Predictability of Extreme Precipitations Over the Conterminous us, 1949-2010

Science.gov (United States)

Jiang, M.; Felzer, B. S.

2015-12-01

Extreme precipitation plays an important role in regulating ecosystem services. Precipitation extremes vary in magnitude and duration both spatially and temporally, making it one of the most challenging climate variables to comprehend and predict. Using information theory, we provide an attempt to improve understanding of the predictability of extreme precipitation in the conterminous U.S. over the period of 1949-2010. We define predictability as the recurrent likelihood of patterns described by the measures of constancy and contingency, with the former describing the inter-annual variability and the latter describing the seasonality. This study shows that there are clear west-east contrasts of predictability over the U.S. landscape, with a generally decreasing gradient from the Northeast to the Southwest for intensity-based extremes and a generally increasing gradient from the West to the East for duration-based extremes. We further identify spatially heterogeneous patterns of temporal changes in predictability over the investigated timeframe. Finally, it is evident that constancy plays a heavier role in regulating predictability increases for both intensity and duration-based extremes and for predictability decreases for duration-based extremes, while contingency contributes equally with constancy to determining the decreases in predictability for intensity-based extremes.

Extreme load alleviation using industrial implementation of active trailing edge flaps in a full design load basis

DEFF Research Database (Denmark)

Barlas, Athanasios; Pettas, Vasilis; Gertz, Drew Patrick

2016-01-01

The application of active trailing edge flaps in an industrial oriented implementation is evaluated in terms of capability of alleviating design extreme loads. A flap system with basic control functionality is implemented and tested in a realistic full Design Load Basis (DLB) for the DTU 10MW...

Extreme Design Loads Calibration of Offshore Wind Turbine Blades through Real Time Measurements

DEFF Research Database (Denmark)

Natarajan, Anand; Vesth, Allan; Lamata, Rebeca Rivera

2014-01-01

Blade Root flap and Edge moments are measured on the blades of a 3.6MW offshore wind turbine in normal operation. Ten minute maxima of the measurements are sampled to determine the extreme blade root flap moment, edge moment and resultant moment over six month duration. A random subset of the mea......Blade Root flap and Edge moments are measured on the blades of a 3.6MW offshore wind turbine in normal operation. Ten minute maxima of the measurements are sampled to determine the extreme blade root flap moment, edge moment and resultant moment over six month duration. A random subset...... of the measurements over a week is taken as input to stochastic load extrapolation whereby the one year extrapolated design extreme is obtained, which are then compared with the maximum extremes obtained from direct measurements over a six month period to validate the magnification in the load levels for the blade...... root flap moment, edge moment obtained by extrapolation. The validation yields valuable information on prescribing the slope of the local extrapolation curve at each mean wind speed. As an alternative to determining the contemporaneous loads for each primary extrapolated load, the blade root resultant...

Effects of normal and extreme turbulence spectral parameters on wind turbine loads

DEFF Research Database (Denmark)

Dimitrov, Nikolay Krasimirov; Natarajan, Anand; Mann, Jakob

2017-01-01

the recommended values in the IEC 61400-1 Ed.3 that is used for wind turbine design. The present paper investigates the impact of Mann turbulence model parameter variations on the design loads envelope for 5 MW and 10 MW reference wind turbines. Specific focus is made on the blade root loads, tower top moments...... of design loads is investigated with a focus on the commonly used Mann turbulence model. Quantification of the Mann model parameters is made through wind measurements acquired from the Høvsøre site. The parameters of the Mann model fitted to site specific observations can differ significantly from...... and tower base loads under normal turbulence and extreme turbulence, whereby the change in operating extreme and fatigue design loads obtained through turbulence model parameter variations is compared with corresponding variations obtained from random seeds of turbulence. The investigations quantify...

Extreme value predictions and critical wave episodes for marine structures by FORM

DEFF Research Database (Denmark)

Jensen, Jørgen Juncher

2007-01-01

The aim of the present paper is to advocate for a very effective stochastic procedure, based on the First Order Reliability Method (FORM), for extreme value predictions related to wave induced loads. Three different applications will be illustrated. The first deals with a jack-up rig where second...... order stochastic waves are included in the analysis. The second application is parametric roll motions of ships. Finally, the motion of a TLP floating foundation for an offshore wind turbine is analysed taking into account large motions....

Extreme value predictions and critical wave episodes for marine structures by FORM

DEFF Research Database (Denmark)

Jensen, Jørgen Juncher

2008-01-01

The aim of the present paper is to advocate for a very effective stochastic procedure, based on the First Order Reliability Method (FORM), for extreme value predictions related to wave induced loads. Three different applications will be illustrated. The first deals with a jack-up rig where second...... order stochastic waves are included in the analysis. The second application is parametric roll motions of ships. Finally, the motion of a TLP floating foundation for an offshore wind turbine is analysed taking into account large motions....

Summary of international extreme load design requirements for nuclear power plant facilities

International Nuclear Information System (INIS)

Stevenson, J.D.

1978-01-01

An attempt is made to trace the development of extreme load criteria as it applies to earthquakes, extreme wind, high energy system rupture (LOCA), floods and other manmade and natural external hazards, from 1965 until the present, in the leading nuclear power nations throughout the world. (Author)

Survey of extreme load design regulatory agency licensing requirements for nuclear power plants

Energy Technology Data Exchange (ETDEWEB)

Stevenson, J D

1976-04-01

Since 1965, when extreme load requirements began to be considered explicitly in nuclear power plant design, there has been a gradual divergence in requirements imposed by national regulatory agencies. However, nuclear plant safety is an international problem because of the potential international effects of any postulated plant failure. For this reason this paper has been prepared in an attempt to highlight the differences in national criteria currently used in the extreme load design of nuclear plant facilities. No attempt has been made to evaluate the relative merit of the criteria established by the various national regulatory agencies. This paper presents the results of a recent survey made of national atomic energy regulatory agencies and major nuclear steam supply design agencies, which requested a summary of current licensing criteria associated with earthquake, extreme wind (tornado), flood, airplane crash and accident (pipe break) loads applicable within the various national jurisdictions. Also presented are a number of comparisons which are meant to illustrate the differences in national regulatory criteria.

Survey of extreme load design regulatory agency licensing requirements for nuclear power plants

International Nuclear Information System (INIS)

Stevenson, J.D.

1976-01-01

Since 1965, when extreme load requirements began to be considered explicitly in nuclear power plant design, there has been a gradual divergence in requirements imposed by national regulatory agencies. However, nuclear plant safety is an international problem because of the potential international effects of any postulated plant failure. For this reason this paper has been prepared in an attempt to highlight the differences in national criteria currently used in the extreme load design of nuclear plant facilities. No attempt has been made to evaluate the relative merit of the criteria established by the various national regulatory agencies. This paper presents the results of a recent survey made of national atomic energy regulatory agencies and major nuclear steam supply design agencies, which requested a summary of current licensing criteria associated with earthquake, extreme wind (tornado), flood, airplane crash and accident (pipe break) loads applicable within the various national jurisdictions. Also presented are a number of comparisons which are meant to illustrate the differences in national regulatory criteria. (Auth.)

Ultimate design load analysis of planetary gearbox bearings under extreme events

DEFF Research Database (Denmark)

Gallego Calderon, Juan Felipe; Natarajan, Anand; Cutululis, Nicolaos Antonio

2017-01-01

This paper investigates the impact of extreme events on the planet bearings of a 5 MW gearbox. The system is simulated using an aeroelastic tool, where the turbine structure is modeled, and MATLAB/Simulink, where the drivetrain (gearbox and generator) are modeled using a lumped-parameter approach....... Three extreme events are assessed: low-voltage ride through, emergency stop and normal stop. The analysis is focused on finding which event has the most negative impact on the bearing extreme radial loads. The two latter events are carried out following the guidelines of the International...

Analysis of Global Sensitivity of Landing Variables on Landing Loads and Extreme Values of the Loads in Carrier-Based Aircrafts

Directory of Open Access Journals (Sweden)

Jin Zhou

2018-01-01

Full Text Available When a carrier-based aircraft is in arrested landing on deck, the impact loads on landing gears and airframe are closely related to landing states. The distribution and extreme values of the landing loads obtained during life-cycle analysis provide an important basis for buffering parameter design and fatigue design. In this paper, the effect of the multivariate distribution was studied based on military standards and guides. By establishment of a virtual prototype, the extended Fourier amplitude sensitivity test (EFAST method is applied on sensitivity analysis of landing variables. The results show that sinking speed and rolling angle are the main influencing factors on the landing gear’s course load and vertical load; sinking speed, rolling angle, and yawing angle are the main influencing factors on the landing gear’s lateral load; and sinking speed is the main influencing factor on the barycenter overload. The extreme values of loads show that the typical condition design in the structural strength analysis is safe. The maximum difference value of the vertical load of the main landing gear is 12.0%. This research may provide some reference for structure design of landing gears and compilation of load spectrum for carrier-based aircrafts.

Cognitive task load in a naval ship control centre: from identification to prediction.

Science.gov (United States)

Grootjen, M; Neerincx, M A; Veltman, J A

Deployment of information and communication technology will lead to further automation of control centre tasks and an increasing amount of information to be processed. A method for establishing adequate levels of cognitive task load for the operators in such complex environments has been developed. It is based on a model distinguishing three load factors: time occupied, task-set switching, and level of information processing. Application of the method resulted in eight scenarios for eight extremes of task load (i.e. low and high values for each load factor). These scenarios were performed by 13 teams in a high-fidelity control centre simulator of the Royal Netherlands Navy. The results show that the method provides good prediction of the task load that will actually appear in the simulator. The model allowed identification of under- and overload situations showing negative effects on operator performance corresponding to controlled experiments in a less realistic task environment. Tools proposed to keep the operator at an optimum task load are (adaptive) task allocation and interface support.

Using bench press load to predict upper body exercise loads in physically active individuals.

Science.gov (United States)

Wong, Del P; Ngo, Kwan-Lung; Tse, Michael A; Smith, Andrew W

2013-01-01

This study investigated whether loads for assistance exercises of the upper body can be predicted from the loads of the bench press exercise. Twenty-nine physically active collegiate students (age: 22.6 ± 2.5; weight training experience: 2.9 ± 2.1 years; estimated 1RM bench press: 54.31 ± 14.60 kg; 1RM: body weight ratio: 0.80 ± 0.22; BMI: 22.7 ± 2.1 kg·m(-2)) were recruited. The 6RM loads for bench press, barbell bicep curl, overhead dumbbell triceps extension, hammer curl and dumbbell shoulder press were measured. Test-retest reliability for the 5 exercises as determined by Pearson product moment correlation coefficient was very high to nearly perfect (0.82-0.98, p bench press load was significantly correlated with the loads of the 4 assistance exercises (r ranged from 0.80 to 0.93, p bench press load was a significant (R(2) range from 0.64 to 0.86, p Bench press load (0.28) + 6.30 kg, (b) Barbell biceps curl = Bench press load (0.33) + 6.20 kg, (c) Overhead triceps extension = Bench press load (0.33) - 0.60 kg, and (d) Dumbbell shoulder press = Bench press load (0.42) + 5.84 kg. The difference between the actual load and the predicted load using the four equations ranged between 6.52% and 8.54%, such difference was not significant. Fitness professionals can use the 6RM bench press load as a time effective and accurate method to predict training loads for upper body assistance exercises. Key pointsThe bench press load was significantly correlated with the loads of the 4 assistance exercises.No significant differences were found between the actual load and the predicted load in the four equations.6RM bench press load can be a time effective and accurate method to predict training loads for upper body assistance exercises.

Power load prediction based on GM (1,1)

Science.gov (United States)

Wu, Di

2017-05-01

Currently, Chinese power load prediction is highly focused; the paper deeply studies grey prediction and applies it to Chinese electricity consumption during the recent 14 years; through after-test test, it obtains grey prediction which has good adaptability to medium and long-term power load.

Investigating extreme event loading on coastal bridges using wireless sensor technology

Science.gov (United States)

Gelineau, Douglas A.; Davis, Justin R.; Rice, Jennifer A.

2017-04-01

Coastal infrastructure, such as bridges, are susceptible to many forms of coastal hazards: particularly hurricane surge and wave loading. These two forms of loading can cause catastrophic damage to aging highway infrastructure. It is estimated that storm damage costs the United States about $50 Billion per year. In light of this, it is crucial that we understand the damaging forces placed on infrastructure during storm events so that we can develop safer and more resilient coastal structures. This paper presents the ongoing research to enable the efficient collection of extreme event loads acting on both the substructure and superstructure of low clearance, simple span, reinforced concrete bridges. Bridges of this type were commonly constructed during the 1950's and 60's and are particularly susceptible to deck unseating caused by hurricane surge and wave loading. The sensing technology used to capture this data must be ruggedized to survive in an extremely challenging environment, be designed to allow for redundancy in the event of sensors or other network components being lost in the storm, and be relatively low cost to allow for more bridges to be instrumented per storm event. The prototype system described in this paper includes wireless technology, rapid data transmission, and, for the sensors, self-contained power. While this specific application focuses on hurricane hazards, the framework can be extended to include other natural hazards.

Hydrogen ion induced ultralow wear of PEEK under extreme load

Science.gov (United States)

Yan, Shuai; Wang, Anying; Fei, Jixiong; Wang, Zhenyang; Zhang, Xiaofeng; Lin, Bin

2018-03-01

As a high-performance engineering polymer, poly(ether ether ketone) (PEEK) is a perfect candidate material for applications under extreme working conditions. However, its high wear rate greatly shortens its service life. In this study, ultralow friction and wear between PEEK and silicon nitride (Si3N4) under extreme-load conditions (with a mean contact pressure above 100 MPa) are found in acid lubricating solutions. Both friction and wear decrease sharply with decreasing pH. At pH = 1, the friction coefficient decreases by an order of magnitude and the wear rate of the PEEK decreases by two orders of magnitude compared to the results with water lubrication. These reductions in friction and wear occur for different speed, load, and surface roughness conditions. The underlying mechanism can be attributed to the formation of hydrogen-ion-induced electrical double layers on the surfaces of PEEK and Si3N4. The combined effect of the resulting repulsive force, electro-viscosity, and low shear strength of the water layer dramatically reduces both friction and wear.

Characterization and prediction of extreme events in turbulence

Science.gov (United States)

Fonda, Enrico; Iyer, Kartik P.; Sreenivasan, Katepalli R.

2017-11-01

Extreme events in Nature such as tornadoes, large floods and strong earthquakes are rare but can have devastating consequences. The predictability of these events is very limited at present. Extreme events in turbulence are the very large events in small scales that are intermittent in character. We examine events in energy dissipation rate and enstrophy which are several tens to hundreds to thousands of times the mean value. To this end we use our DNS database of homogeneous and isotropic turbulence with Taylor Reynolds numbers spanning a decade, computed with different small scale resolutions and different box sizes, and study the predictability of these events using machine learning. We start with an aggressive data augmentation to virtually increase the number of these rare events by two orders of magnitude and train a deep convolutional neural network to predict their occurrence in an independent data set. The goal of the work is to explore whether extreme events can be predicted with greater assurance than can be done by conventional methods (e.g., D.A. Donzis & K.R. Sreenivasan, J. Fluid Mech. 647, 13-26, 2010).

Statistical Model of Extreme Shear

DEFF Research Database (Denmark)

Hansen, Kurt Schaldemose; Larsen, Gunner Chr.

2005-01-01

In order to continue cost-optimisation of modern large wind turbines, it is important to continuously increase the knowledge of wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describes the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of full-scale measurements recorded with a high sampling rate...

Load prediction of stall regulated wind turbines

Energy Technology Data Exchange (ETDEWEB)

Bjoerck, A.; Dahlberg, J.Aa. [Aeronautical Research Inst. of Sweden, Bromma (Sweden); Carlen, I. [Chalmers Univ. of Technology, Goeteborg (Sweden). Div. of Marine Structural Engineering; Ganander, H. [Teknikgruppen AB, Sollentua (Sweden)

1996-12-01

Measurements of blade loads on a turbine situated in a small wind farm shows that the highest blade loads occur during operation close to the peak power i.e. when the turbine operates in the stall region. In this study the extensive experimental data base has been utilised to compare loads in selected campaigns with corresponding load predictions. The predictions are based on time domain simulations of the wind turbine structure, performed by the aeroelastic code VIDYN. In the calculations a model were adopted in order to include the effects of dynamic stall. This paper describes the work carried out so far within the project and key results. 5 refs, 10 figs

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

Directory of Open Access Journals (Sweden)

Guo-Qiang Zeng

2017-11-01

Full Text Available As the penetration level of renewable distributed generations such as wind turbine generator and photovoltaic stations increases, the load frequency control issue of a multi-area interconnected power system becomes more challenging. This paper presents an adaptive model predictive load frequency control method for a multi-area interconnected power system with photovoltaic generation by considering some nonlinear features such as a dead band for governor and generation rate constraint for steam turbine. The dynamic characteristic of this system is formulated as a discrete-time state space model firstly. Then, the predictive dynamic model is obtained by introducing an expanded state vector, and rolling optimization of control signal is implemented based on a cost function by minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have demonstrated the superiority of the proposed model predictive control method to these state-of-the-art control techniques such as firefly algorithm, genetic algorithm, and population extremal optimization-based proportional-integral control methods in cases of normal conditions, load disturbance and parameters uncertainty.

Prediction of contact mechanics in metal-on-metal Total Hip Replacement for parametrically comprehensive designs and loads.

Science.gov (United States)

Donaldson, Finn E; Nyman, Edward; Coburn, James C

2015-07-16

Manufacturers and investigators of Total Hip Replacement (THR) bearings require tools to predict the contact mechanics resulting from diverse design and loading parameters. This study provides contact mechanics solutions for metal-on-metal (MoM) bearings that encompass the current design space and could aid pre-clinical design optimization and evaluation. Stochastic finite element (FE) simulation was used to calculate the head-on-cup contact mechanics for five thousand combinations of design and loading parameters. FE results were used to train a Random Forest (RF) surrogate model to rapidly predict the contact patch dimensions, contact area, pressures and plastic deformations for arbitrary designs and loading. In addition to widely observed polar and edge contact, FE results included ring-polar, asymmetric-polar, and transitional categories which have previously received limited attention. Combinations of design and load parameters associated with each contact category were identified. Polar contact pressures were predicted in the range of 0-200 MPa with no permanent deformation. Edge loading (with subluxation) was associated with pressures greater than 500 MPa and induced permanent deformation in 83% of cases. Transitional-edge contact (with little subluxation) was associated with intermediate pressures and permanent deformation in most cases, indicating that, even with ideal anatomical alignment, bearings may face extreme wear challenges. Surrogate models were able to accurately predict contact mechanics 18,000 times faster than FE analyses. The developed surrogate models enable rapid prediction of MoM bearing contact mechanics across the most comprehensive range of loading and designs to date, and may be useful to those performing bearing design optimization or evaluation. Published by Elsevier Ltd.

Containment bellows testing under extreme loads

International Nuclear Information System (INIS)

Splezter, B.L.; Lambert, L.D.; Parks, M.B.

1993-01-01

Sandia National Laboratories (SNL) is conducting several research programs to help develop validated methods for the prediction of the ultimate pressure capacity, at elevated temperatures, of light water reactor (LWR) containment structures. To help understand the ultimate pressure of the entire containment pressure boundary, each component must be evaluated. The containment pressure boundary consists of the containment shell and many access, piping, and electrical penetrations. The focus of the current research program is to study the ultimate behavior of flexible metal bellows that are used at piping penetrations. Bellows are commonly used at piping penetrations in steel containments; however, they have very few applications in concrete (reinforced or prestressed) containments. The purpose of piping bellows is to provide a soft connection between the containment shell and the pipe are attached while maintaining the containment pressure boundary. In this way, piping loads caused by differential movement between the piping and the containment shell are minimized. SNL is conducting a test program to determine the leaktight capacity of containment bellows when subjected to postulated severe accident conditions. If the test results indicate that containment bellows could be a possible failure mode of the containment pressure boundary, then methods will be developed to predict the deformation, pressure, and temperature conditions that would likely cause a bellows failure. Results from the test program would be used to validate the prediction methods. This paper provides a description of the use and design of bellows in containment piping penetrations, the types of possible bellows loadings during a severe accident, and an overview of the test program, including available test results at the time of writing

Predicting Ultimate Loads for Wind Turbine Design

International Nuclear Information System (INIS)

Madsen, P. H.; Pierce, K.; Buhl, M.

1998-01-01

This paper addresses the statistical uncertainty of loads prediction using structural dynamics simulation codes and the requirements for the number and duration of simulations for obtaining robust load estimates

PREDICTION OF THE EXTREMAL SHAPE FACTOR OF SPHEROIDAL PARTICLES

Directory of Open Access Journals (Sweden)

Daniel Hlubinka

2011-05-01

Full Text Available In the stereological unfolding problem for spheroidal particles the extremal shape factor is predicted. The theory of extreme values has been used to show that extremes of the planar shape factor of particle sections tend to the same limit distribution as extremes of the original shape factor for both the conditional and marginal distribution. Attention is then paid to the extreme shape factor conditioned by the particle size. Normalizing constants are evaluated for a parametric model and the numerical procedure is tested on real data from metallography.

Variability of extreme flap loads during turbine operation

Energy Technology Data Exchange (ETDEWEB)

Ronold, K O [Det Norske Veritas, Hoevik (Norway); Larsen, G C [Risoe National Lab., Wind Energy and Atmospheric Physics Dept., Roskilde (Denmark)

1999-03-01

The variability of extreme flap loads is of utmost importance for design of wind-turbine rotor blades. The flap loads of interest consist of the flap-wise bendin moment response at the blade root whose variability in the short-term, for a given wind climate, can be represented by a stationary process. A model for the short-term bending moment process is presented, and the distribution of its associated maxima is derived. A model for the wind climate is given in terms of the probability distributions for the 10-minute mean wind speed and the standard deviation of the arbitrary wind speed. This is used to establish the distribution of the largest flap-wise bending moment in a specific reference period, and it is outlined how a characteristic bending moment for use in design can be extracted from this distribution. The application of the presented distribution models is demonstrated by a numerical example for a site-specific wind turbine. (au)

Influential Factors for Accurate Load Prediction in a Demand Response Context

DEFF Research Database (Denmark)

Wollsen, Morten Gill; Kjærgaard, Mikkel Baun; Jørgensen, Bo Nørregaard

2016-01-01

Accurate prediction of a buildings electricity load is crucial to respond to Demand Response events with an assessable load change. However, previous work on load prediction lacks to consider a wider set of possible data sources. In this paper we study different data scenarios to map the influence....... Next, the time of day that is being predicted greatly influence the prediction which is related to the weather pattern. By presenting these results we hope to improve the modeling of building loads and algorithms for Demand Response planning.......Accurate prediction of a buildings electricity load is crucial to respond to Demand Response events with an assessable load change. However, previous work on load prediction lacks to consider a wider set of possible data sources. In this paper we study different data scenarios to map the influence...

Predicting extreme rainfall over eastern Asia by using complex networks

International Nuclear Information System (INIS)

He Su-Hong; Gong Yan-Chun; Huang Yan-Hua; Wu Cheng-Guo; Feng Tai-Chen; Gong Zhi-Qiang

2014-01-01

A climate network of extreme rainfall over eastern Asia is constructed for the period of 1971–2000, employing the tools of complex networks and a measure of nonlinear correlation called event synchronization (ES). Using this network, we predict the extreme rainfall for several cases without delay and with n-day delay (1 ≤ n ≤ 10). The prediction accuracy can reach 58% without delay, 21% with 1-day delay, and 12% with n-day delay (2 ≤ n ≤ 10). The results reveal that the prediction accuracy is low in years of a weak east Asia summer monsoon (EASM) or 1 year later and high in years of a strong EASM or 1 year later. Furthermore, the prediction accuracy is higher due to the many more links that represent correlations between different grid points and a higher extreme rainfall rate during strong EASM years. (geophysics, astronomy, and astrophysics)

Predictability and possible earlier awareness of extreme precipitation across Europe

Science.gov (United States)

Lavers, David; Pappenberger, Florian; Richardson, David; Zsoter, Ervin

2017-04-01

Extreme hydrological events can cause large socioeconomic damages in Europe. In winter, a large proportion of these flood episodes are associated with atmospheric rivers, a region of intense water vapour transport within the warm sector of extratropical cyclones. When preparing for such extreme events, forecasts of precipitation from numerical weather prediction models or river discharge forecasts from hydrological models are generally used. Given the strong link between water vapour transport (integrated vapour transport IVT) and heavy precipitation, it is possible that IVT could be used to warn of extreme events. Furthermore, as IVT is located in extratropical cyclones, it is hypothesized to be a more predictable variable due to its link with synoptic-scale atmospheric dynamics. In this research, we firstly provide an overview of the predictability of IVT and precipitation forecasts, and secondly introduce and evaluate the ECMWF Extreme Forecast Index (EFI) for IVT. The EFI is a tool that has been developed to evaluate how ensemble forecasts differ from the model climate, thus revealing the extremeness of the forecast. The ability of the IVT EFI to capture extreme precipitation across Europe during winter 2013/14, 2014/15, and 2015/16 is presented. The results show that the IVT EFI is more capable than the precipitation EFI of identifying extreme precipitation in forecast week 2 during forecasts initialized in a positive North Atlantic Oscillation (NAO) phase. However, the precipitation EFI is superior during the negative NAO phase and at shorter lead times. An IVT EFI example is shown for storm Desmond in December 2015 highlighting its potential to identify upcoming hydrometeorological extremes.

Prediction of future nitrogen loading to Lake Rotorua

International Nuclear Information System (INIS)

Morgenstern, U.; Gordon, D.

2006-01-01

Groundwater that feeds streams and springs in the Lake Rotorua catchment has 15-130 years mean residence times in the aquifer. These long residence times of the water in the ground result in large time-delays of nitrogen loading from historical agricultural and urban development in the catchment. Currently observed increases in nitrogen loading in surface and groundwater are mostly due to the delayed impact of catchment development that occurred around 55 years ago. Further increases in nitrogen are expected. The time-dependence of the arrival of water to the lake that was recharged since landuse development in the 1950's was calculated using the age distribution of the water derived from tritium, CFC and SF 6 data. The arrival of post-landuse water over time was then used to estimate the nitrogen load to the lake for the time prior to landuse development, for the time since then, and for the future. Excellent matches between measured N loads over the last decades and predicted loads demonstrate the robustness of the approach, and that the model assumptions used for future predictions are reasonable. Future groundwater-derived nutrient loads are listed below. No changes are expected in phosphorus loads via groundwater as long as landuse-derived P continues to be absorbed by the volcanic soils in the catchment. The nitrogen loading to Lake Rotorua prior to major landuse development in the catchment in the 1950's was calculated to be 60 t/year. This has slowly increased to a present nitrogen load of 420 t/y, delayed by long travel times of the groundwater. The nitrogen loading is expected to further increase to 532 t/y in 50 years (25% increase from current), 572 t/y in 100 years (35% increase from current), and to 619 t/y at steady-state (47% increase from current). About 75% of the groundwater-derived nitrogen loading at steady-state enters Lake Rotorua via the nine major streams, and about 20% enters the lake from direct groundwater inflow to the lake bed. The

Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

Directory of Open Access Journals (Sweden)

Chan-Uk Yeom

2017-10-01

Full Text Available This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM with automatic knowledge representation from a given input-output data set. For this purpose, we use a Takagi-Sugeno-Kang (TSK-based ELM to develop a systematic approach to generating if-then rules, while the conventional ELM operates without knowledge information. The TSK-ELM design includes a two-phase development. First, we generate an initial random-partition matrix and estimate cluster centers for random clustering. The obtained cluster centers are used to determine the premise parameters of fuzzy if-then rules. Next, the linear weights of the TSK fuzzy type are estimated using the least squares estimate (LSE method. These linear weights are used as the consequent parameters in the TSK-ELM design. The experiments were performed on short-term electricity-load data for forecasting. The electricity-load data were used to forecast hourly day-ahead loads given temperature forecasts; holiday information; and historical loads from the New England ISO. In order to quantify the performance of the forecaster, we use metrics and statistical characteristics such as root mean squared error (RMSE as well as mean absolute error (MAE, mean absolute percent error (MAPE, and R-squared, respectively. The experimental results revealed that the proposed method showed good performance when compared with a conventional ELM with four activation functions such sigmoid, sine, radial basis function, and rectified linear unit (ReLU. It possessed superior prediction performance and knowledge information and a small number of rules.

Investigation of potential extreme load reduction for a two-bladed upwind turbine with partial pitch

DEFF Research Database (Denmark)

Kim, Taeseong; Larsen, Torben J.; Yde, Anders

2015-01-01

This paper presents a wind turbine concept with an innovative design combining partial pitch with a two-bladed (PP-2B) turbine configuration. Special emphasis is on extreme load reduction during storm situations at standstill, but operational loads are also investigated. In order to compare...... loads are reduced by approximately 20% for the PP-2B and 18% for the PP-3B compared with the 3B turbine for the parked condition in a storm situation. Moreover, a huge potential of 60% is observed for the reduction of the extreme tower bottom bending moment for the PP-2B turbine, when the wind direction...... is from ±90° to the turbine, but this also requires that the turbine is parked in a T-configuration. © 2014 The Authors. Wind Energy published by John Wiley & Sons, Ltd....

A new crack growth model for life prediction under random loading

International Nuclear Information System (INIS)

Lee, Ouk Sub; Chen, Zhi Wei

1999-01-01

The load interaction effect in variable amplitude fatigue test is a very important issue for correctly predicting fatigue life. Some prediction methods for retardation are reviewed and the problems discussed. The so-called 'under-load' effect is also of importance for a prediction model to work properly under random load spectrum. A new model that is simple in form but combines overload plastic zone and residual stress considerations together with Elber's closure concept is proposed to fully take account of the load-interaction effects including both over-load and under-load effects. Applying this new model to complex load sequence is explored here. Simulations of tests show the improvement of the new model over other models. The best prediction (mostly closely resembling test curve) is given by the newly proposed Chen-Lee model

Advances in the Assessment of Wind Turbine Operating Extreme Loads via More Efficient Calculation Approaches

Energy Technology Data Exchange (ETDEWEB)

Graf, Peter; Damiani, Rick R.; Dykes, Katherine; Jonkman, Jason M.

2017-01-09

A new adaptive stratified importance sampling (ASIS) method is proposed as an alternative approach for the calculation of the 50 year extreme load under operational conditions, as in design load case 1.1 of the the International Electrotechnical Commission design standard. ASIS combines elements of the binning and extrapolation technique, currently described by the standard, and of the importance sampling (IS) method to estimate load probability of exceedances (POEs). Whereas a Monte Carlo (MC) approach would lead to the sought level of POE with a daunting number of simulations, IS-based techniques are promising as they target the sampling of the input parameters on the parts of the distributions that are most responsible for the extreme loads, thus reducing the number of runs required. We compared the various methods on select load channels as output from FAST, an aero-hydro-servo-elastic tool for the design and analysis of wind turbines developed by the National Renewable Energy Laboratory (NREL). Our newly devised method, although still in its infancy in terms of tuning of the subparameters, is comparable to the others in terms of load estimation and its variance versus computational cost, and offers great promise going forward due to the incorporation of adaptivity into the already powerful importance sampling concept.

Biomechanical loading on the upper extremity increases from single key tapping to directional tapping.

Science.gov (United States)

Qin, Jin; Trudeau, Matthieu; Katz, Jeffrey N; Buchholz, Bryan; Dennerlein, Jack T

2011-08-01

Musculoskeletal disorders associated with computer use span the joints of the upper extremity. Computing typically involves tapping in multiple directions. Thus, we sought to describe the loading on the finger, wrist, elbow and shoulder joints in terms of kinematic and kinetic difference across single key switch tapping to directional tapping on multiple keys. An experiment with repeated measures design was conducted. Six subjects tapped with their right index finger on a stand-alone number keypad placed horizontally in three conditions: (1) on single key switch (the number key 5); (2) left and right on number key 4 and 6; (3) top and bottom on number key 8 and 2. A force-torque transducer underneath the keypad measured the fingertip force. An active-marker infrared motion analysis system measured the kinematics of the fingertip, hand, forearm, upper arm and torso. Joint moments for the metacarpophalangeal, wrist, elbow, and shoulder joints were estimated using inverse dynamics. Tapping in the top-bottom orientation introduced the largest biomechanical loading on the upper extremity especially for the proximal joint, followed by tapping in the left-right orientation, and the lowest loading was observed during single key switch tapping. Directional tapping on average increased the fingertip force, joint excursion, and peak-to-peak joint torque by 45%, 190% and 55%, respectively. Identifying the biomechanical loading patterns associated with these fundamental movements of keying improves the understanding of the risks of upper extremity musculoskeletal disorders for computer keyboard users. Copyright © 2010 Elsevier Ltd. All rights reserved.

A new simulation method for turbines in wake - Applied to extreme response during operation

DEFF Research Database (Denmark)

Thomsen, K.; Aagaard Madsen, H.

2005-01-01

The work focuses on prediction of load response for wind turbines operating in wind forms using a newly developed aeroelostic simulation method The traditionally used concept is to adjust the free flow turbulence intensity to account for increased loads in wind farms-a methodology that might......, the resulting extremes might be erroneous. For blade loads the traditionally used simplified approach works better than for integrated rotor loads-where the instantaneous load gradient across the rotor disc is causing the extreme loads. In the article the new wake simulation approach is illustrated...

DeRisk - Accurate prediction of ULS wave loads. Outlook and first results

DEFF Research Database (Denmark)

Bredmose, Henrik; Dixen, Martin; Ghadirian, Amin

2016-01-01

Loads from extreme waves can be dimensioning for the substructures of offshore wind turbines. The DeRisk project (2015-2019) aims at an improved load evaluation procedure for extreme waves through application of advanced wave models, laboratory tests of load effects, development of hydrodynamic...... load models, aero-elastic response calculations and statistical analysis. This first paper from the project outlines the content and philosophy behind DeRisk. Next, the first results from laboratory tests with irregular waves are presented, including results for 2D and 3D focused wave groups....... The results of focused wave group tests and a 6-hour (full scale duration) test are reproduced numerically by re-application of the wave paddle signal in a fully nonlinear potential flow wave model. A good match for the free surface elevation and associated exceedance probability curve is obtained. Finally...

Wind simulation for extreme and fatigue loads

Energy Technology Data Exchange (ETDEWEB)

Nielsen, M.; Larsen, G.C.; Mann, J.; Ott, S.; Hansen, K.S.; Pedersen, B.J.

2004-01-01

Measurements of atmospheric turbulence have been studied and found to deviate from a Gaussian process, in particular regarding the velocity increments over small time steps, where the tails of the pdf are exponential rather than Gaussian. Principles for extreme event counting and the occurrence of cascading events are presented. Empirical extreme statistics agree with Rices exceedence theory, when it is assumed that the velocity and its time derivative are independent. Prediction based on the assumption that the velocity is a Gaussian process underpredicts the rate of occurrence of extreme events by many orders of magnitude, mainly because the measured pdf is non-Gaussian. Methods for simulation of turbulent signals have been developed and their computational efficiency are considered. The methods are applicable for multiple processes with individual spectra and probability distributions. Non-Gaussian processes are simulated by the correlation-distortion method. Non-stationary processes are obtained by Bezier interpolation between a set of stationary simulations with identical random seeds. Simulation of systems with some signals available is enabled by conditional statistics. A versatile method for simulation of extreme events has been developed. This will generate gusts, velocity jumps, extreme velocity shears, and sudden changes of wind direction. Gusts may be prescribed with a specified ensemble average shape, and it is possible to detect the critical gust shape for a given construction. The problem is formulated as the variational problem of finding the most probable adjustment of a standard simulation of a stationary Gaussian process subject to relevant event conditions, which are formulated as linear combination of points in the realization. The method is generalized for multiple correlated series, multiple simultaneous conditions, and 3D fields of all velocity components. Generalization are presented for a single non-Gaussian process subject to relatively

Predicting Malaysian palm oil price using Extreme Value Theory

OpenAIRE

Chuangchid, K; Sriboonchitta, S; Rahman, S; Wiboonpongse, A

2013-01-01

This paper uses the extreme value theory (EVT) to predict extreme price events of Malaysian palm oil in the future, based on monthly futures price data for a 25 year period (mid-1986 to mid-2011). Model diagnostic has confirmed non-normal distribution of palm oil price data, thereby justifying the use of EVT. Two principal approaches to model extreme values – the Block Maxima (BM) and Peak-Over- Threshold (POT) models – were used. Both models revealed that the palm oil price will peak at ...

Using the load-velocity relationship for 1RM prediction.

OpenAIRE

Jidovtseff, Boris; Harris, N. K.; Crielaard, Jean-Michel; Cronin, J. B.

2011-01-01

Jidovtseff, B, Harris, NK, Crielaard, J-M, and Cronin, JB. Using the load-velocity relationship for 1RM prediction. J Strength Cond Res 24(x): 000-000, 2009-The purpose of this study was to investigate the ability of the load-velocity relationship to accurately predict a bench press 1 repetition maximum (1RM). Data from 3 different bench press studies (n = 112) that incorporated both 1RM assessment and submaximal load-velocity profiling were analyzed. Individual regression analysis was perfor...

Numerical prediction of slamming loads

DEFF Research Database (Denmark)

Seng, Sopheak; Jensen, Jørgen J; Pedersen, Preben T

2012-01-01

It is important to include the contribution of the slamming-induced response in the structural design of large vessels with a significant bow flare. At the same time it is a challenge to develop rational tools to determine the slamming-induced loads and the prediction of their occurrence. Today i...

Prognostic factors for specific lower extremity and spinal musculoskeletal injuries identified through medical screening and training load monitoring in professional football (soccer): a systematic review

Science.gov (United States)

Sergeant, Jamie C; Parkes, Matthew J; Callaghan, Michael J

2017-01-01

Background Medical screening and load monitoring procedures are commonly used in professional football to assess factors perceived to be associated with injury. Objectives To identify prognostic factors (PFs) and models for lower extremity and spinal musculoskeletal injuries in professional/elite football players from medical screening and training load monitoring processes. Methods The MEDLINE, AMED, EMBASE, CINAHL Plus, SPORTDiscus and PubMed electronic bibliographic databases were searched (from inception to January 2017). Prospective and retrospective cohort studies of lower extremity and spinal musculoskeletal injury incidence in professional/elite football players aged between 16 and 40 years were included. The Quality in Prognostic Studies appraisal tool and the modified Grading of Recommendations Assessment, Development and Evaluation synthesis approach was used to assess the quality of the evidence. Results Fourteen studies were included. 16 specific lower extremity injury outcomes were identified. No spinal injury outcomes were identified. Meta-analysis was not possible due to heterogeneity and study quality. All evidence related to PFs and specific lower extremity injury outcomes was of very low to low quality. On the few occasions where multiple studies could be used to compare PFs and outcomes, only two factors demonstrated consensus. A history of previous hamstring injuries (HSI) and increasing age may be prognostic for future HSI in male players. Conclusions The assumed ability of medical screening tests to predict specific musculoskeletal injuries is not supported by the current evidence. Screening procedures should currently be considered as benchmarks of function or performance only. The prognostic value of load monitoring modalities is unknown. PMID:29177074

MESSENGER Observations of Extreme Loading and Unloading of Mercury's Magnetic Tail

Science.gov (United States)

Slavin, James A.; Anderson, Brian J.; Baker, Daniel N.; Benna, Mehdi; Boardsen, Scott A.; Gloeckler, George; Gold, Robert E.; Ho, George C.; Korth, Haje; Krimigis, Stamatios M.;

Extrinsic Cognitive Load Impairs Spoken Word Recognition in High- and Low-Predictability Sentences.

Science.gov (United States)

Hunter, Cynthia R; Pisoni, David B

Listening effort (LE) induced by speech degradation reduces performance on concurrent cognitive tasks. However, a converse effect of extrinsic cognitive load on recognition of spoken words in sentences has not been shown. The aims of the present study were to (a) examine the impact of extrinsic cognitive load on spoken word recognition in a sentence recognition task and (b) determine whether cognitive load and/or LE needed to understand spectrally degraded speech would differentially affect word recognition in high- and low-predictability sentences. Downstream effects of speech degradation and sentence predictability on the cognitive load task were also examined. One hundred twenty young adults identified sentence-final spoken words in high- and low-predictability Speech Perception in Noise sentences. Cognitive load consisted of a preload of short (low-load) or long (high-load) sequences of digits, presented visually before each spoken sentence and reported either before or after identification of the sentence-final word. LE was varied by spectrally degrading sentences with four-, six-, or eight-channel noise vocoding. Level of spectral degradation and order of report (digits first or words first) were between-participants variables. Effects of cognitive load, sentence predictability, and speech degradation on accuracy of sentence-final word identification as well as recall of preload digit sequences were examined. In addition to anticipated main effects of sentence predictability and spectral degradation on word recognition, we found an effect of cognitive load, such that words were identified more accurately under low load than high load. However, load differentially affected word identification in high- and low-predictability sentences depending on the level of sentence degradation. Under severe spectral degradation (four-channel vocoding), the effect of cognitive load on word identification was present for high-predictability sentences but not for low-predictability

Using the load-velocity relationship for 1RM prediction.

Science.gov (United States)

Jidovtseff, Boris; Harris, Nigel K; Crielaard, Jean-Michel; Cronin, John B

2011-01-01

The purpose of this study was to investigate the ability of the load-velocity relationship to accurately predict a bench press 1 repetition maximum (1RM). Data from 3 different bench press studies (n = 112) that incorporated both 1RM assessment and submaximal load-velocity profiling were analyzed. Individual regression analysis was performed to determine the theoretical load at zero velocity (LD0). Data from each of the 3 studies were analyzed separately and also presented as overall group mean. Thereafter, correlation analysis provided quantification of the relationships between 1RM and LD0. Practically perfect correlations (r = ∼0.95) were observed in our samples, confirming the ability of the load-velocity profile to accurately predict bench press 1RM.

Methodologies for predicting the part-load performance of aero-derivative gas turbines

DEFF Research Database (Denmark)

Haglind, Fredrik; Elmegaard, Brian

2009-01-01

Prediction of the part-load performance of gas turbines is advantageous in various applications. Sometimes reasonable part-load performance is sufficient, while in other cases complete agreement with the performance of an existing machine is desirable. This paper is aimed at providing some guidance...... on methodologies for predicting part-load performance of aero-derivative gas turbines. Two different design models – one simple and one more complex – are created. Subsequently, for each of these models, the part-load performance is predicted using component maps and turbine constants, respectively. Comparisons...... with manufacturer data are made. With respect to the design models, the simple model, featuring a compressor, combustor and turbines, results in equally good performance prediction in terms of thermal efficiency and exhaust temperature as does a more complex model. As for part-load predictions, the results suggest...

Predictability of summer extreme precipitation days over eastern China

Science.gov (United States)

Li, Juan; Wang, Bin

2017-08-01

Extreme precipitation events have severe impacts on human activity and natural environment, but prediction of extreme precipitation events remains a considerable challenge. The present study aims to explore the sources of predictability and to estimate the predictability of the summer extreme precipitation days (EPDs) over eastern China. Based on the region- and season-dependent variability of EPDs, all stations over eastern China are divided into two domains: South China (SC) and northern China (NC). Two domain-averaged EPDs indices during their local high EPDs seasons (May-June for SC and July-August for NC) are therefore defined. The simultaneous lower boundary anomalies associated with each EPDs index are examined, and we find: (a) the increased EPDs over SC are related to a rapid decaying El Nino and controlled by Philippine Sea anticyclone anomalies in May-June; (b) the increased EPDs over NC are accompanied by a developing La Nina and anomalous zonal sea level pressure contrast between the western North Pacific subtropical high and East Asian low in July-August. Tracking back the origins of these boundary anomalies, one or two physically meaningful predictors are detected for each regional EPDs index. The causative relationships between the predictors and the corresponding EPDs over each region are discussed using lead-lag correlation analyses. Using these selected predictors, a set of Physics-based Empirical models is derived. The 13-year (2001-2013) independent forecast shows significant temporal correlation skills of 0.60 and 0.74 for the EPDs index of SC and NC, respectively, providing an estimation of the predictability for summer EPDs over eastern China.

The prediction of rotor rotational noise using measured fluctuating blade loads

Science.gov (United States)

Hosier, R. N.; Pegg, R. J.; Ramakrishnan, R.

1974-01-01

In tests conducted at the NASA Langley Research Center Helicopter Rotor Test Facility, simultaneous measurements of the high-frequency fluctuating aerodynamic blade loads and far-field radiated noise were made on a full-scale, nontranslating rotor system. After their characteristics were determined, the measured blade loads were used in an existing theory to predict the far-field rotational noise. A comparison of the calculated and measured rotational noise is presented with specific attention given to the effect of blade loading coefficients, chordwise loading distributions, blade loading phases, and observer azimuthal position on the predictions.

Predictive equations for lumbar spine loads in load-dependent asymmetric one- and two-handed lifting activities.

Science.gov (United States)

Arjmand, N; Plamondon, A; Shirazi-Adl, A; Parnianpour, M; Larivière, C

2012-07-01

Asymmetric lifting activities are associated with low back pain. A finite element biomechanical model is used to estimate spinal loads during one- and two-handed asymmetric static lifting activities. Model input variables are thorax flexion angle, load magnitude as well as load sagittal and lateral positions while response variables are L4-L5 and L5-S1 disc compression and shear forces. A number of levels are considered for each input variable and all their possible combinations are introduced into the model. Robust yet user-friendly predictive equations that relate model responses to its inputs are established. Predictive equations with adequate goodness-of-fit (R(2) ranged from ~94% to 99%, P≤0.001) that relate spinal loads to task (input) variables are established. Contour plots are used to identify combinations of task variable levels that yield spine loads beyond the recommended limits. The effect of uncertainties in the measurements of asymmetry-related inputs on spinal loads is studied. A number of issues regarding the NIOSH asymmetry multiplier are discussed and it is concluded that this multiplier should depend on the trunk posture and be defined in terms of the load vertical and horizontal positions. Due to an imprecise adjustment of the handled load magnitude this multiplier inadequately controls the biomechanical loading of the spine. Ergonomists and bioengineers, faced with the dilemma of using either complex but more accurate models on one hand or less accurate but simple models on the other hand, have hereby easy-to-use predictive equations that quantify spinal loads under various occupational tasks. Copyright © 2011 Elsevier Ltd. All rights reserved.

Analysis of extreme wind events at Høvsøre and the effect on wind turbine loads

DEFF Research Database (Denmark)

Hannesdóttir, Ásta; Kelly, Mark C.; Mann, Jakob

used to simulate wind turbine response in time domain. The simulations are made for the DTU 10 MW reference wind turbine. Load analysis shows that the maximum tilt moment on the tower yaw bearing correlates well with the wind shear of the measurements. When these loads are compared with the extreme...... wind shear load case of the IEC standards, it is seen that they are of similar magnitude and in one case even higher....

Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

Science.gov (United States)

Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

Prediction-based dynamic load-sharing heuristics

Science.gov (United States)

Goswami, Kumar K.; Devarakonda, Murthy; Iyer, Ravishankar K.

1993-01-01

The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.

Extreme value prediction of the wave-induced vertical bending moment in large container ships

DEFF Research Database (Denmark)

Andersen, Ingrid Marie Vincent; Jensen, Jørgen Juncher

2015-01-01

increase the extreme hull girder response significantly. Focus in the present paper is on the influence of the hull girder flexibility on the extreme response amidships, namely the wave-induced vertical bending moment (VBM) in hogging, and the prediction of the extreme value of the same. The analysis...... in the present paper is based on time series of full scale measurements from three large container ships of 8600, 9400 and 14000 TEU. When carrying out the extreme value estimation the peak-over-threshold (POT) method combined with an appropriate extreme value distribution is applied. The choice of a proper...... threshold level as well as the statistical correlation between clustered peaks influence the extreme value prediction and are taken into consideration in the present paper....

Reliability and validity of a low load endurance strength test for upper and lower extremities in patients with fibromyalgia.

Science.gov (United States)

Munguía-Izquierdo, Diego; Legaz-Arrese, Alejandro

2012-11-01

To evaluate the reliability, standard error of the mean (SEM), clinical significant change, and known group validity of 2 assessments of endurance strength to low loads in patients with fibromyalgia syndrome (FS). Cross-sectional reliability and comparative study. University Pablo de Olavide, Seville, Spain. Middle-aged women with FS (n=95) and healthy women (n=64) matched for age, weight, and body mass index (BMI) were recruited for the study. Not applicable. The endurance strength to low loads tests of the upper and lower extremities and anthropometric measures (BMI) were used for the evaluations. The differences between the readings (tests 1 and 2) and the SDs of the differences, intraclass correlation coefficient (ICC) model (2,1), 95% confidence interval for the ICC, coefficient of repeatability, intrapatient SD, SEM, Wilcoxon signed-rank test, and Bland-Altman plots were used to examine reliability. A Mann-Whitney U test was used to analyze the differences in test values between the patient group and the control group. We hypothesized that patients with FS would have an endurance strength to low loads performance in lower and upper extremities at least twice as low as that of the healthy controls. Satisfactory test-retest reliability and SEMs were found for the lower extremity, dominant arm, and nondominant arm tests (ICC=.973-.979; P.05 for all). The Bland-Altman plots showed 95% limits of agreement for the lower extremity (4.7 to -4.5), dominant arm (3.8 to -4.4), and nondominant arm (3.9 to -4.1) tests. The endurance strength to low loads test scores for the patients with FS were 4-fold lower than for the controls in all performed tests (P<.001 for all). The endurance strength to low loads tests showed good reliability and known group validity and can be recommended for evaluating endurance strength to low loads in patients with FS. For individual evaluation, however, an improved score of at least 4 and 5 repetitions for the upper and lower extremities

Spatial extreme learning machines: An application on prediction of disease counts.

Science.gov (United States)

Prates, Marcos O

2018-01-01

Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

Changes in Predictive Task Switching with Age and with Cognitive Load.

Science.gov (United States)

Levy-Tzedek, Shelly

2017-01-01

Predictive control of movement is more efficient than feedback-based control, and is an important skill in everyday life. We tested whether the ability to predictively control movements of the upper arm is affected by age and by cognitive load. A total of 63 participants were tested in two experiments. In both experiments participants were seated, and controlled a cursor on a computer screen by flexing and extending their dominant arm. In Experiment 1, 20 young adults and 20 older adults were asked to continuously change the frequency of their horizontal arm movements, with the goal of inducing an abrupt switch between discrete movements (at low frequencies) and rhythmic movements (at high frequencies). We tested whether that change was performed based on a feed-forward (predictive) or on a feedback (reactive) control. In Experiment 2, 23 young adults performed the same task, while being exposed to a cognitive load half of the time via a serial subtraction task. We found that both aging and cognitive load diminished, on average, the ability of participants to predictively control their movements. Five older adults and one young adult under a cognitive load were not able to perform the switch between rhythmic and discrete movement (or vice versa). In Experiment 1, 40% of the older participants were able to predictively control their movements, compared with 70% in the young group. In Experiment 2, 48% of the participants were able to predictively control their movements with a cognitively loading task, compared with 70% in the no-load condition. The ability to predictively change a motor plan in anticipation of upcoming changes may be an important component in performing everyday functions, such as safe driving and avoiding falls.

Prediction method for cavitation erosion based on measurement of bubble collapse impact loads

International Nuclear Information System (INIS)

Hattori, S; Hirose, T; Sugiyama, K

2009-01-01

The prediction of cavitation erosion rates is important in order to evaluate the exact life of components. The measurement of impact loads in bubble collapses helps to predict the life under cavitation erosion. In this study, we carried out erosion tests and the measurements of impact loads in bubble collapses with a vibratory apparatus. We evaluated the incubation period based on a cumulative damage rule by measuring the impact loads of cavitation acting on the specimen surface and by using the 'constant impact load - number of impact loads curve' similar to the modified Miner's rule which is employed for fatigue life prediction. We found that the parameter Σ(F i α xn i ) (F i : impact load, n i : number of impacts and α: constant) is suitable for the evaluation of the erosion life. Moreover, we propose a new method that can predict the incubation period under various cavitation conditions.

Study on Predicting Axial Load Capacity of CFST Columns

Science.gov (United States)

Ravi Kumar, H.; Muthu, K. U.; Kumar, N. S.

2017-11-01

This work presents an analytical study and experimental study on the behaviour and ultimate load carrying capacity of axially compressed self-compacting concrete-filled steel tubular columns. Results of tests conducted by various researchers on 213 samples concrete-filled steel tubular columns are reported and present authors experimental data are reported. Two theoretical equations were derived for the prediction of the ultimate axial load strength of concrete-filled steel tubular columns. The results from prediction were compared with the experimental data. Validation to the experimental results was made.

Cognitive load predicts point-of-care ultrasound simulator performance.

Science.gov (United States)

Aldekhyl, Sara; Cavalcanti, Rodrigo B; Naismith, Laura M

2018-02-01

The ability to maintain good performance with low cognitive load is an important marker of expertise. Incorporating cognitive load measurements in the context of simulation training may help to inform judgements of competence. This exploratory study investigated relationships between demographic markers of expertise, cognitive load measures, and simulator performance in the context of point-of-care ultrasonography. Twenty-nine medical trainees and clinicians at the University of Toronto with a range of clinical ultrasound experience were recruited. Participants answered a demographic questionnaire then used an ultrasound simulator to perform targeted scanning tasks based on clinical vignettes. Participants were scored on their ability to both acquire and interpret ultrasound images. Cognitive load measures included participant self-report, eye-based physiological indices, and behavioural measures. Data were analyzed using a multilevel linear modelling approach, wherein observations were clustered by participants. Experienced participants outperformed novice participants on ultrasound image acquisition. Ultrasound image interpretation was comparable between the two groups. Ultrasound image acquisition performance was predicted by level of training, prior ultrasound training, and cognitive load. There was significant convergence between cognitive load measurement techniques. A marginal model of ultrasound image acquisition performance including prior ultrasound training and cognitive load as fixed effects provided the best overall fit for the observed data. In this proof-of-principle study, the combination of demographic and cognitive load measures provided more sensitive metrics to predict ultrasound simulator performance. Performance assessments which include cognitive load can help differentiate between levels of expertise in simulation environments, and may serve as better predictors of skill transfer to clinical practice.

Failure mitigation in software defined networking employing load type prediction

KAUST Repository

Bouacida, Nader

2017-07-31

The controller is a critical piece of the SDN architecture, where it is considered as the mastermind of SDN networks. Thus, its failure will cause a significant portion of the network to fail. Overload is one of the common causes of failure since the controller is frequently invoked by new flows. Even through SDN controllers are often replicated, the significant recovery time can be an overkill for the availability of the entire network. In order to overcome the problem of the overloaded controller failure in SDN, this paper proposes a novel controller offload solution for failure mitigation based on a prediction module that anticipates the presence of a harmful long-term load. In fact, the long-standing load would eventually overwhelm the controller leading to a possible failure. To predict whether the load in the controller is short-term or long-term load, we used three different classification algorithms: Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. Our evaluation results demonstrate that Support Vector Machine algorithm is applicable for detecting the type of load with an accuracy of 97.93% in a real-time scenario. Besides, our scheme succeeded to offload the controller by switching between the reactive and proactive mode in response to the prediction module output.

Extremely Randomized Machine Learning Methods for Compound Activity Prediction

Directory of Open Access Journals (Sweden)

Wojciech M. Czarnecki

2015-11-01

Full Text Available Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

TBCs for Gas Turbines under Thermomechanical Loadings: Failure Behaviour and Life Prediction

Directory of Open Access Journals (Sweden)

Herzog R.

2012-10-01

Full Text Available The present contribution gives an overview about recent research on a thermal barrier coating (TBC system consisted of (i an intermetallic MCrAlY-alloy Bondcoat (BC applied by vacuum plasma spraying (VPS and (ii an Yttria Stabilised Zirconia (YSZ top coat air plasma sprayed (APS at Forschungszentrum Juelich, Institute of Energy and Climate Research (IEK-1. The influence of high temperature dwell time, maximum and minimum temperature on crack growth kinetics during thermal cycling of such plasma sprayed TBCs is investigated using infrared pulse thermography (IT, acoustic emission (AE analysis and scanning electron microscopy. Thermocyclic life in terms of accumulated time at maximum temperature decreases with increasing high temperature dwell time and increases with increasing minimum temperature. AE analysis proves that crack growth mainly occurs during cooling at temperatures below the ductile-to-brittle transition temperature of the BC. Superimposed mechanical load cycles accelerate delamination crack growth and, in case of sufficiently high mechanical loadings, result in premature fatigue failure of the substrate. A life prediction model based on TGO growth kinetics and a fracture mechanics approach has been developed which accounts for the influence of maximum and minimum temperature as well as of high temperature dwell time with good accuracy in an extremely wide parameter range.

Improving prediction accuracy of cooling load using EMD, PSR and RBFNN

Science.gov (United States)

Shen, Limin; Wen, Yuanmei; Li, Xiaohong

2017-08-01

To increase the accuracy for the prediction of cooling load demand, this work presents an EMD (empirical mode decomposition)-PSR (phase space reconstruction) based RBFNN (radial basis function neural networks) method. Firstly, analyzed the chaotic nature of the real cooling load demand, transformed the non-stationary cooling load historical data into several stationary intrinsic mode functions (IMFs) by using EMD. Secondly, compared the RBFNN prediction accuracies of each IMFs and proposed an IMF combining scheme that is combine the lower-frequency components (called IMF4-IMF6 combined) while keep the higher frequency component (IMF1, IMF2, IMF3) and the residual unchanged. Thirdly, reconstruct phase space for each combined components separately, process the highest frequency component (IMF1) by differential method and predict with RBFNN in the reconstructed phase spaces. Real cooling load data of a centralized ice storage cooling systems in Guangzhou are used for simulation. The results show that the proposed hybrid method outperforms the traditional methods.

Towards building a neural network model for predicting pile static load test curves

Directory of Open Access Journals (Sweden)

Alzo’ubi A. K.

2018-01-01

Full Text Available In the United Arab Emirates, Continuous Flight Auger piles are the most widely used type of deep foundation. To test the pile behaviour, the Static Load Test is routinely conducted in the field by increasing the dead load while monitoring the displacement. Although the test is reliable, it is expensive to conduct. This test is usually conducted in the UAE to verify the pile capacity and displacement as the load increase and decreases in two cycles. In this paper we will utilize the Artificial Neural Network approach to build a model that can predict a complete Static Load Pile test. We will show that by integrating the pile configuration, soil properties, and ground water table in one artificial neural network model, the Static Load Test can be predicted with confidence. We believe that based on this approach, the model is able to predict the entire pile load test from start to end. The suggested approach is an excellent tool to reduce the cost associated with such expensive tests or to predict pile’s performance ahead of the actual test.

Model for predicting non-linear crack growth considering load sequence effects (LOSEQ)

International Nuclear Information System (INIS)

Fuehring, H.

1982-01-01

A new analytical model for predicting non-linear crack growth is presented which takes into account the retardation as well as the acceleration effects due to irregular loading. It considers not only the maximum peak of a load sequence to effect crack growth but also all other loads of the history according to a generalised memory criterion. Comparisons between crack growth predicted by using the LOSEQ-programme and experimentally observed data are presented. (orig.) [de

Forecasting Strategies for Predicting Peak Electric Load Days

Science.gov (United States)

Saxena, Harshit

Academic institutions spend thousands of dollars every month on their electric power consumption. Some of these institutions follow a demand charges pricing structure; here the amount a customer pays to the utility is decided based on the total energy consumed during the month, with an additional charge based on the highest average power load required by the customer over a moving window of time as decided by the utility. Therefore, it is crucial for these institutions to minimize the time periods where a high amount of electric load is demanded over a short duration of time. In order to reduce the peak loads and have more uniform energy consumption, it is imperative to predict when these peaks occur, so that appropriate mitigation strategies can be developed. The research work presented in this thesis has been conducted for Rochester Institute of Technology (RIT), where the demand charges are decided based on a 15 minute sliding window panned over the entire month. This case study makes use of different statistical and machine learning algorithms to develop a forecasting strategy for predicting the peak electric load days of the month. The proposed strategy was tested for a whole year starting May 2015 to April 2016 during which a total of 57 peak days were observed. The model predicted a total of 74 peak days during this period, 40 of these cases were true positives, hence achieving an accuracy level of 70 percent. The results obtained with the proposed forecasting strategy are promising and demonstrate an annual savings potential worth about $80,000 for a single submeter of RIT.

Drivers and seasonal predictability of extreme wind speeds in the ECMWF System 4 and a statistical model

Science.gov (United States)

Walz, M. A.; Donat, M.; Leckebusch, G. C.

2017-12-01

As extreme wind speeds are responsible for large socio-economic losses in Europe, a skillful prediction would be of great benefit for disaster prevention as well as for the actuarial community. Here we evaluate patterns of large-scale atmospheric variability and the seasonal predictability of extreme wind speeds (e.g. >95th percentile) in the European domain in the dynamical seasonal forecast system ECMWF System 4, and compare to the predictability based on a statistical prediction model. The dominant patterns of atmospheric variability show distinct differences between reanalysis and ECMWF System 4, with most patterns in System 4 extended downstream in comparison to ERA-Interim. The dissimilar manifestations of the patterns within the two models lead to substantially different drivers associated with the occurrence of extreme winds in the respective model. While the ECMWF System 4 is shown to provide some predictive power over Scandinavia and the eastern Atlantic, only very few grid cells in the European domain have significant correlations for extreme wind speeds in System 4 compared to ERA-Interim. In contrast, a statistical model predicts extreme wind speeds during boreal winter in better agreement with the observations. Our results suggest that System 4 does not seem to capture the potential predictability of extreme winds that exists in the real world, and therefore fails to provide reliable seasonal predictions for lead months 2-4. This is likely related to the unrealistic representation of large-scale patterns of atmospheric variability. Hence our study points to potential improvements of dynamical prediction skill by improving the simulation of large-scale atmospheric dynamics.

Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors

Energy Technology Data Exchange (ETDEWEB)

Wang, Bin; Huang, Rui; Wang, Yubo; Nazaripouya, Hamidreza; Qiu, Charlie; Chu, Chi-Cheng; Gadh, Rajit

2016-05-02

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimization module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.

Assessment of extreme design loads for modern wind turbines using the probabilistic approach

DEFF Research Database (Denmark)

Abdallah, Imad

There is a large drive to reduce the cost of energy of wind energy generators. Various tracks are being considered such as enhanced O&M strategies through condition monitoring, increased manufacturing efficiency through higher production volumes and increased automation, improved resource...... and drag coefficients showed (a) a tangible reduction in the load partial safety factor for a blade and (b) generally a larger impact on extreme loads during power production compared to stand-still. Therefore, the way forward is for wind turbine manufactures to further update the stochastic model...... assessment through turbine-mounted real-time site assessment technologies, improved components reliability by increased laboratory testing, increased number of prototype test turbines before serial production, larger rotor and tower concepts for both onshore and offshore installations, advanced drive train...

Prediction of extreme floods in the Central Andes by means of Complex Networks

Science.gov (United States)

Boers, Niklas; Bookhagen, Bodo; Barbosa, Henrique; Marwan, Norbert; Kurths, Jürgen; Marengo, Jose

2014-05-01

Based on a non-linear synchronisation measure and complex network theory, we present a novel framework for the prediction of extreme events of spatially embedded, interrelated time series. This method is general in the sense that it can be applied to any type of spatially sampled time series with significant interrelations, ranging from climate observables to biological or stock market data. In this presentation, we apply our method to extreme rainfall in South America and show how this leads to the prediction of more than 60% (90% during El Niño conditions) of extreme rainfall events in the eastern Central Andes of Bolivia and northern Argentina, with only 1% false alarms. From paleoclimatic to decadal time scales, the Central Andes continue to be subject to pronounced changes in climatic conditions. In particular, our and past work shows that frequency as well as magnitudes of extreme rainfall events have increased significantly during past decades, calling for a better understanding of the involved climatic mechanisms. Due to their large spatial extend and occurrence at high elevations, these extreme events often lead to severe floods and landslides with disastrous socioeconomic impacts. They regularly affect tens of thousands of people and produce estimated costs of the order of several hundred million USD. Alongside with the societal value of predicting natural hazards, our study provides insights into the responsible climatic features and suggests interactions between Rossby waves in polar regions and large scale (sub-)tropical moisture transport as a driver of subseasonal variability of the South American monsoon system. Predictable extreme events result from the propagation of extreme rainfall from the region of Buenos Aires towards the Central Andes given characteristic atmospheric conditions. Our results indicate that the role of frontal systems originating from Rossby waves in polar latitudes is much more dominant for controlling extreme rainfall in

Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network

Directory of Open Access Journals (Sweden)

Dongxiao Niu

2015-01-01

Full Text Available In order to realize the predicting and positioning of short-term load inflection point, this paper made reference to related research in the field of computer image recognition. It got a load sharp degree sequence by the transformation of the original load sequence based on the algorithm of sharp degree. Then this paper designed a forecasting model based on the chaos theory and RBF neural network. It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point. Finally, in the empirical example analysis, this paper predicted the daily load point of a region using the actual load data of the certain region to verify the effectiveness and applicability of this method. Prediction results showed that most of the test sample load points could be accurately predicted.

Prediction of the metabolic cost of walking with and without loads.

Science.gov (United States)

Duggan, A; Haisman, M F

1992-04-01

Measurement of the metabolic cost of walking inconveniences subjects, and requires skilled technical support and expensive equipment. These factors have stimulated interest in predictive equations. The present study assessed existing equations. Under each of 17 combinations of gradient (0-6%) and carried load (4.1-37.4 kg), 7-12 men undertook treadmill walking at 1.67 m/s. Measured oxygen consumption and respiratory exchange ratio were used to calculate metabolic rate (MRobserved). Metabolic rate was also predicted from the equation of Pandolf et al. (1977) (MRpandolf) and, where appropriate, from another five equations relating to walking without loads. MRobserved and MRpandolf did not differ significantly (p greater than 0.05) under any combination of gradient and load. The overall mean MRobserved and MRpandolf of 609 W and 602 W, respectively, also did not differ significantly (p greater than 0.05). These variables were highly correlated (r = 0.94) with a standard deviation about the prediction error of 47 W. For level walking without loads, the mean predictions from the equations of Pandolf et al. (1977) and Cotes and Meade (1960) did not differ significantly (p greater than 0.05) from the mean MRobserved of 428 Watts, but four other equations overestimated by 17-74 W. In conclusion, the Pandolf et al. (1977) equation has given good results across the range of combinations of load and gradient tested, and the errors are considered acceptable for most practical purposes.

Deterministic and Probabilistic Analysis of NPP Communication Bridge Resistance Due to Extreme Loads

Directory of Open Access Journals (Sweden)

Králik Juraj

2014-12-01

Full Text Available This paper presents the experiences from the deterministic and probability analysis of the reliability of communication bridge structure resistance due to extreme loads - wind and earthquake. On the example of the steel bridge between two NPP buildings is considered the efficiency of the bracing systems. The advantages and disadvantages of the deterministic and probabilistic analysis of the structure resistance are discussed. The advantages of the utilization the LHS method to analyze the safety and reliability of the structures is presented

Walking economy is predictably determined by speed, grade, and gravitational load.

Science.gov (United States)

Ludlow, Lindsay W; Weyand, Peter G

2017-11-01

The metabolic energy that human walking requires can vary by more than 10-fold, depending on the speed, surface gradient, and load carried. Although the mechanical factors determining economy are generally considered to be numerous and complex, we tested a minimum mechanics hypothesis that only three variables are needed for broad, accurate prediction: speed, surface grade, and total gravitational load. We first measured steady-state rates of oxygen uptake in 20 healthy adult subjects during unloaded treadmill trials from 0.4 to 1.6 m/s on six gradients: -6, -3, 0, 3, 6, and 9°. Next, we tested a second set of 20 subjects under three torso-loading conditions (no-load, +18, and +31% body weight) at speeds from 0.6 to 1.4 m/s on the same six gradients. Metabolic rates spanned a 14-fold range from supine rest to the greatest single-trial walking mean (3.1 ± 0.1 to 43.3 ± 0.5 ml O 2 ·kg -body -1 ·min -1 , respectively). As theorized, the walking portion (V̇o 2-walk  =  V̇o 2-gross - V̇o 2-supine-rest ) of the body's gross metabolic rate increased in direct proportion to load and largely in accordance with support force requirements across both speed and grade. Consequently, a single minimum-mechanics equation was derived from the data of 10 unloaded-condition subjects to predict the pooled mass-specific economy (V̇o 2-gross , ml O 2 ·kg -body + load -1 ·min -1 ) of all the remaining loaded and unloaded trials combined ( n = 1,412 trials from 90 speed/grade/load conditions). The accuracy of prediction achieved ( r 2  = 0.99, SEE = 1.06 ml O 2 ·kg -1 ·min -1 ) leads us to conclude that human walking economy is predictably determined by the minimum mechanical requirements present across a broad range of conditions. NEW & NOTEWORTHY Introduced is a "minimum mechanics" model that predicts human walking economy across a broad range of conditions from only three variables: speed, surface grade, and body-plus-load mass. The derivation

Manual for the prediction of blast and fragment loadings on structures

Energy Technology Data Exchange (ETDEWEB)

1980-11-01

The purpose of this manual is to provide Architect-Engineer (AE) firms guidance for the prediction of air blast, ground shock and fragment loadings on structures as a result of accidental explosions in or near these structures. Information in this manual is the result of an extensive literature survey and data gathering effort, supplemented by some original analytical studies on various aspects of blast phenomena. Many prediction equations and graphs are presented, accompanied by numerous example problems illustrating their use. The manual is complementary to existing structural design manuals and is intended to reflect the current state-of-the-art in prediction of blast and fragment loads for accidental explosions of high explosives at the Pantex Plant. In some instances, particularly for explosions within blast-resistant structures of complex geometry, rational estimation of these loads is beyond the current state-of-the-art.

Assessing the ability of operational snow models to predict snowmelt runoff extremes (Invited)

Science.gov (United States)

Wood, A. W.; Restrepo, P. J.; Clark, M. P.

2013-12-01

In the western US, the snow accumulation and melt cycle of winter and spring plays a critical role in the region's water management strategies. Consequently, the ability to predict snowmelt runoff at time scales from days to seasons is a key input for decisions in reservoir management, whether for avoiding flood hazards or supporting environmental flows through the scheduling of releases in spring, or for allocating releases for multi-state water distribution in dry seasons of year (using reservoir systems to provide an invaluable buffer for many sectors against drought). Runoff forecasts thus have important benefits at both wet and dry extremes of the climatological spectrum. The importance of the prediction of the snow cycle motivates an assessment of the strengths and weaknesses of the US's central operational snow model, SNOW17, in contrast to process-modeling alternatives, as they relate to simulating observed snowmelt variability and extremes. To this end, we use a flexible modeling approach that enables an investigation of different choices in model structure, including model physics, parameterization and degree of spatiotemporal discretization. We draw from examples of recent extreme events in western US watersheds and an overall assessment of retrospective model performance to identify fruitful avenues for advancing the modeling basis for the operational prediction of snow-related runoff extremes.

Predictive Solar-Integrated Commercial Building Load Control

Energy Technology Data Exchange (ETDEWEB)

Glasgow, Nathan [EdgePower Inc., Aspen, CO (United States)

2017-01-31

This report is the final technical report for the Department of Energy SunShot award number EE0007180 to EdgePower Inc., for the project entitled “Predictive Solar-Integrated Commercial Building Load Control.” The goal of this project was to successfully prove that the integration of solar forecasting and building load control can reduce demand charge costs for commercial building owners with solar PV. This proof of concept Tier 0 project demonstrated its value through a pilot project at a commercial building. This final report contains a summary of the work completed through he duration of the project. Clean Power Research was a sub-recipient on the award.

Climatic extremes improve predictions of spatial patterns of tree species

Science.gov (United States)

Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C.; Meier, E.S.; Thuiller, W.; Guisan, Antoine; Schmatz, D.R.; Pearman, P.B.

2009-01-01

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

Directory of Open Access Journals (Sweden)

Jianzhou Wang

2014-01-01

Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.

Dynamical prediction and pattern mapping in short-term load forecasting

Energy Technology Data Exchange (ETDEWEB)

Aguirre, Luis Antonio; Rodrigues, Daniela D.; Lima, Silvio T. [Departamento de Engenharia Eletronica, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil); Martinez, Carlos Barreira [Departamento de Engenharia Hidraulica e Recursos Hidricos, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil)

2008-01-15

This work will not put forward yet another scheme for short-term load forecasting but rather will provide evidences that may improve our understanding about fundamental issues which underlay load forecasting problems. In particular, load forecasting will be decomposed into two main problems, namely dynamical prediction and pattern mapping. It is argued that whereas the latter is essentially static and becomes nonlinear when weekly features in the data are taken into account, the former might not be deterministic at all. In such cases there is no determinism (serial correlations) in the data apart from the average cycle and the best a model can do is to perform pattern mapping. Moreover, when there is determinism in addition to the average cycle, the underlying dynamics are sometimes linear, in which case there is no need to resort to nonlinear models to perform dynamical prediction. Such conclusions were confirmed using real load data and surrogate data analysis. In a sense, the paper details and organizes some general beliefs found in the literature on load forecasting. This sheds some light on real model-building and forecasting problems and helps understand some apparently conflicting results reported in the literature. (author)

Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Xue-cun Yang

2015-01-01

Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

Locomotion With Loads: Practical Techniques for Predicting Performance Outcomes

Science.gov (United States)

2015-05-01

Metabolic energy consumption as a function of speed and body size in birds and mammals. J Exp Biol. 97, 1-21. Weyand, P., Smith, B., Puyau, M. and...height, weight (including load), speed, and grade algorithms proposed will allow walking metabolic rates to be predicted to within 6.0 and 12.0% in...gait, metabolism , performance, load carriage 16. SECURITY CLASSIFICATION OF: Unclassified 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME

Loading Intensity Prediction by Velocity and the OMNI-RES 0-10 Scale in Bench Press.

Science.gov (United States)

Naclerio, Fernando; Larumbe-Zabala, Eneko

2017-02-01

Naclerio, F and Larumbe-Zabala, E. Loading intensity prediction by velocity and the OMNI-RES 0-10 scale in bench press. J Strength Cond Res 32(1): 323-329, 2017-This study examined the possibility of using movement velocity and the perceived exertion as indicators of relative load in the bench press (BP) exercise. A total of 308 young, healthy, resistance trained athletes (242 men and 66 women) performed a progressive strength test up to the one repetition maximum for the individual determination of the full load-velocity and load-exertion relationships. Longitudinal regression models were used to predict the relative load from the average velocity (AV) and the OMNI-Resistance Exercise Scales (OMNI-RES 0-10 scale), considering sets as the time-related variable. Load associated with the AV and the OMNI-RES 0-10 scale value expressed after performing a set of 1-3 repetitions were used to construct 2 adjusted predictive equations: Relative load = 107.75 - 62.97 × average velocity; and Relative load = 29.03 + 7.26 × OMNI-RES 0-10 scale value. The 2 models were capable of estimating the relative load with an accuracy of 84 and 93%, respectively. These findings confirm the ability of the 2 calculated regression models, using load-velocity and load-exertion from the OMNI-RES 0-10 scale, to accurately predict strength performance in BP.

Load and Global Response of Ships

DEFF Research Database (Denmark)

Jensen, Jørgen Juncher

The present monograph covers wave load and global structural response for ships. It is primary written as a textbook for students with an introductionary background in naval architecture and a basic knowledge of statistics and strength of materials. The subjects are treated in details starting from...... first principles. The aim has been to derive and present the necessary theoretical framework for predicting the extreme loads and the corresponding hull girder stresses the ship may be subjected to during its operational lifetime.Although some account is given to reliabiity analysis, the present...

Integrity of reinforced concrete cooling towers under extreme loads: Wind and earthquake

International Nuclear Information System (INIS)

Louhi, Amine

2015-01-01

The authorities have planned to increase the lifetime of currently operating nuclear power plants. The ageing of reinforced concrete structures such as cooling towers should be evaluated and its impact on the bearing capacity calculated. In the case of significant damage, the strengthening must be considered to ensure the sustainability of these towers facing the risk of storms and earthquakes becoming more and more frequent. This work aims to quantify the adverse effects that can generate concrete cracks and rebar section loss induced by corrosion, especially on the bearing capacity of nuclear power plant cooling towers under monotonic or cyclic extreme load conditions (wind and earthquake). These loads are certainly the most severe, since they take the structure into the nonlinear domain and can induce or amplify cracking damage. Numerical simulations are proposed to determine the quasi-static or dynamic response of the structure, taking into account appearance of concrete cracks and their evolution via an appropriate material concrete law and rebar's yielding. In the case of a seismic load, the responses are evaluated by three different methods; the nonlinear response history analysis (NLRHA), the response spectrum analysis and the modal response history analysis (MRHA) in order to compare the earthquake modeling approaches and to evaluate the robustness of the results. Parametric studies on damping, load combinations and structural configurations, are also performed. In the case of a wind load, the strengthening technique using composite materials, such as carbon fiber reinforced plastic (CFRP) is modeled. The behavior of the damaged structure with an advanced corrosion rate is estimated in the pre- and post-cracking regime, compared to the undamaged structure. The drop of bearing capacity is quantified, a reinforcement designed is proposed to restore the integrity and thus increase the lifetime of the structure. (author)

A study of the importance of occupancy to building cooling load in prediction by intelligent approach

Energy Technology Data Exchange (ETDEWEB)

Kwok, Simon S.K. [Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Hong Kong); Lee, Eric W.M., E-mail: ericlee@cityu.edu.h [Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Hong Kong)

2011-07-15

Research highlights: {yields} The building occupancy affecting the cooling load prediction is studied. {yields} PENN model is adopted in this study for predicting the building cooling load. {yields} Statistical approach is adopted to result a less prejudice prediction performance. {yields} Results show that occupancy data can significantly improve the prediction. -- Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of

A study of the importance of occupancy to building cooling load in prediction by intelligent approach

International Nuclear Information System (INIS)

Kwok, Simon S.K.; Lee, Eric W.M.

2011-01-01

Research highlights: → The building occupancy affecting the cooling load prediction is studied. → PENN model is adopted in this study for predicting the building cooling load. → Statistical approach is adopted to result a less prejudice prediction performance. → Results show that occupancy data can significantly improve the prediction. -- Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of simulation results

Predictive value of the DASH tool for predicting return to work of injured workers with musculoskeletal disorders of the upper extremity.

Science.gov (United States)

Armijo-Olivo, Susan; Woodhouse, Linda J; Steenstra, Ivan A; Gross, Douglas P

2016-12-01

To determine whether the Disabilities of the Arm, Shoulder, and Hand (DASH) tool added to the predictive ability of established prognostic factors, including patient demographic and clinical outcomes, to predict return to work (RTW) in injured workers with musculoskeletal (MSK) disorders of the upper extremity. A retrospective cohort study using a population-based database from the Workers' Compensation Board of Alberta (WCB-Alberta) that focused on claimants with upper extremity injuries was used. Besides the DASH, potential predictors included demographic, occupational, clinical and health usage variables. Outcome was receipt of compensation benefits after 3 months. To identify RTW predictors, a purposeful logistic modelling strategy was used. A series of receiver operating curve analyses were performed to determine which model provided the best discriminative ability. The sample included 3036 claimants with upper extremity injuries. The final model for predicting RTW included the total DASH score in addition to other established predictors. The area under the curve for this model was 0.77, which is interpreted as fair discrimination. This model was statistically significantly different than the model of established predictors alone (pmodels (p=0.34). The DASH tool together with other established predictors significantly helped predict RTW after 3 months in participants with upper extremity MSK disorders. An appealing result for clinicians and busy researchers is that DASH item 23 has equal predictive ability to the total DASH score. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Effects of extreme-duration heavy load carriage on neuromuscular function and locomotion: a military-based study.

Directory of Open Access Journals (Sweden)

Jordane G Grenier

Full Text Available Trekking and military missions generally consist of carrying heavy loads for extreme durations. These factors have been separately shown to be sources of neuromuscular (NM fatigue and locomotor alterations. However, the question of their combined effects remains unresolved, and addressing this issue required a representative context.The aim was to investigate the effects of extreme-duration heavy load carriage on NM function and walking characteristics.Ten experienced infantrymen performed a 21-h simulated military mission (SMM in a middle-mountain environment with equipment weighing ∼27 kg during battles and ∼43 kg during marches. NM function was evaluated for knee extensors (KE and plantar flexors (PF pre- and immediately post-SMM using isometric maximal voluntary contraction (MVC measurement, neural electrical stimulation and surface EMG. The twitch-interpolation method was used to assess central fatigue. Peripheral changes were examined by stimulating the muscle in the relaxed state. The energy cost, mechanical work and spatio-temporal pattern of walking were also evaluated pre-/post-SMM on an instrumented treadmill in three equipment conditions: Sportswear, Battle and March.After the SMM, MVC declined by -10.2±3.6% for KE (P<0.01 and -10.7±16.1% for PF (P = 0.06. The origin of fatigue was essentially peripheral for both muscle groups. A trend toward low-frequency fatigue was detected for KE (5.5%, P = 0.08. These moderate NM alterations were concomitant with a large increase in perceived fatigue from pre- (rating of 8.3±2.2 to post-SMM (15.9±2.1, P<0.01. The SMM-related fatigue did not alter walking energetics or mechanics, and the different equipment carried on the treadmill did not interact with this fatigue either.this study reports the first data on physiological and biomechanical consequences of extreme-duration heavy load carriage. Unexpectedly, NM function alterations due to the 21-h SMM were moderate and did not

Effects of extreme-duration heavy load carriage on neuromuscular function and locomotion: a military-based study.

Science.gov (United States)

Grenier, Jordane G; Millet, Guillaume Y; Peyrot, Nicolas; Samozino, Pierre; Oullion, Roger; Messonnier, Laurent; Morin, Jean-Benoît

2012-01-01

Trekking and military missions generally consist of carrying heavy loads for extreme durations. These factors have been separately shown to be sources of neuromuscular (NM) fatigue and locomotor alterations. However, the question of their combined effects remains unresolved, and addressing this issue required a representative context. The aim was to investigate the effects of extreme-duration heavy load carriage on NM function and walking characteristics. Ten experienced infantrymen performed a 21-h simulated military mission (SMM) in a middle-mountain environment with equipment weighing ∼27 kg during battles and ∼43 kg during marches. NM function was evaluated for knee extensors (KE) and plantar flexors (PF) pre- and immediately post-SMM using isometric maximal voluntary contraction (MVC) measurement, neural electrical stimulation and surface EMG. The twitch-interpolation method was used to assess central fatigue. Peripheral changes were examined by stimulating the muscle in the relaxed state. The energy cost, mechanical work and spatio-temporal pattern of walking were also evaluated pre-/post-SMM on an instrumented treadmill in three equipment conditions: Sportswear, Battle and March. After the SMM, MVC declined by -10.2±3.6% for KE (P<0.01) and -10.7±16.1% for PF (P = 0.06). The origin of fatigue was essentially peripheral for both muscle groups. A trend toward low-frequency fatigue was detected for KE (5.5%, P = 0.08). These moderate NM alterations were concomitant with a large increase in perceived fatigue from pre- (rating of 8.3±2.2) to post-SMM (15.9±2.1, P<0.01). The SMM-related fatigue did not alter walking energetics or mechanics, and the different equipment carried on the treadmill did not interact with this fatigue either. this study reports the first data on physiological and biomechanical consequences of extreme-duration heavy load carriage. Unexpectedly, NM function alterations due to the 21-h SMM were moderate and did not alter

Experimental investigation of ultimate loads

Energy Technology Data Exchange (ETDEWEB)

Petersen, S M; Larsen, G C; Antoniou, I; Lind, S O; Courtney, M [Risoe National Lab., Wind Energy and Atmospheric Physics, Roskilde (Denmark)

1999-03-01

Verification of the structural integrity of a wind turbine involves analysis of fatigue loading as well as ultimate loading. With the trend of persistently growing turbines, the ultimate loading seems to become relatively more important. For wind turbines designed according to the wind conditions prescribed in the IEC-61400 code, the ultimate load is often identified as the leading load parameter. Exemplified by the use of an extensive measurement campaign a procedure for evaluation of the extreme flap-wise bending moments, occurring during normal operating of a wind turbine, is presented. The structural measurements are made on a NEG Micon 650 kW wind turbine erected at a complex high wind site in Oak Creek, California. The turbine is located on the top of a ridge. The prevailing wind direction is perpendicular to the ridge, and the annual mean wind speed is 9.5 m/s. The associated wind field measurement, are taken from two instrumented masts erected less than one rotor diameter in front of the turbine in direction of the prevailing wind direction. Both masts are instrumented at different heights in order to gain insight of the 3D-wind speed structure over the entire rotor plane. Extreme distributions, associated with a recurrence period of 10 minutes, conditioned on the mean wind speed and the turbulence intensity are derived. Combined with the wind climate model proposed in the IEC standard, these distributions are used to predict extreme distributions with recurrence periods equal to one and fifty years, respectively. The synthesis of the conditioned PDF`s and the wind climate model is performed by means of Monte Carlo simulation. (au)

Fear among the extremes: how political ideology predicts negative emotions and outgroup derogation.

Science.gov (United States)

van Prooijen, Jan-Willem; Krouwel, André P M; Boiten, Max; Eendebak, Lennart

2015-04-01

The "rigidity of the right" hypothesis predicts that particularly the political right experiences fear and derogates outgroups. We propose that above and beyond that, the political extremes (at both sides of the spectrum) are more likely to display these responses than political moderates. Results of a large-scale sample reveal the predicted quadratic term on socio-economic fear. Moreover, although the political right is more likely to derogate the specific category of immigrants, we find a quadratic effect on derogation of a broad range of societal categories. Both extremes also experience stronger negative emotions about politics than politically moderate respondents. Finally, the quadratic effects on derogation of societal groups and negative political emotions were mediated by socio-economic fear, particularly among left- and right-wing extremists. It is concluded that negative emotions and outgroup derogation flourish among the extremes. © 2015 by the Society for Personality and Social Psychology, Inc.

Predicting mass loading as a function of pressure difference across prefilter/HEPA filter systems

International Nuclear Information System (INIS)

Novick, V.J.; Klassen, J.F.; Monson, P.R.

1992-01-01

The purpose of this work is to develop a methodology for predicting the mass loading and pressure drop effects on a prefilter/ HEPA filter system. The methodology relies on the use of empirical equations for the specific resistance of the aerosol loaded filter as a function of the particle diameter. These correlations relate the pressure difference across a filter to the mass loading on the filter and accounts for aerosol particle density effects. These predictions are necessary for the efficient design of new filtration systems and for risk assessment studies of existing filter systems. This work specifically addresses the prefilter/HEPA filter Airborne Activity Confinement Systems (AACS) at the Savannah River Plant. In order to determine the mass loading on the system, it is necessary to establish the efficiency characteristics for the prefilter, the mass loading characteristics of the prefilter measured as a function of pressure difference across the prefilter, and the mass loading characteristics of the HEPA filter as a function of pressure difference across the filter. Furthermore, the efficiency and mass loading characteristics need to be determined as a function of the aerosol particle diameter. A review of the literature revealed that no previous work had been performed to characterize the prefilter material of interest. In order to complete the foundation of information necessary to predict total mass loadings on prefilter/HEPA filter systems, it was necessary to determine the prefilter efficiency and mass loading characteristics. The measured prefilter characteristics combined with the previously determined HEPA filter characteristics allowed the resulting pressure difference across both filters to be predicted as a function of total particle mass for a given particle distribution. These predictions compare favorably to experimental measurements (±25%)

Extreme Motion Predictions for Deepwater TLP Floaters for Offshore Wind Turbines

DEFF Research Database (Denmark)

Jensen, Jørgen Juncher; Mansour, A. E.

2006-01-01

illustrates that a very effective, stochastic response analysis can be performed using the First-order reliability method (FORM), well-known within structural reliability. The results presented are the mean outcrossing rates of the horizontal motions, from where extreme value predictions are readily obtained...

Model of analysis of maximum loads in wind generators produced by extreme winds

International Nuclear Information System (INIS)

Herrera – Sánchez, Omar; Schellong, Wolfgang; González – Fernández, Vladimir

2010-01-01

The use of the wind energy by means of the wind turbines in areas of high risk of occurrence of Hurricanes comes being an important challenge for the designers of wind farm at world for some years. The wind generator is not usually designed to support this type of phenomena, for this reason the areas of high incidence of tropical hurricanes of the planning are excluded, that which, in occasions disables the use of this renewable source of energy totally, either because the country is very small, or because it coincides the area of more potential fully with that of high risk. To counteract this situation, a model of analysis of maxims loads has been elaborated taken place the extreme winds in wind turbines of great behavior. This model has the advantage of determining, in a chosen place, for the installation of a wind farm, the micro-areas with higher risk of wind loads above the acceptable for the standard classes of wind turbines. (author)

Prediction of Running Injuries from Training Load: a Machine Learning Approach.

NARCIS (Netherlands)

Dijkhuis, Talko; Otter, Ruby; Velthuijsen, H.; Lemmink, Koen A.P.M.

2017-01-01

The prediction of the running injuries based on selfreported training data on load is difficult. At present, coaches and researchers have no validated system to predict if a runner has an increased risk of injuries. We aim to develop an algorithm to predict the increase of the risk of a runner to

Rod behaviour under base load, load follow and frequency control operation: CYRANO 2 code predictions versus experimental results

International Nuclear Information System (INIS)

Gautier, B.; Raybaud, A.

1984-01-01

The French PWR reactors are now currently operating under load follow and frequency control. In order to demonstrate that these operating conditions were not able to increase the fuel failure rate, fuel rod behaviour calculations have been performed by E.D.F. with CYRANO 2 code. In parallel with these theoretical calculations, code predictions have been compared to experimental results. The paper presents some of the comparisons performed on 17x17 fuel irradiated in FESSENHEIM 2 up to 30 GWd/tU under base load operation and in the CAP reactor under load follow and frequency control conditions. It is shown that experimental results can be predicted with a reasonable accuracy by CYRANO 2 code. The experimental work was carried out under joint R and D programs by EDF, FRAGEMA, CEA, and WESTINGHOUSE (CAP program by French partners only). (author)

Developing a Framework for Seamless Prediction of Sub-Seasonal to Seasonal Extreme Precipitation Events in the United States.

Science.gov (United States)

Rosendahl, D. H.; Ćwik, P.; Martin, E. R.; Basara, J. B.; Brooks, H. E.; Furtado, J. C.; Homeyer, C. R.; Lazrus, H.; Mcpherson, R. A.; Mullens, E.; Richman, M. B.; Robinson-Cook, A.

2017-12-01

Extreme precipitation events cause significant damage to homes, businesses, infrastructure, and agriculture, as well as many injures and fatalities as a result of fast-moving water or waterborne diseases. In the USA, these natural hazard events claimed the lives of more than 300 people during 2015 - 2016 alone, with total damage reaching $24.4 billion. Prior studies of extreme precipitation events have focused on the sub-daily to sub-weekly timeframes. However, many decisions for planning, preparing and resilience-building require sub-seasonal to seasonal timeframes (S2S; 14 to 90 days), but adequate forecasting tools for prediction do not exist. Therefore, the goal of this newly funded project is an enhancement in understanding of the large-scale forcing and dynamics of S2S extreme precipitation events in the United States, and improved capability for modeling and predicting such events. Here, we describe the project goals, objectives, and research activities that will take place over the next 5 years. In this project, a unique team of scientists and stakeholders will identify and understand weather and climate processes connected with the prediction of S2S extreme precipitation events by answering these research questions: 1) What are the synoptic patterns associated with, and characteristic of, S2S extreme precipitation evens in the contiguous U.S.? 2) What role, if any, do large-scale modes of climate variability play in modulating these events? 3) How predictable are S2S extreme precipitation events across temporal scales? 4) How do we create an informative prediction of S2S extreme precipitation events for policymaking and planing? This project will use observational data, high-resolution radar composites, dynamical climate models and workshops that engage stakeholders (water resource managers, emergency managers and tribal environmental professionals) in co-production of knowledge. The overarching result of this project will be predictive models to reduce of

Fatigue crack growth and life prediction under mixed-mode loading

Science.gov (United States)

Sajith, S.; Murthy, K. S. R. K.; Robi, P. S.

2018-04-01

Fatigue crack growth life as a function of crack length is essential for the prevention of catastrophic failures from damage tolerance perspective. In damage tolerance design approach, principles of fracture mechanics are usually applied to predict the fatigue life of structural components. Numerical prediction of crack growth versus number of cycles is essential in damage tolerance design. For cracks under mixed mode I/II loading, modified Paris law (d/a d N =C (ΔKe q ) m ) along with different equivalent stress intensity factor (ΔKeq) model is used for fatigue crack growth rate prediction. There are a large number of ΔKeq models available for the mixed mode I/II loading, the selection of proper ΔKeq model has significant impact on fatigue life prediction. In the present investigation, the performance of ΔKeq models in fatigue life prediction is compared with respect to the experimental findings as there are no guidelines/suggestions available on the selection of these models for accurate and/or conservative predictions of fatigue life. Within the limitations of availability of experimental data and currently available numerical simulation techniques, the results of present study attempt to outline models that would provide accurate and conservative life predictions. Such a study aid the numerical analysts or engineers in the proper selection of the model for numerical simulation of the fatigue life. Moreover, the present investigation also suggests a procedure to enhance the accuracy of life prediction using Paris law.

Thoracolumbar spine model with articulated ribcage for the prediction of dynamic spinal loading.

Science.gov (United States)

Ignasiak, Dominika; Dendorfer, Sebastian; Ferguson, Stephen J

2016-04-11

Musculoskeletal modeling offers an invaluable insight into the spine biomechanics. A better understanding of thoracic spine kinetics is essential for understanding disease processes and developing new prevention and treatment methods. Current models of the thoracic region are not designed for segmental load estimation, or do not include the complex construct of the ribcage, despite its potentially important role in load transmission. In this paper, we describe a numerical musculoskeletal model of the thoracolumbar spine with articulated ribcage, modeled as a system of individual vertebral segments, elastic elements and thoracic muscles, based on a previously established lumbar spine model and data from the literature. The inverse dynamics simulations of the model allow the prediction of spinal loading as well as costal joints kinetics and kinematics. The intradiscal pressure predicted by the model correlated well (R(2)=0.89) with reported intradiscal pressure measurements, providing a first validation of the model. The inclusion of the ribcage did not affect segmental force predictions when the thoracic spine did not perform motion. During thoracic motion tasks, the ribcage had an important influence on the predicted compressive forces and muscle activation patterns. The compressive forces were reduced by up to 32%, or distributed more evenly between thoracic vertebrae, when compared to the predictions of the model without ribcage, for mild thoracic flexion and hyperextension tasks, respectively. The presented musculoskeletal model provides a tool for investigating thoracic spine loading and load sharing between vertebral column and ribcage during dynamic activities. Further validation for specific applications is still necessary. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mean annual precipitation predicts primary production resistance and resilience to extreme drought.

Science.gov (United States)

Stuart-Haëntjens, Ellen; De Boeck, Hans J; Lemoine, Nathan P; Mänd, Pille; Kröel-Dulay, György; Schmidt, Inger K; Jentsch, Anke; Stampfli, Andreas; Anderegg, William R L; Bahn, Michael; Kreyling, Juergen; Wohlgemuth, Thomas; Lloret, Francisco; Classen, Aimée T; Gough, Christopher M; Smith, Melinda D

2018-04-27

Extreme drought is increasing in frequency and intensity in many regions globally, with uncertain consequences for the resistance and resilience of ecosystem functions, including primary production. Primary production resistance, the capacity to withstand change during extreme drought, and resilience, the degree to which production recovers, vary among and within ecosystem types, obscuring generalized patterns of ecological stability. Theory and many observations suggest forest production is more resistant but less resilient than grassland production to extreme drought; however, studies of production sensitivity to precipitation variability indicate that the processes controlling resistance and resilience may be influenced more by mean annual precipitation (MAP) than ecosystem type. Here, we conducted a global meta-analysis to investigate primary production resistance and resilience to extreme drought in 64 forests and grasslands across a broad MAP gradient. We found resistance to extreme drought was predicted by MAP; however, grasslands (positive) and forests (negative) exhibited opposing resilience relationships with MAP. Our findings indicate that common plant physiological mechanisms may determine grassland and forest resistance to extreme drought, whereas differences among plant residents in turnover time, plant architecture, and drought adaptive strategies likely underlie divergent resilience patterns. The low resistance and resilience of dry grasslands suggests that these ecosystems are the most vulnerable to extreme drought - a vulnerability that is expected to compound as extreme drought frequency increases in the future. Copyright © 2018. Published by Elsevier B.V.

Mixed price and load forecasting of electricity markets by a new iterative prediction method

International Nuclear Information System (INIS)

Amjady, Nima; Daraeepour, Ali

2009-01-01

Load and price forecasting are the two key issues for the participants of current electricity markets. However, load and price of electricity markets have complex characteristics such as nonlinearity, non-stationarity and multiple seasonality, to name a few (usually, more volatility is seen in the behavior of electricity price signal). For these reasons, much research has been devoted to load and price forecast, especially in the recent years. However, previous research works in the area separately predict load and price signals. In this paper, a mixed model for load and price forecasting is presented, which can consider interactions of these two forecast processes. The mixed model is based on an iterative neural network based prediction technique. It is shown that the proposed model can present lower forecast errors for both load and price compared with the previous separate frameworks. Another advantage of the mixed model is that all required forecast features (from load or price) are predicted within the model without assuming known values for these features. So, the proposed model can better be adapted to real conditions of an electricity market. The forecast accuracy of the proposed mixed method is evaluated by means of real data from the New York and Spanish electricity markets. The method is also compared with some of the most recent load and price forecast techniques. (author)

Nested-scale discharge and groundwater level monitoring to improve predictions of flow route discharges and nitrate loads

Science.gov (United States)

van der Velde, Y.; Rozemeijer, J. C.; de Rooij, G. H.; van Geer, F. C.; Torfs, P. J. J. F.; de Louw, P. G. B.

2010-10-01

Identifying effective measures to reduce nutrient loads of headwaters in lowland catchments requires a thorough understanding of flow routes of water and nutrients. In this paper we assess the value of nested-scale discharge and groundwater level measurements for predictions of catchment-scale discharge and nitrate loads. In order to relate field-site measurements to the catchment-scale an upscaling approach is introduced that assumes that scale differences in flow route fluxes originate from differences in the relationship between groundwater storage and the spatial structure of the groundwater table. This relationship is characterized by the Groundwater Depth Distribution (GDD) curve that relates spatial variation in groundwater depths to the average groundwater depth. The GDD-curve was measured for a single field site (0.009 km2) and simple process descriptions were applied to relate the groundwater levels to flow route discharges. This parsimonious model could accurately describe observed storage, tube drain discharge, overland flow and groundwater flow simultaneously with Nash-Sutcliff coefficients exceeding 0.8. A probabilistic Monte Carlo approach was applied to upscale field-site measurements to catchment scales by inferring scale-specific GDD-curves from hydrographs of two nested catchments (0.4 and 6.5 km2). The estimated contribution of tube drain effluent (a dominant source for nitrates) decreased with increasing scale from 76-79% at the field-site to 34-61% and 25-50% for both catchment scales. These results were validated by demonstrating that a model conditioned on nested-scale measurements simulates better nitrate loads and better predictions of extreme discharges during validation periods compared to a model that was conditioned on catchment discharge only.

Lower extremity kinematics that correlate with success in lateral load transfers over a low friction surface.

Science.gov (United States)

Catena, Robert D; Xu, Xu

2015-01-01

We previously studied balance during lateral load transfers, but were left without explanation of why some individuals were successful in novel low friction conditions and others were not. Here, we retrospectively examined lower extremity kinematics between successful (SL) and unsuccessful (UL) groups to determine what characteristics may improve low friction performance. Success versus failure over a novel slippery surface was used to dichotomise 35 healthy working-age individuals into the two groups (SL and UL). Participants performed lateral load transfers over three sequential surface conditions: high friction, novel low friction, and practiced low friction. The UL group used a wide stance with rotation mostly at the hips during the high and novel low friction conditions. To successfully complete the practiced low friction task, they narrowed their stance and pivoted both feet and torso towards the direction of the load, similar to the SL group in all conditions. This successful kinematic method potentially results in reduced muscle demand throughout the task. Practitioner Summary: The reason for this paper is to retrospectively examine the different load transfer strategies that are used in a low friction lateral load transfer. We found stance width to be the major source of success, while sagittal plane motion was altered to potentially maintain balance.

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

Energy Technology Data Exchange (ETDEWEB)

Abbas, Nikhar [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Tom, Nathan M [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-06-03

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.

Predicting lower body power from vertical jump prediction equations for loaded jump squats at different intensities in men and women.

Science.gov (United States)

Wright, Glenn A; Pustina, Andrew A; Mikat, Richard P; Kernozek, Thomas W

2012-03-01

The purpose of this study was to determine the efficacy of estimating peak lower body power from a maximal jump squat using 3 different vertical jump prediction equations. Sixty physically active college students (30 men, 30 women) performed jump squats with a weighted bar's applied load of 20, 40, and 60% of body mass across the shoulders. Each jump squat was simultaneously monitored using a force plate and a contact mat. Peak power (PP) was calculated using vertical ground reaction force from the force plate data. Commonly used equations requiring body mass and vertical jump height to estimate PP were applied such that the system mass (mass of body + applied load) was substituted for body mass. Jump height was determined from flight time as measured with a contact mat during a maximal jump squat. Estimations of PP (PP(est)) for each load and for each prediction equation were compared with criterion PP values from a force plate (PP(FP)). The PP(est) values had high test-retest reliability and were strongly correlated to PP(FP) in both men and women at all relative loads. However, only the Harman equation accurately predicted PP(FP) at all relative loads. It can therefore be concluded that the Harman equation may be used to estimate PP of a loaded jump squat knowing the system mass and peak jump height when more precise (and expensive) measurement equipment is unavailable. Further, high reliability and correlation with criterion values suggest that serial assessment of power production across training periods could be used for relative assessment of change by either of the prediction equations used in this study.

System performance modeling of extreme ultraviolet lithographic thermal issues

International Nuclear Information System (INIS)

Spence, P. A.; Gianoulakis, S. E.; Moen, C. D.; Kanouff, M. P.; Fisher, A.; Ray-Chaudhuri, A. K.

1999-01-01

Numerical simulation is used in the development of an extreme ultraviolet lithography Engineering Test Stand. Extensive modeling was applied to predict the impact of thermal loads on key lithographic parameters such as image placement error, focal shift, and loss of CD control. We show that thermal issues can be effectively managed to ensure that their impact on lithographic performance is maintained within design error budgets. (c) 1999 American Vacuum Society

Outlier robustness for wind turbine extrapolated extreme loads

DEFF Research Database (Denmark)

Natarajan, Anand; Verelst, David Robert

2012-01-01

. Stochastic identification of numerical artifacts in simulated loads is demonstrated using the method of principal component analysis. The extrapolation methodology is made robust to outliers through a weighted loads approach, whereby the eigenvalues of the correlation matrix obtained using the loads with its...

Prediction of length-of-day using extreme learning machine

Directory of Open Access Journals (Sweden)

Yu Lei

2015-03-01

Full Text Available Traditional artificial neural networks (ANN such as back-propagation neural networks (BPNN provide good predictions of length-of-day (LOD. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine (ELM, to improve the efficiency of LOD predictions. Earth orientation parameters (EOP C04 time-series provides daily values from International Earth Rotation and Reference Systems Service (IERS, which serves as our database. First, the known predictable effects that can be described by functional models—such as the effects of solid earth, ocean tides, or seasonal atmospheric variations—are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data (i.e., the irregular and quasi-periodic variations are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks (GRNN, and adaptive network-based fuzzy inference systems (ANFIS. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction techniques, the mean-absolute-error (MAE from the proposed method is compared with that from the EOP prediction comparison campaign (EOP PCC. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.

Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches

Directory of Open Access Journals (Sweden)

Yaolin Lin

2018-06-01

Full Text Available Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR, chi-square automatic interaction detector (CHAID, exhaustive CHAID (ECHAID, back-propagation neural network (BPNN, radial basis function network (RBFN, classification and regression trees (CART, and support vector machines (SVM. It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models—MLR (MLR + BPNN and MLR-BPNN—are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour.

Computational data sciences for assessment and prediction of climate extremes

Science.gov (United States)

Ganguly, A. R.

2011-12-01

Climate extremes may be defined inclusively as severe weather events or large shifts in global or regional weather patterns which may be caused or exacerbated by natural climate variability or climate change. This area of research arguably represents one of the largest knowledge-gaps in climate science which is relevant for informing resource managers and policy makers. While physics-based climate models are essential in view of non-stationary and nonlinear dynamical processes, their current pace of uncertainty reduction may not be adequate for urgent stakeholder needs. The structure of the models may in some cases preclude reduction of uncertainty for critical processes at scales or for the extremes of interest. On the other hand, methods based on complex networks, extreme value statistics, machine learning, and space-time data mining, have demonstrated significant promise to improve scientific understanding and generate enhanced predictions. When combined with conceptual process understanding at multiple spatiotemporal scales and designed to handle massive data, interdisciplinary data science methods and algorithms may complement or supplement physics-based models. Specific examples from the prior literature and our ongoing work suggests how data-guided improvements may be possible, for example, in the context of ocean meteorology, climate oscillators, teleconnections, and atmospheric process understanding, which in turn can improve projections of regional climate, precipitation extremes and tropical cyclones in an useful and interpretable fashion. A community-wide effort is motivated to develop and adapt computational data science tools for translating climate model simulations to information relevant for adaptation and policy, as well as for improving our scientific understanding of climate extremes from both observed and model-simulated data.

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong.

Science.gov (United States)

Zhang, Jiangshe; Ding, Weifu

2017-01-24

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

Directory of Open Access Journals (Sweden)

Jiangshe Zhang

2017-01-01

Full Text Available With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

Fusing Simulation Results From Multifidelity Aero-servo-elastic Simulators - Application To Extreme Loads On Wind Turbine

DEFF Research Database (Denmark)

Abdallah, Imad; Sudret, Bruno; Lataniotis, Christos

2015-01-01

Fusing predictions from multiple simulators in the early stages of the conceptual design of a wind turbine results in reduction in model uncertainty and risk mitigation. Aero-servo-elastic is a term that refers to the coupling of wind inflow, aerodynamics, structural dynamics and controls. Fusing...... the response data from multiple aero-servo-elastic simulators could provide better predictive ability than using any single simulator. The co-Kriging approach to fuse information from multifidelity aero-servo-elastic simulators is presented. We illustrate the co-Kriging approach to fuse the extreme flapwise...... bending moment at the blade root of a large wind turbine as a function of wind speed, turbulence and shear exponent in the presence of model uncertainty and non-stationary noise in the output. The extreme responses are obtained by two widely accepted numerical aero-servo-elastic simulators, FAST...

Reliability and Validity of the Load-Velocity Relationship to Predict the 1RM Back Squat.

Science.gov (United States)

Banyard, Harry G; Nosaka, Kazunori; Haff, G Gregory

2017-07-01

Banyard, HG, Nosaka, K, and Haff, GG. Reliability and validity of the load-velocity relationship to predict the 1RM back squat. J Strength Cond Res 31(7): 1897-1904, 2017-This study investigated the reliability and validity of the load-velocity relationship to predict the free-weight back squat one repetition maximum (1RM). Seventeen strength-trained males performed three 1RM assessments on 3 separate days. All repetitions were performed to full depth with maximal concentric effort. Predicted 1RMs were calculated by entering the mean concentric velocity of the 1RM (V1RM) into an individualized linear regression equation, which was derived from the load-velocity relationship of 3 (20, 40, 60% of 1RM), 4 (20, 40, 60, 80% of 1RM), or 5 (20, 40, 60, 80, 90% of 1RM) incremental warm-up sets. The actual 1RM (140.3 ± 27.2 kg) was very stable between 3 trials (ICC = 0.99; SEM = 2.9 kg; CV = 2.1%; ES = 0.11). Predicted 1RM from 5 warm-up sets up to and including 90% of 1RM was the most reliable (ICC = 0.92; SEM = 8.6 kg; CV = 5.7%; ES = -0.02) and valid (r = 0.93; SEE = 10.6 kg; CV = 7.4%; ES = 0.71) of the predicted 1RM methods. However, all predicted 1RMs were significantly different (p ≤ 0.05; ES = 0.71-1.04) from the actual 1RM. Individual variation for the actual 1RM was small between trials ranging from -5.6 to 4.8% compared with the most accurate predictive method up to 90% of 1RM, which was more variable (-5.5 to 27.8%). Importantly, the V1RM (0.24 ± 0.06 m·s) was unreliable between trials (ICC = 0.42; SEM = 0.05 m·s; CV = 22.5%; ES = 0.14). The load-velocity relationship for the full depth free-weight back squat showed moderate reliability and validity but could not accurately predict 1RM, which was stable between trials. Thus, the load-velocity relationship 1RM prediction method used in this study cannot accurately modify sessional training loads because of large V1RM variability.

Lifetime prediction of structures submitted to thermal fatigue loadings

International Nuclear Information System (INIS)

Amiable, S.

2006-01-01

The aim of this work is to predict the lifetime of structures submitted to thermal fatigue loadings. This work lies within the studies undertaken by the CEA on the thermal fatigue problems from the french reactor of Civaux. In particular we study the SPLASH test: a specimen is heated continuously and cyclically cooled down by a water spray. This loading generates important temperature gradients in space and time and leads to the initiation and the propagation of a crack network. We propose a new thermo-mechanical model to simulate the SPLASH experiment and we propose a new fatigue criterion to predict the lifetime of the SPLASH specimen. We propose and compare several numerical models with various complexity to estimate the mechanical response of the SPLASH specimen. The practical implications of this work are the reevaluation of the hypothesis used in the French code RCC, which are used to simulate thermal shock and to interpret the results in terms of fatigue. This work leads to new perspectives on the mechanical interpretation of the fatigue criterion. (author)

Intracranial hypertension prediction using extremely randomized decision trees.

Science.gov (United States)

Scalzo, Fabien; Hamilton, Robert; Asgari, Shadnaz; Kim, Sunghan; Hu, Xiao

2012-10-01

Intracranial pressure (ICP) elevation (intracranial hypertension, IH) in neurocritical care is typically treated in a reactive fashion; it is only delivered after bedside clinicians notice prolonged ICP elevation. A proactive solution is desirable to improve the treatment of intracranial hypertension. Several studies have shown that the waveform morphology of the intracranial pressure pulse holds predictors about future intracranial hypertension and could therefore be used to alert the bedside clinician of a likely occurrence of the elevation in the immediate future. In this paper, a computational framework is proposed to predict prolonged intracranial hypertension based on morphological waveform features computed from the ICP. A key contribution of this work is to exploit an ensemble classifier method based on extremely randomized decision trees (Extra-Trees). Experiments on a representative set of 30 patients admitted for various intracranial pressure related conditions demonstrate the effectiveness of the predicting framework on ICP pulses acquired under clinical conditions and the superior results of the proposed approach in comparison to linear and AdaBoost classifiers. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

Using squat testing to predict training loads for lower-body exercises in elite karate athletes.

Science.gov (United States)

Wong, Del P; Tan, Erik C H; Chaouachi, Anis; Carling, Christopher; Castagna, Carlo; Bloomfield, Jonathan; Behm, David G

2010-11-01

The purpose of this study was to determine the relationship between squat loads and 2 bilateral and 2 unilateral stepping lower-body exercises in predominantly unilateral movement elite athletes (Karate). Equations to predict loads for lower-body exercises based on the squat load were also determined. Fourteen male elite Karate athletes (age = 22.6 ± 1.2 years) performed 6 repetition maximum (RM) of the following free-weight bilateral exercises: back half squat, deadlift, leg press and unilateral stepping exercises, lunge; and step-up. Results showed that 6RM squat load was significantly (p squat load was a significant predictor for deadlift, leg press, lunge, and step-up (R2 range from 0.57 to 0.85, p squat load (1.12)-16.60 kg, (b) Leg press = squat load (1.66) + 16.10 kg, (c) Lunge = squat load (0.61) + 9.39 kg, and (d) step-up = squat load (0.85)-10.36 kg. Coaches and fitness professionals can use the 6RM squat load as a time effective and accurate method to predict training loads for both bilateral and unilateral lower-body exercises with quadriceps as the prime mover. Load prescriptions for unilateral exercises should take into account the type of athletic population.

Angiographic assessment of atherosclerotic load at the lower extremity in patients with diabetic foot and charcot neuro-arthropathy.

Science.gov (United States)

Çildağ, Mehmet B; Ertuğrul, Bülent M; Köseoğlu, Ömer Fk; Çildağ, Songül; Armstrong, David G

2018-06-01

The aim of this study was to investigate atherosclerotic load at the lower extremity in patients with diabetic foot and charcot neuro-arthropathy and compare them with patients with diabetic foot without charcot neuro-arthropathy. This retrospective study consists of 78 patients with diabetic foot who had lower extremity angiography with antegrade approach. All patients were classified into two groups; neuro ischemic wounds with charcot neuro-arthropathy (30/78) and without charcot neuro-arthropathy (48/78).Atherosclerotic load at the side of diabetic foot was determined by using the Bollinger angiogram scoring method. Comparison of atherosclerotic load between the two groups was performed. The mean of total and infrapopliteal level angiogram scoring of all patients was 33.3 (standard deviation, sd:±17.2) and 29.3 (sd:±15.6), respectively. The mean of total and infrapopliteal level angiogram scoring of neuroischemic wounds with charcot neuro-arthropathy group was 18.1 (sd:±11.6) and 15.7 (sd:±10.4), respectively. The mean of total and infrapopliteal level angiogram scoring of neuroischemic wounds without charcot neuro-arthropathy group was 42.8 (sd:±12.7) and 37.7 (sd:±12.0), respectively. There was a statistically significant difference between the two groups of mean total and infrapopliteal angiogram scoring (p diabetic foot and chronic charcot neuro-arthropathy is significantly less than in patients with neuroischemic diabetic foot wounds without chronic charcot neuro-arthropathy. Copyright © 2017. Published by Elsevier Taiwan LLC.

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

Energy Technology Data Exchange (ETDEWEB)

Abbas, Nikhar; Tom, Nathan

2017-09-01

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.

The Influence of Lower Extremity Lean Mass on Landing Biomechanics During Prolonged Exercise.

Science.gov (United States)

Montgomery, Melissa M; Tritsch, Amanda J; Cone, John R; Schmitz, Randy J; Henson, Robert A; Shultz, Sandra J

2017-08-01

  The extent to which lower extremity lean mass (LELM) relative to total body mass influences one's ability to maintain safe landing biomechanics during prolonged exercise when injury incidence increases is unknown.   To examine the influence of LELM on (1) pre-exercise lower extremity biomechanics and (2) changes in biomechanics during an intermittent exercise protocol (IEP) and (3) determine whether these relationships differ by sex. We hypothesized that less LELM would predict higher-risk baseline biomechanics and greater changes toward higher-risk biomechanics during the IEP.   Cohort study.   Controlled laboratory.   A total of 59 athletes (30 men: age = 20.3 ± 2.0 years, height = 1.79 ± 0.05 m, mass = 75.2 ± 7.2 kg; 29 women: age = 20.6 ± 2.3 years, height = 1.67 ± 0.08 m, mass = 61.8 ± 9.0 kg) participated.   Before completing an individualized 90-minute IEP designed to mimic a soccer match, participants underwent dual-energy x-ray absorptiometry testing for LELM.   Three-dimensional lower extremity biomechanics were measured during drop-jump landings before the IEP and every 15 minutes thereafter. A previously reported principal components analysis reduced 40 biomechanical variables to 11 factors. Hierarchical linear modeling analysis then determined the extent to which sex and LELM predicted the baseline score and the change in each factor over time.   Lower extremity lean mass did not influence baseline biomechanics or the changes over time. Sex influenced the biomechanical factor representing knee loading at baseline (P = .04) and the changes in the anterior cruciate ligament-loading factor over time (P = .03). The LELM had an additional influence only on women who possessed less LELM (P = .03 and .02, respectively).   Lower extremity lean mass influenced knee loading during landing in women but not in men. The effect appeared to be stronger in women with less LELM. Continually decreasing knee loading over time may reflect a

Enhancing Accuracy of Sediment Total Load Prediction Using Evolutionary Algorithms (Case Study: Gotoorchay River

Directory of Open Access Journals (Sweden)

K. Roshangar

2016-09-01

Full Text Available Introduction: Exact prediction of transported sediment rate by rivers in water resources projects is of utmost importance. Basically erosion and sediment transport process is one of the most complexes hydrodynamic. Although different studies have been developed on the application of intelligent models based on neural, they are not widely used because of lacking explicitness and complexity governing on choosing and architecting of proper network. In this study, a Genetic expression programming model (as an important branches of evolutionary algorithems for predicting of sediment load is selected and investigated as an intelligent approach along with other known classical and imperical methods such as Larsen´s equation, Engelund-Hansen´s equation and Bagnold´s equation. Materials and Methods: In this study, in order to improve explicit prediction of sediment load of Gotoorchay, located in Aras catchment, Northwestern Iran latitude: 38°24´33.3˝ and longitude: 44°46´13.2˝, genetic programming (GP and Genetic Algorithm (GA were applied. Moreover, the semi-empirical models for predicting of total sediment load and rating curve have been used. Finally all the methods were compared and the best ones were introduced. Two statistical measures were used to compare the performance of the different models, namely root mean square error (RMSE and determination coefficient (DC. RMSE and DC indicate the discrepancy between the observed and computed values. Results and Discussions: The statistical characteristics results obtained from the analysis of genetic programming method for both selected model groups indicated that the model 4 including the only discharge of the river, relative to other studied models had the highest DC and the least RMSE in the testing stage (DC= 0.907, RMSE= 0.067. Although there were several parameters applied in other models, these models were complicated and had weak results of prediction. Our results showed that the model 9

Reversible Watermarking Using Prediction-Error Expansion and Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Guangyong Gao

2015-01-01

Full Text Available Currently, the research for reversible watermarking focuses on the decreasing of image distortion. Aiming at this issue, this paper presents an improvement method to lower the embedding distortion based on the prediction-error expansion (PE technique. Firstly, the extreme learning machine (ELM with good generalization ability is utilized to enhance the prediction accuracy for image pixel value during the watermarking embedding, and the lower prediction error results in the reduction of image distortion. Moreover, an optimization operation for strengthening the performance of ELM is taken to further lessen the embedding distortion. With two popular predictors, that is, median edge detector (MED predictor and gradient-adjusted predictor (GAP, the experimental results for the classical images and Kodak image set indicate that the proposed scheme achieves improvement for the lowering of image distortion compared with the classical PE scheme proposed by Thodi et al. and outperforms the improvement method presented by Coltuc and other existing approaches.

Predicting Tail Buffet Loads of a Fighter Airplane

Science.gov (United States)

Moses, Robert W.; Pototzky, Anthony S.

2006-01-01

Buffet loads on aft aerodynamic surfaces pose a recurring problem on most twin-tailed fighter airplanes: During maneuvers at high angles of attack, vortices emanating from various surfaces on the forward parts of such an airplane (engine inlets, wings, or other fuselage appendages) often burst, immersing the tails in their wakes. Although these vortices increase lift, the frequency contents of the burst vortices become so low as to cause the aft surfaces to vibrate destructively. Now, there exists a new analysis capability for predicting buffet loads during the earliest design phase of a fighter-aircraft program. In effect, buffet pressures are applied to mathematical models in the framework of a finite-element code, complete with aeroelastic properties and working knowledge of the spatiality of the buffet pressures for all flight conditions. The results of analysis performed by use of this capability illustrate those vibratory modes of a tail fin that are most likely to be affected by buffet loads. Hence, the results help in identifying the flight conditions during which to expect problems. Using this capability, an aircraft designer can make adjustments to the airframe and possibly the aerodynamics, leading to a more robust design.

Regression models for predicting peak and continuous three-dimensional spinal loads during symmetric and asymmetric lifting tasks.

Science.gov (United States)

Fathallah, F A; Marras, W S; Parnianpour, M

1999-09-01

Most biomechanical assessments of spinal loading during industrial work have focused on estimating peak spinal compressive forces under static and sagittally symmetric conditions. The main objective of this study was to explore the potential of feasibly predicting three-dimensional (3D) spinal loading in industry from various combinations of trunk kinematics, kinetics, and subject-load characteristics. The study used spinal loading, predicted by a validated electromyography-assisted model, from 11 male participants who performed a series of symmetric and asymmetric lifts. Three classes of models were developed: (a) models using workplace, subject, and trunk motion parameters as independent variables (kinematic models); (b) models using workplace, subject, and measured moments variables (kinetic models); and (c) models incorporating workplace, subject, trunk motion, and measured moments variables (combined models). The results showed that peak 3D spinal loading during symmetric and asymmetric lifting were predicted equally well using all three types of regression models. Continuous 3D loading was predicted best using the combined models. When the use of such models is infeasible, the kinematic models can provide adequate predictions. Finally, lateral shear forces (peak and continuous) were consistently underestimated using all three types of models. The study demonstrated the feasibility of predicting 3D loads on the spine under specific symmetric and asymmetric lifting tasks without the need for collecting EMG information. However, further validation and development of the models should be conducted to assess and extend their applicability to lifting conditions other than those presented in this study. Actual or potential applications of this research include exposure assessment in epidemiological studies, ergonomic intervention, and laboratory task assessment.

Recent developments on SMA actuators: predicting the actuation fatigue life for variable loading schemes

Science.gov (United States)

Wheeler, Robert W.; Lagoudas, Dimitris C.

2017-04-01

Shape memory alloys (SMAs), due to their ability to repeatably recover substantial deformations under applied mechanical loading, have the potential to impact the aerospace, automotive, biomedical, and energy industries as weight and volume saving replacements for conventional actuators. While numerous applications of SMA actuators have been flight tested and can be found in industrial applications, these actuators are generally limited to non-critical components, are not widely implemented and frequently one-off designs, and are generally overdesigned due to a lack of understanding of the effect of the loading path on the fatigue life and the lack of an accurate method for predicting actuator lifetimes. In recent years, multiple research efforts have increased our understanding of the actuation fatigue process of SMAs. These advances can be utilized to predict the fatigue lives and failure loads in SMA actuators. Additionally, these prediction methods can be implemented in order to intelligently design actuators in accordance with their fatigue and failure limits. In the following paper, both simple and complex thermomechanical loading paths have been considered. Experimental data was utilized from two material systems: equiatomic Nickel-Titanium and Nickelrich Nickel-Titanium.

Prediction of elastic-plastic response of structural elements subjected to cyclic loading

International Nuclear Information System (INIS)

El Haddad, M.H.; Samaan, S.

1985-01-01

A simplified elastic-plastic analysis is developed to predict stress strain and force deformation response of structural metallic elements subjected to irregular cyclic loadings. In this analysis a simple elastic-plastic method for predicting the skeleton force deformation curve is developed. In this method, elastic and fully plastic solutions are first obtained for unknown quantities, such as deflection or local strains. Elastic and fully plastic contributions are then combined to obtain an elastic-plastic solution. The skeleton curve is doubled to establish the shape of the hysteresis loop. The complete force deformation response can therefore be simulated through reversal by reversal in accordance with hysteresis looping and material memory. Several examples of structural elements with various cross sections made from various materials and subjected to irregular cyclic loadings, are analysed. A close agreement is obtained between experimental results found in the literature and present predictions. (orig.)

Fatigue life prediction of oil ducts under service loads

Energy Technology Data Exchange (ETDEWEB)

Meggiolaro, Marco A.; Castro, Jaime T.P. [Pontificia Univ. Catolica do Rio de Janeiro, RJ (Brazil)

2003-07-01

A methodology to calculate the residual initiation and propagation lives of fatigue cracks in oil pipelines with corrosion-like defects is proposed and applied to predict the residual life of an old duct made of API 5L Gr. B steel, in service for more than 40 years. Since its inauguration, this pipeline has carried several heated products under variable temperatures and pressures. The calculated (nominal) service stresses are very high, due to thermal loads that induce significant bending in curved parts of the duct, with peaks close to the yield strength of the steel. The elastic- plastic fatigue damage at a notch or a corrosion pit root is calculated using the {epsilon}N method, and the effects of surface semi-elliptical cracks in its internal (or external) wall is studied considering appropriate stress intensity factor expressions and the actual service loads. In the presence of surface flaws associated to stress concentration factors of the order of three, a fatigue crack likely will initiate in the pipeline. However, if these surface cracks are small compared to the duct wall thickness, their predicted propagation rates are very low. (author)

Literature review for Texas Department of Transportation Research Project 0-4695: Guidance for design in areas of extreme bed-load mobility, Edwards Plateau, Texas

Science.gov (United States)

Heitmuller, Franklin T.; Asquith, William H.; Fang, Xing; Thompson, David B.; Wang, Keh-Han

2005-01-01

A review of the literature addressing sediment transport in gravel-bed river systems and structures designed to control bed-load mobility is provided as part of Texas Department of Transportation research project 0–4695: Guidance for Design in Areas of Extreme Bed-Load Mobility. The study area comprises the western half of the Edwards Plateau in central Texas. Three primary foci of the literature review are journal articles, edited volumes, and government publications. Major themes within the body of literature include deterministic sediment transport theory and equations, development of methods to measure and analyze fluvial sediment, applications and development of theory in natural channels and flume experiments, and recommendations for river management and structural design. The literature review provides an outline and foundation for the research project to characterize extreme bed-load mobility in rivers and streams across the study area. The literature review also provides a basis upon which potential modifications to low-water stream-crossing design in the study area can be made.

Data-Driven Predictive Direct Load Control of Refrigeration Systems

DEFF Research Database (Denmark)

Shafiei, Seyed Ehsan; Knudsen, Torben; Wisniewski, Rafal

2015-01-01

A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control...... is proposed to fulfil two important objectives: to secure high coefficient of performance and to participate in power consumption management. Moreover, a new method for design of input signals for system identification is put forward. The control method is fully data driven without an explicit use of model...... against real data. The performance improvement results in a 22% reduction in the energy consumption. A comparative simulation is accomplished showing the superiority of the method over the existing approaches in terms of the load following performance....

Robust nonlinear model predictive control for nuclear power plants in load following operations with bounded xenon oscillations

International Nuclear Information System (INIS)

Eliasi, H.; Menhaj, M.B.; Davilu, H.

2011-01-01

Research highlights: → In this work, a robust nonlinear model predictive control algorithm is developed. → This algorithm is applied to control the power level for load following. → The state constraints are imposed on the predicted trajectory during optimization. → The xenon oscillations are the main constraint for the load following problem. → In this algorithm, xenon oscillations are bounded within acceptable limits. - Abstract: One of the important operations in nuclear power plants is load-following in which imbalance of axial power distribution induces xenon oscillations. These oscillations must be maintained within acceptable limits otherwise the nuclear power plant could become unstable. Therefore, bounded xenon oscillation considered to be a constraint for the load-following operation. In this paper, a robust nonlinear model predictive control for the load-following operation problem is proposed that ensures xenon oscillations are kept bounded within acceptable limits. The proposed controller uses constant axial offset (AO) strategy to maintain xenon oscillations to be bounded. The constant AO is a robust state constraint for load-following problem. The controller imposes restricted state constraints on the predicted trajectory during optimization which guarantees robust satisfaction of state constraints without restoring to a min-max optimization problem. Simulation results show that the proposed controller for the load-following operation is so effective so that the xenon oscillations kept bounded in the given region.

Prediction of laser cutting heat affected zone by extreme learning machine

Science.gov (United States)

Anicic, Obrad; Jović, Srđan; Skrijelj, Hivzo; Nedić, Bogdan

2017-01-01

Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be utilized effectively in applications of HAZ forecasting.

Prediction of wax buildup in 24 inch cold, deep sea oil loading line

Energy Technology Data Exchange (ETDEWEB)

Asperger, R.G.; Sattler, R.E.; Tolonen, W.J.; Pitchford, A.C.

1981-10-01

When designing pipelines for cold environments, it is important to know how to predict potential problems due to wax deposition on the pipeline's inner surface. The goal of this work was to determine the rate of wax buildup and the maximum, equlibrium wax thickness for a North Sea field loading line. The experimental techniques and results used to evaluate the waxing potential of the crude oil (B) are described. Also, the theoretic model which was used for predicting the maximum wax deposit thickness in the crude oil (B) loading pipeline at controlled temperatures of 40 F (4.4 C) and 100 F (38 C), is illustrated. Included is a recommendation of a procedure for using hot oil at the end of a tanker loading period in order to dewax the crude oil (B) line. This technique would give maximum heating of the pipeline and should be followed by shutting the hot oil into the pipeline at the end of the loading cycle which will provide a hot oil soaking to help soften existing wax. 14 references.

SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

International Nuclear Information System (INIS)

Jia, J; Cao, R; Pei, X; Wang, H; Hu, L

2015-01-01

Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy

SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Jia, J; Cao, R; Pei, X; Wang, H; Hu, L [Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, Anhui, 230031 (China); Engineering Technology Research Center of Accurate Radiotherapy of Anhui Province, Hefei 230031 (China); Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, SuZhou (China)

2015-06-15

Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy.

Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads

Science.gov (United States)

Parlos, Alexander G.; Toliyat, Hamid A.

2005-01-01

An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.

CAN UPPER EXTREMITY FUNCTIONAL TESTS PREDICT THE SOFTBALL THROW FOR DISTANCE: A PREDICTIVE VALIDITY INVESTIGATION

Science.gov (United States)

Hanney, William J.; Kolber, Morey J.; Davies, George J.; Riemann, Bryan

2011-01-01

Introduction: Understanding the relationships between performance tests and sport activity is important to the rehabilitation specialist. The purpose of this study was two- fold: 1) To identify if relationships exist between tests of upper body strength and power (Single Arm Seated Shot Put, Timed Push-Up, Timed Modified Pull-Up, and The Davies Closed Kinetic Chain Upper Extremity Stability Test, and the softball throw for distance), 2) To determine which variable or group of variables best predicts the performance of a sport specific task (the softball throw for distance). Methods: One hundred eighty subjects (111 females and 69 males, aged 18-45 years) performed the 5 upper extremity tests. The Pearson product moment correlation and a stepwise regression were used to determine whether relationships existed between performance on the tests and which upper extremity test result best explained the performance on the softball throw for distance. Results: There were significant correlations (r=.33 to r=.70, p=0.001) between performance on all of the tests. The modified pull-up test was the best predictor of the performance on the softball throw for distance (r2= 48.7), explaining 48.7% of variation in performance. When weight, height, and age were added to the regression equation the r2 values increased to 64.5, 66.2, and 67.5 respectively. Conclusion: The results of this study indicate that several upper extremity tests demonstrate significant relationships with one another and with the softball throw for distance. The modified pull up test was the best predictor of performance on the softball throw for distance. PMID:21712942

Deflection Prediction of No-Fines Lightweight Concrete Wall Using Neural Network Caused Dynamic Loads

Directory of Open Access Journals (Sweden)

Ridho Bayuaji

2018-04-01

Full Text Available No-fines lightweight concrete wall with horizontal reinforcement refers to an alternative material for wall construction with an aim of improving the wall quality towards horizontal loads. This study is focused on artificial neural network (ANN application to predicting the deflection deformation caused by dynamic loads. The ANN method is able to capture the complex interactions among input/output variables in a system without any knowledge of interaction nature and without any explicit assumption to model form. This paper explains the existing data research, data selection and process of ANN modelling training process and validation. The results of this research show that the deformation can be predicted more accurately, simply and quickly due to the alternating horizontal loads.

Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study

Directory of Open Access Journals (Sweden)

Fisnik Dalipi

2016-01-01

Full Text Available We present our data-driven supervised machine-learning (ML model to predict heat load for buildings in a district heating system (DHS. Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR, Partial Least Square (PLS, and random forest (RF. We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE, mean absolute percentage error (MAPE, and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.

Prediction of fatigue life under service loading using the relative method

International Nuclear Information System (INIS)

Buch, A.

1982-01-01

Fatigue life estimates obtained with the local strain approach (LSA) and with the conventional nominal stress approach (NSA) were compared with experimental results obtained on notched AlCuMg2 aircraft material specimens with flight simulation random tensile loading. The effect of change of the reference stress, of the loading program and of some changes in the loading frequency distribution, on the ratio Nsub(exp)/Nsub(pred) was investigated. A material strain-life curve, a cyclic stress-strain curve. The Neuber-Topper rule Ksub(sigma) x Ksub(epsilon) = K 2 = const. and a K value estimated with an exact two-parameter notch factor formula for the case R = 0, N = 10 7 were used for life predictions. (orig./RW) [de

Environmental prediction, risk assessment and extreme events: adaptation strategies for the developing world

Science.gov (United States)

Webster, Peter J.; Jian, Jun

2011-01-01

The uncertainty associated with predicting extreme weather events has serious implications for the developing world, owing to the greater societal vulnerability to such events. Continual exposure to unanticipated extreme events is a contributing factor for the descent into perpetual and structural rural poverty. We provide two examples of how probabilistic environmental prediction of extreme weather events can support dynamic adaptation. In the current climate era, we describe how short-term flood forecasts have been developed and implemented in Bangladesh. Forecasts of impending floods with horizons of 10 days are used to change agricultural practices and planning, store food and household items and evacuate those in peril. For the first time in Bangladesh, floods were anticipated in 2007 and 2008, with broad actions taking place in advance of the floods, grossing agricultural and household savings measured in units of annual income. We argue that probabilistic environmental forecasts disseminated to an informed user community can reduce poverty caused by exposure to unanticipated extreme events. Second, it is also realized that not all decisions in the future can be made at the village level and that grand plans for water resource management require extensive planning and funding. Based on imperfect models and scenarios of economic and population growth, we further suggest that flood frequency and intensity will increase in the Ganges, Brahmaputra and Yangtze catchments as greenhouse-gas concentrations increase. However, irrespective of the climate-change scenario chosen, the availability of fresh water in the latter half of the twenty-first century seems to be dominated by population increases that far outweigh climate-change effects. Paradoxically, fresh water availability may become more critical if there is no climate change. PMID:22042897

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

OpenAIRE

Li, Guohui; Zhang, Songling; Yang, Hong

2017-01-01

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

The measured contribution of whipping and springing on the fatigue and extreme loading of container vessels

Science.gov (United States)

Storhaug, Gaute

2014-12-01

Whipping/springing research started in the 50'ies. In the 60'ies inland water vessels design rules became stricter due to whipping/springing. The research during the 70-90'ies may be regarded as academic. In 2000 a large ore carrier was strengthened due to severe cracking from North Atlantic operation, and whipping/springing contributed to half of the fatigue damage. Measurement campaigns on blunt and slender vessels were initiated. A few blunt ships were designed to account for whipping/springing. Based on the measurements, the focus shifted from fatigue to extreme loading. In 2005 model tests of a 4,400 TEU container vessel included extreme whipping scenarios. In 2007 the 4400 TEU vessel MSC Napoli broke in two under similar conditions. In 2009 model tests of an 8,600 TEU container vessel container vessel included extreme whipping scenarios. In 2013 the 8,100 TEU vessel MOL COMFORT broke in two under similar conditions. Several classification societies have published voluntary guidelines, which have been used to include whipping/springing in the design of several container vessels. This paper covers results from model tests and full scale measurements used as background for the DNV Legacy guideline. Uncertainties are discussed and recommendations are given in order to obtain useful data. Whipping/springing is no longer academic.

Snow loads in a changing climate: new risks?

Directory of Open Access Journals (Sweden)

U. Strasser

2008-01-01

Full Text Available In January/February 2006, heavy snowfalls in Bavaria (Germany lead to a series of infrastructural damage of catastrophic nature. Since on many collapsed roofs the total snow load was not exceptional, serious engineering deficiencies in roof construction and a sudden rise in the total snow load were considered to be the trigger of the events. An analysis of the then meteorological conditions reveals, that the early winter of 2005/2006 was characterised by an exceptional continuous snow cover, temperatures remained around the freezing point and no significant snowmelt was evident. The frequent freezing/thawing cycles were followed by a general compaction of the snow load. This resulted in a re-distribution and a new concentration of the snow load on specific locations on roofs. With respect to climate change, the question arises as to whether the risks relating to snow loads will increase. The future probability of a continuous snow cover occurrence with frequent freezing/thawing cycles will probably decline due to predicted higher temperatures. However, where temperatures remain low, an increase in winter precipitation will result in increased snow loads. Furthermore, the variability of extremes is predicted to increase. If heavy snowfall events are more frequent, the risk of a trigger event will likely increase. Finally, an attempt will be made here in this paper to outline a concept for an operational warning system for the Bavarian region. This system envisages to predict the development and risk of critical snow loads for a 3-day time period, utilising a combination of climate and snow modelling data and using this together with a snow pillow device (located on roofs and the results of which.

Predictive Simulations of Neuromuscular Coordination and Joint-Contact Loading in Human Gait.

Science.gov (United States)

Lin, Yi-Chung; Walter, Jonathan P; Pandy, Marcus G

2018-04-18

We implemented direct collocation on a full-body neuromusculoskeletal model to calculate muscle forces, ground reaction forces and knee contact loading simultaneously for one cycle of human gait. A data-tracking collocation problem was solved for walking at the normal speed to establish the practicality of incorporating a 3D model of articular contact and a model of foot-ground interaction explicitly in a dynamic optimization simulation. The data-tracking solution then was used as an initial guess to solve predictive collocation problems, where novel patterns of movement were generated for walking at slow and fast speeds, independent of experimental data. The data-tracking solutions accurately reproduced joint motion, ground forces and knee contact loads measured for two total knee arthroplasty patients walking at their preferred speeds. RMS errors in joint kinematics were joint kinematics, ground forces, knee contact loads and muscle activation patterns measured for slow and fast walking. The results demonstrate the feasibility of performing computationally-efficient, predictive, dynamic optimization simulations of movement using full-body, muscle-actuated models with realistic representations of joint function.

Deep Recurrent Model for Server Load and Performance Prediction in Data Center

Directory of Open Access Journals (Sweden)

Zheng Huang

2017-01-01

Full Text Available Recurrent neural network (RNN has been widely applied to many sequential tagging tasks such as natural language process (NLP and time series analysis, and it has been proved that RNN works well in those areas. In this paper, we propose using RNN with long short-term memory (LSTM units for server load and performance prediction. Classical methods for performance prediction focus on building relation between performance and time domain, which makes a lot of unrealistic hypotheses. Our model is built based on events (user requests, which is the root cause of server performance. We predict the performance of the servers using RNN-LSTM by analyzing the log of servers in data center which contains user’s access sequence. Previous work for workload prediction could not generate detailed simulated workload, which is useful in testing the working condition of servers. Our method provides a new way to reproduce user request sequence to solve this problem by using RNN-LSTM. Experiment result shows that our models get a good performance in generating load and predicting performance on the data set which has been logged in online service. We did experiments with nginx web server and mysql database server, and our methods can been easily applied to other servers in data center.

Effect of fuel assembly mechanical design changes on dynamic response of reactor pressure vessel system under extreme loadings

International Nuclear Information System (INIS)

Bhandari, D.R.; Hankinson, M.F.

1993-01-01

This paper presents the results of a study to assess the effect of fuel assembly mechanical design changes on the dynamic response of a pressurized water reactor vessel and reactor internals under Loss-Of-Coolant Accident (LOCA) conditions. The results of this study show that the dynamic response of the reactor vessel internals and the core under extreme loadings, such as LOCA, is very sensitive to fuel assembly mechanical design changes. (author)

Validation of nonlinear FEA models of a thin-walled elbow under extreme loading conditions for Sodium-cooled Fast Reactors

International Nuclear Information System (INIS)

Watakabe, Tomoyoshi; Wakai, Takashi; Jin, Chuanrong; Usui, Yoshiya; Sakai, Shinkichi; Ooshika, Junji; Tsukimori, Kazuyuki

2015-01-01

For the purpose of confirming failure modes and safety margin, some studies on the ultimate strength of thin-walled piping components for Sodium-cooled Fast Reactors (SFRs) under extreme loading conditions such as large earthquakes have been reported these several years. Nonlinear finite element analysis has been applied in these studies to simulate buckling and yielding with large deformation, whose accuracy is dependent on the element type, the mesh size, the elasto-plastic model and so on. It is important to check the validation of a finite element model for nonlinear analysis especially under extreme loading conditions. This paper presents static and dynamic analyses of a thin-walled elbow with large deformation under large seismic loading, and discusses the validation of the FEA models comparing with experimental results. The finite element analysis models in this study are generated by shell elements for a stainless steel pipe elbow of diameter-to-thickness ratio 59:1 similar to the main pipe of SFRs, which is used for shaking table tests. At first, a static analysis is carried out for an in-plane monotonic bending test, in order to confirm that the shell element is appropriate to the large deformation analysis and the material parameters are proper for the strain level in the experiments. And then, a dynamic in-plane bending test with the maximum acceleration of 11.7G is simulated by the nonlinear FEA with stiffness-proportional damping. The influence of mesh sizes on results is investigated, to determine proper mesh sizes and reduce the computational cost. Finally, comparing the results of the FEM analyses with those of experiments, it is concluded that the appropriately generated FEA models are effective and give accurate results for nonlinear analyses of the thin-walled elbow under large seismic loading. (author)

Narrowing the scope of failure prediction using targeted fault load injection

Science.gov (United States)

Jordan, Paul L.; Peterson, Gilbert L.; Lin, Alan C.; Mendenhall, Michael J.; Sellers, Andrew J.

2018-05-01

As society becomes more dependent upon computer systems to perform increasingly critical tasks, ensuring that those systems do not fail becomes increasingly important. Many organizations depend heavily on desktop computers for day-to-day operations. Unfortunately, the software that runs on these computers is written by humans and, as such, is still subject to human error and consequent failure. A natural solution is to use statistical machine learning to predict failure. However, since failure is still a relatively rare event, obtaining labelled training data to train these models is not a trivial task. This work presents new simulated fault-inducing loads that extend the focus of traditional fault injection techniques to predict failure in the Microsoft enterprise authentication service and Apache web server. These new fault loads were successful in creating failure conditions that were identifiable using statistical learning methods, with fewer irrelevant faults being created.

Ultimate compression after impact load prediction in graphite/epoxy coupons using neural network and multivariate statistical analyses

Science.gov (United States)

Gregoire, Alexandre David

2011-07-01

The goal of this research was to accurately predict the ultimate compressive load of impact damaged graphite/epoxy coupons using a Kohonen self-organizing map (SOM) neural network and multivariate statistical regression analysis (MSRA). An optimized use of these data treatment tools allowed the generation of a simple, physically understandable equation that predicts the ultimate failure load of an impacted damaged coupon based uniquely on the acoustic emissions it emits at low proof loads. Acoustic emission (AE) data were collected using two 150 kHz resonant transducers which detected and recorded the AE activity given off during compression to failure of thirty-four impacted 24-ply bidirectional woven cloth laminate graphite/epoxy coupons. The AE quantification parameters duration, energy and amplitude for each AE hit were input to the Kohonen self-organizing map (SOM) neural network to accurately classify the material failure mechanisms present in the low proof load data. The number of failure mechanisms from the first 30% of the loading for twenty-four coupons were used to generate a linear prediction equation which yielded a worst case ultimate load prediction error of 16.17%, just outside of the +/-15% B-basis allowables, which was the goal for this research. Particular emphasis was placed upon the noise removal process which was largely responsible for the accuracy of the results.

Extreme events and predictability of catastrophic failure in composite materials and in the Earth

Science.gov (United States)

Main, I.; Naylor, M.

2012-05-01

Despite all attempts to isolate and predict extreme earthquakes, these nearly always occur without obvious warning in real time: fully deterministic earthquake prediction is very much a `black swan'. On the other hand engineering-scale samples of rocks and other composite materials often show clear precursors to dynamic failure under controlled conditions in the laboratory, and successful evacuations have occurred before several volcanic eruptions. This may be because extreme earthquakes are not statistically special, being an emergent property of the process of dynamic rupture. Nevertheless, probabilistic forecasting of event rate above a given size, based on the tendency of earthquakes to cluster in space and time, can have significant skill compared to say random failure, even in real-time mode. We address several questions in this debate, using examples from the Earth (earthquakes, volcanoes) and the laboratory, including the following. How can we identify `characteristic' events, i.e. beyond the power law, in model selection (do dragon-kings exist)? How do we discriminate quantitatively between stationary and non-stationary hazard models (is a dragon likely to come soon)? Does the system size (the size of the dragon's domain) matter? Are there localising signals of imminent catastrophic failure we may not be able to access (is the dragon effectively invisible on approach)? We focus on the effect of sampling effects and statistical uncertainty in the identification of extreme events and their predictability, and highlight the strong influence of scaling in space and time as an outstanding issue to be addressed by quantitative studies, experimentation and models.

Predicting fractional bed load transport rates: Application of the Wilcock‐Crowe equations to a regulated gravel bed river

Science.gov (United States)

Gaeuman, David; Andrews, E.D.; Krause, Andreas; Smith, Wes

2009-01-01

Bed load samples from four locations in the Trinity River of northern California are analyzed to evaluate the performance of the Wilcock‐Crowe bed load transport equations for predicting fractional bed load transport rates. Bed surface particles become smaller and the fraction of sand on the bed increases with distance downstream from Lewiston Dam. The dimensionless reference shear stress for the mean bed particle size (τ*rm) is largest near the dam, but varies relatively little between the more downstream locations. The relation between τ*rm and the reference shear stresses for other size fractions is constant across all locations. Total bed load transport rates predicted with the Wilcock‐Crowe equations are within a factor of 2 of sampled transport rates for 68% of all samples. The Wilcock‐Crowe equations nonetheless consistently under‐predict the transport of particles larger than 128 mm, frequently by more than an order of magnitude. Accurate prediction of the transport rates of the largest particles is important for models in which the evolution of the surface grain size distribution determines subsequent bed load transport rates. Values of τ*rm estimated from bed load samples are up to 50% larger than those predicted with the Wilcock‐Crowe equations, and sampled bed load transport approximates equal mobility across a wider range of grain sizes than is implied by the equations. Modifications to the Wilcock‐Crowe equation for determining τ*rm and the hiding function used to scale τ*rm to other grain size fractions are proposed to achieve the best fit to observed bed load transport in the Trinity River.

Verifying a computational method for predicting extreme ground motion

Science.gov (United States)

Harris, R.A.; Barall, M.; Andrews, D.J.; Duan, B.; Ma, S.; Dunham, E.M.; Gabriel, A.-A.; Kaneko, Y.; Kase, Y.; Aagaard, Brad T.; Oglesby, D.D.; Ampuero, J.-P.; Hanks, T.C.; Abrahamson, N.

2011-01-01

In situations where seismological data is rare or nonexistent, computer simulations may be used to predict ground motions caused by future earthquakes. This is particularly practical in the case of extreme ground motions, where engineers of special buildings may need to design for an event that has not been historically observed but which may occur in the far-distant future. Once the simulations have been performed, however, they still need to be tested. The SCEC-USGS dynamic rupture code verification exercise provides a testing mechanism for simulations that involve spontaneous earthquake rupture. We have performed this examination for the specific computer code that was used to predict maximum possible ground motion near Yucca Mountain. Our SCEC-USGS group exercises have demonstrated that the specific computer code that was used for the Yucca Mountain simulations produces similar results to those produced by other computer codes when tackling the same science problem. We also found that the 3D ground motion simulations produced smaller ground motions than the 2D simulations.

Nonhydrostatic and surfbeat model predictions of extreme wave run-up in fringing reef environments

Science.gov (United States)

Lashley, Christopher H.; Roelvink, Dano; van Dongeren, Ap R.; Buckley, Mark L.; Lowe, Ryan J.

2018-01-01

The accurate prediction of extreme wave run-up is important for effective coastal engineering design and coastal hazard management. While run-up processes on open sandy coasts have been reasonably well-studied, very few studies have focused on understanding and predicting wave run-up at coral reef-fronted coastlines. This paper applies the short-wave resolving, Nonhydrostatic (XB-NH) and short-wave averaged, Surfbeat (XB-SB) modes of the XBeach numerical model to validate run-up using data from two 1D (alongshore uniform) fringing-reef profiles without roughness elements, with two objectives: i) to provide insight into the physical processes governing run-up in such environments; and ii) to evaluate the performance of both modes in accurately predicting run-up over a wide range of conditions. XBeach was calibrated by optimizing the maximum wave steepness parameter (maxbrsteep) in XB-NH and the dissipation coefficient (alpha) in XB-SB) using the first dataset; and then applied to the second dataset for validation. XB-NH and XB-SB predictions of extreme wave run-up (Rmax and R2%) and its components, infragravity- and sea-swell band swash (SIG and SSS) and shoreline setup (), were compared to observations. XB-NH more accurately simulated wave transformation but under-predicted shoreline setup due to its exclusion of parameterized wave-roller dynamics. XB-SB under-predicted sea-swell band swash but overestimated shoreline setup due to an over-prediction of wave heights on the reef flat. Run-up (swash) spectra were dominated by infragravity motions, allowing the short-wave (but not wave group) averaged model (XB-SB) to perform comparably well to its more complete, short-wave resolving (XB-NH) counterpart. Despite their respective limitations, both modes were able to accurately predict Rmax and R2%.

SWAT Model Prediction of Phosphorus Loading in a South Carolina Karst Watershed with a Downstream Embayment

Science.gov (United States)

Devendra M. Amatya; Manoj K. Jha; Thomas M. Williams; Amy E. Edwards; Daniel R.. Hitchcock

2013-01-01

The SWAT model was used to predict total phosphorus (TP) loadings for a 1555-ha karst watershed—Chapel Branch Creek (CBC)—which drains to a lake via a reservoir-like embayment (R-E). The model was first tested for monthly streamflow predictions from tributaries draining three potential source areas as well as the downstream R-E, followed by TP loadings using data...

Extremes of shock index predicts death in trauma patients

Directory of Open Access Journals (Sweden)

Stephen R Odom

2016-01-01

Full Text Available Context: We noted a bimodal relationship between mortality and shock index (SI, the ratio of heart rate to systolic blood pressure. Aims: To determine if extremes of SI can predict mortality in trauma patients. Settings and Designs: Retrospective evaluation of adult trauma patients at a tertiary care center from 2000 to 2012 in the United States. Materials and Methods: We examined the SI in trauma patients and determined the adjusted mortality for patients with and without head injuries. Statistical Analysis Used: Descriptive statistics and multivariable logistic regression. Results: SI values demonstrated a U-shaped relationship with mortality. Compared with patients with a SI between 0.5 and 0.7, patients with a SI of 1.3 had an odds ratio of death of 3.1. (95% CI 1.6–5.9. Elevated SI is associated with increased mortality in patients with isolated torso injuries, and is associated with death at both low and high values in patients with head injury. Conclusion: Our data indicate a bimodal relationship between SI and mortality in head injured patients that persists after correction for various co-factors. The distribution of mortality is different between head injured patients and patients without head injuries. Elevated SI predicts death in all trauma patients, but low SI values only predict death in head injured patients.

Advances in the analysis and design of concrete structures, metal containments and liner plate for extreme loads

International Nuclear Information System (INIS)

Stevenson, J.D.; Eibl, J.; Curbach, M.; Johnson, T.E.; Daye, M.A.; Riera, J.D.; Nemet, J.; Iyengar, K.T.S.

1992-01-01

The material presented in this paper summarizes the progress that has been made in the analysis, design, and testing of concrete structures. The material is summarized in the following documents: Part I: Containment Design Criteria and Loading Combinations; Part II: Reinforced and Prestressed Concrete Behavior; Part III: Concrete Containment Analysis, Design and Related Testing; Part IV: Impact and Impulse Loading and Response Prediction; Part V: Metal Containments and Liner Plate Systems; Part VI: Prestressed Reactor Vessel Design, Testing and Analysis. (orig.)

Extrapolation of extreme response for different mooring line systems of floating wave energy converters

DEFF Research Database (Denmark)

Ambühl, Simon; Sterndorff, Martin; Sørensen, John Dalsgaard

2014-01-01

Mooring systems for floating wave energy converters (WECs) are a major cost driver. Failure of mooring systems often occurs due to extreme loads. This paper introduces an extrapolation method for extreme response which accounts for the control system of a WEC that controls the loads onto...... measurements from lab-scaled WEPTOS WEC are taken. Different catenary anchor leg mooring (CALM) systems as well as single anchor legmooring (SALM)mooring systemsare implemented for a dynamic simulation with different number of mooring lines. Extreme tension loads with a return period of 50 years are assessed...... for the hawser as well as at the different mooring lines. Furthermore, the extreme load impact given failure of one mooring line is assessed and compared with extreme loads given no system failure....

The role of atmospheric diagnosis and Big Data science in improving hydroclimatic extreme prediction and the merits of climate informed prediction for future water resources management

Science.gov (United States)

Lu, Mengqian; Lall, Upmanu

2017-04-01

The threats that hydroclimatic extremes pose to sustainable development, safety and operation of infrastructure are both severe and growing. Recent heavy precipitation triggered flood events in many regions and increasing frequency and intensity of extreme precipitation suggested by various climate projections highlight the importance of understanding the associated hydrometeorological patterns and space-time variability of such extreme events, and developing a new approach to improve predictability with a better estimation of uncertainty. This clear objective requires the optimal utility of Big Data analytics on multi-source datasets to extract informative predictors from the complex ocean-atmosphere coupled system and develop a statistical and physical based framework. The proposed presentation includes the essence of our selected works in the past two years, as part of our Global Floods Initiatives. Our approach for an improved extreme prediction begins with a better understanding of the associated atmospheric circulation patterns, under the influence and regulation of slowly changing oceanic boundary conditions [Lu et al., 2013, 2016a; Lu and Lall, 2016]. The study of the associated atmospheric circulation pattern and the regulation of teleconnected climate signals adopted data science techniques and statistical modeling recognizing the nonstationarity and nonlinearity of the system, as the underlying statistical assumptions of the classical extreme value frequency analysis are challenged in hydroclimatic studies. There are two main factors that are considered important for understanding how future flood risk will change. One is the consideration of moisture holding capacity as a function of temperature, as suggested by Clausius-Clapeyron equation. The other is the strength of the convergence or convection associated with extreme precipitation. As convergence or convection gets stronger, rain rates can be expected to increase if the moisture is available. For

Comparison of Methods to Predict Lower Bound Buckling Loads of Cylinders Under Axial Compression

Science.gov (United States)

Haynie, Waddy T.; Hilburger, Mark W.

2010-01-01

Results from a numerical study of the buckling response of two different orthogrid stiffened circular cylindrical shells with initial imperfections and subjected to axial compression are used to compare three different lower bound buckling load prediction techniques. These lower bound prediction techniques assume different imperfection types and include an imperfection based on a mode shape from an eigenvalue analysis, an imperfection caused by a lateral perturbation load, and an imperfection in the shape of a single stress-free dimple. The STAGS finite element code is used for the analyses. Responses of the cylinders for ranges of imperfection amplitudes are considered, and the effect of each imperfection is compared to the response of a geometrically perfect cylinder. Similar behavior was observed for shells that include a lateral perturbation load and a single dimple imperfection, and the results indicate that the predicted lower bounds are much less conservative than the corresponding results for the cylinders with the mode shape imperfection considered herein. In addition, the lateral perturbation technique and the single dimple imperfection produce response characteristics that are physically meaningful and can be validated via testing.

Loading intensity prediction by velocity and the OMNI-RES 0–10 scale in bench press

OpenAIRE

Naclerio, Fernando; Larumbe-Zabala, Eneko

2017-01-01

This study examined the possibility of using movement velocity and the perceived exertion as indicators of relative load in the bench press exercise. Three hundred eight young, healthy, resistance trained athletes (242 male and 66 female) performed a progressive strength test up to the one-repetition maximum for the individual determination of the full load-velocity and load-exertion relationships. Longitudinal regression models were used to predict the relative load from the average velocity...

Mid-term load forecasting of power systems by a new prediction method

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2008-01-01

Mid-term load forecasting (MTLF) becomes an essential tool for today power systems, mainly in those countries whose power systems operate in a deregulated environment. Among different kinds of MTLF, this paper focuses on the prediction of daily peak load for one month ahead. This kind of load forecast has many applications like maintenance scheduling, mid-term hydro thermal coordination, adequacy assessment, management of limited energy units, negotiation of forward contracts, and development of cost efficient fuel purchasing strategies. However, daily peak load is a nonlinear, volatile, and nonstationary signal. Besides, lack of sufficient data usually further complicates this problem. The paper proposes a new methodology to solve it, composed of an efficient data model, preforecast mechanism and combination of neural network and evolutionary algorithm as the hybrid forecast technique. The proposed methodology is examined on the EUropean Network on Intelligent TEchnologies (EUNITE) test data and Iran's power system. We will also compare our strategy with the other MTLF methods revealing its capability to solve this load forecast problem

Lower extremity joint loads in habitual rearfoot and mid/forefoot strike runners with normal and shortened stride lengths.

Science.gov (United States)

Boyer, Elizabeth R; Derrick, Timothy R

2018-03-01

Our purpose was to compare joint loads between habitual rearfoot (hRF) and habitual mid/forefoot strikers (hFF), rearfoot (RFS) and mid/forefoot strike (FFS) patterns, and shorter stride lengths (SLs). Thirty-eight hRF and hFF ran at their normal SL, 5% and 10% shorter, as well as with the opposite foot strike. Three-dimensional ankle, knee, patellofemoral (PF) and hip contact forces were calculated. Nearly all contact forces decreased with a shorter SL (1.2-14.9% relative to preferred SL). In general, hRF had higher PF (hRF-RFS: 10.8 ± 1.4, hFF-FFS: 9.9 ± 2.0 BWs) and hip loads (axial hRF-RFS: -9.9 ± 0.9, hFF-FFS: -9.6 ± 1.0 BWs) than hFF. Many loads were similar between foot strike styles for the two groups, including axial and lateral hip, PF, posterior knee and shear ankle contact forces. Lateral knee and posterior hip contact forces were greater for RFS, and axial ankle and knee contact forces were greater for FFS. The tibia may be under greater loading with a FFS because of these greater axial forces. Summarising, a particular foot strike style does not universally decrease joint contact forces. However, shortening one's SL 10% decreased nearly all lower extremity contact forces, so it may hold potential to decrease overuse injuries associated with excessive joint loads.

Automatic Power Control for Daily Load-following Operation using Model Predictive Control Method

Energy Technology Data Exchange (ETDEWEB)

Yu, Keuk Jong; Kim, Han Gon [KH, Daejeon (Korea, Republic of)

2009-10-15

Under the circumstances that nuclear power occupies more than 50%, nuclear power plants are required to be operated on load-following operation in order to make the effective management of electric grid system and enhanced responsiveness to rapid changes in power demand. Conventional reactors such as the OPR1000 and APR1400 have a regulating system that controls the average temperature of the reactor core relation to the reference temperature. This conventional method has the advantages of proven technology and ease of implementation. However, this method is unsuitable for controlling the axial power shape, particularly the load following operation. Accordingly, this paper reports on the development of a model predictive control method which is able to control the reactor power and the axial shape index. The purpose of this study is to analyze the behavior of nuclear reactor power and the axial power shape by using a model predictive control method when the power is increased and decreased for a daily load following operation. The study confirms that deviations in the axial shape index (ASI) are within the operating limit.

Active load management in an intelligent building using model predictive control strategy

DEFF Research Database (Denmark)

Zong, Yi; Kullmann, Daniel; Thavlov, Anders

2011-01-01

This paper introduces PowerFlexHouse, a research facility for exploring the technical potential of active load management in a distributed power system (SYSLAB) with a high penetration of renewable energy and presents in detail on how to implement a thermal model predictive controller for load...... shifting in PowerFlexHouse heaters' power consumption scheme. With this demand side control study, it is expected that this method of demand response can dramatically raise energy efficiencies and improve grid reliability, when there is a high penetration of intermittent energy resources in the power...

Hourly cooling load prediction of a vehicle in the southern region of Turkey by Artificial Neural Network

International Nuclear Information System (INIS)

Solmaz, Ozgur; Ozgoren, Muammer; Aksoy, Muharrem Hilmi

2014-01-01

Highlights: • An ANN model was developed to predict hourly cooling load of a vehicle. • Hourly meteorological data of 5 different provinces was used. • The agreement of the cooling load values between the calculations and predictions was fairly promising. • The ANN model could be successfully used to design automotive air conditioning systems. - Abstract: In this study, Artificial Neural Networks (ANNs) method for prediction hourly cooling load of a vehicle was implemented. The cooling load of the vehicle was calculated along the cooling season (1 May–30 September) for Antalya, Konya, Mersin, Mugla and Sanliurfa provinces in Turkey. For ANN model, seven neurons determinated as input signals of latitude, longitude, altitude, day of the year, hour of the day, hourly mean ambient air temperature and hourly solar radiation were used for the input layer of the network. One neuron producing an output signal of the hourly cooling load was utilized in the output layer. All data were divided into two categories for training and testing of the ANN. The 80% of the data was reserved to training and the remaining was used for testing of the model. Neuron numbers in the hidden layer from 7 to 40 were tested step by step to find the best matching ANN structure. The obtained results for different numbers of neurons were compared in terms of root mean squared error (RMSE), coefficient of determination (R 2 ) and mean absolute error (MAE). The best matching results for the training and testing were obtained as 8 neurons for the minimum testing RMSE value for the prediction of cooling load by the ANN model on the 23rd day of each month along the cooling season. For the model with 8 neurons RMSE, R 2 and MAE (Training/Testing) were found to be 0.0128/0.0259, 0.9959/0.9818 and 78.81/174.71 W/m 2 , respectively. It is shown that the cooling load of a vehicle can be successfully predicted by means of the ANNs from geographical characteristics and meteorological data

Helicopter Rotor Load Prediction Using a Geometrically Exact Beam with Multicomponent Model

DEFF Research Database (Denmark)

Lee, Hyun-Ku; Viswamurthy, S.R.; Park, Sang Chul

2010-01-01

In this paper, an accurate structural dynamic analysis was developed for a helicopter rotor system including rotor control components, which was coupled to various aerodynamic and wake models in order to predict an aeroelastic response and the loads acting on the rotor. Its blade analysis was based...... rotor-blade/control-system model was loosely coupled with various inflow and wake models in order to simulate both hover and forward-flight conditions. The resulting rotor blade response and pitch link loads are in good agreement with those predicted byCAMRADII. The present analysis features both model...... on an intrinsic formulation of moving beams implemented in the time domain. The rotor control system was modeled as a combination of rigid and elastic components. A multicomponent analysis was then developed by coupling the beam finite element model with the rotor control system model to obtain a complete rotor-blade/control...

Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique

International Nuclear Information System (INIS)

Hou Zhijian; Lian Zhiwei; Yao Ye; Yuan Xinjian

2006-01-01

A novel method integrating rough sets (RS) theory and an artificial neural network (ANN) based on data-fusion technique is presented to forecast an air-conditioning load. Data-fusion technique is the process of combining multiple sensors data or related information to estimate or predict entity states. In this paper, RS theory is applied to find relevant factors to the load, which are used as inputs of an artificial neural-network to predict the cooling load. To improve the accuracy and enhance the robustness of load forecasting results, a general load-prediction model, by synthesizing multi-RSAN (MRAN), is presented so as to make full use of redundant information. The optimum principle is employed to deduce the weights of each RSAN model. Actual prediction results from a real air-conditioning system show that, the MRAN forecasting model is better than the individual RSAN and moving average (AMIMA) ones, whose relative error is within 4%. In addition, individual RSAN forecasting results are better than that of ARIMA

Energy Coordinative Optimization of Wind-Storage-Load Microgrids Based on Short-Term Prediction

Directory of Open Access Journals (Sweden)

Changbin Hu

2015-02-01

Full Text Available According to the topological structure of wind-storage-load complementation microgrids, this paper proposes a method for energy coordinative optimization which focuses on improvement of the economic benefits of microgrids in the prediction framework. First of all, the external characteristic mathematical model of distributed generation (DG units including wind turbines and storage batteries are established according to the requirements of the actual constraints. Meanwhile, using the minimum consumption costs from the external grid as the objective function, a grey prediction model with residual modification is introduced to output the predictive wind turbine power and load at specific periods. Second, based on the basic framework of receding horizon optimization, an intelligent genetic algorithm (GA is applied to figure out the optimum solution in the predictive horizon for the complex non-linear coordination control model of microgrids. The optimum results of the GA are compared with the receding solution of mixed integer linear programming (MILP. The obtained results show that the method is a viable approach for energy coordinative optimization of microgrid systems for energy flow and reasonable schedule. The effectiveness and feasibility of the proposed method is verified by examples.

A Study on Assessment Method of Traffic Load Effect of Bridge in Service

Science.gov (United States)

Ling, Pan; Dajian, Han

2010-05-01

Because of overloading usually occur in highway in China today, it is found that the traffic load and the load effects given by Specification for Inspection and Evaluation of Load-bearing Capacity of Highway Bridge[1] cannot be adequately estimated. Especially, the extreme values in a service period can not be predicted. In this paper, a model is first developed for a better estimation of the actual traffic flow, as well as the service load effect level of a bridge. Based on a five-day collection data of the vehicle samples passing through the exit of Guangyuan to Shahe of the North-Ring Highway in Guangzhou, The Qiaole Bridge of the highway is taken as an example to illustrate the assessment model. Then, a threshold model is applied to estimate the tail distribution of the maximum load effect of a fleet. And threshold point process is used to infer the maximum value distribution for prediction of the load effect level of the bridge in a future time period. Finally, a 0.95 quantile is obtained to compare with the result given by specification[1]. The results show that the assessment method proposed in this paper is valid and feasible.

Ventilator flow data predict bronchopulmonary dysplasia in extremely premature neonates

Directory of Open Access Journals (Sweden)

Mariann H. Bentsen

2018-03-01

Full Text Available Early prediction of bronchopulmonary dysplasia (BPD may facilitate tailored management for neonates at risk. We investigated whether easily accessible flow data from a mechanical ventilator can predict BPD in neonates born extremely premature (EP. In a prospective population-based study of EP-born neonates, flow data were obtained from the ventilator during the first 48 h of life. Data were logged for >10 min and then converted to flow–volume loops using custom-made software. Tidal breathing parameters were calculated and averaged from ≥200 breath cycles, and data were compared between those who later developed moderate/severe and no/mild BPD. Of 33 neonates, 18 developed moderate/severe and 15 no/mild BPD. The groups did not differ in gestational age, surfactant treatment or ventilator settings. The infants who developed moderate/severe BPD had evidence of less airflow obstruction, significantly so for tidal expiratory flow at 50% of tidal expiratory volume (TEF50 expressed as a ratio of peak tidal expiratory flow (PTEF (p=0.007. A compound model estimated by multiple logistic regression incorporating TEF50/PTEF, birthweight z-score and sex predicted moderate/severe BPD with good accuracy (area under the curve 0.893, 95% CI 0.735–0.973. This study suggests that flow data obtained from ventilators during the first hours of life may predict later BPD in premature neonates. Future and larger studies are needed to validate these findings and to determine their clinical usefulness.

Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

International Nuclear Information System (INIS)

Salcedo-Sanz, S.; Pastor-Sánchez, A.; Prieto, L.; Blanco-Aguilera, A.; García-Herrera, R.

2014-01-01

Highlights: • A novel approach for short-term wind speed prediction is presented. • The system is formed by a coral reefs optimization algorithm and an extreme learning machine. • Feature selection is carried out with the CRO to improve the ELM performance. • The method is tested in real wind farm data in USA, for the period 2007–2008. - Abstract: This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks’ training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO–ELM approach in this FSP wind speed prediction problem

Rigid-Plastic Approximations for Predicting Plastic Deformation of Cylindrical Shells Subject to Dynamic Loading

Directory of Open Access Journals (Sweden)

Michelle S. Hoo Fatt

1996-01-01

Full Text Available A theoretical approach was developed for predicting the plastic deformation of a cylindrical shell subject to asymmetric dynamic loads. The plastic deformation of the leading generator of the shell is found by solving for the transverse deflections of a rigid-plastic beam/string-on-foundation. The axial bending moment and tensile force in the beam/string are equivalent to the longitudinal bending moments and membrane forces of the shell, while the plastic foundation force is equivalent to the shell circumferential bending moment and membrane resistances. Closed-form solutions for the transient and final deformation profile of an impulsive loaded shell when it is in a “string” state were derived using the eigenfunction expansion method. These results were compared to DYNA 3D predictions. The analytical predictions of the transient shell and final centerline deflections were within 25% of the DYNA 3D results.

Climate control loads prediction of electric vehicles

International Nuclear Information System (INIS)

Zhang, Ziqi; Li, Wanyong; Zhang, Chengquan; Chen, Jiangping

2017-01-01

Highlights: • A model of vehicle climate control loads is proposed based on experiments. • Main climate control loads of the modeled vehicle are quantitatively analyzed. • Range reductions of the modeled vehicle under different conditions are simulated. - Abstract: A new model of electric vehicle climate control loads is provided in this paper. The mathematical formulations of the major climate control loads are developed, and the coefficients of the formulations are experimentally determined. Then, the detailed climate control loads are analyzed, and the New European Driving Cycle (NEDC) range reductions due to these loads are calculated under different conditions. It is found that in an electric vehicle, the total climate control loads vary with the vehicle speed, HVAC mode and blower level. The ventilation load is the largest climate control load, followed by the solar radiation load. These two add up to more than 80% of total climate control load in summer. The ventilation load accounts for 70.7–83.9% of total heating load under the winter condition. The climate control loads will cause a 17.2–37.1% reduction of NEDC range in summer, and a 17.1–54.1% reduction in winter, compared to the AC off condition. The heat pump system has an advantage in range extension. A heat pump system with an average heating COP of 1.7 will extend the range by 7.6–21.1% based on the simulation conditions.

Predictability of extreme weather events for NE U.S.: improvement of the numerical prediction using a Bayesian regression approach

Science.gov (United States)

Yang, J.; Astitha, M.; Anagnostou, E. N.; Hartman, B.; Kallos, G. B.

2015-12-01

Weather prediction accuracy has become very important for the Northeast U.S. given the devastating effects of extreme weather events in the recent years. Weather forecasting systems are used towards building strategies to prevent catastrophic losses for human lives and the environment. Concurrently, weather forecast tools and techniques have evolved with improved forecast skill as numerical prediction techniques are strengthened by increased super-computing resources. In this study, we examine the combination of two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) by utilizing a Bayesian regression approach to improve the prediction of extreme weather events for NE U.S. The basic concept behind the Bayesian regression approach is to take advantage of the strengths of two atmospheric modeling systems and, similar to the multi-model ensemble approach, limit their weaknesses which are related to systematic and random errors in the numerical prediction of physical processes. The first part of this study is focused on retrospective simulations of seventeen storms that affected the region in the period 2004-2013. Optimal variances are estimated by minimizing the root mean square error and are applied to out-of-sample weather events. The applicability and usefulness of this approach are demonstrated by conducting an error analysis based on in-situ observations from meteorological stations of the National Weather Service (NWS) for wind speed and wind direction, and NCEP Stage IV radar data, mosaicked from the regional multi-sensor for precipitation. The preliminary results indicate a significant improvement in the statistical metrics of the modeled-observed pairs for meteorological variables using various combinations of the sixteen events as predictors of the seventeenth. This presentation will illustrate the implemented methodology and the obtained results for wind speed, wind direction and precipitation, as well as set the research steps that will be

Predicting lake responses to phosphorus loading with measurement-based characterization of P recycling in sediments

Science.gov (United States)

Katsev, S.; Li, J.

2017-12-01

Predicting the time scales on which lake ecosystems respond to changes in anthropogenic phosphorus loadings is critical for devising efficient management strategies and setting regulatory limits on loading. Internal loading of phosphorus from sediments, however, can significantly contribute to the lake P budget and may delay recovery from eutrophication. The efficiency of mineralization and recycling of settled P in bottom sediments, which is ultimately responsible for this loading, is often poorly known and is surprisingly poorly characterized in the societally important systems such as the Great Lakes. We show that a simple mass-balance model that uses only a minimum number of parameters, all of which are measurable, can successfully predict the time scales over which the total phosphorus (TP) content of lakes responds to changes in external loadings, in a range of situations. The model also predicts the eventual TP levels attained under stable loading conditions. We characterize the efficiency of P recycling in Lake Superior based on a detailed characterization of sediments at 13 locations that includes chemical extractions for P and Fe fractions and characterization of sediment-water exchange fluxes of P. Despite the low efficiency of P remobilization in these deeply oxygenated sediments (only 12% of deposited P is recycled), effluxes of dissolved phosphorus (2.5-7.0 μmol m-2 d-1) still contribute 37% to total P inputs into the water column. In this oligotrophic large lake, phosphate effluxes are regulated by organic sedimentation rather than sediment redox conditions. By adjusting the recycling efficiency to conditions in other Laurentian Great Lakes, we show that the model reproduces the historical data for total phosphorus levels. Analysis further suggests that, in the Lower Lakes, the rate of P sequestration from water column into sediments has undergone a significant change in recent decades, possibly in response to their invasion by quagga mussels

Predicting dynamic knee joint load with clinical measures in people with medial knee osteoarthritis.

Science.gov (United States)

Hunt, Michael A; Bennell, Kim L

2011-08-01

Knee joint loading, as measured by the knee adduction moment (KAM), has been implicated in the pathogenesis of knee osteoarthritis (OA). Given that the KAM can only currently be accurately measured in the laboratory setting with sophisticated and expensive equipment, its utility in the clinical setting is limited. This study aimed to determine the ability of a combination of four clinical measures to predict KAM values. Three-dimensional motion analysis was used to calculate the peak KAM at a self-selected walking speed in 47 consecutive individuals with medial compartment knee OA and varus malalignment. Clinical predictors included: body mass; tibial angle measured using an inclinometer; walking speed; and visually observed trunk lean toward the affected limb during the stance phase of walking. Multiple linear regression was performed to predict KAM magnitudes using the four clinical measures. A regression model including body mass (41% explained variance), tibial angle (17% explained variance), and walking speed (9% explained variance) explained a total of 67% of variance in the peak KAM. Our study demonstrates that a set of measures easily obtained in the clinical setting (body mass, tibial alignment, and walking speed) can help predict the KAM in people with medial knee OA. Identifying those patients who are more likely to experience high medial knee loads could assist clinicians in deciding whether load-modifying interventions may be appropriate for patients, whilst repeated assessment of joint load could provide a mechanism to monitor disease progression or success of treatment. Copyright © 2010 Elsevier B.V. All rights reserved.

Power Load Prediction Based on Fractal Theory

OpenAIRE

Jian-Kai, Liang; Cattani, Carlo; Wan-Qing, Song

2015-01-01

The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and...

Multivariate Modelling of Extreme Load Combinations for Wind Turbines

DEFF Research Database (Denmark)

Dimitrov, Nikolay Krasimirov

2015-01-01

into a periodic part and a perturbation term, where each part has a known probability distribution. The proposed model shows good agreement with simulated data under stationary conditions, and a design load envelope based on this model is comparable to the load envelope estimated using the standard procedure...

A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine

International Nuclear Information System (INIS)

Chia, Yen Yee; Lee, Lam Hong; Shafiabady, Niusha; Isa, Dino

2015-01-01

Highlights: • A novel energy management system (EMS) for supercapacitor-battery hybrid energy storage system is implemented. • It is a load predictive EMS which is implemented using Support Vector Machine (SVM). • An optimum SVM load prediction model is obtained, which yields 100% accuracy in 0.004866 s of training time. • The implemented load predictive EMS is compared with the conventional sequential programming control. • This methodology reduces the number of power electronics used and prolong battery lifespan. - Abstract: This paper presents the use of a Support Vector Machine load predictive energy management system to control the energy flow between a solar energy source, a supercapacitor-battery hybrid energy storage combination and the load. The supercapacitor-battery hybrid energy storage system is deployed in a solar energy system to improve the reliability of delivered power. The combination of batteries and supercapacitors makes use of complementary characteristic that allow the overlapping of a battery’s high energy density with a supercapacitors’ high power density. This hybrid system produces a straightforward benefit over either individual system, by taking advantage of each characteristic. When the supercapacitor caters for the instantaneous peak power which prolongs the battery lifespan, it also minimizes the system cost and ensures a greener system by reducing the number of batteries. The resulting performance is highly dependent on the energy controls implemented in the system to exploit the strengths of the energy storage devices and minimize its weaknesses. It is crucial to use energy from the supercapacitor and therefore minimize jeopardizing the power system reliability especially when there is a sudden peak power demand. This study has been divided into two stages. The first stage is to obtain the optimum SVM load prediction model, and the second stage carries out the performance comparison of the proposed SVM-load predictive

Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

Directory of Open Access Journals (Sweden)

Yanjun Zhang

2015-01-01

Full Text Available A new optimized extreme learning machine- (ELM- based method for power system transient stability prediction (TSP using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

Global resistance and resilience of primary production following extreme drought are predicted by mean annual precipitation

Science.gov (United States)

Stuart-Haëntjens, E. J.; De Boeck, H. J.; Lemoine, N. P.; Gough, C. M.; Kröel-Dulay, G.; Mänd, P.; Jentsch, A.; Schmidt, I. K.; Bahn, M.; Lloret, F.; Kreyling, J.; Wohlgemuth, T.; Stampfli, A.; Anderegg, W.; Classen, A. T.; Smith, M. D.

2017-12-01

Extreme drought is increasing globally in frequency and intensity, with uncertain consequences for the resistance and resilience of key ecosystem functions, including primary production. Primary production resistance, the capacity of an ecosystem to withstand change in primary production following extreme climate, and resilience, the degree to which primary production recovers, vary among and within ecosystem types, obscuring global patterns of resistance and resilience to extreme drought. Past syntheses on resistance have focused climatic gradients or individual ecosystem types, without assessing interactions between the two. Theory and many empirical studies suggest that forest production is more resistant but less resilient than grassland production to extreme drought, though some empirical studies reveal that these trends are not universal. Here, we conducted a global meta-analysis of sixty-four grassland and forest sites, finding that primary production resistance to extreme drought is predicted by a common continuum of mean annual precipitation (MAP). However, grasslands and forests exhibit divergent production resilience relationships with MAP. We discuss the likely mechanisms underlying the mixed production resistance and resilience patterns of forests and grasslands, including different plant species turnover times and drought adaptive strategies. These findings demonstrate the primary production responses of forests and grasslands to extreme drought are mixed, with far-reaching implications for Earth System Models, ecosystem management, and future studies of extreme drought resistance and resilience.

Extreme Value Predictions for Wave- and Wind-induced Loads on Floating Offshore Wind Turbines using FORM

DEFF Research Database (Denmark)

Joensen, Sunvard; Jensen, Jørgen Juncher; Mansour, Alaa E.

2007-01-01

duration of the time domain simulations needed (typically 60-300s to cover the hy-drodynamic memory effects in the response) the calcu-lation of the mean out-crossing rates of a given response are very fast. Thus complicated non-linear effects can be included. The FORM analysis also identifies the most...... probable wave episodes leading to given re-sponses. As an example the motions of floating foundations for offshore wind turbines are analysed taking into consid-eration both the wave and wind induced loads and con-sidering different mooring systems. The possible large horizontal motions make it important...

Failure Predictions of Out-of-Autoclave Sandwich Joints with Delaminations Under Flexure Loads

Science.gov (United States)

Nordendale, Nikolas A.; Goyal, Vinay K.; Lundgren, Eric C.; Patel, Dhruv N.; Farrokh, Babak; Jones, Justin; Fischetti, Grace; Segal, Kenneth N.

2015-01-01

An analysis and a test program was conducted to investigate the damage tolerance of composite sandwich joints. The joints contained a single circular delamination between the face-sheet and the doubler. The coupons were fabricated through out-of-autoclave (OOA) processes, a technology NASA is investigating for joining large composite sections. The four-point bend flexure test was used to induce compression loading into the side of the joint where the delamination was placed. The compression side was chosen since it tends to be one of the most critical loads in launch vehicles. Autoclave cure was used to manufacture the composite sandwich sections, while the doubler was co-bonded onto the sandwich face-sheet using an OOA process after sandwich panels were cured. A building block approach was adopted to characterize the mechanical properties of the joint material, including the fracture toughness between the doubler and face-sheet. Twelve four-point-bend samples were tested, six in the sandwich core ribbon orientation and six in sandwich core cross-ribbon direction. Analysis predicted failure initiation and propagation at the pre-delaminated location, consistent with experimental observations. A building block approach using fracture analyses methods predicted failure loads in close agreement with tests. This investigation demonstrated a small strength reduction due to a flaw of significant size compared to the width of the sample. Therefore, concerns of bonding an OOA material to an in-autoclave material was mitigated for the geometries, materials, and load configurations considered.

Fat or lean: adjustment of endogenous energy stores to predictable and unpredictable changes in allostatic load

Science.gov (United States)

Schultner, Jannik; Kitaysky, Alexander S.; Welcker, Jorg; Hatch, Scott

2013-01-01

1. The ability to store energy endogenously is an important ecological mechanism that allows animals to buffer predictable and unpredictable variation in allostatic load. The secretion of glucocorticoids, which reflects changes in allostatic load, is suggested to play a major role in the adjustment of endogenous stores to these varying conditions.

Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

Science.gov (United States)

White, C. J.; Franks, S. W.; McEvoy, D.

2015-06-01

Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

Dorsolateral Prefrontal Cortex GABA Concentration in Humans Predicts Working Memory Load Processing Capacity.

Science.gov (United States)

Yoon, Jong H; Grandelis, Anthony; Maddock, Richard J

2016-11-16

The discovery of neural mechanisms of working memory (WM) would significantly enhance our understanding of complex human behaviors and guide treatment development for WM-related impairments found in neuropsychiatric conditions and aging. Although the dorsolateral prefrontal cortex (DLPFC) has long been considered critical for WM, we still know little about the neural elements and pathways within the DLPFC that support WM in humans. In this study, we tested whether an individual's DLPFC gamma-aminobutryic acid (GABA) content predicts individual differences in WM task performance using a novel behavioral approach. Twenty-three healthy adults completed a task that measured the unique contribution of major WM components (memory load, maintenance, and distraction resistance) to performance. This was done to address the possibility that components have differing GABA dependencies and the failure to parse WM into components would lead to missing true associations with GABA. The subjects then had their DLPFC GABA content measured by single-voxel proton magnetic spectroscopy. We found that individuals with lower DLPFC GABA showed greater performance degradation with higher load, accounting for 31% of variance, p (corrected) = 0.015. This relationship was component, neurochemical, and brain region specific. DLPFC GABA content did not predict performance sensitivity to other components tested; DLPFC glutamate + glutamine and visual cortical GABA content did not predict load sensitivity. These results confirm the involvement of DLPFC GABA in WM load processing in humans and implicate factors controlling DLPFC GABA content in the neural mechanisms of WM and its impairments. This study demonstrated for the first time that the amount of gamma-aminobutryic acid (GABA), the major inhibitory neurotransmitter of the brain, in an individual's prefrontal cortex predicts working memory (WM) task performance. Given that WM is required for many of the most characteristic cognitive and

Prediction calculation of HTR-10 fuel loading for the first criticality

International Nuclear Information System (INIS)

Jing Xingqing; Yang Yongwei; Gu Yuxiang; Shan Wenzhi

2001-01-01

The 10 MW high temperature gas cooled reactor (HTR-10) was built at Institute of Nuclear Energy Technology, Tsinghua University, and the first criticality was attained in Dec. 2000. The high temperature gas cooled reactor physics simulation code VSOP was used for the prediction of the fuel loading for HTR-10 first criticality. The number of fuel element and graphite element was predicted to provide reference for the first criticality experiment. The prediction calculations toke into account the factors including the double heterogeneity of the fuel element, buckling feedback for the spectrum calculation, the effect of the mixture of the graphite and the fuel element, and the correction of the diffusion coefficients near the upper cavity based on the transport theory. The effects of impurities in the fuel and the graphite element in the core and those in the reflector graphite on the reactivity of the reactor were considered in detail. The first criticality experiment showed that the predicted values and the experiment results were in good agreement with little relative error less than 1%, which means the prediction was successful

Amyloid Load in Fat Tissue Reflects Disease Severity and Predicts Survival in Amyloidosis

NARCIS (Netherlands)

Van Gameren, Ingrid I.; Hazenberg, Bouke P. C.; Bijzet, Johan; Haagsma, Elizabeth B.; Vellenga, Edo; Posthumus, Marcel D.; Jager, Pieter L.; Van Rijswijk, Martin H.

Objective. The severity of systemic amyloidosis is thought to be related to the extent of amyloid deposition. We studied whether amyloid load in fat tissue reflects disease severity and predicts survival. Methods. We studied all consecutive patients with systemic amyloidosis seen between January

Model Predictive Control for Load Frequency Control with Wind Turbines

Directory of Open Access Journals (Sweden)

Yi Zhang

2015-01-01

Full Text Available Reliable load frequency (LFC control is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The scheme incorporates the two critical nonlinear constraints, for example, the generation rate constraint (GRC and the valve limit, into convex optimization problems. Furthermore, the algorithm reduces the impact on the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and that without the participation of the wind turbines is carried out. Good performance is obtained in the presence of power system nonlinearities due to the governors and turbines constraints and load change disturbances.

Prediction of the Tensile Load of Drilled CFRP by Artificial Neural Network

Directory of Open Access Journals (Sweden)

Burak Yenigun

2018-04-01

Full Text Available The application areas of carbon fiber reinforced plastics (CFRP have been increasing day by day. The machining of CFRP with incorrect machining parameters leads in huge loss cost and time. Therefore, it is very important that the composite materials are machined with correct machining parameters. The aim of this paper is to examine the influence of drilling parameters on tensile load after drilling of CFRP. The drilling operations were carried out on Computer Numerical Control (CNC by Tungsten Carbide (WC, High Speed Steel (HSS and Brad Spur type drill bits with spindle speeds of 1000, 3000 and 5000 rpm and feed rates of 0.05, 0.10 and 0.15 mm/rev. The results indicate that the surface roughness, delamination and thrust force, were affected by drilling parameters therefore tensile load was also affected by the same parameters. It was observed that increase in surface roughness, delamination and thrust force all lead to the decrease of tensile load of CFRP. If the correct drilling parameters are selected; the decrease in tensile load of CFRP can be saved up to 25%. Furthermore, an artificial neural network (ANN model has been used to predict of tensile load. The results of the ANN model are in close agreement with the experimental results.

Extrapolation of Extreme Response for Wind Turbines based on FieldMeasurements

DEFF Research Database (Denmark)

Toft, Henrik Stensgaard; Sørensen, John Dalsgaard

2009-01-01

extrapolation are presented. The first method is based on the same assumptions as the existing method but the statistical extrapolation is only performed for a limited number of mean wind speeds where the extreme load is likely to occur. For the second method the mean wind speeds are divided into storms which......The characteristic loads on wind turbines during operation are among others dependent on the mean wind speed, the turbulence intensity and the type and settings of the control system. These parameters must be taken into account in the assessment of the characteristic load. The characteristic load...... are assumed independent and the characteristic loads are determined from the extreme load in each storm....

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: a case study.

Science.gov (United States)

Escarela, Gabriel

2012-06-01

The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.

Which screening tools can predict injury to the lower extremities in team sports?: a systematic review.

Science.gov (United States)

Dallinga, Joan M; Benjaminse, Anne; Lemmink, Koen A P M

2012-09-01

Injuries to lower extremities are common in team sports such as soccer, basketball, volleyball, football and field hockey. Considering personal grief, disabling consequences and high costs caused by injuries to lower extremities, the importance for the prevention of these injuries is evident. From this point of view it is important to know which screening tools can identify athletes who are at risk of injury to their lower extremities. The aim of this article is to determine the predictive values of anthropometric and/or physical screening tests for injuries to the leg, anterior cruciate ligament (ACL), knee, hamstring, groin and ankle in team sports. A systematic review was conducted in MEDLINE (1966 to September 2011), EMBASE (1989 to September 2011) and CINAHL (1982 to September 2011). Based on inclusion criteria defined a priori, titles, abstracts and full texts were analysed to find relevant studies. The analysis showed that different screening tools can be predictive for injuries to the knee, ACL, hamstring, groin and ankle. For injuries in general there is some support in the literature to suggest that general joint laxity is a predictive measure for leg injuries. The anterior right/left reach distance >4 cm and the composite reach distance injuries. Furthermore, an increasing age, a lower hamstring/quadriceps (H : Q) ratio and a decreased range of motion (ROM) of hip abduction may predict the occurrence of leg injuries. Hyperextension of the knee, side-to-side differences in anterior-posterior knee laxity and differences in knee abduction moment between both legs are suggested to be predictive tests for sustaining an ACL injury and height was a predictive screening tool for knee ligament injuries. There is some evidence that when age increases, the probability of sustaining a hamstring injury increases. Debate exists in the analysed literature regarding measurement of the flexibility of the hamstring as a predictive screening tool, as well as using the H

Crack under biaxial loading: Two-parameter description and prediction of crack growth direction

Czech Academy of Sciences Publication Activity Database

Seitl, Stanislav

2014-01-01

Roč. 31, APR (2014), s. 44-49 ISSN 0213-3725 R&D Projects: GA MŠk(CZ) 7AMB14AT012 Institutional support: RVO:68081723 Keywords : Concrete * T-stress * cracks growth prediction * numerical calculation * biaxial loading Subject RIV: JL - Materials Fatigue, Friction Mechanics

Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

International Nuclear Information System (INIS)

Li Qiong; Meng Qinglin; Cai Jiejin; Yoshino, Hiroshi; Mochida, Akashi

2009-01-01

This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MRE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods

Prediction and discovery of extremely strong hydrodynamic instabilities due to a velocity jump: theory and experiments

International Nuclear Information System (INIS)

Fridman, A M

2008-01-01

The theory and the experimental discovery of extremely strong hydrodynamic instabilities are described, viz. the Kelvin-Helmholtz, centrifugal, and superreflection instabilities. The discovery of the last two instabilities was predicted and the Kelvin-Helmholtz instability in real systems was revised by us. (reviews of topical problems)

The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

Science.gov (United States)

Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin

2017-08-01

The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

An EMD-ANN based prediction methodology for DR driven smart household load demand

NARCIS (Netherlands)

Tascikaraoglu, A.; Paterakis, N.G.; Catalaõ, J.P.S.; Erdinç, O.; Bakirtzis, A.G.

2015-01-01

This study proposes a model for the prediction of smart household load demand influenced by a dynamic pricing demand response (DR) program. Price-based DR programs have a considerable impact on household demand pattern due to the expected choice of customers or their home energy management systems

Analytical approach for predicting three-dimensional tire-pavement contact load

CSIR Research Space (South Africa)

Hernandez, JA

2014-12-01

Full Text Available stream_source_info De Beer1_2014.pdf.txt stream_content_type text/plain stream_size 38657 Content-Encoding UTF-8 stream_name De Beer1_2014.pdf.txt Content-Type text/plain; charset=UTF-8 75 Transportation Research Record... by measuring the applied forces in each perpendicular direction (15). Analytical Approach for Predicting Three-Dimensional Tire–Pavement Contact Load Jaime A. Hernandez, Angeli Gamez, Imad L. Al-Qadi, and Morris De Beer J. A. Hernandez, A. Gamez, and I. L...

Using subseasonal-to-seasonal (S2S extreme rainfall forecasts for extended-range flood prediction in Australia

Directory of Open Access Journals (Sweden)

C. J. White

2015-06-01

Full Text Available Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal. Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

Large-strain time-temperature equivalence in high density polyethylene for prediction of extreme deformation and damage

Directory of Open Access Journals (Sweden)

Gray G.T.

2012-08-01

Full Text Available Time-temperature equivalence is a widely recognized property of many time-dependent material systems, where there is a clear predictive link relating the deformation response at a nominal temperature and a high strain-rate to an equivalent response at a depressed temperature and nominal strain-rate. It has been found that high-density polyethylene (HDPE obeys a linear empirical formulation relating test temperature and strain-rate. This observation was extended to continuous stress-strain curves, such that material response measured in a load frame at large strains and low strain-rates (at depressed temperatures could be translated into a temperature-dependent response at high strain-rates and validated against Taylor impact results. Time-temperature equivalence was used in conjuction with jump-rate compression tests to investigate isothermal response at high strain-rate while exluding adiabatic heating. The validated constitutive response was then applied to the analysis of Dynamic-Tensile-Extrusion of HDPE, a tensile analog to Taylor impact developed at LANL. The Dyn-Ten-Ext test results and FEA found that HDPE deformed smoothly after exiting the die, and after substantial drawing appeared to undergo a pressure-dependent shear damage mechanism at intermediate velocities, while it fragmented at high velocities. Dynamic-Tensile-Extrusion, properly coupled with a validated constitutive model, can successfully probe extreme tensile deformation and damage of polymers.

Automated system for load flow prediction in power substations using artificial neural networks

Directory of Open Access Journals (Sweden)

Arlys Michel Lastre Aleaga

2015-09-01

Full Text Available The load flow is of great importance in assisting the process of decision making and planning of generation, distribution and transmission of electricity. Ignorance of the values in this indicator, as well as their inappropriate prediction, difficult decision making and efficiency of the electricity service, and can cause undesirable situations such as; the on demand, overheating of the components that make up a substation, and incorrect planning processes electricity generation and distribution. Given the need for prediction of flow of electric charge of the substations in Ecuador this research proposes the concept for the development of an automated prediction system employing the use of Artificial Neural Networks.

Comparative analysis of methods for modelling the short-term probability distribution of extreme wind turbine loads

DEFF Research Database (Denmark)

Dimitrov, Nikolay Krasimirov

2016-01-01

We have tested the performance of statistical extrapolation methods in predicting the extreme response of a multi-megawatt wind turbine generator. We have applied the peaks-over-threshold, block maxima and average conditional exceedance rates (ACER) methods for peaks extraction, combined with four...... levels, based on the assumption that the response tail is asymptotically Gumbel distributed. Example analyses were carried out, aimed at comparing the different methods, analysing the statistical uncertainties and identifying the factors, which are critical to the accuracy and reliability...

Effect of Enhanced Air Temperature (extreme heat, and Load of Non-Linear Against the Use of Electric Power

Directory of Open Access Journals (Sweden)

I Ketut Wijaya

2015-12-01

Full Text Available Usage Electric power is very easy to do, because the infrastructure for connecting  already available and widely sold. Consumption electric power is not accompanied by the ability to recognize electric power. The average increase of electricity power in Bali in extreme weather reaches 10% in years 2014, so that Bali suffered power shortages and PLN as the manager of electric power to perform scheduling on of electric power usage. Scheduling is done because many people use electric power as the load  of fan and Air Conditioner exceeding the previous time. Load of fan, air conditioning, and computers including non-linear loads which can add heat on the conductor of electricity. Non-linear load and hot weather can lead to heat on conductor so  insulation damaged  and cause electrical short circuit. Data of electric power obtained through questionnaires, surveys, measurement and retrieve data from various parties. Fires that occurred in 2014, namely 109 events, 44 is  event caused by an electric short circuit (approximately 40%. Decrease power factors can cause losses of electricity and hot. Heat can cause and adds heat on the  conductor electric. The analysis showed  understanding electric power of the average  is 27,700 with value between 20 to 40. So an understanding of the electrical power away from the understand so that many errors because of the act own. Installation tool ELCB very necessary but very necessary provide counseling   of electricity to the community.

Prediction of equibiaxial loading stress in collagen-based extracellular matrix using a three-dimensional unit cell model.

Science.gov (United States)

Susilo, Monica E; Bell, Brett J; Roeder, Blayne A; Voytik-Harbin, Sherry L; Kokini, Klod; Nauman, Eric A

2013-03-01

Mechanical signals are important factors in determining cell fate. Therefore, insights as to how mechanical signals are transferred between the cell and its surrounding three-dimensional collagen fibril network will provide a basis for designing the optimum extracellular matrix (ECM) microenvironment for tissue regeneration. Previously we described a cellular solid model to predict fibril microstructure-mechanical relationships of reconstituted collagen matrices due to unidirectional loads (Acta Biomater 2010;6:1471-86). The model consisted of representative volume elements made up of an interconnected network of flexible struts. The present study extends this work by adapting the model to account for microstructural anisotropy of the collagen fibrils and a biaxial loading environment. The model was calibrated based on uniaxial tensile data and used to predict the equibiaxial tensile stress-stretch relationship. Modifications to the model significantly improved its predictive capacity for equibiaxial loading data. With a comparable fibril length (model 5.9-8μm, measured 7.5μm) and appropriate fibril anisotropy the anisotropic model provides a better representation of the collagen fibril microstructure. Such models are important tools for tissue engineering because they facilitate prediction of microstructure-mechanical relationships for collagen matrices over a wide range of microstructures and provide a framework for predicting cell-ECM interactions. Copyright © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Potential Hydrodynamic Loads on Coastal Bridges in the Greater New York Area due to Extreme Storm Surge and Wave

Science.gov (United States)

2018-04-18

This project makes a computer modeling study on vulnerability of coastal bridges in New York City (NYC) metropolitan region to storm surges and waves. Prediction is made for potential surges and waves in the region and consequent hydrodynamic load an...

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

Directory of Open Access Journals (Sweden)

Qiang Shang

Full Text Available Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS. Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM is proposed based on singular spectrum analysis (SSA and kernel extreme learning machine (KELM. SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

Recent Successes and Remaining Challenges in Predicting Phosphorus Loading to Surface Waters at Large Scales

Science.gov (United States)

Harrison, J.; Metson, G.; Beusen, A.

2017-12-01

Over the past century humans have greatly accelerated phosphorus (P) flows from land to aquatic ecosystems, causing eutrophication and associated effects such as harmful algal blooms and hypoxia. Effectively addressing this challenge requires understanding geographic and temporal distribution of aquatic P loading, knowledge of major controls on P loading, and the relative importance of various potential P sources. The Global (N)utrient (E)xport from (W)ater(S)heds) NEWS model and recent improvements and extensions of this modeling system can be used to generate this understanding. This presentation will focus on insights global NEWS models grant into past, present, and potential future P sources and sinks, with a focus on the world's large rivers. Early results suggest: 1) that while aquatic P loading is globally dominated by particulate forms, dissolved P can be locally dominant; 2) that P loading has increased substantially at the global scale, but unevenly between world regions, with hotspots in South and East Asia; 3) that P loading is likely to continue to increase globally, but decrease in certain regions that are actively pursuing proactive P management; and 4) that point sources, especially in urban centers, play an important (even dominant) role in determining loads of dissolved inorganic P. Despite these insights, substantial unexplained variance remains when model predictions and measurements are compared at global and regional scales, for example within the U.S. Disagreements between model predictions and measurements suggest opportunities for model improvement. In particular, explicit inclusion of soil characteristics and the concept of temporal P legacies in future iterations of NEWS (and other) models may help improve correspondence between models and measurements.

Application of Model Predictive Control for Active Load Management in a Distributed Power System with High Wind Penetration

DEFF Research Database (Denmark)

Zong, Yi; Kullmann, Daniel; Thavlov, Anders

2012-01-01

management. It also presents in detail how to implement a thermal model predictive controller (MPC) for the heaters' power consumption prediction in the PowerFlexHouse. It demonstrates that this MPC strategy can realize load shifting, and using good predictions in MPC-based control, a better matching...

Thermal deformation prediction in reticles for extreme ultraviolet lithography based on a measurement-dependent low-order model

NARCIS (Netherlands)

Bikcora, C.; Weiland, S.; Coene, W.M.J.

2014-01-01

In extreme ultraviolet lithography, imaging errors due to thermal deformation of reticles are becoming progressively intolerable as the source power increases. Despite this trend, such errors can be mitigated by adjusting the wafer and reticle stages based on a set of predicted deformation-induced

Reliability analysis and prediction of mixed mode load using Markov Chain Model

International Nuclear Information System (INIS)

Nikabdullah, N.; Singh, S. S. K.; Alebrahim, R.; Azizi, M. A.; K, Elwaleed A.; Noorani, M. S. M.

2014-01-01

The aim of this paper is to present the reliability analysis and prediction of mixed mode loading by using a simple two state Markov Chain Model for an automotive crankshaft. The reliability analysis and prediction for any automotive component or structure is important for analyzing and measuring the failure to increase the design life, eliminate or reduce the likelihood of failures and safety risk. The mechanical failures of the crankshaft are due of high bending and torsion stress concentration from high cycle and low rotating bending and torsional stress. The Markov Chain was used to model the two states based on the probability of failure due to bending and torsion stress. In most investigations it revealed that bending stress is much serve than torsional stress, therefore the probability criteria for the bending state would be higher compared to the torsion state. A statistical comparison between the developed Markov Chain Model and field data was done to observe the percentage of error. The reliability analysis and prediction was derived and illustrated from the Markov Chain Model were shown in the Weibull probability and cumulative distribution function, hazard rate and reliability curve and the bathtub curve. It can be concluded that Markov Chain Model has the ability to generate near similar data with minimal percentage of error and for a practical application; the proposed model provides a good accuracy in determining the reliability for the crankshaft under mixed mode loading

Constrained model predictive control for load-following operation of APR reactors

International Nuclear Information System (INIS)

Kim, Jae Hwan; Lee, Sim Won; Kim, Ju Hyun; Na, Man Gyun; Yu, Keuk Jong; Kim, Han Gon

2012-01-01

The load-following operation of APR+ reactor is needed to control the power effectively using the control rods and to restrain the reactivity control from using the boric acid for flexibility of plant operation. Usually, the reason why the disproportion of axial flux distribution occurs during load-following operation is xenon-induced oscillation. The xenon has a very high absorption cross-section and makes the impact on the reactor delayed by the iodine precursor. The power maneuvering using automatically load-following operation has advantage in terms of safety and economic operation of the reactor, so the controller has to be designed efficiently. Therefore, an advanced control method that meets the conditions such as automatic control, flexibility, safety, and convenience is necessary to load-following operation of APR+ reactor. In this paper, the constrained model predictive control (MPC) method is applied to design APR reactor's automatic load-following controller for the integrated thermal power level and axial shape index (ASI) control. Some controllers use only the current tracking command, but MPC considers future commands in addition to the current tracking command. So, MPC can achieve better tracking performance than others. Furthermore, an MPC is to used in many industrial process control systems. The basic concept of the MPC is to solve an optimization problem for a finite future time interval at present time and to implement the first optimal control input as the current control input. The KISPAC-1D code, which models the APR+ nuclear power plants, is interfaced to the proposed controller to verify the tracking performance of the reactor power level and ASI. It is known that the proposed controller exhibits very fast tracking responses

Evaluation of a numerical model's ability to predict bed load transport observed in braided river experiments

Science.gov (United States)

Javernick, Luke; Redolfi, Marco; Bertoldi, Walter

2018-05-01

New data collection techniques offer numerical modelers the ability to gather and utilize high quality data sets with high spatial and temporal resolution. Such data sets are currently needed for calibration, verification, and to fuel future model development, particularly morphological simulations. This study explores the use of high quality spatial and temporal data sets of observed bed load transport in braided river flume experiments to evaluate the ability of a two-dimensional model, Delft3D, to predict bed load transport. This study uses a fixed bed model configuration and examines the model's shear stress calculations, which are the foundation to predict the sediment fluxes necessary for morphological simulations. The evaluation is conducted for three flow rates, and model setup used highly accurate Structure-from-Motion (SfM) topography and discharge boundary conditions. The model was hydraulically calibrated using bed roughness, and performance was evaluated based on depth and inundation agreement. Model bed load performance was evaluated in terms of critical shear stress exceedance area compared to maps of observed bed mobility in a flume. Following the standard hydraulic calibration, bed load performance was tested for sensitivity to horizontal eddy viscosity parameterization and bed morphology updating. Simulations produced depth errors equal to the SfM inherent errors, inundation agreement of 77-85%, and critical shear stress exceedance in agreement with 49-68% of the observed active area. This study provides insight into the ability of physically based, two-dimensional simulations to accurately predict bed load as well as the effects of horizontal eddy viscosity and bed updating. Further, this study highlights how using high spatial and temporal data to capture the physical processes at work during flume experiments can help to improve morphological modeling.

A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

Energy Technology Data Exchange (ETDEWEB)

Jang, Jin-wook [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: Jinwook@kaeri.re.kr; Seong, Seung-Hwan [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: shseong@kaeri.re.kr; Lee, Un-Chul [Department of Nuclear Engineering, Seoul National University, Shinlim-Dong, Gwanak-Gu, Seoul 151-742 (Korea, Republic of)

2007-09-15

A fast prediction model for load-following operations in a soluble boron-free reactor has been proposed, which can predict the core status when three or more control rod groups are moved at a time. This prediction model consists of two multilayer feedforward neural network models to retrieve the axial offset and the reactivity, and compensation models to compensate for the reactivity and axial offset arising from the xenon transient. The neural network training data were generated by taking various overlaps among the control rod groups into consideration for training the neural network models, and the accuracy of the constructed neural network models was verified. Validation results of predicting load following operations for a soluble boron-free reactor show that this model has a good capability to predict the positions of the control rods for sustaining the criticality of a core during load-following operations to ensure that the tolerable axial offset band is not exceeded and it can provide enough corresponding time for the operators to take the necessary actions to prevent a deviation from the tolerable operating band.

A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

International Nuclear Information System (INIS)

Jang, Jin-wook; Seong, Seung-Hwan; Lee, Un-Chul

2007-01-01

A fast prediction model for load-following operations in a soluble boron-free reactor has been proposed, which can predict the core status when three or more control rod groups are moved at a time. This prediction model consists of two multilayer feedforward neural network models to retrieve the axial offset and the reactivity, and compensation models to compensate for the reactivity and axial offset arising from the xenon transient. The neural network training data were generated by taking various overlaps among the control rod groups into consideration for training the neural network models, and the accuracy of the constructed neural network models was verified. Validation results of predicting load following operations for a soluble boron-free reactor show that this model has a good capability to predict the positions of the control rods for sustaining the criticality of a core during load-following operations to ensure that the tolerable axial offset band is not exceeded and it can provide enough corresponding time for the operators to take the necessary actions to prevent a deviation from the tolerable operating band

Decentralized model predictive based load frequency control in an interconnected power system

Energy Technology Data Exchange (ETDEWEB)

Mohamed, T.H., E-mail: tarekhie@yahoo.co [High Institute of Energy, South Valley University (Egypt); Bevrani, H., E-mail: bevrani@ieee.or [Dept. of Electrical Engineering and Computer Science, University of Kurdistan (Iran, Islamic Republic of); Hassan, A.A., E-mail: aahsn@yahoo.co [Faculty of Engineering, Dept. of Electrical Engineering, Minia University, Minia (Egypt); Hiyama, T., E-mail: hiyama@cs.kumamoto-u.ac.j [Dept. of Electrical Engineering and Computer Science, Kumamoto University, Kumamoto (Japan)

2011-02-15

This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the proposed MPC technique.

Decentralized model predictive based load frequency control in an interconnected power system

International Nuclear Information System (INIS)

Mohamed, T.H.; Bevrani, H.; Hassan, A.A.; Hiyama, T.

2011-01-01

This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the proposed MPC technique.

Footbridge Response Predictions and Their Sensitivity to Stochastic Load Assumptions

DEFF Research Database (Denmark)

Pedersen, Lars; Frier, Christian

2011-01-01

Knowledge about footbridges response to actions of walking is important in assessments of vibration serviceability. In a number of design codes for footbridges, the vibration serviceability limit state is assessed using a walking load model in which the walking parameters (step frequency, pedestr......Knowledge about footbridges response to actions of walking is important in assessments of vibration serviceability. In a number of design codes for footbridges, the vibration serviceability limit state is assessed using a walking load model in which the walking parameters (step frequency...... of pedestrians for predicting footbridge response, which is meaningful, and a step forward. Modelling walking parameters stochastically, however, requires decisions to be made in terms of their statistical distribution and the parameters describing the statistical distribution. The paper investigates...... the sensitivity of results of computations of bridge response to some of the decisions to be made in this respect. This is a useful approach placing focus on which decisions (and which information) are important for sound estimation of bridge response. The studies involve estimating footbridge responses using...

Prediction of crack density and electrical resistance changes in indium tin oxide/polymer thin films under tensile loading

KAUST Repository

Mora Cordova, Angel; Khan, Kamran; El Sayed, Tamer

2014-01-01

We present unified predictions for the crack onset strain, evolution of crack density, and changes in electrical resistance in indium tin oxide/polymer thin films under tensile loading. We propose a damage mechanics model to quantify and predict

Fast core prediction simulator for load follow control

International Nuclear Information System (INIS)

Yim, Man Sung; Lee, Sang Hoon; Lee, Un Chul

1990-01-01

An operator-assisting system for the reactor core control under power changing operating condition was developed. The system is consisted of core simulator routine and Xenon and Iodine initial condition generation routine. The initial condition generation routine, without exactly knowing the core status, is capable of providing accurate number densities and axial offset conditions of Xenon and Iodine after several hours of predictor- corrector calculations using the plant instrumentation signals of power level and power axial offset. The core simulator routine, even with the two node core model, gives equivalently accurate results as the one-dimensional model for the core behaviour simulation under power changing condition and can provide proper control strategies for load follow operation. The core simulator can also be used by the operator to develop remedial actions to restore the distorted power distribution by using its prediction capability

The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM

Directory of Open Access Journals (Sweden)

Josphat Igadwa Mwasiagi

2017-03-01

Full Text Available The optimization of the manufacture of cotton yarns involves several processes, while the prediction of yarn quality parameters forms an important area of investigation. This research work concentrated on the prediction of cotton yarn elongation. Cotton lint and yarn samples were collected in textile factories in Kenya.The collected samples were tested under standard testing conditions. Cotton lint parameters, machine parameters and yarn elongation were used to design yarn elongation prediction models. The elongation prediction models used three network training algorithms, including backpropagation (BP, an extreme learning machine (ELM, and a hybrid of differential evolution (DE and an ELM referred to as DE-ELM. The prediction models recorded a mean squared error (mse value of 0.001 using 11, 43 and 2 neurons in the hidden layer for the BP, ELM and DE-ELM models respectively. The ELM models exhibited faster training speeds than the BP algorithms, but required more neurons in the hidden layer than other models. The DEELM hybrid algorithm was faster than the BP algorithm, but slower than the ELM algorithm.

On the predictability of extreme events in records with linear and nonlinear long-range memory: Efficiency and noise robustness

Science.gov (United States)

Bogachev, Mikhail I.; Bunde, Armin

2011-06-01

We study the predictability of extreme events in records with linear and nonlinear long-range memory in the presence of additive white noise using two different approaches: (i) the precursory pattern recognition technique (PRT) that exploits solely the information about short-term precursors, and (ii) the return interval approach (RIA) that exploits long-range memory incorporated in the elapsed time after the last extreme event. We find that the PRT always performs better when only linear memory is present. In the presence of nonlinear memory, both methods demonstrate comparable efficiency in the absence of white noise. When additional white noise is present in the record (which is the case in most observational records), the efficiency of the PRT decreases monotonously with increasing noise level. In contrast, the RIA shows an abrupt transition between a phase of low level noise where the prediction is as good as in the absence of noise, and a phase of high level noise where the prediction becomes poor. In the phase of low and intermediate noise the RIA predicts considerably better than the PRT, which explains our recent findings in physiological and financial records.

Climate extremes and predicted warming threaten Mediterranean Holocene firs forests refugia.

Science.gov (United States)

Sánchez-Salguero, Raúl; Camarero, J Julio; Carrer, Marco; Gutiérrez, Emilia; Alla, Arben Q; Andreu-Hayles, Laia; Hevia, Andrea; Koutavas, Athanasios; Martínez-Sancho, Elisabet; Nola, Paola; Papadopoulos, Andreas; Pasho, Edmond; Toromani, Ervin; Carreira, José A; Linares, Juan C

2017-11-21

Warmer and drier climatic conditions are projected for the 21st century; however, the role played by extreme climatic events on forest vulnerability is still little understood. For example, more severe droughts and heat waves could threaten quaternary relict tree refugia such as Circum-Mediterranean fir forests (CMFF). Using tree-ring data and a process-based model, we characterized the major climate constraints of recent (1950-2010) CMFF growth to project their vulnerability to 21st-century climate. Simulations predict a 30% growth reduction in some fir species with the 2050s business-as-usual emission scenario, whereas growth would increase in moist refugia due to a longer and warmer growing season. Fir populations currently subjected to warm and dry conditions will be the most vulnerable in the late 21st century when climatic conditions will be analogous to the most severe dry/heat spells causing dieback in the late 20th century. Quantification of growth trends based on climate scenarios could allow defining vulnerability thresholds in tree populations. The presented predictions call for conservation strategies to safeguard relict tree populations and anticipate how many refugia could be threatened by 21st-century dry spells.

A critical pressure based panel method for prediction of unsteady loading of marine propellers under cavitation

International Nuclear Information System (INIS)

Liu, P.; Bose, N.; Colbourne, B.

2002-01-01

A simple numerical procedure is established and implemented into a time domain panel method to predict hydrodynamic performance of marine propellers with sheet cavitation. This paper describes the numerical formulations and procedures to construct this integration. Predicted hydrodynamic loads were compared with both a previous numerical model and experimental measurements for a propeller in steady flow. The current method gives a substantial improvement in thrust and torque coefficient prediction over a previous numerical method at low cavitation numbers of less than 2.0, where severe cavitation occurs. Predicted pressure coefficient distributions are also presented. (author)

Numerical modelling of closed-cell aluminium foam under dynamic loading

Science.gov (United States)

Hazell, Paul; Kader, M. A.; Islam, M. A.; Escobedo, J. P.; Saadatfar, M.

2015-06-01

Closed-cell aluminium foams are extensively used in aerospace and automobile industries. The understanding of their behaviour under impact loading conditions is extremely important since impact problems are directly related to design of these engineering structures. This research investigates the response of a closed-cell aluminium foam (CYMAT) subjected to dynamic loading using the finite element software ABAQUS/explicit. The aim of this research is to numerically investigate the material and structural properties of closed-cell aluminium foam under impact loading conditions with interest in shock propagation and its effects on cell wall deformation. A μ-CT based 3D foam geometry is developed to simulate the local cell collapse behaviours. A number of numerical techniques are applied for modelling the crush behaviour of aluminium foam to obtain the more accurate results. The simulation results are compared with experimental data. Comparison of the results shows a good correlation between the experimental results and numerical predictions.

Fatigue life prediction in composites using progressive damage modelling under block and spectrum loading

DEFF Research Database (Denmark)

Passipoularidis, Vaggelis; Philippidis, T.P.; Brøndsted, Povl

2010-01-01

series can be simulated. The predictions are validated against fatigue life data both from repeated block tests at a single stress ratio as well as against spectral fatigue using the WISPER, WISPERX and NEW WISPER load sequences on a Glass/Epoxy multidirectional laminate typical of a Wind Turbine Rotor...

Impact of Madden–Julian Oscillation upon Winter Extreme Rainfall in Southern China: Observations and Predictability in CFSv2

Directory of Open Access Journals (Sweden)

Hong-Li Ren

2017-09-01

Full Text Available The impact of Madden–Julian oscillation (MJO upon extreme rainfall in southern China was studied using the Real-time Multivariate MJO (RMM index and daily precipitation data from high-resolution stations in China. The probability-distribution function (PDF of November–March rainfall in southern China was found to be skewed toward larger (smaller values in phases 2–3 (6–7 of MJO, during which the probability of extreme rainfall events increased (reduced by 30–50% (20–40% relative to all days in the same season. Physical analysis indicated that the favorable conditions for generating extreme rainfall are associated with southwesterly moisture convergence and vertical moisture advection over southern China, while the direct contributions from horizontal moisture advection are insignificant. Based on the above results, the model-based predictability for extreme rainfall in winter was examined using hindcasts from the Climate Forecast System version 2 (CFSv2 of NOAA. It is shown that the modulations of MJO on extreme rainfall are captured and forecasted well by CFSv2, despite the existence of a relatively small bias. This study suggests the feasibility of deriving probabilistic forecasts of extreme rainfall in southern China based on RMM indices.

How extreme is extreme hourly precipitation?

Science.gov (United States)

Papalexiou, Simon Michael; Dialynas, Yannis G.; Pappas, Christoforos

2016-04-01

The importance of accurate representation of precipitation at fine time scales (e.g., hourly), directly associated with flash flood events, is crucial in hydrological design and prediction. The upper part of a probability distribution, known as the distribution tail, determines the behavior of extreme events. In general, and loosely speaking, tails can be categorized in two families: the subexponential and the hyperexponential family, with the first generating more intense and more frequent extremes compared to the latter. In past studies, the focus has been mainly on daily precipitation, with the Gamma distribution being the most popular model. Here, we investigate the behaviour of tails of hourly precipitation by comparing the upper part of empirical distributions of thousands of records with three general types of tails corresponding to the Pareto, Lognormal, and Weibull distributions. Specifically, we use thousands of hourly rainfall records from all over the USA. The analysis indicates that heavier-tailed distributions describe better the observed hourly rainfall extremes in comparison to lighter tails. Traditional representations of the marginal distribution of hourly rainfall may significantly deviate from observed behaviours of extremes, with direct implications on hydroclimatic variables modelling and engineering design.

Prediction of crack density and electrical resistance changes in indium tin oxide/polymer thin films under tensile loading

KAUST Repository

Mora Cordova, Angel

2014-06-11

We present unified predictions for the crack onset strain, evolution of crack density, and changes in electrical resistance in indium tin oxide/polymer thin films under tensile loading. We propose a damage mechanics model to quantify and predict such changes as an alternative to fracture mechanics formulations. Our predictions are obtained by assuming that there are no flaws at the onset of loading as opposed to the assumptions of fracture mechanics approaches. We calibrate the crack onset strain and the damage model based on experimental data reported in the literature. We predict crack density and changes in electrical resistance as a function of the damage induced in the films. We implement our model in the commercial finite element software ABAQUS using a user subroutine UMAT. We obtain fair to good agreement with experiments. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm.

Science.gov (United States)

Zhou, Xiuze; Lin, Fan; Yang, Lvqing; Nie, Jing; Tan, Qian; Zeng, Wenhua; Zhang, Nian

2016-01-01

With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN). The AHPGD makes the aggregative indicator of virtual machines in cloud, and become input parameters of predicted RBFNN. Also, this paper proposes a new dynamic load balancing scheduling algorithm combined with a weighted round-robin algorithm, which uses the predictive periodical load value of nodes based on AHPPGD and RBFNN optimized by HHGA, then calculates the corresponding weight values of nodes and makes constant updates. Meanwhile, it keeps the advantages and avoids the shortcomings of static weighted round-robin algorithm.

Dynamic Loads and Wake Prediction for Large Wind Turbines Based on Free Wake Method

Institute of Scientific and Technical Information of China (English)

Cao Jiufa; Wang Tongguang; Long Hui; Ke Shitang; Xu Bofeng

2015-01-01

With large scale wind turbines ,the issue of aerodynamic elastic response is even more significant on dy-namic behaviour of the system .Unsteady free vortex wake method is proposed to calculate the shape of wake and aerodynamic load .Considering the effect of aerodynamic load ,inertial load and gravity load ,the decoupling dy-namic equations are established by using finite element method in conjunction of the modal method and equations are solved numerically by Newmark approach .Finally ,the numerical simulation of a large scale wind turbine is performed through coupling the free vortex wake modelling with structural modelling .The results show that this coupling model can predict the flexible wind turbine dynamic characteristics effectively and efficiently .Under the influence of the gravitational force ,the dynamic response of flapwise direction contributes to the dynamic behavior of edgewise direction under the operational condition of steady wind speed .The difference in dynamic response be-tween the flexible and rigid wind turbines manifests when the aerodynamics/structure coupling effect is of signifi-cance in both wind turbine design and performance calculation .

Defense plan of Hydro-Quebec for extreme contingencies

International Nuclear Information System (INIS)

Trudel, Guilles; Bernard, Serge; Portales, Esteban

2000-01-01

In the last years, Hydro-Quebec it undertook an important program to improve the dependability of their net of energy transport. They concentrated the efforts on increasing the capacity of the net resist in the event of carries to an extreme contingency caused in general by multiple incidents or for successive disconnection of the lines of energy transport. To neutralize these contingencies, Hydro-Quebec it adopted a series of special measures that are contained under the general title of Plan of Defense for Extreme Contingencies. The objective of this plan is to detect the incidents that surpass the capacity of the net. It is completely automatic and it is based mainly in: A system of automatic disconnection of generation and tele-shot of loads; A system of automatic maneuver (opening and closing) of inductances shunt of 735 kw; A system of disconnection of loads for low voltage; A system of disconnection of loads for low frequency. The present document summarizes the orientations that there is taking Hydro-Quebec to protect its net in the event of extreme contingencies and it describes the different automatism that they are adopts, in particular the system automatic disconnection of generation and tele-shot of loads (RPTC) that is one of the main components of the defense plan. The system RPTC detects the simultaneous loss of several lines directly in 15 substations of 735 kw. It understands four places of automatic disconnection of generation and a centralized system of tele-shot of loads

Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

DEFF Research Database (Denmark)

Preindl, Matthias; Schaltz, Erik

2011-01-01

The widely used cascade speed and torque controllers have a limited control performance in most high power applications due to the low switching frequency of power electronic converters and the convenience to avoid speed overshoots and oscillations for lifetime considerations. Model Predictive...... Direct Current Control (MPDCC) leads to an increase of torque control performance taking into account the discrete nature of inverters but temporary offsets and poor responses to load torque variations are still issues in speed control. A load torque estimator is proposed in this paper in order...

Development of an aerodyanmic theory capable of predicting surface loads on slender wings with vortex flow

Science.gov (United States)

Gloss, B. B.; Johnson, F. T.

1976-01-01

The Boeing Commercial Airplane Company developed an inviscid three-dimensional lifting surface method that shows promise in being able to accurately predict loads, subsonic and supersonic, on wings with leading-edge separation and reattachment.

Investigation of Load Prediction on the Mexico Rotor Using the Technique of Determination of the Angle of Attack

DEFF Research Database (Denmark)

Yang, Hua; Shen, Wen Zhong; Sørensen, Jens Nørkær

2012-01-01

Blade element moment (BEM) is a widely used technique for prediction of wind turbine aerodynamics performance, the reliability of airfoil data is an important factor to improve the prediction accuracy of aerodynamic loads and power using a BEM code. The method of determination of angle of attack ...

Probabilistic methods for condition assessment and life prediction of concrete structures in nuclear power plants

International Nuclear Information System (INIS)

Ellingwood, B.R.; Mori, Yasuhiro

1993-01-01

A probability-based methodology is being developed in support of the NRC Structural Aging Program to assist in evaluating the reliability of existing concrete structures in nuclear power plants under potential future operating loads and extreme evironmental and accidental events. The methodology includes models to predict structural deterioration due to environmental stressors, a database to support the use of these models, and methods for analyzing time-dependent reliability of concrete structural components subjected to stochastic loads. The methodology can be used to support a plant license extension application by providing evidence that safety-related concrete structures in their current (service) condition are able to withstand future extreme events with a level of reliability sufficient for public health and safety. (orig.)

Reliability prediction for structures under cyclic loads and recurring inspections

Directory of Open Access Journals (Sweden)

Alberto W. S. Mello Jr

2009-06-01

Full Text Available This work presents a methodology for determining the reliability of fracture control plans for structures subjected to cyclic loads. It considers the variability of the parameters involved in the problem, such as initial flaw and crack growth curve. The probability of detection (POD curve of the field non-destructive inspection method and the condition/environment are used as important factors for structural confidence. According to classical damage tolerance analysis (DTA, inspection intervals are based on detectable crack size and crack growth rate. However, all variables have uncertainties, which makes the final result totally stochastic. The material properties, flight loads, engineering tools and even the reliability of inspection methods are subject to uncertainties which can affect significantly the final maintenance schedule. The present methodology incorporates all the uncertainties in a simulation process, such as Monte Carlo, and establishes a relationship between the reliability of the overall maintenance program and the proposed inspection interval, forming a “cascade” chart. Due to the scatter, it also defines the confidence level of the “acceptable” risk. As an example, the damage tolerance analysis (DTA results are presented for the upper cockpit longeron splice bolt of the BAF upgraded F-5EM. In this case, two possibilities of inspection intervals were found: one that can be characterized as remote risk, with a probability of failure (integrity nonsuccess of 1 in 10 million, per flight hour; and other as extremely improbable, with a probability of nonsuccess of 1 in 1 billion, per flight hour, according to aviation standards. These two results are compared with the classical military airplane damage tolerance requirements.

Lifetime prediction of structures submitted to thermal fatigue loadings; Prediction de duree de vie de structures sous chargement de fatigue thermique

Energy Technology Data Exchange (ETDEWEB)

Amiable, S

2006-01-15

The aim of this work is to predict the lifetime of structures submitted to thermal fatigue loadings. This work lies within the studies undertaken by the CEA on the thermal fatigue problems from the french reactor of Civaux. In particular we study the SPLASH test: a specimen is heated continuously and cyclically cooled down by a water spray. This loading generates important temperature gradients in space and time and leads to the initiation and the propagation of a crack network. We propose a new thermo-mechanical model to simulate the SPLASH experiment and we propose a new fatigue criterion to predict the lifetime of the SPLASH specimen. We propose and compare several numerical models with various complexity to estimate the mechanical response of the SPLASH specimen. The practical implications of this work are the reevaluation of the hypothesis used in the French code RCC, which are used to simulate thermal shock and to interpret the results in terms of fatigue. This work leads to new perspectives on the mechanical interpretation of the fatigue criterion. (author)

Working Memory Load Strengthens Reward Prediction Errors.

Science.gov (United States)

Collins, Anne G E; Ciullo, Brittany; Frank, Michael J; Badre, David

2017-04-19

Reinforcement learning (RL) in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors (RPEs) are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we investigated how working memory (WM) and incremental RL processes interact to guide human learning. WM load was manipulated by varying the number of stimuli to be learned across blocks. Behavioral results and computational modeling confirmed that learning was best explained as a mixture of two mechanisms: a fast, capacity-limited, and delay-sensitive WM process together with slower RL. Model-based analysis of fMRI data showed that striatum and lateral prefrontal cortex were sensitive to RPE, as shown previously, but, critically, these signals were reduced when the learning problem was within capacity of WM. The degree of this neural interaction related to individual differences in the use of WM to guide behavioral learning. These results indicate that the two systems do not process information independently, but rather interact during learning. SIGNIFICANCE STATEMENT Reinforcement learning (RL) theory has been remarkably productive at improving our understanding of instrumental learning as well as dopaminergic and striatal network function across many mammalian species. However, this neural network is only one contributor to human learning and other mechanisms such as prefrontal cortex working memory also play a key role. Our results also show that these other players interact with the dopaminergic RL system, interfering with its key computation of reward prediction errors. Copyright © 2017 the authors 0270-6474/17/374332-11$15.00/0.

Scaling approach in predicting the seatbelt loading and kinematics of vulnerable occupants: How far can we go?

Science.gov (United States)

Nie, Bingbing; Forman, Jason L; Joodaki, Hamed; Wu, Taotao; Kent, Richard W

2016-09-01

Occupants with extreme body size and shape, such as the small female or the obese, were reported to sustain high risk of injury in motor vehicle crashes (MVCs). Dimensional scaling approaches are widely used in injury biomechanics research based on the assumption of geometrical similarity. However, its application scope has not been quantified ever since. The objective of this study is to demonstrate the valid range of scaling approaches in predicting the impact response of the occupants with focus on the vulnerable populations. The present analysis was based on a data set consisting of 60 previously reported frontal crash tests in the same sled buck representing a typical mid-size passenger car. The tests included two categories of human surrogates: 9 postmortem human surrogates (PMHS) of different anthropometries (stature range: 147-189 cm; weight range: 27-151 kg) and 5 anthropomorphic test devices (ATDs). The impact response was considered including the restraint loads and the kinematics of multiple body segments. For each category of the human surrogates, a mid-size occupant was selected as a baseline and the impact response was scaled specifically to another subject based on either the body mass (body shape) or stature (the overall body size). To identify the valid range of the scaling approach, the scaled response was compared to the experimental results using assessment scores on the peak value, peak timing (the time when the peak value occurred), and the overall curve shape ranging from 0 (extremely poor) to 1 (perfect match). Scores of 0.7 to 0.8 and 0.8 to 1.0 indicate fair and acceptable prediction. For both ATDs and PMHS, the scaling factor derived from body mass proved an overall good predictor of the peak timing for the shoulder belt (0.868, 0.829) and the lap belt (0.858, 0.774) and for the peak value of the lap belt force (0.796, 0.869). Scaled kinematics based on body stature provided fair or acceptable prediction on the overall head

Political extremism predicts belief in conspiracy theories

NARCIS (Netherlands)

van Prooijen, J.W.; Krouwel, A.P.M.; Pollet, T. V.

2015-01-01

Historical records suggest that the political extremes—at both the “left” and the “right”—substantially endorsed conspiracy beliefs about other-minded groups. The present contribution empirically tests whether extreme political ideologies, at either side of the political spectrum, are positively

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint

Energy Technology Data Exchange (ETDEWEB)

Jin, Xin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Baker, Kyri A. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Christensen, Dane T. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Isley, Steven C. [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-08-21

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility and reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.

Early Conventional MRI for Prediction of Neurodevelopmental Impairment in Extremely-Low-Birth-Weight Infants.

Science.gov (United States)

Slaughter, Laurel A; Bonfante-Mejia, Eliana; Hintz, Susan R; Dvorchik, Igor; Parikh, Nehal A

2016-01-01

Extremely-low-birth-weight (ELBW; ≤1,000 g) infants are at high risk for neurodevelopmental impairments. Conventional brain MRI at term-equivalent age is increasingly used for prediction of outcomes. However, optimal prediction models remain to be determined, especially for cognitive outcomes. The aim was to evaluate the accuracy of a data-driven MRI scoring system to predict neurodevelopmental impairments. 122 ELBW infants had a brain MRI performed at term-equivalent age. Conventional MRI findings were scored with a standardized algorithm and tested using a multivariable regression model to predict neurodevelopmental impairment, defined as one or more of the following at 18-24 months' corrected age: cerebral palsy, bilateral blindness, bilateral deafness requiring amplification, and/or cognitive/language delay. Results were compared with a commonly cited scoring system. In multivariable analyses, only moderate-to-severe gyral maturational delay was a significant predictor of overall neurodevelopmental impairment (OR: 12.6, 95% CI: 2.6, 62.0; p neurodevelopmental impairment/death. Diffuse cystic abnormality was a significant predictor of cerebral palsy (OR: 33.6, 95% CI: 4.9, 229.7; p neurodevelopmental impairment. In our cohort, conventional MRI at term-equivalent age exhibited high specificity in predicting neurodevelopmental outcomes. However, sensitivity was suboptimal, suggesting additional clinical factors and biomarkers are needed to enable accurate prognostication. © 2016 S. Karger AG, Basel.

Framing Failures in Wood-Frame Hip Roofs under Extreme Wind Loads

Directory of Open Access Journals (Sweden)

Sarah A. Stevenson

2018-02-01

Full Text Available Wood-frame residential roof failures are among the most common and expensive types of wind damage. Hip roofs are commonly understood to be more resilient during extreme wind in relation to gable roofs. However, inspection of damage survey data from recent tornadoes has revealed a previously unstudied failure mode in which hip roofs suffer partial failure of the framing structure. In the current study, evidence of partial framing failures and statistics of their occurrence are explored and discussed, while the common roof design and construction practice are reviewed. Two-dimensional finite element models are developed to estimate the element-level load effects on hip roof trusses and stick-frame components. The likelihood of failure in each member is defined based on relative demand-to-capacity ratios. Trussed and stick-frame structures are compared to assess the relative performance of the two types of construction. The present analyses verify the common understanding that toenailed roof-to-wall connections are likely to be the most vulnerable elements in the structure of a wood-frame hip roof. However, the results also indicate that certain framing members and connections display significant vulnerability under the same wind uplift, and the possibility of framing failure is not to be discounted. Furthermore, in the case where the roof-to-wall connection uses hurricane straps, certain framing members and joints become the likely points of failure initiation. The analysis results and damage survey observations are used to expand the understanding of wood-frame residential roof failures, as they relate to the Enhanced Fujita Scale and provide assessment of potential gaps in residential design codes.

Instability predictions for circumferentially cracked Type-304 stainless-steel pipes under dynamic loading. Final report

International Nuclear Information System (INIS)

Zahoor, A.; Wilkowski, G.; Abou-Sayed, I.; Marschall, C.; Broek, D.; Sampath, S.; Rhee, H.; Ahmad, J.

1982-04-01

This report provides methods to predict margins of safety for circumferentially cracked Type 304 stainless steel pipes subjected to applied bending loads. An integrated combination of experimentation and analysis research was pursued. Two types of experiments were performed: (1) laboratory-scale tests on center-cracked panels and bend specimens to establish the basic mechanical and fracture properties of Type 304 stainless steel, and (2) full-scale pipe fracture tests under quasi-static and dynamic loadings to assess the analysis procedures. Analyses were based upon the simple plastic collapse criterion, a J-estimation procedure, and elastic-plastic large-deformation finite element models

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Institute of Scientific and Technical Information of China (English)

Yi Zhang; Xiangjie Liu; Bin Qu

2017-01-01

Reliable load frequency control(LFC) is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control(DMPC) based on coordination scheme.The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints(GRCs), load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed–loop performance, and computational burden with the physical constraints.

Proposed Model of Predicting the Reduced Yield Axial Load of Reinforced Concrete Columns Due to Casting Deficiency Effect

Directory of Open Access Journals (Sweden)

Achillopoulou Dimitra

2014-12-01

Full Text Available The study deals with the investigation of the effect of casting deficiencies- both experimentally and analytically on axial yield load or reinforced concrete columns. It includes 6 specimens of square section (150x150x500 mm of 24.37 MPa nominal concrete strength with 4 longitudinal steel bars of 8 mm (500 MPa nominal strength with confinement ratio ωc=0.15. Through casting procedure the necessary provisions defined by International Standards were not applied strictly in order to create construction deficiencies. These deficiencies are quantified geometrically without the use of expensive and expertise non-destructive methods and their effect on the axial load capacity of the concrete columns is calibrated trough a novel and simplified prediction model extracted by an experimental and analytical investigation that included 6 specimens. It is concluded that: a even with suitable repair, load reduction up to 22% is the outcome of the initial construction damage presence, b the lower dispersion is noted for the section damage index proposed, c extended damage alters the failure mode to brittle accompanied with longitudinal bars buckling, d the proposed model presents more than satisfying results to the load capacity prediction of repaired columns.

Robust Distributed Model Predictive Load Frequency Control of Interconnected Power System

Directory of Open Access Journals (Sweden)

Xiangjie Liu

2013-01-01

Full Text Available Considering the load frequency control (LFC of large-scale power system, a robust distributed model predictive control (RDMPC is presented. The system uncertainty according to power system parameter variation alone with the generation rate constraints (GRC is included in the synthesis procedure. The entire power system is composed of several control areas, and the problem is formulated as convex optimization problem with linear matrix inequalities (LMI that can be solved efficiently. It minimizes an upper bound on a robust performance objective for each subsystem. Simulation results show good dynamic response and robustness in the presence of power system dynamic uncertainties.

Assessment of Wind Parameter Sensitivity on Extreme and Fatigue Wind Turbine Loads

Energy Technology Data Exchange (ETDEWEB)

Robertson, Amy N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sethuraman, Latha [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jonkman, Jason [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2018-01-12

Wind turbines are designed using a set of simulations to ascertain the structural loads that the turbine could encounter. While mean hub-height wind speed is considered to vary, other wind parameters such as turbulence spectra, sheer, veer, spatial coherence, and component correlation are fixed or conditional values that, in reality, could have different characteristics at different sites and have a significant effect on the resulting loads. This paper therefore seeks to assess the sensitivity of different wind parameters on the resulting ultimate and fatigue loads on the turbine during normal operational conditions. Eighteen different wind parameters are screened using an Elementary Effects approach with radial points. As expected, the results show a high sensitivity of the loads to the turbulence standard deviation in the primary wind direction, but the sensitivity to wind shear is often much greater. To a lesser extent, other wind parameters that drive loads include the coherence in the primary wind direction and veer.

Predicting Dynamic Response of Structures under Earthquake Loads Using Logical Analysis of Data

Directory of Open Access Journals (Sweden)

Ayman Abd-Elhamed

2018-04-01

Full Text Available In this paper, logical analysis of data (LAD is used to predict the seismic response of building structures employing the captured dynamic responses. In order to prepare the data, computational simulations using a single degree of freedom (SDOF building model under different ground motion records are carried out. The selected excitation records are real and of different peak ground accelerations (PGA. The sensitivity of the seismic response in terms of displacements of floors to the variation in earthquake characteristics, such as soil class, characteristic period, and time step of records, peak ground displacement, and peak ground velocity, have also been considered. The dynamic equation of motion describing the building model and the applied earthquake load are presented and solved incrementally using the Runge-Kutta method. LAD then finds the characteristic patterns which lead to forecast the seismic response of building structures. The accuracy of LAD is compared to that of an artificial neural network (ANN, since the latter is the most known machine learning technique. Based on the conducted study, the proposed LAD model has been proven to be an efficient technique to learn, simulate, and blindly predict the dynamic response behaviour of building structures subjected to earthquake loads.

Seasonal Climate Extremes : Mechanism, Predictability and Responses to Global Warming

NARCIS (Netherlands)

Shongwe, M.E.

2010-01-01

Climate extremes are rarely occurring natural phenomena in the climate system. They often pose one of the greatest environmental threats to human and natural systems. Statistical methods are commonly used to investigate characteristics of climate extremes. The fitted statistical properties are often

Which adherence measure - self-report, clinician recorded or pharmacy refill - is best able to predict detectable viral load in a public ART programme without routine plasma viral load monitoring?

Science.gov (United States)

Mekuria, Legese A; Prins, Jan M; Yalew, Alemayehu W; Sprangers, Mirjam A G; Nieuwkerk, Pythia T

2016-07-01

Combination antiretroviral therapy (cART) suppresses viral replication to an undetectable level if a sufficiently high level of adherence is achieved. We investigated which adherence measurement best distinguishes between patients with and without detectable viral load in a public ART programme without routine plasma viral load monitoring. We randomly selected 870 patients who started cART between May 2009 and April 2012 in 10 healthcare facilities in Addis Ababa, Ethiopia. Six hundred and sixty-four (76.3%) patients who were retained in HIV care and were receiving cART for at least 6 months were included and 642 had their plasma HIV-1 RNA concentration measured. Patients' adherence to cART was assessed according to self-report, clinician recorded and pharmacy refill measures. Multivariate logistic regression model was fitted to identify the predictors of detectable viremia. Model accuracy was evaluated by computing the area under the receiver operating characteristic (ROC) curve. A total of 9.2% and 5.5% of the 642 patients had a detectable viral load of ≥40 and ≥400 RNA copies/ml, respectively. In the multivariate analyses, younger age, lower CD4 cell count at cART initiation, being illiterate and widowed, and each of the adherence measures were significantly and independently predictive of having ≥400 RNA copies/ml. The ROC curve showed that these variables altogether had a likelihood of more than 80% to distinguish patients with a plasma viral load of ≥400 RNA copies/ml from those without. Adherence to cART was remarkably high. Self-report, clinician recorded and pharmacy refill non-adherence were all significantly predictive of detectable viremia. The choice for one of these methods to detect non-adherence and predict a detectable viral load can therefore be based on what is most practical in a particular setting. © 2016 John Wiley & Sons Ltd.

Microcracking in composite laminates under thermal and mechanical loading. Thesis

Science.gov (United States)

Maddocks, Jason R.

1995-01-01

Composites used in space structures are exposed to both extremes in temperature and applied mechanical loads. Cracks in the matrix form, changing the laminate thermoelastic properties. The goal of the present investigation is to develop a predictive methodology to quantify microcracking in general composite laminates under both thermal and mechanical loading. This objective is successfully met through a combination of analytical modeling and experimental investigation. In the analysis, the stress and displacement distributions in the vicinity of a crack are determined using a shear lag model. These are incorporated into an energy based cracking criterion to determine the favorability of crack formation. A progressive damage algorithm allows the inclusion of material softening effects and temperature-dependent material properties. The analysis is implemented by a computer code which gives predicted crack density and degraded laminate properties as functions of any thermomechanical load history. Extensive experimentation provides verification of the analysis. AS4/3501-6 graphite/epoxy laminates are manufactured with three different layups to investigate ply thickness and orientation effects. Thermal specimens are cooled to progressively lower temperatures down to -184 C. After conditioning the specimens to each temperature, cracks are counted on their edges using optical microscopy and in their interiors by sanding to incremental depths. Tensile coupons are loaded monotonically to progressively higher loads until failure. Cracks are counted on the coupon edges after each loading. A data fit to all available results provides input parameters for the analysis and shows them to be material properties, independent of geometry and loading. Correlation between experiment and analysis is generally very good under both thermal and mechanical loading, showing the methodology to be a powerful, unified tool. Delayed crack initiation observed in a few cases is attributed to a

The use of geoinformatic data and spatial analysis to predict faecal pollution during extreme precipitation events

Science.gov (United States)

Ward, Ray; Purnell, Sarah; Ebdon, James; Nnane, Daniel; Taylor, Huw

2013-04-01

The Water Framework Directive (WFD) regulates surface water quality standards in the European Union (EU). The Directive call for the identification and management of point and diffuse sources of pollution and requires the establishment of a 'programme of measures' for identified river basin districts, in order to achieve a "good status" by 2015. The hygienic quality of water is normally monitored using faecal indicator organisms (FIO), such as Escherichia coli, which indicate a potential risk to public health from human waterborne pathogens. Environmental factors influence the transmission of these pathogens and indicator organisms, and statistically significant relationships have been found between rainfall and outbreaks of waterborne disease. Climate change has been predicted to lead to an increase in severe weather events in many parts of Europe, including an increase in the frequency of extreme rainfall events. This in turn is likely to lead to an increase in incidents of human waterborne disease in Europe, unless measures are taken to predict and mitigate for such events. This study investigates a variety of environmental factors that influence the concentration of FIO in surface waters receiving faecal contamination from a variety of sources. Levels of FIO, including Escherichia coli, intestinal enterococci, somatic coliphage and GB124 (a human-specific microbial source tracking marker), were monitored in the Sussex Ouse catchment in Southeast England over a period of 26 months. These data were combined with geoinformatic environmental data within a GIS to map faecal contamination within the river. Previously, precipitation and soil erosion have been identified as major factors that can influence the concentration of these faecal markers, and studies have shown that slope, soil type and vegetation influence both the mechanisms and the rate by which erosion occurs in river catchments. Of the environmental variables studied, extreme precipitation was found to

Directional Considerations for Extreme Wind Climatic Events in the ...

African Journals Online (AJOL)

This paper takes a look at the importance and role of probability concepts structural design of transmission line. The reliability of transmission structure is clearly a function of the maximum loads that may be imposed over the useful life of the structure. These loads are, more often than not, caused by the extreme atmospheric ...

Concurrent Working Memory Load Can Facilitate Selective Attention: Evidence for Specialized Load

Science.gov (United States)

Park, Soojin; Kim, Min-Shik; Chun, Marvin M.

2007-01-01

Load theory predicts that concurrent working memory load impairs selective attention and increases distractor interference (N. Lavie, A. Hirst, J. W. de Fockert, & E. Viding, see record 2004-17825-003). Here, the authors present new evidence that the type of concurrent working memory load determines whether load impairs selective attention or not.…

Likelihood estimators for multivariate extremes

KAUST Repository

Huser, Raphaë l; Davison, Anthony C.; Genton, Marc G.

2015-01-01

The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.

Likelihood estimators for multivariate extremes

KAUST Repository

Huser, Raphaël

2015-11-17

The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.

Adaptive algorithm for predicting increases in central loads of electrical energy systems

Energy Technology Data Exchange (ETDEWEB)

Arbachyauskene, N A; Pushinaytis, K V

1982-01-01

An adaptive algorithm for predicting increases in central loads of the electrical energy system is suggested for the task of evaluating the condition. The algorithm is based on the Kalman filter. In order to calculate the coefficient of intensification, the a priori assigned noise characteristics with low accuracy are used only in the beginning of the calculation. Further, the coefficient of intensification is calculated from the innovation sequence. This approach makes it possible to correct errors in the assignment of the statistical noise characteristics and to follow their changes. The algorithm is experimentally verified.

Validated Loads Prediction Models for Offshore Wind Turbines for Enhanced Component Reliability

DEFF Research Database (Denmark)

Koukoura, Christina

To improve the reliability of offshore wind turbines, accurate prediction of their response is required. Therefore, validation of models with site measurements is imperative. In the present thesis a 3.6MW pitch regulated-variable speed offshore wind turbine on a monopole foundation is built...... are used for the modification of the sub-structure/foundation design for possible material savings. First, the background of offshore wind engineering, including wind-wave conditions, support structure, blade loading and wind turbine dynamics are presented. Second, a detailed description of the site...

Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

Science.gov (United States)

Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J

2016-11-01

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R 2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.

Microstructure-based constitutive modeling of TRIP steel: Prediction of ductility and failure modes under different loading conditions

International Nuclear Information System (INIS)

Choi, K.S.; Liu, W.N.; Sun, X.; Khaleel, M.A.

2009-01-01

We study the ultimate ductility and failure modes of a commercial transformation-induced plasticity (TRIP) 800 steel under different loading conditions with an advanced microstructure-based finite-element analysis. The representative volume element (RVE) for the TRIP 800 under examination is developed based on an actual microstructure obtained from scanning electron microscopy. The ductile failure of the TRIP 800 under different loading conditions is predicted in the form of plastic strain localization without any prescribed failure criteria for the individual phases. This indicates that the microstructure-level inhomogeneity of the various constituent phases can be the key factor influencing the final ductility of the TRIP 800 steel under different loading conditions. Comparisons of the computational results with experimental measurements suggest that the microstructure-based modeling approach accurately captures the overall macroscopic behavior of the TRIP 800 steel under different loading and boundary conditions.

Prediction of bead area contact load at the tire-wheel interface using NASTRAN

Science.gov (United States)

Chen, C. H. S.

1982-01-01

The theoretical prediction of the bead area contact load at the tire wheel interface using NASTRAN is reported. The application of the linear code to a basically nonlinear problem results in excessive deformation of the structure and the tire-wheel contact conditions become impossible to achieve. A psuedo-nonlinear approach was adopted in which the moduli of the cord reinforced composite are increased so that the computed key deformations matched that of the experiment. Numerical results presented are discussed.

Toward Improving Predictability of Extreme Hydrometeorological Events: the Use of Multi-scale Climate Modeling in the Northern High Plains

Science.gov (United States)

Munoz-Arriola, F.; Torres-Alavez, J.; Mohamad Abadi, A.; Walko, R. L.

2014-12-01

Our goal is to investigate possible sources of predictability of hydrometeorological extreme events in the Northern High Plains. Hydrometeorological extreme events are considered the most costly natural phenomena. Water deficits and surpluses highlight how the water-climate interdependence becomes crucial in areas where single activities drive economies such as Agriculture in the NHP. Nonetheless we recognize the Water-Climate interdependence and the regulatory role that human activities play, we still grapple to identify what sources of predictability could be added to flood and drought forecasts. To identify the benefit of multi-scale climate modeling and the role of initial conditions on flood and drought predictability on the NHP, we use the Ocean Land Atmospheric Model (OLAM). OLAM is characterized by a dynamic core with a global geodesic grid with hexagonal (and variably refined) mesh cells and a finite volume discretization of the full compressible Navier Stokes equations, a cut-grid cell method for topography (that reduces error in computational gradient computation and anomalous vertical dispersion). Our hypothesis is that wet conditions will drive OLAM's simulations of precipitation to wetter conditions affecting both flood forecast and drought forecast. To test this hypothesis we simulate precipitation during identified historical flood events followed by drought events in the NHP (i.e. 2011-2012 years). We initialized OLAM with CFS-data 1-10 days previous to a flooding event (as initial conditions) to explore (1) short-term and high-resolution and (2) long-term and coarse-resolution simulations of flood and drought events, respectively. While floods are assessed during a maximum of 15-days refined-mesh simulations, drought is evaluated during the following 15 months. Simulated precipitation will be compared with the Sub-continental Observation Dataset, a gridded 1/16th degree resolution data obtained from climatological stations in Canada, US, and

Instability predictions for circumferentially cracked Type-304 stainless-steel pipes under dynamic loading. Final report. [BWR

Energy Technology Data Exchange (ETDEWEB)

Zahoor, A.; Wilkowski, G.; Abou-Sayed, I.; Marschall, C.; Broek, D.; Sampath, S.; Rhee, H.; Ahmad, J.

1982-04-01

This report provides methods to predict margins of safety for circumferentially cracked Type 304 stainless steel pipes subjected to applied bending loads. An integrated combination of experimentation and analysis research was pursued. Two types of experiments were performed: (1) laboratory-scale tests on center-cracked panels and bend specimens to establish the basic mechanical and fracture properties of Type 304 stainless steel, and (2) full-scale pipe fracture tests under quasi-static and dynamic loadings to assess the analysis procedures. Analyses were based upon the simple plastic collapse criterion, a J-estimation procedure, and elastic-plastic large-deformation finite element models.

Assessment of the reference stress method for combined tensile bending and thermal loading

International Nuclear Information System (INIS)

Philipp, A.; Munz, D.

1984-01-01

The reference stress method has been investigated for combined tensile, bending and thermal loading by considering a uniformly bent beam subjected to superimposed tensile stress and lateral temperature gradients. The creep deformation of the beam can be calculated numerically applying a Norton-type creep law. It turns out that the ratio of curvature rate to strain at the outer fiber depends on the creep exponent. Therefore, the reference stresses for these two quantities must be expected to be different in general. In most load cases, however, it is possible to determine a reference stress which can be used to describe the complete deformation of the beam. The only exception is the case of high tensile loading if the side exposed to higher tensile stress is cooler. Approximate solutions for the reference stress which rely on elastic and limit analyses, can be used only for estimates because they lead to extremely non-conservative predictions. (author)

Rational Calibration of Four IEC 61400-1 Extreme External Conditions

DEFF Research Database (Denmark)

Larsen, Gunner Chr.; Hansen, Kurt Schaldemose

2008-01-01

Based on a set of asymptotic statistical models on closed form this paper presents a rational and consistent calibration of four extreme external conditions defined in the International Electrotechnical Commission (IEC) 61400-1 standard: extreme operating gust, extreme wind shear, extreme coheren...... and proposed specifications of the magnitudes of the extreme external wind conditions are highlighted and discussed using an illustrative example based on two selected terrain types. Copyright © 2008 John Wiley & Sons, Ltd....... gust with direction change and extreme wind direction change. These four extreme external conditions are used in the definition of six of the IEC 61400-1 ultimate load cases. The statistical models are based on simple and easily accessible mean wind speed and turbulence characteristics...

Load-Unload Response Ratio and Accelerating Moment/Energy Release Critical Region Scaling and Earthquake Prediction

Science.gov (United States)

Yin, X. C.; Mora, P.; Peng, K.; Wang, Y. C.; Weatherley, D.

The main idea of the Load-Unload Response Ratio (LURR) is that when a system is stable, its response to loading corresponds to its response to unloading, whereas when the system is approaching an unstable state, the response to loading and unloading becomes quite different. High LURR values and observations of Accelerating Moment/Energy Release (AMR/AER) prior to large earthquakes have led different research groups to suggest intermediate-term earthquake prediction is possible and imply that the LURR and AMR/AER observations may have a similar physical origin. To study this possibility, we conducted a retrospective examination of several Australian and Chinese earthquakes with magnitudes ranging from 5.0 to 7.9, including Australia's deadly Newcastle earthquake and the devastating Tangshan earthquake. Both LURR values and best-fit power-law time-to-failure functions were computed using data within a range of distances from the epicenter. Like the best-fit power-law fits in AMR/AER, the LURR value was optimal using data within a certain epicentral distance implying a critical region for LURR. Furthermore, LURR critical region size scales with mainshock magnitude and is similar to the AMR/AER critical region size. These results suggest a common physical origin for both the AMR/AER and LURR observations. Further research may provide clues that yield an understanding of this mechanism and help lead to a solid foundation for intermediate-term earthquake prediction.

The effect of prosthetic foot push-off on mechanical loading associated with knee osteoarthritis in lower extremity amputees.

Science.gov (United States)

Morgenroth, David C; Segal, Ava D; Zelik, Karl E; Czerniecki, Joseph M; Klute, Glenn K; Adamczyk, Peter G; Orendurff, Michael S; Hahn, Michael E; Collins, Steven H; Kuo, Art D

2011-10-01

Lower extremity amputation not only limits mobility, but also increases the risk of knee osteoarthritis of the intact limb. Dynamic walking models of non-amputees suggest that pushing-off from the trailing limb can reduce collision forces on the leading limb. These collision forces may determine the peak knee external adduction moment (EAM), which has been linked to the development of knee OA in the general population. We therefore hypothesized that greater prosthetic push-off would lead to reduced loading and knee EAM of the intact limb in unilateral transtibial amputees. Seven unilateral transtibial amputees were studied during gait under three prosthetic foot conditions that were intended to vary push-off. Prosthetic foot-ankle push-off work, intact limb knee EAM and ground reaction impulses for both limbs during step-to-step transition were measured. Overall, trailing limb prosthetic push-off work was negatively correlated with leading intact limb 1st peak knee EAM (slope=-.72±.22; p=.011). Prosthetic push-off work and 1st peak intact knee EAM varied significantly with foot type. The prosthetic foot condition with the least push-off demonstrated the largest knee EAM, which was reduced by 26% with the prosthetic foot producing the most push-off. Trailing prosthetic limb push-off impulse was negatively correlated with leading intact limb loading impulse (slope=-.34±.14; p=.001), which may help explain how prosthetic limb push-off can affect intact limb loading. Prosthetic feet that perform more prosthetic push-off appear to be associated with a reduction in 1st peak intact knee EAM, and their use could potentially reduce the risk and burden of knee osteoarthritis in this population. Published by Elsevier B.V.

Rating curve estimation of nutrient loads in Iowa rivers

Science.gov (United States)

Stenback, G.A.; Crumpton, W.G.; Schilling, K.E.; Helmers, M.J.

2011-01-01

Accurate estimation of nutrient loads in rivers and streams is critical for many applications including determination of sources of nutrient loads in watersheds, evaluating long-term trends in loads, and estimating loading to downstream waterbodies. Since in many cases nutrient concentrations are measured on a weekly or monthly frequency, there is a need to estimate concentration and loads during periods when no data is available. The objectives of this study were to: (i) document the performance of a multiple regression model to predict loads of nitrate and total phosphorus (TP) in Iowa rivers and streams; (ii) determine whether there is any systematic bias in the load prediction estimates for nitrate and TP; and (iii) evaluate streamflow and concentration factors that could affect the load prediction efficiency. A commonly cited rating curve regression is utilized to estimate riverine nitrate and TP loads for rivers in Iowa with watershed areas ranging from 17.4 to over 34,600km2. Forty-nine nitrate and 44 TP datasets each comprising 5-22years of approximately weekly to monthly concentrations were examined. Three nitrate data sets had sample collection frequencies averaging about three samples per week. The accuracy and precision of annual and long term riverine load prediction was assessed by direct comparison of rating curve load predictions with observed daily loads. Significant positive bias of annual and long term nitrate loads was detected. Long term rating curve nitrate load predictions exceeded observed loads by 25% or more at 33% of the 49 measurement sites. No bias was found for TP load prediction although 15% of the 44 cases either underestimated or overestimate observed long-term loads by more than 25%. The rating curve was found to poorly characterize nitrate and phosphorus variation in some rivers. ?? 2010 .

Nested-scale discharge and groundwater level monitoring to improve predictions of flow route discharges and nitrate loads

NARCIS (Netherlands)

Velde, Y. van der; Rozemeijer, J.C.; Rooij, G.H.de; Geer, F.C. van; Torfs, P.J.J.F.; Louw, P.G.B. de

2010-01-01

Identifying effective measures to reduce nutrient loads of headwaters in lowland catchments requires a thorough understanding of flow routes of water and nutrients. In this paper we assess the value of nested-scale discharge and groundwater level measurements for predictions of catchment-scale

Application of dimensionless sediment rating curves to predict suspended-sediment concentrations, bedload, and annual sediment loads for rivers in Minnesota

Science.gov (United States)

Ellison, Christopher A.; Groten, Joel T.; Lorenz, David L.; Koller, Karl S.

2016-10-27

variety of basin sizes and flow regimes than DSRC models developed using data collected for Pagosa Springs, Colorado. Minnesota DSRC models retained a substantial portion of the unique sediment signatures for most rivers, although deviations were observed for streams with limited sediment supply and for rivers in southeastern Minnesota, which had markedly larger regression exponents. Compared to Pagosa Springs DSRC models, Minnesota DSRC models had regression slopes that more closely matched the slopes of site-specific regression models, had greater Nash-Sutcliffe Efficiency values, had lower model biases, and approximated measured annual sediment loads more closely. The results presented in this report indicate that regionally based DSRCs can be used to estimate reasonably accurate values of SSC and bedload.Practitioners are cautioned that DSRC reliability is dependent on representative measures of bankfull streamflow, SSC, and bedload. It is, therefore, important that samples of SSC and bedload, which will be used for estimating SSC and bedload at the bankfull streamflow, are collected over a range of conditions that includes the ascending and descending limbs of the event hydrograph. The use of DSRC models may have substantial limitations for certain conditions. For example, DSRC models should not be used to predict SSC and sediment loads for extreme streamflows, such as those that exceed twice the bankfull streamflow value because this constitutes conditions beyond the realm of current (2016) empirical modeling capability. Also, if relations between SSC and streamflow and between bedload and streamflow are not statistically significant, DSRC models should not be used to predict SSC or bedload, as this could result in large errors. For streams that do not violate these conditions, DSRC estimates of SSC and bedload can be used for stream restoration planning and design, and for estimating annual sediment loads for streams where little or no sediment data are available.

Short-term load and wind power forecasting using neural network-based prediction intervals.

Science.gov (United States)

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Ultimate loading of wind turbines

DEFF Research Database (Denmark)

Larsen, Gunner Chr.; Ronold, K.; Ejsing Jørgensen, Hans

1999-01-01

An extreme loading study has been conducted comprising a general wind climate analysis as well as a wind turbine reliability study. In the wind climate analysis, the distribution of the (horizontal) turbulence standard deviation, conditioned on the meanwind speed, has been approximated by fitting......, a design turbulence intensity for off-shore application is proposed which, in the IEC code framework, is applicable for extreme as well as for fatigue loaddetermination. In order to establish a rational method to analyse wind turbine components with respect to failure in ultimate loading, and in addition...... a three parameter Weibull distribution to the measured on-shore and off-shore data for wind speed variations. Specific recommendations on off-shore design turbulence intensities are lacking in the presentIEC-code. Based on the present analysis of the off-shore wind climate on two shallow water sites...

Short-term load forecasting of power system

Science.gov (United States)

Xu, Xiaobin

2017-05-01

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

Thoracolumbar spine loading associated with kinematics of the young and the elderly during activities of daily living.

Science.gov (United States)

Ignasiak, Dominika; Rüeger, Andrea; Sperr, Ramona; Ferguson, Stephen J

2018-03-21

Excessive mechanical loading of the spine is a critical factor in vertebral fracture initiation. Most vertebral fractures develop spontaneously or due to mild trauma, as physiological loads during activities of daily living might exceed the failure load of osteoporotic vertebra. Spinal loading patterns are affected by vertebral kinematics, which differ between elderly and young individuals. In this study, the effects of age-related changes in spine kinematics on thoracolumbar spinal segmental loading during dynamic activities of daily living were investigated using combined experimental and modeling approach. Forty-four healthy volunteers were recruited into two age groups: young (N = 23, age = 27.1 ± 3.8) and elderly (N = 21, age = 70.1 ± 3.9). The spinal curvature was assessed with a skin-surface device and the kinematics of the spine and lower extremities were recorded during daily living tasks (flexion-extension and stand-sit-stand) with a motion capture system. The obtained data were used as input for a musculoskeletal model with a detailed thoracolumbar spine representation. To isolate the effect of kinematics on predicted loads, other model properties were kept constant. Inverse dynamics simulations were performed in the AnyBody Modeling System to estimate corresponding spinal loads. The maximum compressive loads predicted for the elderly motion patterns were lower than those of the young for L2/L3 and L3/L4 lumbar levels during flexion and for upper thoracic levels during stand-to-sit (T1/T2-T8/T9) and sit-to-stand (T3/T4-T6/T7). However, the maximum loads predicted for the lower thoracic levels (T9/T10-L1/L2), a common site of vertebral fractures, were similar compared to the young. Nevertheless, these loads acting on the vertebrae of reduced bone quality might contribute to a higher fracture risk for the elderly. Copyright © 2017 Elsevier Ltd. All rights reserved.

Response Load Extrapolation for Wind Turbines during Operation Based on Average Conditional Exceedance Rates

DEFF Research Database (Denmark)

Toft, Henrik Stensgaard; Naess, Arvid; Saha, Nilanjan

2011-01-01

to cases where the Gumbel distribution is the appropriate asymptotic extreme value distribution. However, two extra parameters are introduced by which a more general and flexible class of extreme value distributions is obtained with the Gumbel distribution as a subclass. The general method is implemented...... within a hierarchical model where the variables that influence the loading are divided into ergodic variables and time-invariant non-ergodic variables. The presented method for statistical response load extrapolation was compared with the existing methods based on peak extrapolation for the blade out......The paper explores a recently developed method for statistical response load (load effect) extrapolation for application to extreme response of wind turbines during operation. The extrapolation method is based on average conditional exceedance rates and is in the present implementation restricted...

A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

Directory of Open Access Journals (Sweden)

Javier Moriano

2016-01-01

Full Text Available In recent years, Secondary Substations (SSs are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected.

Prediction of a Densely Loaded Particle-Laden Jet using a Euler-Lagrange Dense Spray Model

Science.gov (United States)

Pakseresht, Pedram; Apte, Sourabh V.

2017-11-01

Modeling of a dense spray regime using an Euler-Lagrange discrete-element approach is challenging because of local high volume loading. A subgrid cluster of droplets can lead to locally high void fractions for the disperse phase. Under these conditions, spatio-temporal changes in the carrier phase volume fractions, which are commonly neglected in spray simulations in an Euler-Lagrange two-way coupling model, could become important. Accounting for the carrier phase volume fraction variations, leads to zero-Mach number, variable density governing equations. Using pressure-based solvers, this gives rise to a source term in the pressure Poisson equation and a non-divergence free velocity field. To test the validity and predictive capability of such an approach, a round jet laden with solid particles is investigated using Direct Numerical Simulation and compared with available experimental data for different loadings. Various volume fractions spanning from dilute to dense regimes are investigated with and without taking into account the volume displacement effects. The predictions of the two approaches are compared and analyzed to investigate the effectiveness of the dense spray model. Financial support was provided by National Aeronautics and Space Administration (NASA).

Modelling and Simulation of Wave Loads

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle

velocity can be approximated by a Gaussian Markov process. Known approximate results for the first-passage density or equivalently, the distribution of the extremes of wave loads are presented and compared with rather precise simulation results. It is demonstrated that the approximate results......A simple model of the wave load on slender members of offshore structures is described. The wave elevation of the sea state is modelled by a stationary Gaussian process. A new procedure to simulate realizations of the wave loads is developed. The simulation method assumes that the wave particle...

Modelling and Simulation of Wave Loads

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle

1985-01-01

velocity can be approximated by a Gaussian Markov process. Known approximate results for the first passage density or equivalently, the distribution of the extremes of wave loads are presented and compared with rather precise simulation results. It is demonstrated that the approximate results......A simple model of the wave load on stender members of offshore structures is described . The wave elevation of the sea stateis modelled by a stationary Gaussian process. A new procedure to simulate realizations of the wave loads is developed. The simulation method assumes that the wave particle...

Prediction of a Francis turbine prototype full load instability from investigations on the reduced scale model

Science.gov (United States)

Alligné, S.; Maruzewski, P.; Dinh, T.; Wang, B.; Fedorov, A.; Iosfin, J.; Avellan, F.

2010-08-01

The growing development of renewable energies combined with the process of privatization, lead to a change of economical energy market strategies. Instantaneous pricings of electricity as a function of demand or predictions, induces profitable peak productions which are mainly covered by hydroelectric power plants. Therefore, operators harness more hydroelectric facilities at full load operating conditions. However, the Francis Turbine features an axi-symmetric rope leaving the runner which may act under certain conditions as an internal energy source leading to instability. Undesired power and pressure fluctuations are induced which may limit the maximum available power output. BC Hydro experiences such constraints in a hydroelectric power plant consisting of four 435 MW Francis Turbine generating units, which is located in Canada's province of British Columbia. Under specific full load operating conditions, one unit experiences power and pressure fluctuations at 0.46 Hz. The aim of the paper is to present a methodology allowing prediction of this prototype's instability frequency from investigations on the reduced scale model. A new hydro acoustic vortex rope model has been developed in SIMSEN software, taking into account the energy dissipation due to the thermodynamic exchange between the gas and the surrounding liquid. A combination of measurements, CFD simulations and computation of eigenmodes of the reduced scale model installed on test rig, allows the accurate calibration of the vortex rope model parameters at the model scale. Then, transposition of parameters to the prototype according to similitude laws is applied and stability analysis of the power plant is performed. The eigenfrequency of 0.39 Hz related to the first eigenmode of the power plant is determined to be unstable. Predicted frequency of the full load power and pressure fluctuations at the unit unstable operating point is found to be in general agreement with the prototype measurements.

Prediction of a Francis turbine prototype full load instability from investigations on the reduced scale model

International Nuclear Information System (INIS)

Alligne, S; Maruzewski, P; Avellan, F; Dinh, T; Wang, B; Fedorov, A; Iosfin, J

2010-01-01

The growing development of renewable energies combined with the process of privatization, lead to a change of economical energy market strategies. Instantaneous pricings of electricity as a function of demand or predictions, induces profitable peak productions which are mainly covered by hydroelectric power plants. Therefore, operators harness more hydroelectric facilities at full load operating conditions. However, the Francis Turbine features an axi-symmetric rope leaving the runner which may act under certain conditions as an internal energy source leading to instability. Undesired power and pressure fluctuations are induced which may limit the maximum available power output. BC Hydro experiences such constraints in a hydroelectric power plant consisting of four 435 MW Francis Turbine generating units, which is located in Canada's province of British Columbia. Under specific full load operating conditions, one unit experiences power and pressure fluctuations at 0.46 Hz. The aim of the paper is to present a methodology allowing prediction of this prototype's instability frequency from investigations on the reduced scale model. A new hydro acoustic vortex rope model has been developed in SIMSEN software, taking into account the energy dissipation due to the thermodynamic exchange between the gas and the surrounding liquid. A combination of measurements, CFD simulations and computation of eigenmodes of the reduced scale model installed on test rig, allows the accurate calibration of the vortex rope model parameters at the model scale. Then, transposition of parameters to the prototype according to similitude laws is applied and stability analysis of the power plant is performed. The eigenfrequency of 0.39 Hz related to the first eigenmode of the power plant is determined to be unstable. Predicted frequency of the full load power and pressure fluctuations at the unit unstable operating point is found to be in general agreement with the prototype measurements.

Prediction of dynamic blade loading of the Francis-99 turbine

International Nuclear Information System (INIS)

Nicolle, J; Cupillard, S

2015-01-01

CFD simulations focusing on capturing dynamic fluctuations of the flow for three operating points were performed for a scale model of a high head Francis turbine. A mesh sensitivity study showed an influence of the near wall resolution, consequently a low Reynolds mesh with a SST turbulence model was used. Rotor/stator fluctuations are well reproduced with the full turbine simulation at all operating points. Velocity contours and average velocity profiles from LDV measurements in the draft tube confirm that the flow physics is generally well reproduced. Simplified approaches such as profile transform and Fourier transform underestimated the measured fluctuations. As full turbine simulations were time-consuming, a simulation with only the draft tube was performed at part load to predict the fluctuations in the draft tube cone. The SAS-SST turbulence model was able to capture the vortex rope behavior

High and increasing Oxa-51 DNA load predict mortality in Acinetobacter baumannii bacteremia: implication for pathogenesis and evaluation of therapy.

Directory of Open Access Journals (Sweden)

Yu-Chung Chuang

Full Text Available BACKGROUND: While quantification of viral loads has been successfully employed in clinical medicine and has provided valuable insights and useful markers for several viral diseases, the potential of measuring bacterial DNA load to predict outcome or monitor therapeutic responses remains largely unexplored. We tested this possibility by investigating bacterial loads in Acinetobacter baumannii bacteremia, a rapidly increasing nosocomial infection characterized by high mortality, drug resistance, multiple and complicated risk factors, all of which urged the need of good markers to evaluate therapeutics. METHODS AND FINDINGS: We established a quantitative real-time PCR assay based on an A. baumannii-specific gene, Oxa-51, and conducted a prospective study to examine A. baumannii loads in 318 sequential blood samples from 51 adults patients (17 survivors, 34 nonsurvivors with culture-proven A. baumannii bacteremia in the intensive care units. Oxa-51 DNA loads were significantly higher in the nonsurvivors than survivors on day 1, 2 and 3 (P=0.03, 0.001 and 0.006, respectively. Compared with survivors, nonsurvivors had higher maximum Oxa-51 DNA load and a trend of increase from day 0 to day 3 (P<0.001, which together with Pitt bacteremia score were independent predictors for mortality by multivariate analysis (P=0.014 and 0.016, for maximum Oxa-51 DNA and change of Oxa-51 DNA, respectively. Kaplan-Meier analysis revealed significantly different survival curves in patients with different maximum Oxa-51 DNA and change of Oxa-51 DNA from day 0 to day 3. CONCLUSIONS: High Oxa-51 DNA load and its initial increase could predict mortality. Moreover, monitoring Oxa-51 DNA load in blood may provide direct parameters for evaluating new regimens against A. baumannii in future clinical studies.

Extreme Sea Conditions in Shallow Water: Estimation based on in-situ measurements

Science.gov (United States)

Le Crom, Izan; Saulnier, Jean-Baptiste

2013-04-01

The design of marine renewable energy devices and components is based, among others, on the assessment of the environmental extreme conditions (winds, currents, waves, and water level) that must be combined together in order to evaluate the maximal loads on a floating/fixed structure, and on the anchoring system over a determined return period. Measuring devices are generally deployed at sea over relatively short durations (a few months to a few years), typically when describing water free surface elevation, and extrapolation methods based on hindcast data (and therefore on wave simulation models) have to be used. How to combine, in a realistic way, the action of the different loads (winds and waves for instance) and which correlation of return periods should be used are highly topical issues. However, the assessment of the extreme condition itself remains a not-fully-solved, crucial, and sensitive task. Above all in shallow water, extreme wave height, Hmax, is the most significant contribution in the dimensioning process of EMR devices. As a case study, existing methodologies for deep water have been applied to SEMREV, the French marine energy test site. The interest of this study, especially at this location, goes beyond the simple application to SEMREV's WEC and floating wind turbines deployment as it could also be extended to the Banc de Guérande offshore wind farm that are planned close by. More generally to pipes and communication cables as it is a redundant problematic. The paper will first present the existing measurements (wave and wind on site), the prediction chain that has been developed via wave models, the extrapolation methods applied to hindcast data, and will try to formulate recommendations for improving this assessment in shallow water.

The NASA Energy and Water Cycle Extreme (NEWSE) Integration Project

Science.gov (United States)

House, P. R.; Lapenta, W.; Schiffer, R.

2008-01-01

Skillful predictions of water and energy cycle extremes (flood and drought) are elusive. To better understand the mechanisms responsible for water and energy extremes, and to make decisive progress in predicting these extremes, the collaborative NASA Energy and Water cycle Extremes (NEWSE) Integration Project, is studying these extremes in the U.S. Southern Great Plains (SGP) during 2006-2007, including their relationships with continental and global scale processes, and assessment of their predictability on multiple space and time scales. It is our hypothesis that an integrative analysis of observed extremes which reflects the current understanding of the role of SST and soil moisture variability influences on atmospheric heating and forcing of planetary waves, incorporating recently available global and regional hydro- meteorological datasets (i.e., precipitation, water vapor, clouds, etc.) in conjunction with advances in data assimilation, can lead to new insights into the factors that lead to persistent drought and flooding. We will show initial results of this project, whose goals are to provide an improved definition, attribution and prediction on sub-seasonal to interannual time scales, improved understanding of the mechanisms of decadal drought and its predictability, including the impacts of SST variability and deep soil moisture variability, and improved monitoring/attributions, with transition to applications; a bridging of the gap between hydrological forecasts and stakeholders (utilization of probabilistic forecasts, education, forecast interpretation for different sectors, assessment of uncertainties for different sectors, etc.).

A model for Quick Load Analysis for monopile-type offshore wind turbine substructures

DEFF Research Database (Denmark)

Schløer, Signe; Castillo, Laura Garcia; Fejerskov, Morten

2016-01-01

A model for Quick Load Analysis, QuLA, of an offshore wind turbine substructure is presented. The aerodynamic rotor loads and damping are precomputed for a load-based configuration. The dynamic structural response is represented by the first global fore-aft mode only and is computed...... in the frequency domain using the equation of motion. The model is compared against the state of the art aeroelastic code, Flex5, and both life time fatigue and extreme loads are considered in the comparison. In general there is good similarity between the two models. Some derivation for the sectional forces...... are explained in terms of the model simplifications. The difference in the sectional moments are found to be within 14% for the fatigue load case and 10% for the extreme load condition....

Predicting the Response of Electricity Load to Climate Change

Energy Technology Data Exchange (ETDEWEB)

Sullivan, Patrick [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Colman, Jesse [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Kalendra, Eric [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2015-07-28

Our purpose is to develop a methodology to quantify the impact of climate change on electric loads in the United States. We perform simple linear regression, assisted by geospatial smoothing, on paired temperature and load time-series to estimate the heating- and coolinginduced sensitivity to temperature across 300 transmission zones and 16 seasonal and diurnal time periods. The estimated load sensitivities can be coupled with climate scenarios to quantify the potential impact of climate change on load, with a primary application being long-term electricity scenarios. The method allows regional and seasonal differences in climate and load response to be reflected in the electricity scenarios. While the immediate product of this analysis was designed to mesh with the spatial and temporal resolution of a specific electricity model to enable climate change scenarios and analysis with that model, we also propose that the process could be applied for other models and purposes.

Kinematic and kinetic synergies of the lower extremities during the pull in olympic weightlifting.

Science.gov (United States)

Kipp, Kristof; Redden, Josh; Sabick, Michelle; Harris, Chad

2012-07-01

The purpose of this study was to identify multijoint lower extremity kinematic and kinetic synergies in weightlifting and compare these synergies between joints and across different external loads. Subjects completed sets of the clean exercise at loads equal to 65, 75, and 85% of their estimated 1-RM. Functional data analysis was used to extract principal component functions (PCF's) for hip, knee, and ankle joint angles and moments of force during the pull phase of the clean at all loads. The PCF scores were then compared between joints and across loads to determine how much of each PCF was present at each joint and how it differed across loads. The analyses extracted two kinematic and four kinetic PCF's. The statistical comparisons indicated that all kinematic and two of the four kinetic PCF's did not differ across load, but scaled according to joint function. The PCF's captured a set of joint- and load-specific synergies that quantified biomechanical function of the lower extremity during Olympic weightlifting and revealed important technical characteristics that should be considered in sports training and future research.

Treatment efficiency of patients with shin fracture after intraosseous blocked osteosynthesis by using the load dispenser

Directory of Open Access Journals (Sweden)

Yu. V. Sukhin

2017-08-01

Full Text Available The purpose of research: еvaluation of the effectiveness of the device for determining the value of the load on the lower extremity while walking in real time with controlling and signalization of excessive and insufficient load. Materials and methods. Еlaborated and applied device, that allows to determine the load magnitude on the lower extremity in real time, and also to signal about excessive or weak load. The sensory block with the insole and the sensor is located in shoes, under patient's heel, and the main block is fixed on the shin with the help of the strap. Current value of the load on the leg is registered in real time. Received data is recorded in non-volatile memory. The system provides an opportunity to notify patient or doctor by email about the presence of a strong or weak load on the lower extremity, and also about the absence of load for a long period. Results. We used the loading batcher in 38 patients with the shin bones fractures, who were on inpatient treatment at the traumatology and orthopedics center in Odessa in the period from 1.5 to 12 months. The main group included patients, who used the load batcher on the lower extremity in rehabilitation period (transversal fracture of the shin bones diaphysis – 9 patients, oblique fracture – 11 patients. The control group consisted of patients, who didn't use the load batcher (10 patients with oblique fracture of the shin bones in the middle third, 8 patients with transversal fracture of both shin bones in the middle third. As a result of applying the device we succeeded to reduce the fracture fusion period for two weeks and avoid such complications as contracture of joint and fracture non-union. Conclusions. The device allows patients with traumatic consequences reaching the optimal load in rehabilitation period, avoiding excessive load on the lower extremity. The elaboration provides an opportunity to determine the statistics of the load and its transfer to the server

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

Directory of Open Access Journals (Sweden)

Yanjiao Li

2017-08-01

Full Text Available Gas utilization ratio (GUR is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs. In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF, depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces.

Science.gov (United States)

Li, Yanjiao; Zhang, Sen; Yin, Yixin; Xiao, Wendong; Zhang, Jie

2017-08-10

Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

Wrist loading patterns during pommel horse exercises.

Science.gov (United States)

Markolf, K L; Shapiro, M S; Mandelbaum, B R; Teurlings, L

1990-01-01

Gymnastics is a sport which involves substantial periods of upper extremity support as well as frequent impacts to the wrist. Not surprisingly, wrist pain is a common finding in gymnasts. Of all events, the pommel horse is the most painful. In order to study the forces of wrist impact, a standard pommel horse was instrumented with a specially designed load cell to record the resultant force of the hand on the pommel during a series of basic skills performed by a group of seventeen elite male gymnasts. The highest mean peak forces were recorded during the front scissors and flair exercises (1.5 BW) with peaks of up to 2.0 BW for some gymnasts. The mean peak force for hip circles at the center or end of the horse was 1.1 BW. The mean overall loading rate (initial contact to first loading peak) ranged from 5.2 BWs-1 (hip circles) to 10.6 BW s-1 (flairs). However, many recordings displayed localized initial loading spikes which occurred during 'hard' landings on the pommel. When front scissors were performed in an aggressive manner, the initial loading spikes averaged 1.0 BW in magnitude (maximum 1.8 BW) with an average rise time of 8.2 ms; calculated localized loading rates averaged 129 BW s-1 (maximum 219 BW s-1). These loading parameters are comparable to those encountered at heel strike during running. These impact forces and loading rates are remarkably high for an upper extremity joint not normally exposed to weight-bearing loads, and may contribute to the pathogenesis of wrist injuries in gymnastics.

Load consequences when sweeping blades - A case study of a 5 MW pitch controlled wind turbine

Energy Technology Data Exchange (ETDEWEB)

Verelst, D.R.S.; Larsen, Torben J.

2010-08-15

The generic 5 MW NREL wind turbine model is used in Risoe's aeroelastic simulator HAWC2 to investigate 120 different swept blade configurations (forward and backward sweep). Sensitivity for 2 different controllers is considered as well. Backward sweep results in a pitch to feather torsional moment of the blade, effectively reducing blade twist angles under increased loading. This behaviour results in decreased flap-wise fatigue and extreme loads, an increase for edge-wise fatigue loading and status quo or slight decrease in extreme loads (depending on the controller). Tower base and shaft-end bending moments are reduced as well. Forward sweep leads to an increase in angle of attack under loading. For a pitch controlled turbine this leads to an increase in fatigue and extreme loading in all cases. A controller inflicted instability is present for the more extreme forward swept cases. Due to the shape of considered sweep curves, an inherent and significant increase in torsional blade root bending moment is noted. A boomerang shaped sweep curve is proposed to counteract this problematic increased loading. Controller sensitivity shows that adding sweep affects some loadings differently. Power output is reduced for backward sweep since the blade twist is optimized as a rigid structure, ignoring the torsional deformations which for a swept blade can be significant. (author)

Precast concrete sandwich panels subjected to impact loading

Science.gov (United States)

Runge, Matthew W.

Precast concrete sandwich panels are a relatively new product in the construction industry. The design of these panels incorporates properties that allow for great resilience against temperature fluctuation as well as the very rapid and precise construction of facilities. The concrete sandwich panels investigated in this study represent the second generation of an ongoing research and development project. This second generation of panels have been engineered to construct midsized commercial buildings up to three stories in height as well as residential dwellings. The panels consist of a double-tee structural wythe, a foam core and a fascia wythe, joined by shear connectors. Structures constructed from these panels may be subjected to extreme loading including the effects of seismic and blast loading in addition to wind. The aim of this work was to investigate the behaviour of this particular sandwich panel when subjected to structural impact events. The experimental program consisted of fourteen concrete sandwich panels, five of which were considered full-sized specimens (2700 mm X 1200mm X 270 mm) and nine half-sized specimens (2700mm X 600mm X 270 mm) The panels were subjected to impact loads from a pendulum impact hammer where the total energy applied to the panels was varied by changing the mass of the hammer. The applied loads, displacements, accelerations, and strains at the mid-span of the panel as well as the reaction point forces were monitored during the impact. The behaviour of the panels was determined primarily from the experimental results. The applied loads at low energy levels that caused little to no residual deflection as well as the applied loads at high energy levels that represent catastrophic events and thus caused immediate failure were determined from an impact on the structural and the fascia wythes. Applied loads at intermediate energy levels representing extreme events were also used to determine whether or not the panels could withstand

Tool-life prediction under multi-cycle loading during metal forming: a feasibility study

Directory of Open Access Journals (Sweden)

Hu Yiran

2015-01-01

Full Text Available In the present research, the friction and wear behaviour of a hard coating were studied by using ball-on-disc tests to simulate the wear process of the coated tools for sheet metal forming process. The evolution of the friction coefficient followed a typical dual-plateau pattern, i.e. at the initial stage of sliding, the friction coefficient was relatively low, followed by a sharp increase due to the breakdown of the coatings after a certain number of cyclic dynamic loadings. This phenomenon was caused by the interactive response between the friction and wear from a coating tribo-system, which is often neglected by metal forming researchers, and constant friction coefficient values are normally used in the finite element (FE simulations to represent the complex tribological nature at the contact interfaces. Meanwhile, most of the current FE simulations consider single-cycle loading processes, whereas many metal-forming operations are conducted in a form of multi-cycle loading. Therefore, a novel friction/wear interactive friction model was developed to, simultaneously, characterise the evolutions of friction coefficient and the remaining thickness of the coating layer, to enable the wear life of coated tooling to be predicted. The friction model was then implemented into the FE simulation of a sheet metal forming process for feasibility study.

Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory

Directory of Open Access Journals (Sweden)

Jun Yang

2015-03-01

Full Text Available In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G technique, electric vehicles (EVs can act as mobile energy storage units, which could be a solution for load frequency control (LFC in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances.

Stuck pipe prediction

KAUST Repository

Alzahrani, Majed; Alsolami, Fawaz; Chikalov, Igor; Algharbi, Salem; Aboudi, Faisal; Khudiri, Musab

2016-01-01

Disclosed are various embodiments for a prediction application to predict a stuck pipe. A linear regression model is generated from hook load readings at corresponding bit depths. A current hook load reading at a current bit depth is compared with a normal hook load reading from the linear regression model. A current hook load greater than a normal hook load for a given bit depth indicates the likelihood of a stuck pipe.

Stuck pipe prediction

KAUST Repository

Alzahrani, Majed

2016-03-10

Disclosed are various embodiments for a prediction application to predict a stuck pipe. A linear regression model is generated from hook load readings at corresponding bit depths. A current hook load reading at a current bit depth is compared with a normal hook load reading from the linear regression model. A current hook load greater than a normal hook load for a given bit depth indicates the likelihood of a stuck pipe.

Modeling, Forecasting and Mitigating Extreme Earthquakes

Science.gov (United States)

Ismail-Zadeh, A.; Le Mouel, J.; Soloviev, A.

2012-12-01

Recent earthquake disasters highlighted the importance of multi- and trans-disciplinary studies of earthquake risk. A major component of earthquake disaster risk analysis is hazards research, which should cover not only a traditional assessment of ground shaking, but also studies of geodetic, paleoseismic, geomagnetic, hydrological, deep drilling and other geophysical and geological observations together with comprehensive modeling of earthquakes and forecasting extreme events. Extreme earthquakes (large magnitude and rare events) are manifestations of complex behavior of the lithosphere structured as a hierarchical system of blocks of different sizes. Understanding of physics and dynamics of the extreme events comes from observations, measurements and modeling. A quantitative approach to simulate earthquakes in models of fault dynamics will be presented. The models reproduce basic features of the observed seismicity (e.g., the frequency-magnitude relationship, clustering of earthquakes, occurrence of extreme seismic events). They provide a link between geodynamic processes and seismicity, allow studying extreme events, influence of fault network properties on seismic patterns and seismic cycles, and assist, in a broader sense, in earthquake forecast modeling. Some aspects of predictability of large earthquakes (how well can large earthquakes be predicted today?) will be also discussed along with possibilities in mitigation of earthquake disasters (e.g., on 'inverse' forensic investigations of earthquake disasters).

Analysis of extreme events

CSIR Research Space (South Africa)

Khuluse, S

2009-04-01

Full Text Available ) determination of the distribution of the damage and (iii) preparation of products that enable prediction of future risk events. The methodology provided by extreme value theory can also be a powerful tool in risk analysis...

The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil Reservoir

KAUST Repository

Prasetyo Utomo, Chandra

2011-06-01

Permeability is an important parameter connected with oil reservoir. Predicting the permeability could save millions of dollars. Unfortunately, petroleum engineers have faced numerous challenges arriving at cost-efficient predictions. Much work has been carried out to solve this problem. The main challenge is to handle the high range of permeability in each reservoir. For about a hundred year, mathematicians and engineers have tried to deliver best prediction models. However, none of them have produced satisfying results. In the last two decades, artificial intelligence models have been used. The current best prediction model in permeability prediction is extreme learning machine (ELM). It produces fairly good results but a clear explanation of the model is hard to come by because it is so complex. The aim of this research is to propose a way out of this complexity through the design of a hybrid intelligent model. In this proposal, the system combines classification and regression models to predict the permeability value. These are based on the well logs data. In order to handle the high range of the permeability value, a classification tree is utilized. A benefit of this innovation is that the tree represents knowledge in a clear and succinct fashion and thereby avoids the complexity of all previous models. Finally, it is important to note that the ELM is used as a final predictor. Results demonstrate that this proposed hybrid model performs better when compared with support vector machines (SVM) and ELM in term of correlation coefficient. Moreover, the classification tree model potentially leads to better communication among petroleum engineers concerning this important process and has wider implications for oil reservoir management efficiency.

XBeach-G: a tool for predicting gravel barrier response to extreme storm conditions

Science.gov (United States)

Masselink, Gerd; Poate, Tim; McCall, Robert; Roelvink, Dano; Russell, Paul; Davidson, Mark

2014-05-01

Gravel beaches protect low-lying back-barrier regions from flooding during storm events and their importance to society is widely acknowledged. Unfortunately, breaching and extensive storm damage has occurred at many gravel sites and this is likely to increase as a result of sea-level rise and enhanced storminess due to climate change. Limited scientific guidance is currently available to provide beach managers with operational management tools to predict the response of gravel beaches to storms. The New Understanding and Prediction of Storm Impacts on Gravel beaches (NUPSIG) project aims to improve our understanding of storm impacts on gravel coastal environments and to develop a predictive capability by modelling these impacts. The NUPSIG project uses a 5-pronged approach to address its aim: (1) analyse hydrodynamic data collected during a proto-type laboratory experiment on a gravel beach; (2) collect hydrodynamic field data on a gravel beach under a range of conditions, including storm waves with wave heights up to 3 m; (3) measure swash dynamics and beach response on 10 gravel beaches during extreme wave conditions with wave heights in excess of 3 m; (4) use the data collected under 1-3 to develop and validate a numerical model to model hydrodynamics and morphological response of gravel beaches under storm conditions; and (5) develop a tool for end-users, based on the model formulated under (4), for predicting storm response of gravel beaches and barriers. The aim of this presentation is to present the key results of the NUPSIG project and introduce the end-user tool for predicting storm response on gravel beaches. The model is based on the numerical model XBeach, and different forcing scenarios (wave and tides), barrier configurations (dimensions) and sediment characteristics are easily uploaded for model simulations using a Graphics User Interface (GUI). The model can be used to determine the vulnerability of gravel barriers to storm events, but can also be

Determination of Correlation for Extreme Metocean Variables

Directory of Open Access Journals (Sweden)

Nizamani Zafarullah

2017-01-01

Full Text Available Metocean environmental load includes wind, wave and currents. Offshore structures are designed for two environmental load design conditions i.e. extreme and operational load conditions of environmental loads are evaluated. The ccorrelation between load variables using Joint probability distribution, Pearson correlation coefficient and Spearman’s rank correlation coefficients methods in Peninsular Malaysia (PM, Sabah and Sarawak are computed. Joint probability distribution method is considered as a reliable method among three different methods to determine the relationship between load variables. The PM has good correlation between the wind-wave and wave-current; Sabah has both strong relationships of wind-wave and wind-current with 50 year return period; Sarawak has good correlation between wind and current in both 50 years and 100 years return period. Since Sabah has good correlation between the associated load variables, no matter in 50 years or 100 years of return period of load combination. Thus, method 1 of ISO 19901-1, specimen provides guideline for metocean loading conditions, can be adopted for design for offshore structure in Sabah. However, due to weak correlations in PM and Sarawak, this method cannot be applied and method 2, which is current practice in offshore industry, should continueto be used.

Full scale test SSP 34m blade, edgewise loading LTT. Extreme load and PoC{sub I}nvE Data report

Energy Technology Data Exchange (ETDEWEB)

Nielsen, Magda; Roczek-Sieradzan, A.; Jensen, Find M. (and others)

2010-09-15

This report is the second report covering the research and demonstration project 'Experimental blade research: Structural mechanisms in current and future large blades under combined loading', supported by the EUDP program. A 34m wind turbine blade from SSP-Technology A/S has been tested in edgewise direction (LTT). The blade has been submitted to thorough examination by means of strain gauges, displacement transducers and a 3D optical measuring system. This data report presents results obtained during full scale testing of the blade up to 80% Risoe load, where 80% Risoe load corresponds to 100% certification load. These pulls at 80% Risoe load were repeated and the results from these pulls were compared. The blade was reinforced according to a Risoe DTU invention, where the trailing edge panels are coupled. The coupling is implemented to prevent the out of plane deformations and to reduce peeling stresses in the adhesive joints. Test results from measurements with the reinforcement have been compared to results without the coupling. The report presents only the relevant results for the 80% Risoe load and the results applicable for the investigation of the influence of the invention on the profile deformation. (Author)

Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine

Science.gov (United States)

Imani, Moslem; Kao, Huan-Chin; Lan, Wen-Hau; Kuo, Chung-Yen

2018-02-01

The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

The Atlas load protection switch

CERN Document Server

Davis, H A; Dorr, G; Martínez, M; Gribble, R F; Nielsen, K E; Pierce, D; Parsons, W M

1999-01-01

Atlas is a high-energy pulsed-power facility under development to study materials properties and hydrodynamics experiments under extreme conditions. Atlas will implode heavy liner loads (m~45 gm) with a peak current of 27-32 MA delivered in 4 mu s, and is energized by 96, 240 kV Marx generators storing a total of 23 MJ. A key design requirement for Atlas is obtaining useful data for 95601130f all loads installed on the machine. Materials response calculations show current from a prefire can damage the load requiring expensive and time consuming replacement. Therefore, we have incorporated a set of fast-acting mechanical switches in the Atlas design to reduce the probability of a prefire damaging the load. These switches, referred to as the load protection switches, short the load through a very low inductance path during system charge. Once the capacitors have reached full charge, the switches open on a time scale short compared to the bank charge time, allowing current to flow to the load when the trigger pu...

Gaussian vs non-Gaussian turbulence: impact on wind turbine loads

DEFF Research Database (Denmark)

Berg, Jacob; Natarajan, Anand; Mann, Jakob

2016-01-01

taking into account the safety factor for extreme moments. Other extreme load moments as well as the fatigue loads are not affected because of the use of non-Gaussian turbulent inflow. It is suggested that the turbine thus acts like a low-pass filter that averages out the non-Gaussian behaviour, which......From large-eddy simulations of atmospheric turbulence, a representation of Gaussian turbulence is constructed by randomizing the phases of the individual modes of variability. Time series of Gaussian turbulence are constructed and compared with its non-Gaussian counterpart. Time series from the two...

Optimizing the Loads of multi-player online game Servers using Markov Chains

DEFF Research Database (Denmark)

Saeed, Aamir; Olsen, Rasmus Løvenstein; Pedersen, Jens Myrup

2015-01-01

that is created due to the load balancing of servers. Load balancing among servers is sensitive to correct status information. The Markov based load prediction was introduced in this paper to predict load of under-loaded servers, based on arrival (μ) and departure (λ) rates of players. The prediction based...... that need to be considered when developing load balancing algorithm, that is the reliability of the information that is shared. Simulation results show that Markov based prediction of load information performed better from the normal load status information sharing....

The Efficacy of Intraoperative Neurophysiological Monitoring Using Transcranial Electrically Stimulated Muscle-evoked Potentials (TcE-MsEPs) for Predicting Postoperative Segmental Upper Extremity Motor Paresis After Cervical Laminoplasty.

Science.gov (United States)

Fujiwara, Yasushi; Manabe, Hideki; Izumi, Bunichiro; Tanaka, Hiroyuki; Kawai, Kazumi; Tanaka, Nobuhiro

2016-05-01

Prospective study. To investigate the efficacy of transcranial electrically stimulated muscle-evoked potentials (TcE-MsEPs) for predicting postoperative segmental upper extremity palsy following cervical laminoplasty. Postoperative segmental upper extremity palsy, especially in the deltoid and biceps (so-called C5 palsy), is the most common complication following cervical laminoplasty. Some papers have reported that postoperative C5 palsy cannot be predicted by TcE-MsEPs, although others have reported that it can be predicted. This study included 160 consecutive cases that underwent open-door laminoplasty, and TcE-MsEP monitoring was performed in the biceps brachii, triceps brachii, abductor digiti minimi, tibialis anterior, and abductor hallucis. A >50% decrease in the wave amplitude was defined as an alarm point. According to the monitoring alarm, interventions were performed, which include steroid administration, foraminotomies, etc. Postoperative deltoid and biceps palsy occurred in 5 cases. Among the 155 cases without segmental upper extremity palsy, there were no monitoring alarms. Among the 5 deltoid and biceps palsy cases, 3 had significant wave amplitude decreases in the biceps during surgery, and palsy occurred when the patients awoke from anesthesia (acute type). In the other 2 cases in which the palsy occurred 2 days after the operation (delayed type), there were no significant wave decreases. In all of the cases, the palsy was completely resolved within 6 months. The majority of C5 palsies have been reported to occur several days after surgery, but some of them have been reported to occur immediately after surgery. Our results demonstrated that TcE-MsEPs can predict the acute type, whereas the delayed type cannot be predicted. A >50% wave amplitude decrease in the biceps is useful to predict acute-type segmental upper extremity palsy. Further examination about the interventions for monitoring alarm will be essential for preventing palsy.

Investigation of Unsteady Pressure-Sensitive Paint (uPSP) and a Dynamic Loads Balance to Predict Launch Vehicle Buffet Environments

Science.gov (United States)

Schuster, David M.; Panda, Jayanta; Ross, James C.; Roozeboom, Nettie H.; Burnside, Nathan J.; Ngo, Christina L.; Kumagai, Hiro; Sellers, Marvin; Powell, Jessica M.; Sekula, Martin K.;

Moving in extreme environments: what's extreme and who decides?

Science.gov (United States)

Cotter, James David; Tipton, Michael J

2014-01-01

, extreme loading, chronic unloading and high altitude. Ramifications include factors such as health and safety, productivity, enjoyment and autonomy, acute and chronic protection and optimising adaptation.

Life prediction of different commercial dental implants as influence by uncertainties in their fatigue material properties and loading conditions.

Science.gov (United States)

Pérez, M A

2012-12-01

Probabilistic analyses allow the effect of uncertainty in system parameters to be determined. In the literature, many researchers have investigated static loading effects on dental implants. However, the intrinsic variability and uncertainty of most of the main problem parameters are not accounted for. The objective of this research was to apply a probabilistic computational approach to predict the fatigue life of three different commercial dental implants considering the variability and uncertainty in their fatigue material properties and loading conditions. For one of the commercial dental implants, the influence of its diameter in the fatigue life performance was also studied. This stochastic technique was based on the combination of a probabilistic finite element method (PFEM) and a cumulative damage approach known as B-model. After 6 million of loading cycles, local failure probabilities of 0.3, 0.4 and 0.91 were predicted for the Lifecore, Avinent and GMI implants, respectively (diameter of 3.75mm). The influence of the diameter for the GMI implant was studied and the results predicted a local failure probability of 0.91 and 0.1 for the 3.75mm and 5mm, respectively. In all cases the highest failure probability was located at the upper screw-threads. Therefore, the probabilistic methodology proposed herein may be a useful tool for performing a qualitative comparison between different commercial dental implants. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Real-time visual biofeedback during weight bearing improves therapy compliance in patients following lower extremity fractures.

Science.gov (United States)

Raaben, Marco; Holtslag, Herman R; Leenen, Luke P H; Augustine, Robin; Blokhuis, Taco J

2018-01-01

Individuals with lower extremity fractures are often instructed on how much weight to bear on the affected extremity. Previous studies have shown limited therapy compliance in weight bearing during rehabilitation. In this study we investigated the effect of real-time visual biofeedback on weight bearing in individuals with lower extremity fractures in two conditions: full weight bearing and touch-down weight bearing. 11 participants with full weight bearing and 12 participants with touch-down weight bearing after lower extremity fractures have been measured with an ambulatory biofeedback system. The participants first walked 15m and the biofeedback system was only used to register the weight bearing. The same protocol was then repeated with real-time visual feedback during weight bearing. The participants could thereby adapt their loading to the desired level and improve therapy compliance. In participants with full weight bearing, real-time visual biofeedback resulted in a significant increase in loading from 50.9±7.51% bodyweight (BW) without feedback to 63.2±6.74%BW with feedback (P=0.0016). In participants with touch-down weight bearing, the exerted lower extremity load decreased from 16.7±9.77kg without feedback to 10.27±4.56kg with feedback (P=0.0718). More important, the variance between individual steps significantly decreased after feedback (P=0.018). Ambulatory monitoring weight bearing after lower extremity fractures showed that therapy compliance is low, both in full and touch-down weight bearing. Real-time visual biofeedback resulted in significantly higher peak loads in full weight bearing and increased accuracy of individual steps in touch-down weight bearing. Real-time visual biofeedback therefore results in improved therapy compliance after lower extremity fractures. Copyright © 2017 Elsevier B.V. All rights reserved.

A method for predicting the fatigue life of pre-corroded 2024-T3 aluminum from breaking load tests

Science.gov (United States)

Gruenberg, Karl Martin

Characterization of material properties is necessary for design purposes and has been a topic of research for many years. Over the last several decades, much progress has been made in identifying metrics to describe fracture mechanics properties and developing procedures to measure the appropriate values. However, in the context of design, there has not been as much success in quantifying the susceptibility of a material to corrosion damage and its subsequent impact on material behavior in the framework of fracture mechanics. A natural next step in understanding the effects of corrosion damage was to develop a link between standard material test procedures and fatigue life in the presence of corrosion. Simply stated, the goal of this investigation was to formulate a cheaper and quicker method for assessing the consequences of corrosion on remaining fatigue life. For this study, breaking load specimens and fatigue specimens of a single nominal gage (0.063″) of aluminum alloy 2024-T3 were exposed to three levels of corrosion. The breaking load specimens were taken from three different material lots, and the fatigue tests were carried out at three stress levels. All failed specimens, both breaking load and fatigue specimens, were examined to characterize the damage state(s) and failure mechanism(s). Correlations between breaking load results and fatigue life results in the presence of corrosion damage were developed using a fracture mechanics foundation and the observed mechanisms of failure. Where breaking load tests showed a decrease in strength due to increased corrosion exposure, the corresponding set of fatigue tests showed a decrease in life. And where breaking load tests from different specimen orientations exhibited similar levels of strength, the corresponding set of fatigue specimens showed similar lives. The spread from shortest to longest fatigue lives among the different corrosion conditions decreased at the higher stress levels. Life predictions based

Using the Enhanced Daily Load Stimulus Model to Quantify the Mechanical Load and Bone Mineral Density Changes Experienced by Crew Members on the International Space Station

Science.gov (United States)

Genc, K. O.; Gopalakrishnan, R.; Kuklis, M. M.; Maender, C. C.; Rice, A. J.; Cavanagh, P. R.

2009-01-01

Despite the use of exercise countermeasures during long-duration space missions, bone mineral density (BMD) and predicted bone strength of astronauts continue to show decreases in the lower extremities and spine. This site-specific bone adaptation is most likely caused by the effects of microgravity on the mechanical loading environment of the crew member. There is, therefore, a need to quantify the mechanical loading experienced on Earth and on-orbit to define the effect of a given "dose" of loading on bone homeostasis. Gene et al. recently proposed an enhanced DLS (EDLS) model that, when used with entire days of in-shoe forces, takes into account recently developed theories on the importance of factors such as saturation, recovery, and standing and their effects on the osteogenic response of bone to daily physical activity. This algorithm can also quantify the tinting and type of activity (sit/unload, stand, walk, run or other loaded activity) performed throughout the day. The purpose of the current study was to use in-shoe force measurements from entire typical work days on Earth and on-orbit in order to quantify the type and amount of loading experienced by crew members. The specific aim was to use these measurements as inputs into the EDLS model to determine activity timing/type and the mechanical "dose" imparted on the musculoskeletal system of crew members and relate this dose to changes in bone homeostasis.

SORM correction of FORM results for the FBC load combination problem

DEFF Research Database (Denmark)

Ditlevsen, Ove

2005-01-01

The old stochastic load combination model of Ferry Borges and Castanheta and the corresponding extreme random load effect value is considered. The evaluation of the distribution function of the extreme value by use of a particular first order reliability method was first described in a celebrated...... calculations. The calculation gives a limit state curvature correction factor on the probability approximation obtained by the RF algorithm. This correction factor is based on Breitung’s celebrated asymptotic formula. Example calculations with comparisons with exact results show an impressing accuracy...

Solar radiation and cooling load calculation for radiant systems: Definition and evaluation of the Direct Solar Load

DEFF Research Database (Denmark)

Causone, Francesco; Corgnati, Stefano P.; Filippi, Marco

2010-01-01

The study of the influence of solar radiation on the built environment is a basic issue in building physics and currently it is extremely important because glazed envelopes are widely used in contemporary architecture. In the present study, the removal of solar heat gains by radiant cooling systems...... is investigated. Particular attention is given to the portion of solar radiation converted to cooling load, without taking part in thermal absorption phenomena due to the thermal mass of the room. This specific component of the cooling load is defined as the Direct Solar Load. A simplified procedure to correctly...... calculate the magnitude of the Direct Solar Load in cooling load calculations is proposed and it is implemented with the Heat Balance method and the Radiant Time Series method. The F ratio of the solar heat gains directly converted to cooling load, in the case of a low thermal mass radiant ceiling...

A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions.

Science.gov (United States)

Zheng, Yang; Zhou, Jianzhong; Xu, Yanhe; Zhang, Yuncheng; Qian, Zhongdong

2017-05-01

This paper proposes a distributed model predictive control based load frequency control (MPC-LFC) scheme to improve control performances in the frequency regulation of power system. In order to reduce the computational burden in the rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are utilized to approximate the predicted control trajectory. The closed-loop stability of the proposed MPC scheme is achieved by adding a terminal equality constraint to the online quadratic optimization and taking the cost function as the Lyapunov function. Furthermore, the treatments of some typical constraints in load frequency control have been studied based on the specific Laguerre-based formulations. Simulations have been conducted in two different interconnected power systems to validate the effectiveness of the proposed distributed MPC-LFC as well as its superiority over the comparative methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Directional analysis of extreme winds under mixed climate conditions

CSIR Research Space (South Africa)

Kruger, A

2013-07-01

Full Text Available Directional statistics provide design engineers with the opportunity to realise considerable cost savings, but these are not yet provided for in the South African standard for wind loading. The development of the directional statistics of extreme...

Instability predictions for circumferentially cracked Type-304 stainless steel pipes under dynamic loading. Volume 2. Appendixes. Final report

International Nuclear Information System (INIS)

Zahoor, A.; Wilkowski, G.; Abou-Sayed, I.; Marschall, C.; Broek, D.; Sampath, S.; Rhee, H.; Ahmad, J.

1982-04-01

This report provides methods to predict margins of safety for circumferentially cracked Type 304 stainless steel pipes subjected to applied bending loads. An integrated combination of experimentation and analysis research was pursued. Two types of experiments were performed: (1) laboratory-scale tests on center-cracked panels and bend specimens to establish the basic mechanical and fracture properties of Type 304 stainless steel, and (2) full-scale pipe fracture tests under quasi-static and dynamic loadings to assess the analysis procedures. Analyses were based upon the simple plastic collapse criterion, a J-estimation procedure, and elastic-plastic large-deformation finite element models

Future residential loads profiles : scenario-based analysis of high penetration of heavy loads and distributed generation

NARCIS (Netherlands)

Asare-Bediako, B.; Kling, W.L.; Ribeiro, P.F.

2014-01-01

Electric load profiles are useful for accurate load forecasting, network planning and optimal generation capacity. They represent electricity demand patterns and are to a large extent predictable. However, new and heavier loads (heat pumps and electric vehicles), distributed generation, and home

Predictive ability of the Society for Vascular Surgery Wound, Ischemia, and foot Infection (WIfI) classification system after first-time lower extremity revascularizations

OpenAIRE

Darling, Jeremy; McCallum, John C.; Soden, Peter A.; Guzman, R.J. (Raul J.); Wyers, M.C. (Mark C.); Hamdan, A.D. (Allen D.); Verhagen, Hence; Schermerhorn, Marc

2017-01-01

markdownabstract__Objective:__ The Society for Vascular Surgery (SVS) Wound, Ischemia and foot Infection (WIfI) classification system was proposed to predict 1-year amputation risk and potential benefit from revascularization. Our goal was to evaluate the predictive ability of this scale in a real-world selection of patients undergoing a first-time lower extremity revascularization for chronic limb-threatening ischemia (CLTI). __Methods:__ From 2005 to 2014, 1336 limbs underwent a first-time ...

Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading

Science.gov (United States)

Gentz, Steven J.; Ordway, David O; Parsons, David S.; Garrison, Craig M.; Rodgers, C. Steven; Collins, Brian W.

2015-01-01

The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (approx. 9 inches from the source) dominated by direct wave propagation, mid-field environment (approx. 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This report documents the outcome of the assessment.

Full Scale Test SSP 34m blade, edgewise loading LTT. Extreme load and PoC_InvE Data report

DEFF Research Database (Denmark)

Nielsen, Magda; Roczek-Sieradzan, Agnieszka; Jensen, Find Mølholt

This report is the second report covering the research and demonstration project “Eksperimentel vingeforskning: Strukturelle mekanismer i nutidens og fremtidens store vinger under kombineret last”, supported by the EUDP program. A 34m wind turbine blade from SSP-Technology A/S has been tested...... in edgewise direction (LTT). The blade has been submitted to thorough examination by means of strain gauges, displacement transducers and a 3D optical measuring system. This data report presents results obtained during full scale testing of the blade up to 80% Risø load, where 80% Risø load corresponds to 100...... stresses in the adhesive joints. Test results from measurements with the reinforcement have been compared to results without the coupling. The report presents only the relevant results for the 80% Risø load and the results applicable for the investigation of the influence of the invention on the profile...

Muscle Strength Is a Poor Screening Test for Predicting Lower Extremity Injuries in Professional Male Soccer Players: A 2-Year Prospective Cohort Study.

Science.gov (United States)

Bakken, Arnhild; Targett, Stephen; Bere, Tone; Eirale, Cristiano; Farooq, Abdulaziz; Mosler, Andrea B; Tol, Johannes L; Whiteley, Rod; Khan, Karim M; Bahr, Roald

2018-03-01

Lower extremity muscle strength tests are commonly used to screen for injury risk in professional soccer. However, there is limited evidence on the ability of such tests in predicting future injuries. To examine the association between hip and thigh muscle strength and the risk of lower extremity injuries in professional male soccer players. Case-control study; Level of evidence, 3. Professional male soccer players from 14 teams in Qatar underwent a comprehensive strength assessment at the beginning of the 2013/2014 and 2014/2015 seasons. Testing consisted of concentric and eccentric quadriceps and hamstring isokinetic peak torques, eccentric hip adduction and abduction forces, and bilateral isometric adductor force (squeeze test at 45°). Time-loss injuries and exposure in training and matches were registered prospectively by club medical staff throughout each season. Univariate and multivariate Cox regression analyses were used to calculate hazard ratios (HRs) with 95% CIs. In total, 369 players completed all strength tests and had registered injury and exposure data. Of these, 206 players (55.8%) suffered 538 lower extremity injuries during the 2 seasons; acute muscle injuries were the most frequent. Of the 20 strength measures examined, greater quadriceps concentric peak torque at 300 deg/s (HR, 1.005 [95% CI, 1.00-1.01]; P = .037) was the only strength measure identified as significantly associated with a risk of lower extremity injuries in multivariate analysis. Greater quadriceps concentric peak torque at 60 deg/s (HR, 1.004 [95% CI, 1.00-1.01]; P = .026) was associated with the risk of overuse injuries, and greater bilateral adductor strength adjusted for body weight (HR, 0.75 [95% CI, 0.57-0.97; P = .032) was associated with a lower risk for any knee injury. Receiver operating characteristic curve analyses indicated poor predictive ability of the significant strength variables (area under the curve, 0.45-0.56). There was a weak association with the risk of

Prediction of pavement remaining service life based on repetition of load and permanent deformation

Science.gov (United States)

Usman, R. S.; Setyawan, A.; Suprapto, M.

2018-03-01

One of the methods which was applied in the assessment of flexible pavement performance was mechanistic method assuming structures of road pavement to become multi-layer structure for flexible pavement, that the vehicle load working on the pavement layer under repetition with power failure worth 1 (one) unit which was assumed as evenly distributed static load, and therefore the pavement material would provide response in the form of stress, strain, and deflection. This is closely related in order to assess the structure of flexible pavement and to predict the remaining service life on the roads of Pulau Indah sta 0 + 000 to sta. 0 + 845 in Kota Kupang, Nusa Tenggara Timur. The performance appraisal indicator which was used was fatigue cracking happening bottom of the asphalt layer and permanent deformation (rutting) on the surface of subgrade. The strain estimate on the flexible pavement layer structure needs carefulness and high accuracy and therefore a software like KENPAVE which produces horizontal tensile strain of 8,802E-05 and vertical compressive strain of 2,642E-04 was used. By applying equation of The Asphalt Instituteit was obtained repetition of permit load when reaching fatigue cracking (Nf) was 16.071.516 ESAL and permanent deformation (rutting) was 14.703.867 ESAL and also it was predicted the remaining service life of pavement applied the equation of AASTHO 1993 by considering Traffic Multiplier factor (TM 1.8, TM 1.9 and TM 2.0) obtained the remaining life service due to fatigue of 5.51% in the year of 13th (TM 1.8), 7.95% in the year of12th (TM 1.9) and 3.11% (TM 2.0) in the year of 12th, also the remaining service life due to rutting of 4.69% in the year of 12th(TM 1.8), 7.79% in the year of 11th (TM 1.9), and 2.94 in the year of 11th (TM 2.0).

Assessment of the response of reinforced concrete structural members to aircraft crash impact loading

International Nuclear Information System (INIS)

Brandes, K.

1988-01-01

For the containments of nuclear power plants in the Federal Republic of Germany, the loading caused by a striking military aircraft decisively influences their design. The low probability of occurrence of this loading can be associated with an increase of consequences and reduced safety margins used in the design of structures. This requires that the actual response of structures to extreme loads has to be predicted with a higher degree of confidence than is typically the case with conventional structures. The adequacy of the computer-oriented mechanical-mathematical methods and the associated computer-codes used in the design of nuclear power plants can only be verified by comparing results from both, analytical and experimental studies. After having confirmed the adequacy of the analytical tools, a probabilistic risk analysis can be sufficiently performed. A series of tests is described which have been performed with the aim to improve the mechanical and physical modelling of RC-structures. Furthermore, a case study is presented to evolve a feeling for the safety margins which are implicitely included in the deterministic design calculation. (orig.)

Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation

Science.gov (United States)

Tiebin, Wu; Yunlian, Liu; Xinjun, Li; Yi, Yu; Bin, Zhang

2018-06-01

Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

Science.gov (United States)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Full Scale Test of SSP 34m blade, edgewise loading LTT

DEFF Research Database (Denmark)

Nielsen, Magda; Jensen, Find Mølholt; Nielsen, Per Hørlyk

This report is a part of the research project “Eksperimentel vingeforskning: Strukturelle mekanismer i nutidens og fremtidens store vinger under kombineret last” where a 34m wind turbine blade from SSP-Technology A/S has been tested in edgewise direction (LTT). The applied load is 60......% of an unrealistic extreme event, corresponding to 75% of a certificated extreme load. This report describes the background, the test set up, the tests and the results. For this project, a new solution has been used for the load application and the solution for the load application is described in this report...... as well. The blade has been submitted to thorough examination. More areas have been examined with DIC, both global and local deflections have been measured, and also 378 strain gauge measurements have been performed. Furthermore Acoustic Emission has been used in order to detect damage while testing new...

Extreme climatic events in relation to global change and their impact on life histories

Directory of Open Access Journals (Sweden)

Juan MORENO, Anders Pape Møller

2011-06-01

Full Text Available Extreme weather conditions occur at an increasing rate as evidenced by higher frequency of hurricanes and more extreme precipitation and temperature anomalies. Such extreme environmental conditions will have important implications for all living organisms through greater frequency of reproductive failure and reduced adult survival. We review examples of reproductive failure and reduced survival related to extreme weather conditions. Phenotypic plasticity may not be sufficient to allow adaptation to extreme weather for many animals. Theory predicts reduced reproductive effort as a response to increased stochasticity. We predict that patterns of natural selection will change towards truncation selection as environmental conditions become more extreme. Such changes in patterns of selection may facilitate adaptation to extreme events. However, effects of selection on reproductive effort are difficult to detect. We present a number of predictions for the effects of extreme weather conditions in need of empirical tests. Finally, we suggest a number of empirical reviews that could improve our ability to judge the effects of extreme environmental conditions on life history [Current Zoology 57 (3: 375–389, 2011].

Risk assessment of precipitation extremes in northern Xinjiang, China

Science.gov (United States)

Yang, Jun; Pei, Ying; Zhang, Yanwei; Ge, Quansheng

2018-05-01

This study was conducted using daily precipitation records gathered at 37 meteorological stations in northern Xinjiang, China, from 1961 to 2010. We used the extreme value theory model, generalized extreme value (GEV) and generalized Pareto distribution (GPD), statistical distribution function to fit outputs of precipitation extremes with different return periods to estimate risks of precipitation extremes and diagnose aridity-humidity environmental variation and corresponding spatial patterns in northern Xinjiang. Spatiotemporal patterns of daily maximum precipitation showed that aridity-humidity conditions of northern Xinjiang could be well represented by the return periods of the precipitation data. Indices of daily maximum precipitation were effective in the prediction of floods in the study area. By analyzing future projections of daily maximum precipitation (2, 5, 10, 30, 50, and 100 years), we conclude that the flood risk will gradually increase in northern Xinjiang. GEV extreme value modeling yielded the best results, proving to be extremely valuable. Through example analysis for extreme precipitation models, the GEV statistical model was superior in terms of favorable analog extreme precipitation. The GPD model calculation results reflect annual precipitation. For most of the estimated sites' 2 and 5-year T for precipitation levels, GPD results were slightly greater than GEV results. The study found that extreme precipitation reaching a certain limit value level will cause a flood disaster. Therefore, predicting future extreme precipitation may aid warnings of flood disaster. A suitable policy concerning effective water resource management is thus urgently required.

A method of predicting the reliability of CDM coil insulation

International Nuclear Information System (INIS)

Kytasty, A.; Ogle, C.; Arrendale, H.

1992-01-01

This paper presents a method of predicting the reliability of the Collider Dipole Magnet (CDM) coil insulation design. The method proposes a probabilistic treatment of electrical test data, stress analysis, material properties variability and loading uncertainties to give the reliability estimate. The approach taken to predict reliability of design related failure modes of the CDM is to form analytical models of the various possible failure modes and their related mechanisms or causes, and then statistically assess the contributions of the various contributing variables. The probability of the failure mode occurring is interpreted as the number of times one would expect certain extreme situations to combine and randomly occur. One of the more complex failure modes of the CDM will be used to illustrate this methodology

The Load Level of Modern Wind Turbines according to IEC 61400-1

International Nuclear Information System (INIS)

Freudenreich, K; Argyriadis, K

2007-01-01

The paper describes some effects on the load level of state-of-the art multi megawatt wind turbines introduced by the new edition of the standard IEC 61400-1:2005 W ind Turbines - Part 1: Design requirements . Compared to the previous edition, especially the extreme load determination has been modified by applying stochastic and statistical analyses. Within this paper the effect on the overall load level of wind turbines is demonstrated and occurring problems are discussed. Load simulations have been carried out for four state-of-the-art multi-megawatt wind turbines of different design concepts and from different manufacturers. The blade root bending moments and tip deflection have been determined by applying different extrapolation methods. Advantages and disadvantages of these methods and tail fittings for different load components and wind turbine technologies are discussed and interpreted. Further on, the application of the extreme turbulence model is demonstrated. The dependence of the load level on the turbulence intensity and control system, as well as the interaction with extrapolated loads is discussed and limitations outlined. The obtained load level is compared to the overall load level of the turbines according to the previous edition of the standard, IEC 61400-1:1999

Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data

Science.gov (United States)

Ockenden, Mary C.; Tych, Wlodek; Beven, Keith J.; Collins, Adrian L.; Evans, Robert; Falloon, Peter D.; Forber, Kirsty J.; Hiscock, Kevin M.; Hollaway, Michael J.; Kahana, Ron; Macleod, Christopher J. A.; Villamizar, Martha L.; Wearing, Catherine; Withers, Paul J. A.; Zhou, Jian G.; Benskin, Clare McW. H.; Burke, Sean; Cooper, Richard J.; Freer, Jim E.; Haygarth, Philip M.

2017-12-01

Excess nutrients in surface waters, such as phosphorus (P) from agriculture, result in poor water quality, with adverse effects on ecological health and costs for remediation. However, understanding and prediction of P transfers in catchments have been limited by inadequate data and over-parameterised models with high uncertainty. We show that, with high temporal resolution data, we are able to identify simple dynamic models that capture the P load dynamics in three contrasting agricultural catchments in the UK. For a flashy catchment, a linear, second-order (two pathways) model for discharge gave high simulation efficiencies for short-term storm sequences and was useful in highlighting uncertainties in out-of-bank flows. A model with non-linear rainfall input was appropriate for predicting seasonal or annual cumulative P loads where antecedent conditions affected the catchment response. For second-order models, the time constant for the fast pathway varied between 2 and 15 h for all three catchments and for both discharge and P, confirming that high temporal resolution data are necessary to capture the dynamic responses in small catchments (10-50 km2). The models led to a better understanding of the dominant nutrient transfer modes, which will be helpful in determining phosphorus transfers following changes in precipitation patterns in the future.

Loading Analysis of Composite Wind Turbine Blade for Fatigue Life Prediction of Adhesively Bonded Root Joint

Science.gov (United States)

Salimi-Majd, Davood; Azimzadeh, Vahid; Mohammadi, Bijan

2015-06-01

Nowadays wind energy is widely used as a non-polluting cost-effective renewable energy resource. During the lifetime of a composite wind turbine which is about 20 years, the rotor blades are subjected to different cyclic loads such as aerodynamics, centrifugal and gravitational forces. These loading conditions, cause to fatigue failure of the blade at the adhesively bonded root joint, where the highest bending moments will occur and consequently, is the most critical zone of the blade. So it is important to estimate the fatigue life of the root joint. The cohesive zone model is one of the best methods for prediction of initiation and propagation of debonding at the root joint. The advantage of this method is the possibility of modeling the debonding without any requirement to the remeshing. However in order to use this approach, it is necessary to analyze the cyclic loading condition at the root joint. For this purpose after implementing a cohesive interface element in the Ansys finite element software, one blade of a horizontal axis wind turbine with 46 m rotor diameter was modelled in full scale. Then after applying loads on the blade under different condition of the blade in a full rotation, the critical condition of the blade is obtained based on the delamination index and also the load ratio on the root joint in fatigue cycles is calculated. These data are the inputs for fatigue damage growth analysis of the root joint by using CZM approach that will be investigated in future work.

Diagnostic value of microRNA-143 in predicting in-stent restenosis for patients with lower extremity arterial occlusive disease

OpenAIRE

Yu, Zhi-Hai; Wang, Hai-Tao; Tu, Can

2017-01-01

Purpose This study was conducted to explore the diagnostic value of microRNA-143 (miRNA-143) in predicting in-stent restenosis (ISR) of lower extremity arterial occlusive disease (LEAOD). Methods From February 2012 to March 2015, 165 patients (112 males and 53 females) with LEAOD undergoing interventional treatment were enrolled in this study. Serum miRNA-143 expression was detected using quantitative real-time polymerase chain reaction (qRT-PCR). Patients were assigned into the restenosis an...

Comparison of Damage Models for Predicting the Non-Linear Response of Laminates Under Matrix Dominated Loading Conditions

Science.gov (United States)

Schuecker, Clara; Davila, Carlos G.; Rose, Cheryl A.

2010-01-01

Five models for matrix damage in fiber reinforced laminates are evaluated for matrix-dominated loading conditions under plane stress and are compared both qualitatively and quantitatively. The emphasis of this study is on a comparison of the response of embedded plies subjected to a homogeneous stress state. Three of the models are specifically designed for modeling the non-linear response due to distributed matrix cracking under homogeneous loading, and also account for non-linear (shear) behavior prior to the onset of cracking. The remaining two models are localized damage models intended for predicting local failure at stress concentrations. The modeling approaches of distributed vs. localized cracking as well as the different formulations of damage initiation and damage progression are compared and discussed.

CFD-based design load analysis of 5MW offshore wind turbine

Science.gov (United States)

Tran, T. T.; Ryu, G. J.; Kim, Y. H.; Kim, D. H.

2012-11-01

The structure and aerodynamic loads acting on NREL 5MW reference wind turbine blade are calculated and analyzed based on advanced Computational Fluid Dynamics (CFD) and unsteady Blade Element Momentum (BEM). A detailed examination of the six force components has been carried out (three force components and three moment components). Structure load (gravity and inertia load) and aerodynamic load have been obtained by additional structural calculations (CFD or BEM, respectively,). In CFD method, the Reynolds Average Navier-Stokes approach was applied to solve the continuity equation of mass conservation and momentum balance so that the complex flow around wind turbines was modeled. Written in C programming language, a User Defined Function (UDF) code which defines transient velocity profile according to the Extreme Operating Gust condition was compiled into commercial FLUENT package. Furthermore, the unsteady BEM with 3D stall model has also adopted to investigate load components on wind turbine rotor. The present study introduces a comparison between advanced CFD and unsteady BEM for determining load on wind turbine rotor. Results indicate that there are good agreements between both present methods. It is importantly shown that six load components on wind turbine rotor is significant effect under Extreme Operating Gust (EOG) condition. Using advanced CFD and additional structural calculations, this study has succeeded to construct accuracy numerical methodology to estimate total load of wind turbine that compose of aerodynamic load and structure load.

Extreme loads seismic testing of conduit systems

International Nuclear Information System (INIS)

Howard, G.E.; Ibanez, P.; Harrison, S.; Shi, Z.T.

1991-01-01

Rigid steel conduit (thin-wall tubes with threaded connections) containing electrical cabling are a common feature in nuclear power plants. Conduit systems are in many cases classified in U.S.A. practice as Seismic Category I structures. this paper summarizes results and others aspects of a dynamic test program conducted to investigate conduit systems seismic performance under three-axis excitation for designs representative at a nuclear power plant sited near Ft. Worth, Texas (a moderate seismic zone), with a Safe Shutdown Earthquake (SSE) of 0.12 g. Test specimens where subjected to postulated seismic events, including excitation well in excess of Safe Shutdown Earthquake events typical for U.S.A. nuclear power stations. A total of 18 conduit systems of 9-meter nominal lengths were shake table mounted and subjected to a variety of tests. None of the specimens suffered loss of load capacity when subjected to a site-enveloping Safe Shutdown Earthquake (SSE). Clamp/attachment hardware failures only began to occur when earthquake input motion was scaled upward to minimum values of 2.3-4.6 times site enveloping SSE response spectra. Tensile and/or shear failure of clamp attachment bolts or studs was the failure mode in all case in which failure was induced. (author)

Instability predictions for circumferentially cracked Type-304 stainless steel pipes under dynamic loading. Volume 2. Appendixes. Final report. [BWR

Energy Technology Data Exchange (ETDEWEB)

Zahoor, A.; Wilkowski, G.; Abou-Sayed, I.; Marschall, C.; Broek, D.; Sampath, S.; Rhee, H.; Ahmad, J.

1982-04-01

This report provides methods to predict margins of safety for circumferentially cracked Type 304 stainless steel pipes subjected to applied bending loads. An integrated combination of experimentation and analysis research was pursued. Two types of experiments were performed: (1) laboratory-scale tests on center-cracked panels and bend specimens to establish the basic mechanical and fracture properties of Type 304 stainless steel, and (2) full-scale pipe fracture tests under quasi-static and dynamic loadings to assess the analysis procedures. Analyses were based upon the simple plastic collapse criterion, a J-estimation procedure, and elastic-plastic large-deformation finite element models.

Research on dynamic creep strain and settlement prediction under the subway vibration loading.

Science.gov (United States)

Luo, Junhui; Miao, Linchang

2016-01-01

This research aims to explore the dynamic characteristics and settlement prediction of soft soil. Accordingly, the dynamic shear modulus formula considering the vibration frequency was utilized and the dynamic triaxial test conducted to verify the validity of the formula. Subsequently, the formula was applied to the dynamic creep strain function, with the factors influencing the improved dynamic creep strain curve of soft soil being analyzed. Meanwhile, the variation law of dynamic stress with sampling depth was obtained through the finite element simulation of subway foundation. Furthermore, the improved dynamic creep strain curve of soil layer was determined based on the dynamic stress. Thereafter, it could to estimate the long-term settlement under subway vibration loading by norms. The results revealed that the dynamic shear modulus formula is straightforward and practical in terms of its application to the vibration frequency. The values predicted using the improved dynamic creep strain formula closed to the experimental values, whilst the estimating settlement closed to the measured values obtained in the field test.

Transient Vibration Prediction for Rotors on Ball Bearings Using Load-dependent Non-linear Bearing Stiffness

Science.gov (United States)

Fleming, David P.; Poplawski, J. V.

2002-01-01

Rolling-element bearing forces vary nonlinearly with bearing deflection. Thus an accurate rotordynamic transient analysis requires bearing forces to be determined at each step of the transient solution. Analyses have been carried out to show the effect of accurate bearing transient forces (accounting for non-linear speed and load dependent bearing stiffness) as compared to conventional use of average rolling-element bearing stiffness. Bearing forces were calculated by COBRA-AHS (Computer Optimized Ball and Roller Bearing Analysis - Advanced High Speed) and supplied to the rotordynamics code ARDS (Analysis of Rotor Dynamic Systems) for accurate simulation of rotor transient behavior. COBRA-AHS is a fast-running 5 degree-of-freedom computer code able to calculate high speed rolling-element bearing load-displacement data for radial and angular contact ball bearings and also for cylindrical and tapered roller beatings. Results show that use of nonlinear bearing characteristics is essential for accurate prediction of rotordynamic behavior.

Puget Sound Area Electric Reliability Plan. Appendix D, Conservation, Load Management and Fuel Switching Analysis : Draft Environmental Impact Statement.

Energy Technology Data Exchange (ETDEWEB)

United States. Bonneville Power Administration.

1991-09-01

Various conservation, load management, and fuel switching programs were considered as ways to reduce or shift system peak load. These programs operate at the end-use level, such as residential water heat. Figure D-1a shows what electricity consumption for water heat looks like on normal and extreme peak days. Load management programs, such as water heat control, are designed to reduce electricity consumption at the time of system peak. On the coldest day in average winter, system load peaks near 8:00 a.m. In a winter with extremely cold weather, electricity consumption increases fr all hours, and the system peak shifts to later in the morning. System load shapes in the Puget Sound area are shown in Figure D-1b for a normal winter peak day (February 2, 1988) and extreme peak day (February 3, 1989). Peak savings from any program are calculated to be the reduction in loads on the entire system at the hour of system peak. Peak savings for all programs are measured at 8:00 a.m. on a normal peak day and 9:00 a.m. on an extreme peak day. On extremely cold day, some water heat load shifts to much later in the morning, with less load available for shedding at the time of system peak. Models of hourly end-use consumption were constructed to simulate the impact of conservation, land management, and fuel switching programs on electricity consumption. Javelin, a time-series simulating package for personal computers, was chosen for the hourly analysis. Both a base case and a program case were simulated. 15 figs., 7 tabs.

Corrosion pit depth extreme value prediction from limited inspection data

International Nuclear Information System (INIS)

Najjar, D.; Bigerelle, M.; Iost, A.; Bourdeau, L.; Guillou, D.

2004-01-01

Passive alloys like stainless steels are prone to localized corrosion in chlorides containing environments. The greater the depth of the localized corrosion phenomenon, the more dramatic the related damage that can lead to a structure weakening by fast perforation. In practical situations, because measurements are time consuming and expensive, the challenge is usually to predict the maximum pit depth that could be found in a large scale installation from the processing of a limited inspection data. As far as the parent distribution of pit depths is assumed to be of exponential type, the most successful method was found in the application of the statistical extreme-value analysis developed by Gumbel. This study aims to present a new and alternative methodology to the Gumbel approach with a view towards accurately estimating the maximum pit depth observed on a ferritic stainless steel AISI 409 subjected to an accelerated corrosion test (ECC1) used in automotive industry. This methodology consists in characterising and modelling both the morphology of pits and the statistical distribution of their depths from a limited inspection dataset. The heart of the data processing is based on the combination of two recent statistical methods that avoid making any choice about the type of the theoretical underlying parent distribution of pit depths: the Generalized Lambda Distribution (GLD) is used to model the distribution of pit depths and the Bootstrap technique to determine a confidence interval on the maximum pit depth. (authors)

Model-Based Load Estimation for Predictive Condition Monitoring of Wind Turbines

DEFF Research Database (Denmark)

Perisic, Nevena; Pederen, Bo Juul; Grunnet, Jacob Deleuran

signal is performed online, and a Load Indicator Signal (LIS) is formulated as a ratio between current estimated accumulated fatigue loads and its expected value based only on a priori knowledge (WTG dynamics and wind climate). LOT initialisation is based on a priori knowledge and can be obtained using...... programme for pre-maintenance actions. The performance of LOT is demonstrated by applying it to one of the most critical WTG components, the gearbox. Model-based load CMS for gearbox requires only standard WTG SCADA data. Direct measuring of gearbox fatigue loads requires high cost and low reliability...... measurement equipment. Thus, LOT can significantly reduce the price of load monitoring....

Performance of synchrotron x-ray monochromators under heat load: How reliable are the predictions?

International Nuclear Information System (INIS)

Freund, A.K.; Hoszowska, J.; Migliore, J.-S.; Mocella, V.; Zhang, L.; Ferrero, C.

2000-01-01

With the ongoing development of insertion devices with smaller gaps the heat load generated by modern synchrotron sources increases continuously. To predict the overall performance of experiments on beam lines it is of crucial importance to be able to predict the efficiency of x-ray optics and in particular that of crystal monochromators. We report on a detailed comparison between theory and experiment for a water-cooled silicon crystal exposed to bending magnet radiation of up to 237 W total power and 1.3 W/mm2 power density. The thermal deformation has been calculated by the code ANSYS and its output has been injected into a finite difference code based on the Takagi-Taupin diffraction theory for distorted crystals. Several slit settings, filters and reflection orders were used to vary the geometrical conditions and the x-ray penetration depth in the crystal. In general, good agreement has been observed between the calculated and the observed values for the rocking curve width

Multivariable predictive control considering time delay for load-frequency control in multi-area power systems

Directory of Open Access Journals (Sweden)

Daniar Sabah

2016-12-01

Full Text Available In this paper, a multivariable model based predictive control (MPC is proposed for the solution of load frequency control (LFC in a multi-area interconnected power system. The proposed controller is designed to consider time delay, generation rate constraint and multivariable nature of the LFC system, simultaneously. A new formulation of the MPC is presented to compensate time delay. The generation rate constraint is considered by employing a constrained MPC and economic allocation of the generation is further guaranteed by an innovative modification in the predictive control objective function. The effectiveness of proposed scheme is verified through time-based simulations on the standard 39-bus test system and the responses are then compared with the proportional-integral controller. The evaluation of the results reveals that the proposed control scheme offers satisfactory performance with fast responses.

Thermostat Controlled Loads Flexibility Assessment for Enabling Load Shifting – An Experimental Proof in a Low Voltage Grid

DEFF Research Database (Denmark)

Lakshmanan, Venkatachalam; Marinelli, Mattia; Kosek, Anna M.

2017-01-01

This paper investigates the usability of thermostat controlled domestic appliances for load shift in LV distribution grids. The proposed method uses refrigerators for the demonstration of adaptive load prediction to estimate its flexibility and perform scheduling based on load threshold limit. Tw...

Daily Nigerian peak load forecasting using artificial neural network ...

African Journals Online (AJOL)

A daily peak load forecasting technique that uses artificial neural network with seasonal indices is presented in this paper. A neural network of relatively smaller size than the main prediction network is used to predict the daily peak load for a period of one year over which the actual daily load data are available using one ...

Turbulence and turbulence-generated structural loading in wind turbine clusters

DEFF Research Database (Denmark)

Frandsen, Sten Tronæs

2007-01-01

of the model is that it became part of the Danish standard for wind turbine design DS 472 (2001) in August 2001 and it is part of the corresponding international standard, IEC61400-1 (2005). Also, extreme loading under normal operation for wake conditions and the efficiency of very large wind farms......Turbulence - in terms of standard deviation of wind speed fluctuations - and other flow characteristics are different in the interior of wind farms relative to the free flow and action must be taken to ensure sufficient structural sustainability of the wind turbines exposed to “wind farm flow......”. The standard deviation of wind speed fluctuations is a known key parameter for both extreme- and fatigue loading, and it is argued and found to be justified that a model for change in turbulence intensity alone may account for increased fatigue loading in wind farms. Changes in scale of turbulence...

Nonlinear kinematic hardening under non-proportional loading

International Nuclear Information System (INIS)

Ottosen, N.S.

1979-07-01

Within the framework of conventional plasticity theory, it is first determined under which conditions Melan-Prager's and Ziegler's kinematic hardening rules result in identical material behaviour. Next, assuming initial isotropy and adopting the von Mises yield criterion, a nonlinear kinematic hardening function is proposed for prediction of metal behaviour. The model assumes that hardening at a specific stress point depends on the direction of the new incremental loading. Hereby a realistic response is obtained for general reversed loading, and a smooth behaviour is assured, even when loading deviates more and more from proportional loading and ultimately results in reversed loading. The predictions of the proposed model for non-proportional loading under plane stress conditions are compared with those of the classical linear kinematic model, the isotropic model and with published experimental data. Finally, the limitations of the proposaed model are discussed. (author)

Ultramarathon is an outstanding model for the study of adaptive responses to extreme load and stress

Directory of Open Access Journals (Sweden)

Millet Grégoire P

2012-07-01

Full Text Available Abstract Ultramarathons comprise any sporting event involving running longer than the traditional marathon length of 42.195 km (26.2 miles. Studies on ultramarathon participants can investigate the acute consequences of ultra-endurance exercise on inflammation and cardiovascular or renal consequences, as well as endocrine/energetic aspects, and examine the tissue recovery process over several days of extreme physical load. In a study published in BMC Medicine, Schütz et al. followed 44 ultramarathon runners over 4,487 km from South Italy to North Cape, Norway (the Trans Europe Foot Race 2009 and recorded daily sets of data from magnetic resonance imaging, psychometric, body composition and biological measurements. The findings will allow us to better understand the timecourse of degeneration/regeneration of some lower leg tissues such as knee joint cartilage, to differentiate running-induced from age-induced pathologies (for example, retropatelar arthritis and finally to assess the interindividual susceptibility to injuries. Moreover, it will also provide new information about the complex interplay between cerebral adaptations/alterations and hormonal influences resulting from endurance exercise and provide data on the dose-response relationship between exercise and brain structure/function. Overall, this study represents a unique attempt to investigate the limits of the adaptive response of human bodies. Please see related article: http://www.biomedcentral.com/1741-7015/10/78

Extreme Rainfall Mechanisms Exhibited by Typhoon Morakot (2009

Directory of Open Access Journals (Sweden)

Ching-Yuang Huang

2011-01-01

Full Text Available Moderate Typhoon Morakot (2009 became the most catastrophic typhoon in Taiwan on record. The MM5 numerical experiments with and without bogus data assimilation (BDA were used to investigate the extreme rainfall mechanisms in Taiwan associated with the westbound typhoon. The BDA, based on 4DVAR, helps MM5 to maintain a more consolidated typhoon vortex and better predict the observed track after landfall, thus producing realistic extreme rainfall (about 2400 mm at the southern and Central Mountain Range (CMR of Taiwan. Severe rainfall in Taiwan is dominated by the CMR that hence modulates rainfall predictability.

Load-related brain activation predicts spatial working memory performance in youth aged 9-12 and is associated with executive function at earlier ages.

Science.gov (United States)

Huang, Anna S; Klein, Daniel N; Leung, Hoi-Chung

2016-02-01

Spatial working memory is a central cognitive process that matures through adolescence in conjunction with major changes in brain function and anatomy. Here we focused on late childhood and early adolescence to more closely examine the neural correlates of performance variability during this important transition period. Using a modified spatial 1-back task with two memory load conditions in an fMRI study, we examined the relationship between load-dependent neural responses and task performance in a sample of 39 youth aged 9-12 years. Our data revealed that between-subject differences in task performance was predicted by load-dependent deactivation in default network regions, including the ventral anterior cingulate cortex (vACC) and posterior cingulate cortex (PCC). Although load-dependent increases in activation in prefrontal and posterior parietal regions were only weakly correlated with performance, increased prefrontal-parietal coupling was associated with better performance. Furthermore, behavioral measures of executive function from as early as age 3 predicted current load-dependent deactivation in vACC and PCC. These findings suggest that both task positive and task negative brain activation during spatial working memory contributed to successful task performance in late childhood/early adolescence. This may serve as a good model for studying executive control deficits in developmental disorders. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Load-related brain activation predicts spatial working memory performance in youth aged 9–12 and is associated with executive function at earlier ages

Science.gov (United States)

Huang, Anna S.; Klein, Daniel N.; Leung, Hoi-Chung

2015-01-01

Spatial working memory is a central cognitive process that matures through adolescence in conjunction with major changes in brain function and anatomy. Here we focused on late childhood and early adolescence to more closely examine the neural correlates of performance variability during this important transition period. Using a modified spatial 1-back task with two memory load conditions in an fMRI study, we examined the relationship between load-dependent neural responses and task performance in a sample of 39 youth aged 9–12 years. Our data revealed that between-subject differences in task performance was predicted by load-dependent deactivation in default network regions, including the ventral anterior cingulate cortex (vACC) and posterior cingulate cortex (PCC). Although load-dependent increases in activation in prefrontal and posterior parietal regions were only weakly correlated with performance, increased prefrontal-parietal coupling was associated with better performance. Furthermore, behavioral measures of executive function from as early as age 3 predicted current load-dependent deactivation in vACC and PCC. These findings suggest that both task positive and task negative brain activation during spatial working memory contributed to successful task performance in late childhood/early adolescence. This may serve as a good model for studying executive control deficits in developmental disorders. PMID:26562059

Cognitive Demands Influence Lower Extremity Mechanics During a Drop Vertical Jump Task in Female Athletes.

Science.gov (United States)

Almonroeder, Thomas Gus; Kernozek, Thomas; Cobb, Stephen; Slavens, Brooke; Wang, Jinsung; Huddleston, Wendy

2018-05-01

Study Design Cross-sectional study. Background The drop vertical jump task is commonly used to screen for anterior cruciate ligament injury risk; however, its predictive validity is limited. The limited predictive validity of the drop vertical jump task may be due to not imposing the cognitive demands that reflect sports participation. Objectives To investigate the influence of additional cognitive demands on lower extremity mechanics during execution of the drop vertical jump task. Methods Twenty uninjured women (age range, 18-25 years) were required to perform the standard drop vertical jump task, as well as drop vertical jumps that included additional cognitive demands. The additional cognitive demands were related to attending to an overhead goal (ball suspended overhead) and/or temporal constraints on movement selection (decision making). Three-dimensional ground reaction forces and lower extremity mechanics were compared between conditions. Results The inclusion of the overhead goal resulted in higher peak vertical ground reaction forces and lower peak knee flexion angles in comparison to the standard drop vertical jump task. In addition, participants demonstrated greater peak knee abduction angles when trials incorporated temporal constraints on decision making and/or required participants to attend to an overhead goal, in comparison to the standard drop vertical jump task. Conclusion Imposing additional cognitive demands during execution of the drop vertical jump task influenced lower extremity mechanics in a manner that suggested increased loading of the anterior cruciate ligament. Tasks utilized in anterior cruciate ligament injury risk screening may benefit from more closely reflecting the cognitive demands of the sports environment. J Orthop Sports Phys Ther 2018;48(5):381-387. Epub 10 Jan 2018. doi:10.2519/jospt.2018.7739.

A simplified model predicting the weight of the load carrying beam in a wind turbine blade

DEFF Research Database (Denmark)

Mikkelsen, Lars Pilgaard

2016-01-01

from 20 to 90 m. In addition, it can be seen that for a blade using glass fibre reinforced polymers, the design is controlled by the deflection and thereby the material stiffness in order to avoid the blade to hit the tower. On the other hand if using aluminium, the design will be controlled...... to predict the weight of the load carrying beam when using glassfibre reinforced polymers, carbon fibre reinforced polymers or an aluminium alloy as the construction material. Thereby, it is found that the weight of a glass fibre wind turbine blade is increased from 0.5 to 33 tons when the blade length grows...... by the fatigue resistance in orderto making the material survive the 100 to 500 million load cycles experience of the windturbine blade throughout the lifetime. The aluminium blade is also found to be considerably heavier compared with the composite blades....

A coupled diffusion-fluid pressure model to predict cell density distribution for cells encapsulated in a porous hydrogel scaffold under mechanical loading.

Science.gov (United States)

Zhao, Feihu; Vaughan, Ted J; Mc Garrigle, Myles J; McNamara, Laoise M

2017-10-01

Tissue formation within tissue engineering (TE) scaffolds is preceded by growth of the cells throughout the scaffold volume and attachment of cells to the scaffold substrate. It is known that mechanical stimulation, in the form of fluid perfusion or mechanical strain, enhances cell differentiation and overall tissue formation. However, due to the complex multi-physics environment of cells within TE scaffolds, cell transport under mechanical stimulation is not fully understood. Therefore, in this study, we have developed a coupled multiphysics model to predict cell density distribution in a TE scaffold. In this model, cell transport is modelled as a thermal conduction process, which is driven by the pore fluid pressure under applied loading. As a case study, the model is investigated to predict the cell density patterns of pre-osteoblasts MC3T3-e1 cells under a range of different loading regimes, to obtain an understanding of desirable mechanical stimulation that will enhance cell density distribution within TE scaffolds. The results of this study have demonstrated that fluid perfusion can result in a higher cell density in the scaffold region closed to the outlet, while cell density distribution under mechanical compression was similar with static condition. More importantly, the study provides a novel computational approach to predict cell distribution in TE scaffolds under mechanical loading. Copyright © 2017 Elsevier Ltd. All rights reserved.

Design of a model predictive load-following controller by discrete optimization of control rod speed for PWRs

International Nuclear Information System (INIS)

Kim, Jae Hwan; Park, Soon Ho; Na, Man Gyun

2014-01-01

Highlights: • A model predictive controller for load-following operation was developed. • Genetic algorithm optimizes the five nonlinear discrete control rod speeds. • The boron concentration is adjusted with automatic adjustment logic. • The proposed controller reflects the realistic control rod drive mechanism movement. • The performance was confirmed to be satisfactory by simulation from BOC to EOC. - Abstract: Currently, most existing nuclear power plants alter the reactor power by adjusting the boron concentration in the coolant because it has a smaller effect on the reactor power distribution. Frequent control rod movements for load-following operation induce xenon-oscillation. Therefore, a controller that can subdue this phenomenon effectively is needed. At an APR1400 nuclear power plant which is a pressurized water reactor (PWR), the reactor power is controlled automatically using a Reactor Regulating System (RRS) but the power distribution is controlled manually by operators. Therefore, for APR+ nuclear power plants which is an improved version of APR1400 nuclear reactor, a new concept of a reactor controller is needed to control both the reactor power and power distribution automatically. The model predictive control (MPC) method is applicable to multiple-input multiple-output control, and can be applied for complex and nonlinear systems, such as the nuclear power plants. In this study, an MPC controller was developed by applying a genetic algorithm to optimize the discrete control rod speeds and by reflecting the realistic movement of the control rod drive mechanism that moves at only five discrete speeds. The performance of the proposed controller was confirmed to be satisfactory by simulating the load-following operation of an APR+ nuclear power plant through interface with KISPAC-1D code

Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

Science.gov (United States)

Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

2018-04-01

Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

Science.gov (United States)

An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

2017-08-18

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

STRUCTURAL SCALE LIFE PREDICTION OF AERO STRUCTURES EXPERIENCING COMBINED EXTREME ENVIRONMENTS

Science.gov (United States)

2017-07-01

complex loading environments. Today’s state of the art methods cannot address structural reliability under combined environment conditions due to...probabilistically assess the structural life under complex loading environments. Today’s state of the art methods cannot address structural reliability...Institute of Aeronautics and Astronautics, San Diego, CA, January 4th‐8th, 2016. Clark, L. D., Bae, H., Gobal, K., and Penmetsa, R., “ Engineering

Automation Rover for Extreme Environments

Science.gov (United States)

Sauder, Jonathan; Hilgemann, Evan; Johnson, Michael; Parness, Aaron; Hall, Jeffrey; Kawata, Jessie; Stack, Kathryn

2017-01-01

Almost 2,300 years ago the ancient Greeks built the Antikythera automaton. This purely mechanical computer accurately predicted past and future astronomical events long before electronics existed1. Automata have been credibly used for hundreds of years as computers, art pieces, and clocks. However, in the past several decades automata have become less popular as the capabilities of electronics increased, leaving them an unexplored solution for robotic spacecraft. The Automaton Rover for Extreme Environments (AREE) proposes an exciting paradigm shift from electronics to a fully mechanical system, enabling longitudinal exploration of the most extreme environments within the solar system.

Static reliability of concrete structures under extreme temperature, radiation, moisture and force loading

International Nuclear Information System (INIS)

Stepanek, P.; Stastnik, S.; Salajka, V.; Hradil, P.; Skolar, J.; Chlanda, V.

2003-01-01

The contribution presents some aspects of the static reliability of concrete structures under temperature effects and under mechanical loading. The mathematical model of a load-bearing concrete structure was performed using the FEM method. The temperature field and static stress that generated states of stress were taken into account. A brief description of some aspects of evaluation of the reliability within the primary circuit concrete structures is stated. The knowledge of actual physical and mechanical characteristics and chemical composition of concrete were necessary for obtaining correct results of numerical analysis. (author)

Heavy-Load Lifting

DEFF Research Database (Denmark)

Bloomquist, Kira; Oturai, Peter; Steele, Megan L

2018-01-01

of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal....... repetition maximum (RM), two sets of 15-20 repetitions) and heavy-load (85-90% 1RM, three sets of 5-8 repetition) upper-extremity resistance exercise separated by a one-week wash-out period. Swelling was determined by bioimpedance spectroscopy and dual energy x-ray absorptiometry, with breast cancer......-related lymphedema symptoms (heaviness, swelling, pain, tightness) reported using a numeric rating scale (0-10). Order of low- versus heavy-load was randomized. All outcomes were assessed pre-, immediately post-, and 24- and 72-hours post-exercise. Generalized estimating equations were used to evaluate changes over...

Prediction of Lower Extremity Movement by Cyclograms

Directory of Open Access Journals (Sweden)

P. Kutilek

2012-01-01

Full Text Available Human gait is nowadays undergoing extensive analysis. Predictions of leg movements can be used for orthosis and prosthesis programming, and also for rehabilitation. Our work focuses on predicting human gait with the use of angle-angle diagrams, also called cyclograms. In conjunction with artificial intelligence, cyclograms offer a wide area of medical applications. We have identified cyclogram characteristics such as the slope and the area of the cyclogram for a neural network learning algorithm. Neural networks learned by cyclograms offer wide applications in prosthesis control systems.

On Wind Forces in the Forest-Edge Region During Extreme-Gust Passages and Their Implications for Damage Patterns

Science.gov (United States)

Gromke, Christof; Ruck, Bodo

2018-03-01

A damage pattern that is occasionally found after a period of strong winds shows an area of damaged trees inside a forest stand behind an intact stripe of trees directly at the windward edge. In an effort to understand the mechanism leading to this damage pattern, wind loading in the forest-edge region during passages of extreme gusts with different characteristics are investigated using a scaled forest model in the wind tunnel. The interaction of a transient extreme gust with the stationary atmospheric boundary layer (ABL) as a background flow at the forest edge leads to the formation of a vortex at the top of the canopy. This vortex intensifies when travelling downstream and subsequently deflects high-momentum air from above the canopy downwards resulting in increased wind loading on the tree crowns. Under such conditions, the decrease in wind loading in the streamwise direction can be relatively weak compared to stationary ABL approach flows. The resistance of trees with streamwise distance from the forest edge, however, is the result of adaptive growth to wind loading under stationary flow conditions and shows a rapid decline within two to three tree heights behind the windward edge. For some of the extreme gusts realized, an exceedance of the wind loading over the resistance of the trees is found at approximately three tree heights behind the forest edge, suggesting that the damage pattern described above can be caused by the interaction of a transient extreme gust with the stationary ABL flow.

Numerical modelling of extreme waves by Smoothed Particle Hydrodynamics

Directory of Open Access Journals (Sweden)

M. H. Dao

2011-02-01

Full Text Available The impact of extreme/rogue waves can lead to serious damage of vessels as well as marine and coastal structures. Such extreme waves in deep water are characterized by steep wave fronts and an energetic wave crest. The process of wave breaking is highly complex and, apart from the general knowledge that impact loadings are highly impulsive, the dynamics of the breaking and impact are still poorly understood. Using an advanced numerical method, the Smoothed Particle Hydrodynamics enhanced with parallel computing is able to reproduce well the extreme waves and their breaking process. Once the waves and their breaking process are modelled successfully, the dynamics of the breaking and the characteristics of their impact on offshore structures could be studied. The computational methodology and numerical results are presented in this paper.

Extremely Low-Metallicity Stars in the Classical Dwarf Galaxies

NARCIS (Netherlands)

Starkenburg, E.; DART Team, [Unknown; Aoki, W; Ishigaki, M; Suda, T; Tsujimoto, T; Arimoto, N

After careful re-analysis of Ca II triplet calibration at low-metallicity, the classical satellites around the Milky Way are found not to be devoided of extremely low-metallicity stars and their (extremely) metal-poor tails are predicted to be much more in agreement with the Milky Way halo. A first

Forecasting extreme temperature health hazards in Europe

Science.gov (United States)

Di Napoli, Claudia; Pappenberger, Florian; Cloke, Hannah L.

2017-04-01

Extreme hot temperatures, such as those experienced during a heat wave, represent a dangerous meteorological hazard to human health. Heat disorders such as sunstroke are harmful to people of all ages and responsible for excess mortality in the affected areas. In 2003 more than 50,000 people died in western and southern Europe because of a severe and sustained episode of summer heat [1]. Furthermore, according to the Intergovernmental Panel on Climate Change heat waves are expected to get more frequent in the future thus posing an increasing threat to human lives. Developing appropriate tools for extreme hot temperatures prediction is therefore mandatory to increase public preparedness and mitigate heat-induced impacts. A recent study has shown that forecasts of the Universal Thermal Climate Index (UTCI) provide a valid overview of extreme temperature health hazards on a global scale [2]. UTCI is a parameter related to the temperature of the human body and its regulatory responses to the surrounding atmospheric environment. UTCI is calculated using an advanced thermo-physiological model that includes the human heat budget, physiology and clothing. To forecast UTCI the model uses meteorological inputs, such as 2m air temperature, 2m water vapour pressure and wind velocity at body height derived from 10m wind speed, from NWP models. Here we examine the potential of UTCI as an extreme hot temperature prediction tool for the European area. UTCI forecasts calculated using above-mentioned parameters from ECMWF models are presented. The skill in predicting UTCI for medium lead times is also analysed and discussed for implementation to international health-hazard warning systems. This research is supported by the ANYWHERE project (EnhANcing emergencY management and response to extreme WeatHER and climate Events) which is funded by the European Commission's HORIZON2020 programme. [1] Koppe C. et al., Heat waves: risks and responses. World Health Organization. Health and

Temporal variability in the suspended sediment load and streamflow of the Doce River

Science.gov (United States)

Oliveira, Kyssyanne Samihra Santos; Quaresma, Valéria da Silva

2017-10-01

Long-term records of streamflow and suspended sediment load provide a better understanding of the evolution of a river mouth, and its adjacent waters and a support for mitigation programs associated with extreme events and engineering projects. The aim of this study is to investigate the temporal variability in the suspended sediment load and streamflow of the Doce River to the Atlantic Ocean, between 1990 and 2013. Streamflow and suspended sediment load were analyzed at the daily, seasonal, and interannual scales. The results showed that at the daily scale, Doce River flood events are due to high intensity and short duration rainfalls, which means that there is a flashy response to rainfall. At the monthly and season scales, approximately 94% of the suspended sediment supply occurs during the wet season. Extreme hydrological events are important for the interannual scale for Doce River sediment supply to the Atlantic Ocean. The results suggest that a summation of anthropogenic interferences (deforestation, urbanization and soil degradation) led to an increase of extreme hydrological events. The findings of this study shows the importance of understanding the typical behavior of the Doce River, allowing the detection of extreme hydrological conditions, its causes and possible environmental and social consequences.

Predictive characterization of aging and degradation of reactor materials in extreme environments. Final report, December 20, 2013 - September 20, 2017

Energy Technology Data Exchange (ETDEWEB)

Qu, Jianmin [Northwestern Univ., Evanston, IL (United States)

2017-09-20

Understanding of reactor material behavior in extreme environments is vital not only to the development of new materials for the next generation nuclear reactors, but also to the extension of the operating lifetimes of the current fleet of nuclear reactors. To this end, this project conducted a suite of unique experimental techniques, augmented by a mesoscale computational framework, to understand and predict the long-term effects of irradiation, temperature, and stress on material microstructures and their macroscopic behavior. The experimental techniques and computational tools were demonstrated on two distinctive types of reactor materials, namely, Zr alloys and high-Cr martensitic steels. These materials are chosen as the test beds because they are the archetypes of high-performance reactor materials (cladding, wrappers, ducts, pressure vessel, piping, etc.). To fill the knowledge gaps, and to meet the technology needs, a suite of innovative in situ transmission electron microscopy (TEM) characterization techniques (heating, heavy ion irradiation, He implantation, quantitative small-scale mechanical testing, and various combinations thereof) were developed and used to elucidate and map the fundamental mechanisms of microstructure evolution in both Zr and Cr alloys for a wide range environmental boundary conditions in the thermal-mechanical-irradiation input space. Knowledge gained from the experimental observations of the active mechanisms and the role of local microstructural defects on the response of the material has been incorporated into a mathematically rigorous and comprehensive three-dimensional mesoscale framework capable of accounting for the compositional variation, microstructural evolution and localized deformation (radiation damage) to predict aging and degradation of key reactor materials operating in extreme environments. Predictions from this mesoscale framework were compared with the in situ TEM observations to validate the model.

Predictive characterization of aging and degradation of reactor materials in extreme environments. Final report, December 20, 2013 - September 20, 2017

International Nuclear Information System (INIS)

Qu, Jianmin

2017-01-01

Understanding of reactor material behavior in extreme environments is vital not only to the development of new materials for the next generation nuclear reactors, but also to the extension of the operating lifetimes of the current fleet of nuclear reactors. To this end, this project conducted a suite of unique experimental techniques, augmented by a mesoscale computational framework, to understand and predict the long-term effects of irradiation, temperature, and stress on material microstructures and their macroscopic behavior. The experimental techniques and computational tools were demonstrated on two distinctive types of reactor materials, namely, Zr alloys and high-Cr martensitic steels. These materials are chosen as the test beds because they are the archetypes of high-performance reactor materials (cladding, wrappers, ducts, pressure vessel, piping, etc.). To fill the knowledge gaps, and to meet the technology needs, a suite of innovative in situ transmission electron microscopy (TEM) characterization techniques (heating, heavy ion irradiation, He implantation, quantitative small-scale mechanical testing, and various combinations thereof) were developed and used to elucidate and map the fundamental mechanisms of microstructure evolution in both Zr and Cr alloys for a wide range environmental boundary conditions in the thermal-mechanical-irradiation input space. Knowledge gained from the experimental observations of the active mechanisms and the role of local microstructural defects on the response of the material has been incorporated into a mathematically rigorous and comprehensive three-dimensional mesoscale framework capable of accounting for the compositional variation, microstructural evolution and localized deformation (radiation damage) to predict aging and degradation of key reactor materials operating in extreme environments. Predictions from this mesoscale framework were compared with the in situ TEM observations to validate the model.

Real-time prediction of extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using univariate linear stochastic models

International Nuclear Information System (INIS)

Sharma, P.; Khare, M.

2000-01-01

Historical data of the time-series of carbon monoxide (CO) concentration was analysed using Box-Jenkins modelling approach. Univariate Linear Stochastic Models (ULSMs) were developed to examine the degree of prediction possible for situations where only a limited data set, restricted only to the past record of pollutant data are available. The developed models can be used to provide short-term, real-time forecast of extreme CO concentrations for an Air Quality Control Region (AQCR), comprising a major traffic intersection in a Central Business District of Delhi City, India. (author)

Prediction of regular wave loads on a fixed offshore oscillating water column-wave energy converter using CFD

Directory of Open Access Journals (Sweden)

Ahmed Elhanafi

2016-12-01

Full Text Available In this paper, hydrodynamic wave loads on an offshore stationary–floating oscillating water column (OWC are investigated via a 2D and 3D computational fluid dynamics (CFD modeling based on the RANS equations and the VOF surface capturing scheme. The CFD model is validated against previous experiments for nonlinear regular wave interactions with a surface-piercing stationary barge. Following the validation stage, the numerical model is modified to consider the pneumatic damping effect, and an extensive campaign of numerical tests is carried out to study the wave–OWC interactions for different wave periods, wave heights and pneumatic damping factors. It is found that the horizontal wave force is usually larger than the vertical one. Also, there a direct relationship between the pneumatic and hydrodynamic vertical forces with a maximum vertical force almost at the device natural frequency, whereas the pneumatic damping has a little effect on the horizontal force. Additionally, simulating the turbine damping with an orifice plate induces higher vertical loads than utilizing a slot opening. Furthermore, 3D modeling significantly escalates and declines the predicted hydrodynamic vertical and horizontal wave loads, respectively.

Simulations of nearly extremal binary black holes

Science.gov (United States)

Giesler, Matthew; Scheel, Mark; Hemberger, Daniel; Lovelace, Geoffrey; Kuper, Kevin; Boyle, Michael; Szilagyi, Bela; Kidder, Lawrence; SXS Collaboration

2015-04-01

Astrophysical black holes could have nearly extremal spins; therefore, nearly extremal black holes could be among the binaries that current and future gravitational-wave observatories will detect. Predicting the gravitational waves emitted by merging black holes requires numerical-relativity simulations, but these simulations are especially challenging when one or both holes have mass m and spin S exceeding the Bowen-York limit of S /m2 = 0 . 93 . Using improved methods we simulate an unequal-mass, precessing binary black hole coalescence, where the larger black hole has S /m2 = 0 . 99 . We also use these methods to simulate a nearly extremal non-precessing binary black hole coalescence, where both black holes have S /m2 = 0 . 994 , nearly reaching the Novikov-Thorne upper bound for holes spun up by thin accretion disks. We demonstrate numerical convergence and estimate the numerical errors of the waveforms; we compare numerical waveforms from our simulations with post-Newtonian and effective-one-body waveforms; and we compare the evolution of the black-hole masses and spins with analytic predictions.

Hydraulic Soft Yaw System Load Reduction and Prototype Results

DEFF Research Database (Denmark)

Stubkier, Søren; Pedersen, Henrik C.; Markussen, Kristian

2013-01-01

Introducing a hydraulic soft yaw concept for wind turbines leads to significant load reductions in the wind turbine structure. The soft yaw system operates as a shock absorption system on a car, hence absorbing the loading from turbulent wind conditions instead of leading them into the stiff wind...... turbine structure. Results presented shows fatigue reductions of up to 40% and ultimate load reduction of up to 19%. The ultimate load reduction increases even more when the over load protection system in the hydraulic soft yaw system is introduced and results show how the exact extreme load cut off...... operates. Further it is analyzed how the soft yaw system influence the power production of the turbine. It is shown that the influence is minimal, but at larger yaw errors the effect is possitive. Due to the implemeted functions in the hydraulic soft yaw system such as even load distribution on the pinions...

Evaluation of Load Analysis Methods for NASAs GIII Adaptive Compliant Trailing Edge Project

Science.gov (United States)

Cruz, Josue; Miller, Eric J.

2016-01-01

The Air Force Research Laboratory (AFRL), NASA Armstrong Flight Research Center (AFRC), and FlexSys Inc. (Ann Arbor, Michigan) have collaborated to flight test the Adaptive Compliant Trailing Edge (ACTE) flaps. These flaps were installed on a Gulfstream Aerospace Corporation (GAC) GIII aircraft and tested at AFRC at various deflection angles over a range of flight conditions. External aerodynamic and inertial load analyses were conducted with the intention to ensure that the change in wing loads due to the deployed ACTE flap did not overload the existing baseline GIII wing box structure. The objective of this paper was to substantiate the analysis tools used for predicting wing loads at AFRC. Computational fluid dynamics (CFD) models and distributed mass inertial models were developed for predicting the loads on the wing. The analysis tools included TRANAIR (full potential) and CMARC (panel) models. Aerodynamic pressure data from the analysis codes were validated against static pressure port data collected in-flight. Combined results from the CFD predictions and the inertial load analysis were used to predict the normal force, bending moment, and torque loads on the wing. Wing loads obtained from calibrated strain gages installed on the wing were used for substantiation of the load prediction tools. The load predictions exhibited good agreement compared to the flight load results obtained from calibrated strain gage measurements.

Modified Inverse First Order Reliability Method (I-FORM) for Predicting Extreme Sea States.

Energy Technology Data Exchange (ETDEWEB)

Eckert-Gallup, Aubrey Celia; Sallaberry, Cedric Jean-Marie; Dallman, Ann Renee; Neary, Vincent Sinclair

2014-09-01

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulation s as a part of the stand ard current practice for designing marine structure s to survive extreme sea states. Such environmental contours are characterized by combinations of significant wave height ( ) and energy period ( ) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first - order reliability method (IFORM) i s standard design practice for generating environmental contours. In this paper, the traditional appli cation of the IFORM to generating environmental contours representing extreme sea states is described in detail and its merits and drawbacks are assessed. The application of additional methods for analyzing sea state data including the use of principal component analysis (PCA) to create an uncorrelated representation of the data under consideration is proposed. A reexamination of the components of the IFORM application to the problem at hand including the use of new distribution fitting techniques are shown to contribute to the development of more accurate a nd reasonable representations of extreme sea states for use in survivability analysis for marine struc tures. Keywords: In verse FORM, Principal Component Analysis , Environmental Contours, Extreme Sea State Characteri zation, Wave Energy Converters

Time- & Load-Dependence of Triboelectric Effect.

Science.gov (United States)

Pan, Shuaihang; Yin, Nian; Zhang, Zhinan

2018-02-06

Time- and load-dependent friction behavior is considered as important for a long time, due to its time-evolution and force-driving characteristics. However, its electronic behavior, mainly considered in triboelectric effect, has almost never been given the full attention and analyses from the above point of view. In this paper, by experimenting with fcc-latticed aluminum and copper friction pairs, the mechanical and electronic behaviors of friction contacts are correlated by time and load analyses, and the behind physical understanding is provided. Most importantly, the difference of "response lag" in force and electricity is discussed, the extreme points of coefficient of friction with the increasing normal loads are observed and explained with the surface properties and dynamical behaviors (i.e. wear), and the micro and macro theories linking tribo-electricity to normal load and wear (i.e. the physical explanation between coupled electrical and mechanical phenomena) are successfully developed and tested.

Full scale test SSP 34m blade, combined load. Data report

Energy Technology Data Exchange (ETDEWEB)

Nielsen, Per H.; Nielsen, Magda; Jensen, Find M. (and others)

2010-11-15

This report is part of the research project where a 34m wind turbine blade from SSP-Technology A/S was tested in combined flap and edgewise load. The applied load is 55% of an imaginary extreme event based on the certification load of the blade. This report describes the reason for choosing the loads and the load direction and the method of applying the loads to the blade. A novel load introduction allows the blade to deform in a more realistic manner, allowing the observation of e.g. transverse shear distortion. The global and local deformation of the blade as well as the blades' respond to repeated tests has been studied and the result from these investigations are presented, including the measurements performed. (Author)

Evaluation of load rejection to house load test at 50% power for UCN 3

International Nuclear Information System (INIS)

Lee, Chang Gyun; Sohn, Suk Whun; Sohn, Jong Joo; Seo, Jong Tae; Lee, Sang Keun; Kim, Yong Sung; Nam, Kyu Won; Jung, Yang Mook; Chae, Kyeong Sik; Koh, Bum Jae; Oh, Chul Sung; Park, Hee Chool

1998-01-01

The Load Rejection to House Load test at 50% power was successfully performed during the UCN 3 PAT period. In this test, all plant control systems automatically controlled the plant from 50% power to house load operation mode. The KISPAC code, which was used in the performance analysis during the design process of UCN 3 and 4, predictions of the test agreed with the measured data demonstrating the validity of the code as well as completeness of the plant design

Extreme Wave-Induced Oscillation in Paradip Port Under the Resonance Conditions

Science.gov (United States)

Kumar, Prashant; Gulshan

2017-12-01

A mathematical model is constructed to analyze the long wave-induced oscillation in Paradip Port, Odisha, India under the resonance conditions to avert any extreme wave hazards. Boundary element method (BEM) with corner contribution is utilized to solve the Helmholtz equation under the partial reflection boundary conditions. Furthermore, convergence analysis is also performed for the boundary element scheme with uniform and non-uniform discretization of the boundary. The numerical scheme is also validated with analytic approximation and existing studies based on harbor resonance. Then, the amplification factor is estimated at six key record stations in the Paradip Port with multidirectional incident waves and resonance modes are also estimated at the boundary of the port. Ocean surface wave field is predicted in the interior of Paradip Port for the different directional incident wave at various resonance modes. Moreover, the safe locations in the port have been identified for loading and unloading of moored ship with different resonance modes and directional incident waves.

A prediction model for treatment decisions in high-grade extremity soft-tissue sarcomas: Personalised sarcoma care (PERSARC).

Science.gov (United States)

van Praag, Veroniek M; Rueten-Budde, Anja J; Jeys, Lee M; Laitinen, Minna K; Pollock, Rob; Aston, Will; van der Hage, Jos A; Dijkstra, P D Sander; Ferguson, Peter C; Griffin, Anthony M; Willeumier, Julie J; Wunder, Jay S; van de Sande, Michiel A J; Fiocco, Marta

2017-09-01

To support shared decision-making, we developed the first prediction model for patients with primary soft-tissue sarcomas of the extremities (ESTS) which takes into account treatment modalities, including applied radiotherapy (RT) and achieved surgical margins. The PERsonalised SARcoma Care (PERSARC) model, predicts overall survival (OS) and the probability of local recurrence (LR) at 3, 5 and 10 years. Development and validation, by internal validation, of the PERSARC prediction model. The cohort used to develop the model consists of 766 ESTS patients who underwent surgery, between 2000 and 2014, at five specialised international sarcoma centres. To assess the effect of prognostic factors on OS and on the cumulative incidence of LR (CILR), a multivariate Cox proportional hazard regression and the Fine and Gray model were estimated. Predictive performance was investigated by using internal cross validation (CV) and calibration. The discriminative ability of the model was determined with the C-index. Multivariate Cox regression revealed that age and tumour size had a significant effect on OS. More importantly, patients who received RT showed better outcomes, in terms of OS and CILR, than those treated with surgery alone. Internal validation of the model showed good calibration and discrimination, with a C-index of 0.677 and 0.696 for OS and CILR, respectively. The PERSARC model is the first to incorporate known clinical risk factors with the use of different treatments and surgical outcome measures. The developed model is internally validated to provide a reliable prediction of post-operative OS and CILR for patients with primary high-grade ESTS. LEVEL OF SIGNIFICANCE: level III. Copyright © 2017 Elsevier Ltd. All rights reserved.

A rational evaluation of structural design loads

International Nuclear Information System (INIS)

Tasaka, S.

1993-01-01

The reliability-based seismic design of structures is a design method ensuring that the structural seismic capacity is not less than the maximum seismic load or load effect for a prescribed value of the reliability index, wherein the design reference period, n, is used to specify the n-year maximum load. In the conventional Load and Resistance Factor Design (LRFD) method the design load is commonly determined on the basis of the n-year maximum the probability distribution of which may be given in some different ways. However, in contrast with the structural capacity the n-year maximum load usually involves much larger variabilities. The effort to decrease the variability would, hence, be effective for the purpose of avoiding nuclear power plant (NPP) structures having unnecessarily large capacities. A possible way to do this is to consider the joint probability distribution of the n-year 1st and 2nd maxima of the seismic load derived from the formula of extreme order statistics. Since the reliability index is conventionally associated with the n-year 1st maximum, the conditional probability distribution rather than the joint one of the n-year 1st maximum given a value of the n-year 2nd one will be considered. Three conditional extreme value distributions, which correspond to the usual extreme value distributions of Types I, II and III, and their statistical moments up to the second order are presented. Within the framework of the first-order second moment method, the conditional statistical moments are utilized to calculate the reliability index as well as the design value of the seismic load. The seismic load considered herein is represented by the peak ground acceleration (PGA) in n years. The present scheme is applied to evaluate the design PGA's at II sites in Japan where samples of the annual 1st and 2nd PGA's have been obtained by using historical seismic data. In this application the following two points are of our interest: (a) Define the reliability

Validation of Simplified Load Equations Through Loads Measurement and Modeling of a Small Horizontal-Axis Wind Turbine Tower

Energy Technology Data Exchange (ETDEWEB)

Dana, Scott [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Van Dam, Jeroen J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Damiani, Rick R [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2018-04-24

As part of an ongoing effort to improve the modeling and prediction of small wind turbine dynamics, the National Renewable Energy Laboratory (NREL) tested a small horizontal-axis wind turbine in the field at the National Wind Technology Center. The test turbine was a 2.1-kW downwind machine mounted on an 18-m multi-section fiberglass composite tower. The tower was instrumented and monitored for approximately 6 months. The collected data were analyzed to assess the turbine and tower loads and further validate the simplified loads equations from the International Electrotechnical Commission (IEC) 61400-2 design standards. Field-measured loads were also compared to the output of an aeroelastic model of the turbine. In particular, we compared fatigue loads as measured in the field, predicted by the aeroelastic model, and calculated using the simplified design equations. Ultimate loads at the tower base were assessed using both the simplified design equations and the aeroelastic model output. The simplified design equations in IEC 61400-2 do not accurately model fatigue loads and a discussion about the simplified design equations is discussed.

Finite element-based limit load of piping branch junctions under combined loadings

International Nuclear Information System (INIS)

Xuan Fuzhen; Li Peining

2004-01-01

The limit load is an important input parameter in engineering defect-assessment procedures and strength design. In the present work, a total of 100 different piping branch junction models for the limit load calculation were performed under combined internal pressure and moments in use of non-linear finite element (FE) method. Three different existing accumulation rules for limit load, i.e., linear equation, parabolic equation and quadratic equation were discussed on the basis of FE results. A novel limit load solution was developed based on detailed three-dimensional FE limit analyses which accommodated the geometrical parameter influence, together with analytical solutions based on equilibrium stress fields. Finally, six experimental results were provided to justify the presented equation. According to the FE limit analysis, limit load interaction of the piping tees under combined pressure and moments has a relationship with the geometrical parameters, especially with the diameter ratio d/D. The predicted limit loads from the presented formula are very close to the experimental data. The resulting limit load solution is given in a closed form, and thus can be easily used in practice

Wound healing in immediately loaded implants.

Science.gov (United States)

Romanos, Georgios E

2015-06-01

The orthopedic field has accumulated ample evidence that bone formation is related to functional loading and in general to physical activity. However, despite evidence that immediately loaded implants can be predictably successful, many clinicians still use the classical (delayed loading) treatment protocol. This paper examines the effects of loading on dental implants and discusses the advantages of immediate loading. The role of loading on augmented alveolar ridges is also addressed and provides evidence that early bone resorption may be controlled when bone is functionally loaded. Similar data are emerging for advanced augmentation techniques in order to control crestal bone loss. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Calculation of equivalent static loads and its application

International Nuclear Information System (INIS)

Choi, Woo-Seok; Park, K.B.; Park, G.J.

2005-01-01

All the forces in the real world act dynamically on structures. Since dynamic loads are extremely difficult to handle in analysis and design, static loads are usually utilized with dynamic factors. Generally, the dynamic factors are determined from design codes or experience. Therefore, static loads may not give accurate solutions in analysis and design and structural engineers often come up with unreliable solutions. Two different methods are proposed for the transformation of dynamic loads into equivalent static loads (ESLs). One is an analytical method for exact ESLs and the other is an approximation method. The exact ESLs are calculated to generate identical response fields such as displacement and stress with those from dynamic loads at a certain time. Some approximation methods are proposed in engineering applications, which generate similar response fields from dynamic loads. They are divided into the displacement-based approach and the stress-based approach. The process is derived and evaluated mathematically. Standard examples are selected and solved by the proposed method and error analyses are conducted. Applications of the method to structural optimization are discussed

The influence of schizotypal traits on attention under high perceptual load.

Science.gov (United States)

Stotesbury, Hanne; Gaigg, Sebastian B; Kirhan, Saim; Haenschel, Corinna

2018-03-01

Schizophrenia Spectrum Disorders (SSD) are known to be characterised by abnormalities in attentional processes, but there are inconsistencies in the literature that remain unresolved. This article considers whether perceptual resource limitations play a role in moderating attentional abnormalities in SSD. According to perceptual load theory, perceptual resource limitations can lead to attenuated or superior performance on dual-task paradigms depending on whether participants are required to process, or attempt to ignore, secondary stimuli. If SSD is associated with perceptual resource limitations, and if it represents the extreme end of an otherwise normally distributed neuropsychological phenotype, schizotypal traits in the general population should lead to disproportionate performance costs on dual-task paradigms as a function of the perceptual task demands. To test this prediction, schizotypal traits were quantified via the Schizotypal Personality Questionnaire (SPQ) in 74 healthy volunteers, who also completed a dual-task signal detection paradigm that required participants to detect central and peripheral stimuli across conditions that varied in the overall number of stimuli presented. The results confirmed decreasing performance as the perceptual load of the task increased. More importantly, significant correlations between SPQ scores and task performance confirmed that increased schizotypal traits, particularly in the cognitive-perceptual domain, are associated with greater performance decrements under increasing perceptual load. These results confirm that attentional difficulties associated with SSD extend sub-clinically into the general population and suggest that cognitive-perceptual schizotypal traits may represent a risk factor for difficulties in the regulation of attention under increasing perceptual load.

The influence of schizotypal traits on attention under high perceptual load

Directory of Open Access Journals (Sweden)

Hanne Stotesbury

2018-03-01

Full Text Available Schizophrenia Spectrum Disorders (SSD are known to be characterised by abnormalities in attentional processes, but there are inconsistencies in the literature that remain unresolved. This article considers whether perceptual resource limitations play a role in moderating attentional abnormalities in SSD. According to perceptual load theory, perceptual resource limitations can lead to attenuated or superior performance on dual-task paradigms depending on whether participants are required to process, or attempt to ignore, secondary stimuli. If SSD is associated with perceptual resource limitations, and if it represents the extreme end of an otherwise normally distributed neuropsychological phenotype, schizotypal traits in the general population should lead to disproportionate performance costs on dual-task paradigms as a function of the perceptual task demands. To test this prediction, schizotypal traits were quantified via the Schizotypal Personality Questionnaire (SPQ in 74 healthy volunteers, who also completed a dual-task signal detection paradigm that required participants to detect central and peripheral stimuli across conditions that varied in the overall number of stimuli presented. The results confirmed decreasing performance as the perceptual load of the task increased. More importantly, significant correlations between SPQ scores and task performance confirmed that increased schizotypal traits, particularly in the cognitive-perceptual domain, are associated with greater performance decrements under increasing perceptual load. These results confirm that attentional difficulties associated with SSD extend sub-clinically into the general population and suggest that cognitive-perceptual schizotypal traits may represent a risk factor for difficulties in the regulation of attention under increasing perceptual load.

Structural and functional responses of extremity veins to long-term gravitational loading or unloading—lessons from animal systems

Science.gov (United States)

Monos, Emil; Raffai, Gábor; Dörnyei, Gabriella; Nádasy, György L.; Fehér, Erzsébet

2007-02-01

Long, transparent tubular tilt-cages were developed to maintain experimental rats either in 45∘ head-up (orthostasis model), or in 45∘ head-down body position (antiorthostasis model) for several weeks. In order to study the functional and structural changes in extremity blood vessels, also novel pressure angiograph systems, as well as special quantitative electron microscopic methods were applied. It was found that several adaptive mechanisms are activated in the lower limb superficial veins and microvessels of muscles when an organism is exposed to long-term (1-2 weeks) orthostatic-type gravitational load including a reversible amplification of the pressure-dependent myogenic response, tuning of the myogenic tone by Ca++- and voltage-sensitive K+ channels in humans, augmentation of the intramural sympathetic innervation involving an increased nerve terminal density and synaptic vesicle count with functional remodeling, reorganization of vascular network properties (microvascular rarefaction in muscles, decreased branching angles in superficial veins), and responses of an endothelin and platelet-derived growth factor (PDGF) containing vesicle system in the endothelium. On the other hand, when applying long-term head-down tilting, the effects are dichotomous, e.g. it suppresses significantly the pressure-induced myogenic response, however does not diminish the adventitial sympathetic innervation density.

Drug Loading of Mesoporous Silicon

Science.gov (United States)

Moffitt, Anne; Coffer, Jeff; Wang, Mengjia

2011-03-01

The nanostructuring of crystalline solids with low aqueous solubilities by their incorporation into mesoporous host materials is one route to improve the bioavailability of such solids. Earlier studies suggest that mesoporous Si (PSi), with pore widths in the range of 5-50 nm, is a candidate for such an approach. In this presentation, we describe efforts to load curcumin into free-standing microparticles of PSi. Curcumin is a compound extracted from turmeric root, which is an ingredient of curry. Curucmin has shown activity against selected cancer cell lines, bacteria, and other medical conditions. However, curcumin has a very low bioavailability due to its extremely low water solubility (0.6 μ g/mL). Incorporation of curcumin was achieved by straightforward loading of the molten solid at 185circ; C. Loading experiments were performed using PSi particles of two different size ranges, 45-75 μ m and 150-250 μ m. Longer loading times and ratio of curcumin to PSi leads to a higher percentage of loaded curcumin in both PSi particle sizes (as determined by weight difference). The extent of curcumin crystallinity was assessed by x-ray diffraction (XRD). The solubility and release kinetics of loaded curcumin from the PSi was determined by extraction into water at 37circ; C, with analysis using UV-VIS spectrometry. NSF-REU and TCU.

Using Haines Index coupled with fire weather model predicted from high resolution LAM forecasts to asses wildfire extreme behaviour in Southern Europe.

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