Conference Agenda

Application and development of energy efficient techniques to separate ice from different surfaces and substrates is of significant value in the context of building equipment performance. Ice is an important commodity both as a direct use product and as a nuisance in day-to-day operation of refrigeration related equipment. It is utilized: as an energy storage medium at industrial scale, in the food service industry, in domestic household refrigeration, and in supermarkets. Separating ice from a substrate is an energy intensive process and typically ~25% of the total energy is exclusively consumed in this stage. A hybrid approach to help dislocate the ice layer using advanced materials and ultrasonic vibration to lower ice adhesion strength was investigated.

A reliable test methodology was developed to measure the ice adhesion strength of different materials and geometries relevant to building equipment and appliances. The developed test setup was successfully employed in measuring the ice adhesion strength on both tubular and planar substrate geometries of metals including copper, aluminum, stainless steel. Application of advanced polymer materials in lowering the adhesion strength of ice was confirmed where the measured strength was lowered by 50-70% depending on the material and geometry. Additionally, utilization of induced ultrasonic vibration in further lowering the ice harvesting energy was confirmed on multiple materials and geometries. Durability of the coating enhancement was also confirmed in a thermal cycling test under realistic operating conditions. Experimental results of this hybrid approach will be presented in this paper.

This paper provides an overview of research work conducted over the last six years investigating the measurement and presentation of cold store and process refrigeration plant efficiency. The variation in cold store performance across notionally similar facilities is massive and it follows that the amount of energy being wasted (and the opportunities for improvement) are also huge. The difference between best in class and worst in terms of energy use can be a factor of 10. The discrepancy in process freezing and chilling equipment is even greater.

The objective of this work is to provide facility owners and operators with a mechanism for assessing their plant performance and benchmarking it against past performance and other facilities. A mechanism for forecasting the annualised effect of performance improvement interventions is presented and some case study examples of improvements are described.

A roadmap for the adoption of these techniques is detailed and a method of expressing the results in financial terms in order to aid capital investment planning is provided. A new business model, designed to overcome the barriers to adoption of this methodology, is suggested.

System-level intelligent and intensive control is crucial for achieving energy efficiency upgrades in existing refrigeration systems. This study proposed an energy-saving control method for refrigeration systems based on a hybrid mechanism-based and data-based model. The aim of this study is to address the drawbacks of high calculation errors in single mechanism-driven control method and high dependence on historical data in single data-driven control method. Firstly, a big database of system operating parameters was established, which consisted of data collected from sensors and calculation results derived from the mechanism-based system simulation model. This extensive database can cover the entire operating range of the system. Based on this database, a data-driven system simulation model was developed to accurately predict the operating status and energy consumption of the system. Then, in order to achieve real-time energy-saving optimization of system control parameters, a nonlinear multivariate function optimization algorithm was adopted. The application of this energy-saving control method in NH₃-CO₂ cascade refrigeration system significantly reduces energy consumption by 10%-26%. Furthermore, when compared to data-driven models that entirely establish on historical senor data and mechanism-based models that heavily depend on assumptions, the hybrid-based model demonstrated improved system simulation accuracy by more than 2% and 10%, respectively. In conclusion, this study provides a promising direction for improving system performance in refrigeration systems.

At present, a significant percentage of US Navy shipboard cooling is provided by R-134a based centrifugal chillers. Beginning in 2025, the manufacture and general availability of high global warming potential (GWP) hydrofluorocarbon (HFC) refrigerants will be restricted in the United States. The installed shipboard chiller plants are expected to have significant lifetimes that extend into this reduction phase. Therefore, the objective of this study is to consider the performance impact of the replacing R-134a with a lower global warming potential hydrofluroolefin (HFO) refrigerant, R-1234yf (GWP < 1). First, a thermodynamic state point model of a 500-ton Navy chiller plant was developed at baseline conditions. Heat exchanger overall conductance values were evaluated at the baseline condition and used as fixed values in subsequent analysis. In parallel, a conceptual design of an R-134a centrifugal compressor was developed and analyzed using a meanline solver. The compressor model was used to generate operating maps for all refrigerants. The compressor maps were input to the chiller design model and fixed, baseline heat exchanger conductance values to predict performance at different condenser seawater inlet and chiller loads for R-134a and its potential replacements.

The escalating capacity and demand in data centers, driven by advancements in information technology such as artificial intelligence and machine learning, necessitate increased computing power. The heightened performance and energy consumption of central processing units and graphics processing units in data centers pose challenges to traditional air-cooling methods. Fan-based air cooling exhibits limitations with low cooling capacity and high energy consumption. Consequently, there is a growing focus on alternative cooling methods, particularly hybrid cooling with cold plates and immersion cooling using dielectric fluid. Immersion cooling relies on an electrically insulating and thermally stable dielectric fluid to directly cool electronic components.

This study seeks to compare different data center cooling techniques using the computational fluid dynamics simulation tool. A server model, featuring 2 central processing units and 16 memories, is employed for validation purposes and stacked on top of each other for imitation of the server rack. A simplified porous media model is used to replace the heatsink. The investigation involves a comparison of diverse performance parameters including central processing unit temperature, memory temperature and thermal resistance where immersion cooling emerges as the most efficient. Particularly, under controlled conditions of inlet fluid temperature and power consumption, the average central processing unit temperature in the immersion cooling is found to be 102.58°C and 22.89°C lower than that observed in air cooling and hybrid cooling, respectively, at 800 W central processing unit thermal design power. Further comparison is conducted for various central processing unit thermal design power and memory thermal design power.