Thermal performance of thin film heat gauges of gold, silver and nano-composite

Abstract

Abstract Thin film materials along with nanotechnology can be a useful tool to efficiently estimate the surface heat flux. The advancement of soft computing methods can also be used for automated estimation of thermal performances of various heat gauges. Thus, in this study, thin film gauges (TFG) made from Ag, Au, CNT/Ag nano-composite topped on insulating substrate have been tested experimentally under impulsive heat loads from laser. The fabrication of TFGs was done using paint method and physical vapor deposition. The fabricated TFGs were calibrated first and then surface heat fluxes were calculated analytically from temperature history by utilizing one-dimensional heat conduction theory applied on semi-infinite solids. The advanced soft computing methods viz. Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic Programming (GP) were used to estimate heat fluxes for these three TFGs. The statistical evaluation of GP with ANFIS models on the heat flux experimental database reveals that the ANFIS models have performed better in predicting the peak heat fluxes whereas the GP models have performed better in capturing the trend of heat fluxes. This combined approach paved a way to automated estimation of heat fluxes for industrial applications.