The use of machine learning (ML) based surrogate models is a promising technique to significantly accelerate simulation-based design optimization of IC engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, surrogate-based optimization for IC engine applications suffers from two main issues. First, training ML models requires hyperparameter selection, often involving trial-and-error combined with domain expertise. The second issue is that the data required to train these models is often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a Super Learner model to obtain better test performance. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization, is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization, while also reducing the number of simulations needed for performing surrogate-based design optimization.

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