Machine-learning (ML) techniques have bloomed in recent years, especially in fluid mechanics applications. In this paper, we trained, validated and compared two types of ML-based models to augment Reynolds-averaged Navier–Stokes (RANS) simulations. The methodology was tested in a series of flows around bumps, characterized by different levels of flow separation and curvatures. Initially, the ML-based models were trained in three configurations presenting attached flow, small and moderate separation and tested in two configurations presenting incipient and large separation. The output quantity of the machine-learning model is the turbulent viscosity as done in Volpiani et al. (2022). The new models based on artificial neural networks (NN) and random forest (RF) improved the results if compared to the baseline Spalart–Allmaras model, in terms of velocity field and skin-friction profiles. We noted that NN has better extrapolation properties than RF, but the skin-friction distribution can present small oscillations when using certain input features. These oscillations can be reduced if the RF model is employed. One major advantages of RF is that raw quantities can be given as input features, avoiding normalization issues (such as division by zero) and allowing a larger number of universal inputs. At the end, we propose a mixed NN-RF model that combines the strengths of each method and, as a result, improves considerably the RANS prediction capability, even for a case with strong separation where the Boussinesq hypothesis (and therefore the eddy-viscosity assumption) lacks accuracy.

中文翻译：

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随机森林是否比神经网络更适合增强 RANS 湍流模型？

近年来，机器学习 (ML) 技术蓬勃发展，尤其是在流体力学应用领域。在本文中，我们训练、验证和比较了两种基于 ML 的模型，以增强雷诺平均纳维斯托克斯 (RANS) 模拟。该方法在凸块周围的一系列流动中进行了测试，其特征是不同程度的流动分离和曲率。最初，基于机器学习的模型在呈现附着流、小和中等分离的三种配置中进行训练，并在呈现初期和大分离的两种配置中进行测试。机器学习模型的输出量是湍流粘度，如 Volpiani 等人所做的那样。（2022）。与基线 Spalart-Allmaras 模型相比，基于人工神经网络 (NN) 和随机森林 (RF) 的新模型在速度场和皮肤摩擦曲线方面改进了结果。我们注意到，神经网络比 RF 具有更好的外推特性，但在使用某些输入特征时，皮肤摩擦分布可能会出现小振荡。如果采用 RF 模型，则可以减少这些振荡。RF 的一个主要优点是可以将原始数量作为输入特征给出，从而避免标准化问题（例如除以零）并允许大量通用输入。最后，我们提出了一种混合 NN-RF 模型，该模型结合了每种方法的优点，从而显着提高了 RANS 预测能力，即使对于布辛涅斯克假设（因此涡粘性假设）缺乏准确性。