Optimal Location of a Fiber-Optic-based sensing net for SHM applications using a digital twin

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1 9 th European Workshop on Structural Health Monitoring July 10-13, 2018, Manchester, United Kingdom Optimal Location of a Fiber-Optic-based sensing net for SHM applications using a digital twin More info about this article: I.Kressel 1, U. Ben-Simon 1, S. Shoham 1, G. Don-Yehiya 1, S. Sheinkman 2, R. Davidi 3 and M. Tur 3 1 Israel Aerospace Industries, Israel, ikressel@iai.co.il 2 IAF, Engineering Division, Israel, sagigm@gmail.com 3 Tel-Aviv University, Tel-Aviv, Israel, tur@post.tau.ac.il Abstract A strain-based sensing concept for SHM applications has the important advantage that on-ground and in-flight measurements can be directly correlated with the results of structural stress/strain finite element (FE) analysis. In the current work, a detailed FE model of unmanned aerial vehicle (UAV) has been calibrated against actual ground test measurements, as obtained by a fiber-optic sensing net, to be later used as a digital twin platform for optimizing sensors locations for sensitive damage detection. The use of a digital twin for damage detection should prove to be a cost-effective approach for optimizing an SHM damage detection system. 1. Introduction Fiber-optic-based sensors are excellent candidates for monitoring airborne structures due to their ability to sustain both dynamic loads and high strains of more than 6000, using a standard single mode fiber. Optical fibers are also quite flexible, tolerant to extreme environmental conditions and electromagnetic interferences. In addition, their small diameter allows them to be easily embedded within or externally bonded on large composite structural components, such as wings or fuselage skins, at relatively short time and low cost. For example, spatially discrete strain sensing can be performed both on-ground and inflight using Fiber Bragg Grating (FBG)-based nets of point sensors. These sensors have been successfully implemented in many applications, demonstrating reliability and technological maturity [1-9]. Using a strain-based sensing concept for SHM applications has the great advantage that on-ground and in-flight measurements can be correlated with the results of structural stress/strain analysis, provided the finite element twin had been calibrated against actual measurements. Indeed, a detailed stress/strain analysis is available today for most if not all airborne structures, and, moreover, high spatial resolution fiber-optic sensing techniques can currently provide the information required for the calibration stage. The calibrated digital twin can then be used to simulate damages and optimize SHM implementations in terms of sensor location and required sampling rate. In the current work, a fiber-optic-based sensing net, applied as an airworthy system on the Heron Unmanned Aerial Vehicle (UAV) boom structure, is used to calibrate a detailed finite element twin. Using FBG point sensors, the calibration was performed by measuring the boom reaction to both static and dynamic loading during a ground test. At Creative Commons CC-BY-NC licence

2 a second stage, the same FBG sensing net was used to capture the loading spectra and dynamic behavior of the boom during flight. This verified and calibrated simulation twin model was later used to evaluate different sensing net layouts under a variety of landing conditions, characterized by many combinations of UAV weight and center-of-gravity location, rate of decent etc. A total of 75 simulations were performed, leading to an optimized damage detection concept. 2. The digital twin The all-composite-made Heron UAV (Fig. 1), designed and manufactured by Israel Aerospace Industries and used by the IAF, was selected as the test-bed for the evaluation of this fiber-optic based SHM sensing concept. FBG sensors were embedded along each boom, at the wing root and near the outer wing attachment. Each boom is basically a cantilever beam with a relatively large mass at the back end. The main boom loading condition is vertical bending. In order to track these loading conditions, two fibers were embedded at the center of the boom ("Top" and "Bottom" in Fig. 2). Eight FBG sensors were imprinted on each fiber. Thus, eight sections of the boom are monitored by two optical fibers. A detailed FE model representing right half of the A/C was created, Fig. 3. Such a detailed model is capable to accurately simulate sensor readings under both static and dynamic landings design. The strain data extraction from all FBG sensors, enabling accurate comparison and calibration of the model against test data. Comparison between the FE simulation and the static loading test data is presented in Figure 4. In addition, this FE model was calibrated to accurately simulate the natural modes and frequencies as measured during a ground vibration test. The calibrated FE model was further used to evaluate different sensing net layouts under varying landing conditions, different configurations of UAV weight and CG location, rate of decent etc. A total of 75 simulations were performed, including 10 simulations of a damaged boom under various loadings. Having both "healthy" and damaged boom sensor readings made it possible to clearly identify the best sensors layouts as detailed in the next section. Figure 1: The Israel Aerospace Industries Heron UAV 2

3 Top fiber FBG-1 Top fiber FBG-8 FWD Bottom fiber FBG-1 Tail Attachment Bottom fiber FBG-8 Figure 2: Boom general layout and the routing of the embedded optical fibers in the boom. 16 FBGs were located at points of interest (Right hand side boom shown. The left one has a similar constructions) F z F x Figure 3: Heron UAV FE model used for symmetric landing simulations. Note the detailed wing and boom representation and the fuselage coarse model Strain readings (normalized) Boom location (normalized) Figure 4: Concept validation: FE results vs. static test data 3

4 3. Sensor net optimization The calibrated FE model was used to investigate a few sensors layouts in order to optimize the damage detection. Two typical simulated impact damages were introduced and evaluated by performing UAV landing touch-down analysis. One proposed sensing net layout was based on placing the sensors along the boom vertical bending neutral axis (on the side of the boom). For a healthy boom no strain reading due to vertical bending is expected. Therefore, the damage sensitive feature for such a concept is the generation of longitudinal strain caused by the effect of damage on the location of the neutral axis. This concept can be implemented by the side sensors of Fig. 5. DAMAGE SENSORS Figure 5: Boom FEM model with additional sensors and simulated damage Damage was introduced by removing one element at the corner of the boom (Fig. 5). The simulation results of both the healthy and damaged boom is presented in Fig. 6. It is clearly seen that the newly introduced side sensing layout is more sensitive to damage since now the strain amplitude difference and shape of the time history shape are more prominent in respect to those of upper surface longitudinal sensor. Strain[Nor.] No damage Damaged Strain[Nor.] No damage Damaged Time[Sec.] Time[Sec.] (a) (b) Figure 6: Damage effect on sensors strain readings: (a) Boom top longitudinal sensor strain time history readings; (b) Boom side (neutral axis) longitudinal sensor strain time history readings 4

5 3. Conclusions A comprehensive approach for optimizing sensors locations using a calibrated digital twin was presented. It is concluded that sensors that were located for load monitoring are not necessarily optimized for damage detection. For a beam-like structure, similar to the Heron UAV boom, this work demonstrated that the strain along the beam bending neutral axis, being zero for a healthy beam, is a promising damage sensitive feature, which can track damages with high ratio of damage-induced signal to normal operation reading. Such an approach, involving a calibrated digital twin, should enable effective evaluation of sensor layouts for optimized damage detection. References 1. García, I., Zubia, J., Durana, G., Aldabaldetreku, G., Illarramendi, M.A. and Villatoro, J., Optical fiber sensors for aircraft structural health monitoring. Sensors, 15(7), pp Ramakrishnan, M., Rajan, G., Semenova, Y. and Farrell, G., Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials Sensors, 16(1), p Takeda, N., 2008, March. Fiber optic sensor-based SHM technologies for aerospace applications in Japan. In Proceedings of SPIE (Vol. 6933, p ). 4. Boller, C. and Meyendorf, N., 2008, December State-of-the-art in Structural Health monitoring for aeronautics In Proceedings of the International Symposium on NDT in Aerospace 5. Richards, W. L., Madaras, E. I., Prosser, W. H., & Studor, G. NASA Applications of Structural Health Monitoring Technology 9th International Workshop on Structural Health Monitoring, September 10 12, Kressel, I., Balter, J., Mashiach, N., Sovran, I., Shapira, O., Shemesh, N.Y., Glamm, B., Dvorjetski, A., Yehoshua, T. and Tur, M., 2014 High speed, in-flight structural health monitoring system for medium altitude long endurance unmanned air vehicle In EWSHM-7th European workshop on structural health monitoring. 7. Tur, M., Sovran, I., Bergman, A., Motil, A., Shapira, O., Ben-Simon, U. and Kressel, I., 2015, September Structural health monitoring of composite-based UAVs using simultaneous fiber optic interrogation by static Rayleigh-based distributed sensing and dynamic fiber Bragg grating point sensors In International Conference on Optical Fibre Sensors (OFS24) (pp P-96340P). International Society for Optics and Photonics. 8. Kressel, I., Dorfman, B., Botsev, Y., Handelman, A., Balter, J., Pillai, A.C.R., Prasad, M.H., Gupta, N., Joseph, A.M., Sundaram, R. and Tur, M., 2015 Flight validation of an embedded structural health monitoring system for an unmanned aerial vehicle Smart Materials and Structures, 24(7), p Nicolas, M.J., Sullivan, R.W. and Richards, W.L., Large Scale Applications Using FBG Sensors: Determination of In-Flight Loads and Shape of a Composite Aircraft Wing Aerospace 2016, 3(3), 18; doi: / aerospace