4.5 Article

A Neural Network-Inspired Matrix Formulation of Chemical Kinetics for Acceleration on GPUs

Journal

ENERGIES
Volume 14, Issue 9, Pages -

Publisher

MDPI
DOI: 10.3390/en14092710

Keywords

graphics processing units; high-performance computing; chemical kinetics; multi-physics simulation; neural networks; turbulent combustion

Categories

Funding

  1. [DE-AC05-00OR22725]

Ask authors/readers for more resources

This study explores the use of GPU-accelerated neural networks to speed up high-fidelity simulations of turbulent flames with detailed chemical kinetics. By recasting Arrhenius kinetics as a neural network using matrix-based formulations, GPU-optimized linear algebra libraries can be utilized without the need for training. The performance analysis focuses on the scalability of source term calculations with reaction mechanism complexity, revealing trends in cell number effects on GPU saturation and speedup. The matrix-based method enables highly efficient GPU performance and near-peak performance in saturated regimes.
High-fidelity simulations of turbulent flames are computationally expensive when using detailed chemical kinetics. For practical fuels and flow configurations, chemical kinetics can account for the vast majority of the computational time due to the highly non-linear nature of multi-step chemistry mechanisms and the inherent stiffness of combustion chemistry. While reducing this cost has been a key focus area in combustion modeling, the recent growth in graphics processing units (GPUs) that offer very fast arithmetic processing, combined with the development of highly optimized libraries for artificial neural networks used in machine learning, provides a unique pathway for acceleration. The goal of this paper is to recast Arrhenius kinetics as a neural network using matrix-based formulations. Unlike ANNs that rely on data, this formulation does not require training and exactly represents the chemistry mechanism. More specifically, connections between the exact matrix equations for kinetics and traditional artificial neural network layers are used to enable the usage of GPU-optimized linear algebra libraries without the need for modeling. Regarding GPU performance, speedup and saturation behaviors are assessed for several chemical mechanisms of varying complexity. The performance analysis is based on trends for absolute compute times and throughput for the various arithmetic operations encountered during the source term computation. The goals are ultimately to provide insights into how the source term calculations scale with the reaction mechanism complexity, which types of reactions benefit the GPU formulations most, and how to exploit the matrix-based formulations to provide optimal speedup for large mechanisms by using sparsity properties. Overall, the GPU performance for the species source term evaluations reveals many informative trends with regards to the effect of cell number on device saturation and speedup. Most importantly, it is shown that the matrix-based method enables highly efficient GPU performance across the board, achieving near-peak performance in saturated regimes.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available