Aerodynamic Design with Machine Learning

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (22 December 2023) | Viewed by 5824

Special Issue Editors

Department of Mechanical Engineering, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore 119077, Singapore
Interests: aerodynamic shape optimization; aircraft design; advanced machine learning

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Guest Editor
Institut Supérieur de l'Aéronautique et de l'Espace (ISAE - SUPAERO), 10 Av. Edouard Belin, 31400 Toulouse, France
Interests: multidisciplinary design optimization; topology optimization; surrogate modeling; eco-informed material optimization
Department of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, New Territories, Hong Kong
Interests: surrogate modeling; aircraft design and mission analysis; air transportation; data-enhanced modeling; variable-fidelity analysis; multidisciplinary design and optimization; computational modeling for complex systems; machine learning and data analytics

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Guest Editor
Faculty of Mechanical and Aerospace Engineering ITB, Bandung Institute of Technology, Kota Bandung, Jawa Barat 40132, Indonesia
Interests: aerodynamics; optimization methods; robust optimization; multiobjective optimization; machine learning; statistical learning

Special Issue Information

Dear Colleagues,

Machine learning has promoted advances in aerodynamic design optimization in multiple aspects such as aerodynamic modeling, shape parameterization, optimization architectures, etc. In order to provide our community with a briefing on the state-of-the-art and future directions, we organize this special issue to collect relevant studies applied to the design optimization of airfoils, wings, aircraft, turbines, vehicles, etc.

The topics include but are not limited to data-driven surrogate modeling, generalizable off-design constraints, aerodynamic shape parameterization, reinforcement learning, transform learning, multi-fidelity optimization, generative design, data-driven interactive design, etc. We look forward to your high-qualified contributions, especially those with demonstrated benefits compared with conventional methods.

Dr. Jichao Li
Prof. Dr. Joseph Morlier
Dr. Rhea Liem
Dr. Pramudita Satria Palar
Guest Editors

Manuscript Submission Information

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Published Papers (3 papers)

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22 pages, 14228 KiB  
Article
Flow Field Reconstruction of 2D Hypersonic Inlets Based on a Variational Autoencoder
by Zuwei Tan, Runze Li and Yufei Zhang
Aerospace 2023, 10(9), 825; https://doi.org/10.3390/aerospace10090825 - 21 Sep 2023
Viewed by 970
Abstract
The inlet is one of the most important components of a hypersonic vehicle. The design and optimization of the hypersonic inlet is of great significance to the research and development of hypersonic vehicles. In recent years, artificial intelligence techniques have been used to [...] Read more.
The inlet is one of the most important components of a hypersonic vehicle. The design and optimization of the hypersonic inlet is of great significance to the research and development of hypersonic vehicles. In recent years, artificial intelligence techniques have been used to improve the efficiency of aerodynamic optimization. Deep generative models, such as variational autoencoder (VAE) and generative adversarial network (GAN), have been used in a variety of flow problems in the last two years, making fast reconstruction and prediction of the full flow field possible. In this study, a hybrid multilayer perceptron (MLP) combined with a VAE network is used to reconstruct and predict the flow field of a two-dimensional multiwedge hypersonic inlet. The obtained results show that the VAE network can reconstruct the overall flow structure of the hypersonic flow field with high accuracy. The reconstruction accuracy of complex flow structures, such as shockwaves, boundary layers, and separation bubbles, is satisfactory. The flow field prediction model based on the MLP-VAE hybrid model has a strong generalization and generation ability, achieving relatively accurate flow field prediction for inlets with geometric configurations outside the training set. Full article
(This article belongs to the Special Issue Aerodynamic Design with Machine Learning)
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21 pages, 7715 KiB  
Article
Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning Method
by Hao Chen, Chuanqiang Gao, Jifei Wu, Kai Ren and Weiwei Zhang
Aerospace 2023, 10(5), 486; https://doi.org/10.3390/aerospace10050486 - 20 May 2023
Cited by 1 | Viewed by 1715
Abstract
Transonic buffet is a phenomenon of large self-excited shock oscillations caused by shock wave-boundary layer interaction, which is one of the common flow instability problems in aeronautical engineering. This phenomenon involves unsteady flow, which makes optimal design more difficult. In this paper, aerodynamic [...] Read more.
Transonic buffet is a phenomenon of large self-excited shock oscillations caused by shock wave-boundary layer interaction, which is one of the common flow instability problems in aeronautical engineering. This phenomenon involves unsteady flow, which makes optimal design more difficult. In this paper, aerodynamic shape optimization design is combined with reinforcement learning to address the problem of transonic buffet. Using the deep deterministic policy gradient (DDPG) algorithm, a reinforcement learning-based design framework for airfoil shape optimization was constructed to achieve effective suppression of transonic buffet. The aerodynamic characteristics of the airfoil were calculated by the computational fluid dynamics (CFD) method. After optimization, the buffet onset angles of attack of the airfoils NACA0012 and RAE2822 were improved by 2° and 1.2° respectively, and the lift-drag ratios improved by 83.5% and 30% respectively. Summarizing and verifying the optimization results, three general conclusions can be drawn to improve the buffet performance: (1) narrowing of the leading edge of the airfoil; (2) situating the maximum thickness position at approximately 0.4 times the chord length; (3) increasing the thickness of the trailing edge within a certain range. This paper established a reinforcement learning-based unsteady optimal design method that enables the optimization of unsteady problems, including buffet. Full article
(This article belongs to the Special Issue Aerodynamic Design with Machine Learning)
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11 pages, 4178 KiB  
Perspective
Aerodynamic Intelligent Topology Design (AITD)-A Future Technology for Exploring the New Concept Configuration of Aircraft
by Peng Liao, Wei Song, Peng Du, Feng Feng and Yudong Zhang
Aerospace 2023, 10(1), 46; https://doi.org/10.3390/aerospace10010046 - 03 Jan 2023
Cited by 2 | Viewed by 1737
Abstract
With the increasing requirements for aerodynamic performance, aerodynamic configuration design of aircraft based on traditional design experience has gradually failed to meet the needs of the future. Therefore, the new concept aerodynamic shape design will be the development trend for future aircraft, but [...] Read more.
With the increasing requirements for aerodynamic performance, aerodynamic configuration design of aircraft based on traditional design experience has gradually failed to meet the needs of the future. Therefore, the new concept aerodynamic shape design will be the development trend for future aircraft, but the current new concept aerodynamic shape design is still based on the designer’s understanding of the existing flow physics. One novel technology that can be useful is topology design. Compared with traditional design, topology design not only has more undetermined parameters, but also its topology variables have a greater impact on the design goals. In this perspective, we propose the concept of Artificial Intelligent Topology Design (AITD) for aerodynamic configuration design based on topology design and artificial intelligence technology and discuss its potential in the application of the new concept of aerodynamic configuration design. Full article
(This article belongs to the Special Issue Aerodynamic Design with Machine Learning)
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