## 5 Results and Discussion  
Figure 7 presents the comparison between experimental and numerical data concerning suction evolution along four cycles measured by both tensiometers. The arrows identify the beginning of each wetting. Overall, the model can reproduce well the average suction measured in the equipment. The steps on wetting recorded are simplified in the numerical simulation because it is almost impossible to estimate the amount of water in each water drop added to the soil using the syringe. As presented in Figure 7 and intended at model calibration (Tables 3 and 4), the maximum suction values measured were reproduced in almost all cycles, although the values computed for the long shaft are closer to the experimental values than those found for the short one. The shape of the drying curves for the long shaft confirms that drying occurs faster in the model than in the experiment. The shape of the curves corresponding to the small shaft is similar; however, in the model, the volume surrounding this tensiometer also appears to dry faster than in the test. The shape of the wetting curves is well reproduced, \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Soil Surface (Wetting and Drying Cycles)** & & **Lateral Wall** \\ \hline  & **First Drying** & **All Settings** & **Further Dryings** & **All Steps** \\ \hline \(\omega_{g}^{\text{m}}\) (kg/kg) & 0.01 & 0.01 & 0.01 & 0 \\ \(P_{g}\) (MPa) & 0.1 & 0.1 & 0.1 & 0 \\ \(\gamma_{g}\) & 1 & 1 & 1 & 0 \\ \(\beta_{g}\) & 1 & 1 & 1 & 0 \\ \(\rho_{g}\) (kg/m\({}^{3}\)) & 1.436 (HR = 65\%) & 2.209 (HR = 100\%) & 1.661 (HR = 75\%) & 0 \\ \(P_{l}\) (MPa) & 0 & 0.12 & 0 & 0 \\ \(\gamma_{l}\) & 0 & 10 & 0 & 0 \\ \(T\) (\({}^{\circ}\)C) & 24.4 & 24.4 & 24.4 & 24.4 \\ \(\gamma_{e}\) & 4 & 4 & 4 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Loading conditions applied in the model. Figure 7: Comparison between numerical and experimental data measured in the two tensiometers. The arrows indicate the time steps when wetting was carried out. in particular in the second wetting. There is the possibility that this can delay the response of the sensors and the interaction between their porous tips and the surrounding soil. The inclusion of the porous tips of the sensors and the characterization of this material to find the parameters will be done in the future. The analysis of the evolution of the degree of saturation along with the test presented in Figure 8 allows for a better understanding of water balance. Wetting in the first cycle is not complete but, because the evaporation rate is low in the next, the mass of water added to the system is enough to achieve the degree of saturation at the end of the second drying. Further wettings are simulated well by the model, but the maximum values of \(S_{r}\) attained in the test are not reproduced. Nevertheless, the degrees of saturation after each drying computed are quite similar to the experimental values. The differences between the curves during drying may be explained by easy water transport through the unsaturated sample. When in the liquid phase (conduction), the water flow is controlled by the unsaturated permeability of the material: intrinsic permeability and parameter \(\mathbf{k}_{\text{rl}}\) (Equation (7)). The intrinsic permeability adopted was already the smallest value measured in the tests. Higher values would lead to a significant increase in the degree of saturation upon wetting, and following drying, would not be enough to dry the sample. In addition, a sensitivity analysis of the definition of \(\mathbf{k}_{\text{rl}}\) was unsuccessful, and therefore, the default law for this parameter was kept [15]. The relative humidity of the soil is given by parameter \(\rho_{\text{vap}}=\rho_{g}\omega_{g}^{w}\) and was analyzed further to understand the limitations of the model to reproduce drying when dealing with vapor diffusion. The evolution of \(\omega_{g}^{w}\) with the cycles is presented in Figure 9a for the two tensiometers.