Journal articles: 'Flow modeling'

1

Johansen, Stein Tore. "Multiphase flow modeling of metallurgical flows." Experimental Thermal and Fluid Science 26, no. 6-7 (August 2002): 739–45. http://dx.doi.org/10.1016/s0894-1777(02)00183-8.

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Sindeev, S. V., S. V. Frolov, D. Liepsch, and A. Balasso. "MODELING OF FLOW ALTERATIONS INDUCED BY FLOW-DIVERTER USING MULTISCALE MODEL OF HEMODYNAMICS." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 23, no. 1 (2017): 025–32. http://dx.doi.org/10.17277/vestnik.2017.01.pp.025-032.

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3

Carr, John, and Mark Howells. "Modeling pig flow." Livestock 21, no. 3 (May 2, 2016): 180–86. http://dx.doi.org/10.12968/live.2016.21.3.180.

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4

Giovangigli, Vincent. "Multicomponent flow modeling." Science China Mathematics 55, no. 2 (December 20, 2011): 285–308. http://dx.doi.org/10.1007/s11425-011-4346-y.

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Melikyan, V. Sh, V. D. Hovhannisyan, M. T. Grigoryan, A. A. Avetisyan, and H. T. Grigoryan. "Real Number Modeling Flow of Digital to Analog Converter." Proceedings of Universities. Electronics 26, no. 2 (April 2021): 144–53. http://dx.doi.org/10.24151/1561-5405-2021-26-2-144-153.

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This work introduces a flow of digital to analog (DAC) implementation in digital environment of SystemVerilog. Unlike the classical Verilog models, this digital to analog converter behavioral model is analog. Such type of model creation in general is called real number modeling. The DAC model is verified by the HSPICE and SystemVerilog Co-simulations which show its applicability in different register transfer level verification environments. The digital environment with real number modeled DAC runs around 8 times faster than the same environment with SPICE model. At the same time, the output signal’s voltage difference between RNM and SPICE models is less than 2 mV.

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6

Xiong, Jinbiao, Seiichi Koshizuka, and Mikio Sakai. "ICONE19-43282 TURBULENCE MODELING FOR MASS TRANSFER IN SEPARATED AND REATTACHING FLOWS FOR FLOW-ACCELERATED CORROSION." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_119.

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Platonov, Dmitriy Viktorovich, Andrey Viktorovich Minakov, Alexander Anatolyevich Dekterev, and Andrey Vasilyevich Sentyabov. "Numerical modeling of flows with flow swirling." Computer Research and Modeling 5, no. 4 (August 2013): 635–48. http://dx.doi.org/10.20537/2076-7633-2013-5-4-635-648.

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Oussoren, Andrew, Jovica Riznic, and Shripad Revankar. "ICONE23-2115 MODELING CRITICAL FLOW IN CRACK GEOMETRIES USING TRACE." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2015.23 (2015): _ICONE23–2—_ICONE23–2. http://dx.doi.org/10.1299/jsmeicone.2015.23._icone23-2_44.

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9

Slimani, Nadia, Ilham Slimani, Nawal Sbiti, and Mustapha Amghar. "Machine Learning and statistic predictive modeling for road traffic flow." International Journal of Traffic and Transportation Management 03, no. 01 (March 1, 2021): 17–24. http://dx.doi.org/10.5383/jttm.03.01.003.

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Traffic forecasting is a research topic debated by several researchers affiliated to a range of disciplines. It is becoming increasingly important given the growth of motorized vehicles on the one hand, and the scarcity of lands for new transportation infrastructure on the other. Indeed, in the context of smart cities and with the uninterrupted increase of the number of vehicles, road congestion is taking up an important place in research. In this context, the ability to provide highly accurate traffic forecasts is of fundamental importance to manage traffic, especially in the context of smart cities. This work is in line with this perspective and aims to solve this problem. The proposed methodology plans to forecast day-by-day traffic stream using three different models: the Multilayer Perceptron of Artificial Neural Networks (ANN), the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Support Machine Regression (SMOreg). Using those three models, the forecast is realized based on a history of real traffic data recorded on a road section over 42 months. Besides, a recognized traffic manager in Morocco provides this dataset; the performance is then tested based on predefined criteria. From the experiment results, it is clear that the proposed ANN model achieves highest prediction accuracy with the lowest absolute relative error of 0.57%.

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10

Khan, Sarosh I., and Pawan Maini. "Modeling Heterogeneous Traffic Flow." Transportation Research Record: Journal of the Transportation Research Board 1678, no. 1 (January 1999): 234–41. http://dx.doi.org/10.3141/1678-28.

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11

Alley, R. B., and I. Joughin. "Modeling Ice-Sheet Flow." Science 336, no. 6081 (May 3, 2012): 551–52. http://dx.doi.org/10.1126/science.1220530.

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12

Bollinger, L. Andrew, Chris Davis, Igor Nikolić, and Gerard P. J. Dijkema. "Modeling Metal Flow Systems." Journal of Industrial Ecology 16, no. 2 (December 13, 2011): 176–90. http://dx.doi.org/10.1111/j.1530-9290.2011.00413.x.

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13

Ninković, Vladimir. "Dynamic migration flow modeling." Security Dialogues /Безбедносни дијалози 1-2 (2017): 149–67. http://dx.doi.org/10.47054/sd171-20149n.

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14

Yule, A. J., M. Damou, and D. Kostopoulos. "Modeling confined jet flow." International Journal of Heat and Fluid Flow 14, no. 1 (March 1993): 10–17. http://dx.doi.org/10.1016/0142-727x(93)90035-l.

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15

Balcerak, Ernie. "Modeling ice stream flow." Eos, Transactions American Geophysical Union 92, no. 49 (December 6, 2011): 464. http://dx.doi.org/10.1029/2011eo490018.

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16

King, Richard B., Gary M. Raymond, and James B. Bassingthwaighte. "Modeling blood flow heterogeneity." Annals of Biomedical Engineering 24, no. 3 (May 1996): 352–72. http://dx.doi.org/10.1007/bf02660885.

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17

Pohll, G. M., and J. C. Guitjens. "Modeling Regional Flow and Flow to Drains." Journal of Irrigation and Drainage Engineering 120, no. 5 (September 1994): 925–39. http://dx.doi.org/10.1061/(asce)0733-9437(1994)120:5(925).

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18

Nikitin, Vladislav Nikolaevich, and Ekaterina Valerevna Kozhemyakina. "MODELING REDISTRIBUTION CEREBRAL CIRCULATION." SOFT MEASUREMENTS AND COMPUTING 1, no. 4 (2021): 13–18. http://dx.doi.org/10.36871/2618-9976.2021.04.002.

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The brain is one of the most important organs responsible for the health and functioning of the entire body. The blood supply to the brain is carried out through 2 internal carotid and 2 vertebral arteries in norm. The brain, like other body systems, has protective (compensatory) mechanisms aimed at maintaining the necessary blood flow, one of which is the circle of Willis. The article proposes a mechanism for how blood flow is redistributed through the arteries feeding the brain, which is based on the assumption that the central nervous system controls in such a way that it minimizes flows through the connective arteries of the circle of Willis, the flows along which are normal (with symmetry of the left and right sides) practically equal to zero. Сase of the structure of the circle of Willis is considered in norm. The indicated redistribution mechanism is still only the first step towards an attempt to predict cases of changes in blood flow through the cerebral arteries, especially in stroke. In further works, it is planned to consider the inverse problem, i.e. determine the flows through the internal carotid and vertebral arteries, provided that the flows through the cerebral arteries extending from the circle of Willis have normal flow values.

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19

Wang, Yanlin, Bingde Chen, Yanping Huang, and Junfeng Wang. "ICONE19-43704 Modeling on Bubbly to Churn Flow Pattern Transition for Vertical Upward Flows in Narrow Rectangular Channel." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_273.

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20

Chhatkuli, Subas, and Masayuki Fujihara. "Numerical Modeling of Flow in Fishways Embedded with Circular/Rectangular Obstructions." Journal of Rainwater Catchment Systems 14, no. 2 (2009): 9–19. http://dx.doi.org/10.7132/jrcsa.kj00005284804.

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21

Oshima, M., R. Torii, and T. E. Tezduyar. "W02-1-(4) Modeling and Simulation of Cardiovascular Flow(International Minisymposium on Challenger and Advances in Flow Simulation and Modeling,Mechanical Engineering Congress, 2005 Japan (MECJ-05))." Reference Collection of Annual Meeting 2005.8 (2005): 268. http://dx.doi.org/10.1299/jsmemecjsm.2005.8.0_268.

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22

Blue, Victor J., and Jeffrey L. Adler. "Modeling Four-Directional Pedestrian Flows." Transportation Research Record: Journal of the Transportation Research Board 1710, no. 1 (January 2000): 20–27. http://dx.doi.org/10.3141/1710-03.

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The objective of this study is to explore the modeling of multidirectional pedestrian flows. The complex interactions between flow entities within m-directional space present challenges that cannot be readily handled by existing bidirectional flow models. A cellular automata microsimulation model for four-directional flow is prescribed. This model, built on previous bidirectional models developed by the authors, additionally seeks to manage cross-directional conflicts. Performance of this function in the simulation of unidirectional, bidirectional, cross-directional, and four-directional flows is presented. The applications extend to m-directional terminal facility design and to four-directional street corners, a vital component in any network model of pedestrians.

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23

Supa-Amornkul, Savalaxs, Frank R. Steward, and Derek H. Lister. "Modeling Two-Phase Flow in Pipe Bends." Journal of Pressure Vessel Technology 127, no. 2 (December 8, 2004): 204–9. http://dx.doi.org/10.1115/1.1904063.

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In order to have a better understanding of the interaction between the two-phase steam-water coolant in the outlet feeder pipes of the primary heat transport system of some CANDU reactors and the piping material, themalhydraulic modelling is being performed with a commercial computational fluid dynamics (CFD) code—FLUENT 6.1. The modeling has attempted to describe the results of flow visualization experiments performed in a transparent feeder pipe with air-water mixtures at temperatures below 55°C. The CFD code solves two sets of transport equations—one for each phase. Both phases are first treated separately as homogeneous. Coupling is achieved through pressure and interphase exchange coefficients. A symmetric drag model is employed to describe the interaction between the phases. The geometry and flow regime of interest are a 73 deg bend in a 5.9cm diameter pipe containing water with a Reynolds number of ∼1E5-1E6. The modeling predicted single-phase pressure drop and flow accurately. For two-phase flow with an air voidage of 5–50%, the pressure drop measurements were less well predicted. Furthermore, the observation that an air-water mixture tended to flow toward the outside of the bend while a single-phase liquid layer developed at the inside of the bend was not predicted. The CFD modeling requires further development for this type of geometry with two-phase flow of high voidage.

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24

Brill, James P. "Modeling Multiphase Flow in Pipes." Way Ahead 06, no. 02 (June 1, 2010): 16–17. http://dx.doi.org/10.2118/0210-016-twa.

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25

Abdullah, Makola M., Kenneth K. Walsh, Shannon Grady, and G. Dale Wesson. "Modeling Flow around Bluff Bodies." Journal of Computing in Civil Engineering 19, no. 1 (January 2005): 104–7. http://dx.doi.org/10.1061/(asce)0887-3801(2005)19:1(104).

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26

Park, Chul Woong, Jaeman Park, Naree Kim, and Youngchul Kim. "Modeling water flow on Façade." Automation in Construction 93 (September 2018): 265–79. http://dx.doi.org/10.1016/j.autcon.2018.05.021.

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27

Sopasakis, A. "Unstable flow theory and modeling,." Mathematical and Computer Modelling 35, no. 5-6 (March 2002): 623–41. http://dx.doi.org/10.1016/s0895-7177(01)00186-8.

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28

Sopasakis, A. "Unstable flow theory and modeling." Mathematical and Computer Modelling 35, no. 5-6 (March 2002): 623–41. http://dx.doi.org/10.1016/s0895-7177(02)80025-5.

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29

Molenaar, J., and R. J. Koopmans. "Modeling polymer melt‐flow instabilities." Journal of Rheology 38, no. 1 (January 1994): 99–109. http://dx.doi.org/10.1122/1.550603.

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30

Villaret, C., and A. G. Davies. "Modeling Sediment-Turbulent Flow Interactions." Applied Mechanics Reviews 48, no. 9 (September 1, 1995): 601–9. http://dx.doi.org/10.1115/1.3023148.

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Models of widely differing complexity have been used in recent years to quantify sediment transport processes for engineering applications. This paper presents a review of these model types, from simple eddy viscosity models involving the “passive scalar hypothesis” for sediment predication, to complex two-phase flow models. The specific points addressed in this review include, for the suspension layer, the bottom boundary conditions, the relationship between the turbulent eddy viscosity and particle diffusivity, the damping of turbulence by vertical gradients in suspended sediment concentration, and hindered settling. For the high-concentration near-bed layer, the modeling of particle interactions is discussed mainly with reference to two-phase flow models. The paper concludes with a comparison between the predictions of both a classical, one-equation, turbulence k-model and a two-phase flow model, with “starved bed” experimental data sets obtained in steady, open-channel flow.

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31

Freeze, Allan. "Modeling groundwater flow and pollution." Canadian Geotechnical Journal 25, no. 4 (November 1, 1988): 851–52. http://dx.doi.org/10.1139/t88-098.

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32

Long, J., and P. Chen. "MODELING OF CONCENTRATED SUSPENSION FLOW." Transactions of the Canadian Society for Mechanical Engineering 24, no. 1B (May 2000): 151–67. http://dx.doi.org/10.1139/tcsme-2000-0011.

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33

Abbott, M. B. "Range of Tidal Flow Modeling." Journal of Hydraulic Engineering 123, no. 4 (April 1997): 257–77. http://dx.doi.org/10.1061/(asce)0733-9429(1997)123:4(257).

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34

Combinido, Jay Samuel L., and May T. Lim. "Modeling U-turn traffic flow." Physica A: Statistical Mechanics and its Applications 389, no. 17 (September 2010): 3640–47. http://dx.doi.org/10.1016/j.physa.2010.04.009.

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35

Wei, X., Y. Zhao, Z. Fan, W. Li, F. Qiu, S. Yoakum-Stover, and A. E. Kaufman. "Lattice-based flow field modeling." IEEE Transactions on Visualization and Computer Graphics 10, no. 6 (November 2004): 719–29. http://dx.doi.org/10.1109/tvcg.2004.48.

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36

Caputo, Antonio C., and Pacifico M. Pelagagge. "Flow Modeling in Fabric Filters." Journal of Porous Media 2, no. 2 (1999): 191–204. http://dx.doi.org/10.1615/jpormedia.v2.i2.70.

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37

Kemper, Benjamin, Jeroen de Mast, and Michel Mandjes. "Modeling process flow using diagrams." Quality and Reliability Engineering International 26, no. 4 (August 10, 2009): 341–49. http://dx.doi.org/10.1002/qre.1061.

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38

Pandit, Ashok, and Jean M. Abi Aoun. "Numerical Modeling of Axisymmetric Flow." Ground Water 32, no. 3 (May 1994): 458–64. http://dx.doi.org/10.1111/j.1745-6584.1994.tb00663.x.

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39

Gibson, M. M. "Turbulence measurements and flow modeling." International Journal of Heat and Fluid Flow 8, no. 4 (December 1987): 339. http://dx.doi.org/10.1016/0142-727x(87)90078-6.

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40

Andersson, H. I., and B. A. Pettersson. "Modeling plane turbulent Couette flow." International Journal of Heat and Fluid Flow 15, no. 6 (December 1994): 447–55. http://dx.doi.org/10.1016/0142-727x(94)90003-5.

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41

Wang, Xiaoming, Xiaoyong Li, and Dmitri Loguinov. "Modeling Residual-Geometric Flow Sampling." IEEE/ACM Transactions on Networking 21, no. 4 (August 2013): 1090–103. http://dx.doi.org/10.1109/tnet.2012.2231435.

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42

Langevin, Christian D. "Modeling Axisymmetric Flow and Transport." Ground Water 46, no. 4 (July 2008): 579–90. http://dx.doi.org/10.1111/j.1745-6584.2008.00445.x.

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43

Bredehoeft, John. "Modeling Groundwater Flow-The Beginnings." Ground Water 50, no. 3 (April 27, 2012): 325–29. http://dx.doi.org/10.1111/j.1745-6584.2012.00940.x.

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44

Konikow, Leonard F. "Modeling Groundwater Flow and Pollution." Eos, Transactions American Geophysical Union 69, no. 45 (1988): 1557. http://dx.doi.org/10.1029/88eo01182.

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45

Lagha, M., and P. Manneville. "Modeling transitional plane Couette flow." European Physical Journal B 58, no. 4 (August 2007): 433–47. http://dx.doi.org/10.1140/epjb/e2007-00243-y.

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46

Greenspan, D. "Quasimolecular modeling of cavity flow." Computers & Mathematics with Applications 14, no. 4 (1987): 239–48. http://dx.doi.org/10.1016/0898-1221(87)90131-3.

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47

Hutter, K., B. Svendsen, and D. Rickenmann. "Debris flow modeling: A review." Continuum Mechanics and Thermodynamics 8, no. 1 (February 1994): 1–35. http://dx.doi.org/10.1007/bf01175749.

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48

Miller, Andrzej, Krzysztof Badyda, Jaroslaw Dyjas, and Karol Miller. "Modeling of flow system dynamics." Journal of Thermal Science 13, no. 1 (February 2004): 56–61. http://dx.doi.org/10.1007/s11630-004-0009-4.

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49

Verdier, Claude, CÃ©cile Couzon, Alain Duperray, and Pushpendra Singh. "Modeling cell interactions under flow." Journal of Mathematical Biology 58, no. 1-2 (February 22, 2008): 235–59. http://dx.doi.org/10.1007/s00285-008-0164-4.

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50

Konikow, Leonard F., and James W. Mercer. "Groundwater flow and transport modeling." Journal of Hydrology 100, no. 1-3 (July 1988): 379–409. http://dx.doi.org/10.1016/0022-1694(88)90193-x.

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Journal articles on the topic 'Flow modeling'

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