Implementation of a neural network tool for evaluation of thermal performance in a heat exchanger by using double elliptical leaf angle strips with same orientation and same direction

Abstract

Exchange of energy in all processes generally occur in the form of heat & work. The exchange of heat is determined by the rate of heat exchange between hot and cold body or cold and hot body. To exchange this heat we need two energy stacks such as a source & sink. So, whenever heat is rejected or accepted the energy change occurs identically i.e. amount of heat rejected is equal to amount of heat gained in an ideal case but when heat transfer rate is analyzed it is different fsor different processes such as vaporization is an instantaneous process whereas the condensation is slower and takes much more time so, with this idea that heat transfer rate can be altered individually in different processes an idea of analyzing heat exchanger by introducing elliptic double shaped leaf strips within the double pipe heat exchanger and the rate of heat transfer and pressure drop in is planned at various orientations of angles. From these obtained results neural network tool was designed for evaluating the thermal performance named the generalized regression neural network (GRNN).In this process certain input parameters are given (temperatures, mass flow rate) and instantly predefined output parameters (heat transfer rate, pressure drop) are obtained.