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A Hybrid Maximum Power Point Search Method Using Temperature Measurements in Partial Shading Conditions

Janusz Mroczka Mariusz Ostrowski
Metrology and Measurement Systems | 2014 | No 4 | 733-740 | DOI: 10.2478/mms-2014-0056
Keywords maximum power point partial shading perturb and observe temperature measurement
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Abstract

Photovoltaic panels have a non-linear current-voltage characteristics to produce the maximum power at only one point called the maximum power point. In the case of the uniform illumination a single solar panel shows only one maximum power, which is also the global maximum power point. In the case an irregularly illuminated photovoltaic panel many local maxima on the power-voltage curve can be observed and only one of them is the global maximum. The proposed algorithm detects whether a solar panel is in the uniform insolation conditions. Then an appropriate strategy of tracking the maximum power point is taken using a decision algorithm. The proposed method is simulated in the environment created by the authors, which allows to stimulate photovoltaic panels in real conditions of lighting, temperature and shading.

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Authors and Affiliations

Janusz Mroczka
Mariusz Ostrowski

A new approach to water cooling of photovoltaic panels with a tracking system

Kamil Płachta Janusz Mroczka Mariusz Ostrowski
Metrology and Measurement Systems | 2023 | vol. 30 | No 4 | 675-687 | DOI: 10.24425/mms.2023.147961
Keywords photovoltaic system Cooling system solar tracking system
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Abstract

The article presents a water-cooling system for photovoltaic (PV) modules using a two-axis tracking system that tracks the apparent position of the Sun on the celestial sphere. The cooling system consists of 150 adjustable spray nozzles that cool the bottom layer of PV modules. The refrigerant is water taken from a tank with a capacity of 7 m 3. A water recovery system reduces its consumption with efficiency of approximately 90%. The experimental setup consists of a full-size photovoltaic installation made of 10 modules with an output power of 3.5 kWp combined with a tracking system. The article presents an analysis of the cooling system efficiency in various meteorological conditions. Measurements of energy production were performed in the annual cycle using three different types of photovoltaic installations: stationary, two-axis tracking system and two-axis tracking system combined with the cooling system.
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Authors and Affiliations

Kamil Płachta
1
Janusz Mroczka
1
Mariusz Ostrowski
1
ORCID: ORCID
  1. Wroclaw University of Technology, Faculty of Microsystem Electronics and Photonics, Chair of Electronic and Photonic Metrology, Bolesława Prusa 53/55, 50-317 Wrocław, Poland

Mechanical vibrations: recent trends and engineering applications

Sebastian Garus Bartłomiej Błachowski Wojciech Sochacki Anna Jaskot Paweł Kwiatoń Mariusz Ostrowski Michal Šofer Tomasz Kapitaniak
Bulletin of the Polish Academy of Sciences Technical Sciences | 2022 | 70 | 1 | e140351 | DOI: 10.24425/bpasts.2022.140351
Keywords mechanical vibrations energy harvesting modal analysis granular materials
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Abstract

Although the study of oscillatory motion has a long history, going back four centuries, it is still an active subject of scientificr esearch. In this review paper prospective research directions in the field of mechanical vibrations were pointed out. Four groups of important issues in which advanced research is conducted were discussed. The first are energy harvester devices, thanks to which we can obtain or save significant amounts of energy, and thus reduce the amount of greenhouse gases. The next discussed issue helps in the design of structures using vibrations and describes the algorithms that allow to identify and search for optimal parameters for the devices being developed. The next section describes vibration in multi-body systems and modal analysis, which are key to understanding the phenomena in vibrating machines. The last part describes the properties of granulated materials from which modern, intelligent vacuum-packed particles are made. They are used, for example, as intelligent vibration damping devices.
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Bibliography

  1.  K. Di et al., “Dielectric elastomer generator for electromechanical energy conversion: A mini review,” Sustainability, vol. 13, p. 9881, 2021, doi: 10.3390/su13179881.
  2.  D. Wang, J. Mo, X. Wang, H. Ouyang, and Z. Zhou, “Experimental and numerical investigations of the piezoelectric energy harvesting via friction-induced vibration,” Energy Convers. Manage., vol. 171, pp. 1134–1149, 2018, doi: 10.1016/ j.enconman.2018.06.052.
  3.  A. Anand, S. Naval, P.K. Sinha, N.K. Das, and S. Kundu, “Effects of coupling in piezoelectric multi-beam structure,” Microsyst. Technol., vol. 26, no. 4, pp. 1235–1252, 2019, doi: 10.1007/s00542-019-04653-3.
  4.  A. Anand and S. Kundu, “Design of a spiral-shaped piezoelectric energy harvester for powering pacemakers,” Nanomater. Energy, vol. 8, no. 2, pp. 139–150, 2019, doi: 10.1680/jnaen.19.00016.
  5.  S.B. Ayed, A. Abdelkefi, F. Najar, and M.R. Hajj, “Design and performance of variable-shaped piezoelectric energy harvesters,” J. Intell. Mater. Syst. Struct., vol. 25, no. 2, pp. 174– 186, 2013, doi: 10.1177/1045389x13489365.
  6.  S. Kundu and H.B. Nemade, “Piezoelectric vibration energy harvester with tapered substrate thickness for uniform stress,” Microsyst. Technol., vol. 27, no. 1, pp. 105–113, 2020, doi: 10.1007/s00542-020-04922-6.
  7.  S. Paquin and Y. St-Amant, “Improving the performance of a piezoelectric energy harvester using a variable thickness beam,” Smart Mater. Struct., vol. 19, no. 10, p. 105020, 2010, doi: 10.1088/0964-1726/19/10/105020.
  8.  J. Zhang, X. Xie, G. Song, G. Du, and D. Liu, “A study on a near-shore cantilevered sea wave energy harvester with a variable cross section,” Energy Sci. Eng., vol. 7, no. 6, pp. 3174– 3185, 2019, doi: 10.1002/ese3.489.
  9.  R. Hosseini and M. Nouri, “Shape design optimization of unimorph piezoelectric cantilever energy harvester,” J. Comput. Appl. Mech., vol. 47, no. 2, 2016, doi: 10.22059/jcamech.2017. 224975.126.
  10.  A. Anand, S. Pal, and S. Kundu, “Bandwidth and power enhancement in the MEMS based piezoelectric energy harvester using magnetic tip mass,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e137509, 2022, doi: 10.24425/bpasts.2021.137509.
  11.  X. Li et al., “Investigation to the influence of additional magnets positions on four magnet bi-stable piezoelectric energy harvester,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e140151, 2022, doi: 10.24425/bpasts.2022.140151.
  12.  P. Yingyong, P. Thainiramit, S. Jayasvasti, N. ThanachIssarasak, and D. Isarakorn, “Evaluation of harvesting energy from pedestrians using piezoelectric floor tile energy harvester,” Sens. Actuators A, vol. 331, p. 113035, 2021, doi: 10.1016/j.sna. 2021.113035.
  13.  P. Firoozy, S.E. Khadem, and S.M. Pourkiaee, “Broadband energy harvesting using nonlinear vibrations of a magnetopiezoelastic cantilever beam,” Int. J. Eng. Sci., vol. 111, pp. 113–133, 2017, doi: 10.1016/j.ijengsci.2016.11.006.
  14.  Y. Wu, J. Qiu, S. Zhou, H. Ji, Y. Chen, and S. Li, “A piezoelectric spring pendulum oscillator used for multi-directional and ultra-low frequency vibration energy harvesting,” Appl. Energy, vol. 231, pp. 600–614, 2018, doi: 10.1016/j.apenergy.2018. 09.082.
  15.  J. He et al., “Triboelectric piezoelectric electromagnetic hybrid nanogenerator for high efficient vibration energy harvesting and self powered wireless monitoring system,” Nano Energy, vol. 43, pp. 326–339, 2018, doi: 10.1016/j.nanoen.2017.11.039.
  16.  D. Zhu, S. Roberts, M.J. Tudor, and S.P. Beeby, “Design and experimental characterization of a tunable vibration-based electromagnetic micro- generator,” Sens. Actuators A, vol. 158, no. 2, pp. 284–293, Mar. 2010, doi: 10.1016/j.sna.2010.01.002.
  17.  W.-J. Su, J. Zu, and Y. Zhu, “Design and development of a broadband magnet-induced dual-cantilever piezoelectric energy harvester,” J. Intell. Mater. Syst. Struct., vol. 25, no. 4, pp. 430–442, Aug. 2013, doi: 10.1177/1045389x13498315.
  18.  D. Guo, X.F. Zhang, H. Y. Li, and H. Li, “Piezoelectric energy harvester array with magnetic tip mass,” in Volume 4B: Dynamics, Vibration, and Control. American Society of Mechanical Engineers, Nov. 2015, doi: 10.1115/imece2015-51044.
  19.  M. Ostrowski, B. Błachowski, M. Bocheński, D. Piernikarski, P. Filipek, and W. Janicki, “Design of nonlinear electromagnetic energy harvester equipped with mechanical amplifier and spring bumpers,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 6, pp. 1373–1383, 2020, doi: 10.24425/BPASTS.2020.135384.
  20.  S.-C. Kim, J.-G. Kim, Y.-C. Kim, S.-J. Yang, and H. Lee, “A study of electromagnetic vibration energy harvesters: Design optimization and experimental validation,” Int. J. Precis. Eng. Manuf. Green Technol., vol. 6, no. 4, pp. 779–788, Jul. 2019, doi: 10.1007/s40684-019- 00130-4.
  21.  X. Wang et al., “Similarity and duality of electromagnetic and piezoelectric vibration energy harvesters,” Mech. Syst. Sig. Process., vol. 52-53, pp. 672–684, Feb. 2015, doi: 10.1016/j.ymssp.2014.07.007.
  22.  K. Kecik, A. Mitura, S. Lenci, and J. Warminski, “Energy harvesting from a magnetic levitation system,” Int. J. Non Linear Mech., vol. 94, pp. 200–206, Sep. 2017, doi: 10.1016/j.ijnon linmec.2017.03.021.
  23.  A. Preumont, Mechatronics – Dynamics of Electromechanical and Piezoelectric Systems. Springer Netherlands, 2006, doi: 10.1007/1-4020- 4696-0.
  24.  I. Shahosseini and K. Najafi, “Mechanical amplifier for translational kinetic energy harvesters,” J. Phys. Conf. Ser., vol. 557, p. 012135, Nov. 2014, doi: 10.1088/1742-6596/557/1/012135.
  25.  H. Fu, S. Theodossiades, B. Gunn, I. Abdallah, and E. Chatzi, “Ultra-low frequency energy harvesting using bi-stability and rotary-translational motion in a magnet-tethered oscillator,” Nonlinear Dyn., vol. 101, no. 4, pp. 2131–2143, Sep. 2020, doi: 10.1007/s11071-020-05889-9.
  26.  H. Zhang, L. R. Corr, and T. Ma, “Issues in vibration energy harvesting,” J. Sound Vib., vol. 421, pp. 79–90, May 2018, doi: 10.1016/j. jsv.2018.01.057.
  27.  M. Mösch, G. Fischerauer, and D. Hoffmann, “A self-adaptive and self-sufficient energy harvesting system,” Sensors, vol. 20, no. 9, p. 2519, Apr. 2020, doi: 10.3390/s20092519.
  28.  M. Ostrowski, B. Blachowski, B. Poplawski, D. Pisarski, G. Mikulowski, and L. Jankowski, “Semi-active modal control of structures with lockable joints: general methodology and applications,” Struct. Control Health Monit., vol. 28, no. 5, p. e2710, Feb. 2021, doi: 10.1002/ stc.2710.
  29.  Y. Zhao, M. Alashmori, F. Bi, and X. Wang, “Parameter identification and robust vibration control of a truck driver’s seat system using multi- objective optimization and genetic algorithm,” Applied Acoustics, vol. 173, p. 107697, 2021, doi: 10.1016/j.apacoust.2020.107697.
  30.  S.S. Kessler, S. Spearing, M.J. Atalla, C.E. Cesnik, and C. Soutis, “Damage detection in composite materials using frequency response methods,” Composites Part B, vol. 33, no. 1, pp. 87–95, 2002, doi: 10.1016/S1359-8368(01)00050-6.
  31.  R. Hou and Y. Xia, “Review on the new development of vibration-based damage identification for civil eng. struct.: 2010– 2019,” J. Sound Vib., vol. 491, p. 115741, 2021, doi: 10.1016/ j.jsv.2020.115741.
  32.  K. Dziedziech, P. Czop, W.J. Staszewski, and T. Uhl, “Combined non-parametric and parametric approach for identification of time-variant systems,” Mech. Syst. Sig. Process., vol. 103, pp. 295–311, 2018, doi: 10.1016/j.ymssp.2017.10.020.
  33.  A. Abusoua and M. F. Daqaq, “On using a strong high-frequency excitation for parametric identification of nonlinear systems,” J. Vib. Acoust., vol. 139, no. 5, p. 051012, 2017, doi: 10.1115/ 1.4036504.
  34.  B. Zhu, Y. Dong, and Y. Li, “Nonlinear dynamics of a viscoelastic sandwich beam with parametric excitations and internal resonance,” Nonlinear Dyn., vol. 94, no. 4, pp. 2575–2612, 2018, doi: 10.1007/s11071-018-4511-8.
  35.  F. Beltran-Carbajal and G. Silva-Navarro, “Generalized nonlinear stiffness identification on controlled mechanical vibrating systems,” Asian J. Control, vol. 21, no. 3, pp. 1281–1292, 2018, doi: 10.1002/asjc.1807.
  36.  B.S. Razavi, M.R. Mahmoudkelayeh, and S.S. Razavi, “Damage identification under ambient vibration and unpredictable signal nature,” J. Civ. Struct. Health Monit., vol. 11, no. 5, pp. 1253–1273, 2021, doi: 10.1007/s13349-021-00503-x.
  37.  A.C. Altunıs¸ık, F.Y. Okur, and V. Kahya, “Modal parameter identification and vibration based damage detection of a multiple cracked cantilever beam,” Eng. Fail. Anal., vol. 79, pp. 154–170, 2017, doi: 10.1016/j.engfailanal.2017.04.026.
  38.  K. Ciecieląg, A. Skoczylas, J. Matuszak, K. Zaleski, and K. Kęcik, “Defect detection and localization in polymer composites based on drilling force signal by recurrence analysis,” Measurement, vol. 186, p. 110126, 2021, doi: 10.1016/j.measurement.2021.110126.
  39.  M. Bowkett and K. Thanapalan, “Comparative analysis of failure detection methods of composites materials’ systems,” Syst. Sci. Control Eng., vol. 5, no. 1, pp. 168–177, 2017, doi: 10.1080/ 21642583.2017.1311240.
  40.  D. Cekus, P. Kwiatoń, M. Šofer, and P. Šofer, “Application of heuristic methods to identification of the parameters of discretecontinuous models,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e140150, 2022, doi: 10.24425/bpasts.2022.140150.
  41.  S. Garus, W. Sochacki, M. Kubanek, and M. Nabiałek, “Minimizing the number of layers of the quasi one-dimensional phononic structures,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e139394, 2022, doi: 10.24425/bpasts.2021.139394.
  42.  A. Cancelli, S. Laflamme, A. Alipour, S. Sritharan, and F. Ubertini, “Vibration-based damage localization and quantification in a pretensioned concrete girder using stochastic subspace identification and particle swarm model updating,” Struct. Health Monit., vol. 19, no. 2, pp. 587–605, 2019, doi: 10.1177/1475 921718820015.
  43.  S. Barman, M. Mishra, D. Maiti, and D. Maity, “Vibration-based damage detection of structures employing bayesian data fusion coupled with TLBO optimization algorithm,” Struct. Multidiscip. Optim., vol. 64, pp. 2243–2266, 2021, doi: 10.1007/s00158021-02980-6.
  44.  S. Das, S. Mondal, and S. Guchhait, “Particle swarm optimization-based characterization technique of nonproportional viscous damping parameter of a cantilever beam,” J. Vib. Control, p. 107754632110105, 2021, doi: 10.1177/1077546321101 0526.
  45.  R. Zenzen, I. Belaidi, S. Khatir, and M. A. Wahab, “A damage identification technique for beam-like and truss structures based on frf and bat algorithm,” Comptes Rendus Mécanique, vol. 346, pp. 1253–1266, 2018, doi: 10.1016/j.crme.2018.09.003.
  46.  M.-S. Huang, M. Gül, and H.-P. Zhu, “Vibration-based structural damage identification under varying temperature effects,” J. Aerosp. Eng., vol. 31, no. 3, p. 04018014, 2018, doi: 10.1061/(asce)as.1943-5525.0000829.
  47.  Y. Zhang, Y. Miyamori, S. Mikami, and T. Saito, “Vibrationbased structural state identification by a 1-dimensional convolutional neural network,” Comput.-Aided Civ. Infrastruct. Eng., vol. 34, no. 9, pp. 822–839, 2019, doi: 10.1111/mice.12447.
  48.  H. Nick and A. Aziminejad, “Vibration-based damage identification in steel girder bridges using artificial neural network under noisy conditions,” J. Nondestr. Eval., vol. 40, no. 1, p. 15, 2021, doi: 10.1007/s10921-020-00744-8.
  49.  Y. Yang, C. Dorn, C. Farrar, and D. Mascareñas, “Blind, simultaneous identification of full-field vibration modes and large rigid-body motion of output-only structures from digital video measurements,” Eng. Struct., vol. 207, p. 110183, 2020, doi: 10.1016/j.engstruct.2020.110183.
  50.  Z. Fu and J. He, Modal analysis, ser. 1st edition. Delhi, Oxford: Butterworth-Heinemann, 2001.
  51.  R. Craig and A. Kurdila, Fundamentals of Struct. Dyn., ser. 2nd edition. Hoboken, New Jersey: Wiley, 2006.
  52.  D. de Klerk, D.J. Rixen, and S.N. Voormeeren, “General framework for dynamic substructuring: History, review and classification of techniques,” AIAA Journal, vol. 46, no. 5, pp. 1169–1181, 2008, doi: 10.2514/1.33274.
  53.  J. Roy Craig, Coupling of substructures for dynamic analyses – An overview, 2000, doi: 10.2514/6.2000-1573.
  54.  A. Shabana, Dynamics of Multibody Systems, ser. 4th edition. Cambridge, Chicago: Cambridge University Press, 2013.
  55.  B. Rong, X. Rui, L. Tao, and G. Wang, “Theoretical modeling and numerical solution methods for flexible multibody system dynamics,” Nonlinear Dyn., vol. 98, p. 1519–1553, 2019, doi: 10.1007/s11071-019-05191-3.
  56.  V. Sonneville, M. Scapolan, M. Shan, and O. Bauchau, “Modal reduction procedures for flexible multibody dynamics,” Multibody Sys.Dyn., vol. 51, pp. 377–418, 2021, doi: 10.1007/s11044020-09770-w.
  57.  J. Kim, J. Han, H. Lee, and S. Kim, “Flexible multibody dynamics using coordinate reduction improved by dynamic correction,” Multibody Sys.Dyn., vol. 42, pp. 411–429, 2018, doi: 10.1007/s11044-017-9607-2.
  58.  A. Cammarata, “Global modes for the reduction of flexible multibody systems,” Multibody Sys.Dyn., vol. 53, pp. 59–83, 2021, doi: 10.1007/ s11044-021-09790-0.
  59.  Y. Tang, H. Hu, and Q. Tian, “Model order reduction based on successively local linearizations for flexible multibody dynamics,” Int. J. Numer. Methods Eng., vol. 118, no. 3, pp. 159–180, 2019, doi: 10.1002/nme.6011.
  60.  I. Palomba and R. Vidoni, “Flexible-link multibody system eigenvalue analysis parameterized with respect to rigid-body motion,” Applied Sciences, vol. 9, no. 23, p. 5156, 2019, doi: 10.3390/app9235156.
  61.  K. Worden and P. Green, “A machine learning approach to nonlinear modal analysis,” Mech. Syst. Sig. Process., vol. 84, pp. 34–53, 2017, doi: 10.1016/j.ymssp.2016.04.029.
  62.  G. Kerschen, Modal Analysis of Nonlinear Mechanical Systems, ser. CISM International Centre for Mechanical Sciences. Vienna, Udine: Springer, 2014.
  63.  G. Kerschen, M. Peeters, J. C. Golinval, and C. Stéphan, “Nonlinear modal analysis of a full-scale aircraft,” Journal of Aircraft, vol. 50, no. 5, pp. 1409–1419, 2013, doi: 10.2514/1.C031918.
  64.  A. Albu-Schäffer and C. Della Santina, “A review on nonlinear modes in conservative mechanical systems,” Annu. Rev. Control, vol. 50, pp. 49–71, 2020, doi: 10.1016/j.arcontrol.2020.10.002.
  65.  W. Heylen, S. Lammens, and P. Sas, Modal Analysis Theory and Testing, ser. 1st edition. Heverlee, Belgium: Katholieke Universiteit Leuven, 2007.
  66.  E. Orlowitz and A. Brandt, “Comparison of experimental and operational modal analysis on a laboratory test plate,” Measurement, vol. 102, pp. 121–130, 2017, doi: 10.1016/j.measurement. 2017.02.001.
  67.  F. Zahid, Z. Ong, and S. Khoo, “A review of operational modal analysis techniques for in-service modal identification,” J. Braz. Soc. Mech. Sci. Eng., vol. 42, p. 398, 2020, doi: 10.1007/s40430020-02470-8.
  68.  D. Montanari, A. Agostini, M. Bonini, G. Corti, and C. Ventisette, “The use of empirical methods for testing granular materials in analogue modelling,” Materials, vol. 10, no. 6, p. 635, Jun. 2017, doi: 10.3390/ma10060635.
  69.  B. Kou et al., “Granular materials flow like complex fluids,” Nature, vol. 551, no. 7680, pp. 360–363, Nov. 2017, doi: 10.1038/ nature24062.
  70.  C. Sandeep and K. Senetakis, “Effect of young’s modulus and surface roughness on the inter-particle friction of granu lar materials,” Materials, vol. 11, no. 2, p. 217, Jan. 2018, doi: 10.3390/ma11020217.
  71.  A. Wautier et al., “Multiscale modelling of granular materials in boundary value problems accounting for mesoscale mechanisms,” Comput. Geotech., vol. 134, p. 104143, 2021, doi: 10.1016/j.compgeo.2021.104143.
  72.  G. Recchia, H. Cheng, V. Magnanimo, and L. La Ragione, “Failure in granular materials based on acoustic tensor: a numerical analysis,” EPJ Web Conf. Powders and Grains, vol. 249, p. 10005, 2021.
  73.  J. Irazábal, F. Salazar, and E. Oñate, “Numerical modelling of granular materials with spherical discrete particles and the bounded rolling friction model. Application to railway ballast,” Comput. Geotech., vol. 85, pp. 220–229, 2017, doi: 10.1016/ j.compgeo.2016.12.034.
  74.  S. Zhao, T.M. Evans, and X. Zhou, “Shear-induced anisotropy of granular materials with rolling resistance and particle shape effects,” Int. J. Solids Struct., vol. 150, pp. 268–281, 2018, doi: 10.1016/j.ijsolstr.2018.06.024.
  75.  Z. Nie, C. Fang, J. Gong, and Z. Liang, “Dem study on the effect of roundness on the shear behaviour of granular materials,” Comput. Geotech., vol. 121, p. 103457, 2020, doi: 10.1016/ j.compgeo.2020.103457.
  76.  J. Huang, S. Hu, S. Xu, and S. Luo, “Fractal crushing of granular materials under confined compression at different strain rates,” Int. J. Impact Eng., vol. 106, pp. 259–265, 2017, doi: 10.1016/ j.ijimpeng.2017.04.021.
  77.  S. Larsson, J.M.R. Prieto, G. Gustafsson, H.-Å. Häggblad, and P. Jonsén, “The particle finite element method for transient granular material flow: modelling and validation,” Comput. Part. Mech., vol. 8, no. 1, pp. 135–155, Feb. 2020, doi: 10.1007/ s40571-020-00317-6.
  78.  C. Zhai, E. Herbold, S. Hall, and R. Hnourley, “Particle rotations and energy dissipation during mechanical compression of granular materials,” J. Mech. Phys. Solids, vol. 129, pp. 19–38, 2019, doi: 10.1016/j.jmps.2019.04.018.
  79.  S. Liu, Z. Nie, W. Hu, J. Gong, and P. Lei, “Effect of parti cle type on the shear behaviour of granular materials,” Particuology, vol. 56, pp. 124–131, 2021, doi: 10.1016/j.partic.2020. 11.001.
  80.  W. Fei, G.A. Narsilio, J.H. van der Linden, and M.M. Disfani, “Quantifying the impact of rigid interparticle structures on heat transfer in granular materials using networks,” Int. J. Heat Mass Transfer, vol. 143, p. 118514, 2019, doi: 10.1016/j.ijheatmasstransfer.2019.118514.
  81.  A.M. Druckrey, K.A. Alshibli, and R.I. Al-Raoush, “Discrete particle translation gradient concept to expose strain localisation in sheared granular materials using 3d experimental kinematic measurements,” Géotechnique, vol. 68, no. 2, pp. 162–170, Feb. 2018, doi: 10.1680/ jgeot.16.p.148.
  82.  R. Gupta, S. Salager, K. Wang, and W. Sun, “Open-source support toward validating and falsifying discrete mechanics models using synthetic granular materials – part i: Experimental tests with particles manufactured by a 3d printer,” Acta Geotech., vol. 14, no. 4, pp. 923–937, Jul. 2018, doi: 10.1007/s11440-0180703-0.
  83.  Y. Sun, S. Nimbalkar, and C. Chen, “Particle breakage of granular materials during sample preparation,” J. Rock Mech. Geotech. Eng., vol. 11, no. 2, pp. 417–422, 2019, doi: 10.1016/j.jrmge.2018.12.001.
  84.  T. Sweijen, B. Chareyre, S. Hassanizadeh, and N. Karadimitriou, “Grain-scale modelling of swelling granular materials; application to super absorbent polymers,” Powder Technol., vol. 318, pp. 411–422, 2017, doi: 10.1016/j.powtec.2017.06.015.
  85.  H. M. Beakawi Al-Hashemi and O.S. Baghabra Al-Amoudi, “A review on the angle of repose of granular materials,” Powder Technol., vol. 330, pp. 397–417, 2018, doi: 10.1016/j.powtec.2018.02.003.
  86.  P. Bartkowski, H. Bukowiecki, F. Gawiński, and R. Zalewski, “Adaptive crash energy absorber based on a granular jamming mechanism,” Bull. Pol. Acad. Sci. Tech. Sci., p. e139002, 2021.
  87.  P. Bartkowski, R. Zalewski, and P. Chodkiewicz, “Parameter identification of bouc-wen model for vacuum packed particles based on genetic algorithm,” Arch. Civ. Mech. Eng., vol. 19, no. 2, pp. 322–333, 2019, doi: 10.1016/j.acme.2018. 11.002.
  88.  P. Bartkowski, G. Suwała, and R. Zalewski, “Temperature and strain rate effects of jammed granular systems: experiments and modelling,” Granular Matter, vol. 23, no. 4, p. 79, Aug. 2021, doi: 10.1007/s10035-021-01138-x.
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Authors and Affiliations

Sebastian Garus
1
ORCID: ORCID
Bartłomiej Błachowski
2
ORCID: ORCID
Wojciech Sochacki
1
ORCID: ORCID
Anna Jaskot
3
ORCID: ORCID
Paweł Kwiatoń
1
ORCID: ORCID
Mariusz Ostrowski
2
ORCID: ORCID
Michal Šofer
4
ORCID: ORCID
Tomasz Kapitaniak
5
ORCID: ORCID
  1. Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, Poland
  2. Institute of Fundamental Technological Research, Polish Academy of Sciences, Poland
  3. Faculty of Civil Engineering, Czestochowa University of Technology, Poland
  4. Faculty of Mechanical Engineering, VŠB – Technical University of Ostrava, Czech Republic
  5. Division of Dynamics, Lodz University of Technology, Poland

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