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Learning the Hard Parts: Scaling Reacting Flow Simulations with Machine Learning

Adam Moses, Naval Research Lab

GTC 2020

Explore a new technique for overcoming bottlenecks in computational fluid dynamics with machine learning. We'll describe using a neural chemistry solver for an order-of-magnitude speedup, opening the doors to problems that are currently impossible. Simulating chemically reacting flow — fluid flow with a chemical source term (such as a flame) — is computationally prohibitive: an at-scale combustor simulation with a practical hydrocarbon fuel can take years! Solving the chemical source term dominates this process, taking up to 90% of the total compute time. This share increases for more complex reactions, resulting in poor scalability. We'll discuss designing a neural network to model hydrogen combustion; integrating this neural chemistry solver into computational fluid dynamics code; analyzing the performance of this new approach; and discussing how these lessons could be applied to other CFD bottlenecks.

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