To tackle the challenging near-wall turbulence modeling while preserving low computational cost, the near-wall nonoverlapping domain decomposition (NDD) method is proposed, incorporating the machine-learning technique. Using recently proposed implicit NDD (INDD), the solution can be calculated with a Robin-type (slip) wall boundary condition on a relatively coarse mesh and then corrected in the near-wall region at every iteration through an estimated turbulent viscosity profile obtained from a neural network. To maintain a reasonable complexity with acceptable accuracy, only the near-wall field properties, i.e., wall-normal distance, near-wall velocities, streamwise pressure gradients, and one near-wall scale parameter, are employed as the input features for the neural network. The benefit of incorporating machine learning is twofold. First, the near-wall turbulent viscosity is predicted more accurately than by the traditional algebraic functions used in the approximate approach. Second, similarly to the conventional NDD, the present simulations save one order of computational cost over the fully resolved one-block simulations. The accuracy and efficiency of the method are demonstrated on test cases with the $k\text{−}\varepsilon$ model, including turbulent flows in a channel and an asymmetric diffuser at different Reynolds numbers. Results compare favorably with the one-block benchmark solutions and show a better agreement when compared with approximate NDD predictions. In the latter case, a variation of diffuser geometry is also considered to test the model performance on engineering design tasks with another turbulent model (i.e., Spalart-Allmaras), showing good generalization capability on different turbulence closures.

中文翻译：

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近壁湍流建模的机器学习增强域分解方法

为了解决具有挑战性的近壁湍流建模，同时保持较低的计算成本，提出了结合机器学习技术的近壁非重叠域分解（NDD）方法。使用最近提出的隐式 NDD (INDD)，可以在相对粗糙的网格上使用 Robin 型（滑移）壁边界条件计算解，然后在每次迭代时通过估计的湍流粘度剖面在近壁区域进行校正一个神经网络。为了以可接受的精度保持合理的复杂性，仅采用近壁场属性，即壁法线距离、近壁速度、流向压力梯度和一个近壁尺度参数作为神经网络的输入特征。网络。结合机器学习的好处是双重的。首先，与近似方法中使用的传统代数函数相比，可以更准确地预测近壁湍流粘度。其次，与传统的 NDD 类似，本模拟比完全解析的单块模拟节省了一个数量级的计算成本。该方法的准确性和效率在测试用例中得到了证明$k\text{-}\epsilon$模型，包括通道中的湍流和不同雷诺数的不对称扩散器。结果与单块基准解决方案相比毫不逊色，并且与近似 NDD 预测相比显示出更好的一致性。在后一种情况下，还考虑了扩散器几何形状的变化，以使用另一个湍流模型（即Spalart-Allmaras）测试工程设计任务的模型性能，在不同的湍流闭合上显示出良好的泛化能力。