formfactors. Four different optimization quality criteria - variances of four form factor experimental values are considered. Variances resulting form optimization are 1.5-2 times as less as variances in real experiment. The effect of experiment design optimization criterion on form factors determination errors is ascertained. 1 ref.; 3 tabs

Optimal Responsible Investment

DEFF Research Database (Denmark)

Jessen, Pernille

The paper studies retail Socially Responsible Investment and portfolio allocation. It extends conventional portfolio theory by allowing for a personal value based investment decision. When preferences for responsibility enter the framework for mean-variance analysis, it yields an optimal...... responsible investment model. An example of index investing illustrates the theory. Results show that it is crucial for the responsible investor to consider portfolio risk, expected return, and responsibility simultaneously in order to obtain an optimal portfolio. The model enables responsible investors...

Life Cycle Network Modeling Framework and Solution Algorithms for Systems Analysis and Optimization of the Water-Energy Nexus

Directory of Open Access Journals (Sweden)

Daniel J. Garcia

2015-07-01

Full Text Available The water footprint of energy systems must be considered, as future water scarcity has been identified as a major concern. This work presents a general life cycle network modeling and optimization framework for energy-based products and processes using a functional unit of liters of water consumed in the processing pathway. We analyze and optimize the water-energy nexus over the objectives of water footprint minimization, maximization of economic output per liter of water consumed (economic efficiency of water, and maximization of energy output per liter of water consumed (energy efficiency of water. A mixed integer, multiobjective nonlinear fractional programming (MINLFP model is formulated. A mixed integer linear programing (MILP-based branch and refine algorithm that incorporates both the parametric algorithm and nonlinear programming (NLP subproblems is developed to boost solving efficiency. A case study in bioenergy is presented, and the water footprint is considered from biomass cultivation to biofuel production, providing a novel perspective into the consumption of water throughout the value chain. The case study, optimized successively over the three aforementioned objectives, utilizes a variety of candidate biomass feedstocks to meet primary fuel products demand (ethanol, diesel, and gasoline. A minimum water footprint of 55.1 ML/year was found, economic efficiencies of water range from −$1.31/L to $0.76/L, and energy efficiencies of water ranged from 15.32 MJ/L to 27.98 MJ/L. These results show optimization provides avenues for process improvement, as reported values for the energy efficiency of bioethanol range from 0.62 MJ/L to 3.18 MJ/L. Furthermore, the proposed solution approach was shown to be an order of magnitude more efficient than directly solving the original MINLFP problem with general purpose solvers.

Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality.

Science.gov (United States)

Otero-Muras, Irene; Banga, Julio R

2017-07-21

In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.

Constrained optimization via simulation models for new product innovation

Science.gov (United States)

Pujowidianto, Nugroho A.

2017-11-01

We consider the problem of constrained optimization where the decision makers aim to optimize the primary performance measure while constraining the secondary performance measures. This paper provides a brief overview of stochastically constrained optimization via discrete event simulation. Most review papers tend to be methodology-based. This review attempts to be problem-based as decision makers may have already decided on the problem formulation. We consider constrained optimization models as there are usually constraints on secondary performance measures as trade-off in new product development. It starts by laying out different possible methods and the reasons using constrained optimization via simulation models. It is then followed by the review of different simulation optimization approach to address constrained optimization depending on the number of decision variables, the type of constraints, and the risk preferences of the decision makers in handling uncertainties.

A novel approach for optimal chiller loading using particle swarm optimization

Energy Technology Data Exchange (ETDEWEB)

Ardakani, A. Jahanbani; Ardakani, F. Fattahi; Hosseinian, S.H. [Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez Avenue, Tehran 15875-4413 (Iran, Islamic Republic of)

2008-07-01

This study employs two new methods to solve optimal chiller loading (OCL) problem. These methods are continuous genetic algorithm (GA) and particle swarm optimization (PSO). Because of continuous nature of variables in OCL problem, continuous GA and PSO easily overcome deficiencies in other conventional optimization methods. Partial load ratio (PLR) of the chiller is chosen as the variable to be optimized and consumption power of the chiller is considered as fitness function. Both of these methods find the optimal solution while the equality constraint is exactly satisfied. Some of the major advantages of proposed approaches over other conventional methods can be mentioned as fast convergence, escaping from getting into local optima, simple implementation as well as independency of the solution from the problem. Abilities of proposed methods are examined with reference to an example system. To demonstrate these abilities, results are compared with binary genetic algorithm method. The proposed approaches can be perfectly applied to air-conditioning systems. (author)

Equipment cost optimization

International Nuclear Information System (INIS)

Ribeiro, E.M.; Farias, M.A.; Dreyer, S.R.B.

1995-01-01

Considering the importance of the cost of material and equipment in the overall cost profile of an oil company, which in the case of Petrobras, represents approximately 23% of the total operational cost or 10% of the sales, an organization for the optimization of such costs has been established within Petrobras. Programs are developed aiming at: optimization of life-cycle cost of material and equipment; optimization of industrial processes costs through material development. This paper describes the methodology used in the management of the development programs and presents some examples of concluded and ongoing programs, which are conducted in permanent cooperation with suppliers, technical laboratories and research institutions and have been showing relevant results

Process optimization of friction stir welding based on thermal models

DEFF Research Database (Denmark)

Larsen, Anders Astrup

2010-01-01

This thesis investigates how to apply optimization methods to numerical models of a friction stir welding process. The work is intended as a proof-of-concept using different methods that are applicable to models of high complexity, possibly with high computational cost, and without the possibility...... information of the high-fidelity model. The optimization schemes are applied to stationary thermal models of differing complexity of the friction stir welding process. The optimization problems considered are based on optimizing the temperature field in the workpiece by finding optimal translational speed....... Also an optimization problem based on a microstructure model is solved, allowing the hardness distribution in the plate to be optimized. The use of purely thermal models represents a simplification of the real process; nonetheless, it shows the applicability of the optimization methods considered...