Global sensitivity analysis for the efficient solution of optimization problem in design process

Reuter, Uwe; Mehmood, Zeeshan; Liebscher, Marco

doi:10.1002/pamm.201210385

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…In many real problems with sparse data, polynomial models are insufficient. In addition, regression coefficients of various responses may not be normalized and thus may require more sophisticated variance decomposition methods (Reuter et al, 2011).…”

Section: Methods and Techniquesmentioning

confidence: 99%

Analytical global sensitivity analysis with Gaussian processes

Srivastava

Subramaniyan

Wang

2017

AIEDAM

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Methods for efficient variance-based global sensitivity analysis of complex high-dimensional problems are presented and compared. Variance decomposition methods rank inputs according to Sobol indices that can be computationally expensive to evaluate. Main and interaction effect Sobol indices can be computed analytically in the Kennedy and O'Hagan framework with Gaussian processes. These methods use the high-dimensional model representation concept for variance decomposition that presents a unique model representation when inputs are uncorrelated. However, when the inputs are correlated, multiple model representations may be possible leading to ambiguous sensitivity ranking with Sobol indices. In this work, we present the effect of input correlation on sensitivity analysis and discuss the methods presented by Li and Rabitz in the context of Kennedy and O'Hagan's framework with Gaussian processes. Results are demonstrated on simulated and real problems for correlated and uncorrelated inputs and demonstrate the utility of variance decomposition methods for sensitivity analysis.

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Section: Methods and Techniquesmentioning

confidence: 99%

Analytical global sensitivity analysis with Gaussian processes

Srivastava

Subramaniyan

Wang

2017

AIEDAM

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“…It has been shown that this method can be used to identify related input variables for non-uniform nonlinear problems where simple ANOVA methods may fail. 26…”

Section: Correlation Coefficients Matrixmentioning

confidence: 99%

“…It has been shown that this method can be used to identify related input variables for non-uniform nonlinear problems where simple ANOVA methods may fail. 26 Finally, variance-based sensitivity indices are estimated using surrogate models. Figure 11 shows the sensitivity analysis of the thrust chamber.…”

Section: Thrust Chamber Sensitivity Analysismentioning

confidence: 99%

Multi-objective multidisciplinary design optimization of liquid-propellant engines thrust chamber based on a surrogate model

Alimohammadi,

Aghaei-Togh

2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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The design of liquid-propellant engines (LPEs) has several challenges in setting the performance parameters. Accordingly, optimizing the design of a thrust chamber is of considerable importance that has been considered in several research projects. Previous research has focused on multidisciplinary design optimization (MDO). However, despite these efforts, the main issues remain. The present paper proposes a multi-objective multidisciplinary design optimization based on an efficient adaptive surrogate model of the thrust chamber to address these issues. The proposed method introduces a practical multidisciplinary optimization method based on an adaptive surrogate model that uses the moving least squares methodology, CCM, sensitivity analysis, and the elite multi-objective genetic algorithm (NSGAII). Due to the high importance of specific impulse and thrust-to-weight ratio, these two functions were used as target functions and in the NSGAII framework, the Pareto frontier was drawn for them. The proposed method is applied to a thrust-chamber engine test case. The results show that the target performance of the engine is improved, the specific impulse value is increased by 3.4 s, and the thrust-to-weight ratio is increased by 4%. These values represent significant advances in LPE engine design. The results obtained in this study indicate the potential of the proposed method in solving large-scale thrust-chamber design optimization problems.

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“…Structural optimization is characterized by a set of design parameters, constraints, and objective functions formulated on basis of model responses. The result of global sensitivity analysis may be used to select the most significant design parameters from several potential candidates [7]. Geometrical and cross-sectional design variables were also used in the optimization of extradosed cable-stayed bridges [8].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Extradosed Bridge and Analysis of Parameters' Sensitiveness as a Case Study on Abay Bridge

Lamesgin,

Golla,

Rasool

et al. 2023

ce papers

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The paper describes analytical sensitivity analysis and structural optimization for Abay extradosed cable‐stayed bridge. Identify significant design variables using sensitivity analysis by considering cable stiffness, girder weight and stay cable tension force. The aim of the study is weight minimization of superstructure components of Abay bridges. It has span length (100+180+100) m. For structural optimization, it was carried out by taking total material weight of girders, pylon and stayed‐cable as an objective function and all requirements of strength, stability, serviceability, and fatigue as constraint functions. The formulated optimization problem was coded into Matlab R2019a software and analysis results are retrieved from MIDAS CIVIL 2019 software. The paper gives more reduced weight of bridge from conventional design output by using same material grade. The result shown that optimum stay cable length was reduced from conventional design output by 10% from total values, optimum design of pylon height reduced weight of conventional design by 20.03% and optimum design of box girder reduced girder weight by 19.47%.

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Global sensitivity analysis for the efficient solution of optimization problem in design process

Cited by 4 publications

References 10 publications

Analytical global sensitivity analysis with Gaussian processes

Analytical global sensitivity analysis with Gaussian processes

Multi-objective multidisciplinary design optimization of liquid-propellant engines thrust chamber based on a surrogate model

Optimization of Extradosed Bridge and Analysis of Parameters' Sensitiveness as a Case Study on Abay Bridge

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