A review on coolant selection for thermal management of electronics and implementation of multiple-criteria decision-making approach

Electronics cooling requirements are different from those of other applications i.e. metallurgical, nuclear, thermal, and solar power plant, etc. This article primarily focuses on the selection of coolants for electronic cooling applications based on challenging operating conditions and application areas. Electronics cooling usually operates at a lower temperature gradient in comparison to other cooling applications. Therefore, the effect of temperature gradient does not contribute significantly to the heat extraction process. Hence, it is essential to select a suitable coolant for effective heat extraction even at a low -temperature gradient. Thermophysical properties (thermal conductivity, specific heat, latent heat of evaporation, viscosity, and surface tension) and electrical properties (dielectric constant, electrical conductivity) play a significant role depending on the type of cooling technique. The wide operating temperature range (-40 degrees C to125 degrees C) in military -grade and automotive electronics necessitates the importance of other properties and operational constraints i.e., freezing point, boiling point, critical temperature & pressure, material compatibility, and thermal stability of coolant. Several other factors i.e., economic viability, toxicity, fire hazard, and environmental impact of coolants also play an important role in the practical implementation of an electronic coolant. Selection of an appropriate coolant based on operating conditions, cooling technique, fluid properties, environmental and economic factors is not straight forward and there is necessity of robust and effective coolant selection framework. Several approaches for the selection of coolants are discussed with specific emphasis on figure of merit analysis and multiple -criteria decision -making (MCDM) approach. The technique of order preference by similarity to ideal solution (TOPSIS) and gray relational analysis (GRA) methods are implemented to illustrate a sample electronic coolant selection problem. Both the MCDM methods, ranked water to be the best coolant for the sample indirect electronics cooling application. More research on nanofluid, ionanofluid and pcm slurries need to be carried out in future for extending the performance limit of electronics thermal management systems. The use of artificial intelligence based multi -criteria decision making approach is recommended for coolants selection of electronic thermal management systems. The details of different available electronic coolants and the coolant selection approach presented in this study will be beneficial for the design of electronic thermal management systems.