Abstract
Parametric variability is inevitable in actual energy harvesters. It can significantly affect crucial aspects of the system performance, especially in harvesting systems that present geometric parameters, material properties, or excitation conditions that are susceptible to small perturbations. This work aims to develop an investigation to identify the most critical parameters in the dynamic behavior of asymmetric bistable energy harvesters with nonlinear piezoelectric coupling, considering the variability of their physical and excitation properties. For this purpose, a global sensitivity analysis based on orthogonal variance decomposition, employing Sobol indices, is performed to quantify the effect of the harvester parameters on the variance of the recovered power. This technique quantifies the variance concerning each parameter individually and collectively regarding the total variation of the model. The results indicate that the frequency and amplitude of excitation, asymmetric terms and electrical proprieties of the piezoelectric coupling are the most critical parameters that affect the mean power harvested. It is also shown that the order of importance of the parameters can change according to the stability of the harvester’s dynamic response. In this way, a better understanding of the system under analysis is obtained since the study allows the identification of vital parameters that rule the change of dynamic behavior and therefore constitutes a powerful tool in the robust design, optimization, and response prediction of nonlinear harvesters.
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References
Abbiati, G., Marelli, S., Tsokanas, N., Sudret, B., Stojadinovic, B.: A global sensitivity analysis framework for hybrid simulation. Mech. Syst. Signal Process. 146, 106997 (2021). https://doi.org/10.1016/j.ymssp.2020.106997
Alemazkoor, N., Rachunok, B., Chavas, D., Staid, A., Louhghalam, A., Nateghi, R., Tootkaboni, M.: Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency. Sci. Rep. 10, 15270 (2020). https://doi.org/10.1038/s41598-020-72207-z
Aloui, R., Larbi, W., Chouchane, M.: Global sensitivity analysis of piezoelectric energy harvesters. Compos. Struct. 228, 111317 (2019). https://doi.org/10.1016/j.compstruct.2019.111317
Aloui, R., Larbi, W., Chouchane, M.: Uncertainty quantification and global sensitivity analysis of piezoelectric energy harvesting using macro fiber composites. Smart Mater. Struct. 29, 095014 (2020). https://doi.org/10.1088/1361-665X/ab9f12
Arnold, D.: Review of microscale magnetic power generation. IEEE Trans. Magn. 43, 3940–3951 (2007). https://doi.org/10.1109/TMAG.2007.906150
Cacuci, D.: Sensitivity and Uncertainty Analysis: Theory, vol. 1. Chapman Hall/CRC, New York, Boca Raton (2003)
Cao, J., Wang, W., Zhou, S., Inman, D., Lin, J.: Nonlinear time-varying potential bistable energy harvesting from human motion. Appl. Phys. Lett. 107, 143904 (2015). https://doi.org/10.1063/1.4932947
Catacuzzeno, L., Orfei, F., Di Michele, A., Sforna, L., Franciolini, F., Gammaitoni, L.: Energy harvesting from a bio cell. Nano Energy 823–827, 823 (2019). https://doi.org/10.1016/j.nanoen.2018.12.023
Cottone, F., Vocca, H., Gammaitoni, L.: Nonlinear energy harvesting. Phys. Rev. Lett. 102, 080601 (2009). https://doi.org/10.1103/PhysRevLett.102.080601
Crawley, E., Anderson, E.: Detailed models of piezoceramic actuation of beams. J. Intell. Mater. Syst. Struct. 1, 4–25 (1990). https://doi.org/10.1177/1045389X9000100102
Crestaux, T., Le Maıtre, O., Martinez, J.M.: Polynomial chaos expansion for sensitivity analysis. Reliab. Eng. Syst. Saf. 94, 1161–1172 (2009). https://doi.org/10.1016/j.ress.2008.10.008
Cunha, A., Jr.: Enhancing the performance of a bistable energy harvesting device via the cross-entropy method. Nonlinear Dyn. 103, 137–155 (2021). https://doi.org/10.1007/s11071-020-06109-0
Cunha, A., Jr., Nasser, R., Sampaio, R., Lopes, H., Breitman, K.: Uncertainty quantification through the Monte Carlo method in a cloud computing setting. Comput. Phys. Commun. 185, 1355–1363 (2014). https://doi.org/10.1016/j.cpc.2014.01.006
Cunha Jr, A., Norenberg, J., Peterson, J., Lopes, V.G.: STONEHENGE—Suite for Nonlinear Analysis of Energy Harvesting Systems (2021). https://americocunhajr.github.io/STONEHENGE
Daqaq, M., Crespo, R., Ha, S.: On the efficacy of charging a battery using a chaotic energy harvester. Nonlinear Dyn. 99, 1525–1537 (2020). https://doi.org/10.1007/s11071-019-05372-0
Daqaq, M., Masana, R., Erturk, A., Quinn, D.: On the role of nonlinearities in vibratory energy harvesting: a critical review and discussion. Appl. Mech. Rev. 66, 040801 (2014). https://doi.org/10.1115/1.4026278
duToit, N., Wardle, B.: Experimental verification of models for microfabricated piezoelectric vibration energy harvesters. AIAAJ J. (2017). https://doi.org/10.2514/1.25047
Erturk, A., Hoffmann, J., Inman, D.: A piezomagnetoelastic structure for broadband vibration energy harvesting. Appl. Phys. Lett. 94, 254102 (2009). https://doi.org/10.1063/1.3159815
Erturk, A., Inman, D.: An experimentally validated bimorph cantilever model for piezoelectric energy harvesting from base excitations. Smart Mater. Struct. 18, 1–18 (2009). https://doi.org/10.1088/0964-1726/18/2/025009
Erturk, A., Inman, D.: Broadband piezoelectric power generation on high-energy orbits of the bistable duffing oscillator with electromechanical coupling. J. Sound Vibr. 330, 2339–2353 (2011). https://doi.org/10.1016/j.jsv.2010.11.018
Franco, V., Varoto, P.: Parameter uncertainties in the design and optimization of cantilever piezoelectric energy harvesters. Mech. Syst. Signal Process. 93, 593–609 (2017). https://doi.org/10.1016/j.ymssp.2017.02.030
Ghanem, R., Spanos, P.: Stochastic Finite Elements—A Spectral Approach. Springer, Berlin (1991)
Halvorsen, E.: Fundamental issues in nonlinear wideband-vibration energy harvesting. Phys. Rev. E 87, 042129 (2013). https://doi.org/10.1103/PhysRevE.87.042129
He, Q., Daqaq, M.F.: Influence of potential function asymmetries on the performance of nonlinear energy harvesters under white noise. J. Sound Vibr. 333, 3479–3489 (2014). https://doi.org/10.1115/DETC2014-34397
Hoeffding, W.: A class of statistics with asymptotically normal distribution. Ann. Math. Stat. 19, 293–325 (1948). https://doi.org/10.1214/aoms/1177730196
Homma, T., Saltelli, A.: Importance measures in the global sensitivity analysis of nonlinear models. Reliab. Eng. Syst. Saf. 52, 1–17 (1996). https://doi.org/10.1016/0951-8320(96)00002-6
Huang, D., Zhou, S., Litak, G.: Nonlinear analysis of multistable energy harvesters for enhanced energy harvesting. Commun. Nonlinear Sci. Numer. Simul. 69, 270–286 (2019)
Karami, M.A., Inman, D.J.: Powering pacemakers from heartbeat vibrations using linear and nonlinear energy harvesters. Appl. Phys. Lett. 100, 042901 (2012). https://doi.org/10.1063/1.3679102
Kroese, D., Taimre, T., Botev, Z.I.: Handbook of Monte Carlo Methods, vol. 1. Wiley, NJ (2011)
Leadenham, S., Erturk, A.: Unified nonlinear electroelastic dynamics of a bimorph piezoelectric cantilever for energy harvesting, sensing, and actuation. Nonlinear Dyn. 79, 1727–1743 (2015). https://doi.org/10.1007/s11071-014-1770-x
Lee, Y., Qi, Y., Zhou, G., Lua, K.: Vortex-induced vibration wind energy harvesting by piezoelectric mems device information. Sci. Rep. 9, 20404 (2019). https://doi.org/10.1038/s41598-019-56786-0
Li, Y., Zhou, S., Litak, G.: Uncertainty analysis of bistable vibration energy harvesters based on the improved interval extension. J. Vibr. Eng. Technol. 8, 297–306 (2020). https://doi.org/10.1007/s42417-019-00134-z
Lopes, V., Peterson, J., Cunha Jr, A.: Nonlinear characterization of a bistable energy harvester dynamical system. In: Topics in Nonlinear Mechanics and Physics, vol. 228, pp. 71–88. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-9463-8_3
Lund, A., Dyke, J., Song, W., Bilionis, I.: Global sensitivity analysis for the design of nonlinear identification experiments. Nonlinear Dyn. 98, 375–394 (2019). https://doi.org/10.1007/s11071-019-05199-9
Mann, B., Barton, D., Owens, B.: Uncertainty in performance for linear and nonlinear energy harvesting strategies. J. Intell. Mater. Syst. Struct. 23, 1451–1460 (2012). https://doi.org/10.1177/1045389X12439639
Mitcheson, P., Miao, P., Stark, B., Yeatman, E., Holmes, A., Green, T.: Mems electrostatic micropower generator for low frequency operation. Sens. Actuators, A 115, 523–529 (2004). https://doi.org/10.1016/j.sna.2004.04.026
Nagel, J., Rieckermann, J., Sudrer, B.: Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: application to urban drainage simulation. Reliab. Eng. Syst. Saf. 195, 106737 (2020). https://doi.org/10.1016/j.ress.2019.106737
Norenberg, J., Peterson, J., Lopes, V., Luo, R., de la Roca, L., Pereira, M., Ribeiro, J., Cunha Jr, A.: STONEHENGE—suite for nonlinear analysis of energy harvesting systems. Softw. Impacts (2021). https://doi.org/10.1016/j.simpa.2021.100161
Oladyshkin, S., Nowak, W.: Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliab. Eng. Syst. Saf. 106, 179–190 (2012). https://doi.org/10.1016/j.ress.2012.05.002
Palar, P., Zuhal, L., Shimoyama, K., Tsuchiya, T.: Global sensitivity analysis via multi-fidelity polynomial chaos expansion. Reliab. Eng. Syst. Saf. 170, 175–190 (2018). https://doi.org/10.1016/j.ress.2017.10.013
Ruiz, R., Meruane, V.: Uncertainties propagation and global sensitivity analysis of the frequency response function of piezoelectric energy harvesters. Smart Mater. Struct. 26, 065003 (2017). https://doi.org/10.1088/1361-665X/aa6cf3
Saltelli, A., Chan, K., Scott, E.: Sensitivity Analysis. Wiley, New York (2000)
Sepahvand, K., Marburg, S., Hardtke, H.: Uncertainty quantification in stochastic systems using polynomial chaos expansion. Inter. J. App. Mech. 2, 305–353 (2010). https://doi.org/10.1142/S1758825110000524
Sobol, I.: Sensitivity estimates for nonlinear mathematical models. Math. Comput. Model. 1, 407–414 (1993)
Soize, C.: Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering, vol. 1. Springer (2017)
Stanton, S., Erturk, A., Mann, B., Inman, D.: Nonlinear piezoelectricity in electroelastic energy harvesters: Modeling and experimental identification. J. Appl. Phys. 108, 074903 (2010). https://doi.org/10.1063/1.3486519
Sudret, B.: Global sensitivity analysis using polynomial chaos expansions. Reliab. Eng. Syst. Saf. 93, 964–979 (2008). https://doi.org/10.1016/j.ress.2007.04.002
Triplett, A., Quinn, D.: The effect of nonlinear piezoelectric coupling on vibration-based energy harvesting. J. Intell. Mater. Syst. Struct. 20, 1959–1967 (2009). https://doi.org/10.1177/1045389X09343218
Wang, W., Cao, J., Bowen, C., Zhang, Y., Lin, J.: Nonlinear dynamics and performance enhancement of asymmetric potential bistable energy harvesters. Nonlinear Dyn. 94, 1183–1194 (2018). https://doi.org/10.1007/s11071-018-4417-5
Wang, Y., Yang, E., Chen, T., Wang, J., Hu, Z., Mi, J., Pan, X., Xu, M.: A novel humidity resisting and wind direction adapting flag-type triboelectric nanogenerator for wind energy harvesting and speed sensing. Nano Energy 78, 105279 (2020). https://doi.org/10.1016/j.nanoen.2020.105279
Xin, W., Zhang, Z., Huang, X., Hu, Y., Zhou, T., Zhu, C., Kong, X., Jiang, L., Wen, L.: High-performance silk-based hybrid membranes employed for osmotic energy conversion. Nat. Commun. 10, 3876 (2019). https://doi.org/10.1038/s41467-019-11792-8
Xiu, D.: Numerical Methods for Stochastic Computations: A spectral method approach. Princeton University Press, Princeton (2010)
Yang, K., Fei, F., An, H.: Investigation of coupled lever-bistable nonlinear energy harvesters for enhancement of inter-well dynamic response. Nonlinear Dyn. 96, 2369–2392 (2019). https://doi.org/10.1007/s11071-019-04929-3
Yi, F., Wang, X., Niu, S., Li, S., Yin, Y., Dai, K., Zhang, G., Lin, L., Wen, Z., Guo, H., Wang, J., Yeh, M., Zi, Y., Liao, Q., You, Z., Zhang, Y., Wang, Z.: A highly shape-adaptive, stretchable design based on conductive liquid for energy harvesting and self-powered biomechanical monitoring. Sci. Adv. 2, 1501624 (2016). https://doi.org/10.1126/sciadv.1501624
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The authors gratefully acknowledge, for the financial support given to this research, the following Brazilian agencies: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)—Finance Code 001; São Paulo Research Foundation (FAPESP), Grant Number 19/19684-3; Brazilian National Council for Scientific and Technological Development (CNPq) grant number 306526/2019-0; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) Grants 210.167/2019, 211.037/2019 and 201.294/2021.
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Norenberg, J.P., Cunha, A., da Silva, S. et al. Global sensitivity analysis of asymmetric energy harvesters. Nonlinear Dyn 109, 443–458 (2022). https://doi.org/10.1007/s11071-022-07563-8
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DOI: https://doi.org/10.1007/s11071-022-07563-8