Machine learning-accelerated aerodynamic inverse design

[1]  N. Doner,et al.  Artificial neural network models for heat transfer in the freeboard of a bubbling fluidised bed combustion system , 2023, Case Studies in Thermal Engineering.

[2]  Yingxue Yao,et al.  Blade design and analysis of centrifugal compressors for the transcritical carbon dioxide refrigeration cycle , 2023, Case Studies in Thermal Engineering.

[3]  Jie Li,et al.  Double-decoupled inverse design of natural laminar flow nacelle under transonic conditions , 2023, Chinese Journal of Aeronautics.

[4]  Zhonghua Han,et al.  An efficient geometric constraint handling method for surrogate-based aerodynamic shape optimization , 2023, Engineering Applications of Computational Fluid Mechanics.

[5]  Guowei Yang,et al.  Data-driven rapid prediction model for aerodynamic force of high-speed train with arbitrary streamlined head , 2022, Engineering Applications of Computational Fluid Mechanics.

[6]  E. Ferrer,et al.  Aeroacoustic airfoil shape optimization enhanced by autoencoders , 2022, Expert Syst. Appl..

[7]  M. Tyan,et al.  Rapid Airfoil Inverse Design Method with a Deep Neural Network and Hyperparameter Selection , 2022, International Journal of Aeronautical and Space Sciences.

[8]  Cao Shuyang,et al.  Numerical simulation of wind loads and aerodynamic characteristics of streamlined bridge decks under tornado-like vortices , 2022, Journal of Fluids and Structures.

[9]  Chaohe Chen,et al.  An innovative aerodynamic design methodology of wind turbine blade models for wind tunnel real-time hybrid tests based on genetic algorithm , 2022, Ocean Engineering.

[10]  Jianchun Wang,et al.  Subgrid-scale modelling using deconvolutional artificial neural networks in large eddy simulations of chemically reacting compressible turbulence , 2022, International Journal of Heat and Fluid Flow.

[11]  Liming Xuan,et al.  Knowledge-based turbomachinery design system via a deep neural network and multi-output Gaussian process , 2022, Knowl. Based Syst..

[12]  A. Warey,et al.  Generative Inverse Design of Aerodynamic Shapes Using Conditional Invertible Neural Networks , 2022, J. Comput. Inf. Sci. Eng..

[13]  Liang Wang,et al.  CFD simulations of aerodynamic characteristics for the three-blade NREL Phase VI wind turbine model , 2022, Energy.

[14]  J. Martins,et al.  Machine Learning in Aerodynamic Shape Optimization , 2022, Progress in Aerospace Sciences.

[15]  Shuyue Wang,et al.  Framework of Nacelle Inverse Design Method Based on Improved Generative Adversarial Networks , 2022, Aerospace Science and Technology.

[16]  N. Kurimoto,et al.  Unsteady aerodynamic simulations by the lattice Boltzmann method with near-wall modeling on hierarchical Cartesian grids , 2021, Computers & Fluids.

[17]  Kwanjung Yee,et al.  Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil , 2021, Engineering with Computers.

[18]  A.J. Torregrosa,et al.  On the application of artificial neural network for the development of a nonlinear aeroelastic model , 2021 .

[19]  Weiwei Zhang,et al.  Data-driven modeling for unsteady aerodynamics and aeroelasticity , 2021, Progress in Aerospace Sciences.

[20]  Christian Breitsamter,et al.  Data-driven prediction of unsteady pressure distributions based on deep learning , 2021, Journal of Fluids and Structures.

[21]  Dae-Seung Cho,et al.  Development and validation of a hybrid aerodynamic design method for curved diffusers using genetic algorithm and ball-spine inverse design method , 2021, Alexandria Engineering Journal.

[22]  Cheng He,et al.  An inverse design method for supercritical airfoil based on conditional generative models , 2021 .

[23]  J. Ray,et al.  Projection-based model reduction of dynamical systems using space–time subspace and machine learning , 2021, Computer Methods in Applied Mechanics and Engineering.

[24]  Stephan Hoyer,et al.  Machine learning–accelerated computational fluid dynamics , 2021, Proceedings of the National Academy of Sciences.

[25]  Yujie Zhu,et al.  Proper orthogonal decomposition assisted inverse design optimisation method for the compressor cascade airfoil , 2020 .

[26]  Zhenghong Gao,et al.  Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation , 2020, Chinese Journal of Aeronautics.

[27]  Paresh Halder,et al.  Coupled CAD-CFD automated optimization for leading and trailing edge of an axial impulse turbine blade , 2020 .

[28]  Jai Ahuja,et al.  Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization , 2020, 2008.06731.

[29]  Zhao Liu,et al.  Nonlinear unsteady bridge aerodynamics: Reduced-order modeling based on deep LSTM networks , 2020 .

[30]  Xi-yun Lu,et al.  Deep learning methods for super-resolution reconstruction of turbulent flows , 2020, Physics of Fluids.

[31]  Q. Zheng,et al.  Use of computational fluid dynamics to implement an aerodynamic inverse design method based on exact Riemann solution and moving wall boundary , 2020 .

[32]  Gang Sun,et al.  A review of the artificial neural network surrogate modeling in aerodynamic design , 2019, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering.

[33]  K. Taira,et al.  Super-resolution reconstruction of turbulent flows with machine learning , 2018, Journal of Fluid Mechanics.

[34]  Petros Koumoutsakos,et al.  Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks , 2018, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[35]  Ehsanolah Assareh,et al.  Inverse design of airfoils via an intelligent hybrid optimization technique , 2017, Engineering with Computers.

[36]  J. Templeton,et al.  Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , 2016, Journal of Fluid Mechanics.

[37]  M. Kermani,et al.  Three-Dimensional Design of Axial Flow Compressor Blades Using the Ball-Spine Algorithm , 2015 .

[38]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[39]  Gang Sun,et al.  Artificial neural network based inverse design: Airfoils and wings , 2015 .

[40]  E. Shirani,et al.  Inverse design in subsonic and transonic external flow regimes using Elastic Surface Algorithm , 2014 .

[41]  Tom Verstraete,et al.  Multidisciplinary Optimization of a Turbocharger Radial Turbine , 2012 .

[42]  D. M. Titterington,et al.  Bayesian Methods for Neural Networks and Related Models , 2004 .

[43]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[44]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[45]  David Haussler,et al.  Occam's Razor , 1987, Inf. Process. Lett..

[46]  George S. Dulikravich,et al.  Stream function and stream-function-coordinate (SFC) formulation for inviscid flow field calculations , 1986 .

[47]  R. M. Hicks,et al.  Wing Design by Numerical Optimization , 1977 .

[48]  L. C. Woods The design of two-dimensional aerofoils with mixed boundary conditions , 1955 .

[49]  Xinshuai Zhang,et al.  Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization , 2021, Computer Methods in Applied Mechanics and Engineering.

[50]  Jinglei Xu,et al.  Inverse design method on scramjet nozzles based on maximum thrust theory , 2020 .

[51]  Radford M. Neal Bayesian learning for neural networks , 1995 .

[52]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.