{"title":"基于深度学习的三维变形机翼快速气动分析方法","authors":"Ruolong Xie , Zhiqiang Wan , De Yan , Wenwei Qiu","doi":"10.1016/j.ast.2024.109690","DOIUrl":null,"url":null,"abstract":"<div><div>Morphing wings have garnered widespread attention due to their superior aerodynamic efficiency. However, in the design process, accurately and efficiently obtaining the three-dimensional flow field of morphing wings remains a challenging issue. This paper proposes a Deep Learning-based method for predicting the flow field of a Biomimetic Morphing Wings to address this problem. Firstly, a Coordinate Transformation Mechanism is established for the studied Biomimetic Morphing Wing to ensure the consistency of grid point coordinates between different wing shapes. Secondly, a two-level Flow Field Prediction Model is constructed, consisting of grid point prediction level and physical quantity continuity adjustment level. Using this method, the flow field of the Biomimetic Morphing Wing was predicted, and the predication result were similar to those of numerical simulations. This indicates that the proposed method maintains high prediction accuracy while reducing computation time, thereby enhancing the analysis efficiency of the morphing wing's flow field.</div></div>","PeriodicalId":50955,"journal":{"name":"Aerospace Science and Technology","volume":"155 ","pages":"Article 109690"},"PeriodicalIF":5.0000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast aerodynamic analysis method for three-dimensional morphing wings based on deep learning\",\"authors\":\"Ruolong Xie , Zhiqiang Wan , De Yan , Wenwei Qiu\",\"doi\":\"10.1016/j.ast.2024.109690\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Morphing wings have garnered widespread attention due to their superior aerodynamic efficiency. However, in the design process, accurately and efficiently obtaining the three-dimensional flow field of morphing wings remains a challenging issue. This paper proposes a Deep Learning-based method for predicting the flow field of a Biomimetic Morphing Wings to address this problem. Firstly, a Coordinate Transformation Mechanism is established for the studied Biomimetic Morphing Wing to ensure the consistency of grid point coordinates between different wing shapes. Secondly, a two-level Flow Field Prediction Model is constructed, consisting of grid point prediction level and physical quantity continuity adjustment level. Using this method, the flow field of the Biomimetic Morphing Wing was predicted, and the predication result were similar to those of numerical simulations. This indicates that the proposed method maintains high prediction accuracy while reducing computation time, thereby enhancing the analysis efficiency of the morphing wing's flow field.</div></div>\",\"PeriodicalId\":50955,\"journal\":{\"name\":\"Aerospace Science and Technology\",\"volume\":\"155 \",\"pages\":\"Article 109690\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Aerospace Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1270963824008198\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aerospace Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1270963824008198","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
Fast aerodynamic analysis method for three-dimensional morphing wings based on deep learning
Morphing wings have garnered widespread attention due to their superior aerodynamic efficiency. However, in the design process, accurately and efficiently obtaining the three-dimensional flow field of morphing wings remains a challenging issue. This paper proposes a Deep Learning-based method for predicting the flow field of a Biomimetic Morphing Wings to address this problem. Firstly, a Coordinate Transformation Mechanism is established for the studied Biomimetic Morphing Wing to ensure the consistency of grid point coordinates between different wing shapes. Secondly, a two-level Flow Field Prediction Model is constructed, consisting of grid point prediction level and physical quantity continuity adjustment level. Using this method, the flow field of the Biomimetic Morphing Wing was predicted, and the predication result were similar to those of numerical simulations. This indicates that the proposed method maintains high prediction accuracy while reducing computation time, thereby enhancing the analysis efficiency of the morphing wing's flow field.
期刊介绍:
Aerospace Science and Technology publishes articles of outstanding scientific quality. Each article is reviewed by two referees. The journal welcomes papers from a wide range of countries. This journal publishes original papers, review articles and short communications related to all fields of aerospace research, fundamental and applied, potential applications of which are clearly related to:
• The design and the manufacture of aircraft, helicopters, missiles, launchers and satellites
• The control of their environment
• The study of various systems they are involved in, as supports or as targets.
Authors are invited to submit papers on new advances in the following topics to aerospace applications:
• Fluid dynamics
• Energetics and propulsion
• Materials and structures
• Flight mechanics
• Navigation, guidance and control
• Acoustics
• Optics
• Electromagnetism and radar
• Signal and image processing
• Information processing
• Data fusion
• Decision aid
• Human behaviour
• Robotics and intelligent systems
• Complex system engineering.
Etc.