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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Spectral stochastic finite element"

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Ghanem, Roger G., and Pol D. Spanos. "Spectral Stochastic Finite‐Element Formulation for Reliability Analysis." Journal of Engineering Mechanics 117, no. 10 (October 1991): 2351–72. http://dx.doi.org/10.1061/(asce)0733-9399(1991)117:10(2351).

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2

Ghanem, Roger G., and Robert M. Kruger. "Numerical solution of spectral stochastic finite element systems." Computer Methods in Applied Mechanics and Engineering 129, no. 3 (January 1996): 289–303. http://dx.doi.org/10.1016/0045-7825(95)00909-4.

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3

Honda, Riki, Ghanem Roger, and Michihiro KITAHARA. "Spectral Stochastic Finite Element Method for Log-Normal Uncertainty." Journal of applied mechanics 7 (2004): 391–98. http://dx.doi.org/10.2208/journalam.7.391.

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4

Sousedík, Bedřich, and Howard C. Elman. "Inverse Subspace Iteration for Spectral Stochastic Finite Element Methods." SIAM/ASA Journal on Uncertainty Quantification 4, no. 1 (January 2016): 163–89. http://dx.doi.org/10.1137/140999359.

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5

Gaignaire, R., S. Clnet, B. Sudret, and O. Moreau. "3-D Spectral Stochastic Finite Element Method in Electromagnetism." IEEE Transactions on Magnetics 43, no. 4 (April 2007): 1209–12. http://dx.doi.org/10.1109/tmag.2007.892300.

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6

Powell, C. E., and H. C. Elman. "Block-diagonal preconditioning for spectral stochastic finite-element systems." IMA Journal of Numerical Analysis 29, no. 2 (April 2, 2008): 350–75. http://dx.doi.org/10.1093/imanum/drn014.

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7

Chen, Nian-Zhong, and C. Guedes Soares. "Spectral stochastic finite element analysis for laminated composite plates." Computer Methods in Applied Mechanics and Engineering 197, no. 51-52 (October 2008): 4830–39. http://dx.doi.org/10.1016/j.cma.2008.07.003.

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8

Chung, Doo Bo, Miguel A. Gutiérrez, Lori L. Graham-Brady, and Frederik-Jan Lingen. "Efficient numerical strategies for spectral stochastic finite element models." International Journal for Numerical Methods in Engineering 64, no. 10 (2005): 1334–49. http://dx.doi.org/10.1002/nme.1404.

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9

Beddek, K., S. Clénet, O. Moreau, and Y. Le Menach. "Spectral stochastic finite element method for solving 3D stochastic eddy current problems." International Journal of Applied Electromagnetics and Mechanics 39, no. 1-4 (September 5, 2012): 753–60. http://dx.doi.org/10.3233/jae-2012-1539.

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10

Ghanem, R. G., and P. D. Spanos. "Spectral techniques for stochastic finite elements." Archives of Computational Methods in Engineering 4, no. 1 (March 1997): 63–100. http://dx.doi.org/10.1007/bf02818931.

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Дисертації з теми "Spectral stochastic finite element"

1

Fink, Sebastian [Verfasser]. "Simulation of elastic-plastic material behaviour with uncertain material parameters : a spectral stochastic finite element method approach / Sebastian Fink." Hannover : Technische Informationsbibliothek (TIB), 2015. http://d-nb.info/1095501860/34.

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2

Adam, Alexandros. "Finite element, adaptive spectral wave modelling." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/45307.

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Анотація:
The ability to predict the wave climate has a great impact on a wide range of sectors, including coastal and offshore engineering, marine renewable energy and shipping. The state of the art in wave prediction is called spectral wave modelling and is based on a phase-averaged, spectral description of the sea-surface elevation. The governing equation, called the action balance equation, is five-dimensional and describes the generation, propagation and evolution of action density in geographic space, spectral space and time. Due to the multidimensional nature of the equation the feasible resolutions are restricted by the computational costs. The aim of this work is to propose schemes which can increase the range of possible resolutions in spectral wave modelling, with the use of adaptivity in space and angle. Thus, this work focuses on the development of an unstructured, adaptive finite element spectral wave model (Fluidity-SW). A sub-grid scale model for the spatial discretisation is used, which retains the stability of discontinuous systems, with continuous degrees of freedom. Then, a new framework for angular adaptivity is developed, with results in dynamic angular and spatial anisotropy of the angular mesh. Finally a spatially h−adaptive scheme is implemented, which can dynamically treat the spatial gradients of the solution fields. The resulting framework is thoroughly verified and validated in a wide range of test cases and realistic scenarios, against analytical solutions, wave measurements and the results obtained with the widely used SWAN model. Thus, the overall ability of the code to simulate surface gravity wind-waves in fixed and adaptive spatial and angular meshes is demonstrated.
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3

Bakhtiari, Siamak. "Stochastic finite element slope stability analysis." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/stochastic-finite-element-slope-stability-analysis(c1b451d9-8bf6-43ff-9c10-7b5209fb45c1).html.

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In this thesis, the failures that occurred during the construction of the Jamuna Bridge Abutment in Bangladesh have been investigated. In particular, the influence of heterogeneity on slope stability has been studied using statistical methods, random field theory and the finite element method. The research is divided into three main parts: the statistical characterization of the Jamuna River Sand, based on an extensive in-situ and laboratory database available for the site; calibration of the laboratory data against a double-hardening elastoplastic soil model; and stochastic finite element slope stability analyses, using a Monte Carlo simulation, to analyse the slope failures accounting for heterogeneity. The sand state has been characterised in terms of state parameter, a meaningful quantity which can fully represent the mechanical behaviour of the soil. It was found that the site consists of predominantly loose to mildly dilative material and is very variable. Also, a Normal distribution was found to best represent the state parameter and a Lognormal distribution was found to best represent the tip resistance.The calibration of the constitutive model parameters was found to be challenging, as alternative approaches had to be adopted due to lack of appropriate test results available for the site. Single-variate random fields of state parameter were then linked to the constitutive model parameters based on the relationships found between them, and a parametric study of the abutment was then carried out by linking finite elements and random field theory within a Monte Carlo framework.It was found that, as the degree of anisotropy of the heterogeneity increases, the range of structural responses increases as well. For the isotropic cases, the range of responses was relatively smaller and tended to result in more localised failures. For the anisotropic cases, it was found that there are two different types of deformation mechanism. It was also found that, as the vertical scale of fluctuation becomes bigger, the range of possible structural responses increases and failure is more likely. Finally, it was found that the failed zones observed during the excavation of the West Guide Bund of the Jamuna Bridge Abutment could be closely predicted if heterogeneity was considered in the finite element analyses. In particular, it was found that, for such a natural deposit, a large degree of anisotropy (in the range of 20) could account for the deformation mechanisms observed on site.
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4

Ullmann, Elisabeth. "Solution strategies for stochastic finite element discretizations." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola&quot, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:105-8042820.

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The discretization of the stationary diffusion equation with random parameters by the Stochastic Finite Element Method requires the solution of a highly structured but very large linear system of equations. Depending on the stochastic properties of the diffusion coefficient together with the stochastic discretization we consider three solver cases. If the diffusion coefficient is given by a stochastically linear expansion, e.g. a truncated Karhunen-Loeve expansion, and tensor product polynomial stochastic shape functions are employed, the Galerkin matrix can be transformed to a block-diagonal matrix. For the solution of the resulting sequence of linear systems we study Krylov subspace recycling methods whose success depends on the ordering and grouping of the linear systems as well as the preconditioner. If we use complete polynomials for the stochastic discretization instead, we show that decoupling of the Galerkin matrix with respect to the stochastic degrees of freedom is impossible. For a stochastically nonlinear diffusion coefficient, e.g. a lognormal random field, together with complete polynomials serving as stochastic shape functions, we introduce and test the performance of a new Kronecker product preconditioner, which is not exclusively based on the mean value of the diffusion coefficient.
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5

Savvides, Abraham. "Application of two-dimensional spectral/finite-difference and spectral/hp finite-element methods to cylinder flows." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264204.

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6

Xiao, Dong Wen. "Efficiency analysis on element decomposition method for stochastic finite element analysis." Thesis, University of Macau, 2000. http://umaclib3.umac.mo/record=b1636334.

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Starkloff, Hans-Jörg. "Stochastic finite element method with simple random elements." Universitätsbibliothek Chemnitz, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200800596.

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Анотація:
We propose a variant of the stochastic finite element method, where the random elements occuring in the problem formulation are approximated by simple random elements, i.e. random elements with only a finite number of possible values.
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8

Parvini, Mehdi. "Pavement deflection analysis using stochastic finite element method." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0014/NQ42757.pdf.

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9

Zheng, Yuquan. "Stochastic finite element analysis of continuous elastic systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0002/MQ42231.pdf.

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Parvini, Mehdi. "Pavement deflection analysis using stochastic finite element method /." *McMaster only, 1997.

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Книги з теми "Spectral stochastic finite element"

1

D, Spanos P., ed. Stochastic finite elements: A spectral approach. New York: Springer-Verlag, 1991.

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2

D, Spanos P., ed. Stochastic finite elements: A spectral approach. Minneola, N.Y: Dover Publications, 2003.

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3

Ghanem, Roger G., and Pol D. Spanos. Stochastic Finite Elements: A Spectral Approach. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3094-6.

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4

Ghanem, Roger G. Stochastic Finite Elements: A Spectral Approach. New York, NY: Springer New York, 1991.

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5

Papadopoulos, Vissarion, and Dimitris G. Giovanis. Stochastic Finite Element Methods. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-64528-5.

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6

Bernardi, Christine. Coupling finite element and spectral methods: First results. Hampton, Va: ICASE, 1987.

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7

Center, Ames Research, ed. Finite element aircraft simulation of turbulence. Moffet Field, Calif: Ames Research Center, 1995.

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8

Stochastic structural dynamics: Application of finite element methods. Chichester, West Sussex: Wiley, 2014.

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9

Karniadakis, George. Spectral/hp element methods for CFD. New York: Oxford University Press, 1999.

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10

Introduction to finite and spectral element methods using MATLAB. Boca Raton, CA: Chapman & Hall/CRC, 2004.

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Частини книг з теми "Spectral stochastic finite element"

1

Ghanem, Roger G., and Pol D. Spanos. "Stochastic Finite Element Method: Response Representation." In Stochastic Finite Elements: A Spectral Approach, 67–99. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3094-6_3.

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2

Ghanem, Roger G., and Pol D. Spanos. "Stochastic Finite Element Method: Response Statistics." In Stochastic Finite Elements: A Spectral Approach, 101–19. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3094-6_4.

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3

Ghanem, Roger G., and Pol D. Spanos. "Representation of Stochastic Processes." In Stochastic Finite Elements: A Spectral Approach, 15–65. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3094-6_2.

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4

Ghanem, Roger G., and Pol D. Spanos. "Introduction." In Stochastic Finite Elements: A Spectral Approach, 1–13. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3094-6_1.

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5

Ghanem, Roger G., and Pol D. Spanos. "Numerical Examples." In Stochastic Finite Elements: A Spectral Approach, 121–83. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3094-6_5.

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6

Ghanem, Roger G., and Pol D. Spanos. "Summary and Concluding Remarks." In Stochastic Finite Elements: A Spectral Approach, 185–91. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3094-6_6.

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7

Ghanem, R. G., and P. D. Spanos. "A Spectral Formulation of Stochastic Finite Elements." In Solid Mechanics and Its Applications, 289–312. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5614-1_13.

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8

Kundu, Abhishek, and Sondipon Adhikari. "A Novel Reduced Spectral Function Approach for Finite Element Analysis of Stochastic Dynamical Systems." In Computational Methods in Stochastic Dynamics, 31–54. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7_3.

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9

Salandin, P., and V. Fiorotto. "Stochastic Solute Transport in Natural Formations: Finite Element and Spectral Method Solution." In Computational Methods in Water Resources X, 571–78. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-010-9204-3_70.

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10

Papadopoulos, Vissarion, and Dimitris G. Giovanis. "Stochastic Processes." In Stochastic Finite Element Methods, 1–26. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64528-5_1.

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Тези доповідей конференцій з теми "Spectral stochastic finite element"

1

Hemanth, G., K. J. Vinoy, and S. Gopalakrishnan. "Spectral stochastic finite element method for periodic structure." In 2014 IEEE International Microwave and RF Conference (IMaRC). IEEE, 2014. http://dx.doi.org/10.1109/imarc.2014.7038959.

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2

Adhikari, Sondipon, and Abhishek Kundu. "A Reduced Spectral Projection Method for Stochastic Finite Element Analysis." In 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-1846.

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3

Adhikari, Sondipon. "Doubly Spectral Finite Element Method for Stochastic Field Problems in Structural Dynamics." In 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-2291.

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4

Beddek, K., Y. Le Menach, S. Clenet, and O. Moreau. "3D Stochastic Spectral Finite Element Method in static electromagnetism using vector potential formulation." In 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC 2010). IEEE, 2010. http://dx.doi.org/10.1109/cefc.2010.5481525.

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5

Kontsos, A., and P. D. Spanos. "A Monte Carlo Finite Element Method for Determining the Young’s Modulus of Polymer Nanocomposites Using Nanoindentation Data." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34801.

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Анотація:
This article presents a Monte Carlo finite element method (MCFEM) for determining the Young’s modulus (YM) of polymer nanocomposites (PNC) using Nanoindentation (NI) data. The method treats actual NI data as measurements of the local YM of PNC; it further assesses the effect of the nonhomogeneous dispersion of carbon nanotubes in polymers on the statistical variations observed in experimental NI data. First the method simulates numerically NI data by developing a random field and a multiscale homogenization model. Subsequently, the MCFEM applies the spectral representation method to generate a population of samples of local YM values. These local values are then used in conjunction with a stochastic finite element scheme to derive estimates for the YM of PNC. The statistical processing of the ensemble of FE solutions yields overall YM values that agree well with corresponding results reported in the literature.
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6

Karadeniz, H. "Stochastic Earthquake-Analysis of Underwater Storage Tanks." In ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2007. http://dx.doi.org/10.1115/omae2007-29190.

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In this paper, the problem and analysis method of underwater storage tanks resting on a horizontal seabed is presented under stochastic earthquake loading. The tank is axi-symmetrical and has a flexible wall/roof. The finite element method is used for the response solution. A solid axi-symmetrical FE has been formulated to idealize the tank whereas an axi-symmetrical fluid element is used for the idealization of the fluid domain. The Eulerian formulation of the fluid system is used to calculate interactive water pressure acting on the tank during the free motion of the tank and earthquake motion. For the response calculation, the modal analysis technique is used with a special algorithm to obtain natural frequencies of the water-structure coupled-system. For the stochastic description of the earthquake loading the modified Kanai-Tajimi earthquake spectrum is used. Finally, the analysis method presented in the paper is demonstrated by an example.
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7

Zaki, A. S., K. Ghandi, and A. A. Bent. "Optimal Piezoelectric Actuator Sizing Based on Random Disturbance Data." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/ad-23722.

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Abstract In general, active damping actuators are sized to induce a prescribed level of damping in the structure in the presence of specified disturbance. Unfortunately, structural disturbances cannot be measured directly. Instead, the effect of disturbances on structural response (e.g. deflection, strain, acceleration, etc.) are quantified either in a stochasitic (i.e. spectral data such as autospectrum) or deterministic (i.e. time history data). In this paper, a technique for sizing actuators based on stochastic disturbance information is presented. From the power spectral density of the response, the peak disturbance at each structural mode of interest can be computed as well as the total RMS of the response. A structural dynamic model is synthesized using either finite element analysis or Rayleigh-Ritz formulation. Based on this information, the disturbances can be estimated which is later used to determine whether the actuator size and configuration are sufficient to introduce the prescribed level of damping. The above algorithm is used to design and compare actuators configuration to reduce the vibration on F18 tail model. A comparison between active fiber composite (AFC) actuators and regular d31 actuators is presented. Simulation results show that the AFCs have more authority than the regular d31 actuator.
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8

Jimbo, Tomohiko, Akira Kano, Yousuke Hisakuni, Yasutaka Ito, Kenji Hirohata, and Tsuyoshi Ichimura. "Probabilistic Reliability Analysis Method Based on Surrogate Model for Wind Turbine Drivetrain Structure Subjected to Random Dynamic Load." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94043.

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Abstract In order to improve the mechanical reliability for wind turbine drivetrain structure subjected to random dynamic load, commercial load and fatigue calculation method is developed based on stochastic and stochastic methods (extreme statistics) of the mechanical composition part to the random dynamic load due to the effects such as wind and the earthquake. Evolutionary spectrum is introduced from the analogy with a concept of a dynamic design wave. An attempt is made for random dynamic load calculation and fatigue prediction of 2MW wind power generation plant by the strong wind and earthquake using large-scale structural analysis based on Finite Element Method and Surrogate Modeling Method based on machine learning with physical model.
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9

Shah, Ashwin R., and Christos C. Chamis. "Simulation of Probabilistic Wind Loads and Building Analysis." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0002.

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Abstract Probabilistic wind loads likely to occur on a structure during its design life are predicted. This article describes a suitable multifactor interactive equation (MFIE) model and its use in the Composite Load Spectra (CLS) computer program to simulate the wind pressure cumulative distribution functions on four sides of a building. The simulated probabilistic wind pressure load was applied to a building frame, and cumulative distribution functions of sway displacements and concepts of reliability were demonstrated by computing the probability of overturning using NESSUS (Numerical Evaluation of Stochastic Structure Under Stress), a stochastic finite-element computer code. The geometry of the building and the properties of building members were also considered as random in the NESSUS analysis. The uncertainties of wind pressure, building geometry, and member section property were quantified in terms of their respective sensitivities on the structural response.
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Bakhtiari-Nejad, Firooz, Naserodin Sepehry, and Mahnaz Shamshirsaz. "Polynomial Chaos Expansion Sensitivity Analysis for Electromechanical Impedance of Plate." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59129.

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Piezoelectric wafer active sensors (PWAS) have been the widely used in impedance based damage detection applications. A most important matter in impedance method is applied voltage to PWAS and measuring current in PWAS. In this paper, for modeling of impedance based structural health monitoring, a 3D spectral finite element method (SFEM) is developed for plate structure with PWAS. Because of high frequency application of impedance method, high degree of freedom (DOF) is needed for modeling of impedance of PWAS attached on the plate. Uncertainty of plate and PWAS parameters could be effect on the natural frequencies of structure. So, impedance signal of modeling would be different based on uncertainty parameters. Polynomial chaos expansion (PC) is a probabilistic method consisting in the projection of the model output on a basis of orthogonal stochastic polynomials in the random inputs. In this paper, PCE is used for sensitivity analysis of the electromechanical impedance of plate structure with PWAS.
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Звіти організацій з теми "Spectral stochastic finite element"

1

Payette, Gregory Steven. Spectral/hp finite element models for fluids and structures. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1055631.

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2

Karniadakis, George. Stochastic Spectral/HP Element Methods for CFD and MHD Simulations. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada455950.

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3

X. Frank Xu. Numerical Stochastic Homogenization Method and Multiscale Stochastic Finite Element Method - A Paradigm for Multiscale Computation of Stochastic PDEs. Office of Scientific and Technical Information (OSTI), March 2010. http://dx.doi.org/10.2172/1036255.

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4

T.F. Eibert, J.L. Volakis, and Y.E. Erdemli. Hybrid Finite Element-Fast Spectral Domain Multilayer Boundary Integral Modeling of Doubly Periodic Structures. Office of Scientific and Technical Information (OSTI), March 2002. http://dx.doi.org/10.2172/821699.

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5

Ansari, S. M., E. M. Schetselaar, and J. A. Craven. Three-dimensional magnetotelluric modelling of the Lalor volcanogenic massive-sulfide deposit, Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328003.

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Unconstrained magnetotelluric inversion commonly produces insufficient inherent resolution to image ore-system fluid pathways that were structurally thinned during post-emplacement tectonic activity. To improve the resolution in these complex environments, we synthesized the 3-D magnetotelluric (MT) response for geologically realistic models using a finite-element-based forward-modelling tool with unstructured meshes and applied it to the Lalor volcanogenic massive-sulfide deposit in the Snow Lake mining camp, Manitoba. This new tool is based on mapping interpolated or simulated resistivity values from wireline logs onto unstructured tetrahedral meshes to reflect, with the help of 3-D models obtained from lithostratigraphic and lithofacies drillhole logs, the complexity of the host-rock geological structure. The resulting stochastic model provides a more realistic representation of the heterogeneous spatial distribution of the electric resistivity values around the massive, stringer, and disseminated sulfide ore zones. Both models were combined into one seamless tetrahedral mesh of the resistivity field. To capture the complex resistivity distribution in the geophysical forward model, a finite-element code was developed. Comparative analyses of the forward models with MT data acquired at the Earth's surface show a reasonable agreement that explains the regional variations associated with the host rock geological structure and detects the local anomalies associated with the MT response of the ore zones.
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