可泛化数据驱动湍流模型的开发：条件场反演与符号回归
Development of a Generalizable Data-driven Turbulence Model: Conditioned Field Inversion and Symbolic Regression
AI总结：
针对RANS模型预测分离流时降低附着边界层精度的问题，提出条件场反演技术结合符号回归，在提升分离流预测能力的同时保持附着流精度。
摘要：
本文探讨了使用雷诺平均纳维-斯托克斯（RANS）湍流模型预测分离流的问题，该模型对许多工程任务至关重要。传统RANS模型通常难以处理此任务，因此最近的研究集中在数据驱动方法上，如场反演和机器学习（FIML），通过调整基准方程来纠正此问题。然而，这些FIML方法通常会降低附着边界层的精度。为解决此问题，我们开发了一种“条件场反演”技术。该方法调整FIML使用的校正因子\{eta}（在剪切应力输运（SST）模型中）。它将\{eta}与一个屏蔽函数f_d相乘，该函数在边界层中关闭，在其他地方开启。这维持了基准模型对附着流的精度。我们将条件场反演和经典场反演应用于NASA凸起和弯曲后向台阶（CBFS），创建了两个数据集。这些数据集用于训练两个模型：SR-CND（来自我们的新方法）和SR-CLS（来自传统方法）。SR-CND模型在各种场景下预测分离流时与SR-CLS模型相匹配，例如周期性山丘、NLR7301翼型、3D SAE汽车模型和3D Ahmed车身，并在本文呈现的案例中优于基准SST模型。重要的是，SR-CND模型在附着边界层中保持精度，而SR-CLS模型则不能。因此，所提出的方法在改善分离流预测的同时，保持了对附着流的原始模型精度，为创建数据驱动湍流模型提供了更好的途径。
英文摘要：
This paper addresses the issue of predicting separated flows with Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are essential for many engineering tasks. Traditional RANS models usually struggle with this task, so recent efforts have focused on data-driven methods such as field inversion and machine learning (FIML) to correct this issue by adjusting the baseline equations. However, these FIML methods often reduce accuracy in attached boundary layers. To address this issue, we developed a "conditioned field inversion" technique. This method adjusts the corrective factor \b{eta} (used by FIML) in the shear-stress transport (SST) model. It multiplies \b{eta} with a shield function f_d that is off in the boundary layer and on elsewhere. This maintains the accuracy of the baseline model for the attached flows. We applied both conditioned and classic field inversion to the NASA hump and a curved backward-facing step (CBFS), creating two datasets. These datasets were used to train two models: SR-CND (from our new method) and SR-CLS (from the traditional method). The SR-CND model matches the SR-CLS model in predicting separated flows in various scenarios, such as periodic hills, the NLR7301 airfoil, the 3D SAE car model, and the 3D Ahmed body, and outperforms the baseline SST model in the cases presented in the paper. Importantly, the SR-CND model maintains accuracy in the attached boundary layers, whereas the SR-CLS model does not. Therefore, the proposed method improves separated flow predictions while maintaining the accuracy of the original model for attached flows, offering a better way to create data-driven turbulence models.
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。
