Machine Learning Approach to Aerodynamic Analysis of NACA0005 Airfoil: ANN and CFD Integration

IF 3.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Taiba Kouser;Dilek Funda Kurtulus;Srikanth Goli;Abdulrahman Aliyu;Imil Hamda Imran;Luai M. Alhems;Azhar M. Memon
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Abstract

This study presents a machine learning approach to predict the unsteady aerodynamic performance of a NACA0005 airfoil. Data generated by computational fluid dynamics (CFD) is used to train the model for Reynolds numbers $Re \in [{1000-5000}]$ and angles of attack ranging from 9° to 11°. A robust Scaled Conjugate Gradient (SCG) algorithm is employed for efficient training of data. The ANN has a two-layer architecture, with 9 fixed neurons in the first hidden layer and a varying number of neurons in the second layer to achieve optimal performance. The model yielded coefficients of determination ( $R^{2}$ ) of 0.994 (Coefficient of lift ( $C_{l}$ )) and 0.9615 (Coefficient of drag ( $C_{d}$ )) for training, and 0.9563 ( $C_{l}$ ) and 0.9085 ( $C_{d}$ ) for testing. Overall mean errors are found to be less than 1%. It offers a powerful surrogate modeling approach for aerodynamic studies at ultra-low Reynolds numbers. Moreover, it provides rapid and reliable alternatives to traditional CFD simulations in aerodynamic analysis for unseen cases.
NACA0005翼型气动分析的机器学习方法:ANN和CFD集成
本文提出了一种预测NACA0005翼型非定常气动性能的机器学习方法。利用计算流体力学(CFD)生成的数据对雷诺数$Re \in[{1000-5000}]$和攻角范围为9°至11°的模型进行训练。采用鲁棒缩放共轭梯度(SCG)算法对数据进行有效训练。该人工神经网络采用两层架构,第一层隐藏层有9个固定神经元,第二层隐藏层有不同数量的神经元,以达到最优性能。训练模型的决定系数($R^{2}$)为0.994(升力系数($C_{l}$))和0.9615(阻力系数($C_{d}$)),测试模型的决定系数($R^{2}$)为0.9563 ($C_{l}$)和0.9085 ($C_{d}$)。总体平均误差小于1%。它为超低雷诺数下的空气动力学研究提供了一种强大的替代建模方法。此外,它还为未知情况的气动分析提供了替代传统CFD模拟的快速可靠的方法。
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
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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