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Relevant bibliographies by topics / Spectral Stochastic Finite Element Method

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Academic literature on the topic 'Spectral Stochastic Finite Element Method'

Author: Grafiati

Published: 6 September 2023

Last updated: 7 September 2023

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Journal articles on the topic "Spectral Stochastic Finite Element Method"

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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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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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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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Adhikari, Sondipon. "Doubly Spectral Stochastic Finite-Element Method for Linear Structural Dynamics." Journal of Aerospace Engineering 24, no. 3 (July 2011): 264–76. http://dx.doi.org/10.1061/(asce)as.1943-5525.0000070.

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Ghanem, R., and M. Pellissetti. "Adaptive data refinement in the spectral stochastic finite element method." Communications in Numerical Methods in Engineering 18, no. 2 (January 10, 2002): 141–51. http://dx.doi.org/10.1002/cnm.476.

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Lehikoinen, Antti. "Spectral Stochastic Finite Element Method for Electromagnetic Problems with Random Geometry." Electrical, Control and Communication Engineering 6, no. 1 (October 23, 2014): 5–12. http://dx.doi.org/10.2478/ecce-2014-0011.

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Abstract In electromagnetic problems, the problem geometry may not always be exactly known. One example of such a case is a rotating machine with random-wound windings. While spectral stochastic finite element methods have been used to solve statistical electromagnetic problems such as this, their use has been mainly limited to problems with uncertainties in material parameters only. This paper presents a simple method to solve both static and time-harmonic magnetic field problems with source currents in random positions. By using an indicator function, the geometric uncertainties are effectively reduced to material uncertainties, and the problem can be solved using the established spectral stochastic procedures. The proposed method is used to solve a demonstrative single-conductor problem, and the results are compared to the Monte Carlo method. Based on these simulations, the method appears to yield accurate mean values and variances both for the vector potential and current, converging close to the results obtained by time-consuming Monte Carlo analysis. However, further study may be needed to use the method for more complicated multi-conductor problems and to reduce the sensitivity of the method on the mesh used.

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Gaignaire, R., S. Clenet, O. Moreau, and B. Sudret. "Current Calculation in Electrokinetics Using a Spectral Stochastic Finite Element Method." IEEE Transactions on Magnetics 44, no. 6 (June 2008): 754–57. http://dx.doi.org/10.1109/tmag.2008.915801.

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Stavroulakis, G., D. G. Giovanis, V. Papadopoulos, and M. Papadrakakis. "A GPU domain decomposition solution for spectral stochastic finite element method." Computer Methods in Applied Mechanics and Engineering 327 (December 2017): 392–410. http://dx.doi.org/10.1016/j.cma.2017.08.042.

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Ghanem, R. "Higher-Order Sensitivity of Heat Conduction Problems to Random Data Using the Spectral Stochastic Finite Element Method." Journal of Heat Transfer 121, no. 2 (May 1, 1999): 290–99. http://dx.doi.org/10.1115/1.2825979.

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The spectral formulation of the stochastic finite element method is applied to the problem of heat conduction in a random medium. Specifically, the conductivity of the medium, as well as its heat capacity are treated as uncorrelated random processes with spatial random fluctuations. This paper introduces the basic concepts of the spectral stochastic finite element method using a simple one-dimensional heat conduction examples. The implementation of the method is demonstrated for both Gaussian and log-normal material properties. Moreover, the case of the material properties being modeled as random variables is presented as a simple digression of the formulation for the stochastic process case. Both Gaussian and log-normal models for the material properties are treated.

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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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Dissertations / Theses on the topic "Spectral Stochastic Finite Element Method"

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

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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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Antypas, Dionyssios. "Structural response modelling using the stochastic finite element method." Thesis, Imperial College London, 2002. http://hdl.handle.net/10044/1/8314.

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Li, Chenfeng. "Stochastic finite element modelling of elementary random media." Thesis, Swansea University, 2006. https://cronfa.swan.ac.uk/Record/cronfa42770.

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Following a stochastic approach, this thesis presents a numerical framework for elastostatics of random media. Firstly, after a mathematically rigorous investigation of the popular white noise model in an engineering context, the smooth spatial stochastic dependence between material properties is identified as a fundamental feature of practical random media. Based on the recognition of the probabilistic essence of practical random media and driven by engineering simulation requirements, a comprehensive random medium model, namely elementary random media (ERM), is consequently defined and its macro-scale properties including stationarity, smoothness and principles for material measurements are systematically explored. Moreover, an explicit representation scheme, namely the Fourier-Karhunen-Loeve (F-K-L) representation, is developed for the general elastic tensor of ERM by combining the spectral representation theory of wide-sense stationary stochastic fields and the standard dimensionality reduction technology of principal component analysis. Then, based on the concept of ERM and the F-K-L representation for its random elastic tensor, the stochastic partial differential equations regarding elastostatics of random media are formulated and further discretized, in a similar fashion as for the standard finite element method, to obtain a stochastic system of linear algebraic equations. For the solution of the resulting stochastic linear algebraic system, two different numerical techniques, i.e. the joint diagonalization solution strategy and the directed Monte Carlo simulation strategy, are developed. Original contributions include the theoretical analysis of practical random medium modelling, establishment of the ERM model and its F-K-L representation, and development of the numerical solvers for the stochastic linear algebraic system. In particular, for computational challenges arising from the proposed framework, two novel numerical algorithms are developed: (a) a quadrature algorithm for multidimensional oscillatory functions, which reduces the computational cost of the F-K-L representation by up to several orders of magnitude; and (b) a Jacobi-like joint diagonalization solution method for relatively small mesh structures, which can effectively solve the associated stochastic linear algebraic system with a large number of random variables.

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Nešpůrek, Lukáš. "STOCHASTIC CRACK PROPAGATION MODELLING USING THE EXTENDED FINITE ELEMENT METHOD." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-233900.

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Tato disertační práce vychází z výzkumu v rámci francouzsko-českého programu doktorátu pod dvojím vedením na pracovišti Institut français de mécanique avancée v Clermont-Ferrand a na Ústavu fyziky materiálu AV v Brně. Úvodní výzkumný úkol na brněnském pracovišti se zabýval numerickou analýzou pole napětí v okolí čela trhliny v tenké kovové fólii. Zvláštní pozornost byla zaměřena na vliv speciálního typu singularity v průsečíku čela trhliny s volným povrchem. Těžiště disertační práce spočívá v numerickém modelování a stochastické analýze problémů šíření trhlin se složitou geometrií v dvojrozměrném prostoru. Při analýze těchto problémů se dříve zřídka používaly numerické metody, a to z důvodu vysoké náročnosti na výpočtový čas. V této disertaci je ukázáno, že aplikací moderních metod numerické mechaniky a vhodných technik v analýze spolehlivosti lze tyto problémy řešit s pomocí numerických metod i na PC. Ve spolehlivostní analýze byla využita lineární aproximační metoda FORM. Pro rychlost šíření trhlin se vycházelo z Parisova-Erdoganova vztahu. Pro parametry tohoto vztahu byl použit dvourozměrný statistický model, který postihuje vysokou citlivost na korelaci obou parametrů. Mechanická odezva byla počítána rozšířenou metodou konečných prvků (XFEM), která eliminuje výpočetní náročnost a numerický šum související se změnou sítě v klasické metodě konečných prvků. Prostřednictvím přímé diferenciace bylo odvozeno několik vztahů pro derivace funkce odezvy, čímž se dosáhlo lepší numerické stability a konvergence spolehlivostní analýzy a výrazného zkrácení doby výpočtu. Problém zatížení s proměnou amplitudou byl řešen aplikací transformace zatížení metodou PREFFAS. Využití distribuce výpočtů v síti PC umožnilo další zrychlení analýzy.

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Weber, Marc Anton. "Stochastic structural analysis of engineering components using the finite element method." Doctoral thesis, University of Cape Town, 1993. http://hdl.handle.net/11427/8476.

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Bibliography: p. 113-123.
This thesis investigates probabilistic and stochastic methods for structural analysis which can be integrated into existing, commercially available finite element programs. It develops general probabilistic finite element routines which can be implemented within deterministic finite element programs without requiring major code development. These routines are implemented in the general purpose finite element program ABAQUS through its user element subroutine facility and two probabilistic finite elements are developed: a three-dimensional beam element limited to linear material behaviour and a two-dimensional plane element involving elastic-plastic material behaviour. The plane element incorporates plane strain, plane stress and axisymmetric formulations. The numerical accuracy and robustness of the routines are verified and application of the probabilistic finite element method is illustrated in two case studies, one involving a four-story, two-bay frame structure, the other a reactor pressure vessel nozzle. The probabilistic finite element routines developed in this thesis integrate point estimate methods and mean value first order methods within the same program. Both methods require a systematic sequence involving the perturbation of the random parameters to be evaluated, although the perturbation sequence of the methods differ. It is shown that computer-time saving techniques such as Taylor series and iterative perturbation schemes, developed for mean value based methods, can also be used to solve point estimate method problems. These efficient techniques are limited to linear problems; nonlinear problems must use full perturbation schemes. Finally, it is shown that all these probabilistic methods and perturbation schemes can be integrated within one program and can follow many of the existing deterministic program structures and subroutines. An overall strategy for converting deterministic finite element programs to probabilistic finite element programs is outlined.

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Huh, Jungwon. "Dynamic reliability analysis for nonlinear structures using stochastic finite element method." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/289087.

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An efficient and accurate algorithm is developed to evaluate reliability in the time domain for nonlinear structures subjected to short duration dynamic loadings, including earthquake loading. The algorithm is based on the nonlinear stochastic finite element method (SFEM). Uncertainties in the dynamic and seismic excitation, and resistance-related parameters are incorporated by modeling them as realistically as possible. The uncertainty in them is explicitly addressed. The proposed algorithm intelligently integrates the concepts of response surface method (RSM), finite element method (FEM), first-order reliability method (FORM), and an iterative linear interpolation scheme. This leads to the stochastic finite element concept. It has the potential to estimate the risk associated with any linear or nonlinear structure that can be represented by a finite element algorithm subjected to seismic loading or any short duration dynamic loadings. In the context of the finite element method, the assumed stress-based finite element algorithm is used to increase its efficiency. Two iterative response surface schemes consisting of second order polynomials (with and without cross terms) are proposed. A mixture of saturated and central composite designs is used to assure both efficiency and accuracy of the algorithm. Sensitivity analysis is used to improve the efficiency further. The unique feature of the algorithm is that it is capable of calculating risk using both serviceability and strength limit states and actual earthquake loading time histories can be used to excite structures, enabling a realistic representation of the loading condition. The uncertainty in the amplitude of the earthquake is successfully considered in the context of RSM. Uncertainty in the frequency content of an earthquake is considered indirectly by conducting a parametric study to quantify the effect of uncertainty in the frequency content of earthquakes on the overall reliability of structures. The algorithm has been extensively verified using the Monte Carlo simulation technique. The verified algorithm is used to study the reliability of structures excited by actual earthquake time histories. The results of the numerical examples show that the proposed algorithm can be used accurately and efficiently to estimate the risk for nonlinear structures subjected to short duration time-variant loadings including seismic loading.

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Books on the topic "Spectral Stochastic Finite Element Method"

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

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

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3

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

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4

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

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

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

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Introduction to finite and spectral element methods using MATLAB. Boca Raton: CRC Press, Taylor & Francis Group, 2014.

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Duong, Hien Tran, ed. The stochastic finite element method: Basic perturbation technique and computer implementation. Chichester [England]: Wiley, 1992.

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Maday, Yvon. Nonconforming mortar element methods: Application to spectral discretizations. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1988.

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Kleiber, Michael. The stochastic finite element method: Basic perturbation technique and computer implementation. Chichester: Wiley, 1992.

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Book chapters on the topic "Spectral Stochastic Finite Element Method"

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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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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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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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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Ganguli, Ranjan. "Spectral Finite Element Method." In Foundations of Engineering Mechanics, 205–27. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1902-9_8.

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Gopalakrishnan, Srinivasan, Massimo Ruzzene, and Sathyanarayana Hanagud. "Spectral Finite Element Method." In Springer Series in Reliability Engineering, 177–217. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-284-1_5.

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Rawitscher, George, Victo dos Santos Filho, and Thiago Carvalho Peixoto. "Spectral Finite Element Method." In An Introductory Guide to Computational Methods for the Solution of Physics Problems, 77–93. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-42703-4_7.

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Papadopoulos, Vissarion, and Dimitris G. Giovanis. "Stochastic Finite Element Method." In Stochastic Finite Element Methods, 47–70. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64528-5_3.

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Nath, Kamaljyoti, Anjan Dutta, and Budhaditya Hazra. "Stochastic Finite Element Method." In Reliability-Based Analysis and Design of Structures and Infrastructure, 101–16. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003194613-8.

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Chu, Liu. "Stochastic Finite Element Method." In Uncertainty Quantification of Stochastic Defects in Materials, 51–70. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003226628-6.

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Conference papers on the topic "Spectral Stochastic Finite Element Method"

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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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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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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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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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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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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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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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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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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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Feigl, Kathleen, and Deepthika C. Senaratne. "Calculation of Polymer Flow Using Micro-Macro Simulations." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61575.

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A micro-macro simulation algorithm for the calculation of polymeric flow is developed and implemented. The algorithm couples standard finite element techniques to compute velocity and pressure fields with stochastic simulation techniques to compute polymer stress from simulated polymer dynamics. The polymer stress is computed using a microscopic-based rheological model which combines aspects of network and reptation theory with aspects of continuum mechanics. The model dynamics include two Gaussian stochastic processes each of which is destroyed and regenerated according to a survival time randomly generated from the material’s relaxation spectrum. The Eulerian form of the evolution equations for the polymer configurations are spatially discretized using the discontinuous Galerkin method. The algorithm is tested on benchmark contraction domains for a polyisobutylene (PIB) solution. In particular, the flow in the abrupt die entry domain is simulated and the simulation results are compared with experimental data. The results exhibit the correct qualitative behavior of the polymer and agree well with the experimental data.

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Kapsis, Marios, Li He, Yan Sheng Li, Roger Wells, Omar Valero, Senthil Krishnababu, Gaurav Gupta, Jayanta Kapat, and Megan Schaenzer. "Multi-Scale Parallelised CFD Modelling Towards Resolving Manufacturable Roughness." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90314.

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Abstract Typical turbomachinery aerothermal problems of practical interest are characterised by flow structures of wide-ranging scales, which interact with each other. Such multi-scale interactions can be observed between the flow structures produced by surface roughness and by the bulk flow patterns. Moreover, additive manufacturing may sooner or later open a new chapter in component designs by granting designers the ability to control the surface roughness patterns. As a result, surface finish, which so far has been treated largely as a stochastic trait, can be shifted to a set of design parameters that consist of repetitive, discrete micro-elements on a wall surface (‘manufacturable roughness’). Considering this prospective capability requirement, the question would arise regarding how surface micro-structures can be incorporated in computational analyses during a design phase in the future. Semi-empirical methods for predicting aerothermal characteristics and the impact of manufacturable roughness could be used to minimise computational cost. However, the lack of element-to-element resolution may lead to erroneous predictions, as the interactions among the roughness micro-elements have been shown to be significant for adequate performance predictions [1]. In this paper a new multi-scale approach based on the novel Block Spectral method is adopted. This method aims to provide efficient resolution of the detailed local flow variation in space and time of the large scale micro-structures. This resolution is provided without resorting to modelling every single ones in detail, as a conventional large scale CFD simulation would demand, but still demonstrating similar time-accurate and time-averaged flow properties. The main emphasis of the present work is to develop a parallelised solver of the method to enable tackling large problems. The work also includes a first of the kind verification and demonstration of the method for wall surfaces with a large number of micro-structured elements.

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Reports on the topic "Spectral Stochastic Finite Element Method"

1

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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