An active learning surrogate model-based uncertainty quantification method for CFD simulation under the framework of evidence theory

CHEN Hao; WU Muchen; CHEN Jiangtao; XIAHOU Tangfan; ZHAO Zhongrui; LIU Yu

doi:10.7638/kqdlxxb-2024.0125

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An active learning surrogate model-based uncertainty quantification method for CFD simulation under the framework of evidence theory

更新时间：2026-07-27

- An active learning surrogate model-based uncertainty quantification method for CFD simulation under the framework of evidence theory
- Vol. 42, Issue 9, Pages: 86-99(2024)
- 作者机构：
  
  1. 电子科技大学机械与电气工程学院,成都,611731
  2. 航空工业沈阳飞机设计研究所,沈阳,110035
  3. 中国空气动力研究与发展中心,绵阳,621000
  4. 空天飞行空气动力科学与技术全国重点实验室,绵阳,621000
  5. 电子科技大学系统可靠性与安全性研究中心,成都,611731
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2024.0125
  CLC： O355;V211.3
- Published：2024
- 稿件说明：
移动端阅览
陈浩, 吴沐宸, 陈江涛, 等. 证据理论框架下主动学习代理模型驱动的CFD模拟不确定性量化方法[J]. 空气动力学学报, 2024, 42(9): 86−99.

CHEN Hao, WU Muchen, CHEN Jiangtao, et al. An active learning surrogate model-based uncertainty quantification method for CFD simulation under the framework of evidence theory[J]. 2024, 42(9): 86-99.
陈浩, 吴沐宸, 陈江涛, 等. 证据理论框架下主动学习代理模型驱动的CFD模拟不确定性量化方法[J]. 空气动力学学报, 2024, 42(9): 86−99. DOI： 10.7638/kqdlxxb-2024.0125

CHEN Hao, WU Muchen, CHEN Jiangtao, et al. An active learning surrogate model-based uncertainty quantification method for CFD simulation under the framework of evidence theory[J]. 2024, 42(9): 86-99. DOI： 10.7638/kqdlxxb-2024.0125.

摘要

计算流体力学（CFD）模拟中存在模型参数、数值离散和边界条件等诸多形式各异的不确定因素。鉴于证据理论具有灵活的建模框架，且能同时量化CFD模拟中的随机和认知不确定性，基于其提出了一种证据理论框架下主动学习代理模型驱动的CFD模拟不确定性量化方法，以较少的CFD仿真模型调用次数实现对CFD模拟结果的不确定性量化。该方法采用最优最大最小距离策略生成空间分布良好的候选样本点，通过动态熵权-TOPSIS主动学习策略平衡了代理模型的全局探索、局部开发和鲁棒性。此外，提出基于Hartley测度和Jousselme距离的复合收敛准则以判断终止代理模型训练的时间并量化输出响应的不确定性。最后，以采用NASA SC(2)0410翼型剖面的超临界机翼流场CFD模拟为例，分析来流参数和湍流模型参数的不确定性对机翼输出响应升阻比的不确定性量化结果。

Abstract

Computational Fluid Dynamics (CFD) simulations are subject to various uncertainties

stemming from factors such as model parameters

numerical discretization

and boundary conditions. Given the flexibility of the evidence theory in modeling both aleatory and epistemic uncertainties in CFD simulations

this article introduces an active learning surrogate model-driven approach for uncertainty quantification in CFD simulations. This method aims to properly quantify the uncertainty of CFD simulations using minimal simulation model calls while achieving accurate uncertainty quantification results. The method utilizes the optimization-based max-min distance strategy to generate well-distributed candidate sample points. Moreover

it employs a dynamic entropy-weighted TOPSIS multi-criteria decision analysis to balance the surrogate model’s exploration

exploitation

and robustness. Additionally

this article proposes a composite convergence criterion

combining Hartley's measure and Jousselme distance

to formulate the stopping criterion of the surrogate model. Finally

taking the CFD simulation of the flow field of a supercritical wing with a NASASC(2)0410 airfoil as a case study

the uncertainty quantification of lift-to-drag ratio due to uncertainties in inflow and turbulence model parameters is conducted.

关键词

Keywords

references

CHEN X, WANG G, YE Z Y, et al . A review of uncertainty quantification methods for Computational Fluid Dynamics[J ] . Acta Aerodynamica Sinica, 2021 , 39 ( 4 ): 1 − 13 (in Chinese).

XIAHOU T F, CHEN J T, SHAO Z D, et al . Model validation metrics for CFD numerical simulation under aleatory and epistemic uncertainty[J ] . Acta Aeronautica et Astronautica Sinica, 2022 , 43 ( 8 ): 174 − 190 (in Chinese).

CHEN J T, XIAO W, ZHAO W, et al . Advances in verification and validation in computational fluid dynamics[J ] . Advances in Mechanics, 2023 , 53 ( 3 ): 626 − 660 (in Ch inese).

DU X S, LEIFSSON L . Optimum aerodynamic shape design under uncertainty by utility theory and metamodeling[J ] . Aerospace Science and Technology, 2019 , 95 : 105464 .

ZHAO Y T, YAN C, WANG X Y, et al . Uncertainty and sensitivity analysis of SST turbulence model on hypersonic flow heat transfer[J ] . International Journal of Heat and Mass Transfer, 2019 , 136 : 808 − 820 .

ZHANG Z, JIANG C . Evidence-theory-based structural reliability analysis with epistemic uncertainty: A review[J ] . Structural and Multidisciplinary Optimization, 2021 , 63 ( 6 ): 2935 − 2953 .

ALI T, DUTTA P . Methods to obtain basic probability assignment in evidence theory[J ] . International Journal of Computer Applications, 2012 , 38 ( 4 ): 46 − 51 .

JOUSSELME A L, GRENIER D, BOSSÉ É . A new distance between two bodies of evidence[J ] . Information Fusion, 2001 , 2 ( 2 ): 91 − 101 .

XIAHOU T F, LIU Y, ZHANG Q . Multi-objective redundancy allocation for multi-state system design under epistemic uncertainty of component states[J ] . Journal of Mechanical Design, 2020 , 142 ( 11 ): 111703 .

WILLIAMS C K I, RASMUSSEN C E. Gaussian processes for regression[J]. 8th International Conference on Neural Information Processing Systems, 1995: 514-520.

GUTMANN H M . A radial basis function method for global optimization[J ] . Journal of Global Optimization, 2001 , 19 ( 3 ): 201 − 227 .

CHENG K, LU Z Z . Adaptive Bayesian support vector regression model for structural reliability analysis[J ] . Reliability Engineering System Safety, 2021 , 206 : 107286 .

ZHAO H, WANG S K, GAO Z H, et al . Research progress on aerodynamic robust design optimization methods for air vehicles[J ] . Acta Aerodynamica Sinica, 2024 , 42 ( 9 ): 37 − 69 (in Chinese).

XIONG F F, LI Z X, LIU Y, et al . A review of characterization methods for parameter uncertainty in engineering design based on numerical simulation[J ] . Acta Aeronautica et Astronautica Sinica, 2023 , 44 ( 22 ): 86 − 117 (in Chinese).

SHE Z S, TANG F, XIAO M J . Structural ensemble dynamics theory for engineering turbulenc models[J ] . Acta Aerodynamica Sinica, 2019 , 37 ( 1 ): 1 − 18 (in Chinese).

JIANG C, ZHANG Z, HAN X, et al . A novel evidence-theory-based reliability analysis method for structures with epistemic uncertainty[J ] . Computers Structures, 2013 , 129 : 1 − 12 .

HELTON J C, JOHNSON J D, OBERKAMPF W L, et al . A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory[J ] . Computer Methods in Applied Mechanics and Engineering, 2007 , 196 ( 37-40 ): 3980 − 3998 .

LAI Y J, LIU T Y, HWANG C L . Topsis for modm[J ] . European Journal of Operational Research, 1994 , 76 ( 3 ): 486 − 500 .

LOEPPKY J L, SACKS J, WELCH W J . Choosing the sample size of a computer experiment: A practical guide[J ] . Technometrics, 2009 , 51 ( 4 ): 366 − 376 .

BURCH M, VAN LITH J, VAN DE WATERLAAT N, et al. Voronoier: From images to Voronoi diagrams[C]//13th International Symposium on Visual Information Communication and Interaction, Eindhoven Netherlands. ACM, 2020: 1−9.

PICHENY V, WAGNER T, GINSBOURGER D . A benchmark of kriging-based infill criteria for noisy optimization[J ] . Structural and Multidisciplinary Optimization, 2013 , 48 ( 3 ): 607 − 626 .

GHANOONI P, KAZEMI M, MANI M . Investigating the effect of different winglet cant angles on a supercritical wing aerodynamic efficiency at lower Reynolds numbers[J ] . Journal of Aerospace Science and Technology, 2021 , 14 ( 1 ): 111 − 116 .

JOSHI A, ASSAM A, NIVED M R, et al . A generalised wall function including compressibility and pressure-gradient terms for the Spalart-Allmaras turbulence model[J ] . Journal of Turbulence, 2019 , 20 ( 10 ): 626 − 660 .

SCHAEFER J, HOSDER S, WEST T, et al . Uncertainty quantification of turbulence model closure coefficients for transonic wall-bounded flows[J ] . AIAA Journal, 2017 , 55 ( 1 ): 195 − 213 .

WU M C, XIAHOU T F, CHEN J T, et al . Differentiating effects of input aleatory and epistemic uncertainties on system output: A separating sensitivity analysis approach[J ] . Mechanical Systems and Signal Processing, 2022 , 181 : 109421 .

SWILER L P, PAEZ T L, MAYES R L. Epistemic uncertainty quantification tutorialp[C]// 27th International Modal Analysis Conference, Orlando, Florida, 2009.

WĘGLARCZYK S . Kernel density estimation and its application[J ] . ITM Web of Conferences, 2018 , 23 : 00037 .

LIANG X, YANG Y Y, CHEN J T, et al . Mixed uncertainty quantification of ship non-contact underwater explosion system[J ] . Chinese Journal of Ship Research, 2024 , 19 ( 3 ): 115 − 126 (in Chinese).

TONON F . Using random set theory to propagate epistemic uncertainty through a mechanical system[J ] . Reliability Engineering System Safety, 2004 , 85 ( 1-3 ): 169 − 181 .

WU M C. CFD simulation evaluation and sensitivity analysis method of wing aerodynamic performance under mixed uncertainty[D]. Chengdu: University of Electronic Science and Technology of China, 2023 (in Chinese).

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