The Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics
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Numerical solutions to differential equations are at the core of computational fluid dynamics calculations. As the size and complexity of the simulations grow, so does the need for computational power and time. As the size and complexity of the simulations grow, so does the need for computational power and time. Solving the equations in parallel can dramatically reduce the time to solution. While traditionally done on CPU, unlocking the massive number of computational cores on GPU is highly desirable. Many efforts have been made to implement stiff chemistry solvers on GPUs but have not been highly successful because of the logical divergence in traditional stiff algorithms like CVODE or LSODE. This study will demonstrate a machine learned hybrid algorithm implemented in TensorFlow for stiff problems and the speed gains relative to the traditional LSODE solver used in the Multiphase Flow with Interphase eXchanges (MFiX) Computational Fluid Dynamics (CFD) code. The results will show a dramatic decrease in total simulation time while maintaining the same degree of accuracy.
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