  In this work, we introduce DeepFlame, an open-source C++ platform with the
capabilities of utilising machine learning algorithms and pre-trained models to
solve for reactive flows. We combine the individual strengths of the
computational fluid dynamics library OpenFOAM, machine learning framework
Torch, and chemical kinetics program Cantera. The complexity of cross-library
function and data interfacing (the core of DeepFlame) is minimised to achieve a
simple and clear workflow for code maintenance, extension and upgrading. As a
demonstration, we apply our recent work on deep learning for predicting
chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to
highlight the potential of machine learning in accelerating reacting flow
simulation. A thorough code validation is conducted via a broad range of
canonical cases to assess its accuracy and efficiency. The results demonstrate
that the convection-diffusion-reaction algorithms implemented in DeepFlame are
robust and accurate for both steady-state and transient processes. In addition,
a number of methods aiming to further improve the computational efficiency,
e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their
performances are also evaluated and reported. With the deep learning method
implemented in this work, a speed-up of two orders of magnitude is achieved in
a simple hydrogen ignition case when performed on a medium-end graphics
processing unit (GPU). Further gain in computational efficiency is expected for
hydrocarbon and other complex fuels. A similar level of acceleration is
obtained on an AI-specific chip - deep computing unit (DCU), highlighting the
potential of DeepFlame in leveraging the next-generation computing architecture
and hardware.
