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): 1−13. DOI: 10.7638/kqdlxxb-2024.0125
引用本文: 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): 1−13. DOI: 10.7638/kqdlxxb-2024.0125
CHEN H, WU M C, CHEN J T, et al. An Active Learning Surrogate Model-Based Uncertainty Quantification Method for CFD Simulation Under Evidence Theory[J]. Acta Aerodynamica Sinica, 2024, 42(9): 1−13. DOI: 10.7638/kqdlxxb-2024.0125
Citation: CHEN H, WU M C, CHEN J T, et al. An Active Learning Surrogate Model-Based Uncertainty Quantification Method for CFD Simulation Under Evidence Theory[J]. Acta Aerodynamica Sinica, 2024, 42(9): 1−13. DOI: 10.7638/kqdlxxb-2024.0125

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 Evidence Theory

  • 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不确定因素且形式各异。鉴于证据理论灵活的建模框架,且能同时量化CFD模拟中的随机和认知不确定性,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,以剖面翼型为NASASC(2)0410翼型的超临界机翼流场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, including 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 simulation using fewer 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.

     

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