Abstract
Artificial Neural Network Technique for Estimating the Thermo-Physical Properties of Water-Alumina Nanofluid
Highlights
Modern electronic devices are to be cooled properly, in order to perform their functions efficiently and for a longer period of time
On the basis of experimental data, an artificial neural network model was proposed; 70 percent of the data was used for training, while the remaining 30 percent was used for testing
The Levenberg–Marquardt algorithm (LM) is selected for training, data division for validation is used as random, performance of the model is determined by calculating the mean square error and calculations are performed by writing the code in MATLAB
Summary
Modern electronic devices are to be cooled properly, in order to perform their functions efficiently and for a longer period of time. Higher and efficient thermal management systems render the devices compact, which further leads to the reduction in the weight and cost. Heat transfer can be improved using fans and blowers, jet impingement or even with the help of surface vibration. They are specified as active techniques of intensification of heat transfer and require power sources. Heat transfer liquids are characterized by lesser thermal conductivity than solids. In order to enhance the thermal conductivity of base fluid, nano sized particles (less than 100 nm) are disseminated in the carrier fluid to form an efficient homogenous heat transfer liquid known as nanofluid
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