CFD-Guided DoE-ML Optimization Methods in a Heavy-Duty Hydrogen Engine

This study introduces a CFD-guided Design of Experiments (DoE) and Machine Learning (ML) framework for the co-optimization of piston and pre-chamber geometries in a passive pre-chamber heavy-duty hydrogen engine operating at medium and low loads. Starting from a reference configuration—an omega-type piston and a methane-optimized pre-chamber—the design space was parameterized using seven geometric variables. A Sobol sequence was employed to generate 96 randomized design variants in the DoE, each evaluated through high-fidelity 3D-CFD simulations to capture key combustion and performance metrics. The resulting dataset served as the foundation for developing and evaluating several ML regression models. A rigorous ML workflow was adopted, featuring 5-fold cross-validation and hyperparameter tuning via Bayesian optimization to ensure generalization and robustness. Model selection was based on multi-metric performance criteria including prediction accuracy, error stability, and sensitivity to design changes. The selected model demonstrated strong predictive capabilities across the design space and was integrated into an iterative optimization loop that continuously refined geometry predictions by incorporating additional CFD runs. This adaptive simulation-learning framework led to improved model accuracy and enabled rapid exploration of high-potential design regions. Beyond reducing time for technology deployment relative to expert-guided design strategies, the ML models offered interpretability by exposing key geometric sensitivities and highlighting high-impact design directions for enhanced hydrogen combustion.