Modares Mechanical Engineering

Modares Mechanical Engineering

Global sensitivity analysis of predicted strain and voltage from magnetic shape memory alloy based energy harvester using PAWN method

Authors
Mojtaba Effatpanah 1
Mohammad amin Askari farsangi 2
1 sharif
2 Sharif university
Abstract
In this paper, Global sensitivity analysis of predicted strain from clamped–clamped beam magnetic shape memory alloy based energy harvester with PAWN method is presented. In the selected model MSMA units attached to the roots of clamped-clamped beam while coil wrapped around MSMA. A shock load is applied to a proof mass in the middle of the beam. As a result of beam vibration a longitudinal strain is produced in the MSMA and beam. There are limits to harvest energy from this model and one of them is the strain applied to the MSMA. As the strain increases with respect to the applied pre-strain, the MSMA exits the compression region and causes the model to fail. When strain exceeds limits also affect the predicted result. Thus PAWN method as an efficient and easy way for global sensitivity analysis of the inputs has been taken into account. Then the model is analyzed with introduced method to determine the effect of each input on the output. In the following, due to the importance of the harvested voltage, the sensitivity analysis and ranking of inputs are performed. Moreover, using two-sample Kolmogorov-Smirnov test it is shown that for design and optimization of model with respect to strain one can fix thickness, width and length of MSMA while studying model in corresponding maximums and minimums and also with respect to RMS voltage and similar test the width of the beam can be fixed.
Keywords
Magnetic shape memory alloy
Energy harvester
Beam coupled with MSMA
Global sensitivity analysis
Sensitivity Index
[1] T. Ng, W. Liao, Sensitivity analysis and energy harvesting for a self-powered piezoelectric sensor, Journal of Intelligent Material Systems and Structures, Vol. 16, No. 10, pp. 785-797, 2005.
[2] J. M. Rabaey, M. J. Ammer, J. L. da Silva, D. Patel, S. Roundy, PicoRadio supports ad hoc ultra-low power wireless networking, Computer, Vol. 33, No. 7, pp. 42-48, 2000.
[3] S. Roundy, E. S. Leland, J. Baker, E. Carleton, E. Reilly, E. Lai, B. Otis, J. M. Rabaey, P. K. Wright, V. Sundararajan, Improving power output for vibration-based energy scavengers, IEEE Pervasive Computing, Vol. 4, No. 1, pp. 28-36, 2005.
[4] S. R. Anton, D. J. Inman, Vibration energy harvesting for unmanned aerial vehicles, The 15th International Symposium on: Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring, International Society for Optics and Photonics, pp. 692824-692824-12,2008.
[5] A. Abdelkefi, M. R. Hajj, A. H. Nayfeh, Sensitivity analysis of piezoaeroelastic energy harvesters, Journal of Intelligent Material Systems and Structures Vol. 23, No. 13, pp. 1523-1531, 2012.
[6] S. B. Ayed, A. Abdelkefi, F. Najar, M. R. Hajj, Design and performance of variable-shaped piezoelectric energy harvesters, Journal of Intelligent Material Systems and Structures, Vol. 25, No. 2, pp. 174-186, 2014.
[7] Z. Yan, A. Abdelkefi, M. R. Hajj, Piezoelectric energy harvesting from hybrid vibrations, Smart Materials and Structures, Vol. 23, No. 2, pp. 025026, 2014.
[8] H. R. N. H. Sayyaadi, M. A. Askari Farsangi, An investigation on effectiveness of dimension on Magnetic Shape Memory Alloy based energy harvester with two different configurations, Modares Mechanical Engineering, Vol. 17, No. 1, pp. 136-144, 2017. (in Persian فارسی(
[9] I. Karaman, B. Basaran, H. Karaca, A. Karsilayan, Y. Chumlyakov, Energy harvesting using martensite variant reorientation mechanism in a NiMnGa magnetic shape memory alloy, Applied Physics Letters, Vol. 90, No. 17, pp. 172505, 2007.
[10] N. N. Sarawate, Characterization and Modeling of the Ferromagnetic Shape Memory Alloy Ni-Mn-Ga for Sensing and Actuation, Thesis, The Ohio State University, 2008.
[11] N. M. Bruno, Energy Harvesting Using Martensitic Variant Reorientation in Ni50Mn28. 5Ga21. 5 Magnetic Shape Memory Alloy, Thesis, Northern Arizona University, 2011.
[12] I. Nelson, C. Ciocanel, D. LaMaster, H. Feigenbaum, The Impact of Boundary Conditions on the Response of NiMnGa Samples in Actuation and Power Harvesting Applications, ASME 2013 Conference on Smart Materials , American Society of Mechanical Engineers, pp. V001T01A021- V001T01A021 , 2013.
[13] A. Saren, D. Musiienko, A. Smith, J. Tellinen, K. Ullakko, Modeling and design of a vibration energy harvester using the magnetic shape memory effect, Smart Materials and Structures, Vol. 24, No. 9, pp. 095002, 2015.
[14] H. S. M. A. Askari Farsangi, M. R. Zakerzadeh, A novel inertial energy harvester using magnetic shape memory alloy, Smart Materials and Structures, Vol. 25, No. 10, pp. 105024, 2016.
[15] H.Sayyaadi, M.Effatpanah, M.A. Askari Farsangi, Optimization of energy harvesting from vibration of beam coupled with magnetic shape memory alloys, Modares Mechanical Engineering, Vol. 16, No. 12, pp. 675-684, 2016. (in Persian فارسی(
[16] A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, S. Tarantola, Global Sensitivity Analysis: The Primer, pp. 10-40, New York: Wiley, 2008.
[17] Q. Liu, T. Homma, A new computational method of a moment-independent uncertainty importance measure, Reliability Engineering & System Safety, Vol. 94, No. 7, pp. 1205-1211, 2009.
[18] A. Saltelli, Sensitivity analysis for importance assessment, Risk Analysis, Vol. 22, No. 3, pp. 579-590, 2002.
[19] R. L. Iman, S. C. Hora, A robust measure of uncertainty importance for use in fault tree system analysis, Risk Analysis, Vol. 10, No. 3, pp. 401-406, 1990.
[20] F. Pianosi, T. Wagener, A simple and efficient method for global sensitivity analysis based on cumulative distribution functions, Environmental Modelling & Software, Vol. 67, pp. 1-11, 2015.
[21] M. A. Stephens, Introduction to Kolmogorov (1933) On the Empirical Determination of a Distribution, in: S. Kotz, N. L. Johnson, Breakthroughs in Statistics: Methodology and Distribution, Eds., pp. 93-105, New York, NY: Springer New York, 1992.
[22] J. Wall, Practical statistics for astronomers-ii. correlation, data-modelling and sample comparison, Quarterly Journal of the Royal Astronomical Society, Vol. 37, pp. 519, 1996.
Modares Mechanical Engineering
Volume 18, Issue 1
March 2018
Pages 317-326