Coding,evaluation, comparison,ranking and optimal selection of nanoparticles with heat transfer fluids for thermal systems

Performance of nanofluids is affected by various parameters and it is important to identify these parameters and their inheritance that determine the overall performance of nanofluids. The 125 attributes are identified and a coding scheme is developed for an in-depth understanding and visual comparison of nanofluids more precisely. A three-stage methodology named multi-attribute decision making (MADM) is used for the evaluation, comparison and ranking of nanofluids and it selects the optimum nanofluid for a given application in less time and effort. In the first stage of MADM, known as elimination search, a long list of alternatives is converged to a manageable list. Later, the procedure is followed for the ranking of nanofluids by employing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. This ensures that the selected nanofluid is the closest to the hypothetical best nanofluid and the farthest from the hypothetical worst nanofluids. Finally, the optimum nanofluid is selected by considering the cost factor as well as the government rules for a particular application. This methodology has been explained by an example in which nanofluids are developed in the laboratory to evaluate the heat transfer characteristics for cooling devices such as microchannels. This scheme is user friendly and a simple mathematical procedure is required to use it.