边界层
流量(数学)
代表(政治)
人工神经网络
计算机科学
边界(拓扑)
前馈
模拟
机械
气象学
工程类
物理
数学
人工智能
数学分析
控制工程
政治
政治学
法学
作者
Yuanwei Bin,Gaofeng Huang,Xiang I. A. Yang
出处
期刊:AIAA Journal
[American Institute of Aeronautics and Astronautics]
日期:2023-11-01
卷期号:61 (11): 4852-4863
被引量:6
摘要
We use experimental and simulation data to recalibrate the standard Spalart–Allmaras model. Free-shear flow, the buffer layer, the log layer, and flows with adverse pressure gradients are targeted. In this process, the recalibration does not affect untargeted flows. Our approach uses Bayesian optimization and feedforward neural networks. The recalibrated model is implemented in two codes and tested in 11 flows: the majority of which are outside the training dataset and have geometries that are distinctly different from those in the training dataset. We show that the data-enabled recalibration offers improvements while preserving the model’s existing good behavior. In particular, our recalibration improves the model’s behavior in separated flows while preserving its existing good behaviors in flat-plate boundary-layer flows and channel flows. Further analysis indicates that the improvements in separated flow are mainly due to the recalibrated [Formula: see text] function and the resulting, more precise representation of the “slingshot” effect.
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