An automated machine learning-genetic algorithm (AutoML-GA) approach for efficient simulation-driven engine design optimization
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In recent years, the use of machine learning techniques as surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising method for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage of such methods is that the default machine learning 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 which need to be addressed, this work describes and validates an automated active learning approach for surrogate-based optimization of internal combustion engines, AutoML-GA. 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 surrogate surface trained with the optimal hyperparameters. 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 shown that this approach leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The developed approach offers the advantage of being a more hands-off approach that can be easily applied by researchers and engineers in industry who do not have a machine learning background.
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