期刊
JOURNAL OF SOUND AND VIBRATION
卷 506, 期 -, 页码 -出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jsv.2021.116162
关键词
Gene Expression Programming; Machine learning; Turbulent boundary layer; Wall pressure spectral models
资金
- French global automotive Valeo
This paper introduces a new data-driven approach for establishing empirical models describing turbulent boundary layer wall-pressure spectra. The approach directly builds models from a dataset using Gene Expression Programming, and modifications of the GEP algorithm are proposed to address specific issues in modeling wall pressure spectra. The method demonstrates consistency and better data matching, suggesting new ways to predict the influence of moderate pressure gradient.
This paper presents a new data-driven approach for the establishment of empirical models describing turbulent boundary layer wall-pressure spectra. Unlike other models presented in literature, the new models are not derived by extending previously existing ones, but are directly built from a given dataset through symbolic regression using a machine learning algorithm known as Gene Expression Programming. Two modifications of the GEP algorithm presented in literature are proposed in this work to cope with some issues that are specific to the modelling of wall pressure spectra: a new power terminal and a local optimization loop. The validity of the new approach is first demonstrated using as input a dataset synthesized following the Chase-Howe and Goody models. The method is then applied to experimental data for a flat plate boundary layer. The results indicate that the wall pressure model obtained with the proposed approach remains consistent with previous formulations for zero pressure gradient, while showing a better match with the data and suggesting new ways to predict the influence of moderate pressure gradient & nbsp; (c) 2021 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据