Sensors and Instrumentation, Aircraft/Aerospace, Energy Harvesting & Dynamic Environments Testing, Volume 7

252 A. Poblete and R. O. Ruiz Acknowledgments This work was supported by the National Commission for Scientific and Technological Research [project no. CONICYT/FONDECYT/11180812]. References 1. Anton, S.R., Sodano, H.A.: A review of power harvesting using piezoelectric materials (2003–2006). Smart Mater. Struct. 16(3), R1–R21 (2007). https://doi.org/10.1088/0964-1726/16/3/R01 2. Stanton, S.C., Erturk, A., Mann, B.P., Inman, D.J.: Nonlinear piezoelectricity in electroelastic energy harvesters: modeling and experimental identification. J. Appl. Phys. 108(7), 074903 (2010). https://doi.org/10.1063/1.3486519 3. Ruiz, R.O., Meruane, V.: Uncertainties propagation and global sensitivity analysis of the frequency response function of piezoelectric energy harvesters. Smart Mater. Struct. 26(6), 065003 (2017). https://doi.org/10.1088/1361-665X/aa6cf3 4. Peralta, P., Ruiz, R.O., Meruane, V.: Experimental study of the variations in the electromechanical properties of piezoelectric energy harvesters and their impact on the frequency response function. Mech. Syst. Signal Process. 115, 469–482 (2019). https://doi.org/10.1016/ j.ymssp.2018.06.002 5. Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B.: Bayesian Data Analysis. CRC Press (2013) 6. Peralta, P., Ruiz, R.O., Taflanidis, A.A.: Bayesian identification of electromechanical properties in piezoelectric energy harvesters. Mech. Syst. Signal Process. 141, 106506 (2020). https://doi.org/10.1016/j.ymssp.2019.106506 7. Beck, J.L., Taflanidis, A.A.: Prior and posterior robust stochastic predictions for dynamical systems using probability logic. Int. J, Uncertain. Quan. 3(4), Art. no. 4 (2013) 8. Beck, J.L.: Bayesian system identification based on probability logic. Struct. Control. Health Monit. 17(7), 825–847 (2010). https://doi.org/ 10.1002/stc.424 9. Ching, J., Chen, Y.-C.: Transitional Markov Chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. J. Eng. Mech. 133(7), 816–832 (2007). https://doi.org/10.1061/(ASCE)0733-9399(2007)133:7(816) 10. Betz, W., Papaioannou, I., Straub, D.: Transitional Markov Chain Monte Carlo: observations and improvements. J. Eng. Mech. 142(5), 04016016 (2016). https://doi.org/10.1061/(ASCE)EM.1943-7889.0001066

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