Optimization of Automotive Suspension System by Design of Experiments: A Nonderivative Method

Directory of Open Access Journals (Sweden)

Anirban C. Mitra

2016-01-01

Full Text Available A lot of health issues like low back pain, digestive disorders, and musculoskeletal disorders are caused as a result of the whole body vibrations induced by automobiles. This paper is concerned with the enhancement and optimization of suspension performance by using factorial methods of Design of Experiments, a nonderivative method. It focuses on the optimization of ride comfort and determining the parameters which affect the suspension behavior significantly as per the guidelines stated in ISO 2631-1:1997 standards. A quarter car test rig integrated with a LabVIEW based data acquisition system was developed to understand the real time behavior of a vehicle. In the pilot experiment, only three primary suspension parameters, that is, spring-stiffness, damping, and sprung mass, were considered and the full factorial method was implemented for the purpose of optimization. But the regression analysis of the data obtained rendered a very low goodness of fit which indicated that other parameters are likely to influence the response. Subsequently, steering geometry angles, camber and toe and tire pressure, were included in the design. Fractional factorial method with six factors was implemented to optimize ride comfort. The resultant optimum combination was then verified on the test rig with high correlation.

Optimal Design and Model Validation for Combustion Experiments in a Shock Tube

KAUST Repository

Long, Quan

2014-01-06

We develop a Bayesian framework for the optimal experimental design of the shock tube experiments which are being carried out at the KAUST Clean Combustion Center. The unknown parameters are the pre-exponential parameters and the activation energies in the reaction rate functions. The control parameters are the initial hydrogen concentration and the temperature. First, we build a polynomial based surrogate model for the observable related to the reactions in the shock tube. Second, we use a novel MAP based approach to estimate the expected information gain in the proposed experiments and select the best experimental set-ups corresponding to the optimal expected information gains. Third, we use the synthetic data to carry out virtual validation of our methodology.

I-optimal mixture designs

OpenAIRE

GOOS, Peter; JONES, Bradley; SYAFITRI, Utami

2013-01-01

In mixture experiments, the factors under study are proportions of the ingredients of a mixture. The special nature of the factors in a mixture experiment necessitates specific types of regression models, and specific types of experimental designs. Although mixture experiments usually are intended to predict the response(s) for all possible formulations of the mixture and to identify optimal proportions for each of the ingredients, little research has been done concerning their I-optimal desi...

Media milling process optimization for manufacture of drug nanoparticles using design of experiments (DOE).

Science.gov (United States)

Nekkanti, Vijaykumar; Marwah, Ashwani; Pillai, Raviraj

2015-01-01

Design of experiments (DOE), a component of Quality by Design (QbD), is systematic and simultaneous evaluation of process variables to develop a product with predetermined quality attributes. This article presents a case study to understand the effects of process variables in a bead milling process used for manufacture of drug nanoparticles. Experiments were designed and results were computed according to a 3-factor, 3-level face-centered central composite design (CCD). The factors investigated were motor speed, pump speed and bead volume. Responses analyzed for evaluating these effects and interactions were milling time, particle size and process yield. Process validation batches were executed using the optimum process conditions obtained from software Design-Expert® to evaluate both the repeatability and reproducibility of bead milling technique. Milling time was optimized to process variables by applying DOE resulted in considerable decrease in milling time to achieve the desired particle size. The study indicates the applicability of DOE approach to optimize critical process parameters in the manufacture of drug nanoparticles.

Optimization of process parameters for a quasi-continuous tablet coating system using design of experiments.

Science.gov (United States)

Cahyadi, Christine; Heng, Paul Wan Sia; Chan, Lai Wah

2011-03-01

The aim of this study was to identify and optimize the critical process parameters of the newly developed Supercell quasi-continuous coater for optimal tablet coat quality. Design of experiments, aided by multivariate analysis techniques, was used to quantify the effects of various coating process conditions and their interactions on the quality of film-coated tablets. The process parameters varied included batch size, inlet temperature, atomizing pressure, plenum pressure, spray rate and coating level. An initial screening stage was carried out using a 2(6-1(IV)) fractional factorial design. Following these preliminary experiments, optimization study was carried out using the Box-Behnken design. Main response variables measured included drug-loading efficiency, coat thickness variation, and the extent of tablet damage. Apparent optimum conditions were determined by using response surface plots. The process parameters exerted various effects on the different response variables. Hence, trade-offs between individual optima were necessary to obtain the best compromised set of conditions. The adequacy of the optimized process conditions in meeting the combined goals for all responses was indicated by the composite desirability value. By using response surface methodology and optimization, coating conditions which produced coated tablets of high drug-loading efficiency, low incidences of tablet damage and low coat thickness variation were defined. Optimal conditions were found to vary over a large spectrum when different responses were considered. Changes in processing parameters across the design space did not result in drastic changes to coat quality, thereby demonstrating robustness in the Supercell coating process. © 2010 American Association of Pharmaceutical Scientists

D- and I-optimal design of mixture experiments in the presence of ingredient availability constraints

OpenAIRE

SYAFITRI, Utami; SARTONO, Bagus; GOOS, Peter

2015-01-01

Mixture experiments usually involve various constraints on the proportions of the ingredients of the mixture under study. In this paper, inspired by the fact that the available stock of certain ingredients is often limited, we focus on a new type of constraint, which we refer to as an ingredient availability constraint. This type of constraint substantially complicates the search for optimal designs for mixture experiments. One difficulty, for instance, is that the optimal number of experimen...

Portfolio optimization using Mixture Design of Experiments. Scheduling trades within electricity markets

International Nuclear Information System (INIS)

Oliveira, Francisco Alexandre de; Paiva, Anderson Paulo de; Lima, Jose Wanderley Marangon; Balestrassi, Pedro Paulo; Mendes, Rona Rinston Amaury

2011-01-01

Deregulation of the electricity sector has given rise to several approaches to defining optimal portfolios of energy contracts. Financial tools - requiring substantial adjustments - are usually used to determine risk and return. This article presents a novel approach to adjusting the conditional value at risk (CVaR) metric to the mix of contracts on the energy markets; the approach uses Mixture Design of Experiments (MDE). In this kind of experimental strategy, the design factors are treated as proportions in a mixture system considered quite adequate for treating portfolios in general. Instead of using traditional linear programming, the concept of desirability function is here used to combine the multi-response, nonlinear objective functions for mean with the variance of a specific portfolio obtained through MDE. The maximization of the desirability function is implied in the portfolio optimization, generating an efficient recruitment frontier. This approach offers three main contributions: it includes risk aversion in the optimization routine, it assesses interaction between contracts, and it lessens the computational effort required to solve the constrained nonlinear optimization problem. A case study based on the Brazilian energy market is used to illustrate the proposal. The numerical results verify the proposal's adequacy. (author)

Portfolio optimization using Mixture Design of Experiments. Scheduling trades within electricity markets

Energy Technology Data Exchange (ETDEWEB)

Oliveira, Francisco Alexandre de; Paiva, Anderson Paulo de; Lima, Jose Wanderley Marangon; Balestrassi, Pedro Paulo; Mendes, Rona Rinston Amaury [Federal Univ. of Itajuba, Minas Gerais (Brazil)

2011-01-15

Deregulation of the electricity sector has given rise to several approaches to defining optimal portfolios of energy contracts. Financial tools - requiring substantial adjustments - are usually used to determine risk and return. This article presents a novel approach to adjusting the conditional value at risk (CVaR) metric to the mix of contracts on the energy markets; the approach uses Mixture Design of Experiments (MDE). In this kind of experimental strategy, the design factors are treated as proportions in a mixture system considered quite adequate for treating portfolios in general. Instead of using traditional linear programming, the concept of desirability function is here used to combine the multi-response, nonlinear objective functions for mean with the variance of a specific portfolio obtained through MDE. The maximization of the desirability function is implied in the portfolio optimization, generating an efficient recruitment frontier. This approach offers three main contributions: it includes risk aversion in the optimization routine, it assesses interaction between contracts, and it lessens the computational effort required to solve the constrained nonlinear optimization problem. A case study based on the Brazilian energy market is used to illustrate the proposal. The numerical results verify the proposal's adequacy. (author)

Validation of scaffold design optimization in bone tissue engineering: finite element modeling versus designed experiments.

Science.gov (United States)

Uth, Nicholas; Mueller, Jens; Smucker, Byran; Yousefi, Azizeh-Mitra

2017-02-21

This study reports the development of biological/synthetic scaffolds for bone tissue engineering (TE) via 3D bioplotting. These scaffolds were composed of poly(L-lactic-co-glycolic acid) (PLGA), type I collagen, and nano-hydroxyapatite (nHA) in an attempt to mimic the extracellular matrix of bone. The solvent used for processing the scaffolds was 1,1,1,3,3,3-hexafluoro-2-propanol. The produced scaffolds were characterized by scanning electron microscopy, microcomputed tomography, thermogravimetric analysis, and unconfined compression test. This study also sought to validate the use of finite-element optimization in COMSOL Multiphysics for scaffold design. Scaffold topology was simplified to three factors: nHA content, strand diameter, and strand spacing. These factors affect the ability of the scaffold to bear mechanical loads and how porous the structure can be. Twenty four scaffolds were constructed according to an I-optimal, split-plot designed experiment (DE) in order to generate experimental models of the factor-response relationships. Within the design region, the DE and COMSOL models agreed in their recommended optimal nHA (30%) and strand diameter (460 μm). However, the two methods disagreed by more than 30% in strand spacing (908 μm for DE; 601 μm for COMSOL). Seven scaffolds were 3D-bioplotted to validate the predictions of DE and COMSOL models (4.5-9.9 MPa measured moduli). The predictions for these scaffolds showed relative agreement for scaffold porosity (mean absolute percentage error of 4% for DE and 13% for COMSOL), but were substantially poorer for scaffold modulus (51% for DE; 21% for COMSOL), partly due to some simplifying assumptions made by the models. Expanding the design region in future experiments (e.g., higher nHA content and strand diameter), developing an efficient solvent evaporation method, and exerting a greater control over layer overlap could allow developing PLGA-nHA-collagen scaffolds to meet the mechanical requirements for

Accuracy optimization of high-speed AFM measurements using Design of Experiments

DEFF Research Database (Denmark)

Tosello, Guido; Marinello, F.; Hansen, Hans Nørgaard

2010-01-01

Atomic Force Microscopy (AFM) is being increasingly employed in industrial micro/nano manufacturing applications and integrated into production lines. In order to achieve reliable process and product control at high measuring speed, instrument optimization is needed. Quantitative AFM measurement...... results are influenced by a number of scan settings parameters, defining topography sampling and measurement time: resolution (number of profiles and points per profile), scan range and direction, scanning force and speed. Such parameters are influencing lateral and vertical accuracy and, eventually......, the estimated dimensions of measured features. The definition of scan settings is based on a comprehensive optimization that targets maximization of information from collected data and minimization of measurement uncertainty and scan time. The Design of Experiments (DOE) technique is proposed and applied...

Design and optimization of reverse-transcription quantitative PCR experiments.

Science.gov (United States)

Tichopad, Ales; Kitchen, Rob; Riedmaier, Irmgard; Becker, Christiane; Ståhlberg, Anders; Kubista, Mikael

2009-10-01

Quantitative PCR (qPCR) is a valuable technique for accurately and reliably profiling and quantifying gene expression. Typically, samples obtained from the organism of study have to be processed via several preparative steps before qPCR. We estimated the errors of sample withdrawal and extraction, reverse transcription (RT), and qPCR that are introduced into measurements of mRNA concentrations. We performed hierarchically arranged experiments with 3 animals, 3 samples, 3 RT reactions, and 3 qPCRs and quantified the expression of several genes in solid tissue, blood, cell culture, and single cells. A nested ANOVA design was used to model the experiments, and relative and absolute errors were calculated with this model for each processing level in the hierarchical design. We found that intersubject differences became easily confounded by sample heterogeneity for single cells and solid tissue. In cell cultures and blood, the noise from the RT and qPCR steps contributed substantially to the overall error because the sampling noise was less pronounced. We recommend the use of sample replicates preferentially to any other replicates when working with solid tissue, cell cultures, and single cells, and we recommend the use of RT replicates when working with blood. We show how an optimal sampling plan can be calculated for a limited budget. .

Design of Experiments: Optimizing the Polycarboxylation/Functionalization of Tungsten Disulfide Nanotubes

Directory of Open Access Journals (Sweden)

Daniel Raichman

2014-08-01

Full Text Available Design of experiments (DOE methodology was used to identify and optimize factors that influence the degree of functionalization (polycarboxylation of WS2 INTs via a modified acidic Vilsmeier–Haack reagent. The six factors investigated were reaction time, temperature and the concentrations of 2-bromoacetic acid, WS2 INTs, silver acetate and DMF. The significance of each factor and the associated interactive effects were evaluated using a two-level factorial statistical design in conjunction with statistical software (MiniTab® 16 based on quadratic programming. Although statistical analysis indicated that no factors were statistically significant, time, temperature and concentration of silver acetate were found to be the most important contributors to obtaining maximum functionalization/carboxylation. By examining contour plots and interaction plots, it was determined that optimal functionalization is obtained in a temperature range of 115–120 °C with a reaction time of 54 h using a mixture of 6 mL DMF, 200 mg INTs, 800 mg 2-bromoacetic acid and 60 mg silver acetate.

Optimized Experiment Design for Marine Systems Identification

DEFF Research Database (Denmark)

Blanke, M.; Knudsen, Morten

1999-01-01

Simulation of maneuvring and design of motion controls for marine systems require non-linear mathematical models, which often have more than one-hundred parameters. Model identification is hence an extremely difficult task. This paper discusses experiment design for marine systems identification...... and proposes a sensitivity approach to solve the practical experiment design problem. The applicability of the sensitivity approach is demonstrated on a large non-linear model of surge, sway, roll and yaw of a ship. The use of the method is illustrated for a container-ship where both model and full-scale tests...

A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments

KAUST Repository

Harman, Radoslav; Filová , Lenka; Richtarik, Peter

2018-01-01

We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to the performance of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality that also has applications beyond experimental design, such as the construction of the minimum volume ellipsoid containing a given set of data-points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality. These formulas enable one to use REX for computing A-optimal and I-optimal designs.

A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments

KAUST Repository

Harman, Radoslav

2018-01-17

We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to the performance of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality that also has applications beyond experimental design, such as the construction of the minimum volume ellipsoid containing a given set of data-points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality. These formulas enable one to use REX for computing A-optimal and I-optimal designs.

Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments

NARCIS (Netherlands)

Kleijnen, J.P.C.

1995-01-01

This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for

Design optimization of condenser microphone: a design of experiment perspective.

Science.gov (United States)

Tan, Chee Wee; Miao, Jianmin

2009-06-01

A well-designed condenser microphone backplate is very important in the attainment of good frequency response characteristics--high sensitivity and wide bandwidth with flat response--and low mechanical-thermal noise. To study the design optimization of the backplate, a 2(6) factorial design with a single replicate, which consists of six backplate parameters and four responses, has been undertaken on a comprehensive condenser microphone model developed by Zuckerwar. Through the elimination of insignificant parameters via normal probability plots of the effect estimates, the projection of an unreplicated factorial design into a replicated one can be performed to carry out an analysis of variance on the factorial design. The air gap and slot have significant effects on the sensitivity, mechanical-thermal noise, and bandwidth while the slot/hole location interaction has major influence over the latter two responses. An organized and systematic approach of designing the backplate is summarized.

Experiments Planning, Analysis, and Optimization

CERN Document Server

Wu, C F Jeff

2011-01-01

Praise for the First Edition: "If you . . . want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library."-Journal of the American Statistical Association Fully updated to reflect the major progress in the use of statistically designed experiments for product and process improvement, Experiments, Second Edition introduces some of the newest discoveries-and sheds further light on existing ones-on the design and analysis of experiments and their applications in system optimization, robustness, and tre

Experiment design for identification of structured linear systems

NARCIS (Netherlands)

Potters, M.G.

2016-01-01

Experiment Design for system identification involves the design of an optimal input signal with the purpose of accurately estimating unknown parameters in a system. Specifically, in the Least-Costly Experiment Design (LCED) framework, the optimal input signal results from an optimisation problem in

Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments.

Science.gov (United States)

Hecht, Elizabeth S; Oberg, Ann L; Muddiman, David C

2016-05-01

Mass spectrometry (MS) has emerged as a tool that can analyze nearly all classes of molecules, with its scope rapidly expanding in the areas of post-translational modifications, MS instrumentation, and many others. Yet integration of novel analyte preparatory and purification methods with existing or novel mass spectrometers can introduce new challenges for MS sensitivity. The mechanisms that govern detection by MS are particularly complex and interdependent, including ionization efficiency, ion suppression, and transmission. Performance of both off-line and MS methods can be optimized separately or, when appropriate, simultaneously through statistical designs, broadly referred to as "design of experiments" (DOE). The following review provides a tutorial-like guide into the selection of DOE for MS experiments, the practices for modeling and optimization of response variables, and the available software tools that support DOE implementation in any laboratory. This review comes 3 years after the latest DOE review (Hibbert DB, 2012), which provided a comprehensive overview on the types of designs available and their statistical construction. Since that time, new classes of DOE, such as the definitive screening design, have emerged and new calls have been made for mass spectrometrists to adopt the practice. Rather than exhaustively cover all possible designs, we have highlighted the three most practical DOE classes available to mass spectrometrists. This review further differentiates itself by providing expert recommendations for experimental setup and defining DOE entirely in the context of three case-studies that highlight the utility of different designs to achieve different goals. A step-by-step tutorial is also provided.

Optimal Experimental Design of Furan Shock Tube Kinetic Experiments

KAUST Repository

Kim, Daesang

2015-01-07

A Bayesian optimal experimental design methodology has been developed and applied to refine the rate coefficients of elementary reactions in Furan combustion. Furans are considered as potential renewable fuels. We focus on the Arrhenius rates of Furan + OH ↔ Furyl-2 + H2O and Furan ↔ OH Furyl-3 + H2O, and rely on the OH consumption rate as experimental observable. A polynomial chaos surrogate is first constructed using an adaptive pseudo-spectral projection algorithm. The PC surrogate is then exploited in conjunction with a fast estimation of the expected information gain in order to determine the optimal design in the space of initial temperatures and OH concentrations.

Review of design optimization methods for turbomachinery aerodynamics

Science.gov (United States)

Li, Zhihui; Zheng, Xinqian

2017-08-01

In today's competitive environment, new turbomachinery designs need to be not only more efficient, quieter, and ;greener; but also need to be developed at on much shorter time scales and at lower costs. A number of advanced optimization strategies have been developed to achieve these requirements. This paper reviews recent progress in turbomachinery design optimization to solve real-world aerodynamic problems, especially for compressors and turbines. This review covers the following topics that are important for optimizing turbomachinery designs. (1) optimization methods, (2) stochastic optimization combined with blade parameterization methods and the design of experiment methods, (3) gradient-based optimization methods for compressors and turbines and (4) data mining techniques for Pareto Fronts. We also present our own insights regarding the current research trends and the future optimization of turbomachinery designs.

Optimal experiment design in a filtering context with application to sampled network data

OpenAIRE

Singhal, Harsh; Michailidis, George

2010-01-01

We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking network flow volumes using sampled data, where the design variable corresponds to controlling the sampling rate. The optimal design is numerically compared to a myopic and a naive strategy. Finally, w...

Evaluation and optimization of hepatocyte culture media factors by design of experiments (DoE) methodology.

Science.gov (United States)

Dong, Jia; Mandenius, Carl-Fredrik; Lübberstedt, Marc; Urbaniak, Thomas; Nüssler, Andreas K N; Knobeloch, Daniel; Gerlach, Jörg C; Zeilinger, Katrin

2008-07-01

Optimization of cell culture media based on statistical experimental design methodology is a widely used approach for improving cultivation conditions. We applied this methodology to refine the composition of an established culture medium for growth of a human hepatoma cell line, C3A. A selection of growth factors and nutrient supplements were systematically screened according to standard design of experiments (DoE) procedures. The results of the screening indicated that the medium additives hepatocyte growth factor, oncostatin M, and fibroblast growth factor 4 significantly influenced the metabolic activities of the C3A cell line. Surface response methodology revealed that the optimum levels for these factors were 30 ng/ml for hepatocyte growth factor and 35 ng/ml for oncostatin M. Additional experiments on primary human hepatocyte cultures showed high variance in metabolic activities between cells from different individuals, making determination of optimal levels of factors more difficult. Still, it was possible to conclude that hepatocyte growth factor, epidermal growth factor, and oncostatin M had decisive effects on the metabolic functions of primary human hepatocytes.

Optimization of methylene blue removal by stable emulsified liquid membrane using Plackett-Burman and Box-Behnken designs of experiments.

Science.gov (United States)

Djenouhat, Meriem; Bendebane, Farida; Bahloul, Lynda; Samar, Mohamed E H; Ismail, Fadhel

2018-02-01

The stability of an emulsified liquid membrane composed of Span80 as a surfactant, D2EHPA as an extractant and sulfuric acid as an internal phase was first studied according to different diluents and many operating parameters using the Plackett-Burman design of experiments. Then the removal of methylene blue from an aqueous solution has been carried out using this emulsified liquid membrane at its stability conditions. The effects of operating parameters were analysed from the Box-Behnken design of experiments. The optimization of the extraction has been realized applying the response surface methodology and the results showed that the dye extraction yielding 98.72% was achieved at optimized conditions.

Optimal design criteria - prediction vs. parameter estimation

Science.gov (United States)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

Design and Optimization of Reverse-Transcription Quantitative PCR Experiments

Czech Academy of Sciences Publication Activity Database

Tichopád, A.; Kitchen, R.; Riedmaier, I.; Becker, Ch.; Ståhlberg, A.; Kubista, Mikael

2009-01-01

Roč. 55, č. 10 (2009), s. 1816-1823 ISSN 0009-9147 Institutional research plan: CEZ:AV0Z50520701 Keywords : Design * optimization * RT qPCR Subject RIV: EG - Zoology Impact factor: 6.263, year: 2009

Optimization of methylene blue removal by stable emulsified liquid membrane using Plackett–Burman and Box–Behnken designs of experiments

Science.gov (United States)

Djenouhat, Meriem; Bendebane, Farida; Bahloul, Lynda; Samar, Mohamed E. H.

2018-01-01

The stability of an emulsified liquid membrane composed of Span80 as a surfactant, D2EHPA as an extractant and sulfuric acid as an internal phase was first studied according to different diluents and many operating parameters using the Plackett–Burman design of experiments. Then the removal of methylene blue from an aqueous solution has been carried out using this emulsified liquid membrane at its stability conditions. The effects of operating parameters were analysed from the Box–Behnken design of experiments. The optimization of the extraction has been realized applying the response surface methodology and the results showed that the dye extraction yielding 98.72% was achieved at optimized conditions. PMID:29515841

Efficiency Improvements of Antenna Optimization Using Orthogonal Fractional Experiments

Directory of Open Access Journals (Sweden)

Yen-Sheng Chen

2015-01-01

Full Text Available This paper presents an extremely efficient method for antenna design and optimization. Traditionally, antenna optimization relies on nature-inspired heuristic algorithms, which are time-consuming due to their blind-search nature. In contrast, design of experiments (DOE uses a completely different framework from heuristic algorithms, reducing the design cycle by formulating the surrogates of a design problem. However, the number of required simulations grows exponentially if a full factorial design is used. In this paper, a much more efficient technique is presented to achieve substantial time savings. By using orthogonal fractional experiments, only a small subset of the full factorial design is required, yet the resultant response surface models are still effective. The capability of orthogonal fractional experiments is demonstrated through three examples, including two tag antennas for radio-frequency identification (RFID applications and one internal antenna for long-term-evolution (LTE handheld devices. In these examples, orthogonal fractional experiments greatly improve the efficiency of DOE, thereby facilitating the antenna design with less simulation runs.

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

Integrated topology and shape optimization in structural design

Science.gov (United States)

Bremicker, M.; Chirehdast, M.; Kikuchi, N.; Papalambros, P. Y.

1990-01-01

Structural optimization procedures usually start from a given design topology and vary its proportions or boundary shapes to achieve optimality under various constraints. Two different categories of structural optimization are distinguished in the literature, namely sizing and shape optimization. A major restriction in both cases is that the design topology is considered fixed and given. Questions concerning the general layout of a design (such as whether a truss or a solid structure should be used) as well as more detailed topology features (e.g., the number and connectivities of bars in a truss or the number of holes in a solid) have to be resolved by design experience before formulating the structural optimization model. Design quality of an optimized structure still depends strongly on engineering intuition. This article presents a novel approach for initiating formal structural optimization at an earlier stage, where the design topology is rigorously generated in addition to selecting shape and size dimensions. A three-phase design process is discussed: an optimal initial topology is created by a homogenization method as a gray level image, which is then transformed to a realizable design using computer vision techniques; this design is then parameterized and treated in detail by sizing and shape optimization. A fully automated process is described for trusses. Optimization of two dimensional solid structures is also discussed. Several application-oriented examples illustrate the usefulness of the proposed methodology.

Process optimization by use of design of experiments: Application for liposomalization of FK506.

Science.gov (United States)

Toyota, Hiroyasu; Asai, Tomohiro; Oku, Naoto

2017-05-01

Design of experiments (DoE) can accelerate the optimization of drug formulations, especially complexed formulas such as those of drugs, using delivery systems. Administration of FK506 encapsulated in liposomes (FK506 liposomes) is an effective approach to treat acute stroke in animal studies. To provide FK506 liposomes as a brain protective agent, it is necessary to manufacture these liposomes with good reproducibility. The objective of this study was to confirm the usefulness of DoE for the process-optimization study of FK506 liposomes. The Box-Behnken design was used to evaluate the effect of the process parameters on the properties of FK506 liposomes. The results of multiple regression analysis showed that there was interaction between the hydration temperature and the freeze-thaw cycle on both the particle size and encapsulation efficiency. An increase in the PBS hydration volume resulted in an increase in encapsulation efficiency. Process parameters had no effect on the ζ-potential. The multiple regression equation showed good predictability of the particle size and the encapsulation efficiency. These results indicated that manufacturing conditions must be taken into consideration to prepare liposomes with desirable properties. DoE would thus be promising approach to optimize the conditions for the manufacturing of liposomes. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimized beryllium target design for indirectly driven inertial confinement fusion experiments on the National Ignition Facility

Energy Technology Data Exchange (ETDEWEB)

Simakov, Andrei N., E-mail: simakov@lanl.gov; Wilson, Douglas C.; Yi, Sunghwan A.; Kline, John L.; Batha, Steven H. [Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, New Mexico 87545 (United States); Clark, Daniel S.; Milovich, Jose L.; Salmonson, Jay D. [Lawrence Livermore National Laboratory, P.O. Box 808, Livermore, California 94551 (United States)

2014-02-15

For indirect drive inertial confinement fusion, Beryllium (Be) ablators offer a number of important advantages as compared with other ablator materials, e.g., plastic and high density carbon. In particular, the low opacity and relatively high density of Be lead to higher rocket efficiencies giving a higher fuel implosion velocity for a given X-ray drive; and to higher ablation velocities providing more ablative stabilization and reducing the effect of hydrodynamic instabilities on the implosion performance. Be ablator advantages provide a larger target design optimization space and can significantly improve the National Ignition Facility (NIF) [J. D. Lindl et al., Phys. Plasmas 11, 339 (2004)] ignition margin. Herein, we summarize the Be advantages, briefly review NIF Be target history, and present a modern, optimized, low adiabat, Revision 6 NIF Be target design. This design takes advantage of knowledge gained from recent NIF experiments, including more realistic levels of laser-plasma energy backscatter, degraded hohlraum-capsule coupling, and the presence of cross-beam energy transfer.

The construction of optimal stated choice experiments theory and methods

CERN Document Server

Street, Deborah J

2007-01-01

The most comprehensive and applied discussion of stated choice experiment constructions available The Construction of Optimal Stated Choice Experiments provides an accessible introduction to the construction methods needed to create the best possible designs for use in modeling decision-making. Many aspects of the design of a generic stated choice experiment are independent of its area of application, and until now there has been no single book describing these constructions. This book begins with a brief description of the various areas where stated choice experiments are applicable, including marketing and health economics, transportation, environmental resource economics, and public welfare analysis. The authors focus on recent research results on the construction of optimal and near-optimal choice experiments and conclude with guidelines and insight on how to properly implement these results. Features of the book include: Construction of generic stated choice experiments for the estimation of main effects...

Surrogate models and optimal design of experiments for chemical kinetics applications

KAUST Repository

Bisetti, Fabrizio

2015-01-07

Kinetic models for reactive flow applications comprise hundreds of reactions describing the complex interaction among many chemical species. The detailed knowledge of the reaction parameters is a key component of the design cycle of next-generation combustion devices, which aim at improving conversion efficiency and reducing pollutant emissions. Shock tubes are a laboratory scale experimental configuration, which is widely used for the study of reaction rate parameters. Important uncertainties exist in the values of the thousands of parameters included in the most advanced kinetic models. This talk discusses the application of uncertainty quantification (UQ) methods to the analysis of shock tube data as well as the design of shock tube experiments. Attention is focused on a spectral framework in which uncertain inputs are parameterized in terms of canonical random variables, and quantities of interest (QoIs) are expressed in terms of a mean-square convergent series of orthogonal polynomials acting on these variables. We outline the implementation of a recent spectral collocation approach for determining the unknown coefficients of the expansion, namely using a sparse, adaptive pseudo-spectral construction that enables us to obtain surrogates for the QoIs accurately and efficiently. We first discuss the utility of the resulting expressions in quantifying the sensitivity of QoIs to uncertain inputs, and in the Bayesian inference key physical parameters from experimental measurements. We then discuss the application of these techniques to the analysis of shock-tube data and the optimal design of shock-tube experiments for two key reactions in combustion kinetics: the chain-brancing reaction H + O2 ←→ OH + O and the reaction of Furans with the hydroxyl radical OH.

Design optimization for cost and quality: The robust design approach

Science.gov (United States)

Unal, Resit

1990-01-01

Designing reliable, low cost, and operable space systems has become the key to future space operations. Designing high quality space systems at low cost is an economic and technological challenge to the designer. A systematic and efficient way to meet this challenge is a new method of design optimization for performance, quality, and cost, called Robust Design. Robust Design is an approach for design optimization. It consists of: making system performance insensitive to material and subsystem variation, thus allowing the use of less costly materials and components; making designs less sensitive to the variations in the operating environment, thus improving reliability and reducing operating costs; and using a new structured development process so that engineering time is used most productively. The objective in Robust Design is to select the best combination of controllable design parameters so that the system is most robust to uncontrollable noise factors. The robust design methodology uses a mathematical tool called an orthogonal array, from design of experiments theory, to study a large number of decision variables with a significantly small number of experiments. Robust design also uses a statistical measure of performance, called a signal-to-noise ratio, from electrical control theory, to evaluate the level of performance and the effect of noise factors. The purpose is to investigate the Robust Design methodology for improving quality and cost, demonstrate its application by the use of an example, and suggest its use as an integral part of space system design process.

Optimal Bayesian experimental design for priors of compact support with application to shock-tube experiments for combustion kinetics

KAUST Repository

Bisetti, Fabrizio

2016-01-12

The analysis of reactive systems in combustion science and technology relies on detailed models comprising many chemical reactions that describe the conversion of fuel and oxidizer into products and the formation of pollutants. Shock-tube experiments are a convenient setting for measuring the rate parameters of individual reactions. The temperature, pressure, and concentration of reactants are chosen to maximize the sensitivity of the measured quantities to the rate parameter of the target reaction. In this study, we optimize the experimental setup computationally by optimal experimental design (OED) in a Bayesian framework. We approximate the posterior probability density functions (pdf) using truncated Gaussian distributions in order to account for the bounded domain of the uniform prior pdf of the parameters. The underlying Gaussian distribution is obtained in the spirit of the Laplace method, more precisely, the mode is chosen as the maximum a posteriori (MAP) estimate, and the covariance is chosen as the negative inverse of the Hessian of the misfit function at the MAP estimate. The model related entities are obtained from a polynomial surrogate. The optimality, quantified by the information gain measures, can be estimated efficiently by a rejection sampling algorithm against the underlying Gaussian probability distribution, rather than against the true posterior. This approach offers a significant error reduction when the magnitude of the invariants of the posterior covariance are comparable to the size of the bounded domain of the prior. We demonstrate the accuracy and superior computational efficiency of our method for shock-tube experiments aiming to measure the model parameters of a key reaction which is part of the complex kinetic network describing the hydrocarbon oxidation. In the experiments, the initial temperature and fuel concentration are optimized with respect to the expected information gain in the estimation of the parameters of the target

Helium gas turbine conceptual design by genetic/gradient optimization

International Nuclear Information System (INIS)

Yang, Long; Yu, Suyuan

2003-01-01

Helium gas turbine is the key component of the power conversion system for direct cycle High Temperature Gas-cooled Reactors (HTGR), of which an optimal design is essential for high efficiency. Gas turbine design currently is a multidisciplinary process in which the relationships between constraints, objective functions and variables are very noisy. Due to the ever-increasing complexity of the process, it has becomes very hard for the engineering designer to foresee the consequences of changing certain parts. With classic design procedures which depend on adaptation to baseline design, this problem is usually averted by choosing a large number of design variables based on the engineer's judgment or experience in advance, then reaching a solution through iterative computation and modification. This, in fact, leads to a reduction of the degree of freedom of the design problem, and therefore to a suboptimal design. Furthermore, helium is very different in thermal properties from normal gases; it is uncertain whether the operation experiences of a normal gas turbine could be used in the conceptual design of a helium gas turbine. Therefore, it is difficult to produce an optimal design with the general method of adaptation to baseline. Since their appearance in the 1970s, Genetic algorithms (GAs) have been broadly used in many research fields due to their robustness. GAs have also been used recently in the design and optimization of turbo-machines. Researchers at the General Electronic Company (GE) developed an optimization software called Engineous, and used GAs in the basic design and optimization of turbines. The ITOP study group from Xi'an Transportation University also did some work on optimization of transonic turbine blades. However, since GAs do not have a rigorous theory base, many problems in utilities have arisen, such as premature convergence and uncertainty; the GA doesn't know how to locate the optimal design, and doesn't even know if the optimal solution

Sensor Placement via Optimal Experiment Design in EMI Sensing of Metallic Objects

Directory of Open Access Journals (Sweden)

Lin-Ping Song

2016-01-01

Full Text Available This work, under the optimal experimental design framework, investigates the sensor placement problem that aims to guide electromagnetic induction (EMI sensing of multiple objects. We use the linearized model covariance matrix as a measure of estimation error to present a sequential experimental design (SED technique. The technique recursively minimizes data misfit to update model parameters and maximizes an information gain function for a future survey relative to previous surveys. The fundamental process of the SED seeks to increase weighted sensitivities to targets when placing sensors. The synthetic and field experiments demonstrate that SED can be used to guide the sensing process for an effective interrogation. It also can serve as a theoretic basis to improve empirical survey operation. We further study the sensitivity of the SED to the number of objects within the sensing range. The tests suggest that an appropriately overrepresented model about expected anomalies might be a feasible choice.

Optimal mixture experiments

CERN Document Server

Sinha, B K; Pal, Manisha; Das, P

2014-01-01

The book dwells mainly on the optimality aspects of mixture designs. As mixture models are a special case of regression models, a general discussion on regression designs has been presented, which includes topics like continuous designs, de la Garza phenomenon, Loewner order domination, Equivalence theorems for different optimality criteria and standard optimality results for single variable polynomial regression and multivariate linear and quadratic regression models. This is followed by a review of the available literature on estimation of parameters in mixture models. Based on recent research findings, the volume also introduces optimal mixture designs for estimation of optimum mixing proportions in different mixture models, which include Scheffé’s quadratic model, Darroch-Waller model, log- contrast model, mixture-amount models, random coefficient models and multi-response model.  Robust mixture designs and mixture designs in blocks have been also reviewed. Moreover, some applications of mixture desig...

Robust optimization of the output voltage of nanogenerators by statistical design of experiments

KAUST Repository

Song, Jinhui

2010-09-01

Nanogenerators were first demonstrated by deflecting aligned ZnO nanowires using a conductive atomic force microscopy (AFM) tip. The output of a nanogenerator is affected by three parameters: tip normal force, tip scanning speed, and tip abrasion. In this work, systematic experimental studies have been carried out to examine the combined effects of these three parameters on the output, using statistical design of experiments. A statistical model has been built to analyze the data and predict the optimal parameter settings. For an AFM tip of cone angle 70° coated with Pt, and ZnO nanowires with a diameter of 50 nm and lengths of 600 nm to 1 μm, the optimized parameters for the nanogenerator were found to be a normal force of 137 nN and scanning speed of 40 μm/s, rather than the conventional settings of 120 nN for the normal force and 30 μm/s for the scanning speed. A nanogenerator with the optimized settings has three times the average output voltage of one with the conventional settings. © 2010 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

Robust optimization of the output voltage of nanogenerators by statistical design of experiments

KAUST Repository

Song, Jinhui; Xie, Huizhi; Wu, Wenzhuo; Roshan Joseph, V.; Jeff Wu, C. F.; Wang, Zhong Lin

2010-01-01

Nanogenerators were first demonstrated by deflecting aligned ZnO nanowires using a conductive atomic force microscopy (AFM) tip. The output of a nanogenerator is affected by three parameters: tip normal force, tip scanning speed, and tip abrasion. In this work, systematic experimental studies have been carried out to examine the combined effects of these three parameters on the output, using statistical design of experiments. A statistical model has been built to analyze the data and predict the optimal parameter settings. For an AFM tip of cone angle 70° coated with Pt, and ZnO nanowires with a diameter of 50 nm and lengths of 600 nm to 1 μm, the optimized parameters for the nanogenerator were found to be a normal force of 137 nN and scanning speed of 40 μm/s, rather than the conventional settings of 120 nN for the normal force and 30 μm/s for the scanning speed. A nanogenerator with the optimized settings has three times the average output voltage of one with the conventional settings. © 2010 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

Multidisciplinary Optimization Branch Experience Using iSIGHT Software

Science.gov (United States)

Padula, S. L.; Korte, J. J.; Dunn, H. J.; Salas, A. O.

1999-01-01

The Multidisciplinary Optimization (MDO) Branch at NASA Langley is investigating frameworks for supporting multidisciplinary analysis and optimization research. A framework provides software and system services to integrate computational tasks and allows the researcher to concentrate more on the application and less on the programming details. A framework also provides a common working environment and a full range of optimization tools, and so increases the productivity of multidisciplinary research teams. Finally, a framework enables staff members to develop applications for use by disciplinary experts in other organizations. This year, the MDO Branch has gained experience with the iSIGHT framework. This paper describes experiences with four aerospace applications, including: (1) reusable launch vehicle sizing, (2) aerospike nozzle design, (3) low-noise rotorcraft trajectories, and (4) acoustic liner design. Brief overviews of each problem are provided, including the number and type of disciplinary codes and computation time estimates. In addition, the optimization methods, objective functions, design variables, and constraints are described for each problem. For each case, discussions on the advantages and disadvantages of using the iSIGHT framework are provided as well as notes on the ease of use of various advanced features and suggestions for areas of improvement.

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 ...

A procedure for multi-objective optimization of tire design parameters

Directory of Open Access Journals (Sweden)

Nikola Korunović

2015-04-01

Full Text Available The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

Optimal design of condenser weight

International Nuclear Information System (INIS)

Zheng Jing; Yan Changqi; Wang Jianjun

2011-01-01

The condenser is an important component in nuclear power plants, which dimension and weight will effect the economical performance and the arrangement of the nuclear power plants. In this paper, the calculation model is established according to the design experience. The corresponding codes are also developed, and the sensitivity of design parameters which influence the condenser weight is analyzed. The present design optimization of the condenser, taking the weight minimization as the objective, is carried out with the self-developed complex-genetic algorithm. The results show that the reference condenser design is far from the best scheme, and also verify the feasibility of the complex-genetic algorithm. (authors)

Design -|+ Negative emotions for positive experiences

NARCIS (Netherlands)

Fokkinga, S.F.

2015-01-01

Experience-driven design considers all aspects of a product – its appearance, cultural meaning, functionality, interaction, usability, technology, and indirect consequences of use – with the aim to optimize and orchestrate all these aspects and create the best possible user experience. Since the

Systematic optimization of human pluripotent stem cells media using Design of Experiments

Science.gov (United States)

Marinho, Paulo A.; Chailangkarn, Thanathom; Muotri, Alysson R.

2015-05-01

Human pluripotent stem cells (hPSC) are used to study the early stages of human development in vitro and, increasingly due to somatic cell reprogramming, cellular and molecular mechanisms of disease. Cell culture medium is a critical factor for hPSC to maintain pluripotency and self-renewal. Numerous defined culture media have been empirically developed but never systematically optimized for culturing hPSC. We applied design of experiments (DOE), a powerful statistical tool, to improve the medium formulation for hPSC. Using pluripotency and cell growth as read-outs, we determined the optimal concentration of both basic fibroblast growth factor (bFGF) and neuregulin-1 beta 1 (NRG1β1). The resulting formulation, named iDEAL, improved the maintenance and passage of hPSC in both normal and stressful conditions, and affected trimethylated histone 3 lysine 27 (H3K27me3) epigenetic status after genetic reprogramming. It also enhances efficient hPSC plating as single cells. Altogether, iDEAL potentially allows scalable and controllable hPSC culture routine in translational research. Our DOE strategy could also be applied to hPSC differentiation protocols, which often require numerous and complex cell culture media.

Optimization of laccase production from Marasmiellus palmivorus LA1 by Taguchi method of Design of experiments.

Science.gov (United States)

Chenthamarakshan, Aiswarya; Parambayil, Nayana; Miziriya, Nafeesathul; Soumya, P S; Lakshmi, M S Kiran; Ramgopal, Anala; Dileep, Anuja; Nambisan, Padma

2017-02-13

Fungal laccase has profound applications in different fields of biotechnology due to its broad specificity and high redox potential. Any successful application of the enzyme requires large scale production. As laccase production is highly dependent on medium components and cultural conditions, optimization of the same is essential for efficient product production. Production of laccase by fungal strain Marasmiellus palmivorus LA1 under solid state fermentation was optimized by the Taguchi design of experiments (DOE) methodology. An orthogonal array (L8) was designed using Qualitek-4 software to study the interactions and relative influence of the seven selected factors by one factor at a time approach. The optimum condition formulated was temperature (28 °C), pH (5), galactose (0.8%w/v), cupric sulphate (3 mM), inoculum concentration (number of mycelial agar pieces) (6Nos.) and substrate length (0.05 m). Overall yield increase of 17.6 fold was obtained after optimization. Statistical optimization leads to the elimination of an insignificant medium component ammonium dihydrogen phosphate from the process and contributes to a 1.06 fold increase in enzyme production. A final production of 667.4 ± 13 IU/mL laccase activity paves way for the application of this strain for industrial applications. Study optimized lignin degrading laccases from Marasmiellus palmivorus LA1. This laccases can thus be used for further applications in different scales of production after analyzing the properties of the enzyme. Study also confirmed the use of taguchi method for optimizations of product production.

Diameter optimization of VLS-synthesized ZnO nanowires, using statistical design of experiment

International Nuclear Information System (INIS)

Shafiei, Sepideh; Nourbakhsh, Amirhasan; Ganjipour, Bahram; Zahedifar, Mostafa; Vakili-Nezhaad, Gholamreza

2007-01-01

The possibility of diameter optimization of ZnO nanowires by using statistical design of experiment (DoE) is investigated. In this study, nanowires were synthesized using a vapor-liquid-solid (VLS) growth method in a horizontal reactor. The effects of six synthesis parameters (synthesis time, synthesis temperature, thickness of gold layer, distance between ZnO holder and substrate, mass of ZnO and Ar flow rate) on the average diameter of a ZnO nanowire were examined using the fractional factorial design (FFD) coupled with response surface methodology (RSM). Using a 2 III 6-3 FFD, the main effects of the thickness of the gold layer, synthesis temperature and synthesis time were concluded to be the key factors influencing the diameter. Then Box-Behnken design (BBD) was exploited to create a response surface from the main factors. The total number of required runs for the DoE process is 25, 8 runs for FFD parameter screening and 17 runs for the response surface obtained by BBD. Three extra runs are done to confirm the predicted results

Morphology optimization of CCVD-synthesized multiwall carbon nanotubes, using statistical design of experiments

International Nuclear Information System (INIS)

Nourbakhsh, Amirhasan; Ganjipour, Bahram; Zahedifar, Mostafa; Arzi, Ezatollah

2007-01-01

The possibility of optimization of morphological features of multiwall carbon nanotubes (MWCNTs) using the statistical design of experiments (DoE) is investigated. In this study, MWCNTs were synthesized using a catalytic chemical vapour deposition (CCVD) method in a horizontal reactor using acetylene as the carbon source. The effects of six synthesis parameters (synthesis time, synthesis temperature, catalyst mass, reduction time, acetylene flow rate and hydrogen flow rate) on the average diameter and mean rectilinear length (MRL) of carbon nanotubes were examined using fractional-factorial design (FFD) coupled with response surface methodology (RSM). Using a 2 III 6-3 FFD, the main effects of reaction temperature, hydrogen flow rate and chemical reduction time were concluded to be the key factors influencing the diameter and MRL of MWCNTs; then Box-Behnken design (BBD) was exploited to create a response surface from the main factors. The total number of required runs is 26: 8 runs are for FFD parameter screening, 17 runs are for the response surface obtained by the BBD, and the final run is used to confirm the predicted results

Design and Optimization Method of a Two-Disk Rotor System

Science.gov (United States)

Huang, Jingjing; Zheng, Longxi; Mei, Qing

2016-04-01

An integrated analytical method based on multidisciplinary optimization software Isight and general finite element software ANSYS was proposed in this paper. Firstly, a two-disk rotor system was established and the mode, humorous response and transient response at acceleration condition were analyzed with ANSYS. The dynamic characteristics of the two-disk rotor system were achieved. On this basis, the two-disk rotor model was integrated to the multidisciplinary design optimization software Isight. According to the design of experiment (DOE) and the dynamic characteristics, the optimization variables, optimization objectives and constraints were confirmed. After that, the multi-objective design optimization of the transient process was carried out with three different global optimization algorithms including Evolutionary Optimization Algorithm, Multi-Island Genetic Algorithm and Pointer Automatic Optimizer. The optimum position of the two-disk rotor system was obtained at the specified constraints. Meanwhile, the accuracy and calculation numbers of different optimization algorithms were compared. The optimization results indicated that the rotor vibration reached the minimum value and the design efficiency and quality were improved by the multidisciplinary design optimization in the case of meeting the design requirements, which provided the reference to improve the design efficiency and reliability of the aero-engine rotor.

Multidisciplinary Design, Analysis, and Optimization Tool Development Using a Genetic Algorithm

Science.gov (United States)

Pak, Chan-gi; Li, Wesley

2009-01-01

Multidisciplinary design, analysis, and optimization using a genetic algorithm is being developed at the National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) to automate analysis and design process by leveraging existing tools to enable true multidisciplinary optimization in the preliminary design stage of subsonic, transonic, supersonic, and hypersonic aircraft. This is a promising technology, but faces many challenges in large-scale, real-world application. This report describes current approaches, recent results, and challenges for multidisciplinary design, analysis, and optimization as demonstrated by experience with the Ikhana fire pod design.!

Optimization methods in structural design

CERN Document Server

Rothwell, Alan

2017-01-01

This book offers an introduction to numerical optimization methods in structural design. Employing a readily accessible and compact format, the book presents an overview of optimization methods, and equips readers to properly set up optimization problems and interpret the results. A ‘how-to-do-it’ approach is followed throughout, with less emphasis at this stage on mathematical derivations. The book features spreadsheet programs provided in Microsoft Excel, which allow readers to experience optimization ‘hands-on.’ Examples covered include truss structures, columns, beams, reinforced shell structures, stiffened panels and composite laminates. For the last three, a review of relevant analysis methods is included. Exercises, with solutions where appropriate, are also included with each chapter. The book offers a valuable resource for engineering students at the upper undergraduate and postgraduate level, as well as others in the industry and elsewhere who are new to these highly practical techniques.Whi...

Optimization of the NIF ignition point design hohlraum

International Nuclear Information System (INIS)

Callahan, D A; Hinkel, D E; Berger, R L; Divol, L; Dixit, S N; Edwards, M J; Haan, S W; Jones, O S; Lindl, J D; Meezan, N B; Michel, P A; Pollaine, S M; Suter, L J; Town, R P J; Bradley, P A

2008-01-01

In preparation for the start of NIF ignition experiments, we have designed a porfolio of targets that span the temperature range that is consistent with initial NIF operations: 300 eV, 285 eV, and 270 eV. Because these targets are quite complicated, we have developed a plan for choosing the optimum hohlraum for the first ignition attempt that is based on this portfolio of designs coupled with early NIF experiements using 96 beams. These early experiments will measure the laser plasma instabilities of the candidate designs and will demonstrate our ability to tune symmetry in these designs. These experimental results, coupled with the theory and simulations that went into the designs, will allow us to choose the optimal hohlraum for the first NIF ignition attempt

Optimization of the NIF ignition point design hohlraum

Science.gov (United States)

Callahan, D. A.; Hinkel, D. E.; Berger, R. L.; Divol, L.; Dixit, S. N.; Edwards, M. J.; Haan, S. W.; Jones, O. S.; Lindl, J. D.; Meezan, N. B.; Michel, P. A.; Pollaine, S. M.; Suter, L. J.; Town, R. P. J.; Bradley, P. A.

2008-05-01

In preparation for the start of NIF ignition experiments, we have designed a porfolio of targets that span the temperature range that is consistent with initial NIF operations: 300 eV, 285 eV, and 270 eV. Because these targets are quite complicated, we have developed a plan for choosing the optimum hohlraum for the first ignition attempt that is based on this portfolio of designs coupled with early NIF experiements using 96 beams. These early experiments will measure the laser plasma instabilities of the candidate designs and will demonstrate our ability to tune symmetry in these designs. These experimental results, coupled with the theory and simulations that went into the designs, will allow us to choose the optimal hohlraum for the first NIF ignition attempt.

Optimizing Chromatographic Separation: An Experiment Using an HPLC Simulator

Science.gov (United States)

Shalliker, R. A.; Kayillo, S.; Dennis, G. R.

2008-01-01

Optimization of a chromatographic separation within the time constraints of a laboratory session is practically impossible. However, by employing a HPLC simulator, experiments can be designed that allow students to develop an appreciation of the complexities involved in optimization procedures. In the present exercise, a HPLC simulator from "JCE…

Design and Analysis of simulation experiments : Tutorial

NARCIS (Netherlands)

Kleijnen, J.P.C.

2017-01-01

This tutorial reviews the design and analysis of simulation experiments. These experiments may have various goals: validation, prediction, sensitivity analysis, optimization (possibly robust), and risk or uncertainty analysis. These goals may be realized through metamodels. Two types of metamodels

Application of design of experiments and artificial neural networks ...

African Journals Online (AJOL)

This paper discusses the use of Distance based optimal designs in the design of experiments (DOE) and artificial neural networks (ANN) in optimizing the stacking sequence for simply supported laminated composite plate under uniformly distributed load (UDL) for minimizing the deflections and stresses. A number of finite ...

Optimal preprocessing of serum and urine metabolomic data fusion for staging prostate cancer through design of experiment

International Nuclear Information System (INIS)

Zheng, Hong; Cai, Aimin; Zhou, Qi; Xu, Pengtao; Zhao, Liangcai; Li, Chen; Dong, Baijun; Gao, Hongchang

2017-01-01

Accurate classification of cancer stages will achieve precision treatment for cancer. Metabolomics presents biological phenotypes at the metabolite level and holds a great potential for cancer classification. Since metabolomic data can be obtained from different samples or analytical techniques, data fusion has been applied to improve classification accuracy. Data preprocessing is an essential step during metabolomic data analysis. Therefore, we developed an innovative optimization method to select a proper data preprocessing strategy for metabolomic data fusion using a design of experiment approach for improving the classification of prostate cancer (PCa) stages. In this study, urine and serum samples were collected from participants at five phases of PCa and analyzed using a 1 H NMR-based metabolomic approach. Partial least squares-discriminant analysis (PLS-DA) was used as a classification model and its performance was assessed by goodness of fit (R 2 ) and predictive ability (Q 2 ). Results show that data preprocessing significantly affect classification performance and depends on data properties. Using the fused metabolomic data from urine and serum, PLS-DA model with the optimal data preprocessing (R 2  = 0.729, Q 2  = 0.504, P < 0.0001) can effectively improve model performance and achieve a better classification result for PCa stages as compared with that without data preprocessing (R 2  = 0.139, Q 2  = 0.006, P = 0.450). Therefore, we propose that metabolomic data fusion integrated with an optimal data preprocessing strategy can significantly improve the classification of cancer stages for precision treatment. - Highlights: • NMR metabolomic analysis of body fluids can be used for staging prostate cancer. • Data preprocessing is an essential step for metabolomic analysis. • Data fusion improves information recovery for cancer classification. • Design of experiment achieves optimal preprocessing of metabolomic data fusion.

Recent experience with multidisciplinary analysis and optimization in advanced aircraft design

Science.gov (United States)

Dollyhigh, Samuel M.; Sobieszczanski-Sobieski, Jaroslaw

1990-01-01

The task of modern aircraft design has always been complicated due to the number of intertwined technical factors from the various engineering disciplines. Furthermore, this complexity has been rapidly increasing by the development of such technologies as aeroelasticity tailored materials and structures, active control systems, integrated propulsion/airframe controls, thrust vectoring, and so on. Successful designs that achieve maximum advantage from these new technologies require a thorough understanding of the physical phenomena and the interactions among these phenomena. A study commissioned by the Aeronautical Sciences and Evaluation Board of the National Research Council has gone so far as to identify technology integration as a new discipline from which many future aeronautical advancements will arise. Regardless of whether one considers integration as a new discipline or not, it is clear to all engineers involved in aircraft design and analysis that better methods are required. In the past, designers conducted parametric studies in which a relatively small number of principal characteristics were varied to determine the effect on design requirements which were themselves often diverse and contradictory. Once a design was chosen, it then passed through the various engineers' disciplines whose principal task was to make the chosen design workable. Working in a limited design space, the discipline expert sometimes improved the concept, but more often than not, the result was in the form of a penalty to make the original concept workable. If an insurmountable problem was encountered, the process began over. Most design systems that attempt to account for disciplinary interactions have large empirical elements and reliance on past experience is a poor guide in obtaining maximum utilizations of new technologies. Further compounding the difficulty of design is that as the aeronautical sciences have matured, the discipline specialist's area of research has generally

Environmental analytical chemistry: Design of experiments

International Nuclear Information System (INIS)

Sanchez Alonso, F.

1990-01-01

The design of experiments is needed any time a work on analysis research or development is performed, in order to explain a physical phenomenon through a mathematical model or trying to optimize any kind of process. Therefore it results an unavoidable technique since multidimensional approximation are more economical and reliable. An empirical approximation is never so efficient and generally provides lower qualities. It is known as 'design of experiments' a group of mathematical-statistical techniques that have the maximum information about our problem and consequently the results obtained will have the maximum quality. The modelization of a physic phenomenon, the basic concepts in order to design the experiments and the analysis of results are studied in detail

Microencapsulation of Epoxidized Linseed Oil Liquid Cross-Linker in Poly(N-vinyl-pyrrolidone): Optimization by a Design-of-Experiments Approach

NARCIS (Netherlands)

Senatore, D.; Laven, J.; Benthem, van R.A.T.M.; La Camera, D.; With, de G.

2010-01-01

A liquid cross-linker, epoxidized linseed oil (ELO), was encapsulated in a plastic with a high glass transition temperature (poly(N-vinyl-2-pyrrolidone); PVP). The process parameters of the spray-drying employed were optimized by a Design-of-Experiments (DoE) approach. Three factors concerning both

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

Energy Technology Data Exchange (ETDEWEB)

Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

Optimal Foraging by Birds: Experiments for Secondary & Postsecondary Students

Science.gov (United States)

Pecor, Keith W.; Lake, Ellen C.; Wund, Matthew A.

2015-01-01

Optimal foraging theory attempts to explain the foraging patterns observed in animals, including their choice of particular food items and foraging locations. We describe three experiments designed to test hypotheses about food choice and foraging habitat preference using bird feeders. These experiments can be used alone or in combination and can…

Optimal Experimental Design for Large-Scale Bayesian Inverse Problems

KAUST Repository

Ghattas, Omar

2014-01-06

We develop a Bayesian framework for the optimal experimental design of the shock tube experiments which are being carried out at the KAUST Clean Combustion Research Center. The unknown parameters are the pre-exponential parameters and the activation energies in the reaction rate expressions. The control parameters are the initial mixture composition and the temperature. The approach is based on first building a polynomial based surrogate model for the observables relevant to the shock tube experiments. Based on these surrogates, a novel MAP based approach is used to estimate the expected information gain in the proposed experiments, and to select the best experimental set-ups yielding the optimal expected information gains. The validity of the approach is tested using synthetic data generated by sampling the PC surrogate. We finally outline a methodology for validation using actual laboratory experiments, and extending experimental design methodology to the cases where the control parameters are noisy.

Development and Optimization of Polymeric Self-Emulsifying Nanocapsules for Localized Drug Delivery: Design of Experiment Approach

Directory of Open Access Journals (Sweden)

Jyoti Wadhwa

2014-01-01

Full Text Available The purpose of the present study was to formulate polymeric self-emulsifying curcumin nanocapsules with high encapsulation efficiency, good emulsification ability, and optimal globule size for localized targeting in the colon. Formulations were prepared using modified quasiemulsion solvent diffusion method. Concentration of formulation variables, namely, X1 (oil, X2 (polymeric emulsifier, and X3 (adsorbent, was optimized by design of experiments using Box-Behnken design, for its impact on mean globule size (Y1 and encapsulation efficiency (Y2 of the formulation. Polymeric nanocapsules with an average diameter of 100–180 nm and an encapsulation efficiency of 64.85 ± 0.12% were obtained. In vitro studies revealed that formulations released the drug after 5 h lag time corresponding to the time to reach the colonic region. Pronounced localized action was inferred from the plasma concentration profile (Cmax 200 ng/mL that depicts limited systemic absorption. Roentgenography study confirms the localized presence of carrier (0–2 h in upper GIT; 2–4 h in small intestine; and 4–24 h in the lower intestine. Optimized formulation showed significantly higher cytotoxicity (IC50 value 20.32 μM in HT 29 colonic cancer cell line. The present study demonstrates systematic development of polymeric self-emulsifying nanocapsule formulation of curcumin for localized targeting in colon.

The optimal design of UAV wing structure

Science.gov (United States)

Długosz, Adam; Klimek, Wiktor

2018-01-01

The paper presents an optimal design of UAV wing, made of composite materials. The aim of the optimization is to improve strength and stiffness together with reduction of the weight of the structure. Three different types of functionals, which depend on stress, stiffness and the total mass are defined. The paper presents an application of the in-house implementation of the evolutionary multi-objective algorithm in optimization of the UAV wing structure. Values of the functionals are calculated on the basis of results obtained from numerical simulations. Numerical FEM model, consisting of different composite materials is created. Adequacy of the numerical model is verified by results obtained from the experiment, performed on a tensile testing machine. Examples of multi-objective optimization by means of Pareto-optimal set of solutions are presented.

Sb2Te3 and Its Superlattices: Optimization by Statistical Design.

Science.gov (United States)

Behera, Jitendra K; Zhou, Xilin; Ranjan, Alok; Simpson, Robert E

2018-05-02

The objective of this work is to demonstrate the usefulness of fractional factorial design for optimizing the crystal quality of chalcogenide van der Waals (vdW) crystals. We statistically analyze the growth parameters of highly c axis oriented Sb 2 Te 3 crystals and Sb 2 Te 3 -GeTe phase change vdW heterostructured superlattices. The statistical significance of the growth parameters of temperature, pressure, power, buffer materials, and buffer layer thickness was found by fractional factorial design and response surface analysis. Temperature, pressure, power, and their second-order interactions are the major factors that significantly influence the quality of the crystals. Additionally, using tungsten rather than molybdenum as a buffer layer significantly enhances the crystal quality. Fractional factorial design minimizes the number of experiments that are necessary to find the optimal growth conditions, resulting in an order of magnitude improvement in the crystal quality. We highlight that statistical design of experiment methods, which is more commonly used in product design, should be considered more broadly by those designing and optimizing materials.

Optimal Design of a Centrifugal Compressor Impeller Using Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Soo-Yong Cho

2012-01-01

Full Text Available An optimization study was conducted on a centrifugal compressor. Eight design variables were chosen from the control points for the Bezier curves which widely influenced the geometric variation; four design variables were selected to optimize the flow passage between the hub and the shroud, and other four design variables were used to improve the performance of the impeller blade. As an optimization algorithm, an artificial neural network (ANN was adopted. Initially, the design of experiments was applied to set up the initial data space of the ANN, which was improved during the optimization process using a genetic algorithm. If a result of the ANN reached a higher level, that result was re-calculated by computational fluid dynamics (CFD and was applied to develop a new ANN. The prediction difference between the ANN and CFD was consequently less than 1% after the 6th generation. Using this optimization technique, the computational time for the optimization was greatly reduced and the accuracy of the optimization algorithm was increased. The efficiency was improved by 1.4% without losing the pressure ratio, and Pareto-optimal solutions of the efficiency versus the pressure ratio were obtained through the 21st generation.

A Model for Designing Adaptive Laboratory Evolution Experiments

DEFF Research Database (Denmark)

LaCroix, Ryan A.; Palsson, Bernhard O.; Feist, Adam M.

2017-01-01

in suboptimal experiments that can take multiple months to complete. With the availability of automation and computer simulations, we can now perform these experiments in an optimized fashion and can design experiments to generate greater fitness in an accelerated time frame, thereby pushing the limits of what...

QUALITY IMPROVEMENT IN MULTIRESPONSE EXPERIMENTS THROUGH ROBUST DESIGN METHODOLOGY

Directory of Open Access Journals (Sweden)

M. Shilpa

2012-06-01

Full Text Available Robust design methodology aims at reducing the variability in the product performance in the presence of noise factors. Experiments involving simultaneous optimization of more than one quality characteristic are known as multiresponse experiments which are used in the development and improvement of industrial processes and products. In this paper, robust design methodology is applied to optimize the process parameters during a particular operation of rotary driving shaft manufacturing process. The three important quality characteristics of the shaft considered here are of type Nominal-the-best, Smaller-the-better and Fraction defective. Simultaneous optimization of these responses is carried out by identifying the control parameters and conducting the experimentation using L9 orthogonal array.

Optimization and characterization of liposome formulation by mixture design.

Science.gov (United States)

Maherani, Behnoush; Arab-tehrany, Elmira; Kheirolomoom, Azadeh; Reshetov, Vadzim; Stebe, Marie José; Linder, Michel

2012-02-07

This study presents the application of the mixture design technique to develop an optimal liposome formulation by using the different lipids in type and percentage (DOPC, POPC and DPPC) in liposome composition. Ten lipid mixtures were generated by the simplex-centroid design technique and liposomes were prepared by the extrusion method. Liposomes were characterized with respect to size, phase transition temperature, ζ-potential, lamellarity, fluidity and efficiency in loading calcein. The results were then applied to estimate the coefficients of mixture design model and to find the optimal lipid composition with improved entrapment efficiency, size, transition temperature, fluidity and ζ-potential of liposomes. The response optimization of experiments was the liposome formulation with DOPC: 46%, POPC: 12% and DPPC: 42%. The optimal liposome formulation had an average diameter of 127.5 nm, a phase-transition temperature of 11.43 °C, a ζ-potential of -7.24 mV, fluidity (1/P)(TMA-DPH)((¬)) value of 2.87 and an encapsulation efficiency of 20.24%. The experimental results of characterization of optimal liposome formulation were in good agreement with those predicted by the mixture design technique.

Bayesian Optimal Experimental Design Using Multilevel Monte Carlo

KAUST Repository

Ben Issaid, Chaouki; Long, Quan; Scavino, Marco; Tempone, Raul

2015-01-01

Experimental design is very important since experiments are often resource-exhaustive and time-consuming. We carry out experimental design in the Bayesian framework. To measure the amount of information, which can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions. Optimizing this utility function enables us to design experiments that yield the most informative data for our purpose. One of the major difficulties in evaluating the expected information gain is that the integral is nested and can be high dimensional. We propose using Multilevel Monte Carlo techniques to accelerate the computation of the nested high dimensional integral. The advantages are twofold. First, the Multilevel Monte Carlo can significantly reduce the cost of the nested integral for a given tolerance, by using an optimal sample distribution among different sample averages of the inner integrals. Second, the Multilevel Monte Carlo method imposes less assumptions, such as the concentration of measures, required by Laplace method. We test our Multilevel Monte Carlo technique using a numerical example on the design of sensor deployment for a Darcy flow problem governed by one dimensional Laplace equation. We also compare the performance of the Multilevel Monte Carlo, Laplace approximation and direct double loop Monte Carlo.

Bayesian Optimal Experimental Design Using Multilevel Monte Carlo

KAUST Repository

Ben Issaid, Chaouki

2015-01-07

Experimental design is very important since experiments are often resource-exhaustive and time-consuming. We carry out experimental design in the Bayesian framework. To measure the amount of information, which can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions. Optimizing this utility function enables us to design experiments that yield the most informative data for our purpose. One of the major difficulties in evaluating the expected information gain is that the integral is nested and can be high dimensional. We propose using Multilevel Monte Carlo techniques to accelerate the computation of the nested high dimensional integral. The advantages are twofold. First, the Multilevel Monte Carlo can significantly reduce the cost of the nested integral for a given tolerance, by using an optimal sample distribution among different sample averages of the inner integrals. Second, the Multilevel Monte Carlo method imposes less assumptions, such as the concentration of measures, required by Laplace method. We test our Multilevel Monte Carlo technique using a numerical example on the design of sensor deployment for a Darcy flow problem governed by one dimensional Laplace equation. We also compare the performance of the Multilevel Monte Carlo, Laplace approximation and direct double loop Monte Carlo.

Electrostatic afocal-zoom lens design using computer optimization technique

Energy Technology Data Exchange (ETDEWEB)

Sise, Omer, E-mail: omersise@gmail.com

2014-12-15

Highlights: • We describe the detailed design of a five-element electrostatic afocal-zoom lens. • The simplex optimization is used to optimize lens voltages. • The method can be applied to multi-element electrostatic lenses. - Abstract: Electron optics is the key to the successful operation of electron collision experiments where well designed electrostatic lenses are needed to drive electron beam before and after the collision. In this work, the imaging properties and aberration analysis of an electrostatic afocal-zoom lens design were investigated using a computer optimization technique. We have found a whole new range of voltage combinations that has gone unnoticed until now. A full range of voltage ratios and spherical and chromatic aberration coefficients were systematically analyzed with a range of magnifications between 0.3 and 3.2. The grid-shadow evaluation was also employed to show the effect of spherical aberration. The technique is found to be useful for searching the optimal configuration in a multi-element lens system.

Particle Swarm Optimization for Outdoor Lighting Design

Directory of Open Access Journals (Sweden)

Ana Castillo-Martinez

2017-01-01

Full Text Available Outdoor lighting is an essential service for modern life. However, the high influence of this type of facility on energy consumption makes it necessary to take extra care in the design phase. Therefore, this manuscript describes an algorithm to help light designers to get, in an easy way, the best configuration parameters and to improve energy efficiency, while ensuring a minimum level of overall uniformity. To make this possible, we used a particle swarm optimization (PSO algorithm. These algorithms are well established, and are simple and effective to solve optimization problems. To take into account the most influential parameters on lighting and energy efficiency, 500 simulations were performed using DIALux software (4.10.0.2, DIAL, Ludenscheid, Germany. Next, the relation between these parameters was studied using to data mining software. Subsequently, we conducted two experiments for setting parameters that enabled the best configuration algorithm in order to improve efficiency in the proposed process optimization.

Rotorcraft Optimization Tools: Incorporating Rotorcraft Design Codes into Multi-Disciplinary Design, Analysis, and Optimization

Science.gov (United States)

Meyn, Larry A.

2018-01-01

One of the goals of NASA's Revolutionary Vertical Lift Technology Project (RVLT) is to provide validated tools for multidisciplinary design, analysis and optimization (MDAO) of vertical lift vehicles. As part of this effort, the software package, RotorCraft Optimization Tools (RCOTOOLS), is being developed to facilitate incorporating key rotorcraft conceptual design codes into optimizations using the OpenMDAO multi-disciplinary optimization framework written in Python. RCOTOOLS, also written in Python, currently supports the incorporation of the NASA Design and Analysis of RotorCraft (NDARC) vehicle sizing tool and the Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics II (CAMRAD II) analysis tool into OpenMDAO-driven optimizations. Both of these tools use detailed, file-based inputs and outputs, so RCOTOOLS provides software wrappers to update input files with new design variable values, execute these codes and then extract specific response variable values from the file outputs. These wrappers are designed to be flexible and easy to use. RCOTOOLS also provides several utilities to aid in optimization model development, including Graphical User Interface (GUI) tools for browsing input and output files in order to identify text strings that are used to identify specific variables as optimization input and response variables. This paper provides an overview of RCOTOOLS and its use

Network inference via adaptive optimal design

Directory of Open Access Journals (Sweden)

Stigter Johannes D

2012-09-01

Full Text Available Abstract Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion We conclude that a careful experiment design very often (but not always pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.

An Expert System-Driven Method for Parametric Trajectory Optimization During Conceptual Design

Science.gov (United States)

Dees, Patrick D.; Zwack, Mathew R.; Steffens, Michael; Edwards, Stephen; Diaz, Manuel J.; Holt, James B.

2015-01-01

. The methodology is two-fold: first, capture the heuristics developed by human analysts over their many years of experience; and secondly, leverage the power of modern computing to evaluate multiple trajectories simultaneously and therefore enable the exploration of the trajectory's design space early during the pre- conceptual and conceptual phases of design. This methodology is coupled with design of experiments in order to train surrogate models, which enables trajectory design space visualization and parametric optimal ascent trajectory information to be available when early design decisions are being made.

[Optimize preparation of compound licorice microemulsion with D-optimal design].

Science.gov (United States)

Ma, Shu-Wei; Wang, Yong-Jie; Chen, Cheng; Qiu, Yue; Wu, Qing

2018-03-01

In order to increase the solubility of essential oil in compound licorice microemulsion and improve the efficacy of the decoction for treating chronic eczema, this experiment intends to prepare the decoction into microemulsion. The essential oil was used as the oil phase of the microemulsion and the extract was used as the water phase. Then the microemulsion area and maximum ratio of water capacity was obtained by plotting pseudo-ternary phase diagram, to determine the appropriate types of surfactant and cosurfactant, and Km value-the mass ratio between surfactant and cosurfactant. With particle size and skin retention of active ingredients as the index, microemulsion prescription was optimized by D-optimal design method, to investigate the in vitro release behavior of the optimized prescription. The results showed that the microemulsion was optimal with tween-80 as the surfactant and anhydrous ethanol as the cosurfactant. When the Km value was 1, the area of the microemulsion region was largest while when the concentration of extract was 0.5 g·mL⁻¹, it had lowest effect on the particle size distribution of microemulsion. The final optimized formulation was as follows: 9.4% tween-80, 9.4% anhydrous ethanol, 1.0% peppermint oil and 80.2% 0.5 g·mL⁻¹ extract. The microemulsion prepared under these conditions had a small viscosity, good stability and high skin retention of drug; in vitro release experiment showed that microemulsion had a sustained-release effect on glycyrrhizic acid and liquiritin, basically achieving the expected purpose of the project. Copyright© by the Chinese Pharmaceutical Association.

Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles

Science.gov (United States)

Morelli, Eugene A.; Klein, Vladislav

1990-01-01

A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

A novel design procedure for tractor clutch fingers by using optimization and response surface methods

Energy Technology Data Exchange (ETDEWEB)

Dogan, Oguz; Karpat, Fatih; Yuce, Celalettin; Kaya, Necmettin; Yavuz, Nurettin [Uludag University, Gorukle (Turkmenistan); Sen, Hasan [Valeo A. S., Bursa (Turkmenistan)

2016-06-15

This paper presents a methodology for re-designing a failed tractor transmission component subjected to cyclic loading. Unlike other vehicles, tractors cope with tough working conditions. Thus, it is necessary to re-design components by using modern optimization techniques. To extend their service life, we present a design methodology for a failed tractor clutch power take-off finger. The finger was completely re-designed using topology and shape optimization approach. Stress-life based fatigue analyses were performed. Shape optimization and response surface methodology were conducted to obtain optimum dimensions of the finger. Two design parameters were selected for the design of experiment method and 15 cases were analyzed. By using design of the experiment method, three responses were obtained: Maximum stresses, mass, and displacement depending on the selected the design parameters. After solving the optimization problem, we achieved a maximum stress and mass reduction of 14% and 6%, respectively. The stiffness was improved up to 31.6% compared to the initial design.

Optimal Network-Topology Design

Science.gov (United States)

Li, Victor O. K.; Yuen, Joseph H.; Hou, Ting-Chao; Lam, Yuen Fung

1987-01-01

Candidate network designs tested for acceptability and cost. Optimal Network Topology Design computer program developed as part of study on topology design and analysis of performance of Space Station Information System (SSIS) network. Uses efficient algorithm to generate candidate network designs consisting of subsets of set of all network components, in increasing order of total costs and checks each design to see whether it forms acceptable network. Technique gives true cost-optimal network and particularly useful when network has many constraints and not too many components. Program written in PASCAL.

Design optimization and uncertainty analysis of SMA morphing structures

International Nuclear Information System (INIS)

Oehler, S D; Hartl, D J; Lopez, R; Malak, R J; Lagoudas, D C

2012-01-01

The continuing implementation of shape memory alloys (SMAs) as lightweight solid-state actuators in morphing structures has now motivated research into finding optimized designs for use in aerospace control systems. This work proposes methods that use iterative analysis techniques to determine optimized designs for morphing aerostructures and consider the impact of uncertainty in model variables on the solution. A combination of commercially available and custom coded tools is utilized. ModelCenter, a suite of optimization algorithms and simulation process management tools, is coupled with the Abaqus finite element analysis suite and a custom SMA constitutive model to assess morphing structure designs in an automated fashion. The chosen case study involves determining the optimized configuration of a morphing aerostructure assembly that includes SMA flexures. This is accomplished by altering design inputs representing the placement of active components to minimize a specified cost function. An uncertainty analysis is also conducted using design of experiment methods to determine the sensitivity of the solution to a set of uncertainty variables. This second study demonstrates the effective use of Monte Carlo techniques to simulate the variance of model variables representing the inherent uncertainty in component fabrication processes. This paper outlines the modeling tools used to execute each case study, details the procedures for constructing the optimization problem and uncertainty analysis, and highlights the results from both studies. (paper)

Conceptual optimal design of jackets

DEFF Research Database (Denmark)

Sandal, Kasper; Verbart, Alexander; Stolpe, Mathias

Structural optimization can explore a large design space (400 jackets) in a short time (2 hours), and thus lead to better conceptual jacket designs.......Structural optimization can explore a large design space (400 jackets) in a short time (2 hours), and thus lead to better conceptual jacket designs....

Bayesian optimal experimental design for priors of compact support

KAUST Repository

Long, Quan

2016-01-08

In this study, we optimize the experimental setup computationally by optimal experimental design (OED) in a Bayesian framework. We approximate the posterior probability density functions (pdf) using truncated Gaussian distributions in order to account for the bounded domain of the uniform prior pdf of the parameters. The underlying Gaussian distribution is obtained in the spirit of the Laplace method, more precisely, the mode is chosen as the maximum a posteriori (MAP) estimate, and the covariance is chosen as the negative inverse of the Hessian of the misfit function at the MAP estimate. The model related entities are obtained from a polynomial surrogate. The optimality, quantified by the information gain measures, can be estimated efficiently by a rejection sampling algorithm against the underlying Gaussian probability distribution, rather than against the true posterior. This approach offers a significant error reduction when the magnitude of the invariants of the posterior covariance are comparable to the size of the bounded domain of the prior. We demonstrate the accuracy and superior computational efficiency of our method for shock-tube experiments aiming to measure the model parameters of a key reaction which is part of the complex kinetic network describing the hydrocarbon oxidation. In the experiments, the initial temperature and fuel concentration are optimized with respect to the expected information gain in the estimation of the parameters of the target reaction rate. We show that the expected information gain surface can change its shape dramatically according to the level of noise introduced into the synthetic data. The information that can be extracted from the data saturates as a logarithmic function of the number of experiments, and few experiments are needed when they are conducted at the optimal experimental design conditions.

Design of a rotary dielectric elastomer actuator using a topology optimization method based on pairs of curves

Science.gov (United States)

Wang, Nianfeng; Guo, Hao; Chen, Bicheng; Cui, Chaoyu; Zhang, Xianmin

2018-05-01

Dielectric elastomers (DE), known as electromechanical transducers, have been widely used in the field of sensors, generators, actuators and energy harvesting for decades. A large number of DE actuators including bending actuators, linear actuators and rotational actuators have been designed utilizing an experience design method. This paper proposes a new method for the design of DE actuators by using a topology optimization method based on pairs of curves. First, theoretical modeling and optimization design are discussed, after which a rotary dielectric elastomer actuator has been designed using this optimization method. Finally, experiments and comparisons between several DE actuators have been made to verify the optimized result.

Optimal Experimental Design for Large-Scale Bayesian Inverse Problems

KAUST Repository

Ghattas, Omar

2014-01-01

We develop a Bayesian framework for the optimal experimental design of the shock tube experiments which are being carried out at the KAUST Clean Combustion Research Center. The unknown parameters are the pre-exponential parameters and the activation

Optimal tariff design under consumer self-selection

Energy Technology Data Exchange (ETDEWEB)

Raesaenen, M.; Ruusunen, J.; Haemaelaeinen, R.

1995-12-31

This report considers the design of electricity tariffs which guides an individual consumer to select the tariff designed for his consumption pattern. In the model the utility maximizes the weighted sum of individual consumers` benefits of electricity consumption subject to the utility`s revenue requirement constraints. The consumers` free choice of tariffs is ensured with the so-called self-selection constraints. The relationship between the consumers` optimal choice of tariffs and the weights in the aggregated consumers` benefit function is analyzed. If such weights exist, they will guarantee both the consumers` optimal choice of tariffs and the efficient consumption patterns. Also the welfare effects are analyzed by using demand parameters estimated from a Finnish dynamic pricing experiment. The results indicate that it is possible to design an efficient tariff menu with the welfare losses caused by the self-selection constraints being small compared with the costs created when some consumers choose tariffs other than assigned for them. (author)

Optimal covariate designs theory and applications

CERN Document Server

Das, Premadhis; Mandal, Nripes Kumar; Sinha, Bikas Kumar

2015-01-01

This book primarily addresses the optimality aspects of covariate designs. A covariate model is a combination of ANOVA and regression models. Optimal estimation of the parameters of the model using a suitable choice of designs is of great importance; as such choices allow experimenters to extract maximum information for the unknown model parameters. The main emphasis of this monograph is to start with an assumed covariate model in combination with some standard ANOVA set-ups such as CRD, RBD, BIBD, GDD, BTIBD, BPEBD, cross-over, multi-factor, split-plot and strip-plot designs, treatment control designs, etc. and discuss the nature and availability of optimal covariate designs. In some situations, optimal estimations of both ANOVA and the regression parameters are provided. Global optimality and D-optimality criteria are mainly used in selecting the design. The standard optimality results of both discrete and continuous set-ups have been adapted, and several novel combinatorial techniques have been applied for...

Topology optimization based design of unilateral NMR for generating a remote homogeneous field.

Science.gov (United States)

Wang, Qi; Gao, Renjing; Liu, Shutian

2017-06-01

This paper presents a topology optimization based design method for the design of unilateral nuclear magnetic resonance (NMR), with which a remote homogeneous field can be obtained. The topology optimization is actualized by seeking out the optimal layout of ferromagnetic materials within a given design domain. The design objective is defined as generating a sensitive magnetic field with optimal homogeneity and maximal field strength within a required region of interest (ROI). The sensitivity of the objective function with respect to the design variables is derived and the method for solving the optimization problem is presented. A design example is provided to illustrate the utility of the design method, specifically the ability to improve the quality of the magnetic field over the required ROI by determining the optimal structural topology for the ferromagnetic poles. Both in simulations and experiments, the sensitive region of the magnetic field achieves about 2 times larger than that of the reference design, validating validates the feasibility of the design method. Copyright © 2017. Published by Elsevier Inc.

A Statistical Approach to Optimizing Concrete Mixture Design

OpenAIRE

Ahmad, Shamsad; Alghamdi, Saeid A.

2014-01-01

A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (33). A total of 27 concrete mixtures with three replicate...

Design optimization of hydraulic turbine draft tube based on CFD and DOE method

Science.gov (United States)

Nam, Mun chol; Dechun, Ba; Xiangji, Yue; Mingri, Jin

2018-03-01

In order to improve performance of the hydraulic turbine draft tube in its design process, the optimization for draft tube is performed based on multi-disciplinary collaborative design optimization platform by combining the computation fluid dynamic (CFD) and the design of experiment (DOE) in this paper. The geometrical design variables are considered as the median section in the draft tube and the cross section in its exit diffuser and objective function is to maximize the pressure recovery factor (Cp). Sample matrixes required for the shape optimization of the draft tube are generated by optimal Latin hypercube (OLH) method of the DOE technique and their performances are evaluated through computational fluid dynamic (CFD) numerical simulation. Subsequently the main effect analysis and the sensitivity analysis of the geometrical parameters of the draft tube are accomplished. Then, the design optimization of the geometrical design variables is determined using the response surface method. The optimization result of the draft tube shows a marked performance improvement over the original.

Methodology for Plantwide Design and Optimization of Wastewater Treatment Plants

DEFF Research Database (Denmark)

Maria Dragan, Johanna; Zubov, Alexandr; Sin, Gürkan

2017-01-01

Design of Wastewater Treatment Plants (WWTPs) is a complex engineering task which requires integration of knowledge and experience from environmental biotechnology, process engineering, process synthesis and design as well as mathematical programming. A methodology has been formulated and applied...... for the systematic analysis and development of plantwide design of WWTPs using mathematical optimization and statistical methods such as sensitivity and uncertainty analyses....

Design optimization applied in structural dynamics

NARCIS (Netherlands)

Akcay-Perdahcioglu, Didem; de Boer, Andries; van der Hoogt, Peter; Tiskarna, T

2007-01-01

This paper introduces the design optimization strategies, especially for structures which have dynamic constraints. Design optimization involves first the modeling and then the optimization of the problem. Utilizing the Finite Element (FE) model of a structure directly in an optimization process

Combined Use of Mathematical Optimization and Design of Experiments for the Maximization of Profit in a Four-Echelon Supply Chain

Directory of Open Access Journals (Sweden)

Daniel Arturo Olivares Vera

2018-01-01

Full Text Available This paper develops a location-allocation model to optimize a four-echelon supply chain network, addressing manufacturing and distribution centers location, supplier selection and flow allocation for raw materials from suppliers to manufacturers, and finished products for end customers, while searching for system profit maximization. A fractional-factorial design of experiments is performed to analyze the effects of capacity, quality, delivery time, and interest rate on profit and system performance. The model is formulated as a mixed-integer linear programming problem and solved by using well-known commercial software. The usage of factorial experiments combined with mathematical optimization is a novel approach to address supply chain network design problems. The application of the proposed model to a case study shows that this combination of techniques yields satisfying results in terms of both its behavior and the obtained managerial insights. An ANOVA analysis is executed to quantify the effects of each factor and their interactions. In the analyzed case study, the transportation cost is the most relevant cost component, and the most relevant opportunity for profit improvement is found in the factor of quality. The proposed combination of methods can be adapted to different problems and industries.

Versator divertor experiment: preliminary designs

International Nuclear Information System (INIS)

Wan, A.S.; Yang, T.F.

1984-08-01

The emergence of magnetic divertors as an impurity control and ash removal mechanism for future tokamak reactors bring on the need for further experimental verification of the divertor merits and their ability to operate at reactor relevant conditions, such as with auxiliary heating. This paper presents preliminary designs of a bundle and a poloidal divertor for Versator II, which can operate in conjunction with the existing 150 kW of LHRF heating or LH current drive. The bundle divertor option also features a new divertor configuration which should improve the engineering and physics results of the DITE experiment. Further design optimization in both physics and engineering designs are currently under way

APPROACH ON INTELLIGENT OPTIMIZATION DESIGN BASED ON COMPOUND KNOWLEDGE

Institute of Scientific and Technical Information of China (English)

Yao Jianchu; Zhou Ji; Yu Jun

2003-01-01

A concept of an intelligent optimal design approach is proposed, which is organized by a kind of compound knowledge model. The compound knowledge consists of modularized quantitative knowledge, inclusive experience knowledge and case-based sample knowledge. By using this compound knowledge model, the abundant quantity information of mathematical programming and the symbolic knowledge of artificial intelligence can be united together in this model. The intelligent optimal design model based on such a compound knowledge and the automatically generated decomposition principles based on it are also presented. Practically, it is applied to the production planning, process schedule and optimization of production process of a refining & chemical work and a great profit is achieved. Specially, the methods and principles are adaptable not only to continuous process industry, but also to discrete manufacturing one.

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

Flat-plate photovoltaic array design optimization

Science.gov (United States)

Ross, R. G., Jr.

1980-01-01

An analysis is presented which integrates the results of specific studies in the areas of photovoltaic structural design optimization, optimization of array series/parallel circuit design, thermal design optimization, and optimization of environmental protection features. The analysis is based on minimizing the total photovoltaic system life-cycle energy cost including repair and replacement of failed cells and modules. This approach is shown to be a useful technique for array optimization, particularly when time-dependent parameters such as array degradation and maintenance are involved.

Design of experiments, a powerful tool for method development in forensic toxicology: application to the optimization of urinary morphine 3-glucuronide acid hydrolysis.

Science.gov (United States)

Costa, S; Barroso, M; Castañera, A; Dias, M

2010-04-01

The application of the design of experiments to optimize method development in the field of forensic toxicology using the urinary morphine 3-glucuronide acid hydrolysis as an example is described. Morphine and its trideuterated analogue (used as an internal standard) were extracted from urine samples by liquid-liquid extraction (ToxiTubes A) and derivatized by silylation. Chromatographic analysis was done by gas chromatography-mass spectrometry in the selected ion monitoring mode. Using the peak area ratio (morphine-to-internal standard) as the response, we investigated the independent variables that could influence the acid hydrolysis, including temperature (range 70-130 degrees C), acid volume (range 500-1,000 microL) and time (range 15-90 min). A 2(3) full factorial design for the screening and a response surface methodology, including a central composite design for optimization, were applied. The factors which influenced the response to a greater extent were temperature and its interaction both with time and acid volume. By application of a multiple regression analysis to the experimental data, a second-order polynomial equation was obtained. The optimal predicted conditions for morphine 3-glucuronide acid hydrolysis were 115 degrees C, 38 min and 500 microL for temperature, time and acid volume, respectively. Using design of experiments, instead of the one factor at a time approach, we achieved the optimum combination of all factor values, and this allowed the best results to be obtained, simultaneously optimizing resources. In addition, time and money can be saved, since other approaches are in general more time-consuming and laborious, and do not take into account the interactions between factors.

Thermoeconomic optimization of a solar-assisted heat pump based on transient simulations and computer Design of Experiments

International Nuclear Information System (INIS)

Calise, Francesco; Dentice d’Accadia, Massimo; Figaj, Rafal Damian; Vanoli, Laura

2016-01-01

Highlights: • A polygeneration system for a residential house is presented. • Hybrid photovoltaic/thermal collectors are used, coupled with a solar-assisted heat pump. • An optimization has been performed. • The system is profitable even in the absence of incentives. • A simple pay-back period of about 5 year is achieved. - Abstract: In the paper, a model for the simulation and the optimization of a novel solar trigeneration system is presented. The plant simulation model is designed to supply electricity, space heating or cooling and domestic hot water for a small residential building. The system is based on a solar field equipped with flat-plate photovoltaic/thermal collectors, coupled with a water-to-water electric heat pump/chiller. The electrical energy produced by the hybrid collectors is entirely supplied to the building. During the winter, the thermal energy available from the solar field is used as a heat source for the evaporator of the heat pump and/or to produce domestic hot water. During the summer, the heat pump operates in cooling mode, coupled with a closed circuit cooling tower, providing space cooling for the building, and the hot water produced by the collectors is only used to produce domestic hot water. For such a system, a dynamic simulation model was developed in TRNSYS environment, paying special attention to the dynamic simulation of the building, too. The system was analyzed from an energy and economic point of view, considering different time bases. In order to minimize the pay-back period, an optimum set of the main design/control parameters was obtained by means of a sensitivity analysis. Simultaneously, a computer-based Design of Experiment procedure was implemented, aiming at calculating the optimal set of design parameters, using both energy and economic objective functions. The results showed that thermal and electrical efficiencies are above 40% and 10%, respectively. The coefficient of performance of the reversible heat

Gadolinia experience and design for PWR fuel cycles

International Nuclear Information System (INIS)

Stephenson, L. C.

2000-01-01

The purpose of this paper is to describe Siemens Power Corporation's (SPC) current experience with the burnable absorber gadolinia in PWR fuel assemblies, including optimized features of SPC's PWR gadolinia designs, and comparisons with other burnable absorbers. Siemens is the world leader in PWR gadolinia experience. More than 5,900 Siemens PWR gadolinia-bearing fuel assemblies have been irradiated. The use of gadolinia-bearing fuel provides significant flexibility in fuel cycle designs, allows for low radial leakage fuel management and extended operating cycles, and reduces BOC (beginning-of-cycle) soluble boron concentrations. The optimized use of an integral burnable neutron absorber is a design feature which provides improved economic performance for PWR fuel assemblies. This paper includes a comparison between three different types of integral burnable absorbers: gadolinia, Zirconium diboride and erbia. Fuel cycle design studies performed by Siemens have shown that the enrichment requirements for 18-24 month fuel cycles utilizing gadolinia or zirconium diboride integral fuel burnable absorbers can be approximately the same. Although a typical gadolinia residual penalty for a cycle design of this length is as low as 0.02-0.03 wt% U-235, the design flexibility of gadolinia allows for very aggressive low-leakage core loading plans which reduces the enrichment requirements for gadolinia-bearing fuel. SPC has optimized its use of gadolinia in PWR fuel cycles. Typically, low (2-4) weight percent Gd 2 O 3 is used for beginning to middle of cycle reactivity hold down as well as soluble boron concentration holddown at BOC. Higher concentrations of Gd 2 O 3 , such as 6 and 8 wt%, are used to control power peaking in assemblies later in the cycle. SPC has developed core strategies that maximize the use of lower gadolinia concentrations which significantly reduces the gadolinia residual reactivity penalty. This optimization includes minimizing the number of rods with

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

OpenAIRE

Yao, W.; Chen, X.; Ouyang, Q.; Van Tooren, M.

2011-01-01

Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (M...

Optimization of 3D Field Design

Science.gov (United States)

Logan, Nikolas; Zhu, Caoxiang

2017-10-01

Recent progress in 3D tokamak modeling is now leveraged to create a conceptual design of new external 3D field coils for the DIII-D tokamak. Using the IPEC dominant mode as a target spectrum, the Finding Optimized Coils Using Space-curves (FOCUS) code optimizes the currents and 3D geometry of multiple coils to maximize the total set's resonant coupling. The optimized coils are individually distorted in space, creating toroidal ``arrays'' containing a variety of shapes that often wrap around a significant poloidal extent of the machine. The generalized perturbed equilibrium code (GPEC) is used to determine optimally efficient spectra for driving total, core, and edge neoclassical toroidal viscosity (NTV) torque and these too provide targets for the optimization of 3D coil designs. These conceptual designs represent a fundamentally new approach to 3D coil design for tokamaks targeting desired plasma physics phenomena. Optimized coil sets based on plasma response theory will be relevant to designs for future reactors or on any active machine. External coils, in particular, must be optimized for reliable and efficient fusion reactor designs. Work supported by the US Department of Energy under DE-AC02-09CH11466.

Optimal Market Design

NARCIS (Netherlands)

Boone, J.; Goeree, J.K.

2010-01-01

This paper introduces three methodological advances to study the optimal design of static and dynamic markets. First, we apply a mechanism design approach to characterize all incentive-compatible market equilibria. Second, we conduct a normative analysis, i.e. we evaluate alternative competition and

Divertor design through shape optimization

International Nuclear Information System (INIS)

Dekeyser, W.; Baelmans, M.; Reiter, D.

2012-01-01

Due to the conflicting requirements, complex physical processes and large number of design variables, divertor design for next step fusion reactors is a challenging problem, often relying on large numbers of computationally expensive numerical simulations. In this paper, we attempt to partially automate the design process by solving an appropriate shape optimization problem. Design requirements are incorporated in a cost functional which measures the performance of a certain design. By means of changes in the divertor shape, which in turn lead to changes in the plasma state, this cost functional can be minimized. Using advanced adjoint methods, optimal solutions are computed very efficiently. The approach is illustrated by designing divertor targets for optimal power load spreading, using a simplified edge plasma model (copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

Detailed design of a lattice composite fuselage structure by a mixed optimization method

Science.gov (United States)

Liu, D.; Lohse-Busch, H.; Toropov, V.; Hühne, C.; Armani, U.

2016-10-01

In this article, a procedure for designing a lattice fuselage barrel is developed. It comprises three stages: first, topology optimization of an aircraft fuselage barrel is performed with respect to weight and structural performance to obtain the conceptual design. The interpretation of the optimal result is given to demonstrate the development of this new lattice airframe concept for the fuselage barrel. Subsequently, parametric optimization of the lattice aircraft fuselage barrel is carried out using genetic algorithms on metamodels generated with genetic programming from a 101-point optimal Latin hypercube design of experiments. The optimal design is achieved in terms of weight savings subject to stability, global stiffness and strain requirements, and then verified by the fine mesh finite element simulation of the lattice fuselage barrel. Finally, a practical design of the composite skin complying with the aircraft industry lay-up rules is presented. It is concluded that the mixed optimization method, combining topology optimization with the global metamodel-based approach, allows the problem to be solved with sufficient accuracy and provides the designers with a wealth of information on the structural behaviour of the novel anisogrid composite fuselage design.

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 and implementation of new design of numerical experiments for non linear models

International Nuclear Information System (INIS)

Gazut, St.

2007-03-01

This thesis addresses the problem of the construction of surrogate models in numerical simulation. Whenever numerical experiments are costly, the simulation model is complex and difficult to use. It is important then to select the numerical experiments as efficiently as possible in order to minimize their number. In statistics, the selection of experiments is known as optimal experimental design. In the context of numerical simulation where no measurement uncertainty is present, we describe an alternative approach based on statistical learning theory and re-sampling techniques. The surrogate models are constructed using neural networks and the generalization error is estimated by leave-one-out, cross-validation and bootstrap. It is shown that the bootstrap can control the over-fitting and extend the concept of leverage for non linear in their parameters surrogate models. The thesis describes an iterative method called LDR for Learner Disagreement from experiment Re-sampling, based on active learning using several surrogate models constructed on bootstrap samples. The method consists in adding new experiments where the predictors constructed from bootstrap samples disagree most. We compare the LDR method with other methods of experimental design such as D-optimal selection. (author)

Research on Multidisciplinary Optimization Design of Bridge Crane

Directory of Open Access Journals (Sweden)

Tong Yifei

2013-01-01

Full Text Available Bridge crane is one of the most widely used cranes in our country, which is indispensable equipment for material conveying in the modern production. In this paper, the framework of multidisciplinary optimization for bridge crane is proposed. The presented research on crane multidisciplinary design technology for energy saving includes three levels, respectively: metal structures level, transmission design level, and electrical system design level. The shape optimal mathematical model of the crane is established for shape optimization design of metal structure level as well as size optimal mathematical model and topology optimal mathematical model of crane for topology optimization design of metal structure level is established. Finally, system-level multidisciplinary energy-saving optimization design of bridge crane is further carried out with energy-saving transmission design results feedback to energy-saving optimization design of metal structure. The optimization results show that structural optimization design can reduce total mass of crane greatly by using the finite element analysis and multidisciplinary optimization technology premised on the design requirements of cranes such as stiffness and strength; thus, energy-saving design can be achieved.

A Systematic Optimization Design Method for Complex Mechatronic Products Design and Development

Directory of Open Access Journals (Sweden)

Jie Jiang

2018-01-01

Full Text Available Designing a complex mechatronic product involves multiple design variables, objectives, constraints, and evaluation criteria as well as their nonlinearly coupled relationships. The design space can be very big consisting of many functional design parameters, structural design parameters, and behavioral design (or running performances parameters. Given a big design space and inexplicit relations among them, how to design a product optimally in an optimization design process is a challenging research problem. In this paper, we propose a systematic optimization design method based on design space reduction and surrogate modelling techniques. This method firstly identifies key design parameters from a very big design space to reduce the design space, secondly uses the identified key design parameters to establish a system surrogate model based on data-driven modelling principles for optimization design, and thirdly utilizes the multiobjective optimization techniques to achieve an optimal design of a product in the reduced design space. This method has been tested with a high-speed train design. With comparison to others, the research results show that this method is practical and useful for optimally designing complex mechatronic products.

Multidisciplinary design optimization of the diesel particulate filter in the composite regeneration process

International Nuclear Information System (INIS)

Zhang, Bin; E, Jiaqiang; Gong, Jinke; Yuan, Wenhua; Zuo, Wei; Li, Yu; Fu, Jun

2016-01-01

Highlights: • The multidisciplinary design optimization (MDO) for the DPF is presented. • MDO model and multi-objective functions of the DPF are established. • The optimal design parameters are obtained and DPF’s performances are improved. • The optimized results are verified by experiments. • The composite regeneration process of the optimized DPF allows a higher energy saving. - Abstract: In our previous works, the diesel particulate filter (DPF) using a new composite regeneration mode by coupling microwave and ceria-manganese base catalysts is verified as an effective way to reduce the particulate matter emission of the diesel engine. In order to improve the overall performance of this DPF, its multidisciplinary design optimization (MDO) model is established based on objective functions such as pressure drop, regeneration performance, microwave energy consumption, and thermal shock resistance. Then, the DPF is optimized by using MDO method based on adaptive mutative scale chaos optimization algorithm. The optimization results show that with the help of MDO, DPF’s pressure drop is decreased by 14.5%, regeneration efficiency is increased by 17.3%, microwave energy consumption is decreased by 17.6%, and thermal deformation is decreased by 25.3%. The optimization results are also verified by experiments, and the experimental results indicate that the optimized DPF has larger filtration efficiency, better emission performance and regeneration performance, smaller pressure drop, lower wall temperature and temperature gradient, and lower microwave energy consumption.

Simulation-based optimal Bayesian experimental design for nonlinear systems

KAUST Repository

Huan, Xun

2013-01-01

The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general mathematical framework and an algorithmic approach for optimal experimental design with nonlinear simulation-based models; in particular, we focus on finding sets of experiments that provide the most information about targeted sets of parameters.Our framework employs a Bayesian statistical setting, which provides a foundation for inference from noisy, indirect, and incomplete data, and a natural mechanism for incorporating heterogeneous sources of information. An objective function is constructed from information theoretic measures, reflecting expected information gain from proposed combinations of experiments. Polynomial chaos approximations and a two-stage Monte Carlo sampling method are used to evaluate the expected information gain. Stochastic approximation algorithms are then used to make optimization feasible in computationally intensive and high-dimensional settings. These algorithms are demonstrated on model problems and on nonlinear parameter inference problems arising in detailed combustion kinetics. © 2012 Elsevier Inc.

Optimal design of marine steam turbine

International Nuclear Information System (INIS)

Liu Chengyang; Yan Changqi; Wang Jianjun

2012-01-01

The marine steam turbine is one of the key equipment in marine power plant, and it tends to using high power steam turbine, which makes the steam turbine to be heavier and larger, it causes difficulties to the design and arrangement of the steam turbine, and the marine maneuverability is seriously influenced. Therefore, it is necessary to apply optimization techniques to the design of the steam turbine in order to achieve the minimum weight or volume by means of finding the optimum combination of design parameters. The math model of the marine steam turbine design calculation was established. The sensitivities of condenser pressure, power ratio of HP turbine with LP turbine, and the ratio of diameter with height at the end stage of LP turbine, which influence the weight of the marine steam turbine, were analyzed. The optimal design of the marine steam turbine, aiming at the weight minimization while satisfying the structure and performance constraints, was carried out with the hybrid particle swarm optimization algorithm. The results show that, steam turbine weight is reduced by 3.13% with the optimization scheme. Finally, the optimization results were analyzed, and the steam turbine optimization design direction was indicated. (authors)

Heat experiment design to estimate temperature dependent thermal properties

International Nuclear Information System (INIS)

Romanovski, M

2008-01-01

Experimental conditions are studied to optimize transient experiments for estimating temperature dependent thermal conductivity and volumetric heat capacity. A mathematical model of a specimen is the one-dimensional heat equation with boundary conditions of the second kind. Thermal properties are assumed to vary nonlinearly with temperature. Experimental conditions refer to the thermal loading scheme, sampling times and sensor location. A numerical model of experimental configurations is studied to elicit the optimal conditions. The numerical solution of the design problem is formulated on a regularization scheme with a stabilizer minimization without a regularization parameter. An explicit design criterion is used to reveal the optimal sensor location, heating duration and flux magnitude. Results obtained indicate that even the strongly nonlinear experimental design problem admits the aggregation of its solution and has a strictly defined optimal measurement scheme. Additional region of temperature measurements with allowable identification error is revealed.

Optimization Design of Bipolar Plate Flow Field in PEM Stack

Science.gov (United States)

Wen, Ming; He, Kanghao; Li, Peilong; Yang, Lei; Deng, Li; Jiang, Fei; Yao, Yong

2017-12-01

A new design of bipolar plate flow field in proton exchange membrane (PEM) stack was presented to develop a high-performance transfer efficiency of the two-phase flow. Two different flow fields were studied by using numerical simulations and the performance of the flow fields was presented. the hydrodynamic properties include pressure gap between inlet and outlet, the Reynold’s number of the two types were compared based on the Navier-Stokes equations. Computer aided optimization software was implemented in the design of experiments of the preferable flow field. The design of experiments (DOE) for the favorable concept was carried out to study the hydrodynamic properties when changing the design parameters of the bipolar plate.

Design Optimization Toolkit: Users' Manual

Energy Technology Data Exchange (ETDEWEB)

Aguilo Valentin, Miguel Alejandro [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Solid Mechanics and Structural Dynamics

2014-07-01

The Design Optimization Toolkit (DOTk) is a stand-alone C++ software package intended to solve complex design optimization problems. DOTk software package provides a range of solution methods that are suited for gradient/nongradient-based optimization, large scale constrained optimization, and topology optimization. DOTk was design to have a flexible user interface to allow easy access to DOTk solution methods from external engineering software packages. This inherent flexibility makes DOTk barely intrusive to other engineering software packages. As part of this inherent flexibility, DOTk software package provides an easy-to-use MATLAB interface that enables users to call DOTk solution methods directly from the MATLAB command window.

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....

Cost Optimal Design of a Single-Phase Dry Power Transformer

Directory of Open Access Journals (Sweden)

Raju Basak

2015-08-01

Full Text Available The Dry type transformers are preferred to their oil-immersed counterparts for various reasons, particularly because their operation is hazardless. The application of dry transformers was limited to small ratings in the earlier days. But now these are being used for considerably higher ratings.  Therefore, their cost-optimal design has gained importance. This paper deals with the design procedure for achieving cost optimal design of a dry type single-phase power transformer of small rating, subject to usual design constraints on efficiency and voltage regulation. The selling cost for the transformer has been taken as the objective function. Only two key variables have been chosen, the turns/volt and the height: width ratio of window, which affects the cost function to high degrees. Other variables have been chosen on the basis of designers’ experience. Copper has been used as conductor material and CRGOS as core material to achieve higher efficiency, lower running cost and compact design. The electrical and magnetic loadings have been kept at their maximum values without violating the design constraints. The optimal solution has been obtained by the method of exhaustive search using nested loops.

Machine learning paradigms in design optimization: Applications in turbine aerodynamic design

Science.gov (United States)

Goel, Sanjay

Mechanisms of incorporating machine learning paradigms in design optimization have been investigated in the current research. The primary focus of the work is on machine learning algorithms which use computational models that are analogous to the hypothesized principles of natural or biological learning. Examples from structural and aerodynamic optimization have been used to demonstrate the potential of the proposed schemes. The first strategy examined in the current work seeks to improve the convergence of optimization problems by pruning the search space of weak variables. Such variables are identified by learning from a database of existing designs using neural networks. By using clustering techniques, different sets of weak variables are identified in different regions of the design space. Parameter sensitivity information obtained in the process of identifying weak variables provides accurate heuristics for formulating design rules. The impact of this methodology on obtaining converged designs has been investigated for a turbine design problem. Optimization results from a three-stage power turbine and an aircraft engine turbine are presented in this thesis. The second scheme is an evolutionary design optimization technique which gets progressively 'smarter' during the optimization process by learning from computed domain knowledge. This technique employs adaptive learning mechanisms (classifiers) which recognize the influence of the design variables on the problem solution and then generalize them to dynamically create or change design rules during optimization. This technique, when applied to a constrained optimization problem, shows progressive improvement in convergence of search, as successive generations of rules evolve by learning from the environment. To investigate this methodology, a truss optimization problem is solved with an objective of minimizing the truss weight subject to stress constraints in the truss members. A distinct convergent trend is

Entropy-Based Experimental Design for Optimal Model Discrimination in the Geosciences

Directory of Open Access Journals (Sweden)

Wolfgang Nowak

2016-11-01

Full Text Available Choosing between competing models lies at the heart of scientific work, and is a frequent motivation for experimentation. Optimal experimental design (OD methods maximize the benefit of experiments towards a specified goal. We advance and demonstrate an OD approach to maximize the information gained towards model selection. We make use of so-called model choice indicators, which are random variables with an expected value equal to Bayesian model weights. Their uncertainty can be measured with Shannon entropy. Since the experimental data are still random variables in the planning phase of an experiment, we use mutual information (the expected reduction in Shannon entropy to quantify the information gained from a proposed experimental design. For implementation, we use the Preposterior Data Impact Assessor framework (PreDIA, because it is free of the lower-order approximations of mutual information often found in the geosciences. In comparison to other studies in statistics, our framework is not restricted to sequential design or to discrete-valued data, and it can handle measurement errors. As an application example, we optimize an experiment about the transport of contaminants in clay, featuring the problem of choosing between competing isotherms to describe sorption. We compare the results of optimizing towards maximum model discrimination with an alternative OD approach that minimizes the overall predictive uncertainty under model choice uncertainty.

Optimization of Soluble Expression and Purification of Recombinant Human Rhinovirus Type-14 3C Protease Using Statistically Designed Experiments: Isolation and Characterization of the Enzyme.

Science.gov (United States)

Antoniou, Georgia; Papakyriacou, Irineos; Papaneophytou, Christos

2017-10-01

Human rhinovirus (HRV) 3C protease is widely used in recombinant protein production for various applications such as biochemical characterization and structural biology projects to separate recombinant fusion proteins from their affinity tags in order to prevent interference between these tags and the target proteins. Herein, we report the optimization of expression and purification conditions of glutathione S-transferase (GST)-tagged HRV 3C protease by statistically designed experiments. Soluble expression of GST-HRV 3C protease was initially optimized by response surface methodology (RSM), and a 5.5-fold increase in enzyme yield was achieved. Subsequently, we developed a new incomplete factorial (IF) design that examines four variables (bacterial strain, expression temperature, induction time, and inducer concentration) in a single experiment. The new design called Incomplete Factorial-Strain/Temperature/Time/Inducer (IF-STTI) was validated using three GST-tagged proteins. In all cases, IF-STTI resulted in only 10% lower expression yields than those obtained by RSM. Purification of GST-HRV 3C was optimized by an IF design that examines simultaneously the effect of the amount of resin, incubation time of cell lysate with resin, and glycerol and DTT concentration in buffers, and a further 15% increase in protease recovery was achieved. Purified GST-HRV 3C protease was active at both 4 and 25 °C in a variety of buffers.

Design technology co-optimization for 14/10nm metal1 double patterning layer

Science.gov (United States)

Duan, Yingli; Su, Xiaojing; Chen, Ying; Su, Yajuan; Shao, Feng; Zhang, Recco; Lei, Junjiang; Wei, Yayi

2016-03-01

Design and technology co-optimization (DTCO) can satisfy the needs of the design, generate robust design rule, and avoid unfriendly patterns at the early stage of design to ensure a high level of manufacturability of the product by the technical capability of the present process. The DTCO methodology in this paper includes design rule translation, layout analysis, model validation, hotspots classification and design rule optimization mainly. The correlation of the DTCO and double patterning (DPT) can optimize the related design rule and generate friendlier layout which meets the requirement of the 14/10nm technology node. The experiment demonstrates the methodology of DPT-compliant DTCO which is applied to a metal1 layer from the 14/10nm node. The DTCO workflow proposed in our job is an efficient solution for optimizing the design rules for 14/10 nm tech node Metal1 layer. And the paper also discussed and did the verification about how to tune the design rule of the U-shape and L-shape structures in a DPT-aware metal layer.

Optimal Design of an Automotive Exhaust Thermoelectric Generator

Science.gov (United States)

Fagehi, Hassan; Attar, Alaa; Lee, Hosung

2018-07-01

The consumption of energy continues to increase at an exponential rate, especially in terms of conventional automobiles. Approximately 40% of the applied fuel into a vehicle is lost as waste exhausted to the environment. The desire for improved fuel efficiency by recovering the exhaust waste heat in automobiles has become an important subject. A thermoelectric generator (TEG) has the potential to convert exhaust waste heat into electricity as long as it is improving fuel economy. The remarkable amount of research being conducted on TEGs indicates that this technology will have a bright future in terms of power generation. The current study discusses the optimal design of the automotive exhaust TEG. An experimental study has been conducted to verify the model that used the ideal (standard) equations along with effective material properties. The model is reasonably verified by experimental work, mainly due to the utilization of the effective material properties. Hence, the thermoelectric module that was used in the experiment was optimized by using a developed optimal design theory (dimensionless analysis technique).

Optimal Design of an Automotive Exhaust Thermoelectric Generator

Science.gov (United States)

Fagehi, Hassan; Attar, Alaa; Lee, Hosung

2018-04-01

The consumption of energy continues to increase at an exponential rate, especially in terms of conventional automobiles. Approximately 40% of the applied fuel into a vehicle is lost as waste exhausted to the environment. The desire for improved fuel efficiency by recovering the exhaust waste heat in automobiles has become an important subject. A thermoelectric generator (TEG) has the potential to convert exhaust waste heat into electricity as long as it is improving fuel economy. The remarkable amount of research being conducted on TEGs indicates that this technology will have a bright future in terms of power generation. The current study discusses the optimal design of the automotive exhaust TEG. An experimental study has been conducted to verify the model that used the ideal (standard) equations along with effective material properties. The model is reasonably verified by experimental work, mainly due to the utilization of the effective material properties. Hence, the thermoelectric module that was used in the experiment was optimized by using a developed optimal design theory (dimensionless analysis technique).

Designing Experiments to Discriminate Families of Logic Models.

Science.gov (United States)

Videla, Santiago; Konokotina, Irina; Alexopoulos, Leonidas G; Saez-Rodriguez, Julio; Schaub, Torsten; Siegel, Anne; Guziolowski, Carito

2015-01-01

Logic models of signaling pathways are a promising way of building effective in silico functional models of a cell, in particular of signaling pathways. The automated learning of Boolean logic models describing signaling pathways can be achieved by training to phosphoproteomics data, which is particularly useful if it is measured upon different combinations of perturbations in a high-throughput fashion. However, in practice, the number and type of allowed perturbations are not exhaustive. Moreover, experimental data are unavoidably subjected to noise. As a result, the learning process results in a family of feasible logical networks rather than in a single model. This family is composed of logic models implementing different internal wirings for the system and therefore the predictions of experiments from this family may present a significant level of variability, and hence uncertainty. In this paper, we introduce a method based on Answer Set Programming to propose an optimal experimental design that aims to narrow down the variability (in terms of input-output behaviors) within families of logical models learned from experimental data. We study how the fitness with respect to the data can be improved after an optimal selection of signaling perturbations and how we learn optimal logic models with minimal number of experiments. The methods are applied on signaling pathways in human liver cells and phosphoproteomics experimental data. Using 25% of the experiments, we obtained logical models with fitness scores (mean square error) 15% close to the ones obtained using all experiments, illustrating the impact that our approach can have on the design of experiments for efficient model calibration.

Transportation package design using numerical optimization

International Nuclear Information System (INIS)

Harding, D.C.; Witkowski, W.R.

1991-01-01

The purpose of this overview is twofold: first, to outline the theory and basic elements of numerical optimization; and second, to show how numerical optimization can be applied to the transportation packaging industry and used to increase efficiency and safety of radioactive and hazardous material transportation packages. A more extensive review of numerical optimization and its applications to radioactive material transportation package design was performed previously by the authors (Witkowski and Harding 1992). A proof-of-concept Type B package design is also presented as a simplified example of potential improvements achievable using numerical optimization in the design process

Transportation package design using numerical optimization

International Nuclear Information System (INIS)

Harding, D.C.; Witkowski, W.R.

1992-01-01

The design of structures and engineering systems has always been an iterative process whose complexity was dependent upon the boundary conditions, constraints and available analytical tools. Transportation packaging design is no exception with structural, thermal and radiation shielding constraints based on regulatory hypothetical accident conditions. Transportation packaging design is often accomplished by a group of specialists, each designing a single component based on one or more simple criteria, pooling results with the group, evaluating the open-quotes pooledclose quotes design, and then reiterating the entire process until a satisfactory design is reached. The manual iterative methods used by the designer/analyst can be summarized in the following steps: design the part, analyze the part, interpret the analysis results, modify the part, and re-analyze the part. The inefficiency of this design practice and the frequently conservative result suggests the need for a more structured design methodology, which can simultaneously consider all of the design constraints. Numerical optimization is a structured design methodology whose maturity in development has allowed it to become a primary design tool in many industries. The purpose of this overview is twofold: first, to outline the theory and basic elements of numerical optimization; and second, to show how numerical optimization can be applied to the transportation packaging industry and used to increase efficiency and safety of radioactive and hazardous material transportation packages. A more extensive review of numerical optimization and its applications to radioactive material transportation package design was performed previously by the authors (Witkowski and Harding 1992). A proof-of-concept Type B package design is also presented as a simplified example of potential improvements achievable using numerical optimization in the design process

Investigation of Navier-Stokes Code Verification and Design Optimization

Science.gov (United States)

Vaidyanathan, Rajkumar

2004-01-01

With rapid progress made in employing computational techniques for various complex Navier-Stokes fluid flow problems, design optimization problems traditionally based on empirical formulations and experiments are now being addressed with the aid of computational fluid dynamics (CFD). To be able to carry out an effective CFD-based optimization study, it is essential that the uncertainty and appropriate confidence limits of the CFD solutions be quantified over the chosen design space. The present dissertation investigates the issues related to code verification, surrogate model-based optimization and sensitivity evaluation. For Navier-Stokes (NS) CFD code verification a least square extrapolation (LSE) method is assessed. This method projects numerically computed NS solutions from multiple, coarser base grids onto a freer grid and improves solution accuracy by minimizing the residual of the discretized NS equations over the projected grid. In this dissertation, the finite volume (FV) formulation is focused on. The interplay between the xi concepts and the outcome of LSE, and the effects of solution gradients and singularities, nonlinear physics, and coupling of flow variables on the effectiveness of LSE are investigated. A CFD-based design optimization of a single element liquid rocket injector is conducted with surrogate models developed using response surface methodology (RSM) based on CFD solutions. The computational model consists of the NS equations, finite rate chemistry, and the k-6 turbulence closure. With the aid of these surrogate models, sensitivity and trade-off analyses are carried out for the injector design whose geometry (hydrogen flow angle, hydrogen and oxygen flow areas and oxygen post tip thickness) is optimized to attain desirable goals in performance (combustion length) and life/survivability (the maximum temperatures on the oxidizer post tip and injector face and a combustion chamber wall temperature). A preliminary multi-objective optimization

Interactive Reliability-Based Optimal Design

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle; Siemaszko, A.

1994-01-01

Interactive design/optimization of large, complex structural systems is considered. The objective function is assumed to model the expected costs. The constraints are reliability-based and/or related to deterministic code requirements. Solution of this optimization problem is divided in four main...... tasks, namely finite element analyses, sensitivity analyses, reliability analyses and application of an optimization algorithm. In the paper it is shown how these four tasks can be linked effectively and how existing information on design variables, Lagrange multipliers and the Hessian matrix can...

Construction of a 21-Component Layered Mixture Experiment Design

International Nuclear Information System (INIS)

Piepel, Gregory F.; Cooley, Scott K.; Jones, Bradley

2004-01-01

This paper describes the solution to a unique and challenging mixture experiment design problem involving: (1) 19 and 21 components for two different parts of the design, (2) many single-component and multi-component constraints, (3) augmentation of existing data, (4) a layered design developed in stages, and (5) a no-candidate-point optimal design approach. The problem involved studying the liquidus temperature of spinel crystals as a function of nuclear waste glass composition. The statistical objective was to develop an experimental design by augmenting existing glasses with new nonradioactive and radioactive glasses chosen to cover the designated nonradioactive and radioactive experimental regions. The existing 144 glasses were expressed as 19-component nonradioactive compositions and then augmented with 40 new nonradioactive glasses. These included 8 glasses on the outer layer of the region, 27 glasses on an inner layer, 2 replicate glasses at the centroid, and one replicate each of three existing glasses. Then, the 144 + 40 = 184 glasses were expressed as 21-component radioactive compositions and augmented with 5 radioactive glasses. A D-optimal design algorithm was used to select the new outer layer, inner layer, and radioactive glasses. Several statistical software packages can generate D-optimal experimental designs, but nearly all require a set of candidate points (e.g., vertices) from which to select design points. The large number of components (19 or 21) and many constraints made it impossible to generate the huge number of vertices and other typical candidate points. JMP(R) was used to select design points without candidate points. JMP uses a coordinate-exchange algorithm modified for mixture experiments, which is discussed in the paper

Optimization methods applied to hybrid vehicle design

Science.gov (United States)

Donoghue, J. F.; Burghart, J. H.

1983-01-01

The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.

Intelligent stochastic optimization routine for in-core fuel cycle design

International Nuclear Information System (INIS)

Parks, G.T.

1988-01-01

Any reactor fuel management strategy must specify the fuel design, batch sizes, loading configurations, and operational procedures for each cycle. To permit detailed design studies, the complex core characteristics must necessarily be computer modeled. Thus, the identification of an optimal fuel cycle design represents an optimization problem with a nonlinear objective function (OF), nonlinear safety constraints, many control variables, and no direct derivative information. Most available library routines cannot tackle such problems; this paper introduces an intelligent stochastic optimization routine that can. There has been considerable interest recently in the application of stochastic methods to difficult optimization problems, based on the statistical mechanics algorithms originally attributed to Metropolis. Previous work showed that, in optimizing the performance of a British advanced gas-cooled reactor fuel stringer, a rudimentary version of the Metropolis algorithm performed as efficiently as the only suitable routine in the Numerical Algorithms Group library. Since then the performance of the Metropolis algorithm has been considerably enhanced by the introduction of self-tuning capabilities by which the routine adjusts its control parameters and search pattern as it progresses. Both features can be viewed as examples of artificial intelligence, in which the routine uses the accumulation of data, or experience, to guide its future actions

Design of microfluidic bioreactors using topology optimization

DEFF Research Database (Denmark)

Okkels, Fridolin; Bruus, Henrik

2007-01-01

We address the design of optimal reactors for supporting biological cultures using the method of topology optimization. For some years this method have been used to design various optimal microfluidic devices.1-4 We apply this method to distribute optimally biologic cultures within a flow...

A new efficient mixture screening design for optimization of media.

Science.gov (United States)

Rispoli, Fred; Shah, Vishal

2009-01-01

Screening ingredients for the optimization of media is an important first step to reduce the many potential ingredients down to the vital few components. In this study, we propose a new method of screening for mixture experiments called the centroid screening design. Comparison of the proposed design with Plackett-Burman, fractional factorial, simplex lattice design, and modified mixture design shows that the centroid screening design is the most efficient of all the designs in terms of the small number of experimental runs needed and for detecting high-order interaction among ingredients. (c) 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009.

Design of Thermal Systems Using Topology Optimization

DEFF Research Database (Denmark)

Haertel, Jan Hendrik Klaas

printeddry-cooled power plant condensers using a simpliffed thermouid topology optimizationmodel is presented in another study. A benchmarking of the optimized geometriesagainst a conventional heat exchanger design is conducted and the topologyoptimized designs show a superior performance. A thermouid......The goalof this thesis is to apply topology optimization to the design of differentthermal systems such as heat sinks and heat exchangers in order to improve thethermal performance of these systems compared to conventional designs. Thedesign of thermal systems is a complex task that has...... of optimized designs are presentedwithin this thesis.  The maincontribution of the thesis is the development of several numerical optimizationmodels that are applied to different design challenges within thermalengineering.  Topology optimization isapplied in an industrial project to design the heat rejection...

Wind farm design optimization

Energy Technology Data Exchange (ETDEWEB)

Carreau, Michel; Morgenroth, Michael; Belashov, Oleg; Mdimagh, Asma; Hertz, Alain; Marcotte, Odile

2010-09-15

Innovative numerical computer tools have been developed to streamline the estimation, the design process and to optimize the Wind Farm Design with respect to the overall return on investment. The optimization engine can find the collector system layout automatically which provide a powerful tool to quickly study various alternative taking into account more precisely various constraints or factors that previously would have been too costly to analyze in details with precision. Our Wind Farm Tools have evolved through numerous projects and created value for our clients yielding Wind Farm projects with projected higher returns.

Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Xianfu Cheng

2014-01-01

Full Text Available The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

Science.gov (United States)

Lin, Yuqun

2014-01-01

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system. PMID:24683334

Multiobjective robust design of the double wishbone suspension system based on particle swarm optimization.

Science.gov (United States)

Cheng, Xianfu; Lin, Yuqun

2014-01-01

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Design optimization for active twist rotor blades

Science.gov (United States)

Mok, Ji Won

This dissertation introduces the process of optimizing active twist rotor blades in the presence of embedded anisotropic piezo-composite actuators. Optimum design of active twist blades is a complex task, since it involves a rich design space with tightly coupled design variables. The study presents the development of an optimization framework for active helicopter rotor blade cross-sectional design. This optimization framework allows for exploring a rich and highly nonlinear design space in order to optimize the active twist rotor blades. Different analytical components are combined in the framework: cross-sectional analysis (UM/VABS), an automated mesh generator, a beam solver (DYMORE), a three-dimensional local strain recovery module, and a gradient based optimizer within MATLAB. Through the mathematical optimization problem, the static twist actuation performance of a blade is maximized while satisfying a series of blade constraints. These constraints are associated with locations of the center of gravity and elastic axis, blade mass per unit span, fundamental rotating blade frequencies, and the blade strength based on local three-dimensional strain fields under worst loading conditions. Through pre-processing, limitations of the proposed process have been studied. When limitations were detected, resolution strategies were proposed. These include mesh overlapping, element distortion, trailing edge tab modeling, electrode modeling and foam implementation of the mesh generator, and the initial point sensibility of the current optimization scheme. Examples demonstrate the effectiveness of this process. Optimization studies were performed on the NASA/Army/MIT ATR blade case. Even though that design was built and shown significant impact in vibration reduction, the proposed optimization process showed that the design could be improved significantly. The second example, based on a model scale of the AH-64D Apache blade, emphasized the capability of this framework to

Optimal Design of Gradient Materials and Bi-Level Optimization of Topology Using Targets (BOTT)

Science.gov (United States)

Garland, Anthony

of gradient material designs. A macroscopic gradient can be achieved by varying the microstructure or the mesostructures of an object. The mesostructure interpretation allows for more design freedom since the mesostructures can be tuned to have non-isotropic material properties. A new algorithm called Bi-level Optimization of Topology using Targets (BOTT) seeks to find the best distribution of mesostructure designs throughout a single object in order to minimize an objective value. On the macro level, the BOTT algorithm optimizes the macro topology and gradient material properties within the object. The BOTT algorithm optimizes the material gradient by finding the best constitutive matrix at each location with the object. In order to enhance the likelihood that a mesostructure can be generated with the same equivalent constitutive matrix, the variability of the constitutive matrix is constrained to be an orthotropic material. The stiffness in the X and Y directions (of the base coordinate system) can change in addition to rotating the orthotropic material to align with the loading at each region. Second, the BOTT algorithm designs mesostructures with macroscopic properties equal to the target properties found in step one while at the same time the algorithm seeks to minimize material usage in each mesostructure. The mesostructure algorithm maximizes the strain energy of the mesostructures unit cell when a pseudo strain is applied to the cell. A set of experiments reveals the fundamental relationship between target cell density and the strain (or pseudo strain) applied to a unit cell and the output effective properties of the mesostructure. At low density, a few mesostructure unit cell design are possible, while at higher density the mesostructure unit cell designs have many possibilities. Therefore, at low densities the effective properties of the mesostructure are a step function of the applied pseudo strain. At high densities, the effective properties of the

Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2004-01-01

We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2006-01-01

We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

Design of Experiment Using Simulation of a Discrete Dynamical System

Directory of Open Access Journals (Sweden)

Mašek Jan

2016-12-01

Full Text Available The topic of the presented paper is a promising approach to achieve optimal Design of Experiment (DoE, i.e. spreading of points within a design domain, using a simulation of a discrete dynamical system of interacting particles within an n-dimensional design space. The system of mutually repelling particles represents a physical analogy of the Audze-Eglājs (AE optimization criterion and its periodical modification (PAE, respectively. The paper compares the performance of two approaches to implementation: a single-thread process using the JAVA language environment and a massively parallel solution employing the nVidia CUDA platform.

A statistical approach to optimizing concrete mixture design.

Science.gov (United States)

Ahmad, Shamsad; Alghamdi, Saeid A

2014-01-01

A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (3(3)). A total of 27 concrete mixtures with three replicates (81 specimens) were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48), cementitious materials content (350, 375, and 400 kg/m(3)), and fine/total aggregate ratio (0.35, 0.40, and 0.45). The experimental data were utilized to carry out analysis of variance (ANOVA) and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options.

A Statistical Approach to Optimizing Concrete Mixture Design

Directory of Open Access Journals (Sweden)

Shamsad Ahmad

2014-01-01

Full Text Available A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (33. A total of 27 concrete mixtures with three replicates (81 specimens were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48, cementitious materials content (350, 375, and 400 kg/m3, and fine/total aggregate ratio (0.35, 0.40, and 0.45. The experimental data were utilized to carry out analysis of variance (ANOVA and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options.

Design of acoustic devices by topology optimization

DEFF Research Database (Denmark)

Sigmund, Ole; Jensen, Jakob Søndergaard

2003-01-01

The goal of this study is to design and optimize structures and devices that are subjected to acoustic waves. Examples are acoustic lenses, sound walls, waveguides and loud speakers. We formulate the design problem as a topology optimization problem, i.e. distribute material in a design domain...... such that the acoustic response is optimized....

Backbone cup – a structure design competition based on topology optimization and 3D printing

Directory of Open Access Journals (Sweden)

Zhu Ji-Hong

2016-01-01

Full Text Available This paper addresses a structure design competition based on topology optimization and 3D Printing, and proposes an experimental approach to efficiently and quickly measure the mechanical performance of the structures designed using topology optimization. Since the topology optimized structure designs are prone to be geometrically complex, it is extremely inconvenient to fabricate these designs with traditional machining. In this study, we not only fabricated the topology optimized structure designs using one kind of 3D Printing technology known as stereolithography (SLA, but also tested the mechanical performance of the produced prototype parts. The finite element method is used to analyze the structure responses, and the consistent results of the numerical simulations and structure experiments prove the validity of this new structure testing approach. This new approach will not only provide a rapid access to topology optimized structure designs verifying, but also cut the turnaround time of structure design significantly.

Comparison of optimal design methods in inverse problems

International Nuclear Information System (INIS)

Banks, H T; Holm, K; Kappel, F

2011-01-01

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric-based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher information matrix. A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criterion with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst–Pearl logistic population model (Banks H T and Tran H T 2009 Mathematical and Experimental Modeling of Physical and Biological Processes (Boca Raton, FL: Chapman and Hall/CRC)), the standard harmonic oscillator model (Banks H T and Tran H T 2009) and a popular glucose regulation model (Bergman R N, Ider Y Z, Bowden C R and Cobelli C 1979 Am. J. Physiol. 236 E667–77; De Gaetano A and Arino O 2000 J. Math. Biol. 40 136–68; Toffolo G, Bergman R N, Finegood D T, Bowden C R and Cobelli C 1980 Diabetes 29 979–90)

Comparison of optimal design methods in inverse problems

Science.gov (United States)

Banks, H. T.; Holm, K.; Kappel, F.

2011-07-01

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric-based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher information matrix. A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criterion with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model (Banks H T and Tran H T 2009 Mathematical and Experimental Modeling of Physical and Biological Processes (Boca Raton, FL: Chapman and Hall/CRC)), the standard harmonic oscillator model (Banks H T and Tran H T 2009) and a popular glucose regulation model (Bergman R N, Ider Y Z, Bowden C R and Cobelli C 1979 Am. J. Physiol. 236 E667-77 De Gaetano A and Arino O 2000 J. Math. Biol. 40 136-68 Toffolo G, Bergman R N, Finegood D T, Bowden C R and Cobelli C 1980 Diabetes 29 979-90).

Optimization of ciprofloxacin complex loaded PLGA nanoparticles for pulmonary treatment of cystic fibrosis infections: Design of experiments approach.

Science.gov (United States)

Günday Türeli, Nazende; Türeli, Akif Emre; Schneider, Marc

2016-12-30

Design of Experiments (DoE) is a powerful tool for systematic evaluation of process parameters' effect on nanoparticle (NP) quality with minimum number of experiments. DoE was employed for optimization of ciprofloxacin loaded PLGA NPs for pulmonary delivery against Pseudomonas aeruginosa infections in cystic fibrosis (CF) lungs. Since the biofilm produced by bacteria was shown to be a complicated 3D barrier with heterogeneous meshes ranging from 100nm to 500nm, nanoformulations small enough to travel through those channels were assigned as target quality. Nanoprecipitation was realized utilizing MicroJet Reactor (MJR) technology based on impinging jets principle. Effect of MJR parameters flow rate, temperature and gas pressure on particle size and PDI was investigated using Box-Behnken design. The relationship between process parameters and particle quality was demonstrated by constructed fit functions (R 2 =0.9934 p65%. Response surface plots provided experimental data-based understanding of MJR parameters' effect, thus NP quality. Presented work enables ciprofloxacin loaded PLGA nanoparticle preparations with pre-defined quality to fulfill the requirements of local drug delivery under CF disease conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

Optimization of Superconducting Focusing Quadrupoles for the HighCurrent Experiment

Energy Technology Data Exchange (ETDEWEB)

Sabbi, GianLuca; Gourlay, Steve; Gung, Chen-yu; Hafalia, Ray; Lietzke, Alan; Martovetski, Nicolai; Mattafirri, Sara; Meinke, Rainer; Minervini, Joseph; Schultz, Joel; Seidl, Peter

2005-09-16

The Heavy Ion Fusion (HIF) program is progressing through a series of physics and technology demonstrations leading to an inertial fusion power plant. The High Current Experiment (HCX) at Lawrence Berkeley National Laboratory is exploring the physics of intense beams with high line-charge density. Superconducting focusing quadrupoles have been developed for the HCX magnetic transport studies. A baseline design was selected following several pre-series models. Optimization of the baseline design led to the development of a first prototype that achieved a conductor-limited gradient of 132 T/m in a 70 mm bore, without training, with measured field errors at the 0.1% level. Based on these results, the magnet geometry and fabrication procedures were adjusted to improve the field quality. These modifications were implemented in a second prototype. In this paper, the optimized design is presented and comparisons between the design harmonics and magnetic measurements performed on the new prototype are discussed.

Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.

Science.gov (United States)

Qiu, Jiaheng; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee

2014-10-01

Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.

Optimal experimental design with R

CERN Document Server

Rasch, Dieter; Verdooren, L R; Gebhardt, Albrecht

2011-01-01

Experimental design is often overlooked in the literature of applied and mathematical statistics: statistics is taught and understood as merely a collection of methods for analyzing data. Consequently, experimenters seldom think about optimal design, including prerequisites such as the necessary sample size needed for a precise answer for an experimental question. Providing a concise introduction to experimental design theory, Optimal Experimental Design with R: Introduces the philosophy of experimental design Provides an easy process for constructing experimental designs and calculating necessary sample size using R programs Teaches by example using a custom made R program package: OPDOE Consisting of detailed, data-rich examples, this book introduces experimenters to the philosophy of experimentation, experimental design, and data collection. It gives researchers and statisticians guidance in the construction of optimum experimental designs using R programs, including sample size calculations, hypothesis te...

Optimal Design of Gravity Pipeline Systems Using Genetic Algorithm and Mathematical Optimization

Directory of Open Access Journals (Sweden)

maryam rohani

2015-03-01

Full Text Available In recent years, the optimal design of pipeline systems has become increasingly important in the water industry. In this study, the two methods of genetic algorithm and mathematical optimization were employed for the optimal design of pipeline systems with the objective of avoiding the water hammer effect caused by valve closure. The problem of optimal design of a pipeline system is a constrained one which should be converted to an unconstrained optimization problem using an external penalty function approach in the mathematical programming method. The quality of the optimal solution greatly depends on the value of the penalty factor that is calculated by the iterative method during the optimization procedure such that the computational effort is simultaneously minimized. The results obtained were used to compare the GA and mathematical optimization methods employed to determine their efficiency and capabilities for the problem under consideration. It was found that the mathematical optimization method exhibited a slightly better performance compared to the GA method.

Optimal design of impeller for centrifugal compressor under the influence of one-way fluid-structure interaction

Energy Technology Data Exchange (ETDEWEB)

Kang, Hyun Soo; Kim, Youn Jae [Sungkyunkwan University, Suwon (Korea, Republic of)

2016-09-15

In this study, a method for optimal design of impeller for centrifugal compressor under the influence of Fluid-structure interaction (FSI) and Response surface method (RSM) was studied. Numerical simulation was conducted using ANSYS Multi-physics with various configurations of impeller geometry. Each of the design parameters was divided into 3 levels. Total 45 design points were planned by Central composite design (CCD) method, which is one of the Design of experiment (DOE) techniques. Response surfaces generated based on the DOE results were used to find the optimal shape of impeller for high aerodynamic performance. The whole process of optimization was conducted using ANSYS Design xplorer (DX). Through the optimization, structural safety and aerodynamic performance of centrifugal compressor were improved.

Optimal design of impeller for centrifugal compressor under the influence of one-way fluid-structure interaction

International Nuclear Information System (INIS)

Kang, Hyun Soo; Kim, Youn Jae

2016-01-01

In this study, a method for optimal design of impeller for centrifugal compressor under the influence of Fluid-structure interaction (FSI) and Response surface method (RSM) was studied. Numerical simulation was conducted using ANSYS Multi-physics with various configurations of impeller geometry. Each of the design parameters was divided into 3 levels. Total 45 design points were planned by Central composite design (CCD) method, which is one of the Design of experiment (DOE) techniques. Response surfaces generated based on the DOE results were used to find the optimal shape of impeller for high aerodynamic performance. The whole process of optimization was conducted using ANSYS Design xplorer (DX). Through the optimization, structural safety and aerodynamic performance of centrifugal compressor were improved

User Experience Re-Mastered Your Guide to Getting the Right Design

CERN Document Server

Wilson, Chauncey

2009-01-01

Good user interface design isn’t just about aesthetics or using the latest technology. Designers also need to ensure their product is offering an optimal user experience. This requires user needs analysis, usability testing, persona creation, prototyping, design sketching, and evaluation through-out the design and development process. User Experience Re-Mastered takes tried and tested material from best-selling books in Morgan Kaufmann’s Series in Interactive Technologies and presents it in typical project framework. Chauncey Wilson guides the reader through each chapter, introducing each stag

Site-specific design optimization of wind turbines

DEFF Research Database (Denmark)

Fuglsang, P.; Bak, C.; Schepers, J.G.

2002-01-01

This article reports results from a European project, where site characteristics were incorporated into the design process of wind turbines, to enable site-specific design. Two wind turbines of different concept were investigated at six different sites comprising normal flat terrain, offshore...... and complex terrain wind farms. Design tools based on numerical optimization and aeroelastic calculations were combined with a cost model to allow optimization for minimum cost of energy. Different scenarios were optimized ranging from modifications of selected individual components to the complete design...... of a new wind turbine. Both annual energy yield and design-determining loads depended on site characteristics, and this represented a potential for site-specific design. The maximum variation in annual energy yield was 37% and the maximum variation in blade root fatigue loads was 62%. Optimized site...

Preliminary Design Optimization For A Supersonic Turbine For Rocket Propulsion

Science.gov (United States)

Papila, Nilay; Shyy, Wei; Griffin, Lisa; Huber, Frank; Tran, Ken; McConnaughey, Helen (Technical Monitor)

2000-01-01

In this study, we present a method for optimizing, at the preliminary design level, a supersonic turbine for rocket propulsion system application. Single-, two- and three-stage turbines are considered with the number of design variables increasing from 6 to 11 then to 15, in accordance with the number of stages. Due to its global nature and flexibility in handling different types of information, the response surface methodology (RSM) is applied in the present study. A major goal of the present Optimization effort is to balance the desire of maximizing aerodynamic performance and minimizing weight. To ascertain required predictive capability of the RSM, a two-level domain refinement approach has been adopted. The accuracy of the predicted optimal design points based on this strategy is shown to he satisfactory. Our investigation indicates that the efficiency rises quickly from single stage to 2 stages but that the increase is much less pronounced with 3 stages. A 1-stage turbine performs poorly under the engine balance boundary condition. A portion of fluid kinetic energy is lost at the turbine discharge of the 1-stage design due to high stage pressure ratio and high-energy content, mostly hydrogen, of the working fluid. Regarding the optimization technique, issues related to the design of experiments (DOE) has also been investigated. It is demonstrated that the criteria for selecting the data base exhibit significant impact on the efficiency and effectiveness of the construction of the response surface.

Optimal Design of Stiffeners for Bucket Foundations

DEFF Research Database (Denmark)

Courtney, William Tucker; Stolpe, Mathias; Buhl, Thomas

2015-01-01

Tosca Structure coupled with the finite element software Abaqus. The solutions to these optimization problems are then manually interpreted as a new design concept. Results show that shape optimization of the initial design can reduce stress concentrations by 38%. Additionally, topology optimization has...

Fast Bayesian optimal experimental design and its applications

KAUST Repository

Long, Quan

2015-01-07

We summarize our Laplace method and multilevel method of accelerating the computation of the expected information gain in a Bayesian Optimal Experimental Design (OED). Laplace method is a widely-used method to approximate an integration in statistics. We analyze this method in the context of optimal Bayesian experimental design and extend this method from the classical scenario, where a single dominant mode of the parameters can be completely-determined by the experiment, to the scenarios where a non-informative parametric manifold exists. We show that by carrying out this approximation the estimation of the expected Kullback-Leibler divergence can be significantly accelerated. While Laplace method requires a concentration of measure, multi-level Monte Carlo method can be used to tackle the problem when there is a lack of measure concentration. We show some initial results on this approach. The developed methodologies have been applied to various sensor deployment problems, e.g., impedance tomography and seismic source inversion.

Optimized design of low energy buildings

DEFF Research Database (Denmark)

Rudbeck, Claus Christian; Esbensen, Peter Kjær; Svendsen, Sv Aa Højgaard

1999-01-01

concern which can be seen during the construction of new buildings. People want energy-friendly solutions, but they should be economical optimized. An exonomical optimized building design with respect to energy consumption is the design with the lowest total cost (investment plus operational cost over its...... to evaluate different separate solutions when they interact in the building.When trying to optimize several parameters there is a need for a method, which will show the correct price-performance of each part of a building under design. The problem with not having such a method will first be showed...

On simultaneous shape and orientational design for eigenfrequency optimization

DEFF Research Database (Denmark)

Pedersen, Niels Leergaard

2007-01-01

Plates with an internal hole of fixed area are designed in order to maximize the performance with respect to eigenfrequencies. The optimization is performed by simultaneous shape, material, and orientational design. The shape of the hole is designed, and the material design is the design of an or......Plates with an internal hole of fixed area are designed in order to maximize the performance with respect to eigenfrequencies. The optimization is performed by simultaneous shape, material, and orientational design. The shape of the hole is designed, and the material design is the design...... of an orthotropic material that can be considered as a fiber-net within each finite element. This fiber-net is optimally oriented in the individual elements of the finite element discretization. The optimizations are performed using the finite element method for analysis, and the optimization approach is a two......-step method. In the first step, we find the best design on the basis of a recursive optimization procedure based on optimality criteria. In the second step, mathematical programming and sensitivity analysis are applied to find the final optimized design....

Design analysis for optimal calibration of diffusivity in reactive multilayers

KAUST Repository

Vohra, Manav

2017-05-29

Calibration of the uncertain Arrhenius diffusion parameters for quantifying mixing rates in Zr–Al nanolaminate foils have been previously performed in a Bayesian setting [M. Vohra, J. Winokur, K.R. Overdeep, P. Marcello, T.P. Weihs, and O.M. Knio, Development of a reduced model of formation reactions in Zr–Al nanolaminates, J. Appl. Phys. 116(23) (2014): Article No. 233501]. The parameters were inferred in a low-temperature, homogeneous ignition regime, and a high-temperature self-propagating reaction regime. In this work, we extend the analysis to determine optimal experimental designs that would provide the best data for inference. We employ a rigorous framework that quantifies the expected information gain in an experiment, and find the optimal design conditions using Monte Carlo techniques, sparse quadrature, and polynomial chaos surrogates. For the low-temperature regime, we find the optimal foil heating rate and pulse duration, and confirm through simulation that the optimal design indeed leads to sharp posterior distributions of the diffusion parameters. For the high-temperature regime, we demonstrate the potential for increasing the expected information gain concerning the posteriors by increasing the sample size and reducing the uncertainty in measurements. Moreover, posterior marginals are also obtained to verify favourable experimental scenarios.

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 a Turbine Intake Structure

Directory of Open Access Journals (Sweden)

P. Fošumpaur

2005-01-01

Full Text Available The appropriate design of the turbine intake structure of a hydropower plant is based on assumptions about its suitable function, and the design will increase the total efficiency of operation. This paper deals with optimal design of the turbine structure of run-of-river hydropower plants. The study focuses mainly on optimization of the hydropower plant location with respect to the original river banks, and on the optimal design of a separating pier between the weir and the power plant. The optimal design of the turbine intake was determined with the use of 2-D mathematical modelling. A case study is performed for the optimal design of a turbine intake structure on the Nemen river in Belarus. 

LMI–based robust controller design approach in aircraft multidisciplinary design optimization problem

Directory of Open Access Journals (Sweden)

Qinghua Zeng

2015-07-01

Full Text Available This article proposes a linear matrix inequality–based robust controller design approach to implement the synchronous design of aircraft control discipline and other disciplines, in which the variation in design parameters is treated as equivalent perturbations. Considering the complicated mapping relationships between the coefficient arrays of aircraft motion model and the aircraft design parameters, the robust controller designed is directly based on the variation in these coefficient arrays so conservative that the multidisciplinary design optimization problem would be too difficult to solve, or even if there is a solution, the robustness of design result is generally poor. Therefore, this article derives the uncertainty model of disciplinary design parameters based on response surface approximation, converts the design problem of the robust controller into a problem of solving a standard linear matrix inequality, and theoretically gives a less conservative design method of the robust controller which is based on the variation in design parameters. Furthermore, the concurrent subspace approach is applied to the multidisciplinary system with this kind of robust controller in the design loop. A multidisciplinary design optimization of a tailless aircraft as example is shown that control discipline can be synchronous optimal design with other discipline, especially this method will greatly reduce the calculated amount of multidisciplinary design optimization and make multidisciplinary design optimization results more robustness of flight performance.

Optimal design of condenser volume in nuclear power plant

International Nuclear Information System (INIS)

Zheng Jing; Yan Changqi; Wang Jianjun

2011-01-01

The condenser is an important component in the nuclear power plant,whose dimension will influence the economy and the arrangement of the nuclear power plant.In this paper, the calculation model was established according to the design experience. The corresponding codes were also developed. The sensitivity of design parameters which influence the condenser Janume was analyzed. The present optimal design of the condenser, aiming at the volume minimization, was carried out with the self-developed complex-genetic algorithm. The results show that the reference condenser design is far from the best scheme. In addition, the results also verify the feasibility of the complex-genetic algorithm. Furthermore, the results of this paper can provide reference for the design of the condenser. (authors)

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

Optimization design of blade shapes for wind turbines

DEFF Research Database (Denmark)

Chen, Jin; Wang, Xudong; Shen, Wen Zhong

2010-01-01

For the optimization design of wind turbines, the new normal and tangential induced factors of wind turbines are given considering the tip loss of the normal and tangential forces based on the blade element momentum theory and traditional aerodynamic model. The cost model of the wind turbines...... and the optimization design model are developed. In the optimization model, the objective is the minimum cost of energy and the design variables are the chord length, twist angle and the relative thickness. Finally, the optimization is carried out for a 2 MW blade by using this optimization design model....... The performance of blades is validated through the comparison and analysis of the results. The reduced cost shows that the optimization model is good enough for the design of wind turbines. The results give a proof for the design and research on the blades of large scale wind turbines and also establish...

Robust experiment design for estimating myocardial β adrenergic receptor concentration using PET

International Nuclear Information System (INIS)

Salinas, Cristian; Muzic, Raymond F. Jr.; Ernsberger, Paul; Saidel, Gerald M.

2007-01-01

Myocardial β adrenergic receptor (β-AR) concentration can substantially decrease in congestive heart failure and significantly increase in chronic volume overload, such as in severe aortic valve regurgitation. Positron emission tomography (PET) with an appropriate ligand-receptor model can be used for noninvasive estimation of myocardial β-AR concentration in vivo. An optimal design of the experiment protocol, however, is needed for sufficiently precise estimates of β-AR concentration in a heterogeneous population. Standard methods of optimal design do not account for a heterogeneous population with a wide range of β-AR concentrations and other physiological parameters and consequently are inadequate. To address this, we have developed a methodology to design a robust two-injection protocol that provides reliable estimates of myocardial β-AR concentration in normal and pathologic states. A two-injection protocol of the high affinity β-AR antagonist [ 18 F]-(S)-fluorocarazolol was designed based on a computer-generated (or synthetic) population incorporating a wide range of β-AR concentrations. Timing and dosage of the ligand injections were optimally designed with minimax criterion to provide the least bad β-AR estimates for the worst case in the synthetic population. This robust experiment design for PET was applied to experiments with pigs before and after β-AR upregulation by chemical sympathectomy. Estimates of β-AR concentration were found by minimizing the difference between the model-predicted and experimental PET data. With this robust protocol, estimates of β-AR concentration showed high precision in both normal and pathologic states. The increase in β-AR concentration after sympathectomy predicted noninvasively with PET is consistent with the increase shown by in vitro assays in pig myocardium. A robust experiment protocol was designed for PET that yields reliable estimates of β-AR concentration in a population with normal and pathologic

A Review of Design Optimization Methods for Electrical Machines

Directory of Open Access Journals (Sweden)

Gang Lei

2017-11-01

Full Text Available Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

DETERMINATION OF BRAKING OPTIMAL MODE OF CONTROLLED CUT OF DESIGN GROUP

Directory of Open Access Journals (Sweden)

A. S. Dorosh

2015-06-01

Full Text Available Purpose. The application of automation systems of breaking up process on the gravity hump is the efficiency improvement of their operation, absolute provision of trains breaking up safety demands, as well as improvement of hump staff working conditions. One of the main tasks of the indicated systems is the assurance of cuts reliable separation at all elements of their rolling route to the classification track. This task is a sophisticated optimization problem and has not received a final decision. Therefore, the task of determining the cuts braking mode is quite relevant. The purpose of this research is to find the optimal braking mode of control cut of design group. Methodology. In order to achieve the purpose is offered to use the direct search methods in the work, namely the Box complex method. This method does not require smoothness of the objective function, takes into account its limitations and does not require calculation of the function derivatives, and uses only its value. Findings. Using the Box method was developed iterative procedure for determining the control cut optimal braking mode of design group. The procedure maximizes the smallest controlled time interval in the group. To evaluate the effectiveness of designed procedure the series of simulation experiments of determining the control cut braking mode of design group was performed. The results confirmed the efficiency of the developed optimization procedure. Originality. The author formalized the task of optimizing control cut braking mode of design group, taking into account the cuts separation of design group at all elements (switches, retarders during cuts rolling to the classification track. The problem of determining the optimal control cut braking mode of design group was solved. The developed braking mode ensures cuts reliable separation of the group not only at the switches but at the retarders of brake position. Practical value. The developed procedure can be

Bayesian Optimal Experimental Design Using Multilevel Monte Carlo

KAUST Repository

Ben Issaid, Chaouki

2015-05-12

Experimental design can be vital when experiments are resource-exhaustive and time-consuming. In this work, we carry out experimental design in the Bayesian framework. To measure the amount of information that can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions. Optimizing this utility function enables us to design experiments that yield the most informative data about the model parameters. One of the major difficulties in evaluating the expected information gain is that it naturally involves nested integration over a possibly high dimensional domain. We use the Multilevel Monte Carlo (MLMC) method to accelerate the computation of the nested high dimensional integral. The advantages are twofold. First, MLMC can significantly reduce the cost of the nested integral for a given tolerance, by using an optimal sample distribution among different sample averages of the inner integrals. Second, the MLMC method imposes fewer assumptions, such as the asymptotic concentration of posterior measures, required for instance by the Laplace approximation (LA). We test the MLMC method using two numerical examples. The first example is the design of sensor deployment for a Darcy flow problem governed by a one-dimensional Poisson equation. We place the sensors in the locations where the pressure is measured, and we model the conductivity field as a piecewise constant random vector with two parameters. The second one is chemical Enhanced Oil Recovery (EOR) core flooding experiment assuming homogeneous permeability. We measure the cumulative oil recovery, from a horizontal core flooded by water, surfactant and polymer, for different injection rates. The model parameters consist of the endpoint relative permeabilities, the residual saturations and the relative permeability exponents for the three phases: water, oil and

On Optimal Input Design for Feed-forward Control

OpenAIRE

Hägg, Per; Wahlberg, Bo

2013-01-01

This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in a system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are an...

A reduced scale two loop PWR core designed with particle swarm optimization technique

International Nuclear Information System (INIS)

Lima Junior, Carlos A. Souza; Pereira, Claudio M.N.A; Lapa, Celso M.F.; Cunha, Joao J.; Alvim, Antonio C.M.

2007-01-01

Reduced scale experiments are often employed in engineering projects because they are much cheaper than real scale testing. Unfortunately, designing reduced scale thermal-hydraulic circuit or equipment, with the capability of reproducing, both accurately and simultaneously, all physical phenomena that occur in real scale and at operating conditions, is a difficult task. To solve this problem, advanced optimization techniques, such as Genetic Algorithms, have been applied. Following this research line, we have performed investigations, using the Particle Swarm Optimization (PSO) Technique, to design a reduced scale two loop Pressurized Water Reactor (PWR) core, considering 100% of nominal power and non accidental operating conditions. Obtained results show that the proposed methodology is a promising approach for forced flow reduced scale experiments. (author)

Enhancing product robustness in reliability-based design optimization

International Nuclear Information System (INIS)

Zhuang, Xiaotian; Pan, Rong; Du, Xiaoping

2015-01-01

Different types of uncertainties need to be addressed in a product design optimization process. In this paper, the uncertainties in both product design variables and environmental noise variables are considered. The reliability-based design optimization (RBDO) is integrated with robust product design (RPD) to concurrently reduce the production cost and the long-term operation cost, including quality loss, in the process of product design. This problem leads to a multi-objective optimization with probabilistic constraints. In addition, the model uncertainties associated with a surrogate model that is derived from numerical computation methods, such as finite element analysis, is addressed. A hierarchical experimental design approach, augmented by a sequential sampling strategy, is proposed to construct the response surface of product performance function for finding optimal design solutions. The proposed method is demonstrated through an engineering example. - Highlights: • A unifying framework for integrating RBDO and RPD is proposed. • Implicit product performance function is considered. • The design problem is solved by sequential optimization and reliability assessment. • A sequential sampling technique is developed for improving design optimization. • The comparison with traditional RBDO is provided

Robust D-optimal designs under correlated error, applicable invariantly for some lifetime distributions

International Nuclear Information System (INIS)

Das, Rabindra Nath; Kim, Jinseog; Park, Jeong-Soo

2015-01-01

In quality engineering, the most commonly used lifetime distributions are log-normal, exponential, gamma and Weibull. Experimental designs are useful for predicting the optimal operating conditions of the process in lifetime improvement experiments. In the present article, invariant robust first-order D-optimal designs are derived for correlated lifetime responses having the above four distributions. Robust designs are developed for some correlated error structures. It is shown that robust first-order D-optimal designs for these lifetime distributions are always robust rotatable but the converse is not true. Moreover, it is observed that these designs depend on the respective error covariance structure but are invariant to the above four lifetime distributions. This article generalizes the results of Das and Lin [7] for the above four lifetime distributions with general (intra-class, inter-class, compound symmetry, and tri-diagonal) correlated error structures. - Highlights: • This paper presents invariant robust first-order D-optimal designs under correlated lifetime responses. • The results of Das and Lin [7] are extended for the four lifetime (log-normal, exponential, gamma and Weibull) distributions. • This paper also generalizes the results of Das and Lin [7] to more general correlated error structures

Design of modern experiments

International Nuclear Information System (INIS)

Park, Sung Hweon

1984-03-01

This book is for researchers and engineers, which is written to focus on practical design of experiments. It gives descriptions of conception of design of experiments, basic statistics theory, one way design of experiment, two-way layout without repetition, two-way layout with repetition, partition, a correlation analysis and regression analysis, latin squares, factorial design, design of experiment by table of orthogonal arrays, design of experiment of response surface, design of experiment on compound, Evop, and design of experiment of taguchi.

Dual stator winding variable speed asynchronous generator: optimal design and experiments

International Nuclear Information System (INIS)

Tutelea, L N; Deaconu, S I; Popa, G N

2015-01-01

In the present paper is carried out a theoretical and experimental study of dual stator winding squirrel cage asynchronous generator (DSWA) behavior in the presence of saturation regime (non-sinusoidal) due to the variable speed operation. The main aims are the determination of the relations of calculating the equivalent parameters of the machine windings to optimal design using a Matlab code. Issue is limited to three phase range of double stator winding cage-induction generator of small sized powers, the most currently used in the small adjustable speed wind or hydro power plants. The tests were carried out using three-phase asynchronous generator having rated power of 6 [kVA]. (paper)

An overview of the design and analysis of simulation experiments for sensitivity analysis

NARCIS (Netherlands)

Kleijnen, J.P.C.

2005-01-01

Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models. This review surveys 'classic' and 'modern' designs for experiments with simulation models. Classic designs were developed for real, non-simulated systems in agriculture, engineering, etc. These designs

Optimal Design of Pumped Pipeline Systems Using Genetic Algorithm and Mathematical Optimization

Directory of Open Access Journals (Sweden)

Mohammadhadi Afshar

2007-12-01

Full Text Available In recent years, much attention has been paid to the optimal design of pipeline systems. In this study, the problem of pipeline system optimal design has been solved through genetic algorithm and mathematical optimization. Pipe diameters and their thicknesses are considered as decision variables to be designed in a manner that water column separation and excessive pressures are avoided in the event of pump failure. Capabilities of the genetic algorithm and the mathematical programming method are compared for the problem under consideration. For simulation of transient streams, explicit characteristic method is used in which devices such as pumps are defined as boundary conditions of the equations defining the hydraulic behavior of pipe segments. The problem of optimal design of pipeline systems is a constrained problem which is converted to an unconstrained optimization problem using an external penalty function approach. The efficiency of the proposed approaches is verified in one example and the results are presented.

Topology optimization aided structural design: Interpretation, computational aspects and 3D printing.

Science.gov (United States)

Kazakis, Georgios; Kanellopoulos, Ioannis; Sotiropoulos, Stefanos; Lagaros, Nikos D

2017-10-01

Construction industry has a major impact on the environment that we spend most of our life. Therefore, it is important that the outcome of architectural intuition performs well and complies with the design requirements. Architects usually describe as "optimal design" their choice among a rather limited set of design alternatives, dictated by their experience and intuition. However, modern design of structures requires accounting for a great number of criteria derived from multiple disciplines, often of conflicting nature. Such criteria derived from structural engineering, eco-design, bioclimatic and acoustic performance. The resulting vast number of alternatives enhances the need for computer-aided architecture in order to increase the possibility of arriving at a more preferable solution. Therefore, the incorporation of smart, automatic tools in the design process, able to further guide designer's intuition becomes even more indispensable. The principal aim of this study is to present possibilities to integrate automatic computational techniques related to topology optimization in the phase of intuition of civil structures as part of computer aided architectural design. In this direction, different aspects of a new computer aided architectural era related to the interpretation of the optimized designs, difficulties resulted from the increased computational effort and 3D printing capabilities are covered here in.

An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis

NARCIS (Netherlands)

Kleijnen, J.P.C.

2004-01-01

Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models.This review surveys classic and modern designs for experiments with simulation models.Classic designs were developed for real, non-simulated systems in agriculture, engineering, etc.These designs assume a

Design and sampling plan optimization for RT-qPCR experiments in plants: a case study in blueberry

Directory of Open Access Journals (Sweden)

Jose V Die

2016-03-01

Full Text Available The qPCR assay has become a routine technology in plant biotechnology and agricultural research. It is unlikely to be technically improved, but there are still challenges which center around minimizing the variability in results and transparency when reporting technical data in support of the conclusions of a study. There are a number of aspects of the pre- and post-assay workflow that contribute to variability of results. Here, through the study of the introduction of error in qPCR measurements at different stages of the workflow, we describe the most important causes of technical variability in a case study using blueberry. In this study, we found that the stage for which increasing the number of replicates would be the most beneficial depends on the tissue used. For example, we would recommend the use of more RT replicates when working with leaf tissue, while the use of more sampling (RNA extraction replicates would be recommended when working with stems or fruits to obtain the most optimal results. The use of more qPCR replicates provides the least benefit as it is the most reproducible step. By knowing the distribution of error over an entire experiment and the costs at each step, we have developed a script to identify the optimal sampling plan within the limits of a given budget. These findings should help plant scientists improve the design of qPCR experiments and refine their laboratory practices in order to conduct qPCR assays in a more reliable-manner to produce more consistent and reproducible data.

Design and fabrication of topologically optimized structures;

DEFF Research Database (Denmark)

Feringa, Jelle; Søndergaard, Asbjørn

2012-01-01

Integral structural optimization and fabrication seeks the synthesis of two original approaches; that of topological optimization (TO) and robotic hotwire cutting (HWC) (Mcgee 2011). TO allows for the reduction of up to 70% of the volume of concrete to support a given structure (Sondergaard...... & Dombernowsky 2011). A strength of the method is that it allows to come up with structural designs that lie beyond the grasp of traditional means of design. A design space is a discretized volume, delimiting where the optimization will take place. The number of cells used to discretize the design space thus...

Enabling Parametric Optimal Ascent Trajectory Modeling During Early Phases of Design

Science.gov (United States)

Holt, James B.; Dees, Patrick D.; Diaz, Manuel J.

2015-01-01

-modal due to the interaction of various constraints. Additionally, when these obstacles are coupled with The Program to Optimize Simulated Trajectories [1] (POST), an industry standard program to optimize ascent trajectories that is difficult to use, it requires expert trajectory analysts to effectively optimize a vehicle's ascent trajectory. As it has been pointed out, the paradigm of trajectory optimization is still a very manual one because using modern computational resources on POST is still a challenging problem. The nuances and difficulties involved in correctly utilizing, and therefore automating, the program presents a large problem. In order to address these issues, the authors will discuss a methodology that has been developed. The methodology is two-fold: first, a set of heuristics will be introduced and discussed that were captured while working with expert analysts to replicate the current state-of-the-art; secondly, leveraging the power of modern computing to evaluate multiple trajectories simultaneously, and therefore, enable the exploration of the trajectory's design space early during the pre-conceptual and conceptual phases of design. When this methodology is coupled with design of experiments in order to train surrogate models, the authors were able to visualize the trajectory design space, enabling parametric optimal ascent trajectory information to be introduced with other pre-conceptual and conceptual design tools. The potential impact of this methodology's success would be a fully automated POST evaluation suite for the purpose of conceptual and preliminary design trade studies. This will enable engineers to characterize the ascent trajectory's sensitivity to design changes in an arbitrary number of dimensions and for finding settings for trajectory specific variables, which result in optimal performance for a "dialed-in" launch vehicle design. The effort described in this paper was developed for the Advanced Concepts Office [2] at NASA Marshall

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

NARCIS (Netherlands)

Yao, W.; Chen, X.; Ouyang, Q.; Van Tooren, M.

2011-01-01

Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with

A robust approach to optimal matched filter design in ultrasonic non-destructive evaluation (NDE)

Science.gov (United States)

Li, Minghui; Hayward, Gordon

2017-02-01

The matched filter was demonstrated to be a powerful yet efficient technique to enhance defect detection and imaging in ultrasonic non-destructive evaluation (NDE) of coarse grain materials, provided that the filter was properly designed and optimized. In the literature, in order to accurately approximate the defect echoes, the design utilized the real excitation signals, which made it time consuming and less straightforward to implement in practice. In this paper, we present a more robust and flexible approach to optimal matched filter design using the simulated excitation signals, and the control parameters are chosen and optimized based on the real scenario of array transducer, transmitter-receiver system response, and the test sample, as a result, the filter response is optimized and depends on the material characteristics. Experiments on industrial samples are conducted and the results confirm the great benefits of the method.

Optimizing the design and impact behavior of a polymeric enclosure

International Nuclear Information System (INIS)

Singh, R.; Mattoo, A.; Saigal, A.

2006-01-01

This paper focuses on the application of finite element analysis to design an electronic enclosure with improved impact resistance properties. With the growing push towards miniaturization there is a constant decrease in the wall thickness of the enclosure applications. This necessitates use of ribs to enhance the impact resistance. This study aims at investigating optimal design of ribs for improving impact resistance. The 'DSGZ' phenomenological constitutive model, which uniformly describes the entire range of stress-strain constitutive relationship of polymers under any monotonic loading mode is used to predict the plastic failure energies. Several simulation runs were performed based on the design parameters using a 2 3 factorial design of experiments. The results from these simulations were used to analyze and study the various design parameters and its influence on the impact energy. It was found that when designing enclosures with ribs with an objective to maximize the impact failure energy, stress should be laid on optimizing the ratio of wall thickness to rib height within permissible limits while center-to-center spacing between the ribs and rib thickness do not have a significant effect

Optimizing the design and impact behavior of a polymeric enclosure

Energy Technology Data Exchange (ETDEWEB)

Singh, R. [Department of Mechanical Engineering, Tufts University, Medford, MA 02155 (United States); Mattoo, A. [Department of Mechanical Engineering, Tufts University, Medford, MA 02155 (United States); Saigal, A. [Department of Mechanical Engineering, Tufts University, Medford, MA 02155 (United States)]. E-mail: anil.saigal@tufts.edu

2006-07-01

This paper focuses on the application of finite element analysis to design an electronic enclosure with improved impact resistance properties. With the growing push towards miniaturization there is a constant decrease in the wall thickness of the enclosure applications. This necessitates use of ribs to enhance the impact resistance. This study aims at investigating optimal design of ribs for improving impact resistance. The 'DSGZ' phenomenological constitutive model, which uniformly describes the entire range of stress-strain constitutive relationship of polymers under any monotonic loading mode is used to predict the plastic failure energies. Several simulation runs were performed based on the design parameters using a 2{sup 3} factorial design of experiments. The results from these simulations were used to analyze and study the various design parameters and its influence on the impact energy. It was found that when designing enclosures with ribs with an objective to maximize the impact failure energy, stress should be laid on optimizing the ratio of wall thickness to rib height within permissible limits while center-to-center spacing between the ribs and rib thickness do not have a significant effect.

Telemanipulator design and optimization software

Science.gov (United States)

Cote, Jean; Pelletier, Michel

1995-12-01

For many years, industrial robots have been used to execute specific repetitive tasks. In those cases, the optimal configuration and location of the manipulator only has to be found once. The optimal configuration or position where often found empirically according to the tasks to be performed. In telemanipulation, the nature of the tasks to be executed is much wider and can be very demanding in terms of dexterity and workspace. The position/orientation of the robot's base could be required to move during the execution of a task. At present, the choice of the initial position of the teleoperator is usually found empirically which can be sufficient in the case of an easy or repetitive task. In the converse situation, the amount of time wasted to move the teleoperator support platform has to be taken into account during the execution of the task. Automatic optimization of the position/orientation of the platform or a better designed robot configuration could minimize these movements and save time. This paper will present two algorithms. The first algorithm is used to optimize the position and orientation of a given manipulator (or manipulators) with respect to the environment on which a task has to be executed. The second algorithm is used to optimize the position or the kinematic configuration of a robot. For this purpose, the tasks to be executed are digitized using a position/orientation measurement system and a compact representation based on special octrees. Given a digitized task, the optimal position or Denavit-Hartenberg configuration of the manipulator can be obtained numerically. Constraints on the robot design can also be taken into account. A graphical interface has been designed to facilitate the use of the two optimization algorithms.

Optimal Design of Porous Materials

DEFF Research Database (Denmark)

Andreassen, Erik

The focus of this thesis is topology optimization of material microstructures. That is, creating new materials, with attractive properties, by combining classic materials in periodic patterns. First, large-scale topology optimization is used to design complicated three-dimensional materials......, throughout the thesis extra attention is given to obtain structures that can be manufactured. That is also the case in the final part, where a simple multiscale method for the optimization of structural damping is presented. The method can be used to obtain an optimized component with structural details...

Optimal design of lossy bandgap structures

DEFF Research Database (Denmark)

Jensen, Jakob Søndergaard

2004-01-01

The method of topology optimization is used to design structures for wave propagation with one lossy material component. Optimized designs for scalar elastic waves are presented for mininimum wave transmission as well as for maximum wave energy dissipation. The structures that are obtained...... are of the 1D or 2D bandgap type depending on the objective and the material parameters....

The Primary Experiments of an Analysis of Pareto Solutions for Conceptual Design Optimization Problem of Hybrid Rocket Engine

Science.gov (United States)

Kudo, Fumiya; Yoshikawa, Tomohiro; Furuhashi, Takeshi

Recentry, Multi-objective Genetic Algorithm, which is the application of Genetic Algorithm to Multi-objective Optimization Problems is focused on in the engineering design field. In this field, the analysis of design variables in the acquired Pareto solutions, which gives the designers useful knowledge in the applied problem, is important as well as the acquisition of advanced solutions. This paper proposes a new visualization method using Isomap which visualizes the geometric distances of solutions in the design variable space considering their distances in the objective space. The proposed method enables a user to analyze the design variables of the acquired solutions considering their relationship in the objective space. This paper applies the proposed method to the conceptual design optimization problem of hybrid rocket engine and studies the effectiveness of the proposed method.

Learning from Experiments in Optimization

DEFF Research Database (Denmark)

Winthereik, Brit Ross; Jensen, Casper Bruun

2017-01-01

This article examines attempts by professionals in the Danish branch of the environmental NGO NatureAid to optimize their practice by developing a local standard. Describing these efforts as an experiment in optimization, we outline a post-critical alternative to critiques that centre on the redu...... of management as ‘broken up;’ as a distributed, ambient activity, variably performed by different actors using different standards....

Collaborative Systems Driven Aircraft Configuration Design Optimization

OpenAIRE

Shiva Prakasha, Prajwal; Ciampa, Pier Davide; Nagel, Björn

2016-01-01

A Collaborative, Inside-Out Aircraft Design approach is presented in this paper. An approach using physics based analysis to evaluate the correlations between the airframe design, as well as sub-systems integration from the early design process, and to exploit the synergies within a simultaneous optimization process. Further, the disciplinary analysis modules involved in the optimization task are located in different organization. Hence, the Airframe and Subsystem design tools are integrated ...

Electrical Parasitics and Thermal Modeling for Optimized Layout Design of High Power SiC Modules

DEFF Research Database (Denmark)

Bahman, Amir Sajjad; Blaabjerg, Frede; Dutta, Atanu

2016-01-01

, the presented models are verified by a conventional and an optimized power module layout. The optimized layout is designed based on the reduction of stray inductance and temperature in a P-cell and N-cell half-bridge module. The presented models are verified by FEM simulations and also experiment....

Parametric Optimization of Hospital Design

DEFF Research Database (Denmark)

Holst, Malene Kirstine; Kirkegaard, Poul Henning; Christoffersen, L.D.

2013-01-01

Present paper presents a parametric performancebased design model for optimizing hospital design. The design model operates with geometric input parameters defining the functional requirements of the hospital and input parameters in terms of performance objectives defining the design requirements...... and preferences of the hospital with respect to performances. The design model takes point of departure in the hospital functionalities as a set of defined parameters and rules describing the design requirements and preferences....

Solid Rocket Motor Design Using Hybrid Optimization

Directory of Open Access Journals (Sweden)

Kevin Albarado

2012-01-01

Full Text Available A particle swarm/pattern search hybrid optimizer was used to drive a solid rocket motor modeling code to an optimal solution. The solid motor code models tapered motor geometries using analytical burn back methods by slicing the grain into thin sections along the axial direction. Grains with circular perforated stars, wagon wheels, and dog bones can be considered and multiple tapered sections can be constructed. The hybrid approach to optimization is capable of exploring large areas of the solution space through particle swarming, but is also able to climb “hills” of optimality through gradient based pattern searching. A preliminary method for designing tapered internal geometry as well as tapered outer mold-line geometry is presented. A total of four optimization cases were performed. The first two case studies examines designing motors to match a given regressive-progressive-regressive burn profile. The third case study studies designing a neutrally burning right circular perforated grain (utilizing inner and external geometry tapering. The final case study studies designing a linearly regressive burning profile for right circular perforated (tapered grains.

Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

Science.gov (United States)

Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

2002-06-01

This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

Application of surrogate-based global optimization to aerodynamic design

CERN Document Server

Pérez, Esther

2016-01-01

Aerodynamic design, like many other engineering applications, is increasingly relying on computational power. The growing need for multi-disciplinarity and high fidelity in design optimization for industrial applications requires a huge number of repeated simulations in order to find an optimal design candidate. The main drawback is that each simulation can be computationally expensive – this becomes an even bigger issue when used within parametric studies, automated search or optimization loops, which typically may require thousands of analysis evaluations. The core issue of a design-optimization problem is the search process involved. However, when facing complex problems, the high-dimensionality of the design space and the high-multi-modality of the target functions cannot be tackled with standard techniques. In recent years, global optimization using meta-models has been widely applied to design exploration in order to rapidly investigate the design space and find sub-optimal solutions. Indeed, surrogat...

Extracting Insights from Experience Designers to Enhance User Experience Design

OpenAIRE

Kremer, Simon; Lindemann, Udo

2016-01-01

User Experience (UX) summarizes how a user expects, perceives and assesses an encounter with a product. User Experience Design (UXD) aims at creating meaningful experiences. While UXD is a rather young discipline with-in product development and traditional processes predominate, other disciplines traditionally focus on creating experiences. We engaged with experience de-signers from the fields of arts, movies, sports, music and event management. By analyzing their working processes via interv...

Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm

International Nuclear Information System (INIS)

Rao, R.V.; More, K.C.

2015-01-01

Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. - Highlights: • The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe. • Two examples of heat pipe design and optimization are presented. • The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence

An expert system for integrated structural analysis and design optimization for aerospace structures

Science.gov (United States)

1992-04-01

The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and

Solidification of hesperidin nanosuspension by spray drying optimized by design of experiment (DoE).

Science.gov (United States)

Wei, Qionghua; Keck, Cornelia M; Müller, Rainer H

2018-01-01

To accelerate the determination of optimal spray drying parameters, a "Design of Experiment" (DoE) software was applied to produce well redispersible hesperidin nanocrystals. For final solid dosage forms, aqueous liquid nanosuspensions need to be solidified, whereas spray drying is a large-scale cost-effective industrial process. A nanosuspension with 18% (w/w) of hesperidin stabilized by 1% (w/w) of poloxamer 188 was produced by wet bead milling. The sizes of original and redispersed spray-dried nanosuspensions were determined by laser diffractometry (LD) and photon correlation spectroscopy (PCS) and used as effect parameters. In addition, light microscopy was performed to judge the redispersion quality. After a two-step design of MODDE 9, screening model and response surface model (RSM), the inlet temperature of spray dryer and the concentration of protectant (polyvinylpyrrolidone, PVP K25) were identified as the most important factors affecting the redispersion of nanocrystals. As predicted in the RSM modeling, when 5% (w/w) of PVP K25 was added in an 18% (w/w) of hesperidin nanosuspension, subsequently spray-dried at an inlet temperature of 100 °C, well redispersed solid nanocrystals with an average particle size of 276 nm were obtained. By the use of PVP K25, the saturation solubility of the redispersed nanocrystals in water was improved to 86.81 µg/ml, about 2.5-fold of the original nanosuspension. In addition, the dissolution velocity was accelerated. This was attributed to the additional effects of steric stabilization on the nanocrystals and solubilization by the PVP polymer from spray drying.

Optimization design of turbo-expander gas bearing for a 500W helium refrigerator

Science.gov (United States)

Li, S. S.; Fu, B.; Y Zhang, Q.

2017-12-01

Turbo-expander is the core machinery of the helium refrigerator. Bearing as the supporting element is the core technology to impact the design of turbo-expander. The perfect design and performance study for the gas bearing are essential to ensure the stability of turbo-expander. In this paper, numerical simulation is used to analyze the performance of gas bearing for a 500W helium refrigerator turbine, and the optimization design of the gas bearing has been completed. And the results of the gas bearing optimization have a guiding role in the processing technology. Finally, the turbine experiments verify that the gas bearing has good performance, and ensure the stable operation of the turbine.

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.

Design and Validation of Optimized Feedforward with Robust Feedback Control of a Nuclear Reactor

International Nuclear Information System (INIS)

Shaffer, Roman; He Weidong; Edwards, Robert M.

2004-01-01

Design applications for robust feedback and optimized feedforward control, with confirming results from experiments conducted on the Pennsylvania State University TRIGA reactor, are presented. The combination of feedforward and feedback control techniques complement each other in that robust control offers guaranteed closed-loop stability in the presence of uncertainties, and optimized feedforward offers an approach to achieving performance that is sometimes limited by overly conservative robust feedback control. The design approach taken in this work combines these techniques by first designing robust feedback control. Alternative methods for specifying a low-order linear model and uncertainty specifications, while seeking as much performance as possible, are discussed and evaluated. To achieve desired performance characteristics, the optimized feedforward control is then computed by using the nominal nonlinear plant model that incorporates the robust feedback control

Optimal design of distributed control and embedded systems

CERN Document Server

Çela, Arben; Li, Xu-Guang; Niculescu, Silviu-Iulian

2014-01-01

Optimal Design of Distributed Control and Embedded Systems focuses on the design of special control and scheduling algorithms based on system structural properties as well as on analysis of the influence of induced time-delay on systems performances. It treats the optimal design of distributed and embedded control systems (DCESs) with respect to communication and calculation-resource constraints, quantization aspects, and potential time-delays induced by the associated  communication and calculation model. Particular emphasis is put on optimal control signal scheduling based on the system state. In order to render  this complex optimization problem feasible in real time, a time decomposition is based on periodicity induced by the static scheduling is operated. The authors present a co-design approach which subsumes the synthesis of the optimal control laws and the generation of an optimal schedule of control signals on real-time networks as well as the execution of control tasks on a single processor. The a...

Performance-based Pareto optimal design

NARCIS (Netherlands)

Sariyildiz, I.S.; Bittermann, M.S.; Ciftcioglu, O.

2008-01-01

A novel approach for performance-based design is presented, where Pareto optimality is pursued. Design requirements may contain linguistic information, which is difficult to bring into computation or make consistent their impartial estimations from case to case. Fuzzy logic and soft computing are

Optimal configuration for programmable Moessbauer experiments

Energy Technology Data Exchange (ETDEWEB)

Pasquevich, Gustavo A; Veiga, Alejandro L; Zelis, Pedro Mendoza; Sanchez, Francisco H, E-mail: gpasquev@fisica.unlp.edu.a [Departamento de Fisica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata (Argentina)

2010-03-01

Based on channel independency of recently developed Moessbauer instrumentation an approximation to optimal configuration of experiments is presented. The analysis relies on the presumption that all the available channels of the spectrum are not equally efficient for a given experimental application. A quantification of this concept is presented and a method for different channel layout comparison is proposed. The optimization of recorded spectra is important in dynamic experiments where efficiency in data taking imposes feasibility limits as well as in static applications as a way of reducing experimental time.

A new approach to designing reduced scale thermal-hydraulic experiments

International Nuclear Information System (INIS)

Lapa, Celso M.F.; Sampaio, Paulo A.B. de; Pereira, Claudio M.N.A.

2004-01-01

Reduced scale experiments are often employed in engineering because they are much cheaper than real scale testing. Unfortunately, though, it is difficult to design a thermal-hydraulic circuit or equipment in reduced scale capable of reproducing, both accurately and simultaneously, all the physical phenomena that occur in real scale and operating conditions. This paper presents a methodology to designing thermal-hydraulic experiments in reduced scale based on setting up a constrained optimization problem that is solved using genetic algorithms (GAs). In order to demonstrate the application of the methodology proposed, we performed some investigations in the design of a heater aimed to simulate the transport of heat and momentum in the core of a pressurized water reactor (PWR) at 100% of nominal power and non-accident operating conditions. The results obtained show that the proposed methodology is a promising approach for designing reduced scale experiments

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.

PARAMETER COORDINATION AND ROBUST OPTIMIZATION FOR MULTIDISCIPLINARY DESIGN

Institute of Scientific and Technical Information of China (English)

HU Jie; PENG Yinghong; XIONG Guangleng

2006-01-01

A new parameter coordination and robust optimization approach for multidisciplinary design is presented. Firstly, the constraints network model is established to support engineering change, coordination and optimization. In this model, interval boxes are adopted to describe the uncertainty of design parameters quantitatively to enhance the design robustness. Secondly, the parameter coordination method is presented to solve the constraints network model, monitor the potential conflicts due to engineering changes, and obtain the consistency solution space corresponding to the given product specifications. Finally, the robust parameter optimization model is established, and genetic arithmetic is used to obtain the robust optimization parameter. An example of bogie design is analyzed to show the scheme to be effective.

A review of optimization techniques used in the design of fibre composite structures for civil engineering applications

International Nuclear Information System (INIS)

Awad, Ziad K.; Aravinthan, Thiru; Zhuge, Yan; Gonzalez, Felipe

2012-01-01

Highlights: → We reviewed existing optimization techniques of fibre composite structures. → Proposed an improved methodology for design optimization. → Comparison showed the MRDO is most suitable. -- Abstract: Fibre composite structures have become the most attractive candidate for civil engineering applications. Fibre reinforced plastic polymer (FRP) composite materials have been used in the rehabilitation and replacement of the old degrading traditional structures or build new structures. However, the lack of design standards for civil infrastructure limits their structural applications. The majority of the existing applications have been designed based on the research and guidelines provided by the fibre composite manufacturers or based on the designer's experience. It has been a tendency that the final structure is generally over-designed. This paper provides a review on the available studies related to the design optimization of fibre composite structures used in civil engineering such as; plate, beam, box beam, sandwich panel, bridge girder, and bridge deck. Various optimization methods are presented and compared. In addition, the importance of using the appropriate optimization technique is discussed. An improved methodology, which considering experimental testing, numerical modelling, and design constrains, is proposed in the paper for design optimization of composite structures.

OPTIMAL CAMERA NETWORK DESIGN FOR 3D MODELING OF CULTURAL HERITAGE

Directory of Open Access Journals (Sweden)

B. S. Alsadik

2012-07-01

Full Text Available Digital cultural heritage documentation in 3D is subject to research and practical applications nowadays. Image-based modeling is a technique to create 3D models, which starts with the basic task of designing the camera network. This task is – however – quite crucial in practical applications because it needs a thorough planning and a certain level of expertise and experience. Bearing in mind todays computational (mobile power we think that the optimal camera network should be designed in the field, and, therefore, making the preprocessing and planning dispensable. The optimal camera network is designed when certain accuracy demands are fulfilled with a reasonable effort, namely keeping the number of camera shots at a minimum. In this study, we report on the development of an automatic method to design the optimum camera network for a given object of interest, focusing currently on buildings and statues. Starting from a rough point cloud derived from a video stream of object images, the initial configuration of the camera network assuming a high-resolution state-of-the-art non-metric camera is designed. To improve the image coverage and accuracy, we use a mathematical penalty method of optimization with constraints. From the experimental test, we found that, after optimization, the maximum coverage is attained beside a significant improvement of positional accuracy. Currently, we are working on a guiding system, to ensure, that the operator actually takes the desired images. Further next steps will include a reliable and detailed modeling of the object applying sophisticated dense matching techniques.

Modeling, experiments and optimization of an on-pipe thermoelectric generator

International Nuclear Information System (INIS)

Chen, Jie; Zuo, Lei; Wu, Yongjia; Klein, Jackson

2016-01-01

Highlights: • A novel design of on-pipe thermoelectric generator using heat pipe. • A heat pipe is used and increases power output by more than 6 times. • Detailed system level modeling on the heat transfer and energy conversion. • Lab-based experiments shows that system can harvest more than 2 W of energy. • An optimization towards the design indicates further improvement can be achieved. - Abstract: A thermoelectric energy harvester composed of two thermoelectric modules, a wicked copper-water heat pipe, and finned heat sinks has been designed, modeled, and tested. The harvester is proposed to power sensor nodes on heating/cooling, steam, or exhaust pipes like these in power stations, chemical plants and vehicle systems. A model to analyze the heat transfer and thermoelectric performance of the energy harvesting system has been developed and validated against experiments. The results show that the model predicts the system power output and temperature response with reasonable accuracy. The model developed in this paper can be adapted for use with general heat sink, heat pipe, and thermoelectric systems. The design, incorporating a heat pipe and two 1.1″ by 1.1″ Bi_2Te_3 modules generates 2.25 W ± 0.13 W power output with a temperature difference of 128 °C ± 1.12 °C and source temperature of 246 °C ± 1.9 °C, which is more than enough to operate wireless sensors or some actuators. The use of a heat pipe in this design increased the power output by 6 times over conventional designs. Based on the model, further improvement of the power output and energy harvesting efficiency of the system has been suggested by optimizing the number of thermoelectric modules.

Evolutionary optimization methods for accelerator design

Science.gov (United States)

Poklonskiy, Alexey A.

Many problems from the fields of accelerator physics and beam theory can be formulated as optimization problems and, as such, solved using optimization methods. Despite growing efficiency of the optimization methods, the adoption of modern optimization techniques in these fields is rather limited. Evolutionary Algorithms (EAs) form a relatively new and actively developed optimization methods family. They possess many attractive features such as: ease of the implementation, modest requirements on the objective function, a good tolerance to noise, robustness, and the ability to perform a global search efficiently. In this work we study the application of EAs to problems from accelerator physics and beam theory. We review the most commonly used methods of unconstrained optimization and describe the GATool, evolutionary algorithm and the software package, used in this work, in detail. Then we use a set of test problems to assess its performance in terms of computational resources, quality of the obtained result, and the tradeoff between them. We justify the choice of GATool as a heuristic method to generate cutoff values for the COSY-GO rigorous global optimization package for the COSY Infinity scientific computing package. We design the model of their mutual interaction and demonstrate that the quality of the result obtained by GATool increases as the information about the search domain is refined, which supports the usefulness of this model. We Giscuss GATool's performance on the problems suffering from static and dynamic noise and study useful strategies of GATool parameter tuning for these and other difficult problems. We review the challenges of constrained optimization with EAs and methods commonly used to overcome them. We describe REPA, a new constrained optimization method based on repairing, in exquisite detail, including the properties of its two repairing techniques: REFIND and REPROPT. We assess REPROPT's performance on the standard constrained

Performative Computation-aided Design Optimization

Directory of Open Access Journals (Sweden)

Ming Tang

2012-12-01

Full Text Available This article discusses a collaborative research and teaching project between the University of Cincinnati, Perkins+Will’s Tech Lab, and the University of North Carolina Greensboro. The primary investigation focuses on the simulation, optimization, and generation of architectural designs using performance-based computational design approaches. The projects examine various design methods, including relationships between building form, performance and the use of proprietary software tools for parametric design.

Development of a parameter optimization technique for the design of automatic control systems

Science.gov (United States)

Whitaker, P. H.

1977-01-01

Parameter optimization techniques for the design of linear automatic control systems that are applicable to both continuous and digital systems are described. The model performance index is used as the optimization criterion because of the physical insight that can be attached to it. The design emphasis is to start with the simplest system configuration that experience indicates would be practical. Design parameters are specified, and a digital computer program is used to select that set of parameter values which minimizes the performance index. The resulting design is examined, and complexity, through the use of more complex information processing or more feedback paths, is added only if performance fails to meet operational specifications. System performance specifications are assumed to be such that the desired step function time response of the system can be inferred.

Airfoil design and optimization

Energy Technology Data Exchange (ETDEWEB)

Lutz, T. [Stuttgart Univ. (Germany). Inst. fuer Aerodynamik und Gasdynamik

2001-07-01

The aerodynamic efficiency of mildly swept wings is mainly influenced by the characteristics of the airfoil sections. The specific design of airfoils is therefore one of the classical tasks of aerodynamics. Since the airfoil characteristics are directly dependent on the inviscid pressure distribution the application of inverse calculation methods is obvious. The direct numerical airfoil optimization offers an alternative to the manual design and attracts increasing interest. (orig.)

Analysis and optimization of coupled windings in magnetic resonant wireless power transfer systems with orthogonal experiment method

DEFF Research Database (Denmark)

Yudi, Xiao; Xingkui, Mao; Mao, Lin

2017-01-01

The coupled magnetic resonant unit (CMRU) has great effect on the transmitting power capability and efficiency of magnetic resonant wireless power transfer system. The key objective i.e. the efficiency coefficient kQ is introduced in the design of CMRU or the coupled windings based on the mutual...... inductance model. Then the design method with orthogonal experiments and finite element method simulation is proposed to maximize the kQ due to low precise analytical model of AC resistance and inductance for PCB windings at high- frequency. The method can reduce the design iterations and thereby can get...... more optimal design results. The experiments verified the design objective of kQ as well as the design method effectively. In the optimal PCB windings prototype at operating frequency of 4 MHz, the kQ and the maximum efficiency are increased by about 12% and 4% respectively....

Comparison of Traditional Design Nonlinear Programming Optimization and Stochastic Methods for Structural Design

Science.gov (United States)

Patnaik, Surya N.; Pai, Shantaram S.; Coroneos, Rula M.

2010-01-01

Structural design generated by traditional method, optimization method and the stochastic design concept are compared. In the traditional method, the constraints are manipulated to obtain the design and weight is back calculated. In design optimization, the weight of a structure becomes the merit function with constraints imposed on failure modes and an optimization algorithm is used to generate the solution. Stochastic design concept accounts for uncertainties in loads, material properties, and other parameters and solution is obtained by solving a design optimization problem for a specified reliability. Acceptable solutions were produced by all the three methods. The variation in the weight calculated by the methods was modest. Some variation was noticed in designs calculated by the methods. The variation may be attributed to structural indeterminacy. It is prudent to develop design by all three methods prior to its fabrication. The traditional design method can be improved when the simplified sensitivities of the behavior constraint is used. Such sensitivity can reduce design calculations and may have a potential to unify the traditional and optimization methods. Weight versus reliabilitytraced out an inverted-S-shaped graph. The center of the graph corresponded to mean valued design. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure. Weight can be reduced to a small value for a most failure-prone design. Probabilistic modeling of load and material properties remained a challenge.

A divide and conquer approach to determine the Pareto frontier for optimization of protein engineering experiments

Science.gov (United States)

He, Lu; Friedman, Alan M.; Bailey-Kellogg, Chris

2016-01-01

In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability vs. novelty, affinity vs. specificity, activity vs. immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not “dominated”; i.e., no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), in order to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, PEPFR (Protein Engineering Pareto FRontier), that hierarchically subdivides the objective space, employing appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria. PMID:22180081

A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments.

Science.gov (United States)

He, Lu; Friedman, Alan M; Bailey-Kellogg, Chris

2012-03-01

In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability versus novelty, affinity versus specificity, activity versus immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not "dominated"; that is, no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, Protein Engineering Pareto FRontier (PEPFR), that hierarchically subdivides the objective space, using appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria. Copyright © 2011 Wiley Periodicals, Inc.

New approaches to optimization in aerospace conceptual design

Science.gov (United States)

Gage, Peter J.

1995-01-01

Aerospace design can be viewed as an optimization process, but conceptual studies are rarely performed using formal search algorithms. Three issues that restrict the success of automatic search are identified in this work. New approaches are introduced to address the integration of analyses and optimizers, to avoid the need for accurate gradient information and a smooth search space (required for calculus-based optimization), and to remove the restrictions imposed by fixed complexity problem formulations. (1) Optimization should be performed in a flexible environment. A quasi-procedural architecture is used to conveniently link analysis modules and automatically coordinate their execution. It efficiently controls a large-scale design tasks. (2) Genetic algorithms provide a search method for discontinuous or noisy domains. The utility of genetic optimization is demonstrated here, but parameter encodings and constraint-handling schemes must be carefully chosen to avoid premature convergence to suboptimal designs. The relationship between genetic and calculus-based methods is explored. (3) A variable-complexity genetic algorithm is created to permit flexible parameterization, so that the level of description can change during optimization. This new optimizer automatically discovers novel designs in structural and aerodynamic tasks.

Systematic design of microstructures by topology optimization

DEFF Research Database (Denmark)

Sigmund, Ole

2003-01-01

The topology optimization method can be used to determine the material distribution in a design domain such that an objective function is maximized and constraints are fulfilled. The method which is based on Finite Element Analysis may be applied to all kinds of material distribution problems like...... extremal material design, sensor and actuator design and MEMS synthesis. The state-of-the-art in topology optimization will be reviewed and older as well as new applications in phononic and photonic crystals design will be presented....

Poly-optimization: a paradigm in engineering design in mechatronics

Energy Technology Data Exchange (ETDEWEB)

Tarnowski, Wojciech [Koszalin University of Technology, Department of Control and Driving Systems, Institute of Mechatronics, Nanotechnology and Vacuum Technique, Koszalin (Poland); Krzyzynski, Tomasz; Maciejewski, Igor; Oleskiewicz, Robert [Koszalin University of Technology, Department of Mechatronics and Applied Mechanics, Institute of Mechatronics, Nanotechnology and Vacuum Technique, Koszalin (Poland)

2011-02-15

The paper deals with the Engineering Design that is a general methodology of a design process. It is assumed that a designer has to solve a design task as an inverse problem in an iterative way. After each iteration, a decision should be taken on the information that is called a centre of integration in a systematic design system. For this purpose, poly-optimal solutions may be used. The poly-optimization is presented and contrasted against the Multi Attribute Decision Making, and a set of the poly-optimal solutions is defined. Then Mechatronics is defined and its characteristics given, to prove that mechatronic design process vitally needs CAD tools. Three examples are quoted to demonstrate a key role of the poly-optimization in the mechatronic design. (orig.)

Parametric Design and Multiobjective Optimization of Maglev Actuators for Active Vibration Isolation System

Directory of Open Access Journals (Sweden)

Qianqian Wu

2014-05-01

Full Text Available The microvibration has a serious impact on science experiments on the space station and on image quality of high resolution satellites. As an important component of the active vibration isolation platform, the maglev actuator has a large stroke and exhibits excellent isolating performance benefiting from its noncontact characteristic. A maglev actuator with good linearity was designed in this paper. Fundamental features of the maglev actuator were obtained by finite element simulation. In order to minimize the coil weight and the heat dissipation of the maglev actuator, parametric design was carried out and multiobjective optimization based on the genetic algorithm was adopted. The optimized actuator has better mechanical properties than the initial one. Active vibration isolation platforms for different-scale payload were designed by changing the arrangement of the maglev actuators. The prototype to isolate vibration for small-scale payload was manufactured and the experiments for verifying the characteristics of the actuators were set up. The linearity of the actuator and the mechanical dynamic response of the vibration isolation platform were obtained. The experimental results highlight the effectiveness of the proposed design.

Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science.

Science.gov (United States)

Cavagnaro, Daniel R; Myung, Jay I; Pitt, Mark A; Kujala, Janne V

2010-04-01

Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying model in the fewest possible steps. When the models under consideration are nonlinear, as is often the case in cognitive science, this problem can be impossible to solve analytically without simplifying assumptions. However, as we show in this letter, a full solution can be found numerically with the help of a Bayesian computational trick derived from the statistics literature, which recasts the problem as a probability density simulation in which the optimal design is the mode of the density. We use a utility function based on mutual information and give three intuitive interpretations of the utility function in terms of Bayesian posterior estimates. As a proof of concept, we offer a simple example application to an experiment on memory retention.

Design for solid-state Rayleigh-Taylor experiments in tantalum at Omega

International Nuclear Information System (INIS)

Pollaine, S M; Remington, B A; Park, H S; Prisbrey, S T; Cavallo, R M

2010-01-01

We have designed an experiment for the Omega - EP laser facility to measure the Rayleigh-Taylor (RT) growth rate of solid-state Ta samples at ∼1 Mbar pressures and very high strain rates, 10 7 -10 8 s -1 . A thin walled, hohlraum based, ramp-wave, quasi-isentropic drive has been developed for this experiment. Thick samples (∼50 um) of Ta, with a pre-imposed sinusoidal rippled on the driven side, will be accelerated. The ripple growth due to the RT instability is greatly reduced due to the dynamic material strength. We will show detailed designs, and a thorough error analysis used to optimize the experiment and minimize uncertainty.

Multi-objective optimization design method of radiation shielding

International Nuclear Information System (INIS)

Yang Shouhai; Wang Weijin; Lu Daogang; Chen Yixue

2012-01-01

Due to the shielding design goals of diversification and uncertain process of many factors, it is necessary to develop an optimization design method of intelligent shielding by which the shielding scheme selection will be achieved automatically and the uncertainties of human impact will be reduced. For economical feasibility to achieve a radiation shielding design for automation, the multi-objective genetic algorithm optimization of screening code which combines the genetic algorithm and discrete-ordinate method was developed to minimize the costs, size, weight, and so on. This work has some practical significance for gaining the optimization design of shielding. (authors)

Design and Optimization of Capacitated Supply Chain Networks Including Quality Measures

Directory of Open Access Journals (Sweden)

Krystel K. Castillo-Villar

2014-01-01

Full Text Available This paper presents (1 a novel capacitated model for supply chain network design which considers manufacturing, distribution, and quality costs (named SCND-COQ model and (2 five combinatorial optimization methods, based on nonlinear optimization, heuristic, and metaheuristic approaches, which are used to solve realistic instances of practical size. The SCND-COQ model is a mixed-integer nonlinear problem which can be used at a strategic planning level to design a supply chain network that maximizes the total profit subject to meeting an overall quality level of the final product at minimum costs. The SCND-COQ model computes the quality-related costs for the whole supply chain network considering the interdependencies among business entities. The effectiveness of the proposed solution approaches is shown using numerical experiments. These methods allow solving more realistic (capacitated supply chain network design problems including quality-related costs (inspections, rework, opportunity costs, and others within a reasonable computational time.

Adaptive stimulus optimization and model-based experiments for sensory systems neuroscience

Directory of Open Access Journals (Sweden)

Christopher eDiMattina

2013-06-01

Full Text Available In this paper we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical textit{optimal stimulus} paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the textit{iso-response} paradigm which finds sets of stimuli giving rise to constant responses, and the textit{system identification} paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of nonlinear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify nonlinear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and towards a new paradigm of real-time model estimation and comparison.

Design optimization of grid-connected PV inverters

DEFF Research Database (Denmark)

Koutroulis, Eftichios; Blaabjerg, Frede

2011-01-01

The DC/AC inverters are the key elements in grid-connected PV energy production systems. In this paper, new design optimization techniques focused on transformerless (very high efficiency) PV inverters are proposed. They have been developed based on an analysis of the deficiencies of the current......, state-of-the-art PV inverters design technology, which limits the amount of PV energy supplied into the electric grid. The influences of the electric grid regulations and standards and the PV array operational characteristics on the design of grid-connected PV inverters have also been considered....... The simulation results verify that the proposed optimization techniques enable the maximization of the PV energy injected into the electric grid by the optimized PV installation....

Concurrent Aeroservoelastic Design and Optimization of Wind Turbines

DEFF Research Database (Denmark)

Tibaldi, Carlo

This work develops and investigates methods to integrate controllers in the wind turbine design process and to perform wind turbine optimization. These techniques can exploit the synergy between wind turbine components and generate new design solutions. Two frameworks to perform wind turbine...... optimization design are presented. These tools handle workflows to model a wind turbine and to evaluate loads and performances under specific conditions. Three approaches to evaluate loads are proposed and integrated in the optimization codes. The first method is based on time domain simulations, the second...... simulations, allows the selection of any controller parameter. The methods to evaluate loads and the pole-placement technique are then employed to carry out wind turbine optimization design from an aeroservoelastic prospective. Several analysis of the NREL 5 MW Reference Wind Turbine and the DTU 10 MW...

Models and Methods for Structural Topology Optimization with Discrete Design Variables

DEFF Research Database (Denmark)

Stolpe, Mathias

in the conceptual design phase to find innovative designs. The strength of topology optimization is the capability of determining both the optimal shape and the topology of the structure. In some cases also the optimal material properties can be determined. Optimal structural design problems are modeled...... such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used in the conceptual design phase to find innovative designs. The strength of topology optimization is the capability of determining both the optimal......Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used...

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology.

Science.gov (United States)

Cankorur-Cetinkaya, Ayca; Dias, Joao M L; Kludas, Jana; Slater, Nigel K H; Rousu, Juho; Oliver, Stephen G; Dikicioglu, Duygu

2017-06-01

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).

Dynamic optimization and adaptive controller design

Science.gov (United States)

Inamdar, S. R.

2010-10-01

In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

Ground Vehicle System Integration (GVSI) and Design Optimization Model

National Research Council Canada - National Science Library

Horton, William

1996-01-01

This report documents the Ground Vehicle System Integration (GVSI) and Design Optimization Model GVSI is a top-level analysis tool designed to support engineering tradeoff studies and vehicle design optimization efforts...

Genetic algorithms applied to nuclear reactor design optimization

International Nuclear Information System (INIS)

Pereira, C.M.N.A.; Schirru, R.; Martinez, A.S.

2000-01-01

A genetic algorithm is a powerful search technique that simulates natural evolution in order to fit a population of computational structures to the solution of an optimization problem. This technique presents several advantages over classical ones such as linear programming based techniques, often used in nuclear engineering optimization problems. However, genetic algorithms demand some extra computational cost. Nowadays, due to the fast computers available, the use of genetic algorithms has increased and its practical application has become a reality. In nuclear engineering there are many difficult optimization problems related to nuclear reactor design. Genetic algorithm is a suitable technique to face such kind of problems. This chapter presents applications of genetic algorithms for nuclear reactor core design optimization. A genetic algorithm has been designed to optimize the nuclear reactor cell parameters, such as array pitch, isotopic enrichment, dimensions and cells materials. Some advantages of this genetic algorithm implementation over a classical method based on linear programming are revealed through the application of both techniques to a simple optimization problem. In order to emphasize the suitability of genetic algorithms for design optimization, the technique was successfully applied to a more complex problem, where the classical method is not suitable. Results and comments about the applications are also presented. (orig.)

Tuning Parameters in Heuristics by Using Design of Experiments Methods

Science.gov (United States)

Arin, Arif; Rabadi, Ghaith; Unal, Resit

2010-01-01

With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently.

Quality by design approach for optimizing the formulation and physical properties of extemporaneously prepared orodispersible films

NARCIS (Netherlands)

Visser, J. Caroline; Dohmen, Willem M. C.; Hinrichs, Wouter L. J.; Breitkreutz, Joerg; Frijlink, Henderik W.; Woerdenbag, Herman J.

2015-01-01

The quality by design (QbD) approach was applied for optimizing the formulation of extemporaneously prepared orodispersible films (ODFs) using Design-Expert Software. The starting formulation was based on earlier experiments and contained the film forming agents hypromellose and carbomer 974P and

Transportation package design using numerical optimization

International Nuclear Information System (INIS)

Harding, D.C.; Witkowski, W.R.

1993-01-01

Since the design of transportation packages involves a complex coupling of structural, thermal and radiation shielding analyses and must follow very strict design constraints, numerical optimization provides the potential for more efficient container designs. In numerical optimization, the requirements of the design problem are mathematically formulated through the use of an objective function and constraints. The objective function(s), e.g., package weight, cost, volume, or combination thereof, is the function to be minimized or maximized by altering a set of design variables that define the package's shape and dimensions. Constraints are limitations on the performance of the system, such as resisting structural and thermal accident environments. Two constraints defined for an example wire mesh composite Type B package are: 1) deformation in the containment vessel seal region remains small enough throughout the 10 CFR-71 accident conditions to meet containment criteria, and 2) the elastomeric seal region remains below its operational temperature limit to guarantee seal integrity in the fire environment. The first constraint of a minimum energy absorbing layer thickness is evaluated with finite element analyses of the proposed dynamic crush accident criteria. The second constraint is evaluated with a 1-D transient thermal finite difference code parametrized for variable composite layer thicknesses, and is integrated with the optimization process. (J.P.N.)

Design optimization of shell-and-tube heat exchangers using single objective and multiobjective particle swarm optimization

International Nuclear Information System (INIS)

Elsays, Mostafa A.; Naguib Aly, M; Badawi, Alya A.

2010-01-01

The Particle Swarm Optimization (PSO) algorithm is used to optimize the design of shell-and-tube heat exchangers and determine the optimal feasible solutions so as to eliminate trial-and-error during the design process. The design formulation takes into account the area and the total annual cost of heat exchangers as two objective functions together with operating as well as geometrical constraints. The Nonlinear Constrained Single Objective Particle Swarm Optimization (NCSOPSO) algorithm is used to minimize and find the optimal feasible solution for each of the nonlinear constrained objective functions alone, respectively. Then, a novel Nonlinear Constrained Mult-objective Particle Swarm Optimization (NCMOPSO) algorithm is used to minimize and find the Pareto optimal solutions for both of the nonlinear constrained objective functions together. The experimental results show that the two algorithms are very efficient, fast and can find the accurate optimal feasible solutions of the shell and tube heat exchangers design optimization problem. (orig.)

Installation and first operation of the negative ion optimization experiment

International Nuclear Information System (INIS)

De Muri, Michela; Cavenago, Marco; Serianni, Gianluigi; Veltri, Pierluigi; Bigi, Marco; Pasqualotto, Roberto; Barbisan, Marco; Recchia, Mauro; Zaniol, Barbara; Kulevoy, Timour; Petrenko, Sergey; Baseggio, Lucio; Cervaro, Vannino; Agostini, Fabio Degli; Franchin, Luca; Laterza, Bruno; Minarello, Alessandro; Rossetto, Federico; Sattin, Manuele; Zucchetti, Simone

2015-01-01

Highlights: • Negative ion sources are key components of the neutral beam injectors. • The NIO1 experiment is a RF ion source, 60 kV–135 mA hydrogen negative ion beam. • NIO1 can contribute to beam extraction and optics thanks to quick replacement and upgrading of parts. • This work presents installation, status and first experiments results of NIO1. - Abstract: Negative ion sources are key components of the neutral beam injectors for thermonuclear fusion experiments. The NIO1 experiment is a radio frequency ion source generating a 60 kV–135 mA hydrogen negative ion beam. The beam is composed of nine beamlets over an area of about 40 × 40 mm"2. This experiment is jointly developed by Consorzio RFX and INFN-LNL, with the purpose of providing and optimizing a test ion source, capable of working in continuous mode and in conditions similar to those foreseen for the larger ion sources of the ITER neutral beam injectors. At present research and development activities on these ion sources still address several important issues related to beam extraction and optics optimization, to which the NIO1 test facility can contribute thanks to its modular design, which allows for quick replacement and upgrading of components. This contribution presents the installation phases, the status of the test facility and the results of the first experiments, which have demonstrated that the source can operate in continuous mode.

Acoustic design by topology optimization

DEFF Research Database (Denmark)

Dühring, Maria Bayard; Jensen, Jakob Søndergaard; Sigmund, Ole

2008-01-01

To bring down noise levels in human surroundings is an important issue and a method to reduce noise by means of topology optimization is presented here. The acoustic field is modeled by Helmholtz equation and the topology optimization method is based on continuous material interpolation functions...... in the density and bulk modulus. The objective function is the squared sound pressure amplitude. First, room acoustic problems are considered and it is shown that the sound level can be reduced in a certain part of the room by an optimized distribution of reflecting material in a design domain along the ceiling...

Optimal Pid Controller Design Using Adaptive Vurpso Algorithm

Science.gov (United States)

Zirkohi, Majid Moradi

2015-04-01

The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.

Optimal Design of Modern Transformerless PV Inverter Topologies

DEFF Research Database (Denmark)

Saridakis, Stefanos; Koutroulis, Eftichios; Blaabjerg, Frede

2013-01-01

the operational lifetime period of the PV installation, is also considered in the optimization process. According to the results of the proposed design method, different optimal values of the PV inverter design variables are derived for each PV inverter topology and installation site. The H5, H6, neutral point...... clamped, active-neutral point clamped and conergy-NPC PV inverters designed using the proposed optimization process feature lower levelized cost of generated electricity and lifetime cost, longer mean time between failures and inject more PV-generated energy into the electric grid than their nonoptimized......The design optimization of H5, H6, neutral point clamped, active-neutral point clamped, and conergy-NPC transformerless photovoltaic (PV) inverters is presented in this paper. The components reliability in terms of the corresponding malfunctions, affecting the PV inverter maintenance cost during...

Regression analysis as a design optimization tool

Science.gov (United States)

Perley, R.

1984-01-01

The optimization concepts are described in relation to an overall design process as opposed to a detailed, part-design process where the requirements are firmly stated, the optimization criteria are well established, and a design is known to be feasible. The overall design process starts with the stated requirements. Some of the design criteria are derived directly from the requirements, but others are affected by the design concept. It is these design criteria that define the performance index, or objective function, that is to be minimized within some constraints. In general, there will be multiple objectives, some mutually exclusive, with no clear statement of their relative importance. The optimization loop that is given adjusts the design variables and analyzes the resulting design, in an iterative fashion, until the objective function is minimized within the constraints. This provides a solution, but it is only the beginning. In effect, the problem definition evolves as information is derived from the results. It becomes a learning process as we determine what the physics of the system can deliver in relation to the desirable system characteristics. As with any learning process, an interactive capability is a real attriubute for investigating the many alternatives that will be suggested as learning progresses.

Multidisciplinary Analysis and Optimal Design: As Easy as it Sounds?

Science.gov (United States)

Moore, Greg; Chainyk, Mike; Schiermeier, John

2004-01-01

The viewgraph presentation examines optimal design for precision, large aperture structures. Discussion focuses on aspects of design optimization, code architecture and current capabilities, and planned activities and collaborative area suggestions. The discussion of design optimization examines design sensitivity analysis; practical considerations; and new analytical environments including finite element-based capability for high-fidelity multidisciplinary analysis, design sensitivity, and optimization. The discussion of code architecture and current capabilities includes basic thermal and structural elements, nonlinear heat transfer solutions and process, and optical modes generation.

Design and optimization of thermoacoustic devices

International Nuclear Information System (INIS)

Babaei, Hadi; Siddiqui, Kamran

2008-01-01

Thermoacoustics deals with the conversion of heat energy into sound energy and vice versa. It is a new and emerging technology which has a strong potential towards the development of sustainable and renewable energy systems by utilizing waste heat or solar energy. Although simple to fabricate, the designing of thermoacoustic devices is very challenging. In the present study, a comprehensive design and optimization algorithm is developed for designing thermoacoustic devices. The unique feature of the present algorithm is its ability to design thermoacoustically-driven thermoacoustic refrigerators that can serve as sustainable refrigeration systems. In addition, new features based on the energy balance are also included to design individual thermoacoustic engines and acoustically-driven thermoacoustic refrigerators. As a case study, a thermoacoustically-driven thermoacoustic refrigerator has been designed and optimized based on the developed algorithm. The results from the algorithm are in good agreement with that obtained from the computer code DeltaE

[Optimization of vacuum belt drying process of Gardeniae Fructus in Reduning injection by Box-Behnken design-response surface methodology].

Science.gov (United States)

Huang, Dao-sheng; Shi, Wei; Han, Lei; Sun, Ke; Chen, Guang-bo; Wu Jian-xiong; Xu, Gui-hong; Bi, Yu-an; Wang, Zhen-zhong; Xiao, Wei

2015-06-01

To optimize the belt drying process conditions optimization of Gardeniae Fructus extract from Reduning injection by Box-Behnken design-response surface methodology, on the basis of single factor experiment, a three-factor and three-level Box-Behnken experimental design was employed to optimize the drying technology of Gardeniae Fructus extract from Reduning injection. With drying temperature, drying time, feeding speed as independent variables and the content of geniposide as dependent variable, the experimental data were fitted to a second order polynomial equation, establishing the mathematical relationship between the content of geniposide and respective variables. With the experimental data analyzed by Design-Expert 8. 0. 6, the optimal drying parameter was as follows: the drying temperature was 98.5 degrees C , the drying time was 89 min, the feeding speed was 99.8 r x min(-1). Three verification experiments were taked under this technology and the measured average content of geniposide was 564. 108 mg x g(-1), which was close to the model prediction: 563. 307 mg x g(-1). According to the verification test, the Gardeniae Fructus belt drying process is steady and feasible. So single factor experiments combined with response surface method (RSM) could be used to optimize the drying technology of Reduning injection Gardenia extract.

A Simulation Modeling Approach Method Focused on the Refrigerated Warehouses Using Design of Experiment

Science.gov (United States)

Cho, G. S.

2017-09-01

For performance optimization of Refrigerated Warehouses, design parameters are selected based on the physical parameters such as number of equipment and aisles, speeds of forklift for ease of modification. This paper provides a comprehensive framework approach for the system design of Refrigerated Warehouses. We propose a modeling approach which aims at the simulation optimization so as to meet required design specifications using the Design of Experiment (DOE) and analyze a simulation model using integrated aspect-oriented modeling approach (i-AOMA). As a result, this suggested method can evaluate the performance of a variety of Refrigerated Warehouses operations.

Optimal design of water supply networks for enhancing seismic reliability

International Nuclear Information System (INIS)

Yoo, Do Guen; Kang, Doosun; Kim, Joong Hoon

2016-01-01

The goal of the present study is to construct a reliability evaluation model of a water supply system taking seismic hazards and present techniques to enhance hydraulic reliability of the design into consideration. To maximize seismic reliability with limited budgets, an optimal design model is developed using an optimization technique called harmony search (HS). The model is applied to actual water supply systems to determine pipe diameters that can maximize seismic reliability. The reliabilities between the optimal design and existing designs were compared and analyzed. The optimal design would both enhance reliability by approximately 8.9% and have a construction cost of approximately 1.3% less than current pipe construction cost. In addition, the reinforcement of the durability of individual pipes without considering the system produced ineffective results in terms of both cost and reliability. Therefore, to increase the supply ability of the entire system, optimized pipe diameter combinations should be derived. Systems in which normal status hydraulic stability and abnormal status available demand could be maximally secured if configured through the optimal design. - Highlights: • We construct a seismic reliability evaluation model of water supply system. • We present technique to enhance hydraulic reliability in the aspect of design. • Harmony search algorithm is applied in optimal designs process. • The effects of the proposed optimal design are improved reliability about by 9%. • Optimized pipe diameter combinations should be derived indispensably.

Systematic design of acoustic devices by topology optimization

DEFF Research Database (Denmark)

Jensen, Jakob Søndergaard; Sigmund, Ole

2005-01-01

We present a method to design acoustic devices with topology optimization. The general algorithm is exemplified by the design of a reflection chamber that minimizes the transmission of acoustic waves in a specified frequency range.......We present a method to design acoustic devices with topology optimization. The general algorithm is exemplified by the design of a reflection chamber that minimizes the transmission of acoustic waves in a specified frequency range....

Integrated Reliability-Based Optimal Design of Structures

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle

1987-01-01

In conventional optimal design of structural systems the weight or the initial cost of the structure is usually used as objective function. Further, the constraints require that the stresses and/or strains at some critical points have to be less than some given values. Finally, all variables......-based optimal design is discussed. Next, an optimal inspection and repair strategy for existing structural systems is presented. An optimization problem is formulated , where the objective is to minimize the expected total future cost of inspection and repair subject to the constraint that the reliability...... value. The reliability can be measured from an element and/or a systems point of view. A number of methods to solve reliability-based optimization problems has been suggested, see e.g. Frangopol [I]. Murotsu et al. (2], Thoft-Christensen & Sørensen (3] and Sørensen (4). For structures where...

GPU-accelerated CFD Simulations for Turbomachinery Design Optimization

NARCIS (Netherlands)

Aissa, M.H.

2017-01-01

Design optimization relies heavily on time-consuming simulations, especially when using gradient-free optimization methods. These methods require a large number of simulations in order to get a remarkable improvement over reference designs, which are nowadays based on the accumulated engineering

Controller Design Automation for Aeroservoelastic Design Optimization of Wind Turbines

NARCIS (Netherlands)

Ashuri, T.; Van Bussel, G.J.W.; Zaayer, M.B.; Van Kuik, G.A.M.

2010-01-01

The purpose of this paper is to integrate the controller design of wind turbines with structure and aerodynamic analysis and use the final product in the design optimization process (DOP) of wind turbines. To do that, the controller design is automated and integrated with an aeroelastic simulation

The Potential Role of Cache Mechanism for Complicated Design Optimization

International Nuclear Information System (INIS)

Noriyasu, Hirokawa; Fujita, Kikuo

2002-01-01

This paper discusses the potential role of cache mechanism for complicated design optimization While design optimization is an application of mathematical programming techniques to engineering design problems over numerical computation, its progress has been coevolutionary. The trend in such progress indicates that more complicated applications become the next target of design optimization beyond growth of computational resources. As the progress in the past two decades had required response surface techniques, decomposition techniques, etc., any new framework must be introduced for the future of design optimization methods. This paper proposes a possibility of what we call cache mechanism for mediating the coming challenge and briefly demonstrates some promises in the idea of Voronoi diagram based cumulative approximation as an example of its implementation, development of strict robust design, extension of design optimization for product variety

Topology Optimization - Engineering Contribution to Architectural Design

Science.gov (United States)

Tajs-Zielińska, Katarzyna; Bochenek, Bogdan

2017-10-01

The idea of the topology optimization is to find within a considered design domain the distribution of material that is optimal in some sense. Material, during optimization process, is redistributed and parts that are not necessary from objective point of view are removed. The result is a solid/void structure, for which an objective function is minimized. This paper presents an application of topology optimization to multi-material structures. The design domain defined by shape of a structure is divided into sub-regions, for which different materials are assigned. During design process material is relocated, but only within selected region. The proposed idea has been inspired by architectural designs like multi-material facades of buildings. The effectiveness of topology optimization is determined by proper choice of numerical optimization algorithm. This paper utilises very efficient heuristic method called Cellular Automata. Cellular Automata are mathematical, discrete idealization of a physical systems. Engineering implementation of Cellular Automata requires decomposition of the design domain into a uniform lattice of cells. It is assumed, that the interaction between cells takes place only within the neighbouring cells. The interaction is governed by simple, local update rules, which are based on heuristics or physical laws. The numerical studies show, that this method can be attractive alternative to traditional gradient-based algorithms. The proposed approach is evaluated by selected numerical examples of multi-material bridge structures, for which various material configurations are examined. The numerical studies demonstrated a significant influence the material sub-regions location on the final topologies. The influence of assumed volume fraction on final topologies for multi-material structures is also observed and discussed. The results of numerical calculations show, that this approach produces different results as compared with classical one

Optimal design of a composite space shield based on numerical simulations

International Nuclear Information System (INIS)

Son, Byung Jin; Yoo, Jeong Hoon; Lee, Min Hyung

2015-01-01

In this study, optimal design of a stuffed Whipple shield is proposed by using numerical simulations and new penetration criterion. The target model was selected based on the shield model used in the Columbus module of the international space station. Because experimental results can be obtained only in the low velocity region below 7 km/s, it is required to derive the Ballistic limit curve (BLC) in the high velocity region above 7 km/s by numerical simulation. AUTODYN-2D, the commercial hydro-code package, was used to simulate the nonlinear transient analysis for the hypervelocity impact. The Smoothed particle hydrodynamics (SPH) method was applied to projectile and bumper modeling to represent the debris cloud generated after the impact. Numerical simulation model and selected material properties were validated through a quantitative comparison between numerical and experimental results. A new criterion to determine whether the penetration occurs or not is proposed from kinetic energy analysis by numerical simulation in the velocity region over 7 km/s. The parameter optimization process was performed to improve the protection ability at a specific condition through the Design of experiment (DOE) method and the Response surface methodology (RSM). The performance of the proposed optimal design was numerically verified.

Topology optimization problems with design-dependent sets of constraints

DEFF Research Database (Denmark)

Schou, Marie-Louise Højlund

Topology optimization is a design tool which is used in numerous fields. It can be used whenever the design is driven by weight and strength considerations. The basic concept of topology optimization is the interpretation of partial differential equation coefficients as effective material...... properties and designing through changing these coefficients. For example, consider a continuous structure. Then the basic concept is to represent this structure by small pieces of material that are coinciding with the elements of a finite element model of the structure. This thesis treats stress constrained...... structural topology optimization problems. For such problems a stress constraint for an element should only be present in the optimization problem when the structural design variable corresponding to this element has a value greater than zero. We model the stress constrained topology optimization problem...

A Tool to Support Optimal Industrial Wastewater Treatment Design and Analysis

DEFF Research Database (Denmark)

Quaglia, Alberto; Pennati, Alessandra; Bozkurt, Hande

2013-01-01

may be suboptimal or disregard opportunities for water recycle or resource recovery and reuse. In this contribution, we propose a model-based toolbox developed to help wastewater professionals to screen among a large number of alternatives in order to identify the optimal treatment configuration from......Industrial Wastewater Treatment Plant (IWWTP) design is often based on in-house expert knowledge and experience. Because of time and resources constraints, only a small number of alternative treatment configurations and ideas are evaluated while designing an IWWTP. Consequently, the selected design...... an economic cost-benefit perspective. The toolbox is demonstrated through a case study, dealing with oil refinery wastewater treatment and water recycle....

Metamodel-based design optimization of injection molding process variables and gates of an automotive glove box for enhancing its quality

International Nuclear Information System (INIS)

Kang, Gyung Ju; Park, Chang Hyun; Choi, Dong Hoon

2016-01-01

Injection molding process variables and gates of an automotive glove box were optimally determined to enhance its injection molding quality. We minimized warpage with satisfying constraints on clamp force, weldline, and profiles of filling and packing. Design variables concerning the injection molding process are temperatures of the mold and the resin, ram speeds, and packing pressures and durations; design variables concerning the gates are the shape of the center gate and locations of two side gates. To optimally determine the design variables in an efficient way, we adopted metamodel-based design optimization, sequentially using an optimal Latin hypercube design as a design of experiment, Kriging models as metamodels that replace time-consuming injection molding simulations, and a micro genetic algorithm as an optimization algorithm. In the optimization process, a commercial injection molding analysis software, MoldflowTM, was employed to evaluate the injection molding quality at design points specified. Using the proposed design approach, the warpage was found reduced by 20.5% compared to the initial warpage, while all the design constraints were satisfied, which clearly shows the validity of the proposed design approach

Metamodel-based design optimization of injection molding process variables and gates of an automotive glove box for enhancing its quality

Energy Technology Data Exchange (ETDEWEB)

Kang, Gyung Ju [Pusan National University, Busan (Korea, Republic of); Park, Chang Hyun; Choi, Dong Hoon [Hanyang University, Seoul (Korea, Republic of)

2016-04-15

Injection molding process variables and gates of an automotive glove box were optimally determined to enhance its injection molding quality. We minimized warpage with satisfying constraints on clamp force, weldline, and profiles of filling and packing. Design variables concerning the injection molding process are temperatures of the mold and the resin, ram speeds, and packing pressures and durations; design variables concerning the gates are the shape of the center gate and locations of two side gates. To optimally determine the design variables in an efficient way, we adopted metamodel-based design optimization, sequentially using an optimal Latin hypercube design as a design of experiment, Kriging models as metamodels that replace time-consuming injection molding simulations, and a micro genetic algorithm as an optimization algorithm. In the optimization process, a commercial injection molding analysis software, MoldflowTM, was employed to evaluate the injection molding quality at design points specified. Using the proposed design approach, the warpage was found reduced by 20.5% compared to the initial warpage, while all the design constraints were satisfied, which clearly shows the validity of the proposed design approach.

Design of a microwave calorimeter for the microwave tokamak experiment

International Nuclear Information System (INIS)

Marinak, M.

1988-01-01

The initial design of a microwave calorimeter for the Microwave Tokamak Experiment is presented. The design is optimized to measure the refraction and absorption of millimeter rf microwaves as they traverse the toroidal plasma of the Alcator C tokamak. Techniques utilized can be adapted for use in measuring high intensity pulsed output from a microwave device in an environment of ultra high vacuum, intense fields of ionizing and non-ionizing radiation and intense magnetic fields. 16 refs

Software for CATV Design and Frequency Plan Optimization

OpenAIRE

Hala, O.

2007-01-01

The paper deals with the structure of a software medium used for design and sub-optimization of frequency plan in CATV networks, their description and design method. The software performance is described and a simple design example of energy balance of a simplified CATV network is given. The software was created in programming environment called Delphi and local optimization was made in Matlab.

Analog Circuit Design Optimization Based on Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Mansour Barari

2014-01-01

Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.

Optimization of a serum-free culture medium for mouse embryonic stem cells using design of experiments (DoE) methodology.

Science.gov (United States)

Knöspel, Fanny; Schindler, Rudolf K; Lübberstedt, Marc; Petzolt, Stephanie; Gerlach, Jörg C; Zeilinger, Katrin

2010-12-01

The in vitro culture behaviour of embryonic stem cells (ESC) is strongly influenced by the culture conditions. Current culture media for expansion of ESC contain some undefined substances. Considering potential clinical translation work with such cells, the use of defined media is desirable. We have used Design of Experiments (DoE) methods to investigate the composition of a serum-free chemically defined culture medium for expansion of mouse embryonic stem cells (mESC). Factor screening analysis according to Plackett-Burman revealed that insulin and leukaemia inhibitory factor (LIF) had a significant positive influence on the proliferation activity of the cells, while zinc and L: -cysteine reduced the cell growth. Further analysis using minimum run resolution IV (MinRes IV) design indicates that following factor adjustment LIF becomes the main factor for the survival and proliferation of mESC. In conclusion, DoE screening assays are applicable to develop and to refine culture media for stem cells and could also be employed to optimize culture media for human embryonic stem cells (hESC).

Optimization of infobutton design and Implementation: A systematic review.

Science.gov (United States)

Teixeira, Miguel; Cook, David A; Heale, Bret S E; Del Fiol, Guilherme

2017-10-01

in clinical settings. Improved content indexing in one study led to improved content retrieval across three health care organizations. Best practice technical approaches to ensure optimal infobutton functionality, design and implementation remain understudied. The HL7 Infobutton standard has supported wide adoption of infobutton functionality among clinical information systems and knowledge resources. Limited evidence supports infobutton enhancements such as links to specific subtopics, configuration of optimal resources for specific tasks and users, and improved indexing and content coverage. Further research is needed to investigate user experience improvements to increase infobutton use and effectiveness. Copyright © 2017 Elsevier Inc. All rights reserved.

Improving Battery Reactor Core Design Using Optimization Method

International Nuclear Information System (INIS)

Son, Hyung M.; Suh, Kune Y.

2011-01-01

The Battery Omnibus Reactor Integral System (BORIS) is a small modular fast reactor being designed at Seoul National University to satisfy various energy demands, to maintain inherent safety by liquid-metal coolant lead for natural circulation heat transport, and to improve power conversion efficiency with the Modular Optimal Balance Integral System (MOBIS) using the supercritical carbon dioxide as working fluid. This study is focused on developing the Neutronics Optimized Reactor Analysis (NORA) method that can quickly generate conceptual design of a battery reactor core by means of first principle calculations, which is part of the optimization process for reactor assembly design of BORIS

Optimal design of RTCs in digital circuit fault self-repair based on global signal optimization

Institute of Scientific and Technical Information of China (English)

Zhang Junbin; Cai Jinyan; Meng Yafeng

2016-01-01

Since digital circuits have been widely and thoroughly applied in various fields, electronic systems are increasingly more complicated and require greater reliability. Faults may occur in elec-tronic systems in complicated environments. If immediate field repairs are not made on the faults, elec-tronic systems will not run normally, and this will lead to serious losses. The traditional method for improving system reliability based on redundant fault-tolerant technique has been unable to meet the requirements. Therefore, on the basis of (evolvable hardware)-based and (reparation balance technology)-based electronic circuit fault self-repair strategy proposed in our preliminary work, the optimal design of rectification circuits (RTCs) in electronic circuit fault self-repair based on global sig-nal optimization is deeply researched in this paper. First of all, the basic theory of RTC optimal design based on global signal optimization is proposed. Secondly, relevant considerations and suitable ranges are analyzed. Then, the basic flow of RTC optimal design is researched. Eventually, a typical circuit is selected for simulation verification, and detailed simulated analysis is made on five circumstances that occur during RTC evolution. The simulation results prove that compared with the conventional design method based RTC, the global signal optimization design method based RTC is lower in hardware cost, faster in circuit evolution, higher in convergent precision, and higher in circuit evolution success rate. Therefore, the global signal optimization based RTC optimal design method applied in the elec-tronic circuit fault self-repair technology is proven to be feasible, effective, and advantageous.

Design optimization of jacket structures for mass production

DEFF Research Database (Denmark)

Sandal, Kasper

This thesis presents models and applications for structural optimization of jacket structures for offshore wind turbines. The motivation is that automatic design procedures can be used to obtain more cost efficient designs, and thus reduce the levelized cost of energy from offshore wind. A struct......This thesis presents models and applications for structural optimization of jacket structures for offshore wind turbines. The motivation is that automatic design procedures can be used to obtain more cost efficient designs, and thus reduce the levelized cost of energy from offshore wind....... A structural finite element model is developed specifically for the analysis and optimization of jacket structures. The model uses Timoshenko beam elements, and assumes thin walled tubular beams and a linear elastic structural response. The finite element model is implemented in a Matlab package called JADOP...... (Jacket Design Optimization), and the static and dynamic structural response is verified with the commercial finite element software Abaqus. A parametric mesh of the offshore wind turbine structure makes it relatively easy to represent various structures from the literature, as well as exploring...

Multi-objective three stage design optimization for island microgrids

International Nuclear Information System (INIS)

Sachs, Julia; Sawodny, Oliver

2016-01-01

Highlights: • An enhanced multi-objective three stage design optimization for microgrids is given. • Use of an optimal control problem for the calculation of the optimal operation. • The inclusion of a detailed battery model with CC/CV charging control. • The determination of a representative profile with optimized number of days. • The proposed method finds its direct application in a design tool for microgids. - Abstract: Hybrid off-grid energy systems enable a cost efficient and reliable energy supply to rural areas around the world. The main potential for a low cost operation and uninterrupted power supply lies in the optimal sizing and operation of such microgrids. In particular, sudden variations in load demand or in the power supply from renewables underline the need for an optimally sized system. This paper presents an efficient multi-objective model based optimization approach for the optimal sizing of all components and the determination of the best power electronic layout. The presented method is divided into three optimization problems to minimize economic and environmental objectives. This design optimization includes detailed components models and an optimized energy dispatch strategy which enables the optimal design of the energy system with respect to an adequate control for the specific configuration. To significantly reduce the computation time without loss of accuracy, the presented method contains the determination of a representative load profile using a k-means clustering method. The k-means algorithm itself is embedded in an optimization problem for the calculation of the optimal number of clusters. The benefits in term of reduced computation time, inclusion of optimal energy dispatch and optimization of power electronic architecture, of the presented optimization method are illustrated using a case study.

Optimal Design for Reactivity Ratio Estimation: A Comparison of Techniques for AMPS/Acrylamide and AMPS/Acrylic Acid Copolymerizations

Directory of Open Access Journals (Sweden)

Alison J. Scott

2015-11-01

Full Text Available Water-soluble polymers of acrylamide (AAm and acrylic acid (AAc have significant potential in enhanced oil recovery, as well as in other specialty applications. To improve the shear strength of the polymer, a third comonomer, 2-acrylamido-2-methylpropane sulfonic acid (AMPS, can be added to the pre-polymerization mixture. Copolymerization kinetics of AAm/AAc are well studied, but little is known about the other comonomer pairs (AMPS/AAm and AMPS/AAc. Hence, reactivity ratios for AMPS/AAm and AMPS/AAc copolymerization must be established first. A key aspect in the estimation of reliable reactivity ratios is design of experiments, which minimizes the number of experiments and provides increased information content (resulting in more precise parameter estimates. However, design of experiments is hardly ever used during copolymerization parameter estimation schemes. In the current work, copolymerization experiments for both AMPS/AAm and AMPS/AAc are designed using two optimal techniques (Tidwell-Mortimer and the error-in-variables-model (EVM. From these optimally designed experiments, accurate reactivity ratio estimates are determined for AMPS/AAm (rAMPS = 0.18, rAAm = 0.85 and AMPS/AAc (rAMPS = 0.19, rAAc = 0.86.

Numerical optimization of Combined Heat and Power Organic Rankine Cycles – Part A: Design optimization

International Nuclear Information System (INIS)

Martelli, Emanuele; Capra, Federico; Consonni, Stefano

2015-01-01

This two-part paper proposes an approach based on state-of-the-art numerical optimization methods for simultaneously determining the most profitable design and part-load operation of Combined Heat and Power Organic Rankine Cycles. Compared to the usual design practice, the important advantages of the proposed approach are (i) to consider the part-load performance of the ORC at the design stage, (ii) to optimize not only the cycle variables, but also the main turbine design variables (number of stages, stage loads, rotational speed). In this first part (Part A), the design model and the optimization algorithm are presented and tested on a real-world test case. PGS-COM, a recently proposed hybrid derivative-free algorithm, allows to efficiently tackle the challenging non-smooth black-box problem. - Highlights: • Algorithm for the simultaneous optimization Organic Rakine Cycle and turbine. • Thermodynamic and economic models of boiler, cycle, turbine are developed. • Non-smooth black-box optimization problem is successfully tackled with PGS-COM. • Test cases show that the algorithm returns optimal solutions within 4 min. • Toluene outperforms MDM (a siloxane) in terms of efficiency and costs.

Design Buildings Optimally: A Lifecycle Assessment Approach

KAUST Repository

Hosny, Ossama

2013-01-01

This paper structures a generic framework to support optimum design for multi-buildings in desert environment. The framework is targeting an environmental friendly design with minimum lifecycle cost, using Genetic Algorithms (Gas). GAs function through a set of success measures which evaluates the design, formulates a proper objective, and reflects possible tangible/intangible constraints. The framework optimizes the design and categorizes it under a certain environmental category at minimum Life Cycle Cost (LCC). It consists of three main modules: (1) a custom Building InformationModel (BIM) for desert buildings with a compatibility checker as a central interactive database; (2) a system evaluator module to evaluate the proposed success measures for the design; and (3) a GAs optimization module to ensure optimum design. The framework functions through three levels: the building components, integrated building, and multi-building levels. At the component level the design team should be able to select components in a designed sequence to ensure compatibility among various components, while at the building level; the team can relatively locate and orient each individual building. Finally, at the multi-building (compound) level the whole design can be evaluated using success measures of natural light, site capacity, shading impact on natural lighting, thermal change, visual access and energy saving. The framework through genetic algorithms optimizes the design by determining proper types of building components and relative buildings locations and orientations which ensure categorizing the design under a specific category or meet certain preferences at minimum lifecycle cost.

Optimal Design of Modern Transformerless PV Inverter Topologies

OpenAIRE

Saridakis, Stefanos; Koutroulis, Eftichios; Blaabjerg, Frede

2013-01-01

The design optimization of H5, H6, neutral point clamped, active-neutral point clamped, and conergy-NPC transformerless photovoltaic (PV) inverters is presented in this paper. The components reliability in terms of the corresponding malfunctions, affecting the PV inverter maintenance cost during the operational lifetime period of the PV installation, is also considered in the optimization process. According to the results of the proposed design method, different optimal values of the PV inver...

a Comparison of Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization in Optimal First-Order Design of Indoor Tls Networks

Science.gov (United States)

Jia, F.; Lichti, D.

2017-09-01

The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn't guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

A procedure for multi-objective optimization of tire design parameters

OpenAIRE

Nikola Korunović; Miloš Madić; Miroslav Trajanović; Miroslav Radovanović

2015-01-01

The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zo...

Software for CATV Design and Frequency Plan Optimization

Directory of Open Access Journals (Sweden)

O. Hala

2007-09-01

Full Text Available The paper deals with the structure of a software medium used for design and sub-optimization of frequency plan in CATV networks, their description and design method. The software performance is described and a simple design example of energy balance of a simplified CATV network is given. The software was created in programming environment called Delphi and local optimization was made in Matlab.

Multiobjective hyper heuristic scheme for system design and optimization

Science.gov (United States)

Rafique, Amer Farhan

2012-11-01

As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.

Methodology for designing aircraft having optimal sound signatures

NARCIS (Netherlands)

Sahai, A.K.; Simons, D.G.

2017-01-01

This paper presents a methodology with which aircraft designs can be modified such that they produce optimal sound signatures on the ground. With optimal sound it is implied in this case sounds that are perceived as less annoying by residents living near airport vicinities. A novel design and

Experimental design for optimizing MALDI-TOF-MS analysis of palladium complexes

Directory of Open Access Journals (Sweden)

Rakić-Kostić Tijana M.

2017-01-01

Full Text Available This paper presents optimization of matrix-assisted laser desorption/ionization (MALDI time-of-flight (TOF mass spectrometer (MS instrumental parameters for the analysis of chloro(2,2'',2"-terpyridinepalladium(II chloride dihydrate complex applying design of experiments methodology (DoE. This complex is of interest for potential use in the cancer therapy. DoE methodology was proved to succeed in optimization of many complex analytical problems. However, it has been poorly used for MALDI-TOF-MS optimization up to now. The theoretical mathematical relationships which explain the influence of important experimental factors (laser energy, grid voltage and number of laser shots on the selected responses (signal to noise – S/N ratio and the resolution – R of the leading peak is established. The optimal instrumental settings providing maximal S/N and R are identified and experimentally verified. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 172052 and Grant no. 172011

High-efficiency design optimization of a centrifugal pump

Energy Technology Data Exchange (ETDEWEB)

Heo, Man Woong; Ma, Sang Bum; Shim, Hyeon Seok; Kim, Kwang Yong [Dept. of Mechanical Engineering, Inha University, Incheon (Korea, Republic of)

2016-09-15

Design optimization of a backward-curved blades centrifugal pump with specific speed of 150 has been performed to improve hydraulic performance of the pump using surrogate modeling and three-dimensional steady Reynolds-averaged Navier-Stokes analysis. The shear stress transport model was used for the analysis of turbulence. Four geometric variables defining the blade hub inlet angle, hub contours, blade outlet angle, and blade angle profile of impeller were selected as design variables, and total efficiency of the pump at design flow rate was set as the objective function for the optimization. Thirty-six design points were chosen using the Latin hypercube sampling, and three different surrogate models were constructed using the objective function values calculated at these design points. The optimal point was searched from the constructed surrogate model by using sequential quadratic programming. The optimum designs of the centrifugal pump predicted by the surrogate models show considerable increases in efficiency compared to a reference design. Performance of the best optimum design was validated compared to experimental data for total efficiency and head.

Design of experiments for microencapsulation applications: A review.

Science.gov (United States)

Paulo, Filipa; Santos, Lúcia

2017-08-01

Microencapsulation techniques have been intensively explored by many research sectors such as pharmaceutical and food industries. Microencapsulation allows to protect the active ingredient from the external environment, mask undesired flavours, a possible controlled release of compounds among others. The purpose of this review is to provide a background of design of experiments in microencapsulation research context. Optimization processes are required for an accurate research in these fields and therefore, the right implementation of micro-sized techniques at industrial scale. This article critically reviews the use of the response surface methodologies in pharmaceutical and food microencapsulation research areas. A survey of optimization procedures in the literature, in the last few years is also presented. Copyright © 2017 Elsevier B.V. All rights reserved.

Discriminating Among Probability Weighting Functions Using Adaptive Design Optimization

Science.gov (United States)

Cavagnaro, Daniel R.; Pitt, Mark A.; Gonzalez, Richard; Myung, Jay I.

2014-01-01

Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within cumulative prospect theory. While several different parametric forms have been proposed, their qualitative similarities make it challenging to discriminate among them empirically. In this paper, we use both simulation and choice experiments to investigate the extent to which different parametric forms of the probability weighting function can be discriminated using adaptive design optimization, a computer-based methodology that identifies and exploits model differences for the purpose of model discrimination. The simulation experiments show that the correct (data-generating) form can be conclusively discriminated from its competitors. The results of an empirical experiment reveal heterogeneity between participants in terms of the functional form, with two models (Prelec-2, Linear in Log Odds) emerging as the most common best-fitting models. The findings shed light on assumptions underlying these models. PMID:24453406

Robust Design Optimization of an Aerospace Vehicle Prolusion System

Directory of Open Access Journals (Sweden)

Muhammad Aamir Raza

2011-01-01

Full Text Available This paper proposes a robust design optimization methodology under design uncertainties of an aerospace vehicle propulsion system. The approach consists of 3D geometric design coupled with complex internal ballistics, hybrid optimization, worst-case deviation, and efficient statistical approach. The uncertainties are propagated through worst-case deviation using first-order orthogonal design matrices. The robustness assessment is measured using the framework of mean-variance and percentile difference approach. A parametric sensitivity analysis is carried out to analyze the effects of design variables variation on performance parameters. A hybrid simulated annealing and pattern search approach is used as an optimizer. The results show the objective function of optimizing the mean performance and minimizing the variation of performance parameters in terms of thrust ratio and total impulse could be achieved while adhering to the system constraints.

Statistical aspects of quantitative real-time PCR experiment design.

Science.gov (United States)

Kitchen, Robert R; Kubista, Mikael; Tichopad, Ales

2010-04-01

Experiments using quantitative real-time PCR to test hypotheses are limited by technical and biological variability; we seek to minimise sources of confounding variability through optimum use of biological and technical replicates. The quality of an experiment design is commonly assessed by calculating its prospective power. Such calculations rely on knowledge of the expected variances of the measurements of each group of samples and the magnitude of the treatment effect; the estimation of which is often uninformed and unreliable. Here we introduce a method that exploits a small pilot study to estimate the biological and technical variances in order to improve the design of a subsequent large experiment. We measure the variance contributions at several 'levels' of the experiment design and provide a means of using this information to predict both the total variance and the prospective power of the assay. A validation of the method is provided through a variance analysis of representative genes in several bovine tissue-types. We also discuss the effect of normalisation to a reference gene in terms of the measured variance components of the gene of interest. Finally, we describe a software implementation of these methods, powerNest, that gives the user the opportunity to input data from a pilot study and interactively modify the design of the assay. The software automatically calculates expected variances, statistical power, and optimal design of the larger experiment. powerNest enables the researcher to minimise the total confounding variance and maximise prospective power for a specified maximum cost for the large study. Copyright 2010 Elsevier Inc. All rights reserved.

Optimal cure cycle design of a resin-fiber composite laminate

Science.gov (United States)

Hou, Jean W.; Sheen, Jeenson

1987-01-01

A unified computed aided design method was studied for the cure cycle design that incorporates an optimal design technique with the analytical model of a composite cure process. The preliminary results of using this proposed method for optimal cure cycle design are reported and discussed. The cure process of interest is the compression molding of a polyester which is described by a diffusion reaction system. The finite element method is employed to convert the initial boundary value problem into a set of first order differential equations which are solved simultaneously by the DE program. The equations for thermal design sensitivities are derived by using the direct differentiation method and are solved by the DE program. A recursive quadratic programming algorithm with an active set strategy called a linearization method is used to optimally design the cure cycle, subjected to the given design performance requirements. The difficulty of casting the cure cycle design process into a proper mathematical form is recognized. Various optimal design problems are formulated to address theses aspects. The optimal solutions of these formulations are compared and discussed.

Towards robust optimal design of storm water systems

Science.gov (United States)

Marquez Calvo, Oscar; Solomatine, Dimitri

2015-04-01

In this study the focus is on the design of a storm water or a combined sewer system. Such a system should be capable to handle properly most of the storm to minimize the damages caused by flooding due to the lack of capacity of the system to cope with rain water at peak times. This problem is a multi-objective optimization problem: we have to take into account the minimization of the construction costs, the minimization of damage costs due to flooding, and possibly other criteria. One of the most important factors influencing the design of storm water systems is the expected amount of water to deal with. It is common that this infrastructure is developed with the capacity to cope with events that occur once in, say 10 or 20 years - so-called design rainfall events. However, rainfall is a random variable and such uncertainty typically is not taken explicitly into account in optimization. Rainfall design data is based on historical information of rainfalls, but many times this data is based on unreliable measures; or in not enough historical information; or as we know, the patterns of rainfall are changing regardless of historical information. There are also other sources of uncertainty influencing design, for example, leakages in the pipes and accumulation of sediments in pipes. In the context of storm water or combined sewer systems design or rehabilitation, robust optimization technique should be able to find the best design (or rehabilitation plan) within the available budget but taking into account uncertainty in those variables that were used to design the system. In this work we consider various approaches to robust optimization proposed by various authors (Gabrel, Murat, Thiele 2013; Beyer, Sendhoff 2007) and test a novel method ROPAR (Solomatine 2012) to analyze robustness. References Beyer, H.G., & Sendhoff, B. (2007). Robust optimization - A comprehensive survey. Comput. Methods Appl. Mech. Engrg., 3190-3218. Gabrel, V.; Murat, C., Thiele, A. (2014

Optimal Bayesian Experimental Design for Combustion Kinetics

KAUST Repository

Huan, Xun

2011-01-04

Experimental diagnostics play an essential role in the development and refinement of chemical kinetic models, whether for the combustion of common complex hydrocarbons or of emerging alternative fuels. Questions of experimental design—e.g., which variables or species to interrogate, at what resolution and under what conditions—are extremely important in this context, particularly when experimental resources are limited. This paper attempts to answer such questions in a rigorous and systematic way. We propose a Bayesian framework for optimal experimental design with nonlinear simulation-based models. While the framework is broadly applicable, we use it to infer rate parameters in a combustion system with detailed kinetics. The framework introduces a utility function that reflects the expected information gain from a particular experiment. Straightforward evaluation (and maximization) of this utility function requires Monte Carlo sampling, which is infeasible with computationally intensive models. Instead, we construct a polynomial surrogate for the dependence of experimental observables on model parameters and design conditions, with the help of dimension-adaptive sparse quadrature. Results demonstrate the efficiency and accuracy of the surrogate, as well as the considerable effectiveness of the experimental design framework in choosing informative experimental conditions.

An effective and optimal quality control approach for green energy manufacturing using design of experiments framework and evolutionary algorithm

Science.gov (United States)

Saavedra, Juan Alejandro

Quality Control (QC) and Quality Assurance (QA) strategies vary significantly across industries in the manufacturing sector depending on the product being built. Such strategies range from simple statistical analysis and process controls, decision-making process of reworking, repairing, or scraping defective product. This study proposes an optimal QC methodology in order to include rework stations during the manufacturing process by identifying the amount and location of these workstations. The factors that are considered to optimize these stations are cost, cycle time, reworkability and rework benefit. The goal is to minimize the cost and cycle time of the process, but increase the reworkability and rework benefit. The specific objectives of this study are: (1) to propose a cost estimation model that includes energy consumption, and (2) to propose an optimal QC methodology to identify quantity and location of rework workstations. The cost estimation model includes energy consumption as part of the product direct cost. The cost estimation model developed allows the user to calculate product direct cost as the quality sigma level of the process changes. This provides a benefit because a complete cost estimation calculation does not need to be performed every time the processes yield changes. This cost estimation model is then used for the QC strategy optimization process. In order to propose a methodology that provides an optimal QC strategy, the possible factors that affect QC were evaluated. A screening Design of Experiments (DOE) was performed on seven initial factors and identified 3 significant factors. It reflected that one response variable was not required for the optimization process. A full factorial DOE was estimated in order to verify the significant factors obtained previously. The QC strategy optimization is performed through a Genetic Algorithm (GA) which allows the evaluation of several solutions in order to obtain feasible optimal solutions. The GA

Fusion blanket design and optimization techniques

International Nuclear Information System (INIS)

Gohar, Y.

2005-01-01

In fusion reactors, the blanket design and its characteristics have a major impact on the reactor performance, size, and economics. The selection and arrangement of the blanket materials, dimensions of the different blanket zones, and different requirements of the selected materials for a satisfactory performance are the main parameters, which define the blanket performance. These parameters translate to a large number of variables and design constraints, which need to be simultaneously considered in the blanket design process. This represents a major design challenge because of the lack of a comprehensive design tool capable of considering all these variables to define the optimum blanket design and satisfying all the design constraints for the adopted figure of merit and the blanket design criteria. The blanket design techniques of the First Wall/Blanket/Shield Design and Optimization System (BSDOS) have been developed to overcome this difficulty and to provide the state-of-the-art techniques and tools for performing blanket design and analysis. This report describes some of the BSDOS techniques and demonstrates its use. In addition, the use of the optimization technique of the BSDOS can result in a significant blanket performance enhancement and cost saving for the reactor design under consideration. In this report, examples are presented, which utilize an earlier version of the ITER solid breeder blanket design and a high power density self-cooled lithium blanket design for demonstrating some of the BSDOS blanket design techniques

Model-based Organization Manning, Strategy, and Structure Design via Team Optimal Design (TOD) Methodology

National Research Council Canada - National Science Library

Levchuk, Georgiy; Chopra, Kari; Paley, Michael; Levchuk, Yuri; Clark, David

2005-01-01

This paper describes a quantitative Team Optimal Design (TOD) methodology and its application to the design of optimized manning for E-10 Multi-sensor Command and Control Aircraft. The E-10 (USAF, 2002...

Multi-objective optimal design of sandwich panels using a genetic algorithm

Science.gov (United States)

Xu, Xiaomei; Jiang, Yiping; Pueh Lee, Heow

2017-10-01

In this study, an optimization problem concerning sandwich panels is investigated by simultaneously considering the two objectives of minimizing the panel mass and maximizing the sound insulation performance. First of all, the acoustic model of sandwich panels is discussed, which provides a foundation to model the acoustic objective function. Then the optimization problem is formulated as a bi-objective programming model, and a solution algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is provided to solve the proposed model. Finally, taking an example of a sandwich panel that is expected to be used as an automotive roof panel, numerical experiments are carried out to verify the effectiveness of the proposed model and solution algorithm. Numerical results demonstrate in detail how the core material, geometric constraints and mechanical constraints impact the optimal designs of sandwich panels.

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

Energy Technology Data Exchange (ETDEWEB)

Adams, Brian M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ebeida, Mohamed Salah [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eldred, Michael S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jakeman, John Davis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Swiler, Laura Painton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stephens, John Adam [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vigil, Dena M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wildey, Timothy Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bohnhoff, William J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hu, Kenneth T. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Dalbey, Keith R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bauman, Lara E [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hough, Patricia Diane [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2014-05-01

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

Optimal Design of Laminated Composite Beams

DEFF Research Database (Denmark)

Blasques, José Pedro Albergaria Amaral

model for the analysis of laminated composite beams is proposed. The structural analysis is performed in a beam finite element context. The development of a finite element based tool for the analysis of the cross section stiffness properties is described. The resulting beam finite element formulation...... is able to account for the effects of material anisotropy and inhomogeneity in the global response of the beam. Beam finite element models allow for a significant reduction in problem size and are therefore an efficient alternative in computationally intensive applications like optimization frameworks...... design of laminated composite beams. The devised framework is applied in the optimal design of laminated composite beams with different cross section geometries and subjected to different load cases. Design criteria such as beam stiffness, weight, magnitude of the natural frequencies of vibration...

Optimizing flurbiprofen-loaded NLC by central composite factorial design for ocular delivery

Energy Technology Data Exchange (ETDEWEB)

Gonzalez-Mira, E; Egea, M A; Garcia, M L [Department of Physical Chemistry, Faculty of Pharmacy, Institute of Nanoscience and Nanotechnology, University of Barcelona, Avenida Joan XXIII s/n, E-08028 Barcelona (Spain); Souto, E B [Faculty of Health Sciences, Fernando Pessoa University, Rua Carlos da Maia, Nr. 296, Office S.1, P-4200-150 Porto (Portugal); Calpena, A C, E-mail: eligonzalezmi@ub.edu [Department of Biopharmacy and Pharmaceutical Technology, Faculty of Pharmacy, University of Barcelona, Avenida Joan XXIII s/n, E-08028 Barcelona (Spain)

2011-01-28

The purpose of this study was to design and optimize a new topical delivery system for ocular administration of flurbiprofen (FB), based on lipid nanoparticles. These particles, called nanostructured lipid carriers (NLC), were composed of a fatty acid (stearic acid (SA)) as the solid lipid and a mixture of Miglyol 812 and castor oil (CO) as the liquid lipids, prepared by the hot high pressure homogenization method. After selecting the critical variables influencing the physicochemical characteristics of the NLC (the liquid lipid (i.e. oil) concentration with respect to the total lipid (cOil/L (wt%)), the surfactant and the flurbiprofen concentration, on particle size, polydispersity index and encapsulation efficiency), a three-factor five-level central rotatable composite design was employed to plan and perform the experiments. Morphological examination, crystallinity and stability studies were also performed to accomplish the optimization study. The results showed that increasing cOil/L (wt%) was followed by an enhanced tendency to produce smaller particles, but the liquid to solid lipid proportion should not exceed 30 wt% due to destabilization problems. Therefore, a 70:30 ratio of SA to oil (miglyol + CO) was selected to develop an optimal NLC formulation. The smaller particles obtained when increasing surfactant concentration led to the selection of 3.2 wt% of Tween 80 (non-ionic surfactant). The positive effect of the increase in FB concentration on the encapsulation efficiency (EE) and its total solubilization in the lipid matrix led to the selection of 0.25 wt% of FB in the formulation. The optimal NLC showed an appropriate average size for ophthalmic administration (228.3 nm) with a narrow size distribution (0.156), negatively charged surface (-33.3 mV) and high EE ({approx}90%). The in vitro experiments proved that sustained release FB was achieved using NLC as drug carriers. Optimal NLC formulation did not show toxicity on ocular tissues.

Optimizing flurbiprofen-loaded NLC by central composite factorial design for ocular delivery

International Nuclear Information System (INIS)

Gonzalez-Mira, E; Egea, M A; Garcia, M L; Souto, E B; Calpena, A C

2011-01-01

The purpose of this study was to design and optimize a new topical delivery system for ocular administration of flurbiprofen (FB), based on lipid nanoparticles. These particles, called nanostructured lipid carriers (NLC), were composed of a fatty acid (stearic acid (SA)) as the solid lipid and a mixture of Miglyol 812 and castor oil (CO) as the liquid lipids, prepared by the hot high pressure homogenization method. After selecting the critical variables influencing the physicochemical characteristics of the NLC (the liquid lipid (i.e. oil) concentration with respect to the total lipid (cOil/L (wt%)), the surfactant and the flurbiprofen concentration, on particle size, polydispersity index and encapsulation efficiency), a three-factor five-level central rotatable composite design was employed to plan and perform the experiments. Morphological examination, crystallinity and stability studies were also performed to accomplish the optimization study. The results showed that increasing cOil/L (wt%) was followed by an enhanced tendency to produce smaller particles, but the liquid to solid lipid proportion should not exceed 30 wt% due to destabilization problems. Therefore, a 70:30 ratio of SA to oil (miglyol + CO) was selected to develop an optimal NLC formulation. The smaller particles obtained when increasing surfactant concentration led to the selection of 3.2 wt% of Tween 80 (non-ionic surfactant). The positive effect of the increase in FB concentration on the encapsulation efficiency (EE) and its total solubilization in the lipid matrix led to the selection of 0.25 wt% of FB in the formulation. The optimal NLC showed an appropriate average size for ophthalmic administration (228.3 nm) with a narrow size distribution (0.156), negatively charged surface (-33.3 mV) and high EE (∼90%). The in vitro experiments proved that sustained release FB was achieved using NLC as drug carriers. Optimal NLC formulation did not show toxicity on ocular tissues.

On the design of compliant mechanisms using topology optimization

DEFF Research Database (Denmark)

Sigmund, Ole

1997-01-01

This paper presents a method for optimal design of compliant mechanism topologies. The method is based on continuum-type topology optimization techniques and finds the optimal compliant mechanism topology within a given design domain and a given position and direction of input and output forces....... By constraining the allowed displacement at the input port, it is possible to control the maximum stress level in the compliant mechanism. The ability of the design method to find a mechanism with complex output behavior is demonstrated by several examples. Some of the optimal mechanism topologies have been...... manufactured, both in macroscale (hand-size) made in Nylon, and in microscale (

The Usefulness of Systematic Reviews of Animal Experiments for the Design of Preclinical and Clinical Studies

Science.gov (United States)

de Vries, Rob B. M.; Wever, Kimberley E.; Avey, Marc T.; Stephens, Martin L.; Sena, Emily S.; Leenaars, Marlies

2014-01-01

The question of how animal studies should be designed, conducted, and analyzed remains underexposed in societal debates on animal experimentation. This is not only a scientific but also a moral question. After all, if animal experiments are not appropriately designed, conducted, and analyzed, the results produced are unlikely to be reliable and the animals have in effect been wasted. In this article, we focus on one particular method to address this moral question, namely systematic reviews of previously performed animal experiments. We discuss how the design, conduct, and analysis of future (animal and human) experiments may be optimized through such systematic reviews. In particular, we illustrate how these reviews can help improve the methodological quality of animal experiments, make the choice of an animal model and the translation of animal data to the clinic more evidence-based, and implement the 3Rs. Moreover, we discuss which measures are being taken and which need to be taken in the future to ensure that systematic reviews will actually contribute to optimizing experimental design and thereby to meeting a necessary condition for making the use of animals in these experiments justified. PMID:25541545

Surrogate Assisted Design Optimization of an Air Turbine

Directory of Open Access Journals (Sweden)

Rameez Badhurshah

2014-01-01

Full Text Available Surrogates are cheaper to evaluate and assist in designing systems with lesser time. On the other hand, the surrogates are problem dependent and they need evaluation for each problem to find a suitable surrogate. The Kriging variants such as ordinary, universal, and blind along with commonly used response surface approximation (RSA model were used in the present problem, to optimize the performance of an air impulse turbine used for ocean wave energy harvesting by CFD analysis. A three-level full factorial design was employed to find sample points in the design space for two design variables. A Reynolds-averaged Navier Stokes solver was used to evaluate the objective function responses, and these responses along with the design variables were used to construct the Kriging variants and RSA functions. A hybrid genetic algorithm was used to find the optimal point in the design space. It was found that the best optimal design was produced by the universal Kriging while the blind Kriging produced the worst. The present approach is suggested for renewable energy application.

Optimization of straight-sided spline design

DEFF Research Database (Denmark)

Pedersen, Niels Leergaard

2011-01-01

and the subject of improving the design. The present paper concentrates on the optimization of splines and the predictions of stress concentrations, which are determined by finite element analysis (FEA). Using different design modifications, that do not change the spline load carrying capacity, it is shown...

A Bayesian Optimal Design for Sequential Accelerated Degradation Testing

Directory of Open Access Journals (Sweden)

Xiaoyang Li

2017-07-01

Full Text Available When optimizing an accelerated degradation testing (ADT plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to address this problem by using prior distributions to capture these uncertainties. Nevertheless, when the difference between a prior distribution and actual situation is large, the existing Bayesian optimal design might cause some over-testing or under-testing issues. For example, the implemented ADT following the optimal ADT plan consumes too much testing resources or few accelerated degradation data are obtained during the ADT. To overcome these obstacles, a Bayesian sequential step-down-stress ADT design is proposed in this article. During the sequential ADT, the test under the highest stress level is firstly conducted based on the initial prior information to quickly generate degradation data. Then, the data collected under higher stress levels are employed to construct the prior distributions for the test design under lower stress levels by using the Bayesian inference. In the process of optimization, the inverse Gaussian (IG process is assumed to describe the degradation paths, and the Bayesian D-optimality is selected as the optimal objective. A case study on an electrical connector’s ADT plan is provided to illustrate the application of the proposed Bayesian sequential ADT design method. Compared with the results from a typical static Bayesian ADT plan, the proposed design could guarantee more stable and precise estimations of different reliability measures.

Implementation of quality by design principles in the development of microsponges as drug delivery carriers: Identification and optimization of critical factors using multivariate statistical analyses and design of experiments studies.

Science.gov (United States)

Simonoska Crcarevska, Maja; Dimitrovska, Aneta; Sibinovska, Nadica; Mladenovska, Kristina; Slavevska Raicki, Renata; Glavas Dodov, Marija

2015-07-15

Microsponges drug delivery system (MDDC) was prepared by double emulsion-solvent-diffusion technique using rotor-stator homogenization. Quality by design (QbD) concept was implemented for the development of MDDC with potential to be incorporated into semisolid dosage form (gel). Quality target product profile (QTPP) and critical quality attributes (CQA) were defined and identified, accordingly. Critical material attributes (CMA) and Critical process parameters (CPP) were identified using quality risk management (QRM) tool, failure mode, effects and criticality analysis (FMECA). CMA and CPP were identified based on results obtained from principal component analysis (PCA-X&Y) and partial least squares (PLS) statistical analysis along with literature data, product and process knowledge and understanding. FMECA identified amount of ethylcellulose, chitosan, acetone, dichloromethane, span 80, tween 80 and water ratio in primary/multiple emulsions as CMA and rotation speed and stirrer type used for organic solvent removal as CPP. The relationship between identified CPP and particle size as CQA was described in the design space using design of experiments - one-factor response surface method. Obtained results from statistically designed experiments enabled establishment of mathematical models and equations that were used for detailed characterization of influence of identified CPP upon MDDC particle size and particle size distribution and their subsequent optimization. Copyright © 2015 Elsevier B.V. All rights reserved.

NDDP multi-stage flash desalination process simulator design process optimization

International Nuclear Information System (INIS)

Sashi Kumar, G.N.; Mahendra, A.K.; Sanyal, A.; Gouthaman, G.

2009-03-01

The improvement of NDDP-MSF plant's performance ratio (PR) from design value of 9.0 to 13.1 was achieved by optimizing the plant's operating parameters within the feasible zone of operation. This plant has 20% excess heat transfer area over the design condition which helped us to get a PR of 15.1 after optimization. Thus we have obtained, (1) A 45% increase in the output over design value by the optimization carried out with design heat transfer area. (2) A 68% increase in the output over design value by the optimization carried out with increased heat transfer area. This report discusses the approach, methodology and results of the optimization study carried out. A simulator, MSFSIM which predicts the performance of a multi-stage flash (MSF) desalination plant has been coupled with Genetic Algorithm (GA) optimizer. Exhaustive optimization case studies have been conducted on this plant with an objective to increase the performance ratio (PR). The steady state optimization performed was based on obtaining the best stage wise pressure profile to enhance thermal efficiency which in-turn improves the performance ratio. Apart from this, the recirculating brine flow rate was also optimized. This optimization study enabled us to increase the PR of NDDP-MSF plant from design value of 9.0 to an optimized value 13.1. The actual plant is provided with 20% additional heat transfer area over and above the design heat transfer area. Optimization with this additional heat transfer area has taken the PR to 15.1. A desire to maintain equal flashing rates in all of the stages (a feature required for long life of the plant and to avoid cascading effect of non-flashing triggered by any stage) of the MSF plant has also been achieved. The deviation in the flashing rates within stages has been reduced. The startup characteristic of the plant (i.e the variation of stage pressure and the variation of recirculation flow rate with time), have been optimized with a target to minimize the

Optimal design of link systems using successive zooming genetic algorithm

Science.gov (United States)

Kwon, Young-Doo; Sohn, Chang-hyun; Kwon, Soon-Bum; Lim, Jae-gyoo

2009-07-01

Link-systems have been around for a long time and are still used to control motion in diverse applications such as automobiles, robots and industrial machinery. This study presents a procedure involving the use of a genetic algorithm for the optimal design of single four-bar link systems and a double four-bar link system used in diesel engine. We adopted the Successive Zooming Genetic Algorithm (SZGA), which has one of the most rapid convergence rates among global search algorithms. The results are verified by experiment and the Recurdyn dynamic motion analysis package. During the optimal design of single four-bar link systems, we found in the case of identical input/output (IO) angles that the initial and final configurations show certain symmetry. For the double link system, we introduced weighting factors for the multi-objective functions, which minimize the difference between output angles, providing balanced engine performance, as well as the difference between final output angle and the desired magnitudes of final output angle. We adopted a graphical method to select a proper ratio between the weighting factors.

Mixture experiment methods in the development and optimization of microemulsion formulations.

Science.gov (United States)

Furlanetto, S; Cirri, M; Piepel, G; Mennini, N; Mura, P

2011-06-25

Microemulsion formulations represent an interesting delivery vehicle for lipophilic drugs, allowing for improving their solubility and dissolution properties. This work developed effective microemulsion formulations using glyburide (a very poorly-water-soluble hypoglycaemic agent) as a model drug. First, the area of stable microemulsion (ME) formations was identified using a new approach based on mixture experiment methods. A 13-run mixture design was carried out in an experimental region defined by constraints on three components: aqueous, oil and surfactant/cosurfactant. The transmittance percentage (at 550 nm) of ME formulations (indicative of their transparency and thus of their stability) was chosen as the response variable. The results obtained using the mixture experiment approach corresponded well with those obtained using the traditional approach based on pseudo-ternary phase diagrams. However, the mixture experiment approach required far less experimental effort than the traditional approach. A subsequent 13-run mixture experiment, in the region of stable MEs, was then performed to identify the optimal formulation (i.e., having the best glyburide dissolution properties). Percent drug dissolved and dissolution efficiency were selected as the responses to be maximized. The ME formulation optimized via the mixture experiment approach consisted of 78% surfactant/cosurfacant (a mixture of Tween 20 and Transcutol, 1:1, v/v), 5% oil (Labrafac Hydro) and 17% aqueous phase (water). The stable region of MEs was identified using mixture experiment methods for the first time. Copyright © 2011 Elsevier B.V. All rights reserved.

Automated magnetic divertor design for optimal power exhaust

Energy Technology Data Exchange (ETDEWEB)

Blommaert, Maarten

2017-07-01

The so-called divertor is the standard particle and power exhaust system of nuclear fusion tokamaks. In essence, the magnetic configuration hereby 'diverts' the plasma to a specific divertor structure. The design of this divertor is still a key issue to be resolved to evolve from experimental fusion tokamaks to commercial power plants. The focus of this dissertation is on one particular design requirement: avoiding excessive heat loads on the divertor structure. The divertor design process is assisted by plasma edge transport codes that simulate the plasma and neutral particle transport in the edge of the reactor. These codes are computationally extremely demanding, not in the least due to the complex collisional processes between plasma and neutrals that lead to strong radiation sinks and macroscopic heat convection near the vessel walls. One way of improving the heat exhaust is by modifying the magnetic confinement that governs the plasma flow. In this dissertation, automated design of the magnetic configuration is pursued using adjoint based optimization methods. A simple and fast perturbation model is used to compute the magnetic field in the vacuum vessel. A stable optimal design method of the nested type is then elaborated that strictly accounts for several nonlinear design constraints and code limitations. Using appropriate cost function definitions, the heat is spread more uniformly over the high-heat load plasma-facing components in a practical design example. Furthermore, practical in-parts adjoint sensitivity calculations are presented that provide a way to an efficient optimization procedure. Results are elaborated for a fictituous JET (Joint European Torus) case. The heat load is strongly reduced by exploiting an expansion of the magnetic flux towards the solid divertor structure. Subsequently, shortcomings of the perturbation model for magnetic field calculations are discussed in comparison to a free boundary equilibrium (FBE) simulation

Automated magnetic divertor design for optimal power exhaust

International Nuclear Information System (INIS)

Blommaert, Maarten

2017-01-01

The so-called divertor is the standard particle and power exhaust system of nuclear fusion tokamaks. In essence, the magnetic configuration hereby 'diverts' the plasma to a specific divertor structure. The design of this divertor is still a key issue to be resolved to evolve from experimental fusion tokamaks to commercial power plants. The focus of this dissertation is on one particular design requirement: avoiding excessive heat loads on the divertor structure. The divertor design process is assisted by plasma edge transport codes that simulate the plasma and neutral particle transport in the edge of the reactor. These codes are computationally extremely demanding, not in the least due to the complex collisional processes between plasma and neutrals that lead to strong radiation sinks and macroscopic heat convection near the vessel walls. One way of improving the heat exhaust is by modifying the magnetic confinement that governs the plasma flow. In this dissertation, automated design of the magnetic configuration is pursued using adjoint based optimization methods. A simple and fast perturbation model is used to compute the magnetic field in the vacuum vessel. A stable optimal design method of the nested type is then elaborated that strictly accounts for several nonlinear design constraints and code limitations. Using appropriate cost function definitions, the heat is spread more uniformly over the high-heat load plasma-facing components in a practical design example. Furthermore, practical in-parts adjoint sensitivity calculations are presented that provide a way to an efficient optimization procedure. Results are elaborated for a fictituous JET (Joint European Torus) case. The heat load is strongly reduced by exploiting an expansion of the magnetic flux towards the solid divertor structure. Subsequently, shortcomings of the perturbation model for magnetic field calculations are discussed in comparison to a free boundary equilibrium (FBE) simulation. These flaws

Fast Bayesian optimal experimental design for seismic source inversion

KAUST Repository

Long, Quan

2015-07-01

We develop a fast method for optimally designing experiments in the context of statistical seismic source inversion. In particular, we efficiently compute the optimal number and locations of the receivers or seismographs. The seismic source is modeled by a point moment tensor multiplied by a time-dependent function. The parameters include the source location, moment tensor components, and start time and frequency in the time function. The forward problem is modeled by elastodynamic wave equations. We show that the Hessian of the cost functional, which is usually defined as the square of the weighted L2 norm of the difference between the experimental data and the simulated data, is proportional to the measurement time and the number of receivers. Consequently, the posterior distribution of the parameters, in a Bayesian setting, concentrates around the "true" parameters, and we can employ Laplace approximation and speed up the estimation of the expected Kullback-Leibler divergence (expected information gain), the optimality criterion in the experimental design procedure. Since the source parameters span several magnitudes, we use a scaling matrix for efficient control of the condition number of the original Hessian matrix. We use a second-order accurate finite difference method to compute the Hessian matrix and either sparse quadrature or Monte Carlo sampling to carry out numerical integration. We demonstrate the efficiency, accuracy, and applicability of our method on a two-dimensional seismic source inversion problem. © 2015 Elsevier B.V.

Fast Bayesian Optimal Experimental Design for Seismic Source Inversion

KAUST Repository

Long, Quan

2016-01-06

We develop a fast method for optimally designing experiments [1] in the context of statistical seismic source inversion [2]. In particular, we efficiently compute the optimal number and locations of the receivers or seismographs. The seismic source is modeled by a point moment tensor multiplied by a time-dependent function. The parameters include the source location, moment tensor components, and start time and frequency in the time function. The forward problem is modeled by the elastic wave equations. We show that the Hessian of the cost functional, which is usually defined as the square of the weighted L2 norm of the difference between the experimental data and the simulated data, is proportional to the measurement time and the number of receivers. Consequently, the posterior distribution of the parameters, in a Bayesian setting, concentrates around the true parameters, and we can employ Laplace approximation and speed up the estimation of the expected Kullback-Leibler divergence (expected information gain), the optimality criterion in the experimental design procedure. Since the source parameters span several magnitudes, we use a scaling matrix for efficient control of the condition number of the original Hessian matrix. We use a second-order accurate finite difference method to compute the Hessian matrix and either sparse quadrature or Monte Carlo sampling to carry out numerical integration. We demonstrate the efficiency, accuracy, and applicability of our method on a two-dimensional seismic source inversion problem.

Fast Bayesian Optimal Experimental Design for Seismic Source Inversion

KAUST Repository

Long, Quan; Motamed, Mohammad; Tempone, Raul

2016-01-01

We develop a fast method for optimally designing experiments [1] in the context of statistical seismic source inversion [2]. In particular, we efficiently compute the optimal number and locations of the receivers or seismographs. The seismic source is modeled by a point moment tensor multiplied by a time-dependent function. The parameters include the source location, moment tensor components, and start time and frequency in the time function. The forward problem is modeled by the elastic wave equations. We show that the Hessian of the cost functional, which is usually defined as the square of the weighted L2 norm of the difference between the experimental data and the simulated data, is proportional to the measurement time and the number of receivers. Consequently, the posterior distribution of the parameters, in a Bayesian setting, concentrates around the true parameters, and we can employ Laplace approximation and speed up the estimation of the expected Kullback-Leibler divergence (expected information gain), the optimality criterion in the experimental design procedure. Since the source parameters span several magnitudes, we use a scaling matrix for efficient control of the condition number of the original Hessian matrix. We use a second-order accurate finite difference method to compute the Hessian matrix and either sparse quadrature or Monte Carlo sampling to carry out numerical integration. We demonstrate the efficiency, accuracy, and applicability of our method on a two-dimensional seismic source inversion problem.

Optimization of minoxidil microemulsions using fractional factorial design approach.

Science.gov (United States)

Jaipakdee, Napaphak; Limpongsa, Ekapol; Pongjanyakul, Thaned

2016-01-01

The objective of this study was to apply fractional factorial and multi-response optimization designs using desirability function approach for developing topical microemulsions. Minoxidil (MX) was used as a model drug. Limonene was used as an oil phase. Based on solubility, Tween 20 and caprylocaproyl polyoxyl-8 glycerides were selected as surfactants, propylene glycol and ethanol were selected as co-solvent in aqueous phase. Experiments were performed according to a two-level fractional factorial design to evaluate the effects of independent variables: Tween 20 concentration in surfactant system (X1), surfactant concentration (X2), ethanol concentration in co-solvent system (X3), limonene concentration (X4) on MX solubility (Y1), permeation flux (Y2), lag time (Y3), deposition (Y4) of MX microemulsions. It was found that Y1 increased with increasing X3 and decreasing X2, X4; whereas Y2 increased with decreasing X1, X2 and increasing X3. While Y3 was not affected by these variables, Y4 increased with decreasing X1, X2. Three regression equations were obtained and calculated for predicted values of responses Y1, Y2 and Y4. The predicted values matched experimental values reasonably well with high determination coefficient. By using optimal desirability function, optimized microemulsion demonstrating the highest MX solubility, permeation flux and skin deposition was confirmed as low level of X1, X2 and X4 but high level of X3.

The design of macromolecular crystallography diffraction experiments

International Nuclear Information System (INIS)

Evans, Gwyndaf; Axford, Danny; Owen, Robin L.

2011-01-01

Thoughts about the decisions made in designing macromolecular X-ray crystallography experiments at synchrotron beamlines are presented. The measurement of X-ray diffraction data from macromolecular crystals for the purpose of structure determination is the convergence of two processes: the preparation of diffraction-quality crystal samples on the one hand and the construction and optimization of an X-ray beamline and end station on the other. Like sample preparation, a macromolecular crystallography beamline is geared to obtaining the best possible diffraction measurements from crystals provided by the synchrotron user. This paper describes the thoughts behind an experiment that fully exploits both the sample and the beamline and how these map into everyday decisions that users can and should make when visiting a beamline with their most precious crystals

Optimal designs for one- and two-color microarrays using mixed models: a comparative evaluation of their efficiencies.

Science.gov (United States)

Lima Passos, Valéria; Tan, Frans E S; Winkens, Bjorn; Berger, Martijn P F

2009-01-01

Comparative studies between the one- and two-color microarrays provide supportive evidence for similarities of results on differential gene expression. So far, no design comparisons between the two platforms have been undertaken. With the objective of comparing optimal designs of one- and two-color microarrays in their statistical efficiencies, techniques of design optimization were applied within a mixed model framework. A- and D-optimal designs for the one- and two-color platforms were sought for a 3 x 3 factorial experiment. The results suggest that the choice of the platform will not affect the "subjects to groups" allocation, being concordant in the two designs. However, under financial constraints, the two-color arrays are expected to have a slight upper hand in terms of efficiency of model parameters estimates, once the price of arrays is more expensive than that of subjects. This statement is especially valid for microarray studies envisaging class comparisons.

DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.

Energy Technology Data Exchange (ETDEWEB)

Eldred, Michael Scott; Vigil, Dena M.; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Lefantzi, Sophia (Sandia National Laboratories, Livermore, CA); Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Eddy, John P.

2011-12-01

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.

Refining design of superconducting magnets synchronous with winding using particle swarm optimization

International Nuclear Information System (INIS)

Du, J.J.; Wu, W.; Mei, E.M.; Yuan, P.; Ma, L.Z.; Dong, Z.W.

2013-01-01

Highlights: ► A method of synchronous optimization design of superconducting magnets is proposed. ► We get a refining design of a main magnet on Lanzhou Penning Trap by the method. ► We expounds the necessity of tracking optimizing of coils for magnets. ► Particle swarm optimization shows effectiveness in magnet optimization. ► The expected homogeneity of the magnet improves considerably. -- Abstract: A methodology of synchronous optimization design of magnets under construction according to original design scheme is put forward in this paper, and it has been successfully used for refining design of a superconducting magnet on Lanzhou Penning Trap (LPT). This paper expounds the necessity of tracking optimization of magnet coil in the process of traditional manufacturing, and optimization design of magnet coils by particle swarm optimization is proposed. Particle swarm optimization is turned out to be an effective design method for magnet optimization. The expected homogeneity of the magnet is improved to 200 ppm from 1150 ppm through the refining optimizing, which provides important guarantee for required homogeneity of the whole magnet

On CAD-integrated Structural Topology and Design Optimization

DEFF Research Database (Denmark)

Olhoff, Niels; Bendsøe, M.P.; Rasmussen, John

1991-01-01

Concepts underlying an interactive CAD-based engineering design optimization system are developed, and methods of optimizing the topology, shape and sizing of mechanical components are presented. These methods are integrated in the system, and the method for determining the optimal topology is used...

Application of analytical target cascading method in multidisciplinary design optimization of ship conceptual design

Directory of Open Access Journals (Sweden)

WANG Jian

2017-10-01

Full Text Available [Objectives] Ship conceptual design requires the coordination of many different disciplines for comprehensive optimization, which presents a complicated system design problem affecting several fields of technology. However, the development of overall ship design is relatively slow compared with other subjects. [Methods] The decomposition and coordination strategy of ship design is presented, and the analytical target cascading (ATC method is applied to the multidisciplinary design optimization of the conceptual design phase of ships on this basis. A tank ship example covering the 5 disciplines of buoyancy and stability, rapidity, maneuverability, capacity and economy is established to illustrate the analysis process in the present study. [Results] The results demonstrate the stability, convergence and validity of the ATC method in dealing with the complex coupling effect occurring in ship conceptual design.[Conclusions] The proposed method provides an effective basis for optimization of ship conceptual design.

Strategies for Optimal Design of Structural Systems

DEFF Research Database (Denmark)

Enevoldsen, I.; Sørensen, John Dalsgaard

1992-01-01

Reliability-based design of structural systems is considered. Especially systems where the reliability model is a series system of parallel systems are analysed. A sensitivity analysis for this class of problems is presented. Direct and sequential optimization procedures to solve the optimization...

Design of modern experiments(revised version)

International Nuclear Information System (INIS)

Park, Sung Hweon

1984-03-01

This book mentions design of modern experiments. It includes conception of design of experiments, a key statistics theory, one way design of experiment, two-way layout without repetition and with repetition, multi layout and analysis of enumerated data, partition, correlation and regression analysis, latin squares, factorial design, design of experiment by table of orthogonal arrays I, II, incomplete block design, design of response surface, design of compound experiment, Evop and steepest ascent or descent method and design of experiment of taguchi.

Hybrid Design Optimization of High Voltage Pulse Transformers for Klystron Modulators

CERN Document Server

Sylvain, Candolfi; Davide, Aguglia; Jerome, Cros

2015-01-01

This paper presents a hybrid optimization methodology for the design of high voltage pulse transformers used in klystron modulators. The optimization process is using simplified 2D FEA design models of the 3D transformer structure. Each intermediate optimal solution is evaluated by 3D FEA and correction coefficients of the 2D FEA models are derived. A new optimization process using 2D FEA models is then performed. The convergence of this hybrid optimal design methodology is obtained with a limited number of time consuming 3D FEA simulations. The method is applied to the optimal design of a monolithic high voltage pulse transformer for the CLIC klystron modulator.

Design Optimization of Hybrid FRP/RC Bridge

Science.gov (United States)

Papapetrou, Vasileios S.; Tamijani, Ali Y.; Brown, Jeff; Kim, Daewon

2018-04-01

The hybrid bridge consists of a Reinforced Concrete (RC) slab supported by U-shaped Fiber Reinforced Polymer (FRP) girders. Previous studies on similar hybrid bridges constructed in the United States and Europe seem to substantiate these hybrid designs for lightweight, high strength, and durable highway bridge construction. In the current study, computational and optimization analyses were carried out to investigate six composite material systems consisting of E-glass and carbon fibers. Optimization constraints are determined by stress, deflection and manufacturing requirements. Finite Element Analysis (FEA) and optimization software were utilized, and a framework was developed to run the complete analyses in an automated fashion. Prior to that, FEA validation of previous studies on similar U-shaped FRP girders that were constructed in Poland and Texas is presented. A finer optimization analysis is performed for the case of the Texas hybrid bridge. The optimization outcome of the hybrid FRP/RC bridge shows the appropriate composite material selection and cross-section geometry that satisfies all the applicable Limit States (LS) and, at the same time, results in the lightest design. Critical limit states show that shear stress criteria determine the optimum design for bridge spans less than 15.24 m and deflection criteria controls for longer spans. Increased side wall thickness can reduce maximum observed shear stresses, but leads to a high weight penalty. A taller cross-section and a thicker girder base can efficiently lower the observed deflections and normal stresses. Finally, substantial weight savings can be achieved by the optimization framework if base and side-wall thickness are treated as independent variables.

Detecting and locating light atoms from high-resolution STEM images: The quest for a single optimal design.

Science.gov (United States)

Gonnissen, J; De Backer, A; den Dekker, A J; Sijbers, J; Van Aert, S

2016-11-01

In the present paper, the optimal detector design is investigated for both detecting and locating light atoms from high resolution scanning transmission electron microscopy (HR STEM) images. The principles of detection theory are used to quantify the probability of error for the detection of light atoms from HR STEM images. To determine the optimal experiment design for locating light atoms, use is made of the so-called Cramér-Rao Lower Bound (CRLB). It is investigated if a single optimal design can be found for both the detection and location problem of light atoms. Furthermore, the incoming electron dose is optimised for both research goals and it is shown that picometre range precision is feasible for the estimation of the atom positions when using an appropriate incoming electron dose under the optimal detector settings to detect light atoms. Copyright © 2016 Elsevier B.V. All rights reserved.

Integrated design optimization research and development in an industrial environment

Science.gov (United States)

Kumar, V.; German, Marjorie D.; Lee, S.-J.

1989-01-01

An overview is given of a design optimization project that is in progress at the GE Research and Development Center for the past few years. The objective of this project is to develop a methodology and a software system for design automation and optimization of structural/mechanical components and systems. The effort focuses on research and development issues and also on optimization applications that can be related to real-life industrial design problems. The overall technical approach is based on integration of numerical optimization techniques, finite element methods, CAE and software engineering, and artificial intelligence/expert systems (AI/ES) concepts. The role of each of these engineering technologies in the development of a unified design methodology is illustrated. A software system DESIGN-OPT has been developed for both size and shape optimization of structural components subjected to static as well as dynamic loadings. By integrating this software with an automatic mesh generator, a geometric modeler and an attribute specification computer code, a software module SHAPE-OPT has been developed for shape optimization. Details of these software packages together with their applications to some 2- and 3-dimensional design problems are described.

Design optimization of GaAs betavoltaic batteries

International Nuclear Information System (INIS)

Chen Haiyanag; Jiang Lan; Chen Xuyuan

2011-01-01

GaAs junctions are designed and fabricated for betavoltaic batteries. The design is optimized according to the characteristics of GaAs interface states and the diffusion length in the depletion region of GaAs carriers. Under an illumination of 10 mCi cm -2 63 Ni, the open circuit voltage of the optimized batteries is about ∼0.3 V. It is found that the GaAs interface states induce depletion layers on P-type GaAs surfaces. The depletion layer along the P + PN + junction edge isolates the perimeter surface from the bulk junction, which tends to significantly reduce the battery dark current and leads to a high open circuit voltage. The short circuit current density of the optimized junction is about 28 nA cm -2 , which indicates a carrier diffusion length of less than 1 μm. The overall results show that multi-layer P + PN + junctions are the preferred structures for GaAs betavoltaic battery design.

On the design of 1-3 piezo-composites using topology optimization

DEFF Research Database (Denmark)

Sigmund, Ole; Torquato, S.; Aksay, I.A.

1998-01-01

(h)((*))g(h)((*)), and the electromechanical coupling factor k(h)((*)). The piezocomposite consists of piezoelectric rods embedded in an optimal polymer matrix. We use the topology optimization method to design the optimal (porous) matrix microstructure. When we design for maximum d(h)((*)) and d(h)((*))g(h)((*)) the optimal transversally......We use a topology optimization method to design 1-3 piezocomposites with optimal performance characteristics for hydrophone applications. The performance characteristics we focus on are the hydrostatic charge coefficient d(h)((*)), the hydrophone figure of merit d...

Optimal Design of a Center Support Quadruple Mass Gyroscope (CSQMG

Directory of Open Access Journals (Sweden)

Tian Zhang

2016-04-01

Full Text Available This paper reports a more complete description of the design process of the Center Support Quadruple Mass Gyroscope (CSQMG, a gyro expected to provide breakthrough performance for flat structures. The operation of the CSQMG is based on four lumped masses in a circumferential symmetric distribution, oscillating in anti-phase motion, and providing differential signal extraction. With its 4-fold symmetrical axes pattern, the CSQMG achieves a similar operation mode to Hemispherical Resonant Gyroscopes (HRGs. Compared to the conventional flat design, four Y-shaped coupling beams are used in this new pattern in order to adjust mode distribution and enhance the synchronization mechanism of operation modes. For the purpose of obtaining the optimal design of the CSQMG, a kind of applicative optimization flow is developed with a comprehensive derivation of the operation mode coordination, the pseudo mode inhibition, and the lumped mass twisting motion elimination. The experimental characterization of the CSQMG was performed at room temperature, and the center operation frequency is 6.8 kHz after tuning. Experiments show an Allan variance stability 0.12°/h (@100 s and a white noise level about 0.72°/h/√Hz, which means that the CSQMG possesses great potential to achieve navigation grade performance.

Optimal Control Design for a Solar Greenhouse

NARCIS (Netherlands)

Ooteghem, van R.J.C.

2010-01-01

Abstract: An optimal climate control has been designed for a solar greenhouse to achieve optimal crop production with sustainable instead of fossil energy. The solar greenhouse extends a conventional greenhouse with an improved roof cover, ventilation with heat recovery, a heat pump, a heat

eCodonOpt: a systematic computational framework for optimizing codon usage in directed evolution experiments

OpenAIRE

Moore, Gregory L.; Maranas, Costas D.

2002-01-01

We present a systematic computational framework, eCodonOpt, for designing parental DNA sequences for directed evolution experiments through codon usage optimization. Given a set of homologous parental proteins to be recombined at the DNA level, the optimal DNA sequences encoding these proteins are sought for a given diversity objective. We find that the free energy of annealing between the recombining DNA sequences is a much better descriptor of the extent of crossover formation than sequence...

Optimal design of robust piezoelectric microgrippers undergoing large displacements

DEFF Research Database (Denmark)

Ruiz, D.; Sigmund, Ole

2018-01-01