用户名: 密码: 验证码:
Application of stochastic finite element approaches to wood-based products
详细信息    查看全文
  • 作者:Josef Füssl ; Georg Kandler ; Josef Eberhardsteiner
  • 关键词:Glued ; laminated timber ; Wooden boards ; Random process ; Stochastic finite element method ; Effective stiffness
  • 刊名:Archive of Applied Mechanics (Ingenieur Archiv)
  • 出版年:2016
  • 出版时间:January 2016
  • 年:2016
  • 卷:86
  • 期:1-2
  • 页码:89-110
  • 全文大小:1,171 KB
  • 参考文献:1.Blatman, G., Sudret, B.: An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis. Probab. Eng. Mech. 25(2), 183–197 (2010). doi:10.​1016/​j.​probengmech.​2009.​10.​003 CrossRef
    2.Bucher, C.: Computational Analysis of Randomness in Structural Mechanics: Structures and Infrastructures, vol. 3. Illustrated, CRC Press (2009). ISBN: 0203876539, 9780203876534
    3.Bulleit, W.M., Chapman, R.A.: Characterization of the correlation structure of lumber strength properties. Wood Sci. Technol. 38, 285–296 (2004). doi:10.​1007/​s00226-004-0234-8 CrossRef
    4.Casella, G., Berger, R.L.: Statistical Inference, 2nd edn. Duxbury Press (2001). ISBN: 0534243126
    5.Chen, N.Z., Guedes Soares, C.: Spectral stochastic finite element analysis for laminated composite plates. Comput. Methods Appl. Mech. Eng. 197(51–52), 4830–4839 (2008). doi:10.​1016/​j.​cma.​2008.​07.​003 CrossRef MATH
    6.Choi, S.K., Grandhi, R.V., Canfield, R.A.: Propagation of non-Gaussian stochastic behavior using the polynomial chaos expansion. In: Bathe, K. (ed.) Computational Fluid and Solid Mechanics 2003, pp. 1896–1899. Elsevier, Oxford (2003). doi:10.​1016/​B978-008044046-0.​50464-4 CrossRef
    7.Choi, S.K., Grandhi, R.V., Canfield, R.A.: Structural reliability under non-Gaussian stochastic behavior. Comput. Struct. 82(13–14), 1113–1121 (2004). doi:10.​1016/​j.​compstruc.​2004.​03.​015 . (advances in Probabilistic Mechanics and Structural Reliability)CrossRef
    8.Ehlbeck, J., Colling, F., Görlacher, R.: Einfluss keilgezinkter Lamellen auf die Biegefestigkeit von Brettschichtholzträgern. Überprüfung des Modells mit Hilfe von Trägerversuchen [Influence of finger-joints on the bending strength of GLT beams. Model validation; Published in German]. Holz als Roh- und Werkstoff 43, 439–442 (1985c)CrossRef
    9.Ehlbeck, J., Colling, F., Görlacher, R.: Einfluss keilgezinkter Lamellen auf die Biegefestigkeit von Brettschichtholzträgern. Eingangsdaten für das Rechenmodell [Influence of finger-joints on the bending strength of GLT beams. Input data for the numerical model; Published in German]. Holz als Roh- und Werkstoff 43, 369–373 (1985a)CrossRef
    10.Ehlbeck, J., Colling, F., Görlacher, R.: Einfluss keilgezinkter Lamellen auf die Biegefestigkeit von Brettschichtholzträgern. Entwicklung eines Rechenmodells [Influence of finger-joints on the bending strength of GLT beams. Development of a numerical model; Published in German]. Holz als Roh- und Werkstoff 43, 333–337 (1985b)CrossRef
    11.Elishakoff, I., Ren, Y.: Finite Element Methods for Structures with Large Stochastic Variations. Oxford Texts in Applied and Engineering Mathematics, Oxford University Press (2003). http://​books.​google.​at/​books?​id=​o-dUx0HuXpoC
    12.Falsone, G., Impollonia, N.: A new approach for the stochastic analysis of finite element modelled structures with uncertain parameters. Comput. Methods Appl. Mech. Eng. 191(44), 5067–5085 (2002). doi:10.​1016/​S0045-7825(02)00437-1 CrossRef MATH
    13.Falsone, G., Impollonia, N.: About the accuracy of a novel response surface method for the analysis of finite element modeled uncertain structures. Probab. Eng. Mech. 19(19), 53–63 (2004). doi:10.​1016/​j.​probengmech.​2003.​11.​005 CrossRef
    14.Ghanem, R., Kruger, R.: Numerical solution of spectral stochastic finite element systems. Comput. Methods Appl. Mech. Eng. 129(3), 289–303 (1996). http://​www.​scopus.​com/​inward/​record.​url?​eid=​2-s2.​0-0029776146&​partnerID=​40&​md5=​fe06a9dd3e81bc39​d7cc546559dcaaf8​
    15.Ghanem, R.: The nonlinear Gaussian spectrum of log-normal stochastic processes and variables. J. Appl. Mech. 66(4), 964–973 (1999) http://​www.​scopus.​com/​inward/​record.​url?​eid=​2-s2.​0-0033293545&​partnerID=​40&​md5=​26f4cbea653a3a60​6cd043d1ee14fac4​
    16.Ghanem, R.G., Doostan, A.: On the construction and analysis of stochastic models: characterization and propagation of the errors associated with limited data. J. Comput. Phys. 217(1), 63–81 (2006). doi:10.​1016/​j.​jcp.​2006.​01.​037 CrossRef MathSciNet MATH
    17.Ghanem, R.G., Spanos, P.D.: Stochastic Finite Elements: A Spectral Approach. Springer, New York (2003)
    18.Hurtado, J., Barbat, A.: Monte Carlo techniques in computational stochastic mechanics. Arch. Comput. Methods Eng. 5, 3–29 (1998). doi:10.​1007/​BF02736747 CrossRef MathSciNet
    19.Johansson, C.J.: Grading of timber with respect to mechanical properties. In: Thelandersson, S., Larsen, H. (eds.) Timber Engineering, pp. 23–43. Wiley, Chichester (2003)
    20.Kamiński, M., Kleiber, M.: Perturbation based stochastic finite element method for homogenization of two-phase elastic composites. Comput. Struct. 78(6), 811–826 (2000) http://​www.​scopus.​com/​inward/​record.​url?​eid=​2-s2.​0-0342955063&​partnerID=​40&​md5=​76aec082bb799c1b​bf8e3a8f4ba64ae7​
    21.Kamiński, M.: Generalized perturbation-based stochastic finite element method in elastostatics. Comput. Struct. 85(10), 586–594 (2007), http://​www.​scopus.​com/​inward/​record.​url?​eid=​2-s2.​0-33947265438&​partnerID=​40&​md5=​05fb74e43816a81b​525e5f7df0b1b719​
    22.Kamiński, M.: The Stochastic Perturbation Method for Computational Mechanics. Wiley, Chichester (2013) http://​eu.​wiley.​com/​WileyCDA/​WileyTitle/​productCd-0470770821.​html
    23.Kamiński, M., Świta, P.: Generalized stochastic finite element method in elastic stability problems. Comput. Struct. 89(11–12), 1241–1252 (2011). doi:10.​1016/​j.​compstruc.​2010.​08.​009 . (computational Fluid and Solid Mechanics 2011, Proceedings Sixth MIT Conference on Computational Fluid and Solid Mechanics)CrossRef
    24.Kroese, D.P., Taimre, T., Botev, Z.: Handbook of Monte Carlo Methods. Wiley, Hoboken (2011) http://​www.​myilibrary.​com . ISBN: 9781283072427
    25.Li, C.C., Der Kiureghian, A.: Optimal discretization of random fields. J. Eng. Mech. 119(6), 1136–1154 (1993) http://​www.​scopus.​com/​inward/​record.​url?​eid=​2-s2.​0-0027608710&​partnerID=​40&​md5=​cc99c7fcbe667ec7​e0678419258e9d5d​
    26.Matthies, H.G., Brenner, C.E., Bucher, C.G.: Guedes Soares, C.: Uncertainties in probabilistic numerical analysis of structures and solids-stochastic finite elements. Struct. Saf. 19(3), 283–336 (1997). doi:10.​1016/​S0167-4730(97)00013-1 . (devoted to the work of the Joint Committee on Structural Safety)CrossRef
    27.Noh, H.C., Park, T.: Monte Carlo simulation-compatible stochastic field for application to expansion-based stochastic finite element method. Comput. Struct. 84(31–32), 2363–2372 (2006). doi:10.​1016/​j.​compstruc.​2006.​07.​001 CrossRef MathSciNet
    28.Nyström, J.: Automatic measurement of fiber orientation in softwoods by using the tracheid effect. Comput. Electron. Agric. 41, 91–99 (2003). doi:10.​1016/​S0168-1699(03)00045-0 CrossRef
    29.Olsson, A., Oscarsson, J., Serrano, E., Källsner, B., Johansson, M., Enquist, B.: Prediction of timber bending strength and in-member cross-sectional stiffness variation on basis of local wood fibre orientation. Eur. J. Wood Wood Prod. 71(3), 319–333 (2013)CrossRef
    30.Panayirci, H., Schuëller, G.: On the capabilities of the polynomial chaos expansion method within SFE analysis: an overview. Arch. Comput. Methods Eng. 18(1), 43–55 (2011) http://​www.​scopus.​com/​inward/​record.​url?​eid=​2-s2.​0-79951852512&​partnerID=​40&​md5=​080694c5aa392648​29aef06b53541001​
    31.Pellissetti, M., Ghanem, R.: Iterative solution of systems of linear equations arising in the context of stochastic finite elements. Adv. Eng. Softw. 31(8–9), 607–616 (2000). doi:10.​1016/​S0965-9978(00)00034-X CrossRef MATH
    32.Petersson, H.: Use of optical and laser scanning techniques as tools for obtaining improved FE-input data for strength and shape stability analysis of wood and timber. In: IV European Conference on Computational Mechanics, Paris (2010)
    33.Sachdeva, S.K., Nair, P.B., Keane, A.J.: Hybridization of stochastic reduced basis methods with polynomial chaos expansions. Probab. Eng. Mech. 21(2), 182–192 (2006). doi:10.​1016/​j.​probengmech.​2005.​09.​003 CrossRef
    34.Sachdeva, S.K., Nair, P.B., Keane, A.J.: On using deterministic FEA software to solve problems in stochastic structural mechanics. Comput. Struct. 85(5–6), 277–290 (2007). doi:10.​1016/​j.​compstruc.​2006.​10.​008 . (computational Stochastic Mechanics)CrossRef
    35.Sakamoto, S., Ghanem, R.: Polynomial chaos decomposition for the simulation of non-Gaussian nonstationary stochastic processes. J. Eng. Mech. 128(2), 190–201 (2002) http://​www.​scopus.​com/​inward/​record.​url?​eid=​2-s2.​0-0036472444&​partnerID=​40&​md5=​2fb3d9f6108c11d5​3fba5a0d0032ca3e​
    36.Shinozuka, M., Deodatis, G.: Simulation of stochastic processes by spectral representation. Appl. Mech. Rev. 44(4), 191–204 (1991) http://​www.​scopus.​com/​inward/​record.​url?​eid=​2-s2.​0-84861979006&​partnerID=​40&​md5=​8a818ecc2a3a51fb​fc013c6772feef2d​ , cited By (since 1996) 285
    37.Stefanou, G.: The stochastic finite element method: Past, present and future. Comput. Methods Appl. Mech. Eng. 198(9–12), 1031–1051 (2009). doi:10.​1016/​j.​cma.​2008.​11.​007 CrossRef MATH
    38.Stefanou, G., Papadrakakis, M.: Assessment of spectral representation and Karhunen-Loève expansion methods for the simulation of Gaussian stochastic fields. Comput. Methods Appl. Mech. Eng. 196(21–24), 2465–2477 (2007). doi:10.​1016/​j.​cma.​2007.​01.​009 CrossRef MATH
    39.Sudret, B., Der Kiureghian, A.: Stochastic finite element methods and reliability: A state-of-the-art report. Report (University of California, Berkeley. Structural Engineering, Mechanics and Materials), Department of Civil and Environmental Engineering, University of California (2000) http://​books.​google.​com/​books?​id=​cRxAHAAACAAJ
    40.Vanmarcke, E.: Random Fields: Analysis and Synthesis. Illustrated, World Scientific (2010). ISBN: 9812562974, 9789812562975
    41.Vanmarcke, E., Shinozuka, M., Nakagiri, S., Schuëller, G., Grigoriu, M.: Random fields and stochastic finite elements. Struct. Saf. 3(3–4), 143–166 (1986). doi:10.​1016/​0167-4730(86)90002-0 CrossRef
    42.Zhang, J., Ellingwood, B.: Orthogonal series expansions of random fields in reliability analysis. J. Eng. Mech. 120(12), 2660–2677 (1994) http://​www.​scopus.​com/​inward/​record.​url?​eid=​2-s2.​0-0028734886&​partnerID=​40&​md5=​ea54aedbba1a9efb​bf528b1f70bb2002​
  • 作者单位:Josef Füssl (1)
    Georg Kandler (1)
    Josef Eberhardsteiner (1)

    1. Institute for Mechanics of Materials and Structures, Vienna University of Technology, Karlsplatz 13, 1040, Vienna, Austria
  • 刊物类别:Engineering
  • 刊物主题:Theoretical and Applied Mechanics
    Mechanics
    Complexity
    Fluids
    Thermodynamics
    Systems and Information Theory in Engineering
  • 出版者:Springer Berlin / Heidelberg
  • ISSN:1432-0681
文摘
Due to the natural growing process of wood, the mechanical properties of wooden boards are subject to high variability, mainly introduced by knots and the resulting deviation of the wood fibre directions around them. This variability has a great impact on the serviceability limit state performance of wood-based products, such as glued-laminated timber, and thus should be considered within design concepts. Numerous applications of random process models for numerically representing the fluctuation of the mechanical properties along wooden boards can be found in the literature. But, the corresponding mechanical probabilistic investigation, however, is limited almost exclusively to Monte Carlo simulations so far. For this reason, the focus of this work is laid on alternative probabilistic approaches, in particular the perturbation and the spectral stochastic finite element method. Both methods are combined with several discretization methods for the random process, programmed in a consistent environment, and compared to the Monte Carlo simulation, regarding computational effort as well as quality of results. For this purpose, the second-order moments (mean and standard deviation) of the system response of a glued-laminated timber beam with random lamination stiffness are computed. The performance of the different approaches is compared and, in particular, the influence of the variability of the ‘raw’ material on the structural response is shown. Well-known effects, such as the decrease in the variability of effective properties of GLT with increasing number of lamellas, are numerically reproduced and quantified. Moreover, a significant influence of the correlation length, specifying the rate of material property fluctuation within each lamella, on the effective stiffness of the resulting glued-laminated timber beams is demonstrated.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700