A Comparative Study of RANS and Machine Learning Techniques for Aerodynamic Analysis of Aerofoils

Author:

M N Lochan1,N Rakshitha1,Prasad B K Swathi1,Sivasubramanian Jayahar1

Affiliation:

1. Ramaiah University of Applied Sciences

Abstract

<div class="section abstract"><div class="htmlview paragraph">The design of aerospace applications necessities precise predictions of aerodynamic properties, often obtained through resource-intensive numerical simulations. These simulations, though they are accurate, but are unsuitable for iterative design processes due to their computational complexity and time-consuming nature. To address this challenge, machine learning, with its data-driven approach and advanced algorithms, offers a novel and cost-effective solution for predicting airfoil characteristics with exceptional precision and speed. This study explores the application of the Back-Propagation Neural Network (BPNN), a machine learning model, to forecast critical aerodynamic coefficients such as lift and drag for airfoils. The BPNN model is fed with input parameters including the airfoils name, flow Reynolds number, and angle of attack in relation to incoming flows. Training the BPNN model is accomplished using a dataset derived from CFD simulations employing the Spalart–Allmaras turbulence model on three distinct NACA series airfoils under varying aerodynamic conditions. The data from these simulations are divided into training (70%) and validation/testing (30%) subsets. The BPNN demonstrates a high level of accuracy in predicting these coefficients, evident through low root mean square error (RMSE) and a close alignment between predicted and actual values.</div></div>

Publisher

SAE International

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3