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arXiv 2103.13898physics.flu-dyn
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循环神经网络用于层流-湍流转捩的端到端模拟

Recurrent Neural Network for End-to-End Modeling of Laminar-Turbulent Transition

Muhammad I. Zafar, Meelan M. Choudhari, Pedro Paredes, Heng Xiao

摘要:

层流-湍流转捩的准确预测是多流态气动设计计算流体动力学模拟的关键。传统的过渡预测方法不能很容易地扩展到流动配置的过渡过程取决于一个大的参数集。相比之下,神经网络方法允许考虑更高维的输入特征,而不会损害传统数据驱动模型的效率和准确性。早期提出的神经网络方法遵循在宽频率范围内预测不稳定性增长率的繁琐方法,然后对其进行处理以获得N因子包络,然后基于相关的N因子获得过渡位置。本文提出了一种基于循环神经网络的端到端转捩模型,该模型通过对二维翼型表面的平均边界层廓线进行序列处理,直接预测二维翼型的N因子包络线和转捩位置。建议的过渡模型已经开发和评估使用的大型数据库的53个翼型在广泛的弦雷诺数和攻角。本文提出的序列到序列转换模型提供了一种比早期神经网络方法更直接的方法来准确预测过渡位置,早期神经网络方法预测沿翼型的固定位置处的单个不稳定模式的局部放大率。在各种应用中遇到的翼型的大范围导致额外的困难。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。

英文摘要:

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 sequence-to-sequence transduction model proposed herein provides a more direct approach for accurate predictions of the transition location than the earlier neural network methods, which predict the local amplification rate of a single instability mode at a fixed location along the airfoil. 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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