Enhancing and Expanding Conventional Simulation Models of Refrigeration Systems for Improved Correlations

Date of Award


Degree Name

Ph.D. in Mechanical Engineering


Department of Mechanical and Aerospace Engineering and Renewable and Clean Energy


Advisor: David Myszka


This research presents engineering models that simulate steady-state and transient operations of air-cooled condensing units and an automatic commercial ice making machines ACIM, respectively.The models use easily-obtainable inputs and strategies that promote quick computations. Packaged, air-cooled condensing units include a compressor, condensing coil, tubing, and fans, fastened to a base or installed within an enclosure. A steady-state standard condensing unit system simulation model is assembled from conventional, physics-based component equations. Specifically, a four-section, lumped-parameter approach is used to represent the condenser, while well-established equations model compressor mass flow and power. To increase capacity and efficiency, enhanced condensing units include an economizer loop, configured in either upstream or downstream extraction schemes. The economizer loop uses an injection valve, brazed-plate heat exchanger (BPHE) and scroll compressor adapted for vapor injection. An artificial neural network is used to simulate the performance of the BPHE, as physics-based equations provided insufficient accuracy. The capacity and power results from the condensing unit model are generally within 5% when compared to the experimental data.A transient ice machine model calculates time-varying changes in the system properties and aggregates performance results as a function of machine capacity and environmental conditions. Rapid "what if" analyses can be readily completed, enabling engineers to quickly evaluate the impact of a variety of system design options, including the size of the air-cooled heat exchanger, finned surfaces, air flow rate, ambient air and inlet water temperatures, compressor capacity and/or efficiency for freeze and harvest modes, refrigerants, suction/liquid line heat exchanger and thermal expansion valve properties. Simulation results from the ACIM model were compared with the experimental data of a fully instrumented, standard 500 lb capacity ice machine, operating under various ambient air and water inlet temperatures. Key aggregate measures of the ice machine's performance are: 1) cycle time (duration of freeze plus harvest modes), 2) energy input per 100 lb of ice, and 3) energy usage during 24 hours. For these measures, the model's accuracy is within 5% for a variety of operating conditions. Different strategies for improving the ACIM efficiency are explored. The improvement strategies focus on compressor efficiency and reducing energy during harvest mode. The primary component of ACIM energy use is the compressor, which accounts for approximately 80% of energy input per 24 hrs. The explored strategies include compressor efficiency, electric heaters to assist harvest, and a waste heat recovery scheme. In waste heat recovery strategy, the wasted energy during the freezing mode is stored in a storage media and used during the harvest mode. As a result, harvest time is reduced up to 48.8%.


Mechanical Engineering, Engineering, Energy, Endocrinology, Conservation, Condensation, Computer Engineering, Design, Environmental Education, Environmental Economics, Environmental Engineering, Environmental Science, refrigeration systems, simulation mode, ice machine, transient model, steady-state model, neural network, economizer, brazed-plate, heat exchanger, reducing energy, harvest mode, waste heat recovery, heat recovery strategy, physics-based

Rights Statement

Copyright 2018, author