稀疏流计算中数据驱动的非线性本构关系

IF 2.9 3区 工程技术 Q2 ENGINEERING, MECHANICAL
Wenwen Zhao, Lijian Jiang, Shaobo Yao, Weifang Chen
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引用次数: 0

摘要

为了克服直接模拟蒙特卡罗(DSMC)方法和统一的玻尔兹曼方程格式等传统稀疏数值方法的缺陷,扩大高克努森数流中宏观方程的覆盖范围,首先通过机器学习方法提出了数据驱动的非线性本构关系(DNCR)。基于Navier-Stokes(NS)求解器和统一气体动力学方案(UGKS)求解器的训练数据,在训练阶段后建立了应力张量与热通量和特征向量的响应图。通过所获得的离线训练模型,通过求解具有修正应力张量和热通量的传统方程组,可以快速准确地预测从训练数据集中排除的新测试用例。最后,通过对DNCR、NS、UGKS、DSMC和实验结果的各种比较,给出了常规的一维冲击波情况和钝圆柱周围的二维高超音速流动,以评估所开发方法的能力。粗颗粒化模型预测能力的提高可以使DNCR方法成为稀薄气体群落中的一种有效工具,特别是在高超音速工程应用中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven nonlinear constitutive relations for rarefied flow computations
To overcome the defects of traditional rarefied numerical methods such as the Direct Simulation Monte Carlo (DSMC) method and unified Boltzmann equation schemes and extend the covering range of macroscopic equations in high Knudsen number flows, data-driven nonlinear constitutive relations (DNCR) are proposed first through the machine learning method. Based on the training data from both Navier-Stokes (NS) solver and unified gas kinetic scheme (UGKS) solver, the map between responses of stress tensors and heat flux and feature vectors is established after the training phase. Through the obtained off-line training model, new test cases excluded from training data set could be predicated rapidly and accurately by solving conventional equations with modified stress tensor and heat flux. Finally, conventional one-dimensional shock wave cases and two-dimensional hypersonic flows around a blunt circular cylinder are presented to assess the capability of the developed method through various comparisons between DNCR, NS, UGKS, DSMC and experimental results. The improvement of the predictive capability of the coarse-graining model could make the DNCR method to be an effective tool in the rarefied gas community, especially for hypersonic engineering applications.
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来源期刊
CiteScore
4.50
自引率
4.30%
发文量
35
审稿时长
11 weeks
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