Umetani, Nobuyuki; Bickel, BerndISTA
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.
ACM Trans. Graph.
Umetani N, Bickel B. Learning three-dimensional flow for interactive aerodynamic design. ACM Trans Graph. 2018;37(4). doi:10.1145/3197517.3201325
Umetani, N., & Bickel, B. (2018). Learning three-dimensional flow for interactive aerodynamic design. ACM Trans. Graph. ACM. https://doi.org/10.1145/3197517.3201325
Umetani, Nobuyuki, and Bernd Bickel. “Learning Three-Dimensional Flow for Interactive Aerodynamic Design.” ACM Trans. Graph. ACM, 2018. https://doi.org/10.1145/3197517.3201325.
N. Umetani and B. Bickel, “Learning three-dimensional flow for interactive aerodynamic design,” ACM Trans. Graph., vol. 37, no. 4. ACM, 2018.
Umetani N, Bickel B. 2018. Learning three-dimensional flow for interactive aerodynamic design. ACM Trans. Graph. 37(4), 89.
Umetani, Nobuyuki, and Bernd Bickel. “Learning Three-Dimensional Flow for Interactive Aerodynamic Design.” ACM Trans. Graph., vol. 37, no. 4, 89, ACM, 2018, doi:10.1145/3197517.3201325.
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