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

陈浩; 吴沐宸; 陈江涛; 夏侯唐凡; 赵忠锐; 刘宇

doi:10.7638/kqdlxxb-2024.0125

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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

研究专栏——飞行器气动/多学科不确定性量化与稳健优化设计 | 更新时间：2026-07-27

- 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
- An active learning surrogate model-based uncertainty quantification method for CFD simulation under the framework of evidence theory
- 空气动力学学报 2024年42卷第9期页码：86-99
- 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：
  
  陈浩（2001—），男，四川南充人，硕士研究生，研究方向：不确定性量化与分析. E-mail：haochen20010530@163.com
  夏侯唐凡*（1992—），副教授，研究方向：不确定性量化与分析，多状态系统可靠性建模与评估. E-mail： xiahoutf@uestc.edu.cn
- 基金信息：
  
  国家自然科学基金（72301057, 72271044, 72331002, 52305010）;中国博士后面上项目（2023M730500）;四川省自然科学基金（2024NSFSC0904）
- DOI：10.7638/kqdlxxb-2024.0125
  中图分类号： O355;V211.3
- 纸质出版：2024
- 稿件说明：
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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

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