## 3 Results and Discussion
### Thermal Imaging and Heat Transfer Simulations
This hypothesis may be confirmed (in addition to micro-CT tests) by thermograms (shown in Figure 13) illustrating the temperature of the lining in the next five minutes of the heating process of both composites. To reduce the negative influence of boundary conditions on temperature measurement using a thermal imaging camera, the measurement area was limited to a square with a side of 2 cm. However, even within this area, the temperature distribution is not uniform. In the process of designing the models, it was assumed that all layers constituting both composites have a homogeneous structure throughout their entire volume (constant average density, constant average porosity). Moreover, it was assumed that these layers are perfectly flat and adjacent layers connect with each other over their entire surface. One of the effects of this assumption in the design of models of both composites are the temperature distributions of the models in the next five minutes of the heating process of both composites obtained during the simulation of the experiment (Figure 14). Figure 14: Temperature distributions on 3D models of tested composites were obtained in five consecutive minutes of the modeled experiment. Figure 13: Thermograms of tested composites obtained in five consecutive minutes of the experiment (analyzed surface area: 20 mm \(\times\) 20 mm—indicated by square). The assumption of uniformity in the spatial geometry of the layers resulted in flat temperature distributions on the vertical walls of the composite models. The most effective heat transfer (mainly by conduction) from the hot plate (through subsequent layers to the cooler environment) took place along these walls. The radar chart (Figure 15) presents the percentage differences between the experimental and modeled lining temperature values (\(\Delta T_{\mathrm{L}}\)) obtained in subsequent minutes of the heating process of the tested composites. The largest differences were obtained in the first minute (11.5% for composite \(A\) and 10.8% for composite \(B\)), which decreased over time to reach the lowest values in the fifth minute (5.7% for composite \(A\) and 6.1% for composite \(B\)). Based on the temperature distribution over the thickness of each composite model (Figure 13), the heat losses in each layer of the composite were computed and shown in Figure 16. Analogous computations were not possible in the case of thermal imaging data due to the lack of spatial resolution of the thermogram; however, a close correlation of \(T_{\mathrm{L}}\) occurred between the simulations and the experiment, and it can be supposed that the simulated heat losses are not much different from the corresponding experimental values. The pie charts present that in both tested composites, the most effective thermal barrier was the second thickest (37% of the composite thickness) and the most porous (57%) layer,  
Figure 16: Percentage heat loss in layers of tested composites. Figure 15: The percentage difference between experimental and simulated lining temperatures (\(\Delta T_{\mathrm{L}}\)) obtained in five consecutive minutes of the heating process. which was the basalt fabric. In the case of composite \(A\), it retained 69% of the heat, and in the case of composite \(B\), it retained 68% of the heat. An important thermal insulation role in both composites was played by the thickest layer (48.2% of the composite thickness) and the almost non-porous (0.2%) silicone layer, which retained 21% of heat in composite \(A\) and 16% of heat in composite \(B\). The third thickest layer of the composite (10.4%)--the slightly porous (2%) Mylar(r) foil retained 6% of heat in composite \(A\) and 4% of heat in composite \(B\). The observed differences between both tested composites in terms of heat losses in the three layers mentioned above (basalt fabric, silicone, and Mylar(r) foil) were influenced by the use of different coatings in them.