Abstract
The paper presents a novel technique for determination of loss coefficients due to pressure by use of artificial neural network (ANN) in tee junctions. Geometry and flow parameters are feed into ANN as the inputs for purpose of training the network. Efficacy of the network is demonstrated by comparison of the ANN and experimentally obtained pressure loss coefficients for combining flows in a Tee Junction. Reynolds numbers ranging from 200 to 14000 and discharge ratios varying from minimum to maximum flow for calculation of pressure loss coefficients have been used. Pressure loss coefficients calculated using ANN is compared to the models from literature used in junction flows. The results achieved after the application of ANN agrees reasonably to the experimental values.
Highlights
Due to industrialization and automation in almost every walk of life, energy consumption is on the rise and scientists are finding new alternatives to conserve the fossils reservoirs
The same property i.e. Reynolds number has been studied for side pipe which is shown by subscript ‘s’
The values of pressure loss coefficients are higher than straight pipe due to the fact the there is greater loss of pressure resulting from mixing of two the streams as well as from the bending of pipe at an angle of 90 degrees
Summary
Due to industrialization and automation in almost every walk of life, energy consumption is on the rise and scientists are finding new alternatives to conserve the fossils reservoirs. In this regard, the sun is the sole largest source of energy that can provide enough energy to mankind without being explored like conventional energy. Solar energy based absorption refrigeration system uses a solar collector to harness the freely available solar energy to heat the working fluid.
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