Heat Transfer from Blower through Bend Pipe with Machine Learning Algorithm

Forced convection is one of the modes of heat transfer. In the present paper a U-shaped bend tube is considered as a flow domain through which the air is blown for an experimental study. The heat is supplied to the bend pipe with the help of a heater and varying heat rate with voltage and current. The study will predict the heat transfer coefficient (h), temperature profile and Nusselt Number (Nu) on the variation of input heat transfer rate (Q) and the Reynolds Number (Re) of air. After collecting the data experimentally, the Machine learning (ML) based algorithms are used to predict the comparative result data. The ML method applied in the present work is Gradient boosting regression (GBR). The experimental and ML data are compared and predict a correlation. The correlation will be used to design a U-shaped bend tube used in heat exchanger applications.