Generalizable super-resolution turbulence reconstruction from minimal training data
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866914135793467392 |
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| author | Haokai, Wu Yong, Cao Yaoran, Chen Shujin, Laima Wenli, Chen Dai, Zhou Hui, Li |
| author_facet | Haokai, Wu Yong, Cao Yaoran, Chen Shujin, Laima Wenli, Chen Dai, Zhou Hui, Li |
| contents | Fully resolving turbulent flows remains challenging due to turbulent systems' multiscale complexity. Existing data-driven approaches typically demand expensive retraining for each flow scenario and struggle to generalize beyond their training conditions. Leveraging the universality of small-scale turbulent motions (Kolmogorov's K41 theory), we propose a Scale-oriented Zonal Generative Adversarial Network (SoZoGAN) framework for high-fidelity, zero-shot turbulence generation across diverse domains. Unlike conventional methods, SoZoGAN is trained exclusively on a single dataset of moderate-Reynolds-number homogeneous isotropic turbulence (HIT). The framework employs a zonal decomposition strategy, partitioning turbulent snapshots into subdomains based on scale-sensitive physical quantities. Within each subdomain, turbulence is synthesized using scale-indexed models pre-trained solely on the HIT database. SoZoGAN demonstrates high accuracy, cross-domain generalizability, and robustness in zero-shot super-resolution of unsteady flows, as validated on untrained HIT, turbulent boundary layer, and channel flow. Its strong generalization, demonstrated for homogenous and inhomogenous turbulence cases, suggests potential applicability to a wider range of industrial and natural turbulent flows. The scale-oriented zonal framework is architecture-agnostic, readily extending beyond GANs to other deep learning models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalizable super-resolution turbulence reconstruction from minimal training data Haokai, Wu Yong, Cao Yaoran, Chen Shujin, Laima Wenli, Chen Dai, Zhou Hui, Li Fluid Dynamics Fully resolving turbulent flows remains challenging due to turbulent systems' multiscale complexity. Existing data-driven approaches typically demand expensive retraining for each flow scenario and struggle to generalize beyond their training conditions. Leveraging the universality of small-scale turbulent motions (Kolmogorov's K41 theory), we propose a Scale-oriented Zonal Generative Adversarial Network (SoZoGAN) framework for high-fidelity, zero-shot turbulence generation across diverse domains. Unlike conventional methods, SoZoGAN is trained exclusively on a single dataset of moderate-Reynolds-number homogeneous isotropic turbulence (HIT). The framework employs a zonal decomposition strategy, partitioning turbulent snapshots into subdomains based on scale-sensitive physical quantities. Within each subdomain, turbulence is synthesized using scale-indexed models pre-trained solely on the HIT database. SoZoGAN demonstrates high accuracy, cross-domain generalizability, and robustness in zero-shot super-resolution of unsteady flows, as validated on untrained HIT, turbulent boundary layer, and channel flow. Its strong generalization, demonstrated for homogenous and inhomogenous turbulence cases, suggests potential applicability to a wider range of industrial and natural turbulent flows. The scale-oriented zonal framework is architecture-agnostic, readily extending beyond GANs to other deep learning models. |
| title | Generalizable super-resolution turbulence reconstruction from minimal training data |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2511.02604 |