Surrogate Modeling of Refrigerant Circuits Using Machine Learning

Naoto Naka, Bisser Raytchev, Takumi Uemura, Mikimasa Kawaguchi, Kenta Kobayashi, Yoshiharu Ueki, Yoshitaka Wada, Toru Higaki, Kazufumi Kaneda

Abstract: In this paper we address the computational inefficiency of parameter studies for automotive refrigerant circuits, which currently rely on high-fidelity but time-consuming CAE simulations. To improve time efficiency, we propose two novel machine learning models—DeepOTransformer and DeepOLSTM—trained on control values, intermediate physical parameters, power consumption, and heat exchange data. These models predict the refrigerant circuit’s physical quantities, power usage, and heat transfer rates more rapidly than conventional physics-based simulations. Compared to a baseline model, our approach achieves higher accuracy and significantly faster computation, making it a promising method for analyzing various operating conditions in automotive refrigerant systems efficiently.

External IDs:dblp:conf/ijcnn/NakaRUKKUWHK25