  Chemical kinetic models are an essential component in the development and
optimisation of combustion devices through their coupling to multi-dimensional
simulations such as computational fluid dynamics (CFD). Low-dimensional kinetic
models which retain good fidelity to the reality are needed, the production of
which requires considerable human-time cost and expert knowledge. Here, we
present a novel automated compute intensification methodology to produce
overly-reduced and optimised (compact) chemical kinetic models. This algorithm,
termed Machine Learned Optimisation of Chemical Kinetics (MLOCK),
systematically perturbs each of the four sub-models of a chemical kinetic model
to discover what combinations of terms results in a good model. A virtual
reaction network comprised of n species is first obtained using conventional
mechanism reduction. To counteract the imposed decrease in model performance,
the weights (virtual reaction rate constants) of important connections (virtual
reactions) between each node (species) of the virtual reaction network are
numerically optimised to replicate selected calculations across four sequential
phases. The first version of MLOCK, (MLOCK1.0) simultaneously perturbs all
three virtual Arrhenius reaction rate constant parameters for important
connections and assesses the suitability of the new parameters through
objective error functions, which quantify the error in each compact model
candidate's calculation of the optimisation targets, which may be comprised of
detailed model calculations and/or experimental data. MLOCK1.0 is demonstrated
by creating compact models for the archetypal case of methane air combustion.
It is shown that the NUGMECH1.0 detailed model comprised of 2,789 species is
reliably compacted to 15 species (nodes), whilst retaining an overall fidelity
of ~87% to the detailed model calculations, outperforming the prior
state-of-art.
