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Advisor(s)
Abstract(s)
Nowadays, the increasing energy prices and associated environmental concerns lead the refrigeration systems’ developers and manufacturers to develop more energy efficient and sustainable equipment and devices. On the most demanding systems, intense usage results in the fast accumulation of ice on the evaporator fins that reduces the efficiency and may even clog the system. These systems often have time-controlled defrost cycles, that heat the evaporator, melting the ice and allowing the system to keep working normally after the defrost cycle. This cycle consumes extra energy and causes a thermal imbalance on the refrigerated space, that may result in a worst refrigeration quality. If it was possible to avoid the defrosting cycle passively (without energy consumption) its efficiency would greatly increase, and the refrigeration temperature would be more stable. Currently defrost cycles cannot be avoided in an economically viable way, although new designs, materials and configurations show promising results, and are currently being investigated. These studies require experimental tests that may become expensive as several geometries, topologies, materials and surface treatment combinations should be evaluated. To access the efficiency before these experimental tests, computational models that simulate frost formation could predict with some accuracy which of the most promising configurations should be then tested experimentally. The present paper aims to review the computational methods to predict frost formation and compare them for possible usage in the computational study of evaporators. Additionally, the future trends of the simulations are discussed, taking into account physical and mathematical models, numerical procedures and the accuracy of the dynamic pattern of the predictions.
Description
Keywords
Demand defrosting Frost measurement Controlling strategy Frost detection Evaporator design Finned tube evaporators
Citation
Publisher
AGRO 2018 - International Congress on Organizational Management