## 3 Results
### Phase 2--Sensor Sensitivity Adjustment  
With \(\alpha\) calibrated from the previous phase, the verification of heat fluxes from both experimental and computational outputs could be done. Theoretically, if the computational model is perfectly accurate and the experimental measurement comes without noise and errors, then there should be an almost perfect match between the experimentally (\(q_{\rm IN}\) \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Description** & **Period** \\ \hline \multirow{2}{*}{Phase 1} & Initialization 1 & 11 August 2020–24 August 2020 \\  & Initialization 2 & 1 February 2021–7 February 2021 \\ \hline \multirow{4}{*}{Phase 2} & Sensor adjustment 1 & 28 September 2020–4 October 2020 \\  & Sensor adjustment 2 & 31 November 2020–6 December 2020 \\  & Sensor adjustment 3 & 1 February 2021–7 February 2021 \\  & Sensor adjustment 4 & 29 March 2021–4 April 2021 \\ \hline \multirow{4}{*}{Phase 3} & Performance verification 1 & 5 October 2020–12 October 2020 \\  & Performance verification 2 & 7 December 2020–14 December 2020 \\ \cline{1-1}  & Performance verification 3 & 8 February 2021–15 February 2021 \\ \cline{1-1}  & Performance verification 4 & 5 April 2021–12 April 2021 \\ \hline Phase 4 & Validation & 1 March 2022–17 March 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of experimental data used in the calibration. and computationally (\(q_{\text{OUT}}\)) determined heat fluxes (see Figure 1). Also, if the factory calibration of the heat flux probe is reasonably accurate under local experimental conditions, the measured heat fluxes (\(q_{\text{EXP}}\)) should match with those derived from computational-experimental data indicating that a specific calibration experiment is not necessary at all. On the other hand, if the derived heat fluxes, \(q_{\text{IN}}\) and \(q_{\text{OUT}}\), correspond to each other while the measured heat flux \(q_{\text{EXP}}\) significantly differs from them, it will clearly justify the necessity for additional calibration. As mentioned previously, the sensor adjustment was performed for four different periods (see Table 1). Figure 8: Adjusted performance of the computational model and its comparison with experimental data. Figure 7: Adjustment of the computational model. In Figure 9, there is a comparison of heat fluxes derived from experimental (\(q_{\rm IN}\), see Equation (2)) and simulation data (\(q_{\rm OUT}\), see Equation (3)), together with the heat flux measured using the heat flux sensor FHF01 with default sensitivity declared by the producer. The negative values of heat flux represent heat losses, while the positive ones stand for heat gains. The data can be effectively used as a pre-calibration check as well as a computational model adjustment back-check. If the computational model is well calibrated, a significant agreement between \(q_{\rm IN}\) and \(q_{\rm OUT}\) should be apparent. Any discrepancies detected would indicate insufficient performance of the computational model, which should be prevented by executing of the reinitialization phase. Figure 9 shows that the heat fluxes calculated from experimental and computational data using Equations (2) and (3) corresponded to each other (blue and red lines) to an acceptable extent in most cases. The occasional differences between those data could be explained by model imperfections or neglecting heat storage properties of surface material, which might affect the results even if the thickness of the surface element in the computational model is reasonably small. The differences might also result from changing parameters of the computational model, environmental conditions, or heat flux sensor properties. If substantial differences between \(q_{\rm IN}\) and \(q_{\rm OUT}\) are detected, such as at the end of the second period (see highlighted part of Figure 9), re-initialization should be done using recent data according to Section 3.1 in order to achieve a higher level of accuracy.