abstract = {{This paper introduces a new platform to accelerate the modeling of complex aerothermochemical interactions in new turbomachines, called turbo-reactors, to decarbonize chemical processes. While previous work has aerothermally demonstrated the potential to decarbonize the heat input to the reaction, optimizing the reaction efficiency has been a challenge. This is because measuring reaction performance with aerochemical simulations is computationally prohibitive due to the uniquely complex aerodynamics and chemistry within turbomachines. To address this, we introduce a new multifidelity machine-learning-assisted methodology, called ChemZIP, to mitigate this bottleneck. Although data-driven acceleration methodologies exist for combustion, modeling reactive flows along the bladed path of a turbomachine poses new challenges. This has led to a novel training data generation process. Our approach allows rich dynamic responses of the chemical system to be embedded into the training dataset at a fraction of the cost of reacting flow simulations. The resulting high-dimensional composition vector is compressed onto a low-dimensional basis using an autoencoder-like neural network. This data-driven method is inspired by flamelet-generated manifolds but is more universal. Verification against 1,000 unseen one-dimensional test conditions shows an R2 score exceeding 95% across all quantities of interest. Following this, ChemZIP is coupled into a fully-fledged turbulent computational fluid dynamics solver. For a set of process-relevant three-dimensional configurations entirely different from the training data, the predictive accuracy of the thermochemical state remains within 10% of an industry-standard solver (Fluent) while convergence is achieved almost 50 times faster, even for a small mechanism. Therefore, numerical computations are sufficiently fast that aerothermochemical optimization is now feasible for the first time in the design cycle of the turbo-reactor.}},
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