Extraction of Moment Closures for Strongly Non-Equilibrium Flows via Machine Learning
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909881833881600 |
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| author | Song, Hang Singh, Satyvir Torrilhon, Manuel Cayci, Semih |
| author_facet | Song, Hang Singh, Satyvir Torrilhon, Manuel Cayci, Semih |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00545 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extraction of Moment Closures for Strongly Non-Equilibrium Flows via Machine Learning Song, Hang Singh, Satyvir Torrilhon, Manuel Cayci, Semih Fluid Dynamics 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. |
| title | Extraction of Moment Closures for Strongly Non-Equilibrium Flows via Machine Learning |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2511.00545 |