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TEMPERATURE DISTRIBUTION AND THERMAL CONDUCTIVITY PREDICTION
Dr Manisha Saxena
Abstract:
Numerous factors affect the thermal conductivity of nanofluids; nevertheless, some of them stand out more than others, such as temperature, the size and type of the nanoparticles, and volumetric concentration. In this study, artificial neural networks and least square assist vector with machining (LSSVM) are used to forecast the degree of thermal conductivity of an alumina/water nanofluid as a function of particle size, temperature, and volumetric preoccupation. Self-Organizing Map, Levenberg-Marquardt, and LSSVM Back Estimates of propagation are used to forecast the extent of thermal conductivity. The obtained results demonstrated that these computations are a reliable tool for estimating warm conductivity extent. The association coefficient values are all excellent, and the high warm conductivity can be controlled by choosing the part materials and organising the design.