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Combining digital image correlation and probabilistic approaches for the reliability analysis of composite pressure vessels

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The accuracy of reliability analysis of composite solutions depends on the robust estimation of the uncertainties associated with the mechanical properties of this material. On this basis, we propose a methodology able to exploit the full field strain data provided by the digital image correlation approach in order to extract the probabilistic density functions of the mechanical properties. These probabilistic density functions are complemented by a global sensitivity analysis based on the polynomial chaos expansion and a random variable approach, based on the latin hypercube sampling method, with the aim of obtaining a stochastic evaluation of composite pressure vessels.
Rocznik
Strony
224--239
Opis fizyczny
Bibliogr. 46 poz., fot., rys., tab., wykr.
Twórcy
  • Department of Mechanical Engineering, University of Salamanca, Higher Polytechnic School of Zamora, Campus Viriato, Avenida Requejo, 33, 49022 Zamora, Spain
  • Department of Cartographic and Land Engineering, University of Salamanca, Higher Polytechnic School of Ávila, Hornos Caleros, 50, 05003 Ávila, Spain
  • Department of Cartographic and Land Engineering, University of Salamanca, Higher Polytechnic School of Ávila, Hornos Caleros, 50, 05003 Ávila, Spain
  • Department of Mechanical Engineering, University of Salamanca, Higher Polytechnic School of Zamora, Campus Viriato, Avenida Requejo, 33, 49022 Zamora, Spain
  • Department of Cartographic and Land Engineering, University of Salamanca, Higher Polytechnic School of Ávila, Hornos Caleros, 50, 05003 Ávila, Spain
Bibliografia
  • [1] M. Delogu, L. Zanchi, C. Dattilo, M. Pierini, Innovative composites and hybrid materials for electric vehicles lightweight design in a sustainability perspective, Mater. Today Commun. 13 (2017) 192–209, https://doi.org/10.1016/j. mtcomm.2017.09.012.
  • [2] A. Vicario, S.S. Ramanan, S. Arun, 3.4 Composites in Missiles and Launch Vehicles, 2018.
  • [3] L. Sutherland, A review of impact testing on marine composite materials: Part I – marine impacts on marine composites, Compos. Struct. (2017), https://doi.org/10.1016/j.compstruct.12.073.
  • [4] R.F. Gibson, A review of recent research on mechanics of multifunctional composite materials and structures, Compos. Struct. 92 (2010) 2793–2810, https://doi.org/10.1016/j.compstruct.2010.05.003.
  • [5] R. Rafiee, M.A. Torabi, Stochastic prediction of burst pressure in composite pressure vessels, Compos. Struct. 185 (2018) 573–583, https://doi.org/10.1016/j.compstruct.2017.11.068.
  • [6] L.J. Sánchez-Aparicio, L.F. Ramos, J. Sena-Cruz, J.O. Barros, B. Riveiro, Experimental and numerical approaches for structural assessment in new footbridge designs (SFRSCC–GFPR hybrid structure), Compos. Struct. 134 (2015) 95–105, https://doi.org/10.1016/j.compstruct.2015.07.041.
  • [7] N. Movahedi, E. Linul, Mechanical properties of light expanded clay aggregated (LECA) filled tubes, Mater. Lett. 217 (2018) 194–197, https://doi.org/10.1016/j.matlet.2018.01.078.
  • [8] E. Linul, L. Marsavina, J. Kováčik, Collapse mechanisms of metal foam matrix composites under static and dynamic loading conditions, Mater. Sci. Eng.: A 690 (2017) 214–224, https://doi.org/10.1016/j.msea.2017.03.009.
  • [9] T. Fiedler, M. Taherishargh, L. Krstulović-Opara, M. Vesenjak, Dynamic compressive loading of expanded perlite/aluminium syntactic foam, Mater. Sci. Eng.: A 626 (2015) 296–304, https://doi.org/10.1016/j.msea.2014.12.032.
  • [10] R. Rafiee, On the mechanical performance of glass-fibrereinforced thermosetting-resin pipes: a review, Compos. Struct. 143 (2016) 151–164, https://doi.org/10.1016/j. compstruct.2016.02.037.
  • [11] S. Sriramula, M.K. Chryssanthopoulos, Quantification of uncertainty modelling in stochastic analysis of FRP composites, Compos. Part A: Appl. Sci. Manufact. 40 (2009) 1673–1684, https://doi.org/10.1016/j.compositesa.2009.08.020.
  • [12] J.N. Reddy, Mechanics of Laminated Composite Plates and Shells: Theory and Analysis, CRC Press, 2004.
  • [13] P. Sasikumar, R. Suresh, P. Vijayaghosh, S. Gupta, Experimental characterisation of random field models for CFRP composite panels, Compos. Struct. 120 (2015) 451–471, https://doi.org/10.1016/j.compstruct.2014.10.023.
  • [14] O. Orell, J. Vuorinen, J. Jokinen, H. Kettunen, P. Hytönen, J. Turunen, et al., Characterization of elastic constants of anisotropic composites in compression using digital image correlation, Compos. Struct. 185 (2018) 176–185, https://doi.org/10.1016/j.compstruct.2017.11.008.
  • [15] M. Tekieli, S. De Santis, G. de Felice, A. Kwiecień, F. Roscini, Application of digital image correlation to composite reinforcements testing, Compos. Struct. 160 (2017) 670–688, https://doi.org/10.1016/j.compstruct.2016.10.096.
  • [16] S. Sharifi, S. Gohari, M. Sharifiteshnizi, R. Alebrahim, C. Burvill, Y. Yahya, et al., Fracture of laminated woven GFRP composite pressure vessels under combined low-velocity impact and internal pressure, Arch. Civil Mech. Eng. 18 (2018) 1715–1728, https://doi.org/10.1016/j.acme.2018.07.006.
  • [17] M.A. Seif, U.A. Khashaba, R. Rojas-Oviedo, Measuring delamination in carbon/epoxy composites using a shadow moiré laser based imaging technique, Compos. Struct. 79 (2007) 113–118, https://doi.org/10.1016/j.compstruct.2005.11.039.
  • [18] P. Callaway, M. Gilbert, C.C. Smith, Influence of backfill on the capacity of masonry arch bridges, in: Proceedings of the Institution of Civil Engineers: Bridge Engineering, ICE Publishing, 2012 147–157, https://doi.org/10.1680/bren.11.00038.
  • [19] F. Hild, S. Roux, Digital image correlation: from displacement measurement to identification of elastic properties – a review, Strain 42 (2006) 69–80, https://doi.org/10.1111/j.1475-1305.2006.00258.x.
  • [20] H.C. Biscaia, N. Franco, C. Chastre, Development of a simple bond-slip model for joints monitored with the DIC technique, Arch. Civil Mech. Eng. 18 (2018) 1535–1546, https://doi.org/10.1016/j.acme.2018.06.009.
  • [21] L.J. Sánchez-Aparicio, A. Villarino, J. García-Gago, D. González-Aguilera, Photogrammetric, geometrical, and numerical strategies to evaluate initial and current conditions in historical constructions: a test case in the church of San Lorenzo (Zamora, Spain), Remote Sensing 8 (2016), http://dx.doi.org/10.3390/rs8010060.60.
  • [22] T. Gajewski, T. Garbowski, Calibration of concrete parameters based on digital image correlation and inverse analysis, Arch. Civil Mech. Eng. 14 (2014) 170–180, https://doi.org/10.1016/j.acme.2013.05.012.
  • [23] ISOB, Plastics – Determination of Tensile Properties, 1997.
  • [24] B. Pan, K. Qian, H. Xie, A. Asundi, Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review, Meas. Sci. Technol. 20 (2009) 062001, http://dx.doi.org/10.1088/0957-0233/20/6/062001.
  • [25] L. Luu, Z. Wang, M. Vo, T. Hoang, J. Ma, Accuracy enhancement of digital image correlation with B-spline interpolation, Opt. Lett. 36 (2011) 3070–3072, https://doi.org/10.1364/OL.36.003070.
  • [26] J. Blaber, B. Adair, A. Antoniou, Ncorr: open-source 2D digital image correlation matlab software, Exp. Mech. 55 (2015) 1105–1122. , http://dx.doi.org/10.1007/s11340-015-0009-1.
  • [27] B. Pan, Reliability-guided digital image correlation for image deformation measurement, Appl. Opt. 48 (2009) 1535–1542, https://doi.org/10.1364/AO.48.001535.
  • [28] Y. Dong, B. Pan, A review of speckle pattern fabrication and assessment for digital image correlation, Exp. Mech. 57 (2017) 1161–1181. , http://dx.doi.org/10.1007/s11340-017-0283-1.
  • [29] Z. Chen, C. Quan, F. Zhu, X. He, A method to transfer speckle patterns for digital image correlation, Meas. Sci. Technol. 26 (2015) 095201, http://dx.doi.org/10.1088/0957-0233/26/9/095201.
  • [30] M.N. Vo, Z. Wang, L. Luu, J. Ma, Advanced geometric camera calibration for machine vision, Opt. Eng. 50 (2011) 110503, https://doi.org/10.1117/1.3647521.
  • [31] D. Lecompte, H. Sol, J. Vantomme, A. Habraken, Analysis of speckle patterns for deformation measurements by digital image correlation, in: SPECKLE06: Speckles, From Grains to Flowers: International Society for Optics and Photonics, 2006, 63410E, https://doi.org/10.1117/12.695276.
  • [32] B. Pan, Z. Lu, H. Xie, Mean intensity gradient: an effective global parameter for quality assessment of the speckle patterns used in digital image correlation, Opt. Lasers Eng. 48 (2010) 469–477, https://doi.org/10.1016/j.optlaseng.2009.08.010.
  • [33] A. Ab Ghani, M. Ali, S. Dharmalingam, J. Mahmud, Digital Image Correlation (DIC) Technique in Measuring Strain Using Opensource Platform Ncorr, 2016.
  • [34] S.W. Tsai, E.M. Wu, A general theory of strength for anisotropic materials, J. Compos. Mater. 5 (1971) 58–80, https://doi.org/10.1177/002199837100500106.
  • [35] S.S. Shapiro, M.B. Wilk, An analysis of variance test for normality (complete samples), Biometrika 52 (1965) 591–611. , http://dx.doi.org/10.2307/2333709.
  • [36] Certificación AEdNy, UNE-EN 3-8: extintores portátiles de incendios. Requisitos adicionales a la Norma Europea EN 3-7 para la construcción resistencia a la presión y los ensayos mecánicos para extintores con una presión máxima admisible igual o inferior a 30 bar, AENOR, 2007.
  • [37] IyM. Sobol', On sensitivity estimation for nonlinear mathematical models, Matematich. Model. 2 (1990) 112–118.
  • [38] F. Zhu, Q. Zhou, F. Wang, X. Yang, Spatial variability and sensitivity analysis on the compressive strength of hollow concrete block masonry wallettes, Construct. Build. Mater. 140 (2017) 129–138, https://doi.org/10.1016/j.conbuildmat. 2017.02.099.
  • [39] B. Sudret, Global sensitivity analysis using polynomial chaos expansions, Reliab. Eng. Syst. Saf. 93 (2008) 964–979, https://doi.org/10.1016/j.ress.2007.04.002.
  • [40] R. Ghanem, P. Spanos, Stochastic Finite Elements: A Spectral Approach, revised edition, Dover Publications, New York, 2003.
  • [41] G. Blatman, B. Sudret, Adaptive sparse polynomial chaos expansion based on least angle regression, J. Comput. Phys. 230 (2011) 2345–2367, https://doi.org/10.1016/j.jcp.2010.12.021.
  • [42] M. Stone, Cross-validatory choice and assessment of statistical predictions, J. R. Stat. Soc. Ser. B (Methodol.) (1974) 111–147.
  • [43] S. Geisser, The predictive sample reuse method with applications, J. Am. Stat. Assoc. 70 (1975) 320–328.
  • [44] M.D. McKay, R.J. Beckman, W.J. Conover, Comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 21 (1979) 239–245, https://doi.org/10.1080/00401706.1979.10489755.
  • [45] E. Moradabadi, D.F. Laefer, J.A. Clarke, P.B. Lourenço, A semirandom field finite element method to predict the maximum eccentric compressive load for masonry prisms, Construct. Build. Mater. 77 (2015) 489–500, https://doi.org/10.1016/j. conbuildmat.2014.12.027.
  • [46] J.C. Helton, F.J. Davis, Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliab. Eng. Syst. Saf. 81 (2003) 23–69, https://doi.org/10.1016/S0951-8320(03)00058-9.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8ef88b5e-21ae-4f04-84b8-e34528300f93
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