A generative design method of airfoil based on conditional variational autoencoder
The challenges in multi-objective and multi-dimensional optimization design of airfoils, marked by prolonged optimization cycles and low accuracy, call for an efficient solution to expedite airfoil design. This study presents an innovative airfoil generative design model based on a conditional varia...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 139; s. 109461 |
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| Jazyk: | angličtina |
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01.01.2025
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| ISSN: | 0952-1976 |
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| Abstract | The challenges in multi-objective and multi-dimensional optimization design of airfoils, marked by prolonged optimization cycles and low accuracy, call for an efficient solution to expedite airfoil design. This study presents an innovative airfoil generative design model based on a conditional variational autoencoder (CVAE). Initially, to overcome the limitation of insufficient training data, the model leverages the variational autoencoder (VAE) to learn the spatial distribution of University of Illinois at Urbana-Champaign (UIUC) airfoils, enabling the generation of a diverse set of airfoils with similar distributions. Subsequently, two CVAE-based airfoil generation models, the airfoil freedom design model and the airfoil precision design model, are proposed, which can realize diverse airfoil design under different conditions, such as shape and aerodynamic conditions. Furthermore, two measurements of roughness and diversity are introduced to evaluate the quality of the generated airfoils. The impact of different conditions and network parameters on the model’s generation performance is thoroughly analyzed. Results indicate that our proposed model achieves a 65% lower error compared to physics-guided conditional Wasserstein generative adversarial networks (PG-cWGAN) when generating airfoils that satisfy a specific lift coefficient and a 99.99% lower error compared to airfoil pressure distributions generative adversarial networks (Airfoil-Cp-GAN) when generating airfoils that satisfy specific pressure distributions. This method introduces a more creative and accurate approach for aircraft designers in the realm of airfoil design. The code used for this paper is available at https://github.com/liujun39/airfoilvae.
•Airfoil-VAE generates diverse airfoils similar to UIUC, solving data scarcity.•AFD-CVAE reduces error by 65% compared to PG-cWGAN.•APD-CVAE reduces error by 99.99% compared to Airfoil-Cp-GAN. |
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| AbstractList | The challenges in multi-objective and multi-dimensional optimization design of airfoils, marked by prolonged optimization cycles and low accuracy, call for an efficient solution to expedite airfoil design. This study presents an innovative airfoil generative design model based on a conditional variational autoencoder (CVAE). Initially, to overcome the limitation of insufficient training data, the model leverages the variational autoencoder (VAE) to learn the spatial distribution of University of Illinois at Urbana-Champaign (UIUC) airfoils, enabling the generation of a diverse set of airfoils with similar distributions. Subsequently, two CVAE-based airfoil generation models, the airfoil freedom design model and the airfoil precision design model, are proposed, which can realize diverse airfoil design under different conditions, such as shape and aerodynamic conditions. Furthermore, two measurements of roughness and diversity are introduced to evaluate the quality of the generated airfoils. The impact of different conditions and network parameters on the model’s generation performance is thoroughly analyzed. Results indicate that our proposed model achieves a 65% lower error compared to physics-guided conditional Wasserstein generative adversarial networks (PG-cWGAN) when generating airfoils that satisfy a specific lift coefficient and a 99.99% lower error compared to airfoil pressure distributions generative adversarial networks (Airfoil-Cp-GAN) when generating airfoils that satisfy specific pressure distributions. This method introduces a more creative and accurate approach for aircraft designers in the realm of airfoil design. The code used for this paper is available at https://github.com/liujun39/airfoilvae.
•Airfoil-VAE generates diverse airfoils similar to UIUC, solving data scarcity.•AFD-CVAE reduces error by 65% compared to PG-cWGAN.•APD-CVAE reduces error by 99.99% compared to Airfoil-Cp-GAN. |
| ArticleNumber | 109461 |
| Author | Wang, Xu Tian, Yuan Zhao, Tun Chen, Hai Qian, Weiqi Sun, Haisheng He, Lei |
| Author_xml | – sequence: 1 givenname: Xu orcidid: 0000-0001-7730-0296 surname: Wang fullname: Wang, Xu organization: Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China – sequence: 2 givenname: Weiqi surname: Qian fullname: Qian, Weiqi organization: Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China – sequence: 3 givenname: Tun orcidid: 0000-0003-4428-161X surname: Zhao fullname: Zhao, Tun email: zhaotun.aero@live.com organization: Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China – sequence: 4 givenname: Hai orcidid: 0000-0003-4145-4917 surname: Chen fullname: Chen, Hai organization: Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China – sequence: 5 givenname: Lei surname: He fullname: He, Lei organization: Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China – sequence: 6 givenname: Haisheng surname: Sun fullname: Sun, Haisheng organization: Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China – sequence: 7 givenname: Yuan surname: Tian fullname: Tian, Yuan organization: Chengdu Aeronautic Polytechnic, Chengdu, 610100, China |
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| Cites_doi | 10.1145/15922.15903 10.1016/j.engappai.2023.107388 10.1109/TNNLS.2021.3111911 10.1063/1.5094943 10.2514/1.29958 10.1016/j.apm.2021.03.019 10.1016/j.ast.2023.108268 10.1016/j.cma.2024.116746 10.2514/1.J059317 10.1016/j.oceaneng.2022.113000 10.3390/sym12040544 10.1061/JAEEEZ.ASENG-4508 10.1021/ac60214a047 10.1016/j.ast.2015.01.030 10.2514/1.J061234 10.1016/j.ast.2011.08.010 10.2514/1.J057894 10.1016/j.paerosci.2022.100849 10.1016/j.engappai.2021.104560 10.1016/j.ast.2020.105949 |
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| Keywords | Deep learning Conditional variational autoencoder Variational autoencoder Airfoil design Generative model |
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| Title | A generative design method of airfoil based on conditional variational autoencoder |
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