Keywords: pantograph-catenary system, contact force, Gaussian process regression, surrogate model, physical-based model, computational fluid dynamics.

It is important to evaluate the contact force between pantograph and catenary for stable energy supply to the trains. The magnitude and variation range of contact force determine the quality of current receiving and safe operation of the train. Therefore, a rapid and accurate prediction of contact force is of great significance. However, collecting contact force data through experiments is challenging, and obtaining timely results using numerical simulations is not always feasible. In this study, we propose an efficient simulation-based surrogate approach based on Gaussian process regression, combined with meta-heuristic optimization, to predict key parameters of pantograph-catenary system, which are responsible for the energy transfer quality. A pantograph-catenary model is established and validated using Finite Element Method, which serves to generate training and test data. Gaussian process regression (GPR) is utilized for estimation. A new developed meta-heuristic optimization, i.e binary Hunger Game Search (HGS), is applied on feature selection. To enhance the performance of HGS, chaos mechanism is embedded, resulting in Chaos-HGS GPR (CHGS-GPR). It is found that the proposed CHGS-GPR provides rather accurate prediction for the mean value of contact force, and can be extended to the preliminary design of railway lines, real-time evaluation and control of train operations.

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