预测强制湍流通道流中表皮摩擦力的演变

IF 2.5 3区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

摘要

本文报告了神经网络(NN)和线性随机估算(LSE)工具预测最小湍流通道(Reτ=165)中的表皮摩擦力演变的能力,该通道是在空间和时间上定位的壁附近施加激励后形成的。比较了两种不同的神经网络架构,即多层感知器(MLP)和卷积神经网络(CNN)。论文描述了预测范围和基于墙壁的传感器类型/大小/数量对每种估计器性能的影响。MLP 和 LSE 的性能非常相似,当传感器小于 60 个墙面单位时,其性能与传感器的大小无关。对于足够小的传感器,CNN 的性能优于 MLP 和 LSE,这表明 CNN 能够将近壁循环的一些非线性特性纳入其对致动后皮肤摩擦演变的预测中。事实上,CNN 是仅使用压力传感器就能实现合理预测能力的唯一架构。预测范围对 NN 和 LSE 的预测能力有很大影响,其皮尔逊相关系数从短时间(即执行时间数量级)的 0.95 到涡旋翻转时间数量级的小于 0.4 不等。对 LSE 和 NN 中的权重和滤波器进行的分析表明,所有估计器的目标都是与条纹相一致的壁面特征,这可以解释为条纹是目前强迫作用下近壁面周期中最有因果关系的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the skin friction’s evolution in a forced turbulent channel flow

The present paper reports on the ability of neural networks (NN) and linear stochastic estimation (LSE) tools to predict the evolution of skin friction in a minimal turbulent channel (Reτ=165) after applying an actuation near the wall that is localized in space and time. Two different NN architectures are compared, namely multilayer perceptrons (MLP) and convolutional neural networks (CNN). The paper describes the effect that the predictive horizon and the type/size/number of wall-based sensors have on the performance of each estimator. The performance of MLPs and LSEs is very similar, and becomes independent of the sensor’s size when they are smaller than 60 wall units. For sufficiently small sensors, the CNN outperforms MLPs and LSEs, suggesting that CNNs are able incorporate some of the non-linearities of the near-wall cycle in their prediction of the skin friction evolution after the actuation. Indeed, the CNN is the only architecture able to achieve reasonable predictive capabilities using pressure sensors only. The predictive horizon has a strong effect on the predictive capacity of both NN and LSE, with a Pearson correlation coefficient that varies from 0.95 for short times (i.e., of the order of the actuation time) to less than 0.4 for times of the order of an eddy turn-over time. The analysis of the weights and filters in the LSE and NNs show that all estimators are targeting wall-signatures consistent with streaks, which is interpreted as the streak being the most causal feature in the near-wall cycle for the present forcing.

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来源期刊
Computers & Fluids
Computers & Fluids 物理-计算机:跨学科应用
CiteScore
5.30
自引率
7.10%
发文量
242
审稿时长
10.8 months
期刊介绍: Computers & Fluids is multidisciplinary. The term ''fluid'' is interpreted in the broadest sense. Hydro- and aerodynamics, high-speed and physical gas dynamics, turbulence and flow stability, multiphase flow, rheology, tribology and fluid-structure interaction are all of interest, provided that computer technique plays a significant role in the associated studies or design methodology.
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