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CSP-inspired adaptive chemistry reduction for combustion simulations without tabulation

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Abstract
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Modern combustion systems increasingly operate under challenging conditions, including Moderate and Intense Low Oxygen Dilution (MILD) combustion, high Flue Gas Recirculation (FGR), and hydrogen-rich fuels, driven by stringent emission reduction requirements. Traditional chemistry reduction methods, such as the Flamelet Generated Manifold (FGM), rely on restrictive assumptions about flame structure and fail under these emerging operating conditions where classical flame fronts become ill-defined or entirely absent. This work presents a novel Computational Singular Perturbation (CSP)-inspired chemistry reduction framework that addresses these limitations. The methodology employs analysis of chemical timescales to identify dominant species followed by a realtime homogeneous correction method designed to compute the complete chemical species profile from the reduced species basis and local flow conditions. This strategic adaptive retention of chemical information creates an intermediate level of detail based on first principles, eliminating concerns about user fine-tuning and the definitions and tabulation of basis flamelets, while achieving significant computational savings. To further enhance efficiency, machine learning acceleration through deep neural networks is explored as an alternative implementation pathway. Validation of the framework through one-dimensional freely propagating flames demonstrates exceptional accuracy, with flame temperature prediction error of 0.08% and flame speed prediction error of 0.24% using only a third of the participating species. Two-dimensional axisymmetric combustor simulations show significant improvements in flame position and structure prediction compared to detailed chemistry simulations with the Eddy Dissipation Concept (EDC). These results are highly encouraging and warrant first-party implementations of the proposed algorithm with further investigation into more complex industrial reactive flows.

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