Sensitivity Analysis of Worst-Case Distribution for Probability Optimization Problems

2000 ◽  
pp. 132-147
Author(s):  
Yu. S. Kan ◽  
A. I. Kibzun
Keyword(s):  
Sensitivity Analysis ◽  
Optimization Problems ◽  
Worst Case ◽  
Probability Optimization ◽  
Case Distribution

Sensitivity Analysis of Cylinder Head Fatigue Life Prediction Using Statistical Models

2014 ◽  
Author(s):  
Christoph Szasz ◽  
Sven Lauer
Keyword(s):  
Sensitivity Analysis ◽  
Cylinder Head ◽  
Information Quality ◽  
Development Process ◽  
Optimization Problems ◽  
Combustion Engine ◽  
Probabilistic Method ◽  
Probabilistic Approach ◽  
Variable Loading ◽  
Worst Case

For the efficient virtual development of combustion engine cylinder heads in terms of high cycle fatigue (HCF) it is highly important to have a reliable development process that represents reality in the best possible way. Most of today’s standard HCF procedures are capable of delivering high quality results for a specific load combination. However, loads are usually subject to variation. This is also valid for loads the cylinder head is subjected to. Assembly loads and operating loads considered during the virtual development process are widely determined by the production process which again is subject to variation due to certain tolerances, wear of the tooling equipment etc. As it is highly important to ensure the fatigue design of a cylinder head, there is the need for new analysis models capable of capturing every possible load variation. Within the framework of this paper the influence of different variable loading parameters on the cylinder head HCF margin of a heavy duty diesel engine will be discussed. A design of experiments (DoE) analysis is used together with the 3-d finite element method (FEM) for the investigations. Furthermore a methodology for the probabilistic assessment of the cylinder head HCF margin based on stochastic loading data is introduced. At the same time an effective methodology for the identification of the worst case boundary conditions for HCF analysis will be presented. With the presented probabilistic method it is possible to achieve a highly accurate prediction of the HCF design margin. Due to the probabilistic approach a better understanding of the entire system is possible, as the interaction between input and output parameters can be illustrated. Therefore HCF optimization problems can be encountered more effectively. Furthermore the presented methodology can be used for error estimation of analysis results and assessment of the result sensitivity. Thus, a borderline layout of the cylinder head can be achieved. Also the minimum input information quality, which is required for a profound HCF analysis, can be assessed by using the sensitivity analysis presented. Therefore the proposed methods enable a fast and reliable development of cylinder heads and other combustion engine components.

Parameter Sensitivity Analysis of a Large-Scale System With Several Components Interacting in Series

1989 ◽  
Author(s):  
H. Torab
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Optimization Problems ◽  
Parameter Sensitivity ◽  
Engineering Optimization ◽  
Parameter Sensitivity Analysis ◽  
Large Scale Systems ◽  
In Series ◽  
Special Case ◽  
Engineering Optimization Problems

Abstract Parameter sensitivity for large-scale systems that include several components which interface in series is presented. Large-scale systems can be divided into components or sub-systems to avoid excessive calculations in determining their optimum design. Model Coordination Method of Decomposition (MCMD) is one of the most commonly used methods to solve large-scale engineering optimization problems. In the Model Coordination Method of Decomposition, the vector of coordinating variables can be partitioned into two sub-vectors for systems with several components interacting in series. The first sub-vector consists of those variables that are common among all or most of the elements. The other sub-vector consists of those variables that are common between only two components that are in series. This study focuses on a parameter sensitivity analysis for this special case using MCMD.

Robustness of Discrete Decisions to the Worst Case Distribution

1978 ◽  
Vol 29 (2) ◽  
pp. 159
Author(s):  
Stanley Schenkerman
Keyword(s):  
Worst Case ◽  
Case Distribution

scholarly journals Multi-Objective Robust Optimization Using a Postoptimality Sensitivity Analysis Technique: Application to a Wind Turbine Design

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis ◽  
Ricardo Soto ◽  
Broderick Crawford
Keyword(s):  
Sensitivity Analysis ◽  
Robust Optimization ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimization Problems ◽  
Design Environment ◽  
Multi Objective ◽  
Pareto Ranking ◽  
Analysis Technique ◽  
Robust Optimization Design

Toward a multi-objective optimization robust problem, the variations in design variables (DVs) and design environment parameters (DEPs) include the small variations and the large variations. The former have small effect on the performance functions and/or the constraints, and the latter refer to the ones that have large effect on the performance functions and/or the constraints. The robustness of performance functions is discussed in this paper. A postoptimality sensitivity analysis technique for multi-objective robust optimization problems (MOROPs) is discussed, and two robustness indices (RIs) are introduced. The first one considers the robustness of the performance functions to small variations in the DVs and the DEPs. The second RI characterizes the robustness of the performance functions to large variations in the DEPs. It is based on the ability of a solution to maintain a good Pareto ranking for different DEPs due to large variations. The robustness of the solutions is treated as vectors in the robustness function space (RF-Space), which is defined by the two proposed RIs. As a result, the designer can compare the robustness of all Pareto optimal solutions and make a decision. Finally, two illustrative examples are given to highlight the contributions of this paper. The first example is about a numerical problem, whereas the second problem deals with the multi-objective robust optimization design of a floating wind turbine.

scholarly journals Accelerated Aging Characterization of Lithium-ion Cells: Using Sensitivity Analysis to Identify the Stress Factors Relevant to Cyclic Aging

Batteries ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 6 ◽  
Author(s):  
Tanja Gewald ◽  
Adrian Candussio ◽  
Leo Wildfeuer ◽  
Dirk Lehmkuhl ◽  
Alexander Hahn ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Accelerated Aging ◽  
Lithium Ion ◽  
Simulation Models ◽  
Cell Types ◽  
Stress Factors ◽  
Worst Case ◽  
Lithium Ion Cells ◽  
Aging Models ◽  
Semi Empirical

As storage technology in electric vehicles, lithium-ion cells are subject to a continuous aging process during their service life that, in the worst case, can lead to a premature system failure. Battery manufacturers thus have an interest in the aging prediction during the early design phase, for which semi-empirical aging models are often used. The progress of aging is dependent on the application-specific load profile, more precisely on the aging-relevant stress factors. Still, a literature review reveals a controversy on the aging-relevant stress factors to use as input parameters for the simulation models. It shows that, at present, a systematic and efficient procedure for stress factor selection is missing, as the aging characteristic is cell-specific. In this study, an accelerated sensitivity analysis as a prior step to aging modeling is proposed, which is transferable and allows to determine the actual aging-relevant stress factors for a specific lithium-ion cell. For the assessment of this accelerated approach, two test series with different acceleration levels and cell types are performed and evaluated. The results show that a certain amount of charge throughput, 100 equivalent full cycles in this case, is necessary to conduct a statistically significant sensitivity analysis.

A New Deterministic Approach Using Sensitivity Region Measures for Multi-Objective Robust and Feasibility Robust Design Optimization

2005 ◽  
Vol 128 (4) ◽  
pp. 874-883 ◽  
Author(s):  
Mian Li ◽  
Shapour Azarm ◽  
Art Boyars
Keyword(s):  
Pareto Front ◽  
Optimization Problems ◽  
Continuous Functions ◽  
Robust Design Optimization ◽  
Worst Case ◽  
New Approach ◽  
Multi Objective ◽  
Tolerance Region ◽  
Parameter Variations ◽  
Gradient Based

We present a deterministic non-gradient based approach that uses robustness measures in multi-objective optimization problems where uncontrollable parameter variations cause variation in the objective and constraint values. The approach is applicable for cases that have discontinuous objective and constraint functions with respect to uncontrollable parameters, and can be used for objective or feasibility robust optimization, or both together. In our approach, the known parameter tolerance region maps into sensitivity regions in the objective and constraint spaces. The robustness measures are indices calculated, using an optimizer, from the sizes of the acceptable objective and constraint variation regions and from worst-case estimates of the sensitivity regions’ sizes, resulting in an outer-inner structure. Two examples provide comparisons of the new approach with a similar published approach that is applicable only with continuous functions. Both approaches work well with continuous functions. For discontinuous functions the new approach gives solutions near the nominal Pareto front; the earlier approach does not.

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