Capacitive sensing of frost growth dynamics on aluminum surfaces with different wettabilities

Abstract

Frost formation is a critical issue in many industries including air conditioning and refrigeration, aviation, transportation, and cryogenics. Frost buildup on heat exchanger surfaces reduces their efficiency by acting as a thermal resistance and by reducing or blocking the airflow. Defrosting is therefore necessary to maintain system efficiency. However, defrost initiation and cycle duration are critical parameters that govern system performance. Excessively early or late defrosting can result in energy loss. Frost detection is thus essential to carry out defrosting efficiently. To know when to initiate defrost, both the density and thickness of the frost must be known as both affect the frost thermal resistance. However, state of the art approaches are not sensitive to both frost density and thickness. Here, a capacitive sensing method is proposed that is sensitive to both the density and thickness of the frost. The method can achieve precise frost detection, offering insights into the ideal time to initiate defrosting by being sensitive to factors that affect frost specific thermal resistance. Experiments are carried out on plain (hydrophilic), superhydrophobic, and superhydrophilic aluminum surfaces at three different surface temperatures (−6 °C, −10 °C, −16 °C) to investigate the sensing performance. The capacitance measured increases with time as frost forms and grows. This change in capacitance, along with mass and thickness data, was used as a tool to characterize frost growth. The results indicate that decreasing wettability (superhydrophilic to plain (hydrophilic) to superhydrophobic) results in a decreasing trend in mass accumulation, frost density, and change in capacitance and an increasing trend in frost thicknesses and thermal resistance. It was also demonstrated that increasing surface temperature results in decreasing changes in capacitance (measurement sensitivity), mass accumulation, frost thicknesses, and thermal resistance, with an increasing trend in frost density. A strong correlation between frost mass and change in capacitance was observed regardless of surface temperature or wettability. This rendered implications for the best time of defrosting initiation. The proposed approach offers high accuracy, sensitivity, and convenience as a non-invasive method to study dynamic frost growth with insights into more optimal defrosting cycles, contributing to the development of more efficient energy systems.