Author(s): , ,

Keywords: , , , , , , , , , , ,

Abstract: In this paper, the simplified hot-plate method (SHOP) measured the thermal conductivity of a prototype instrument, and artificial neural networks (ANNs) were employed to confirm the results. The goal of this paper was to produce ANNs to calibrate and improve the accuracy of SHOP thermal conductivity measurements. A thermal constants analyzer verified the results of the SHOP by measuring the thermal conductivity of plywood and calcium silicate boards using the transient plane source (TPS) technique. The one-dimensional heat conduction equation displayed higher thermal conductivity results than the method and the predicted values, which was expected. Good agreement between the various tests indicated that ANNs can successfully predict thermal conductivity values from SHOP measurements.

Reference: Applied Thermal Engineering, 29, 8-9 (2009) 1818-1824

DOI: 10.1016/j.applthermaleng.2008.08.017