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Relevant bibliographies by topics / Flow modeling

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Academic literature on the topic 'Flow modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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Journal articles on the topic "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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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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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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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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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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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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Dissertations / Theses on the topic "Flow modeling"

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Cappiello, Alessandra 1972. "Modeling traffic flow emissions." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/84328.

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Boulay, Fabienne. "Suspension-flow modeling : curvilinear flows and normal stress differences." Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/11689.

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Rycroft, Christopher Harley. "Multiscale modeling in granular flow." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41557.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2007.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 245-254).
Granular materials are common in everyday experience, but have long-resisted a complete theoretical description. Here, we consider the regime of slow, dense granular flow, for which there is no general model, representing a considerable hurdle to industry, where grains and powders must frequently be manipulated. Much of the complexity of modeling granular materials stems from the discreteness of the constituent particles, and a key theme of this work has been the connection of the microscopic particle motion to a bulk continuum description. This led to development of the "spot model", which provides a microscopic mechanism for particle rearrangement in dense granular flow, by breaking down the motion into correlated group displacements on a mesoscopic length scale. The spot model can be used as the basis of a multiscale simulation technique which can accurately reproduce the flow in a large-scale discrete element simulation of granular drainage, at a fraction of the computational cost. In addition, the simulation can also successfully track microscopic packing signatures, making it one of the first models of a flowing random packing. To extend to situations other than drainage ultimately requires a treatment of material properties, such as stress and strain-rate, but these quantities are difficult to define in a granular packing, due to strong heterogeneities at the level of a single particle. However, they can be successfully interpreted at the mesoscopic spot scale, and this information can be used to directly test some commonly-used hypotheses in modeling granular materials, providing insight into formulating a general theory.
by Christopher Harley Rycroft.
Ph.D.

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El, Kheiashy Karim. "Flow-Transport Modeling and Quantification." ScholarWorks@UNO, 2007. http://scholarworks.uno.edu/td/548.

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Several research investigations have been conducted on the flow and sediment transport over bed forms in alluvial rivers (e.g. mean flow field, turbulence, shear partitioning, bed load transport and bed form geometry). Much of this work was either laboratory studies or small scale field investigations. Recently, advance in technology have improved the way data are collected and analyzed, e.g. flow data, velocity data and detailed bathymetric information that provide greater knowledge about the bed form geometry. Recent advances in computing power have also reduced the computational restrictions on using three dimensional numerical models in modeling flow applications to predict the temporal and spatial changes of flow and sediment environments. The work performed in this research quantified the periodic nature of bed forms types and geometries along the Lower Mississippi river. Correlations were performed relating the hydrodynamics of the river to the bed form types and geometries. The research work showed the inability of hydrostatic numerical modeling systems to accurately predict flow separation at the bed form crest but indicated that these models could reasonably predict the out of phase relationship between the bed form and the water surface profile. Furthermore the hydrostatic models predicted the total bed resistance as adequately as the non-hydrostatic models. It was found that non-hydrostatic models are required to properly simulate flow separation at bed form crests. Models such as MIKE 3 with constant z-level vertical discretization failed to capture the observed boundary layers unless very fine grids are used. A new procedure was developed as a part of this research, in which relations and dependencies between the hydrodynamic resistance and the bed form dimensions relative to the numerical model spatial scale were derived. This procedure can be used to aid in numerical riverine model calibration and to provide a better representation of flow resistance in hydrodynamic modeling codes.

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Daniel, Michael M. "Multiresolution statistical modeling with application to modeling groundwater flow." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10749.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
Includes bibliographical references (p. 205-211).
by Michael M. Daniel.
Ph.D.

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Tao, Ye. "Optimal power flow via quadratic modeling." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/45766.

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Optimal power flow (OPF) is the choice tool for determining the optimal operating status of the power system by managing controllable devices. The importance of the OPF approach has increased due to increasing energy prices and availability of more control devices. Existing OPF approaches exhibit shortcomings. Current OPF algorithms can be classified into (a) nonlinear programming, (b) intelligent search methods, and (c) sequential algorithms. Nonlinear programming algorithms focus on the solution of the Kuhn-Tucker conditions; they require a starting feasible solution and the model includes all constraints; these characteristics limit the robustness and efficiency of these methods. Intelligent search methods are first-order methods and are totally inefficient for large-scale systems. Traditional sequential algorithms require a starting feasible solution, a requirement that limits their robustness. Present implementations of sequential algorithms use traditional modeling that result in inefficient algorithms. The research described in this thesis has overcome the shortcomings by developing a robust and highly efficient algorithm. Robustness is defined as the ability to provide a solution for any system; the proposed approach achieves robustness by operating on suboptimal points and moving toward feasible, it stops at a suboptimal solution if an optimum does not exist. Efficiency is achieved by (a) converting the nonlinear OPF problem to a quadratic problem (b) and limiting the size of the model; the quadratic model enables fast convergence and the algorithm that identifies the active constraints, limits the size of the model by only including the active constraints. A concise description of the method is as follows: The proposed method starts from an arbitrary state which may be infeasible; model equations and system constraints are satisfied by introducing artificial mismatch variables at each bus. Mathematically this is an optimal but infeasible point. At each iteration, the artificial mismatches are reduced while the solution point maintains optimality. When mismatches reach zero, the solution becomes feasible and the optimum has been found; otherwise, the mismatch residuals are converted to load shedding and the algorithm provides a suboptimal but feasible solution. Therefore, the algorithm operates on infeasible but optimal points and moves towards feasibility. The proposed algorithm maximizes efficiency with two innovations: (a) quadratization that converts the nonlinear model to quadratic with excellent convergence properties and (b) minimization of model size by identifying active constraints, which are the only constraints included in the model. Finally sparsity technique is utilized that provide the best computational efficiency for large systems. This dissertation work demonstrates the proposed OPF algorithm using various systems up to three hundred buses and compares it with several well-known OPF software packages. The results show that the proposed algorithm converges fast and its runtime is competitive. Furthermore, the proposed method is extended to a three-phase OPF (TOPF) algorithm for unbalanced networks using the quadratized three-phase power system model. An example application of the TOPF is presented. Specifically, TOPF is utilized to address the problem of fault induced delayed voltage recovery (FIDVR) phenomena, which lead to unwanted relay operations, stalling of motors and load disruptions. This thesis presents a methodology that will optimally enhance the distribution system to mitigate/eliminate the onset of FIDVR. The time domain simulation method has been integrated with a TOPF model and a dynamic programming optimization algorithm to provide the optimal reinforcing strategy for the circuits.

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Sharma, Yugdutt. "Modeling transient two-phase slug flow /." Access abstract and link to full text, 1985. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/8605319.

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Kouba, Gene E. "Horizontal slug flow modeling and metering /." Access abstract and link to full text, 1986. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/8700712.

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Yu, Tungsheng. "Traffic flow modeling in highway networks." Master's thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-12232009-020154/.

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Gallant, Elisabeth. "Modeling and Assessing Lava Flow Hazards." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7792.

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Lava flow hazards are one of the few constant themes across the wide spectrum of volcanic research in the solar system. These dynamic hazards are controlled by the location of the eruption, the topography and material properties of the land upon which the flow spreads, and the properties of the lava (e.g., volume, temperature, and rheology). Understanding the influences on eruption location and how lava flows modify the landscape are important steps to accurately forecast volcanic hazards. Three studies are presented in this dissertation that address di˙erent aspects of modeling and assessing vent opening and lava flow hazards. The first study uses hierarchical clustering to explore the distribution of activity at Craters of the Moon (COM) lava field on the eastern Snake River Plain (ESRP). Volcanism at COM is characterized by 53 mapped eruptive vents and 60+ lava flows over the last 15 ka. Temporal, spatial, and spatio-temporal clustering methods that examine different aspects of the distribution of volcanic vents are introduced. The sensitivity of temporal clustering to different criteria that capture the age range of magma generation and ascent is examined Spatial clustering is dictated by structures on the ESRP that attempt to capture the footprint of an emplacing dike. A combined spatio-temporal is the best approach to understanding the distribution of linked eruptive centers and can also provide insight into the evolution of volcanism for the region. Spatial density estimation is used to visualize the differences between these models. The goal of this work is to improve vent opening forecasting tools for use in assessing lava flow hazards. The second study presents a new probabilistic lava flow hazard assessment for the U.S. Department of Energy’s Idaho National Laboratory (INL) nuclear facility that (1) explores the way eruptions are defined and modeled, (2) stochastically samples lava flow parameters from observed values for use in MOLASSES, a lava flow simulator, (3) calculates the likelihood of a new vent opening within the boundaries of INL, (4) determines probabilities of lava flow inundation for INL through Monte Carlo simulation, and (5) couples inundation probabilities with recurrence rates to determine the annual likelihood of lava flow inundation for INL. Results show a 30% probability of partial inundation of the INL given an e˙usive eruption on the ESRP, with an annual inundation probability of 8.4×10^−5 to 1.8×10^−4. An annual probability of 6.2×10^−5 to 1.2×10^−4 is estimated for the opening of a new eruptive center within INL boundaries. The third study models thermo-mechanical erosion of a pyroclastic substrate by flow-ing lava on Volcán Momotombo, Nicaragua. It describes the unique morphology of a lava flow channel using TanDEM-X/TerraSAR-X and terrestrial radar digital elevation models. New methods for modeling paleotopography on steep-sided cones are introduced to mea-sure incision depths and document cross-channel profiles. The channel is incised ~35 m into the edifice at the summit and transitions into a constructional feature halfway down the ~1,300 m high cone. An eroded volume of ~4×10^5 m3 was calculated. It is likely that a lava flow eroded into the cone as it emplaced during an eruption in 1905. There is not suÿcient energy to thermally erode this volume, given the observed morphology of the flow. Models are tested that explore the relationship of shearing and material properties of the lava and substrate against measured erosion depths and find that thermo-mechanical erosion is the most likely mode of channel formation. Additionally, it is likely that all forms of erosion via lava flow are impacted by thermal conditions due to the relationship between temperature and substrate hardness. The evolution of these structures (their creation and subsequent infilling) plays an important role in the growth of young volcanoes and also controls future lava flows hazards, as seen by the routing of the 2015 flow into the 1905 channel.

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Books on the topic "Flow modeling"

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Multicomponent flow modeling. Boston: Birkhäuser, 1999.

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Integrated flow modeling. Amsterdam: Elsevier Science B.V., 2000.

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Chin, Wilson C. Borehole flow modeling. Houston: Gulf Pub. Co., 1992.

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Giovangigli, Vincent. Multicomponent Flow Modeling. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1580-6.

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Morel-Seytoux, H. J., ed. Unsaturated Flow in Hydrologic Modeling. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2352-2.

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Sheng, Chunhua. Advances in Transitional Flow Modeling. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32576-7.

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Papadimitriou, Dimitri B., and Gennaro Zezza, eds. Contributions in Stock-flow Modeling. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230367357.

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Bear, Jacob, and Arnold Verruijt. Modeling Groundwater Flow and Pollution. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3379-8.

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Rajan, M. T. Regional groundwater modeling. New Delhi: Capital Pub. Co., 2004.

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Sarkar, Sutanu. Compressible homogeneous shear: simulation and modeling. Hampton, Va: Institute for Computer Applications in Science and Engineering, 1992.

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Book chapters on the topic "Flow modeling"

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Holzbecher, Ekkehard. "Flow Modeling." In Environmental Modeling, 217–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22042-5_11.

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Greenspan, Donald. "Cavity Flow." In Particle Modeling, 71–82. Boston, MA: Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-1992-7_7.

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Paquier, André, Patrick Chassé, Nicole Goutal, and Amélie Besnard. "1D Flow Models." In Modeling Software, 177–200. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557891.ch15.

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Jakobsen, Hugo A. "Multiphase Flow." In Chemical Reactor Modeling, 369–536. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05092-8_3.

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Giovangigli, Vincent. "Introduction." In Multicomponent Flow Modeling, 1–4. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1580-6_1.

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Giovangigli, Vincent. "Chemical Equilibrium Flows." In Multicomponent Flow Modeling, 245–64. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1580-6_10.

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Giovangigli, Vincent. "Anchored Waves." In Multicomponent Flow Modeling, 265–300. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1580-6_11.

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Giovangigli, Vincent. "Numerical Simulations." In Multicomponent Flow Modeling, 301–15. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1580-6_12.

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Giovangigli, Vincent. "Fundamental Equations." In Multicomponent Flow Modeling, 5–36. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1580-6_2.

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Giovangigli, Vincent. "Approximate and Simplified Models." In Multicomponent Flow Modeling, 37–58. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1580-6_3.

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Conference papers on the topic "Flow modeling"

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Myers, T. M., A. W. Marshall, and H. R. Baum. "Simplified modeling of sprinkler head fluid mechanics." In MULTIPHASE FLOW 2013. Southampton, UK: WIT Press, 2013. http://dx.doi.org/10.2495/mpf130211.

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Ramakrishnan, Srinivas, and Samuel Collis. "Variational Multiscale Modeling for Turbulence Control." In 1st Flow Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-3280.

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Vorobieff, P., M. Anderson, J. Conroy, C. Randall Truman, and S. Kumar. "Morphology of shock-accelerated multiphase flow: experiment and modeling." In MULTIPHASE FLOW 2013. Southampton, UK: WIT Press, 2013. http://dx.doi.org/10.2495/mpf130021.

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Truman, C. Randall, M. Anderson, P. Vorobieff, P. Wayne, C. Corbin, T. Bernard, and G. Kuehner. "Morphology of shock-accelerated multiphase flow: experiment and modeling." In MULTIPHASE FLOW 2013. Southampton, UK: WIT Press, 2013. http://dx.doi.org/10.2495/mpf130111.

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Ali, T. Ait, S. Khelladi, L. Ramirez, and X. Nogueira. "Cavitation modeling using compressible Navier–Stokes and Korteweg equations." In MULTIPHASE FLOW 2015. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/mpf150361.

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Seifert, A., R. Joslin, and Vassilis Theofilis. "Flow Control Experiments, Simulation and Modeling Approaches (Invited)." In 1st Flow Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-3277.

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Kayakol, N. "CFD modeling of cavitation in solenoid valves for diesel fuel injection." In MULTIPHASE FLOW 2015. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/mpf150351.

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Bisantino, T., P. Fischer, F. Gentile, and G. Trisorio Liuzzi. "Rheological properties and debris-flow modeling in a southern Italy watershed." In DEBRIS FLOW 2010. Southampton, UK: WIT Press, 2010. http://dx.doi.org/10.2495/deb100201.

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Tran, A. T. T., and M. M. Hyland. "Modeling of micrometre-sized molten metallic droplet impact on a solid wall." In MULTIPHASE FLOW 2015. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/mpf150321.

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Campos, L. D. O., P. Gardin, S. Vincent, and J. P. Caltagirone. "Physical modeling of turbulent multiphase flow in a continuous casting steel mold." In MULTIPHASE FLOW 2015. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/mpf150371.

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Reports on the topic "Flow modeling"

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Allen, John S. Modeling of Coastal Ocean Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada398915.

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Le MaÒitre, Olivier P., Matthew T. Reagan, Omar M. Knio, Roger Georges Ghanem, and Habib N. Najm. Uncertainty quantification in reacting flow modeling. Office of Scientific and Technical Information (OSTI), October 2003. http://dx.doi.org/10.2172/918251.

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Patnaik, Soumya S., Eugeniya Iskrenova-Ekiert, and Hui Wan. Multiscale Modeling of Multiphase Fluid Flow. Fort Belvoir, VA: Defense Technical Information Center, August 2016. http://dx.doi.org/10.21236/ad1016834.

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Allen, John S. Modeling of Coastal Ocean Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada300401.

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Allen, John S. Modeling of Coastal Ocean Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630171.

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Winters, Kraig B. Modeling Non-Hydrostatic Flow Over Topography. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada629083.

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Allen, John S. Modeling of Coastal Ocean Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada629791.

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Allen, John S. Modeling of Coastal Ocean Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada626225.

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Allen, John S. Modeling of Coastal Ocean Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada609936.

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Allen, John S. Modeling of Coastal Ocean Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada627902.

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