Neural Networks for Finite-Rate Chemistry in Combustion Simulations: Physical Requirements, Predictive Capability, and Cost

This review examines when neural networks can replace or accelerate finite-rate chemistry in combustion simulations. It analyzes how reaction-step errors propagate, computes the accuracy each target requires for methane, hydrogen, and ammonia/hydrogen, and assesses 125 studies against these requirements. Set by the true increment of the state per step, they differ between targets. The ignition delay responds logarithmically to a bias but nearly proportionally to a relative error in the radical increments where the radicals carry the explosive mode, as in hydrogen below the extended second explosion limit. For NO, the formation route matters: thermal NO responds linearly to a bias accumulated over the residence time, whereas fuel NO forms with large increments during ignition. Root-mean-square errors reported for a hydrogen flame exceed the computed bias tolerances of hydrogen ignition by three to six orders of magnitude and cannot show whether ignition is predicted. In controlled comparisons, coverage of the flow states, exact element conservation, and training on sequences of steps made coupled runs stable. Networks reproduced flame speeds and mean temperatures within a few percent; low-temperature ignition, extinction, nitrogen species, and heat loss needed training aimed at their states and time scales. With equal hardware and mechanisms, networks accelerated the chemistry about tenfold and whole simulations a few times, within the bound set by the chemistry share.