Computer Methods in Applied Mechanics and Engineering ( IF 7.2 ) Pub Date : 2022-09-16 , DOI: 10.1016/j.cma.2022.115396 Kenny Chowdhary, Chi Hoang, Kookjin Lee, Jaideep Ray, V.G. Weirs, Brian Carnes
In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. We develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.
中文翻译:
使用数据驱动的机器学习模型通过 HIFiRE-1 实验校准高超声速湍流模型
在本文中,我们研究了将机器学习方法与基于投影的模型缩减技术相结合以创建数据驱动的代理模型的效果计算昂贵的高保真物理模型。这样的代理模型对于多查询应用程序是必不可少的,例如工程设计优化和参数估计,在这些应用程序中,需要多次顺序调用高保真模型。代理模型通常是为单个标量构建的。然而,在某些情况下,需要将空间变化的场建模为模型输入参数的函数。我们开发了一种方法来做到这一点,使用投影来表示空间可变性,而机器学习模型捕获模型响应对输入的依赖性。该方法通过在 7.16 马赫湍流中模拟 HIFiRE-1 几何体表面的热通量和压力进行了演示。然后使用代理模型来执行使用冲击隧道数据,嵌入高保真(雷诺平均纳维-斯托克斯)流动模拟器中的自由流条件和 SST(剪切应力传递)湍流模型参数的贝叶斯估计。该论文首次对复杂高超音速湍流的湍流模型进行贝叶斯校准。我们发现,SST模型参数估计的主要问题是热通量和压力测量的信息内容有限,以及在流动的某个部分遇到的较大的模型形式误差。