Numerical Simulation of Three-dimensional High-Lift Configurations Using Data-Driven Turbulence Model
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911131000373248 |
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| author | Zhang, Shaoguang Wu, Chenyu Zhang, Yufei |
| author_facet | Zhang, Shaoguang Wu, Chenyu Zhang, Yufei |
| contents | Traditional Reynolds-averaged Navier-Stokes (RANS) equations often struggle to predict separated flows accurately. Recent studies have employed data-driven methods to enhance predictions by modifying baseline equations, such as field inversion and machine learning (FIML) with symbolic regression. However, data-driven turbulence models exhibit limited adaptability and are rarely applied to complex engineering problems. This study examines the application of data-driven turbulence models to complex three-dimensional high-lift configurations, extending their usability beyond previous applications. First, the generalizability of the SST-CND model, derived from conditioned field inversion and symbolic regression, is validated. Then, the spatially varying correction factor obtained through conditioned field inversion is transferred to the three-equation k-(v^2)-w model. The 30P30N three-element airfoil, the JAXA Standard Model (JSM), and the high-lift version of the NASA Common Research Model (CRM-HL) are numerically simulated. The results indicated that the SST-CND model significantly improves the prediction of stall characteristics, demonstrating satisfactory generalizability. The corrected k-(v^2 )-w-CND model accurately predicts the stall characteristics of CRM-HL, with a relative error of less than 5% compared to experimental results. This confirms the strong transferability of the model correction derived from conditioned field inversion across different turbulence models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14859 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Numerical Simulation of Three-dimensional High-Lift Configurations Using Data-Driven Turbulence Model Zhang, Shaoguang Wu, Chenyu Zhang, Yufei Fluid Dynamics Traditional Reynolds-averaged Navier-Stokes (RANS) equations often struggle to predict separated flows accurately. Recent studies have employed data-driven methods to enhance predictions by modifying baseline equations, such as field inversion and machine learning (FIML) with symbolic regression. However, data-driven turbulence models exhibit limited adaptability and are rarely applied to complex engineering problems. This study examines the application of data-driven turbulence models to complex three-dimensional high-lift configurations, extending their usability beyond previous applications. First, the generalizability of the SST-CND model, derived from conditioned field inversion and symbolic regression, is validated. Then, the spatially varying correction factor obtained through conditioned field inversion is transferred to the three-equation k-(v^2)-w model. The 30P30N three-element airfoil, the JAXA Standard Model (JSM), and the high-lift version of the NASA Common Research Model (CRM-HL) are numerically simulated. The results indicated that the SST-CND model significantly improves the prediction of stall characteristics, demonstrating satisfactory generalizability. The corrected k-(v^2 )-w-CND model accurately predicts the stall characteristics of CRM-HL, with a relative error of less than 5% compared to experimental results. This confirms the strong transferability of the model correction derived from conditioned field inversion across different turbulence models. |
| title | Numerical Simulation of Three-dimensional High-Lift Configurations Using Data-Driven Turbulence Model |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2503.14859 |