DIGITAL TWIN MODELING OF AN AMBIENT-AIR-SOURCE, STEAM-GENERATING HEAT PUMP FOR REFRIGERANT TRANSITION PLANNING

Abstract

As the impacts of climate change intensify, electrification of industrial heat generation offers a promising pathway toward decarbonization. Steam-generating heat pumps provide high thermodynamic efficiency but have seen limited adoption due to uncertainties in performance under dynamic operating conditions, across varying heat-source temperatures, and when using regulatory-compliant refrigerants. Reliable predictive modeling can bridge the gap between laboratory validation and real-world deployment. In this work, a physics-based digital twin of an ambient-air-source, steam-generating heat pump was developed in GT-SUITE and calibrated with experimental training data to enhance model fidelity. The digital twin simulated the operation of a laboratory pilot system constructed by AtmosZero and demonstrated strong agreement with experimental measurements under both steady state and transient conditions, within 3% average error across comparison metrics. The model was then adapted to predict system performance with an alternative refrigerant, and subsequent experimental validation of the modified laboratory pilot confirmed that predicted performance matched measured results within 3% average error. This work demonstrates that physics-based digital twins can serve as reliable predictive tools for industrial heat pump systems by accurately capturing performance across a wide range of operating conditions while enabling future refrigerant transitions, system optimization, and off-design quality assurance.