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arXiv 2511.00545physics.flu-dyn
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基于机器学习的强非平衡流矩闭包提取

Extraction of Moment Closures for Strongly Non-Equilibrium Flows via Machine Learning

Hang Song, Satyvir Singh, Manuel Torrilhon, Semih Cayci

AI总结:

提出一种基于机器学习的矩方程建模框架,利用高阶矩和碰撞积分从DSMC数据中学习热力学一致闭包,R13-ML模型准确预测激波结构并推广至高超声速及非定常波场景。

摘要:

我们提出了一种用于稀薄气体流动矩方程建模的机器学习框架,旨在解决传统计算流体动力学无法触及的强非平衡条件。该方法利用对非平衡效应高度敏感的高阶矩和碰撞积分作为关键预测变量。训练数据集由一维稳态激波模拟生成,并开发了计算碰撞积分的方法。通过直接从DSMC数据中学习热力学一致的闭包,我们的R13-ML模型结合矩输运方程的间断Galerkin求解器,保持了物理结构并准确预测了正激波结构,同时可推广至高超声速及部分非定常一维波场景。本研究将机器学习与连续介质力学相结合,为下一代超声速飞行器的高保真气动热预测提供了路线图。

英文摘要:

We introduce a machine learning framework for moment-equation modeling of rarefied gas flows, addressing strongly non-equilibrium conditions inaccessible to conventional computational fluid dynamics. Our approach utilizes high-order moments and collision integrals, highly sensitive to non-equilibrium effects, as key predictive variables. Training datasets are created from one-dimensional steady shock simulations, and a methodology of computing collision integrals is developed. By learning thermodynamically consistent closures directly from DSMC data, our R13-ML model, combined with a discontinuous Galerkin solver for the transfer equations of moments, preserves physical structure and accurately predicts normal shock structures and generalizes to hypersonic and some unsteady, one-dimensional wave scenarios. This work bridges machine learning with continuum mechanics, offering a road map for high-fidelity aerothermal predictions in next-generation supersonic vehicles.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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