Heat Exchanger Surrogates for a Vapor Compression System

Abstract

Given the computationally intensive nature of heat exchanger simulators, utilizing a data-driven surrogate model for efficiently computing the heat exchanger outputs is desirable. This study focuses on developing integrated surrogate models of heat exchangers for a vapor compression system in Modelica. The surrogate models are designed to serve as steady-state equivalents based on an efficient physics-based model which was calibrated using reference data obtained from a more advanced simulation model. Subsequently, the calibrated model was employed to generate the training and testing data for the development of Gaussian Process (GP) and Multi-Layer Perceptron (MLP) surrogates. The obtained findings indicate that GPs exhibit high accuracy when applied to the heat exchanger's outputs with smooth behavior. GPs also demonstrate excellent data efficiency compared to MLPs. In cases where the GP struggles to model specific outputs effectively, MLPs are able to capture the more complex behavior. Moreover, hyperparameter optimization is employed to identify optimal MLP topologies. Finally, the fast and compact surrogate model was integrated into the Modelica/Dymola environment. This adaptation allowed the surrogate models to be directly combined with the physical model of the heat exchanger.