Abstract
The ability to efficiently optimise the charging system as part of the complete powertrain for a given duty is attracting significant research interest. A hybrid meanline model integrating Artificial Neural Networks as surrogate models for loss and blockage prediction has shown great potential in wide-range radial turbine performance prediction, demonstrating enhanced accuracy compared to traditional approaches. However, the configuration of surrogate models employed in the hybrid meanline modelling approach has not been studied thoroughly considering the wide range of geometrical variables and the dimensionality of the problem. This paper presents an investigation into a hybrid meanline model with regard to the choice of the surrogate model algorithm and the corresponding impact of the training database size. By optimizing the surrogate model hyperparameters via Bayesian Optimization, the effect of the hyperparameters on the performance of the surrogate models has been isolated. Various hybrid meanline models with different surrogate models were tested on unseen radial turbine geometries, and a comparison of the predicted efficiency is presented.
