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In part 1 of the paper, the discrete model of a nonstationary heat flow process in the sample of material with a hot probe and an auxiliary thermometer based on a twodimensional heat-conduction model was presented. To create the model the finite element method (FEM) implemented in the Matlab environment was used. The part two of the paper is concentrated on possibility of using a neural network for the thermal parameters determination. The artificial neural network (ANN) is used to estimate the coefficients of the inverse heat conduction problem for solid. The network determines the value of the effective thermal conductivity and the effective thermal diffusivity on the basis of temperature responses of the hot probe and the auxiliary thermometer. During selection of optimal ANN architecture several configurations were evaluated. The influence of measurands uncertainty on identified values of the thermal parameters was also analyzed. Training process and simulation analysis were conducted in the Matlab environment.

The article presents an idea of a measurement system with hot probe for testing thermal parameters of heat insulation materials. In comparison to the classical method of the line heat source (LHS) numerous assumptions about model of heat flow in the sample of material are not needed. The model of a nonstationary heat flow process in the sample of material with hot probe and an auxiliary thermometer is based on a twodimensional heat-conduction model and includes heat capacity of probe handle. For solving the system of partial differential equations describing the model, the finite element method (FEM) was applied. Simulations of heat flow process were carried out in the Matlab environment and the results are presented in part 1 of the paper. Part 2 is concentrated on the usability of the artificial neural network in solving the inverse heat transfer problem in a sample of heat insulation material. The proposed method is suitable for immediate measurement in building site or factory.

This paper presents results of research, developing methods for determining the coefficient of thermal diffusivity of a thermal insulating material. This method applies periodic heating as an excitation, and an infrared camera is used to measure the temperature distribution on the surface of the tested material. The usefulness of known analytical solution of the inverse problem was examined in simulation study, using a three-dimensional model of the heat diffusion phenomenon in the sample of material under test. To solve the coefficient inverse problem an approach using an artificial neural network is proposed. The measurements were performed on an experimental setup equipped with an infrared camera and a frame grabber. The experiment allowed verification of the chosen 3D model of the heat diffusion phenomenon and proved the suitability of the proposed test method.