Don't sweat the small stuff - a sensitivity analysis of cost estimate input parameters

2010 ◽  
Author(s):  
Robert Kellogg ◽  
Eric Mahr ◽  
Robert Bitten
Keyword(s):  
Sensitivity Analysis ◽  
Cost Estimate ◽  
Input Parameters

Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code

2013 ◽  
Author(s):  
Fabrice Fouet ◽  
Pierre Probst
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Nuclear Power ◽  
Uncertainty Propagation ◽  
Correlation Coefficients ◽  
Computer Code ◽  
Numerical Application ◽  
Loss Of Coolant Accident ◽  
Input Parameters ◽  
Artificial Neural Network Ann

In nuclear safety, the Best-Estimate (BE) codes may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters, which comes mainly from the lack of knowledge of the input parameters, is evaluated by estimating the 95% percentile with a high degree of confidence. IRSN, technical support of the French Safety Authority, developed a method of uncertainty propagation. This method has been tested with the BE code used is CATHARE-2 V2.5 in order to evaluate the Peak Cladding Temperature (PCT) of the fuel during a Large Break Loss Of Coolant Accident (LB-LOCA) event, starting from a large number of input parameters. A sensitivity analysis is needed in order to limit the number of input parameters and to quantify the influence of each one on the response variability of the numerical model. Generally, the Global Sensitivity Analysis (GSA) is done with linear correlation coefficients. This paper presents a new approach to perform a more accurate GSA to determine and to classify the main uncertain parameters: the Sobol′ methodology. The GSA requires simulating many sets of parameters to propagate uncertainties correctly, which makes of it a time-consuming approach. Therefore, it is natural to replace the complex computer code by an approximate mathematical model, called response surface or surrogate model. We have tested Artificial Neural Network (ANN) methodology for its construction and the Sobol′ methodology for the GSA. The paper presents a numerical application of the previously described methodology on the ZION reactor, a Westinghouse 4-loop PWR, which has been retained for the BEMUSE international problem [8]. The output is the first maximum PCT of the fuel which depends on 54 input parameters. This application outlined that the methodology could be applied to high-dimensional complex problems.

Seismic hazard sensitivity analysis: A multi-parameter approach

1991 ◽  
Vol 81 (3) ◽  
pp. 796-817
Author(s):  
Nitzan Rabinowitz ◽  
David M. Steinberg
Keyword(s):  
Sensitivity Analysis ◽  
Seismic Hazard ◽  
Subjective Probability ◽  
Probability Distributions ◽  
Input Parameter ◽  
The Other ◽  
Probabilistic Assessment ◽  
Input Parameters ◽  
Parameter Approach

Abstract We propose a novel multi-parameter approach for conducting seismic hazard sensitivity analysis. This approach allows one to assess the importance of each input parameter at a variety of settings of the other input parameters and thus provides a much richer picture than standard analyses, which assess each input parameter only at the default settings of the other parameters. We illustrate our method with a sensitivity analysis of seismic hazard for Jerusalem. In this example, we find several input parameters whose importance depends critically on the settings of other input parameters. This phenomenon, which cannot be detected by a standard sensitivity analysis, is easily diagnosed by our method. The multi-parameter approach can also be used in the context of a probabilistic assessment of seismic hazard that incorporates subjective probability distributions for the input parameters.

Validation and Parametric Investigations of an Internal Permanent Magnet Motor Using a Lumped Parameter Thermal Model

2021 ◽  
Author(s):  
Sebastien Sequeira ◽  
Kevin Bennion ◽  
J. Emily Cousineau ◽  
Sreekant Narumanchi ◽  
Gilberto Moreno ◽  
...  
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Permanent Magnet ◽  
Power Density ◽  
Heat Removal ◽  
Maximum Difference ◽  
Modeling Framework ◽  
Element Analysis ◽  
Lumped Parameter ◽  
Input Parameters

Abstract One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multi-physics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a Lumped Parameter Thermal Network (LPTN) model of an Internal Permanent Magnet synchronous motor within the commercially available Motor-CAD® modeling environment. The validation is based on temperature comparison with experimental data and with more detailed Finite Element Analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% - for the highest temperature in the slot-winding and the end-winding - was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot-winding and end-winding. As for the LTPN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance.

scholarly journals Application of sensitivity analysis and genopt to optimize the energy performance of a building in Morocco

2018 ◽  
Vol 7 (4) ◽  
pp. 2068 ◽  
Author(s):  
Abdelhadi Serbouti ◽  
Mourad Rattal ◽  
Abdellah Boulal ◽  
Mohammed Harmouchi ◽  
Azeddine Mouhsen
Keyword(s):  
Sensitivity Analysis ◽  
Residential Buildings ◽  
Energy Performance ◽  
High Energy ◽  
Performance Requirement ◽  
Input Parameters ◽  
Sensitivity Analysis Method ◽  
Python Programming ◽  
Industry Sectors ◽  
High Energy Consumption

The worldwide demographic and economic growth increases the global need for energy and directly contributes to climate change. In Morocco, the residential real estate is the third largest consumer of energy after transport and industry sectors. Thus, the aim of this study is to help engineers improve the energy performance of residential buildings by coupling the TRNSYS software both with a sensitivity analysis method and with an optimization tool. In fact, sensitivity analysis allows reducing the number of input parameters of any studied model, by ranking their degree of impact on any chosen output, and then discard the parameters with the least influence on that output. To do so, we developed algorithms in Python programming language to combine the open source library SALib, available in Github platform, with the TRNSYS software. Then, the chosen input parameters can be optimized through coupling the generic optimization program Genopt with TRNSYS. This article will also explain how these tools were applied to reduce the heating & air-conditioning needs of a high-energy consumption building in Morocco, while studying the variation of nineteen input parameters in TRNSYS. The main aim is to meet the energy performance requirement of the Moroccan thermal regulation for buildings.  

scholarly journals The Dependence of Water Consumption on the Pressure Conditions and Sensitivity Analysis of the Input Parameters

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 592 ◽  
Author(s):  
Ladislav Tuhovcak ◽  
Tomas Suchacek ◽  
Jan Rucka
Keyword(s):  
Sensitivity Analysis ◽  
Water Consumption ◽  
Office Building ◽  
Time Step ◽  
Input Parameters ◽  
Water Meter

The paper presents results and sensitivity analysis of the results of a real detailed study focused on changes in water consumption and its unevenness with changing pressure conditions in a particular observed office building. The dependence of water consumption on pressure is expressed using the FAVAD equation using the N3 coefficient. Parameters for sensitivity analysis are number of workers in the building, pulse value from water meter and length of time step for expressing unevenness of water consumption during the day.

Sensitivity Analysis for Dynamical Response of Reactor Coolant System Based on OPTIMUS

2020 ◽  
Author(s):  
Yuan Yanli ◽  
Ye Xianhui ◽  
Li Lijuan ◽  
Yuan Feng
Keyword(s):  
Sensitivity Analysis ◽  
Steam Generator ◽  
Nuclear Power ◽  
Reactor System ◽  
Response Analysis ◽  
Dynamical Response ◽  
Calculation Error ◽  
Reactor Coolant ◽  
Input Parameters ◽  
Coolant System

Abstract The sensitivity analysis of the dynamical response of reactor coolant system to the input parameters is an important precondition for the design optimization. In this paper, the sensitivity of the dynamical loads at the nozzles of the equipment under seismic conditions is analyzed with an integrated platform called OPTIMUS, taking the stiffness of the dampers in the steam generator and the main pump as the input variables. The key parameters of the reactor system are usually different from the design value due to the calculation error, random and other uncontrollable errors in the manufacturing process and installation process. In a nuclear power project, the measured stiffness values of the dampers on the steam generator and the main pump in the manufacturer are deviated from the requirements in the equipment specification, and it is necessary to evaluate the influence of the deviation on the dynamical response analysis of the reactor system. According to the traditional method, it is necessary to establish the models of the reactor coolant system for nonlinear analysis according to the different stiffness of the dampers, and then the calculation results are compared by EXCEL. In this paper, the sensitivity analysis of output parameters which are the loads at the nozzles of the equipment to the input parameters which are the stiffness of the dampers on the steam generator and pump is realized by OPTIMUS, which is a kind of integration platform. Not only can ANSYS simulation calculations be carried out automatically on the OPTIMUS, but also the output data can be processed rapidly automatically, and the influence of manufacturing deviation of the stiffness of the dampers on the dynamical response of the reactor coolant system can be analyzed quantitatively in the above-mentioned problems, and the data support is provided for the determination of the design variables for subsequent optimization analysis.

scholarly journals Pyrolysis modeling, sensitivity analysis, and optimization techniques for combustible materials: A review

2019 ◽  
Vol 37 (4-6) ◽  
pp. 377-433
Author(s):  
Tatenda Nyazika ◽  
Maude Jimenez ◽  
Fabienne Samyn ◽  
Serge Bourbigot
Keyword(s):  
Sensitivity Analysis ◽  
Material Properties ◽  
Input Parameter ◽  
Fire Behavior ◽  
Sensitivity Study ◽  
Optimization Techniques ◽  
Decomposition Reactions ◽  
Input Parameters ◽  
Gradient Based ◽  
Complete Set

Over the past years, pyrolysis models have moved from thermal models to comprehensive models with great flexibility including multi-step decomposition reactions. However, the downside is the need for a complete set of input data such as the material properties and the parameters related to the decomposition kinetics. Some of the parameters are not directly measurable or are difficult to determine and they carry a certain degree of uncertainty at high temperatures especially for materials that can melt, shrink, or swell. One can obtain input parameters by searching through the literature; however, certain materials may have the same nomenclature but the material properties may vary depending on the manufacturer, thereby inducing uncertainties in the model. Modelers have resorted to the use of optimization techniques such as gradient-based and direct search methods to estimate input parameters from experimental bench-scale data. As an integral part of the model, a sensitivity study allows to identify the role of each input parameter on the outputs. This work presents an overview of pyrolysis modeling, sensitivity analysis, and optimization techniques used to predict the fire behavior of combustible solids when exposed to an external heat flux.

scholarly journals Sensitivity analysis on the pollutant trapping efficiencies of a novel sewerage overflow screening device

2016 ◽  
Vol 18 (6) ◽  
pp. 1007-1018
Author(s):  
M. A. Aziz ◽  
M. A. Imteaz ◽  
H. M. Rasel ◽  
M. Samsuzzoha
Keyword(s):  
Sensitivity Analysis ◽  
Sampling Technique ◽  
Latin Hypercube Sampling ◽  
Capture Efficiency ◽  
Industry Standard ◽  
Informed Decisions ◽  
Input Parameters ◽  
Perform Sensitivity Analysis ◽  
Hydro Jet ◽  
Screening Device

A novel ‘Comb Separator’ was developed and tested with the aim of improving sewer solids capture efficiency and reducing blockages on the screen. Experimental results were compared against the industry standard ‘Hydro-Jet™’ screen. Analysing the parameter sensitivity of a hydraulic screen is a standard practice to get better understanding of the device performance. In order to understand the uncertainties of the Comb Separator's input parameters, it is necessary to undertake sensitivity analysis; this will assist in making informed decisions regarding the use of this device. Such analysis will validate the device's performance in urban sewerage overflow scenarios. The methodology includes multiple linear regression and sampling using the standard Latin hypercube sampling technique to perform sensitivity analysis on different experimental parameters, such as flowrate, effective comb spacing, device runtime, weir opening and comb layers. The input parameters ‘weir opening’ and ‘comb layers’ have an insignificant influence on capture efficiency; hence, they were omitted from further analysis. Among the input parameters, ‘effective spacing’ was the most influential, followed by ‘inflow’ and ‘runtime’. These analyses provide better insights about the sensitivities of the parameters for practical application. This will assist device managers and operators to make informed decisions.

A Procedure for Evaluating the Food Biomass Consumed by a Fish Population

1981 ◽  
Vol 38 (10) ◽  
pp. 1199-1208 ◽  
Author(s):  
Jacek Majkowski ◽  
Kenneth G. Waiwood
Keyword(s):  
Sensitivity Analysis ◽  
Food Consumption ◽  
Atlantic Cod ◽  
Fish Population ◽  
Fish Growth ◽  
Growth Theory ◽  
Food Level ◽  
Fish Food ◽  
Growth Physiology ◽  
Input Parameters

An application of the Ursin fish growth theory for evaluating the food biomass consumed by a fish population is proposed. In this procedure the total catabolism of a fish is treated as dependent on the level of available food. This is the major advantage of the procedure in comparison with that based on the most frequently used Winberg fish growth theory, in which the total catabolism rate is assumed to be independent of the available food level. The Atlantic cod (Gadus morhua) population inhabiting the southern Gulf of St. Lawrence (NAFO subarea 4T) in 1978 is considered in order to illustrate the procedure. The food biomass consumed by the cod population in this area and period is evaluated to be 0.727 million tonnes (the food consumption of the 0th cod age-group is not included). The uncertainty in the estimate of this food biomass due to uncertainties in the input parameters for the procedure is estimated to be in the range of 20% (standard error). Possible ways of improving the results of the procedure are discussed in the light of uncertainties in the input parameters and the sensitivity of the model upon them. It is concluded on the basis of a sensitivity analysis performed for the numerical example of procedure and the foraging theory for fish that the assumption introduced by Winberg can, in certain cases, introduce considerable bias to the procedure results.Key words: fish, food consumption, growth, physiology, bioenergetics, cod, model, sensitivity analysis

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