An integrated data-driven workflow for kinetic model development and optimisation: theory and application to OMEs combustion

The development of computationally efficient kinetic mechanisms for alternative fuels remains a critical bottleneck for large-scale CFD simulations in engine design. This work presents a novel integrated data-driven workflow that automates kinetic mechanism development by coupling chemical lumping, skeletal reduction, and parameter optimisation within a unified framework, demonstrated through a compact OME2 combustion mechanism. Using the SciExpeM data ecosystem, the workflow automatically manages mechanism construction, reduction, and optimisation with minimal manual intervention. The approach treats aggressive skeletal reduction as the foundation for two-stage optimisation, where temporary accuracy loss is systematically recovered through targeted parameter adjustment within physically consistent uncertainty bounds. The integrated workflow achieved a decrease in the number of species from 150 to 55 using DRGEP-based reduction, followed by evolutionary parameter optimisation through OptiSMOKE++. Comprehensive validation against experimental data spanning ignition delay times, jet-stirred reactor speciation, and laminar flame speeds demonstrated reliability across operating conditions relevant to compression ignition engines (650–1700 K, 1–50 atm, φ = 0.3–2.0). The optimised mechanism successfully recovered the accuracy lost during reduction, particularly in the critical intermediate temperature regime (770–910 K). The integrated workflow further improved the traditional size-accuracy trade-off through systematic parameter recalibration, achieving computational efficiency for CFD applications while maintaining chemical fidelity comparable to detailed mechanisms. This methodology establishes a foundation for rapid development of compact kinetic mechanisms for alternative fuels with automated workflows ensuring physical consistency.