Open AccessUsing a Generalized Regression Neural Network Prediction Tool to Estimate Thermal Performance in A Heat Exchanger By using Triple Elliptical Leaf Angle Strips with Opposite Orientation and Same Direction
Author(s) -
J. Bala Bhaskara Rao,
V. Ramachandra Raju
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k1564.0881119
Subject(s) - strips , heat exchanger , orientation (vector space) , artificial neural network , pressure drop , thermal , schedule , heat transfer , regression , volumetric flow rate , mechanical engineering , computer science , tube (container) , simulation , mechanics , mathematics , engineering , artificial intelligence , geometry , statistics , thermodynamics , physics , operating system
In our day to day hectic schedule humans have got so adaptive to technology that tremendous pressure is built on researchers to produce better equipment with greater output & easier way of human usage. One among these is Heat exchanger which is a device for trading heat and providing comfortable environment either for humans or the equipment .This paper aims at finding a solution in improvement of the thermal performance of the heat exchanger by implementing a statistical tool derived from Artificial Neural Network. The name of the tool is GRNN. (Generalized Regression Neural Network) From a sparse data of inputs (Temperatures, Angle orientation & mass flow rates) the outputs of (outlet temperatures & drop in pressure) are found out using this tool. An experiment is also conducted to find the heat transfer rates and pressure drops. To enhance the heat transfer rate three elliptical shaped leaf strips are introduced in the tube with opposite orientation and same direction. The results obtained from both the sources are compared and the percentage of error is calculated.