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。
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 entropyweighted 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.
作者
陈浩
吴沐宸
陈江涛
夏侯唐凡
赵忠锐
刘宇
CHEN Hao;WU Muchen;CHEN Jiangtao;XIAHOU Tangfan;ZHAO Zhongrui;LIU Yu(School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China;AVIC Shenyang Aircraft Design and Research Institute,Shenyang 110035,China;China Aerodynamics Research and Development Center,Mianyang 621000,China;State Key Laboratory of Aerodynamics,Mianyang 621000,China;Center for System Reliability and Safety,University of Electronic Science and Technology of China,Chengdu 611731,China)
出处
《空气动力学学报》
CSCD
北大核心
2024年第9期86-99,I0002,共15页
Acta Aerodynamica Sinica
基金
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)。
关键词
CFD
不确定性量化
证据理论
主动学习
代理模型
动态熵权-TOPSIS
Computational Fluid Dynamics
uncertainty quantification
evidence theory
active learning
based surrogate model
dynamic entropy-based weighted-TOPSIS
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;通信作者: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。
