Impact of Sensor Accuracy and Model Calibration on Simulation of Heat Pumps with Refrigerant Leakage Faults

Soft operational faults can noticeably degrade the performance of heat pumps and influence key monitored variables, emphasizing the need for reliable Fault Detection, Diagnosis, and Evaluation (FDDE) strategies. The BEYOND project tackles this challenge by analyzing simultaneous soft faults using a calibrated simulation model informed by data from a dedicated test rig. Achieving reliable results depends on both accurate measurements and proper model calibration. However, sensor uncertainty and errors in sub-models and correlations calibration can compromise model reliability. This work investigates the influence of measurement accuracy and calibration quality on both experimental variables and simulation outcomes for a residential air-to-water heat pump operating in cooling mode, with particular focus on refrigerant charge estimation. Two sensor configurations—“low accuracy” and “high accuracy”—are assessed, representing commercial- and laboratory-grade instruments, respectively, along with two corresponding calibration strategies. In the low-accuracy case, uncertainties around 10% were found for cooling capacity, energy efficiency ratio, and refrigerant mass flow rate, whereas high-accuracy setups reduced these to approximately 3%. Ultimately, the comparison between experimental and model-derived uncertainties confirms that achieving reliable predictions requires a balanced investment in both high-quality instrumentation and careful model calibration. Overall, this study serves as a crucial tool during the preliminary design of an experimental setup, assisting in the selection of a sensor suite that ensures not only the reliability of secondary variables and KPIs but also a robust and accurate calibration of physics-based models using the acquired experimental data.