Conference Agenda

There are several methods available for defrosting or deicing the surfaces of heat exchangers in cold rooms. One of these methods is interlaced glycol (brine) defrost system, where hot liquid is circulated through a seperate circuit within the evaporator to facilitate the defrosting process. In this study, a mathematical model has been developed to analyze interlaced brine defrost. Experimental results show that developed model is predicting key defrost parameters very well. Besides, based on the existing literature, the performance and duration of interlaced glycol defrost are aimed to be predicted. This method, which consists of one main loop and two sub-routines, determines the exit temperature of glycol through an iterative approach. Ultimately, the glycol mass flow rate is determined, allowing glycol to enter the tray at the desired temperature and exit the coil at the required temperature. To validate the developed methodology, a prototype is manufactured and placed in a test chamber. Numerous tests had been conducted under various conditions to verify the methodology's accuracy. To validate this methodology, tests were conducted at four different Reynolds numbers. These Reynolds numbers were selected to represent three different flow regimes (laminar, transition, and turbulent). Based on the results of these experiments, methodology’s performans under different flow regimes has been observed. As a result of the comparisons made, it has been observed that the experimental data is consistent with the mathematical model

During winter operations, the effectiveness of air source heat pump (ASHP) systems can be substantially compromised by the buildup of frost on evaporator coil surfaces. A numerical model for frost growth has been developed that predicts frost accumulation on the outdoor coil of the ASHP and the performance degradation caused by the frost aggregation. The moving boundary technique is used on the refrigerant side of the heat exchanger while on the air side epsilon-NTU is used for quasi steady state heat transfer from the air to the refrigerant. The air side pressure drop increases as frost grows on the fins. The air flowrate is adjusted by a fan model for each time step as the frost thickness increases. The evaporator model can predict non-uniform frost formation, includes air- and refrigerant-flow maldistribution, and predicts superheat using a moving boundary approach. The system model predicts a reduction in the total capacity as the frost thickness increases. This decrease in overall capacity is due to the drop in both suction and discharge temperatures, and the air mass flowrate across the coil, leading to a reduction in the system's coefficient of performance. The model predictions are within ±5% of experimental results.

In heat pump systems, frost formation is a critical issue during cold winter days, significantly impacting heat pump system COP. Despite considerable efforts to simulate the frost growth process, the initial ice propagation stage has been neglected. This work presents a comprehensive study integrating the ice propagation model with the Euler method’s frost growth model to simulate frost formation on various surfaces considering the ice propagation. Our model significantly enhances understanding of frost layer distribution and its impact on thermal and hydraulic boundary layers at the early stages of frost growth. Notably, it captures how the volume fraction of ice, especially the "bumping" shape at the leading and trailing edges, affects both the hydraulic and thermal boundary layers. The model effectively illustrates the critical edge effect on frost growth, providing new insights into the optimization of surface structure design for anti-frosting. This research advances the fundamental knowledge of frost formation and offers novel insights for anti-frosting optimization of heat pump system design.

The defrosting process is essential for maintaining the Coefficient of Performance (COP) of heat pumps during cold winter days. Reverse cycle is a commonly used approach for heat pump defrosting. However, without an appropriate defrosting control strategy, this method can lead to significant energy waste and incomplete defrosting can decrease COP during heat pump mode. To resolve this issue, we developed a defrost model that predicts the defrosting process and can be used to optimize the control strategy of defrosting process. This model incorporates detailed analysis of fin-tube geometry and frost distribution along the airflow direction, enabling accurate predictions of both defrosting time and energy consumption for individual fin-tube elements. Compared to conventional 1-D models, our defrosting model significantly increases accuracy by considering the complex fin-tube geometry. Additionally, it provides insights into how extra defrosting can lead to excessive energy wastage emphasizing the importance of optimizing defrost strategies to improve defrosting efficiency.

The formation of frost on the evaporator coil surface of a heat pump is a notable issue. This buildup of frost or ice creates an insulating layer that significantly reduces the efficiency of the heat exchange process, resulting in decreased heating capacity and overall efficiency. Several factors influence the speed of frost formation, including ambient humidity, evaporator surface area and volume, compressor speed, refrigerant type, and more. For instance, microchannel heat exchangers tend to develop frost more rapidly than fin-and-tube coils due to their compact volume. Additionally, newer refrigerant mixtures with a global warming potential of less than 150 often exhibit significant temperature glides, leading to varying frosting behaviors.

In the context of defrosting, a typical operation involves a reverse cycle, where the heat pump operates in cooling mode, drawing heat from the indoor space, and turning off the outdoor fan to melt the ice on the outdoor coil. The initiation of the heat pump defrosting operation is usually based on sensing the temperature differential between the outdoor coil surface and the ambient air, and it is terminated when the coil temperature exceeds a certain threshold.

Accurately modeling frost/defrost cycles is crucial for guiding heat exchanger design, appropriately sizing the compressor, and quantifying seasonal energy consumption for heating. While previous studies have predominantly focused on the component level of fin-and-tube coils, there has been limited development of models for defrosting cycles. This study enhances the existing knowledge by improving a detailed, segment-to-segment heat exchanger model to simulate frost/defrost cycles. Furthermore, it integrates this component-level model into a quasi-steady-state system modeling framework to simulate complete frost/defrost processes. This comprehensive approach will unveil differences in coil types, refrigerant choices, and their interactions with the outdoor fan. It will also predict energy consumption patterns and time intervals associated with these processes.

The system-level modeling developed in this study will facilitate the optimization of defrost control strategies, ultimately achieving reduced energy consumption while maintaining the desired comfort levels in heat pump systems.

Defrosting is a crucial procedure in refrigeration applications, employed to eliminate frost and ice from evaporators and maintain optimal working performance. Typically performed routinely, the challenge is in determining the precise time for defrosting, as the traditional approach of establishing minimum defrosting times may lead to excessively frequent defrosting. This not only results in wasted energy but also impacts cabinet temperatures.

This study focuses on a system utilizing two evaporating coils, each designated for specific evaporating temperatures – namely, the latent and sensible coils. The experiment is using ratio 20/80. Operating under typical temperature characteristics, deviations from the established trend trigger the need for defrosting.

To improve the efficiency of conventional defrosting, we introduce an adaptive defrosting indicator capable of accurately detecting the optimal time for defrosting. Through the implementation of this indicator with K_defrost values starting from 1.5, defrosting times can be extended from the conventional 4 hours per cycle to an extended 10 hours per cycle. Reducing the defrosting cycles from 5 times/day to 2 times/day. This adaptive approach aims to minimize energy wastage 1.38kWh (64%) of defrosting process, enhance system efficiency, and maintain consistent cabinet temperatures, thereby offering a more sustainable and responsive solution to the defrosting process in refrigeration systems.