  A novel compute intensification methodology to the construction of
low-dimensional, high-fidelity "compact" kinetic models for NOX formation is
designed and demonstrated. The method adapts the data intensive Machine Learned
Optimization of Chemical Kinetics (MLOCK) algorithm for compact model
generation by the use of a Latin Square method for virtual reaction network
generation. A set of logical rules are defined which construct a minimally
sized virtual reaction network comprising three additional nodes (N, NO, NO2).
This NOX virtual reaction network is appended to a pre-existing compact model
for methane combustion comprising fifteen nodes.
  The resulting eighteen node virtual reaction network is processed by the
MLOCK coded algorithm to produce a plethora of compact model candidates for NOX
formation during methane combustion. MLOCK automatically; populates the terms
of the virtual reaction network with candidate inputs; measures the success of
the resulting compact model candidates (in reproducing a broad set of gas
turbine industry-defined performance targets); selects regions of input
parameters space showing models of best performance; refines the input
parameters to give better performance; and makes an ultimate selection of the
best performing model or models.
  By this method, it is shown that a number of compact model candidates exist
that show fidelities in excess of 75% in reproducing industry defined
performance targets, with one model valid to >75% across fuel/air equivalence
ratios of 0.5-1.0. However, to meet the full fuel/air equivalence ratio
performance envelope defined by industry, we show that with this minimal
virtual reaction network, two further compact models are required.
