3/18/2024 0 Comments Interactive airfoil databaseTurbo expo: power for land, sea, and air 3A: General Shelton ML, Gregory BA, Lamson SH, Moses HL, Doughty RL, Kiss T (1993) Optimization of a transonic turbine airfoil using artificial intelligence, CFD and cascade testing. Sekar V, Zhang M, Shu C, Khoo BC (2019) Inverse design of airfoil using a deep convolutional neural network. Press WH, Teukolsky SA (1990) Savitzky-Golay smoothing filters. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch Oh S, Jung Y, Kim S, Lee I, Kang N (2019) Deep generative design: integration of topology optimization and generative models. Obayashi S, Takanashi S (1996) Genetic optimization of target pressure distributions for inverse design methods. Nash C, Williams CKI (2017) The shape variational autoencoder: a deep generative model of part-segmented 3D objects. Li J, Zhang M (2021) On deep-learning-based geometric filtering in aerodynamic shape optimization. Jameson A (1995) Optimum aerodynamic design using CFD and control theory pp 926–949 Jahangirian A, Shahrokhi A (2009) Inverse design of transonic airfoils using genetic algorithm and a new parametric shape method. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of Wasserstein GANs In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14, MIT Press, Cambridge, MA, USA pp 2672–2680 Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Goodfellow I (2017) NIPS 2016 tutorial: generative adversarial networks Gaggero S, Vernengo G, Villa D, Bonfiglio L (2020) A reduced order approach for optimal design of efficient marine propellers. Inverse Probl Eng 11(1):21–38įilippone A (1995) Airfoil inverse design and optimization by means of viscous-inviscid techniques. vol 54, Berlin, Heidelberg, pp 1–12ĭu X, He P, Martins JRRA (2020) A B-Spline-based generative adversarial network model for fast interactive airfoil aerodynamic optimizationįainekos GE, Giannakoglou KC (2003) Inverse design of airfoils based on a novel formulation of the ant colony optimization method. In: M TJ (Ed) Low reynolds number aerodynamics, Lecture Notes in Engineering. AIAA J 58(11):4723–4735Ĭhen W, Ramamurthy A (2021) Deep generative model for efficient 3D airfoil parameterization and generationĭrela M (1989) Xfoil: An analysis and design system for low Reynolds number airfoils. AIAA J 42(8):1505–1516Ĭhen W, Chiu K, Fuge MD (2020) Airfoil design parameterization and optimization using Bézier generative adversarial networks. Int J Archit Comput 17(1):36–52īui-Thanh T, Damodaran M, Willcox K (2004) Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition. 1904.01083īrown NC, Mueller CT (2019) Design variable analysis and generation for performance-based parametric modeling in architecture. The application of a data-driven, generative model in design. p 2261Īrjovsky M, Bottou L (2017) Towards principled methods for training generative adversarial networksĪrjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networksīarrett TR, Bressloff NW, Keane AJ (2006) Airfoil shape design and optimization using multifidelity analysis and embedded inverse design. United StatesĪchour G, Sung WJ, Pinon-Fischer OJ, Mavris DN (2020) Development of a conditional generative adversarial network for airfoil shape optimization. von Doenhoff AE, Stivers Jr L (1945) Summary of airfoil data. By adopting the proposed method, no additional smoothing method is required to conduct flow analysis.Ībbot I. Then, the results obtained from the proposed model are compared with those of ordinal GANs and variational autoencoders in addition, the proposed method outputs the smoothest shape owing to the earth mover's distance used in cWGAN-gp. In the proposed method, the cWGAN-gp model outputs a shape that indicates the specified lift coefficient. This study employed conditional Wasserstein GAN with gradient penalty (cWGAN-gp) to generate smooth airfoil shapes without any smoothing method. Therefore, Bézier curves or smoothing methods are required. However, the shapes obtained from ordinal GAN models are not smooth hence, flow analysis cannot be conducted. These inverse design problems can be solved by generative adversarial networks (GAN). A typical task is to obtain airfoil shapes that satisfy the required lift coefficient. Machine learning models are recently adopted to generate airfoil shapes.
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