Learning three-dimensional flow for interactive aerodynamic design Journal Article


Author(s): Umetani, Nobuyuki; Bickel, Bernd
Article Title: Learning three-dimensional flow for interactive aerodynamic design
Affiliation IST Austria
Abstract: We present a data-driven technique to instantly predict how fluid flows around various three-dimensional objects. Such simulation is useful for computational fabrication and engineering, but is usually computationally expensive since it requires solving the Navier-Stokes equation for many time steps. To accelerate the process, we propose a machine learning framework which predicts aerodynamic forces and velocity and pressure fields given a threedimensional shape input. Handling detailed free-form three-dimensional shapes in a data-driven framework is challenging because machine learning approaches usually require a consistent parametrization of input and output. We present a novel PolyCube maps-based parametrization that can be computed for three-dimensional shapes at interactive rates. This allows us to efficiently learn the nonlinear response of the flow using a Gaussian process regression. We demonstrate the effectiveness of our approach for the interactive design and optimization of a car body.
Keywords: fluid simulation; Machine learning; Gaussian process; Parameterization
Journal Title: ACM Trans. Graph.
Volume: 37
Issue 4
ISSN: 0730-0301
Publisher: ACM  
Date Published: 2018-08-04
Start Page: Article number: A50
DOI: 10.1145/3197517.3201325
Open access: no
IST Austria Authors
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