A new method to predict temperature distribution on a tube at constant heat flux

Abstract

Surface temperature distribution on a tube is one of the main factors affecting the calculation of the heat transfer coefficient calculation. When an electric heater heats the tube, a magnetic flux is generated that affects the thermocouples readings; therefore, an efficient fitting technique is needed to represent these readings. This work proposes an interpolated spline method to mathematically represent experimental data of a thermal distribution on a tube with heat flux. Linear regression was compared with a double linear interpolation process with an optimization algorithm and cubic spline curve method on the proposed problem. The results show that the interpolated experimental data can highly improve the regression of the spline curve. Consequently, an interpolated spline curve gives better surface temperature distribution and better estimation for the average temperature. The interpolated points on spline segments are chosen by an optimization algorithm, which is particle swarm optimization, in a way that provides more minor errors.