A computational fluid dynamics (CFD) simulation framework for predicting complex flows is developed on the Tensor Processing Unit (TPU) platform. The TPU architecture is featured with accelerated performance of dense matrix multiplication, large high bandwidth memory, and a fast inter-chip interconnect, which makes it attractive for high-performance scientific computing. The CFD framework solves the variable-density Navier-Stokes equation using a Low-Mach approximation, and the governing equations are discretized by a finite difference method on a collocated structured mesh. It uses the graph-based TensorFlow as the programming paradigm. The accuracy and performance of this framework is studied both numerically and analytically, specifically focusing on effects of TPU-native single precision floating point arithmetic on solution accuracy. The algorithm and implementation are validated with canonical 2D and 3D Taylor Green vortex simulations. To demonstrate the capability for simulating turbulent flows, simulations are conducted for two configurations, namely the decaying homogeneous isotropic turbulence and a turbulent planar jet. Both simulations show good statistical agreement with reference solutions. The performance analysis shows a linear weak scaling and a super-linear strong scaling up to a full TPU v3 pod with 2048 cores.
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