Learning Three-Dimensional Flow for Interactive Aerodynamic Design

Umetani, Nobuyuki and Bickel, Bernd (2018) Learning Three-Dimensional Flow for Interactive Aerodynamic Design. ACM Transactions on Graphics, 37 (4). ISSN 1557-7368

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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.

Item Type: Article
DOI: 10.1145/3197517.3201325
Subjects: 000 Computer science, knowledge & general works > 000 Computer science, knowledge & systems > 003 Systems
000 Computer science, knowledge & general works > 000 Computer science, knowledge & systems > 004 Data processing & computer science
Research Group: Bickel Group
Depositing User: Bernd Bickel
Date Deposited: 01 Oct 2018 14:26
Last Modified: 01 Oct 2018 14:26
URI: https://repository.ist.ac.at/id/eprint/1049

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