Recurrent neural network for end-to-end modeling of laminar-turbulent transition

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-03-25 DOI:10.1017/dce.2021.11
M. Zafar, Meelan Choudhari, P. Paredes, Heng Xiao
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引用次数: 11

Abstract

Abstract Accurate prediction of laminar-turbulent transition is a critical element of computational fluid dynamics simulations for aerodynamic design across multiple flow regimes. Traditional methods of transition prediction cannot be easily extended to flow configurations where the transition process depends on a large set of parameters. In comparison, neural network methods allow higher dimensional input features to be considered without compromising the efficiency and accuracy of the traditional data-driven models. Neural network methods proposed earlier follow a cumbersome methodology of predicting instability growth rates over a broad range of frequencies, which are then processed to obtain the N-factor envelope, and then, the transition location based on the correlating N-factor. This paper presents an end-to-end transition model based on a recurrent neural network, which sequentially processes the mean boundary-layer profiles along the surface of the aerodynamic body to directly predict the N-factor envelope and the transition locations over a two-dimensional airfoil. The proposed transition model has been developed and assessed using a large database of 53 airfoils over a wide range of chord Reynolds numbers and angles of attack. The large universe of airfoils encountered in various applications causes additional difficulties. As such, we provide further insights on selecting training datasets from large amounts of available data. Although the proposed model has been analyzed for two-dimensional boundary layers in this paper, it can be easily generalized to other flows due to embedded feature extraction capability of convolutional neural network in the model.
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层流-湍流过渡端到端模型的递归神经网络
摘要层流湍流过渡的精确预测是跨多个流态的气动设计计算流体动力学模拟的关键因素。传统的过渡预测方法不能很容易地扩展到其中过渡过程依赖于一组大参数的流动配置。相比之下,神经网络方法允许在不影响传统数据驱动模型的效率和准确性的情况下考虑更高维度的输入特征。早期提出的神经网络方法遵循了一种繁琐的方法,即预测宽频率范围内的不稳定性增长率,然后对其进行处理以获得N因子包络,然后基于相关的N因子来获得过渡位置。本文提出了一种基于递归神经网络的端到端过渡模型,该模型顺序处理沿气动体表面的平均边界层轮廓,以直接预测二维翼型上的N因子包络和过渡位置。所提出的过渡模型是使用53个翼型的大型数据库在广泛的弦雷诺数和攻角范围内开发和评估的。在各种应用中遇到的翼型的大范围导致了额外的困难。因此,我们提供了从大量可用数据中选择训练数据集的进一步见解。尽管本文已经对所提出的模型进行了二维边界层的分析,但由于模型中嵌入了卷积神经网络的特征提取能力,它可以很容易地推广到其他流。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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