Using triple elliptical leaf angle strips in the same orientation and direction, an extended regression neural network prediction tool is implemented to assess the heat exchanger's thermal performance

Abstract

Heat exchangers have the ability to drastically alter our perception of the world. Since heat exchangers are used practically everywhere in the world, improvements to them lead to significant innovations. In this case, the passive leaf strip insert augmentation approach is applied. For fluid flow, three elliptical leaf strips are put into the tube side in the same orientation and direction. The strips are arranged in groups of 100 at 50 mm intervals and set at various angular orientations between 00 and 1800. A statistical method called GRNN (GENERALISED REGRESSION NEURAL NETWORK) is used to compare the results.Utilizing the functions of independent and dependent variables, an algorithm is developed, and results are obtained. The proportion of error between these experiment and GRNN values is calculated by comparing them