Non-Invasive ML-Enhanced Ultrasonic Sensing System for Refrigerant Flow Characterization in Building Heat Pump Systems

This paper introduces a non-invasive ultrasonic sensing system for two-phase refrigerant flow characterization in heat pump circuits used in building energy systems, validated through machine learning (ML)-based regression of the acquired signals. Heat pumps play a crucial role in energy-efficient buildings. However, the absence of a low-cost, non-invasive instrument capable of measuring mass flow rate, mixture density, and vapor quality without disrupting the thermodynamics of a refrigerant circuit remains a gap for smart HVAC systems. A carbon dioxide (CO2) refrigerant circuit was designed to calibrate such a sensing system under representative operating heat pump conditions. Ultrasonic measurements were conducted using piezoelectric transducers clamped onto the refrigerant pipeline. A calibration framework was structured with ground-truth flowmeter labels, establishing a thermodynamic envelope across 10–20 bar and down to −25 °C, and achieving R2 = 0.901 for flow rate, 0.997 for density, and 0.971 for quality, with an overall R2 = 0.956. The proposed measurement system is a plug-and-play kit enabling more efficient next-generation heat pumps, supporting building energy management and performance monitoring. The labeled dataset and calibration methodology provide a basis for training and validating ML regression models for real-time flow property inference in operational HVAC systems.