Deep Learning Method for Airfoil Flow Field Simulation Based on Unet++

IF 1.8 4区工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal for Numerical Methods in Fluids Pub Date : 2025-01-15 DOI:10.1002/fld.5375

Xie Ruiling, Xu Jie, Chen Jianping, Tan Peizhi

引用次数: 0

Abstract

This paper investigates the accuracy of U-Net++ networks in predicting Reynolds-Averaged Navier-Stokes (RANS) solutions. The study employs the symbolic distance function (SDF) to represent geometry and flow conditions, utilizing parameterized airfoil data from the UIUC (University of Illinois at Urbana-Champaign) airfoil datasets. The research assesses the performance of multiple trained neural networks in predicting pressure and velocity distributions. Specifically, the study examines the influence of varying network weights on solution accuracy. Through the optimization of the model, the research demonstrates that the mean relative error is below 1.72% for a range of previously unseen wing shapes, with a computational speedup factor of up to 1,000× in certain scenarios. The accuracy achieved by this model underscores the significant potential of deep learning-based approaches as reliable tools for aerodynamic design and optimization.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于Unet++的翼型流场模拟深度学习方法

本文研究了 U-Net++ 网络预测雷诺平均纳维-斯托克斯（RANS）解的准确性。研究采用符号距离函数（SDF）来表示几何形状和流动条件，并利用来自 UIUC（伊利诺伊大学香槟分校）机翼数据集的参数化机翼数据。研究评估了多个训练有素的神经网络在预测压力和速度分布方面的性能。具体而言，研究考察了不同网络权重对解决方案准确性的影响。通过对模型进行优化，研究表明，对于一系列以前从未见过的翼型，平均相对误差低于 1.72%，在某些情况下计算速度可提高 1000 倍。该模型所达到的精确度凸显了基于深度学习的方法作为空气动力学设计和优化的可靠工具所具有的巨大潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

International Journal for Numerical Methods in Fluids 物理-计算机：跨学科应用

CiteScore

3.70

自引率

5.60%

发文量

111

审稿时长

8 months

期刊介绍： The International Journal for Numerical Methods in Fluids publishes refereed papers describing significant developments in computational methods that are applicable to scientific and engineering problems in fluid mechanics, fluid dynamics, micro and bio fluidics, and fluid-structure interaction. Numerical methods for solving ancillary equations, such as transport and advection and diffusion, are also relevant. The Editors encourage contributions in the areas of multi-physics, multi-disciplinary and multi-scale problems involving fluid subsystems, verification and validation, uncertainty quantification, and model reduction. Numerical examples that illustrate the described methods or their accuracy are in general expected. Discussions of papers already in print are also considered. However, papers dealing strictly with applications of existing methods or dealing with areas of research that are not deemed to be cutting edge by the Editors will not be considered for review. The journal publishes full-length papers, which should normally be less than 25 journal pages in length. Two-part papers are discouraged unless considered necessary by the Editors.

期刊最新文献

Issue Information Issue Information Issue Information Airfoil Shape Optimization in Ultralow Reynolds Flows Applying a Deep Learning–Genetic Algorithm Framework on a Shear-Stress-Based Inverse Design Method A Hierarchical Multi-Resolution WENO Scheme for Hyperbolic Conservation Laws