ChemZIP: Accelerated Modeling of Complex Aerothermochemical Interactions in Novel Turbomachines for Sustainable High-Temperature Chemical Processes

TL;DR

This paper presents ChemZIP, a machine-learning-based platform that significantly accelerates and improves the modeling of complex aerothermochemical interactions in turbomachines, enabling efficient optimization for sustainable chemical processes.

Contribution

Introduction of ChemZIP, a multifidelity machine-learning methodology with a novel training data generation process and neural network compression, to efficiently model reactive flows in turbomachines.

Findings

Achieves over 95% R2 score on unseen test conditions.

Reduces convergence time by 50 times compared to industry-standard solvers.

Maintains within 10% accuracy in predictive thermochemical states for 3D configurations.

Abstract

This paper introduces a new platform to accelerate the modeling of complex aerothermochemical interactions in new turbomachines, turbo-reactors, to decarbonise 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 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, which 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 into a low-dimensional basis using an autoencoder-like neural network, inspired by but more universal than traditional flamelet-generated manifolds. Verification against 10,000 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 viscous 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 while convergence is achieved 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