## 5 Results
### Simulations Results
Finally, in (f), DPH index evolution, which was computed using the elastic energy obtained from the overall energy balance of the simulation (\(dE_{e}/dt\)), is depicted. Figure 15: Numerical results for Config. 1. (**a**) Load vs. normalized time; (**b**) temporal distribution of the amplitude signals for sensor 1 (left device) and sensor 2 (right device) and the signal computed from the kinetic energy measured during the simulation (\(\Delta E_{k}\)). (**c**) The accumulated number of signals for both sensors, and the accumulated energy magnitudes, measured using the RILEM methodology; the same information was computed from \(\Delta E_{k}\), an alternative measure of AE information. (**d**) \(b\)-value evolution obtained using sensor 1, sensor 2, and \(\Delta E_{k}\). (**e**) DPH index evolution, obtained using the methodology of [26]. (**f**) DPH index evolution, computed using the elastic energy in the simulation. In the analysis of these figures, we make the following observations:  
1. For Config. 1, it is evidenced that, in Figure 15b, sensor 1 recorded more signal activity than sensor 2 until \(t_{n}\approx 0.95\). After this time, the same activity in both sensors is observed. This fact is coherent with the evolution of the configurations presented in Figure 11a,b; until \(t_{n}\approx 0.95\), high AE activity comes from the pressure head. This source is close to sensor 1. This tendency changes after \(t_{n}\approx 0.95\), when the failure mechanism changes, as is depicted in Figure 11c,d. In the second mechanism, the source of AE activity is close to sensor 2. Figure 16: Numerical results for Config. 2. (**a**) Load vs. normalized time; (**b**) temporal distribution of the amplitude signals for sensor 1 (left device) and sensor 2 (right device) and the signal computed from the kinetic energy measured during the simulation (\(\Delta E_{k}\)). (**c**) The accumulated number of signals for both sensors and the accumulated energy magnitudes, measured using the RILEM methodology; the same information was computed from \(\Delta E_{k}\), an alternative measure of AE information. (**d**) \(b\)-value evolution obtained using sensor 1, sensor 2, and \(\Delta E_{k}\). (**e**) DPH index evolution, obtained using the methodology of [26]. (**f**) DPH index evolution, computed using elastic energy in the simulation. * On the other hand, sensor 1 registers more AE activity during all of the simulations in Config. 2, as shown in Figure 16b, and, in this case, this fact is coherent with the configuration presented in Figure 12a-d, which show the crack tip as the main failure source of AE activity during all simulations. * In Figures 15b and 16b, it is interesting to compare the signal obtained from the two sensors and the equivalent information obtained from \(\Delta E_{k}\). In this comparison, it is possible to measure how the travel between the event source and the device modified the acoustic emission signal captured. The simulation of this situation might suggest placing the AE sensor in a better position in the experimental test. Particularly, in the present case, sensor 1 is better positioned than sensor 2. In general, it will be possible to determine if the combination of specimen geometry, boundary condition, and damage distribution can help identify the best position for the AE sensors. * In Figures 15c and 16c, the number of accumulated signals and the change in signal energy during the simulation are presented. In both configurations, the results in terms of the number of signals and energy indicate higher activity registered in sensor 1 until the final part of the simulation in Config. 1 and during all the simulations in Config. 2. The measure of the accumulated number of events and the accumulated energy computed from \(\Delta E_{k}\) are coherent with the results obtained from the signal information obtained with the AE sensors. * In Figures 15d and 16d, \(b\)-value evolution for the signals from sensors 1 and 2 and the \(\Delta E_{k}\) information are shown.