Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914614792421376 |
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| author | Liu, Yunfei Wang, Hao Qi, Yuhang Yue, Hao Meng, Dehong Li, Wei Wang, Rui Li, Tiejun Liu, Jie Hong, Junwu Chen, Xinhai |
| author_facet | Liu, Yunfei Wang, Hao Qi, Yuhang Yue, Hao Meng, Dehong Li, Wei Wang, Rui Li, Tiejun Liu, Jie Hong, Junwu Chen, Xinhai |
| contents | High-fidelity computational fluid dynamics is essential for aerospace design, but engineering-scale simulations of practical three-dimensional aircraft remain computationally expensive. Learning-based flow-field initialization can improve efficiency by reducing the numerical distance between the initial and converged solutions, yet existing deep learning approaches remain difficult to scale to large three-dimensional aircraft flows with multiscale regional heterogeneity. Most prior studies therefore focus on two-dimensional problems, surface quantities, integral aerodynamic coefficients, or simplified three-dimensional cases with limited grid resolution.Here we propose MHLF, a multigrid-hierarchical learning framework for accelerating engineering-scale aircraft flow simulations while preserving high-fidelity numerical accuracy. MHLF combines a topologically consistent geometric multigrid representation with a hierarchical strategy that captures regional flow heterogeneity during both prediction and subsequent CFD correction. Across three engineering-scale aircraft cases spanning Mach 0.15 to 6.0 and covering subsonic, transonic and supersonic regimes, MHLF accelerates convergence without sacrificing flow-field accuracy, achieving a 3 to 8 times efficiency improvement over conventional initialization. These results demonstrate practical full-flow-field prediction for large three-dimensional aircraft within the CFD domain and provide a foundation for data-driven acceleration of high-fidelity aircraft flow simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30375 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning Liu, Yunfei Wang, Hao Qi, Yuhang Yue, Hao Meng, Dehong Li, Wei Wang, Rui Li, Tiejun Liu, Jie Hong, Junwu Chen, Xinhai Fluid Dynamics Artificial Intelligence High-fidelity computational fluid dynamics is essential for aerospace design, but engineering-scale simulations of practical three-dimensional aircraft remain computationally expensive. Learning-based flow-field initialization can improve efficiency by reducing the numerical distance between the initial and converged solutions, yet existing deep learning approaches remain difficult to scale to large three-dimensional aircraft flows with multiscale regional heterogeneity. Most prior studies therefore focus on two-dimensional problems, surface quantities, integral aerodynamic coefficients, or simplified three-dimensional cases with limited grid resolution.Here we propose MHLF, a multigrid-hierarchical learning framework for accelerating engineering-scale aircraft flow simulations while preserving high-fidelity numerical accuracy. MHLF combines a topologically consistent geometric multigrid representation with a hierarchical strategy that captures regional flow heterogeneity during both prediction and subsequent CFD correction. Across three engineering-scale aircraft cases spanning Mach 0.15 to 6.0 and covering subsonic, transonic and supersonic regimes, MHLF accelerates convergence without sacrificing flow-field accuracy, achieving a 3 to 8 times efficiency improvement over conventional initialization. These results demonstrate practical full-flow-field prediction for large three-dimensional aircraft within the CFD domain and provide a foundation for data-driven acceleration of high-fidelity aircraft flow simulation. |
| title | Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning |
| topic | Fluid Dynamics Artificial Intelligence |
| url | https://arxiv.org/abs/2605.30375 |