An adaptive Sequential Enhanced PCE approach and its application in aerodynamic uncertainty quantification

Zhang, Wei; Wang, Qiang; Zeng, Fangfang; Yan, Chao

doi:10.1016/j.ast.2021.106911

Cited by 13 publications

(2 citation statements)

References 35 publications

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“…It can clearly be seen that the optimization prevents the samples from gathering. The proposed IOLHS has been employed in a recent study [33]. When the design space is partitioned based on the marginal CDFs, the division in regions with low probability are sparse.…”

Section: Inherited Optimal-lhsmentioning

confidence: 99%

Adaptive Model Refinement Approach for Bayesian Uncertainty Quantification in Turbulence Model

Zeng

Zhang

et al. 2022

AIAA Journal

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Section: Inherited Optimal-lhsmentioning

confidence: 99%

Adaptive Model Refinement Approach for Bayesian Uncertainty Quantification in Turbulence Model

Zeng

Zhang

et al. 2022

AIAA Journal

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“…When PCE is taken as the uncertainty analysis method, the efficiency of the design optimization based on aerodynamic uncertainty decreases substantially with the increase in the number of design variables [34]. A properly designed program can be much more efficient in parallel computing than in a serial one.…”

Section: Parallel Pcesmentioning

confidence: 99%

Aerodynamic Performance Uncertainty Analysis and Optimization of a Conventional Axisymmetric Vehicle Based on Parallel Polynomial Chaos Expansions

Peng

Zhu

et al. 2022

Aerospace

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In this study, the aerodynamic uncertainty analysis and optimization of a conventional axisymmetric vehicle with an aerodynamic configuration were investigated. The prediction precision of the typical aerodynamic performance estimating methods, namely, engineering estimation and numerical simulation, was compared using the wind tunnel test data of the vehicle. Then, using a modified missile data compendium (DATCOM) software, a high-efficiency and high-precision method was developed, which was applied to analyze and characterize the aerodynamic parameters of the axisymmetric vehicle. To enhance the robustness and reliability of aerodynamic performance, an uncertainty-based design optimization (UDO) framework was established. The design space was scaled by parameter sensitivity analysis, and improved computational efficiency was achieved by developing parallel polynomial chaos expansions (PCEs). The optimized results show that the modified method exhibits high accuracy in predicting aerodynamic performance. For the same constraints, the results of the deterministic design optimization (DDO) showed that compared with the initial scheme, the probability of the controllability-to-stability ratio satisfying the constraint decreased from 98.8% to 72.4%, and this value increased to 99.9% in the case of UDO. Compared with the results of the initial scheme and DDO, UDO achieved a considerable reduction in mean values and standard deviation of aerodynamic performances, which can ensure a higher probability of constraints meeting the design requirements, thereby, realizing a reliable and robust design.

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Uncertainty Quantification in Impulse Thruster Performance Using Polynomial Chaos Expansion

An,

Zhang,

Tao

et al. 2024

Lecture Notes in Mechanical Engineering

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An adaptive Sequential Enhanced PCE approach and its application in aerodynamic uncertainty quantification

Cited by 13 publications

References 35 publications

Adaptive Model Refinement Approach for Bayesian Uncertainty Quantification in Turbulence Model

Adaptive Model Refinement Approach for Bayesian Uncertainty Quantification in Turbulence Model

Aerodynamic Performance Uncertainty Analysis and Optimization of a Conventional Axisymmetric Vehicle Based on Parallel Polynomial Chaos Expansions

Uncertainty Quantification in Impulse Thruster Performance Using Polynomial Chaos Expansion

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