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Journal articles on the topic 'Numerical modeling'

To see the other types of publications on this topic, follow the link: Numerical modeling.

Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

Makokha, Mary, Akira Kobayashi, and Shigeyasu Aoyama. "Numerical Modeling of Seawater Intrusion Management Measures." Journal of Rainwater Catchment Systems 14, no. 1 (2008): 17–24. http://dx.doi.org/10.7132/jrcsa.kj00004978338.

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2

O. B. Silva, Augusto, Newton O. P. Júnior, and João A. V. Requena. "Numerical Modeling of a Composite Hollow Vierendeel-Truss." International Journal of Engineering and Technology 7, no. 3 (June 2015): 176–82. http://dx.doi.org/10.7763/ijet.2015.v7.788.

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3

ADETU, Alina-Elena, Cătălin ADETU, and Vasile NĂSTĂSESCU. "NUMERICAL MODELING OF ACOUSTIC WAVE PROPAGATION IN UNLIMITED SPACE." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 21, no. 1 (October 8, 2019): 80–87. http://dx.doi.org/10.19062/2247-3173.2019.21.12.

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4

ITO, Yusuke, Toru KIZAKI, Naohiko SUGITA, and Mamoru MITSUISHI. "1206 Numerical Modeling of Picosecond Laser Drilling of Glass." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _1206–1_—_1206–5_. http://dx.doi.org/10.1299/jsmelem.2015.8._1206-1_.

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5

Troyani, N., L. E. Montano, and O. M. Ayala. "Numerical modeling of thermal evolution in hot metal coiling." Revista de Metalurgia 41, Extra (December 17, 2005): 488–92. http://dx.doi.org/10.3989/revmetalm.2005.v41.iextra.1082.

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6

Miano, Giovanni, Guglielmo Rubinacci, and Antonello Tamburrino. "Numerical modeling for plasmonics." International Journal of Applied Electromagnetics and Mechanics 35, no. 2 (February 9, 2011): 79–91. http://dx.doi.org/10.3233/jae-2011-1331.

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7

TOKUDA, Daisuke. "Numerical Modeling and Science." JOURNAL OF JAPAN SOCIETY OF HYDROLOGY AND WATER RESOURCES 32, no. 4 (July 5, 2019): 204. http://dx.doi.org/10.3178/jjshwr.32.204.

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8

Isbăşoiu, Eliza Consuela. "Numerical Modeling and Simulation." Advanced Science Letters 19, no. 1 (January 1, 2013): 166–69. http://dx.doi.org/10.1166/asl.2013.4663.

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9

Carper, Kenneth L. "Numerical Modeling: Special Issue." Journal of Performance of Constructed Facilities 27, no. 1 (February 2013): 1. http://dx.doi.org/10.1061/(asce)cf.1943-5509.0000414.

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10

Favreau, P., A. Mangeney, A. Lucas, G. Crosta, and F. Bouchut. "Numerical modeling of landquakes." Geophysical Research Letters 37, no. 15 (August 2010): n/a. http://dx.doi.org/10.1029/2010gl043512.

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11

Laigle, Dominique, and Philippe Coussot. "Numerical Modeling of Mudflows." Journal of Hydraulic Engineering 123, no. 7 (July 1997): 617–23. http://dx.doi.org/10.1061/(asce)0733-9429(1997)123:7(617).

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12

Fujimoto, S. "Numerical Modeling for Corrosion." Interface magazine 23, no. 4 (January 1, 2014): 45. http://dx.doi.org/10.1149/2.f01144if.

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13

Chowdhury, D., M. Mlejnek, and Y. Mauro. "Numerical Modeling of PMD." Journal of Optical and Fiber Communications Reports 1, no. 2 (October 2004): 141–49. http://dx.doi.org/10.1007/s10297-004-0006-0.

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14

Ćurić, M. "Numerical modeling of thunderstorm." Theoretical and Applied Climatology 40, no. 4 (December 1989): 227–35. http://dx.doi.org/10.1007/bf00865973.

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15

Harry, Dennis L. "Comment on [“Seeking explanation affects numerical modeling strategies”] Numerical modeling strategies revisited." Eos, Transactions American Geophysical Union 84, no. 11 (2003): 100. http://dx.doi.org/10.1029/2003eo110007.

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16

TAGUCHI, Y.-h. "NUMERICAL MODELING OF VIBRATED BEDS." International Journal of Modern Physics B 07, no. 09n10 (April 20, 1993): 1839–58. http://dx.doi.org/10.1142/s0217979293002626.

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I propose a numerical model which describes the dynamical features of vibrated beds. It succeeds in reproducing convective motion in vibrated beds which was observed by Faraday for the first time in 1831. In addition to this, this modeling can explain threshold value of instability and surface fluidization. Moreover, I numerically show vibrated bed without side wall can exhibit strong non-linear feature like turbulence or anomalous diffusion.

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17

Barakhovskaia, E. V., I. V. Marchuk, and A. A. Fedorets. "Numerical modeling of thermocapillary deformations in locally heated liquid layer." Eurasian Journal of Mathematical and Computer Applications 5, no. 4 (2017): 4–13. http://dx.doi.org/10.32523/2306-3172-2017-5-4-4-13.

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18

Spiridonov, Alexander Olegovich, and Evgenii Mikhailovich Karchevskii. "Mathematical and numerical modeling of a drop-shaped microcavity laser." Computer Research and Modeling 11, no. 6 (December 2019): 1083–90. http://dx.doi.org/10.20537/2076-7633-2019-11-6-1083-1090.

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19

Ertas, A., and T. J. Kozik. "A Review of Current Approaches to Riser Modeling." Journal of Energy Resources Technology 109, no. 3 (September 1, 1987): 155–60. http://dx.doi.org/10.1115/1.3231341.

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The purpose of this paper is to review the progress made during the past two decades towards the structural modeling and the numerical approaches of a more realistic riser analysis. Two or three-dimensional, linear or nonlinear, either static or dynamic riser modelings are reviewed. Suitable numerical solution techniques for different kinds of modelings are discussed and compared.

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20

Faisal, Fahim, Md Rayhan Mirza, Sadia Afrin, and Debasish Sen. "Modeling and Verification of Multi-Strut Modeling Approach of Masonry Panel Surrounded by RC Frame." Journal of Engineering Science 13, no. 2 (January 15, 2023): 21–29. http://dx.doi.org/10.3329/jes.v13i2.63723.

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Bangladesh which lies in an earthquake-prone region, possesses many seismically vulnerable structures and would need to be strengthened for future usage following the current building code. In this context, assessing the responses of the existing building frames with or without infilled masonry is essential for designing an adequate strengthening scheme that would be suitable for the building. The current study intends to model and simulate one of the available masonry infilled test specimens where the fiber modeling approach of RC member and the multi-strut model of infill masonry have been considered. In addition, the numerically obtained lateral strength has also been compared with analytically evaluated lateral strength. The lateral responses obtained from the numerical analysis showed a fair agreement with the experimental cyclic behavior having a ratio of experimental to the numerical lateral capacity of 0.97. The numerical lateral strength also indicates good conformity with analytical evaluation having an analytical to numerical lateral capacity ratio of 1.13. Journal of Engineering Science 13(2), 2022, 21-29

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21

Tian, Fang-Bao, and Li Wang. "Numerical Modeling of Sperm Swimming." Fluids 6, no. 2 (February 7, 2021): 73. http://dx.doi.org/10.3390/fluids6020073.

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Due to rising human infertility, sperm motility has been an important subject. Among the hundreds of millions of sperms on the journey up the oviducts, only a few excellent travelers will reach the eggs. This journey is affected by many factors, some of which include sperm quality, sperm density, fluid rheology and chemotaxis. In addition, the sperm swimming through different body tracks and fluids involves complex sperm flagellar, complex fluid environment, and multi-sperm and sperm-wall interactions. Therefore, this topic has generated substantial research interest. In this paper, we present a review of computational studies on sperm swimming from an engineering perspective with focus on both simplified theoretical methods and fluid–structure interaction methods. Several open issues in this field are highlighted.

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22

Namdar, Abdoullah, and Asima Nusrath. "Tsunami numerical modeling and mitigation." Frattura ed Integrità Strutturale 4, no. 12 (April 1, 2010): 57–62. http://dx.doi.org/10.3221/igf-esis.12.06.

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23

Zykov, A. P., and S. E. Yakush. "Numerical Modeling of Compartment Fires." Heat Transfer Research 36, no. 7 (2005): 573–84. http://dx.doi.org/10.1615/heattransres.v36.i7.40.

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24

Takagi, Masahide. "Fuel Spray - Numerical Simulation Modeling." Journal of The Japan Institute of Marine Engineering 44, no. 3 (2009): 387–92. http://dx.doi.org/10.5988/jime.44.387.

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25

Heyrani, Mehdi, Abdolmajid Mohammadian, Ioan Nistor, and Omerul Faruk Dursun. "Numerical Modeling of Venturi Flume." Hydrology 8, no. 1 (February 4, 2021): 27. http://dx.doi.org/10.3390/hydrology8010027.

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In order to measure flow rate in open channels, including irrigation channels, hydraulic structures are used with a relatively high degree of reliance. Venturi flumes are among the most common and efficient type, and they can measure discharge using only the water level at a specific point within the converging section and an empirical discharge relationship. There have been a limited number of attempts to simulate a venturi flume using computational fluid dynamics (CFD) tools to improve the accuracy of the readings and empirical formula. In this study, simulations on different flumes were carried out using a total of seven different models, including the standard k–ε, RNG k–ε, realizable k–ε, k–ω, and k–ω SST models. Furthermore, large-eddy simulation (LES) and detached eddy simulation (DES) were performed. Comparison of the simulated results with physical test data shows that among the turbulence models, the k–ε model provides the most accurate results, followed by the dynamic k LES model when compared to the physical experimental data. The overall margin of error was around 2–3%, meaning that the simulation model can be reliably used to estimate the discharge in the channel. In different cross-sections within the flume, the k–ε model provides the lowest percentage of error, i.e., 1.93%. This shows that the water surface data are well calculated by the model, as the water surface profiles also follow the same vertical curvilinear path as the experimental data.

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26

Bouacha, N., H. Chelghafe, and A. Seboui. "Numerical Modeling of Flexible Pavements." International Review on Modelling and Simulations (IREMOS) 8, no. 1 (February 28, 2015): 111. http://dx.doi.org/10.15866/iremos.v8i1.5258.

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27

Kore, S. D., P. Dhanesh, S. V. Kulkarni, and P. P. Date. "Numerical modeling of electromagnetic welding." International Journal of Applied Electromagnetics and Mechanics 32, no. 1 (January 11, 2010): 1–19. http://dx.doi.org/10.3233/jae-2010-1062.

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28

Shukla, A. K., A. Mondal, and A. Upadhyaya. "Numerical modeling of microwave heating." Science of Sintering 42, no. 1 (2010): 99–124. http://dx.doi.org/10.2298/sos1001099s.

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The present study compares the temperature distribution within cylindrical samples heated in microwave furnace with those achieved in radiatively-heated (conventional) furnace. Using a two-dimensional finite difference approach the thermal profiles were simulated for cylinders of varying radii (0.65, 6.5, and 65 cm) and physical properties. The influence of susceptor-assisted microwave heating was also modeled for the same. The simulation results reveal differences in the heating behavior of samples in microwaves. The efficacy of microwave heating depends on the sample size and its thermal conductivity.

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29

Ge, Q., Y. F. Yap, F. M. Vargas, M. Zhang, and John C. Chai. "NUMERICAL MODELING OF ASPHALTENE DEPOSITION." Computational Thermal Sciences 5, no. 2 (2013): 153–63. http://dx.doi.org/10.1615/computthermalscien.2013006316.

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30

Vyroubal, Petr, and Martin Mačák. "Numerical Modeling of Cyclic Voltametry." ECS Transactions 105, no. 1 (November 30, 2021): 561–66. http://dx.doi.org/10.1149/10501.0561ecst.

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This paper deals with a new approach to numerical modeling of cyclic voltammetry using the CFD solver FLUENT. The standard use of this solver is in the field of flow and heat transfer calculations, however, it is possible to model electrochemical reactions and it also includes basic models for calculations related to batteries, such as charging and discharging processes, temperature fields, etc. Thanks to the possibility of scripting, however, it is possible to extend these tasks to a much more complex level.

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31

Romm, Ya E., and S. G. Bulanov. "NUMERICAL MODELING OF LYAPUNOV STABILITY." Современные наукоемкие технологии (Modern High Technologies), no. 7 2021 (2021): 42–60. http://dx.doi.org/10.17513/snt.38752.

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32

CAIN, Joseph C., Bill HOLTER, and Daan SANDEE. "Numerical experiments in geomagnetic modeling." Journal of geomagnetism and geoelectricity 42, no. 9 (1990): 973–87. http://dx.doi.org/10.5636/jgg.42.973.

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33

Pitanguy, Ivo, Djenane Pamplona, Hans I. Weber, Fabiana Leta, Francisco Salgado, and Henrique N. Radwanski. "Numerical Modeling of Facial Aging." Plastic and Reconstructive Surgery 102, no. 1 (July 1998): 200–204. http://dx.doi.org/10.1097/00006534-199807000-00033.

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34

Klimenko, Stanislav, Igor Nikitin, Lialia Nikitina, Kira Konich, and Kevin Reinartz. "Numerical modeling of relativistic networks." International Journal of Modern Physics C 28, no. 03 (March 2017): 1750035. http://dx.doi.org/10.1142/s0129183117500358.

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In this paper, we consider a model of relativistic networks, a topological extension of the model of relativistic particles. Numerical experiments are performed to study thermodynamical properties of the model and their relationship with explicit symmetry of solutions under time reversal. An efficient algorithm is constructed, allowing to generate numerical solutions of high complexity in the given model. The algorithm includes a generator of random topology, an optimal choice of stiffness coefficients for the network and a solver for constrained optimization problem, describing an equilibrium of the network. A system, studied in the given paper, contains about 100 thousands of equations and inequalities. Possible extensions of the algorithm are discussed, necessary for processing of relativistic networks of higher complexity, containing millions of equations.

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35

Zhou, P., W. N. Fu, D. Lin, S. Stanton, and Z. J. Cendes. "Numerical Modeling of Magnetic Devices." IEEE Transactions on Magnetics 40, no. 4 (July 2004): 1803–9. http://dx.doi.org/10.1109/tmag.2004.830511.

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36

Mader, Charles L., and Robert L. Street. "Numerical Modeling of Water Waves." Computers in Physics 3, no. 4 (1989): 103. http://dx.doi.org/10.1063/1.4822852.

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37

Jirásek, Milan. "Numerical modeling of strong discontinuities." Revue Française de Génie Civil 6, no. 6 (January 2002): 1133–46. http://dx.doi.org/10.1080/12795119.2002.9692736.

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38

Chalikov, D. V. "Numerical Modeling of Sea Waves." Izvestiya, Atmospheric and Oceanic Physics 56, no. 3 (May 2020): 312–23. http://dx.doi.org/10.1134/s0001433820030032.

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39

Haine, Thomas W. N. "Numerical Modeling of Ocean Circulation." Eos, Transactions American Geophysical Union 89, no. 24 (June 10, 2008): 221. http://dx.doi.org/10.1029/2008eo240008.

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40

Guo, Yakun. "Numerical Modeling of Free Overfall." Journal of Hydraulic Engineering 131, no. 2 (February 2005): 134–38. http://dx.doi.org/10.1061/(asce)0733-9429(2005)131:2(134).

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41

Schreüder, Willem A., J. Prieur du Plessis, and Devraj Sharma. "NUMERICAL MODELING OF ATMOSPHERIC BOUNDARIES." Numerical Heat Transfer, Part B: Fundamentals 17, no. 2 (January 1990): 171–96. http://dx.doi.org/10.1080/10407799008961738.

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42

Schreüder, Willem A., and J. Prieur du Plessis. "NUMERICAL MODELING OF INTERIOR BOUNDARIES." Numerical Heat Transfer, Part B: Fundamentals 17, no. 2 (January 1990): 197–215. http://dx.doi.org/10.1080/10407799008961739.

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43

Musolino, Antonino, and Rocco Rizzo. "Numerical Modeling of Helical Launchers." IEEE Transactions on Plasma Science 39, no. 3 (March 2011): 935–40. http://dx.doi.org/10.1109/tps.2010.2102046.

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44

Bailie, Neil A., Geraldine Gallagher, John K. Watterson, and Jonathan Cole. "Numerical Modeling of Nasal Airflow." Otolaryngology–Head and Neck Surgery 131, no. 2 (August 2004): P187. http://dx.doi.org/10.1016/j.otohns.2004.06.342.

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45

Khan, Sadia M., Jasim Imran, Scott Bradford, and James Syvitski. "Numerical modeling of hyperpycnal plume." Marine Geology 222-223 (November 2005): 193–211. http://dx.doi.org/10.1016/j.margeo.2005.06.025.

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46

Sun, Ximing, Zhenzhong Zhang, and Da-Ren Chen. "Numerical modeling of miniature cyclone." Powder Technology 320 (October 2017): 325–39. http://dx.doi.org/10.1016/j.powtec.2017.07.053.

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47

Frydman, Marcelo, and Sérgio da Fontoura. "Numerical modeling of borehole pressurization." International Journal of Rock Mechanics and Mining Sciences 34, no. 3-4 (April 1997): 82.e1–82.e14. http://dx.doi.org/10.1016/s1365-1609(97)00130-5.

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48

Deutschmann, O., R. Schmidt, F. Behrendt, and J. Warnat. "Numerical modeling of catalytic ignition." Symposium (International) on Combustion 26, no. 1 (January 1996): 1747–54. http://dx.doi.org/10.1016/s0082-0784(96)80400-0.

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49

Gil', D. V., and O. V. Dikhtievskii. "Numerical modeling of capillary hysteresis." Journal of Engineering Physics and Thermophysics 67, no. 3-4 (1995): 896–98. http://dx.doi.org/10.1007/bf00853015.

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50

Cao, Yinghui, Jie Zheng, and Yushu Zhang. "Numerical modeling of fiber grating." Optik 120, no. 17 (November 2009): 911–15. http://dx.doi.org/10.1016/j.ijleo.2008.02.027.

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