  In recent years, the use of machine learning-based surrogate models for
computational fluid dynamics (CFD) simulations has emerged as a promising
technique for reducing the computational cost associated with engine design
optimization. However, such methods still suffer from drawbacks. One main
disadvantage of is that the default machine learning (ML) hyperparameters are
often severely suboptimal for a given problem. This has often been addressed by
manually trying out different hyperparameter settings, but this solution is
ineffective in a high-dimensional hyperparameter space. Besides this problem,
the amount of data needed for training is also not known a priori. In response
to these issues that need to be addressed, the present work describes and
validates an automated active learning approach, AutoML-GA, for surrogate-based
optimization of internal combustion engines. In this approach, a Bayesian
optimization technique is used to find the best machine learning
hyperparameters based on an initial dataset obtained from a small number of CFD
simulations. Subsequently, a genetic algorithm is employed to locate the design
optimum on the ML surrogate surface. In the vicinity of the design optimum, the
solution is refined by repeatedly running CFD simulations at the projected
optimum and adding the newly obtained data to the training dataset. It is
demonstrated that AutoML-GA leads to a better optimum with a lower number of
CFD simulations, compared to the use of default hyperparameters. The proposed
framework offers the advantage of being a more hands-off approach that can be
readily utilized by researchers and engineers in industry who do not have
extensive machine learning expertise.
