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Journal articles on the topic 'Numerical modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 23 November 2024

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1

Jaichuang, Atit, and Wirawan Chinviriyasit. "Numerical Modelling of Influenza Model with Diffusion." International Journal of Applied Physics and Mathematics 4, no. 1 (2014): 15–21. http://dx.doi.org/10.7763/ijapm.2014.v4.247.

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2

Gerya, Taras V., David Fossati, Curdin Cantieni, and Diane Seward. "Dynamic effects of aseismic ridge subduction: numerical modelling." European Journal of Mineralogy 21, no. 3 (June 29, 2009): 649–61. http://dx.doi.org/10.1127/0935-1221/2009/0021-1931.

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3

Higdon, Robert L. "Numerical modelling of ocean circulation." Acta Numerica 15 (May 2006): 385–470. http://dx.doi.org/10.1017/s0962492906250013.

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Computational simulations of ocean circulation rely on the numerical solution of partial differential equations of fluid dynamics, as applied to a relatively thin layer of stratified fluid on a rotating globe. This paper describes some of the physical and mathematical properties of the solutions being sought, some of the issues that are encountered when the governing equations are solved numerically, and some of the numerical methods that are being used in this area.

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4

Constantin, Albert Titus, Marie Alice Ghitescu, Gheorghe I. Lazar, and Serban Vlad Nicoara. "Fish Ladder Numerical Modelling." Revista de Chimie 69, no. 3 (April 15, 2018): 591–96. http://dx.doi.org/10.37358/rc.18.3.6156.

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The paper presents a 1D numerical modeling of the sanitary water flow passing through a fish ladder designed for the low head step built across the Alb (White) River near Coroiesti Vilage in Hunedoara County. The model aims to evaluate the water velocity spectrum, emphasizing the maximum values, in the cross sections along this passing structure and in the same time to establish the water levels development. In order to reach this goal, the numerical model will consider a sinthetical hydrograph based on the maximum value of the sanitary water flow required on the river.

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5

Pritchard, M. A., and K. W. Savigny. "Numerical modelling of toppling." Canadian Geotechnical Journal 27, no. 6 (December 1, 1990): 823–34. http://dx.doi.org/10.1139/t90-095.

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Evidence of large-scale toppling deformation has been reported in association with deep-seated landslides affecting mountain slopes along the Beaver River valley, Glacier National Park, British Columbia, Canada. A study has been undertaken to quantitatively investigate the relationship between the toppling mass movement process and the deep-seated landslides; specifically, whether the landslides represent a limiting condition of the toppling process. This is the first of two papers that describe the study. Methods of toppling analysis, including limit-equilibrium, finite-element, and distinct-element methods, are critically reviewed. The distinct-element method emerges as the best technique for modelling both block and flexural modes of toppling. The method is verified by modelling three examples of toppling: a theoretical block topple, a physical model of flexural toppling, and an engineered slope from the Brenda mine near Peachland, British Columbia. The results demonstrate that the Universal Distinct Element Code (UDEC) is capable of modelling both block and flexural types of toppling, that the toppling mass movement process limits to deep-seated planar aswell as curvilinear landslides, and that other landforms such as obsequent scarps and grabens are a manifestation of the toppling process. The research reported here contributes to understanding of the deformation behaviour of engineered slopes and the evolution of natural slopes in rock masses containing pervasive discontinuities. Key words: block toppling, flexural toppling, landslide, numerical modelling, distinct element, DDEC, sackung.

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6

CUNDALL, PETER A., and ROGER D. HART. "NUMERICAL MODELLING OF DISCONTINUA." Engineering Computations 9, no. 2 (February 1992): 101–13. http://dx.doi.org/10.1108/eb023851.

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7

Jeremic, Radun. "Numerical modelling of detonation." Vojnotehnicki glasnik 50, no. 2 (2002): 155–65. http://dx.doi.org/10.5937/vojtehg0202155j.

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8

Lindgren, L. E. "Numerical modelling of welding." Computer Methods in Applied Mechanics and Engineering 195, no. 48-49 (October 2006): 6710–36. http://dx.doi.org/10.1016/j.cma.2005.08.018.

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9

Gyasi Agyemang, Eugene. "Numerical Modelling of Nanopores." ECS Meeting Abstracts MA2024-01, no. 52 (August 9, 2024): 3035. http://dx.doi.org/10.1149/ma2024-01523035mtgabs.

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Nanopores are used for resistive pulse sensing of single analytes and as nanopipette probes for electrochemical scanned probe microscopy. The current-voltage responses of nanopores display a rich range of behaviors, which arise due to interactions between the electric fields (applied potential and surface charges), ion concentrations, and fluid flows.1 To understand these behaviors and interpret experimental results, numerical simulation of the coupled physics are typically performed.2 Yet despite simulations of conical nanopores being commonplace, they remain challenging. As the electric double layer requires resolution of concentrations and electric fields at sub-nm length scales, while concentration enhancements and depletions occur 10s of μm inside the pore simulations must resolve differences over length scales that vary by ~5 orders of magnitude. While various strategies have been employed to make these simulations more manageable including simulating a small portion of the pore, ignoring surface charge beyond a certain distance [others]. However, no consensus exists as to best practices or the impact of these simplifications. In this work we assess the impact of commonly made simplifications and describe best practices for efficient modelling of conical nanopores to ensure accurate results are readily obtained. Suggestions include effective choices of mesh, initial conditions, and the handling of semi-infinite boundaries. (1) Lan, W.-J.; Holden, D. A.; White, H. S. Pressure-Dependent Ion Current Rectification in Conical-Shaped Glass Nanopores. J. Am. Chem. Soc. 2011, 133 (34), 13300–13303. https://doi.org/10.1021/ja205773a. (2) White, H. S.; Bund, A. Ion Current Rectification at Nanopores in Glass Membranes. Langmuir 2008, 24 (5), 2212–2218. https://doi.org/10.1021/la702955k.

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10

Chenari, B., S. S. Saadatian, and Almerindo D. Ferreira. "Numerical Modelling of Regular Waves Propagation and Breaking Using Waves2Foam." Journal of Clean Energy Technologies 3, no. 4 (2015): 276–81. http://dx.doi.org/10.7763/jocet.2015.v3.208.

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11

Russell, James K., Daniele Giordano, Donald B. Dingwell, and Kai-Uwe Hess. "Modelling the non-Arrhenian rheology of silicate melts: Numerical considerations." European Journal of Mineralogy 14, no. 2 (March 22, 2002): 417–28. http://dx.doi.org/10.1127/0935-1221/2002/0014-0417.

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12

Fujihara, Masayuki. "Numerical Modelling of Flow in Fishways Using Shallow Water Equations." Journal of Rainwater Catchment Systems 14, no. 2 (2009): 97–98. http://dx.doi.org/10.7132/jrcsa.kj00005284815.

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13

Vala, Jiří. "Numerical approaches to the modelling of quasi-brittle crack propagation." Archivum Mathematicum, no. 3 (2023): 295–303. http://dx.doi.org/10.5817/am2023-3-295.

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14

Benim, Ali Cemal, Aydin Cicek, and Arif Mert Eker. "A numerical analysis of the thermohydraulics of an EGS project in Turkey." MATEC Web of Conferences 240 (2018): 05001. http://dx.doi.org/10.1051/matecconf/201824005001.

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A numerical study of the thermohydraulics of an enhanced geothermal system project in Turkey is presented. The solid structures are modelled as porous media, using the numerically determined hydraulic fracturing data of other authors. The influence of several numerical modelling aspects such as the domain size, grid resolution, temporal resolution as well as the discretization scheme are investigated and assessed to obtain highly accurate numerical solutions under the applied modelling assumptions. Using the suggested mathematical and numerical model, different production scenarios are investigated.

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15

Agraine, Hana, and Meriem Fakhreddine Bouali. "Numerical Modelling of Oedometer Test." Selected Scientific Papers - Journal of Civil Engineering 15, no. 2 (December 1, 2020): 127–36. http://dx.doi.org/10.1515/sspjce-2020-0025.

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Abstract The oedometric test is a test widely used in civil engineering. The main objective of this article has been to investigate the primary consolidation behaviour of the intact soil samples by comparing the results obtained from finite element analysis computations in PlAXIS2D with the experimental result of the soil samples obtained from the site of the Al-Ahdab oil field in the east of Iraq. Three different material models were utilized during the finite element analysis, comparing the performance of the more advanced constitutive Soft Soil material model against the modified Cam Clay and Mohr-Coulomb material models. Numerical results of Oedomter test show that the Soft Soil model behaviour is the most appropriate model to describe the observed behaviour.

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16

Bladé Castellet, Ernest, Luis Cea, and Georgina Corestein. "Numerical modelling of river inundations." Ingeniería del agua 18, no. 1 (August 4, 2014): 68. http://dx.doi.org/10.4995/ia.2014.3144.

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17

Matsubara, Hitoshi, Kosaburo Hirose, Taka-aki Edo, Kei-ichi Tamanaha, Hisao Hara, and Tomonori Yamada. "Numerical modelling of mudcrack growth." Japanese Geotechnical Society Special Publication 2, no. 31 (2016): 1143–47. http://dx.doi.org/10.3208/jgssp.atc1-3-17.

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18

Szakály, Ferenc, Imre Bojtár, and Gábor Szebényi. "Numerical modelling of human ligaments." Biomechanica Hungarica 9, no. 1 (July 2016): 7–15. http://dx.doi.org/10.17489/biohun/2016/1/04.

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19

Rubinacci, Guglielmo, Antonello Tamburrino, Salvatore Ventre, and Fabio Villone. "Numerical modelling of volumetric defects." International Journal of Applied Electromagnetics and Mechanics 19, no. 1-4 (April 24, 2004): 345–49. http://dx.doi.org/10.3233/jae-2004-588.

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20

Tkadlečková, Markéta. "Numerical Modelling in Steel Metallurgy." Metals 11, no. 6 (May 28, 2021): 885. http://dx.doi.org/10.3390/met11060885.

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Steel production represents a complex process which is accompanied by a series of physical–chemical processes from melting, through the multiphase flow of steel and chemical reactions (processes taking place between the slag, metal, and an inert gas) after solidification [...]

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21

Fokina, K. V., K. Yu Bulgakov, and K. L. Voskanyan. "NUMERICAL MODELLING OF BREEZE CIRCULATIION." Proceedings of the Russian State Hydrometeorological University, no. 56 (2019): 50–60. http://dx.doi.org/10.33933/2074-2762-2019-56-50-60.

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22

Melentijevic, Svetlana, Javier Moreno Robles, and Pablo Martín Blanco. "Numerical modelling of vertical drains." Geotecnia 144 (November 2018): 71–87. http://dx.doi.org/10.24849/j.geot.2018.144.07.

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23

Trefilík, Jiří, Karel Kozel, and Jaromír Příhoda. "Numerical experiments modelling turbulent flows." EPJ Web of Conferences 67 (2014): 02118. http://dx.doi.org/10.1051/epjconf/20146702118.

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24

Pryds, N. H., and J. H. Hattel. "Numerical modelling of rapid solidification." Modelling and Simulation in Materials Science and Engineering 5, no. 5 (September 1, 1997): 451–72. http://dx.doi.org/10.1088/0965-0393/5/5/002.

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25

Kresic, Neven, and Sorab Panday. "Numerical groundwater modelling in karst." Geological Society, London, Special Publications 466, no. 1 (December 14, 2017): 319–30. http://dx.doi.org/10.1144/sp466.12.

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26

Durán-Olivencia, F. J., F. Pontiga, and A. Castellanos. "Numerical Modelling of Electrical Discharges." Journal of Physics: Conference Series 490 (March 11, 2014): 012209. http://dx.doi.org/10.1088/1742-6596/490/1/012209.

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27

NGUYEN, Y. Q., and John C. WELLS. "NUMERICAL MODELLING OF BEDFORM DEVELOPMENT." PROCEEDINGS OF HYDRAULIC ENGINEERING 52 (2008): 163–68. http://dx.doi.org/10.2208/prohe.52.163.

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28

Dalewski, Rafał, and Jerzy Jachimowicz. "Numerical modelling of welded joints." Welding International 25, no. 3 (March 2011): 182–87. http://dx.doi.org/10.1080/09507116.2010.540831.

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29

Talemi, R. Hojjati, M. Abdel Wahab, and P. De Baets. "Numerical modelling of fretting fatigue." Journal of Physics: Conference Series 305 (July 19, 2011): 012061. http://dx.doi.org/10.1088/1742-6596/305/1/012061.

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30

De Moortel, I., and K. Galsgaard. "Numerical modelling of 3D reconnection." Astronomy & Astrophysics 459, no. 2 (September 12, 2006): 627–39. http://dx.doi.org/10.1051/0004-6361:20065716.

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31

Deepu, M., S. S. Gokhale, and S. Jayaraj. "Numerical Modelling of Scramjet Combustor." Defence Science Journal 57, no. 4 (July 20, 2007): 367–79. http://dx.doi.org/10.14429/dsj.57.1784.

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32

Abdulle, A., and Y. Bai. "Reduced-order modelling numerical homogenization." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2021 (August 6, 2014): 20130388. http://dx.doi.org/10.1098/rsta.2013.0388.

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A general framework to combine numerical homogenization and reduced-order modelling techniques for partial differential equations (PDEs) with multiple scales is described. Numerical homogenization methods are usually efficient to approximate the effective solution of PDEs with multiple scales. However, classical numerical homogenization techniques require the numerical solution of a large number of so-called microproblems to approximate the effective data at selected grid points of the computational domain. Such computations become particularly expensive for high-dimensional, time-dependent or nonlinear problems. In this paper, we explain how numerical homogenization method can benefit from reduced-order modelling techniques that allow one to identify offline and online computational procedures. The effective data are only computed accurately at a carefully selected number of grid points (offline stage) appropriately ‘interpolated’ in the online stage resulting in an online cost comparable to that of a single-scale solver. The methodology is presented for a class of PDEs with multiple scales, including elliptic, parabolic, wave and nonlinear problems. Numerical examples, including wave propagation in inhomogeneous media and solute transport in unsaturated porous media, illustrate the proposed method.

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33

van Dijk, J., G. M. W. Kroesen, and A. Bogaerts. "Plasma modelling and numerical simulation." Journal of Physics D: Applied Physics 42, no. 19 (September 18, 2009): 190301. http://dx.doi.org/10.1088/0022-3727/42/19/190301.

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34

SRP�I�, Gregor. "Numerical modelling of linear generators." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 1 (January 5, 2019): 6–8. http://dx.doi.org/10.15199/48.2019.01.02.

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35

Gao, Yan, Alexandre C. M. Correia, Peter P. Eggleton, and Zhanwen Han. "Numerical modelling of tertiary tides." Monthly Notices of the Royal Astronomical Society 479, no. 3 (June 14, 2018): 3604–15. http://dx.doi.org/10.1093/mnras/sty1558.

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36

Frankovská, Jana, Miloslav Kopecký, Jakub Panuška, and Juraj Chalmovský. "Numerical Modelling of Slope Instability." Procedia Earth and Planetary Science 15 (2015): 309–14. http://dx.doi.org/10.1016/j.proeps.2015.08.076.

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37

White, David A., and Nicola Verdone. "Numerical modelling of sedimentation processes." Chemical Engineering Science 55, no. 12 (June 2000): 2213–22. http://dx.doi.org/10.1016/s0009-2509(99)00496-0.

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38

Nieminen, T. A., H. Rubinsztein-Dunlop, N. R. Heckenberg, and A. I. Bishop. "Numerical modelling of optical trapping." Computer Physics Communications 142, no. 1-3 (December 2001): 468–71. http://dx.doi.org/10.1016/s0010-4655(01)00391-5.

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39

Passchier, C. W., and E. Druguet. "Numerical modelling of asymmetric boudinage." Journal of Structural Geology 24, no. 11 (November 2002): 1789–803. http://dx.doi.org/10.1016/s0191-8141(01)00163-8.

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40

Bungum, Hilmar. "Numerical modelling of fault activities." Computers & Geosciences 33, no. 6 (June 2007): 808–20. http://dx.doi.org/10.1016/j.cageo.2006.10.011.

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41

Casperson, Lee W. "Numerical modelling of laser instabilities." Mathematical and Computer Modelling 11 (1988): 298–302. http://dx.doi.org/10.1016/0895-7177(88)90502-x.

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42

Kornfeld, Matthias, Tino Lindner-Silwester, Emanuel Hummel, and Bernhard Streibl. "Numerical Modelling of Explosion Protection." MTZ industrial 4, no. 2 (August 23, 2014): 30–37. http://dx.doi.org/10.1007/s40353-014-0137-6.

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43

Wiles, T. D. "Reliability of numerical modelling predictions." International Journal of Rock Mechanics and Mining Sciences 43, no. 3 (April 2006): 454–72. http://dx.doi.org/10.1016/j.ijrmms.2005.08.001.

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44

Mazzucco, G., B. Pomaro, V. A. Salomoni, and C. E. Majorana. "Numerical modelling of ellipsoidal inclusions." Construction and Building Materials 167 (April 2018): 317–24. http://dx.doi.org/10.1016/j.conbuildmat.2018.01.160.

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45

Davies, G. A. O., X. Zhang, G. Zhou, and S. Watson. "Numerical modelling of impact damage." Composites 25, no. 5 (May 1994): 342–50. http://dx.doi.org/10.1016/s0010-4361(94)80004-9.

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46

Kamnis, S., and S. Gu. "Numerical modelling of droplet impingement." Journal of Physics D: Applied Physics 38, no. 19 (September 16, 2005): 3664–73. http://dx.doi.org/10.1088/0022-3727/38/19/015.

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47

Duh, J. C. "Numerical modelling of enclosure convection." Acta Astronautica 22 (January 1990): 367–74. http://dx.doi.org/10.1016/0094-5765(90)90041-i.

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48

Kaltenbacher, M., M. Rausch, H. Landes, and R. Lerch. "Numerical modelling of electrodynamic loudspeakers." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 18, no. 3 (September 1999): 504–14. http://dx.doi.org/10.1108/03321649910275189.

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49

Arbuzov, V. I., and R. A. Turusov. "Numerical modelling of hardening polymers." Mechanics of Composite Materials 31, no. 6 (1996): 603–7. http://dx.doi.org/10.1007/bf00634912.

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50

Klemp, J. B., and D. R. Durran. "Numerical modelling of Bora winds." Meteorology and Atmospheric Physics 36, no. 1-4 (1987): 215–27. http://dx.doi.org/10.1007/bf01045150.

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