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We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design.
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Characterisation of wake bi-stability for a square-back geometry with rotating wheels
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Airfrans: High fidelity computational fluid dynamics dataset for approximating reynolds-averaged navier–stokes solutions
Florent Bonnet, Jocelyn Mazari, Paola Cinnella, and Patrick Gallinari · 2022
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Deep learning for real-time aerodynamic evaluations of arbitrary vehicle shapes
Sam Jacob Jacob, Markus Mrosek, Carsten Othmer, and Harald Köstler · 2022
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Physics-informed pointnet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries
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Summary of the 4th high-lift prediction workshop hybrid rans/les technology focus group
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Deep learning-based prediction of aerodynamic performance for airfoils in transonic regime
Tarek Ayman, Mayar A Elrefaie, Eman Sayed, Mohammed Elrefaie, Mahmoud Ayyad, Ahmed A Hamada, and Mohamed M Abdelrahman · 2023
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Pytorch: An imperative style, high-performance deep learning library
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Thiago Rios, Patricia Wollstadt, Bas Van Stein, Thomas Back, Zhao Xu, Bernhard Sendhoff, and Stefan Menzel · 2019
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Surrogate modeling of the aerodynamic performance for airfoils in transonic regime
Mohamed Elrefaie, Tarek Ayman, Mayar A Elrefaie, Eman Sayed, Mahmoud Ayyad, and Mohamed M AbdelRahman · 2024
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Drivaer model geometry
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Cfd simulation of aerodynamic forces on the drivaer car model: Impact of computational parameters
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