## 3 Results: Load Identification  
Measurements were conducted for the planned test cases. As an example of the wing deformation, the measured strain distributions for AoA = \(-4^{\circ}\) and \(10^{\circ}\) in the \"controlled\" cases are shown in Figure 8. In order to better understand the mechanical implication of the data, we converted the measured Bragg wavelength shifts to strain values by using the Bragg wavelength-strain coefficient of 827 \(\upmu\epsilon\)/nm. The coefficient was experimentally calibrated in advance. In general, the amplitudes of strain variations were greater for the main spar than the aft spar due to the thickness difference under wing deflection. The strain amplitude was smaller at the wing tip, and this was in good agreement with typical strain distributions of a cantilever beam. However, the strain amplitude decreased at the wing root because of the greater cross-sectional area as well as the 400-mm stainless-steel shaft inserted at the wing root that increased the stiffness. The maximum strain amplitude was observed at approximately 400 mm. In the case of AoA = \(-4^{\circ}\), it exhibited tensile strains, which indicated that the wing model deflected downward. In the case of AoA = \(10^{\circ}\), it exhibited compressive strains as the wing model deflected upward. The neural network was trained using the measured data. Thereafter, we used the trained neural network to identify load distributions. Figure 9 shows examples of the load identification results for the test cases used to train the neural network. We plotted an \"aligned\" test case where AoA = \(0^{\circ}\) and \"single\" test cases with AoA = \(-4^{\circ}\) and Flap 2 at \(15^{\circ}\), and AoA = \(4^{\circ}\) and Flap 7 at \(-15^{\circ}\). In the \"aligned\" test case, the load distribution was relatively uniform, however, in the \"single\" test cases, load variations were observed at the repositioned flaps. The positive flap angle of Flap 2 at AoA = \(-4^{\circ}\) produced a lift increase, and the negative flap angle of Flap 7 at AoA = \(4^{\circ}\) produced a decrease in lift. The lift variations were observed locally at the wing segments with the repositioned flaps. The three plots also indicated that greater AoA values produced greater lift loads. The solid lines represent the estimated loads and the dashed lines the applied loads that were calculated from the pressure values, and there is good agreement between them. This indicated that the neural network training was satisfactorily completed and the neural network could be applied to the load identification problem. In order to examine the neural network performance for new conditions, we input the FBG and flap angle data of the \"controlled\" test cases, which were not used for the training. Figure 10 shows the load identification results for AoA = \(-4^{\circ}\) and \(10^{\circ}\). It could be seen that for the \"controlled\" flap angles, the wing root was subjected to greater lift loads and the wing tip to smaller or negative lift loads. The total lift loads increased with increasing AoA. The solid lines represent the estimated loads and the dashed lines the applied loads. The results were in good agreement, which indicated that the neural network successfully learned to generalize unexperienced conditions. The errors appeared greater for the data not used for training, and thus for a quantitative comparison, we plotted the estimated loads with regard to the applied loads in Figure 11. The load values correspond to the lift loads on the individual wing segments. The black circles represent the results for the training data that includes the \"aligned,\" \"random,\" and \"single\" test cases. The red squares represent the results for other data, the \"controlled\" test cases. The error ranges were from \(-0.89\)-\(0.84\) N and from \(-1.5\)-\(1.4\) N for the training and other data, respectively. The standard deviations of the errors were \(0.14\) N and \(0.57\) N for the training and other data, respectively.