Purpose

This paper aims to improve Reynolds-averaged Navier Stokes (RANS) turbulence models using a data-driven approach based on machine learning (ML). A special focus is put on determining the optimal input features used for the ML model.

Design/methodology/approach

The field inversion and machine learning (FIML) approach is applied to the negative Spalart-Allmaras turbulence model for transonic flows over an airfoil where shock-induced separation occurs.

Findings

Optimal input features and an ML model are developed, which improve the existing negative Spalart-Allmaras turbulence model with respect to shock-induced flow separation.

Originality/value

A comprehensive workflow is demonstrated that yields insights on which input features and which ML model should be used in the context of the FIML approach

中文翻译：

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用于跨音速流预测的 RANS 湍流模型的数据驱动扩充

目的

本文旨在使用基于机器学习 (ML) 的数据驱动方法改进雷诺平均纳维斯托克斯 (RANS) 湍流模型。特别关注确定用于 ML 模型的最佳输入特征。

设计/方法/途径

场反演和机器学习 (FIML) 方法应用于负 Spalart-Allmaras 湍流模型，用于跨翼型上的跨音速流动，其中发生冲击引起的分离。

发现

开发了最佳输入特征和 ML 模型，改进了现有的关于冲击引起的流动分离的负 Spalart-Allmaras 湍流模型。

原创性/价值

展示了一个全面的工作流程，可以深入了解在 FIML 方法的上下文中应该使用哪些输入特征和哪个 ML 模型