Готові списки джерел за темами / Fluid Dynamic Modeling

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Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Fluid Dynamic Modeling"

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Tao, Jin, Qinglin Sun, Wei Liang, Zengqiang Chen, Yingping He, and Matthias Dehmer. "Computational fluid dynamics based dynamic modeling of parafoil system." Applied Mathematical Modelling 54 (February 2018): 136–50. http://dx.doi.org/10.1016/j.apm.2017.09.008.

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Domanskii, A. V., and V. V. Ershov. "Fluid-dynamic modeling of mud volcanism." Russian Geology and Geophysics 52, no. 3 (March 2011): 368–76. http://dx.doi.org/10.1016/j.rgg.2011.02.009.

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Charles, Dawari David, and Xiaopeng Xie. "New concepts in dynamic fluid-loss modeling of fracturing fluids." Journal of Petroleum Science and Engineering 17, no. 1-2 (February 1997): 29–40. http://dx.doi.org/10.1016/s0920-4105(96)00054-x.

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Martirosyan, Karen S., Maxim Zyskin, Charles M. Jenkins, and Yasuyuki (Yuki) Horie. "Fluid dynamic modeling of nano-thermite reactions." Journal of Applied Physics 115, no. 10 (March 14, 2014): 104903. http://dx.doi.org/10.1063/1.4867936.

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Cohen, Andrew J., Nima Baradaran, Jorge Mena, Daniel Krsmanovich, and Benjamin N. Breyer. "Computational Fluid Dynamic Modeling of Urethral Strictures." Journal of Urology 202, no. 2 (August 2019): 347–53. http://dx.doi.org/10.1097/ju.0000000000000187.

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TRANCOSSI, Michele, and Jose PASCOA. "Modeling Fluid dynamics and Aerodynamics by Second Law and Bejan Number (Part 1 - Theory)." INCAS BULLETIN 11, no. 3 (September 9, 2019): 169–80. http://dx.doi.org/10.13111/2066-8201.2019.11.3.15.

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Two fundamental questions are still open about the complex relation between fluid dynamics and thermodynamics. Is it possible (and convenient) to describe fluid dynamic in terms of second law based thermodynamic equations? Is it possible to solve and manage fluid dynamics problems by mean of second law of thermodynamics? This chapter analyses the problem of the relationships between the laws of fluid dynamics and thermodynamics in both first and second law of thermodynamics in the light of constructal law. In particular, taking into account constructal law and the diffusive formulation of Bejan number, it defines a preliminary step through an extensive thermodynamic vision of fluid dynamic phenomena.
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Pei, Pei, Yongbo Peng, and Canxing Qiu. "Magnetorheological damper modeling based on a refined constitutive model for MR fluids." Journal of Intelligent Material Systems and Structures 33, no. 10 (October 26, 2021): 1271–91. http://dx.doi.org/10.1177/1045389x211048231.

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A systematic modeling study is conducted to predict the dynamic response of magnetorheological (MR) damper based on a refined constitutive model for MR fluids. A particle-level simulation method is first employed to probe the microstructured behavior and rheological properties of MR fluids, based on which the refined constitutive model is developed. The constitutive model is further validated by comparing the predicted results with the data obtained from microscopic simulations and existing experiments. It is revealed that the proposed constitutive model has comparable accuracy and good applicability in representing MR fluids. Subsequently, a computational fluid dynamics (CFD) model is established to explore MR damper’s behavior by using the proposed constitutive model to describe the fluid rheology. For better capturing the dynamic hysteretic behavior of MR damper, a modified parametric model is developed by combing the Bingham plastic model and the proposed constitutive model. The modified model for MR damper shows its validity and superiority over the existing Bingham plastic models.
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Khabibullin, R. A. "Local density dynamics in a supercritical Lennard-Jones fluid." Journal of Physics: Conference Series 2270, no. 1 (May 1, 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2270/1/012037.

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Abstract The collective particle dynamics of supercritical Lennard-Jones fluid is investigated on the basis of molecular dynamic modeling data. The intermediate scattering functions and dynamic structure factor spectra for the wavenumber range k ∈ [0.18; 3.26] σ−1 were calculated. The characteristics of dynamic structure factor spectra such as the thermal diffusivity and the sound velocity were estimated.
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Thomas, Justin, Thomas M. Holsen, and Suresh Dhaniyala. "Computational fluid dynamic modeling of two passive samplers." Environmental Pollution 144, no. 2 (November 2006): 384–92. http://dx.doi.org/10.1016/j.envpol.2005.12.042.

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Suh, Sang-Ho, Hyoug-Ho Kim, Young Ho Choi, and Jeong Sang Lee. "Computational fluid dynamic modeling of femoral artery pseudoaneurysm." Journal of Mechanical Science and Technology 26, no. 12 (December 2012): 3865–72. http://dx.doi.org/10.1007/s12206-012-1012-4.

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Дисертації з теми "Fluid Dynamic Modeling"

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Cardillo, Giulia. "Fluid Dynamic Modeling of Biological Fluids : From the Cerebrospinal Fluid to Blood Thrombosis." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX110.

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Dans cette thèse, trois modèles mathématiques ont été proposés, avec l’objectif de modéliser autant d’aspects complexes de la biomédecine, dans lesquels la dynamique des fluides du système joue un rôle fondamental: i) les interactions fluide-structure entre la pulsatilité du liquide céphalo-rachidien et la moelle épinière (modélisation analytique); ii) dispersion efficace d’un médicament dans l’espace sous-arachnoïdien (modélisation numérique); et iii) la formation et l’évolution d’un thrombus au sein du système cardiovasculaire (modélisation numérique).Le liquide céphalorachidien est un fluide aqueux qui entoure le cerveau et la moelle épinière afin de les protéger. Une connaissance détaillée de la circulation du liquide céphalorachidien et de son interaction avec les tissus peut être importante dans l’étude de la pathogenèse de maladies neurologiques graves, telles que la syringomyélie, un trouble qui implique la formation de cavités remplies de liquide (seringues) dans la moelle épinière.Par ailleurs, dans certains cas, des analgésiques - ainsi que des médicaments pour le traitement de maladies graves telles que les tumeurs et les infections du liquide céphalorachidien - doivent être administrés directement dans le liquide céphalorachidien. L’importance de connaître et de décrire l’écoulement du liquide céphalorachidien, ses interactions avec les tissus environnants et les phénomènes de transport qui y sont liés devient claire. Dans ce contexte, nous avons proposé: un modèle capable de décrire les interactions du liquide céphalo-rachidien avec la moelle épinière, considérant cela, pour la première fois, comme un milieu poreux imprégné de différents fluides (sang capillaire et veineux et liquide céphalo-rachidien); et un modèle capable d’évaluer le transport d’un médicament dans l’espace sousarachnoïdien, une cavité annulaire remplie de liquide céphalo-rachidien qui entoure la moelle épinière.Avec le troisième modèle proposé, nous entrons dans le système cardiovasculaire.Dans le monde entière, les maladies cardiovasculaires sont la cause principale de mortalité. Parmi ceux-ci, nous trouvons la thrombose, une condition qui implique la formation d’un caillot à l’intérieur d’un vaisseau sanguin, qui peut causer sa occlusion. À cet égard, un modèle numérique a été développé qui étudie la formation et l’évolution des thrombus, en considérant simultanément les aspects chimico-biomécaniques et dynamiques des fluides du problème. Dans le modèle proposé pour la première fois, l'importance du rôle joué par les gradients de contrainte de cisaillement dans le processus de thrombogenèse est pris en compte.Les modèles sélectionnés ont fourni des résultats intéressants. Tout d’abord, l’étude des interactions fluide-structure entre le liquide céphalo-rachidien et la moelle épinière a mis en évidence es conditions pouvant induire l’apparition de la syringomyélie. Il a été observé comment la déviation des valeurs physiologiques du module d’Young de la moelle épinière, les pressions capillaires dans l’interface moelle-espace sousarachnoïdien et la perméabilité des compartiments capillaire et veineux, conduisent à la formation de seringues.Le modèle de calcul pour l’évaluation de la dispersion pharmacologique dans l’espace sousarachnoïdien a permis une estimation quantitatif de la diffusivité effective du médicament, une quantité qui peut aider à l’optimisation des protocoles d’injections intrathécales.Le modèle de thrombogenèse a fourni un instrument capable d’étudier quantitativement l’évolution des dépôts de plaquettes dans la circulation sanguine. En particulier, les résultats ont fourni des informations importantes sur la nécessité de considérer le rôle de l’activation mécanique et de l’agrégation des plaquettes aux côtés de la substance chimique
In the present thesis, three mathematical models are described. Three different biomedical issues, where fluid dynamical aspects are of paramount importance, are modeled: i) Fluid-structure interactions between cerebro-spinal fluid pulsatility and the spinal cord (analytical modeling); ii) Enhanced dispersion of a drug in the subarachnoid space (numerical modeling); and iii) Thrombus formation and evolution in the cardiovascular system (numerical modeling).The cerebrospinal fluid (CSF) is a liquid that surrounds and protects the brain and the spinal cord. Insights into the functioning of cerebrospinal fluid are expected to reveal the pathogenesis of severe neurological diseases, such as syringomyelia that involves the formation of fluid-filled cavities (syrinxes) in the spinal cord.Furthermore, in some cases, analgesic drugs -- as well drugs for treatments of serious diseases such as cancers and cerebrospinal fluid infections -- need to be delivered directly into the cerebrospinal fluid. This underscores the importance of knowing and describing cerebrospinal fluid flow, its interactions with the surrounding tissues and the transport phenomena related to it. In this framework, we have proposed: a model that describes the interactions of the cerebrospinal fluid with the spinal cord that is considered, for the first time, as a porous medium permeated by different fluids (capillary and venous blood and cerebrospinal fluid); and a model that evaluates drug transport within the cerebrospinal fluid-filled space around the spinal cord --namely the subarachnoid space--.The third model deals with the cardiovascular system. Cardiovascular diseases are the leading cause of death worldwide, among these diseases, thrombosis is a condition that involves the formation of a blood clot inside a blood vessel. A computational model that studies thrombus formation and evolution is developed, considering the chemical, bio-mechanical and fluid dynamical aspects of the problem in the same computational framework. In this model, the primary novelty is the introduction of the role of shear micro-gradients into the process of thrombogenesis.The developed models have provided several outcomes. First, the study of the fluid-structure interactions between cerebro-spinal fluid and the spinal cord has shed light on scenarios that may induce the occurrence of Syringomyelia. It was seen how the deviation from the physiological values of the Young modulus of the spinal cord, the capillary pressures at the SC-SAS interface and the permeability of blood networks can lead to syrinx formation.The computational model of the drug dispersion has allowed to quantitatively estimate the drug effective diffusivity, a feature that can aid the tuning of intrathecal delivery protocols.The comprehensive thrombus formation model has provided a quantification tool of the thrombotic deposition evolution in a blood vessel. In particular, the results have given insight into the importance of considering both mechanical and chemical activation and aggregation of platelets
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Kachani, Soulaymane, and Georgia Perakis. "Modeling Travel Times in Dynamic Transportation Networks; A Fluid Dynamics Approach." Massachusetts Institute of Technology, Operations Research Center, 2001. http://hdl.handle.net/1721.1/5224.

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In this paper, we take a fluid dynamics approach to determine the travel time in traversing a network's link. We propose a general model for travel time functions that utilizes fluid dynamics laws for compressible flow to capture a variety of flow patterns such as the formation and dissipation of queues, drivers' response to upstream congestion or decongestion and drivers' reaction time. We examine two variants of the model, in the case of separable velocity functions, which gives rise to two families of travel time functions for the problem; a polynomial and an exponential family. We analyze these travel time functions and examine several special cases. Our investigation also extends to the case of non-separable velocity functions starting with an analysis of the interaction between two links, and then extending it to the general case of acyclic networks.
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Clinkinbeard, Nicholus Ryan. "Computational fluid dynamic modeling of acoustic liquid manipulation." [Ames, Iowa : Iowa State University], 2006.

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Jupp, Laurence. "Dynamic modeling of complex fluids under flow." Thesis, University of Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288304.

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Jacobsson, Krister. "Dynamic modeling of Internet congestion control." Doctoral thesis, Stockholm : Electrical Engineering, Elektrotekniska system, Kungliga Tekniska högskolan, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4708.

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Cortes, Capetillo Azael Jesus. "Computational fluid dynamic modeling of in-duct UV air sterilisation systems." Thesis, University of Leeds, 2015. http://etheses.whiterose.ac.uk/9591/.

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In-duct UVC air sterilisation is a technology that can help in the reduction and control of airborne diseases. Nevertheless, improvements in sterilisation performance efficiency are required for the technology to succeed in an increasingly restricted energy society. Computational fluid Dynamics (CFD) was used to systematically improve the performance of in-duct UVC air sterilisation systems. The Discrete Ordinates method (DO) was used to model lamp irradiation, and a user defined function (UDF) to model the injection of microorganisms inside the duct to then calculate the average UV dose of the system, with this it was possible to reproduce test results published by EPA. After the CFD model was validated, operation parameters such as wall reflectivity, lamp location, lamp position, air velocity and airflow patterns were analysed. It was found that accurate information of UVC susceptibility for microorganisms in air was essential for the correct modeling of UVC air sterilisation systems using CFD, and current available data contain considerable variations that needed to be analysed and interpreted in an appropriate manner. It was also found that the DO method was appropriate to model lamp irradiation and could account for reflectivity, and that CFD was robust enough to reproduce lab tests results. Moreover it was found that airflow patterns, and lamp location and position influenced the sterilisation performance of a UVC system. Results include a comprehensive list of microorganisms UVC susceptibilities in air (Chapter 3); a set of CFD models that can be used for validation or calibration for future studies and a confirmation that CFD is capable to model in-duct UVC air sterilisation systems (Chapter 5). Ultimately this research presents a series of conclusions that will help on the design of more efficient in-duct UVC air sterilisation systems.
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Surendran, Mahesh. "Computational Fluid Dynamic Modeling of Natural Convection in Vertically Heated Rods." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5168.

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Natural convection is a phenomenon that occurs in a wide range of applications such as cooling towers, air conditioners, and power plants. Natural convection may be used in decay heat removal systems such as spent fuel casks, where the higher reliability inherent of natural convection is more desirable than forced convection. Passive systems, such as natural convection, may provide better safety, and hence have received much attention recently. Cooling of spent fuel rods is conventionally done using water as the coolant. However, it involves contaminating the water with radiation from the fuel rods. Contamination becomes dangerous and difficult for humans to handle. Further, the recent nuclear tragedy in Fukushima, Japan has taught us the dangers of contamination of water with nuclear radiation. Natural convection can perhaps significantly reduce the risk since it is self-sufficient and does not rely on other secondary system such as a blower as in cases of forced convection. The Utah State University Experimental Fluid Dynamics lab has recently designed an experiment that models natural convection using heated rod bundles enclosed in a rectangular cavity. The data available from this experiment provides and opportunity to study and validate computational fluid dynamics(CFD)models. The validated CFD models can be used to study multiple configurations, boundary conditions, and changes in physics(natural and/or forced convection). The results are to be validated using experimental data such as the velocity field from particle image velocimetry (PIV), pressure drops across various sections of the geometry, and temperature distributions along the vertically heated rods. This research work involves modeling natural convection using two-layer turbulence models such as k - ε and RST (Reynolds stress transport) using both shear driven (Wolfstein) and buoyancy driven (Xu) near-wall formulations. The interpolation scheme employed is second-order upwinding using the general purpose code STAR-CCM+. The pressure velocity coupling is done using the SIMPLE method. It is ascertained that turbulence models with two-layer formulations are well suited for modeling natural convection. Further it is established that k - ε and Reynolds stress turbulence models with the buoyancy driven (Xu)formulation are able to accurately predict the flow rate and temperature distribution.
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Chakraborty, Sanjib. "Dynamic Modeling and Simulation of Digital Displacement Machine." Thesis, Linköpings universitet, Fluida och mekatroniska system, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-85277.

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Improved efficiency, better controllability and low noise are the most demanding features form a displacement machine now-a-days. Most of the conventional displacement machines are basically a reciprocating pumping element controlled by valve plates or with the help of check valve [1]. This kind of hydraulic machines loose efficiency dramatically at partial displacement because all of the pistons remain at high pressure at the cycle time and due to pressure inside the piston leakage and shear losses increases. One approach to improve the efficiency of the displacement machine can be controlling each hydraulic piston by using programmable faster valves called digital valve. As the total displacement will be controlled digitally, the total system is called Digital Displacement Technology. In digital displacement machine it is possible to disconnect some of the pistons from the load and the piston will connect only with the low pressure side, minimizing losses due to leakage and shear. As the valve will control directly with digital controller it will eliminate the necessity of servo-hydraulic control required by conventional systems. Digital valves can open fully and close again with the input signal within one revaluation of the shaft, so it gives better control to the pumping element results reduction in hysteresis and increase the linearity of the pumping element. In Digital Displacement machines by controlling the valves pistons are connected with the machine when pressure is equal, but in the traditional machines piston connection was pre-determined with the shaft angle. By doing the piston control efficiency of the machine will improve and the sound generates for the decompression flow will be reduced [17]. Also energy storage and recovery can be possible by using accumulator.
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Larsen, Joshua. "Pore Scale Computational Fluid Dynamic Modeling| Approaches for Permeability Modeling and Particle Tracking Using Lattice Boltzmann Methods." Thesis, The University of Arizona, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10978423.

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Knowledge of colloid mobility is important for understanding nutrient cycling, the transport of some contaminants, and for developing environmental remediation systems such as geologic filters. The interaction forces between colloids and soil materials are central to colloid transport and immobilization. These forces act at the microscale (nanometers to microns) and include: fluid drag (friction), Brownian motion, gravity and buoyancy, and fluid chemical forces (including DLVO and van der Waals mechanisms). Most vadose zone studies, however, consider colloids at the continuum scale in terms of solute transport mechanisms using parametrized forms of the advection-dispersion equation and absorption isotherms. A comprehensive, generally applicable, well-documented and publicly available framework for simulating colloids at the microscale is still lacking.

Colloid transport and mobility are mechanisms that fundamentally occur at the microscale. As such, representation of the pore-structure needs to be obtained that is meaningful for the pore-scale fluid flow field and colloid mobility (pore-scale colloidal force balances cause the colloidal transport field to be different from the fluid flow field). At the same time, the pore-structure needs to be relevant for continuum-scale experiments or simulations. There are two ways by which a pore-structure can be obtained: by direct three-dimensional imaging (typically with x-ray tomographic techniques) or by reconstruction techniques that yield a synthetic, but presumably representative, pore-structure. Both techniques are examined in this dissertation, but the synthetic route must be used if little micro-scale information is available.

This dissertation addresses three main objectives. In chapter 2 it addresses the relation between image quality obtained with two different x-ray tomography techniques (a synchrotron and an industrial scanner) and the obtained flow field. Chapter 3 discusses the development of the LB-Colloids software package, while chapter 4 applies the code to data obtained from a breakthrough experiment of nanoparticulate TiO2.

In chapter 2, pore-scale flow fields for Berea sand stone and a macropore soil sample were obtained with lattice Boltzmann simulations which were volume-averaged to a sample-scale permeability and verified with an observed sample-scale permeability. In addition, the lattice Boltzmann simulations were verified with a Kozeny-Carman equation. Results indicate that the simulated flow field strongly depends on the quality of the x-ray tomographic imagery and the segmentation algorithm used to convert gray-scale tomography data into binary pore-structures. More complex or advanced segmentation algorithms do not necessarily produce better segmentations when dealing with ambiguous imagery. It was found that the KC equation provided a reliable initial assessment of error when predicting permeability and can be used as a quick evaluation of whether simulations of the micro-scale flow field should be pursued. In the context of this study, this chapter indicated that LB is able to generate relevant pore-scale flow fields that represent sample-scale permeabilities. However, because the remainder of the study was focused on the development of a pore-scale colloid mobility framework we decided to focus primarily on synthetically-generated pore-structures. This also allowed us to focus on actual mechanisms that were free of imaging and segmentation artifacts.

Chapter 3 discusses the development of the LB-Colloids package. This simulation framework is able to simulate large collections of individual colloids through pore representations and porous media. The general workflow for users is as follows: 1) Obtain a pore structure by tomographic imaging or by synthetic means. The latter can be accomplished though the included PSPHERE module which is able to generate a random porous medium using user-supplied porosity and particle size. 2) The pore-scale fluid flow field in the porous medium is generated with a lattice Boltzmann method and a user-specified body force that controls the volume averaged Darcy velocity. 3) Mobility and attachment/detachment of colloids is simulated by accounting of the force balance (fluid drag, Brownian motion, gravity and buoyancy forces, and fluid-chemical forces including DLVO and van der Waals mechanisms). Colloid mobility is carried out at a submicron to nanometer scale and requires grid refinement of the LB flow field. To speed up computations the fluid-chemical forces are precomputed for every grid cell.

Because of computational considerations, the LB-Colloids package is presently only able to deal with 2D representations of the porous medium. Code-development and testing (chapter 4) would have taken too long for a full 3D approach. The main draw-back of the 2D approach is that these cannot accurately represent 3D pore-structures. However, no fundamental “new” mechanisms are needed for a 3D approach and we expect that this can be easily built into the clean and well-documented LB-colloids code. The LB-Colloids framework is applied on data obtained from a break-through experiment of TiO2 nanoparticles. (Abstract shortened by ProQuest.)

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Scharf, Frank H. "Fluid dynamic and kinetic modeling of the near cathode region in thermal plasmas." Berlin Logos-Verl, 2008. http://d-nb.info/994080492/04.

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Книги з теми "Fluid Dynamic Modeling"

1

Wilcox, David C. Turbulence modeling for CFD. La Cañada, CA: DCW Industries, 1994.

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2

Wilcox, David C. Turbulence modeling for CFD. La Cãnada, CA: DCW Industries, Inc., 1993.

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3

Wilcox, David C. Turbulence modeling for CFD. 2nd ed. La Cãnada, Calif: DCW Industries, 1998.

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4

Wilcox, David C. Solutions manual: Turbulence modeling for CFD. La Cañada, Calif: DCW Industries, Inc., 1993.

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5

Zai-chao, Liang, Chen Ching Jen 1936-, and Cai Shutang, eds. Flow modeling and turbulence measurements. Washington: Hemisphere Pub., 1992.

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6

Zeytounian, R. Kh. Asymptotic modeling of atmospheric flows. Berlin: Springer-Verlag, 1990.

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7

United States. National Aeronautics and Space Administration. Scientific and Technical Information Program, ed. Large-eddy simulation of laminar-turbulent breakdown at high speeds with dynamic subgrid-scale modeling. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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El-Hady, Nabil M. Large-eddy simulation of laminar-turbulent breakdown at high speeds with dynamic subgrid-scale modeling. Hampton, Va: Langley Research Center, 1993.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Large-eddy simulation of laminar-turbulent breakdown at high speeds with dynamic subgrid-scale modeling. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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10

United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Large-eddy simulation of laminar-turbulent breakdown at high speeds with dynamic subgrid-scale modeling. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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Частини книг з теми "Fluid Dynamic Modeling"

1

Shalaby, Ahlam I. "Dynamic Similitude and Modeling." In Fluid Mechanics for Civil and Environmental Engineers, 1403–561. Boca Raton : Taylor & Francis a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781315156637-11.

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Esfandiari, Ramin S., and Bei Lu. "Fluid and Thermal Systems." In Modeling and Analysis of Dynamic Systems, 329–71. Third edition. | Boca Raton : Taylor & Francis, CRC Press, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b22138-7.

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Vitello, P. "Fluid Dynamic Modeling of Plasma Reactors." In Molecular Physics and Hypersonic Flows, 477–84. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0267-1_30.

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Wang, Jiacun, and Daniela Rosca. "Dynamic Workflow Modeling and Verification." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 303–18. Cham: Springer International Publishing, 2006. http://dx.doi.org/10.1007/11767138_21.

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Lespérance, Yves, Todd G. Kelley, John Mylopoulos, and Eric S. K. Yu. "Modeling Dynamic Domains with ConGolog." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 365–80. Cham: Springer International Publishing, 1999. http://dx.doi.org/10.1007/3-540-48738-7_27.

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Pogorelov, Nikolai V. "Numerical Modeling of Discontinuous Gas Dynamic and MHD Astrophysical Flows." In Computational Fluid Dynamics 2000, 145–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56535-9_19.

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Huang, S. X., X. Chen, and C. J. Lu. "Modeling of Dynamic Extrusion Swelling Using Cross Model." In New Trends in Fluid Mechanics Research, 554–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75995-9_181.

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Olufsen, Mette S. "A One-Dimensional Fluid Dynamic Model of the Systemic Arteries." In Computational Modeling in Biological Fluid Dynamics, 167–87. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0151-6_9.

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Burg, J. F. M., and R. P. van de Riet. "COLOR-X: Linguistically-based event modeling: A general approach to dynamic modeling." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 26–39. Cham: Springer International Publishing, 1995. http://dx.doi.org/10.1007/3-540-59498-1_235.

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Majd, S., J. Vossoughi, and A. Johnson. "Computational Fluid Dynamic Modeling of the Airflow Perturbation Device." In IFMBE Proceedings, 397–400. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14998-6_101.

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Тези доповідей конференцій з теми "Fluid Dynamic Modeling"

1

Darbandi, M., Iman Mazaberi, Asghar Dehkordi, and Gerry Schneider. "The Level set Modeling of Droplet Dynamic in Fluid-Fluid Interaction." In 39th AIAA Fluid Dynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-4292.

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2

Denissen, Nicholas A., Bertrand Rollin, Jon M. Reisner, and Malcolm Andrews. "Modeling Turbulent Rayleigh-Taylor Mixing with Dynamic Interfaces." In 43rd AIAA Fluid Dynamics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2487.

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3

Parthasarathy, Girija, and Dinkar Mylaraswamy. "Computational Fluid Dynamic Modeling for Engine Diagnosis." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38567.

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This paper presents the results of a demonstration problem where computational fluid dynamics modeling (CFD) is used for engine diagnosis. As computational resources become faster and cheaper and detailed numerical models of heat transfer, fluid dynamics and chemical kinetics become more accurate, these numerical models can become viable alternatives for seeded fault tests. The work done here is one of the ways this could be done; that is, by using the results of a CFD model to map the effects of certain faults to a model parameter computed by a less detailed lumped parameter model.
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4

Zhao, Kun, and Takayuki Osogami. "Modeling fluid simulation with dynamic Boltzmann machine." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8248180.

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Mazher, Abdel. "A New Approach to Dynamic Modeling of Turbulence." In 4th AIAA Theoretical Fluid Mechanics Meeting. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-5316.

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Huang, Shenghua, Yangming Liu, Ning Ye, and Bo Yang. "Fluid Structure Interaction Modeling for Dynamic Wire Sweep." In 2021 IEEE 71st Electronic Components and Technology Conference (ECTC). IEEE, 2021. http://dx.doi.org/10.1109/ectc32696.2021.00233.

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7

Li, Ding, Xiaoqiang Zeng, Charles Merkle, E. Felderman, and J. Sheeley. "Coupled Fluid-Dynamic Electromagnetic Modeling of Arc Heaters." In 37th AIAA Plasmadynamics and Lasers Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-3768.

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8

Nash, Austin L., and Neera Jain. "Second Law Modeling and Robust Control for Thermal-Fluid Systems." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9056.

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In this paper we present a dynamic model formulation for thermal-fluid systems that enables direct minimization of dissipative and frictional losses, otherwise known as entropy generation. While analysis based upon the second law of thermodynamics has long been used for optimizing steady-state design of thermodynamic systems, transient performance and efficiency have largely been overlooked. Across a range of sectors, from power generation to microelectronics, answering the question of how to control a system to minimize these transient losses is becoming increasingly important. Unfortunately, thermodynamic expressions for entropy generation are typically highly nonlinear and not easily amenable to control synthesis and design. Here we derive a control-oriented dynamic model of a notional thermal-fluid system with entropy generation rate as part of the dynamic state vector. The proposed model formulation technique is generalizable to a wide range of energy conversion systems and, importantly, enables the synthesis of model-based state feedback controllers which can in turn optimize transient system performance to minimize irreversibilities due to energy conversion and transport in real-time. To illustrate the utility of the formulation, we design a state feedback H∞ controller to minimize the total entropy generation rate of the notional system in the presence of pulsed, episodic load disturbances.
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9

Wang, Xiaojie, and Faramarz Gordaninejad. "Dynamic modeling of semi-active ER/MR fluid dampers." In SPIE's 8th Annual International Symposium on Smart Structures and Materials, edited by Daniel J. Inman. SPIE, 2001. http://dx.doi.org/10.1117/12.432736.

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10

Roemer, Daniel B., Per Johansen, Henrik C. Pedersen, and Torben O. Andersen. "Modeling of Dynamic Fluid Forces in Fast Switching Valves." In ASME/BATH 2015 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/fpmc2015-9594.

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Switching valves experience opposing fluid forces due to movement of the moving member itself, as the surrounding fluid volume must move to accommodate the movement. This movement-induced fluid force may be divided into three main components; the added mass term, the viscous term and the so-called history term. For general valve geometries there are no simple solution to either of these terms. During development and design of such switching valves, it is therefore, common practice to use simple models to describe the opposing fluid forces, neglecting all but the viscous term which is determined based on shearing areas and venting channels. For fast acting valves the opposing fluid force may retard the valve performance significantly, if appropriate measures are not taken during the valve design. Unsteady Computational Fluid Dynamics (CFD) simulations are available to simulate the total fluid force, but these models are computationally expensive and are not suitable for evaluating large numbers of different operation conditions or even design optimization. In the present paper, an effort is done to describe these fluid forces and their origin. An example of the total opposing fluid force is given using an analytically solvable example, showing the explicit form of the force terms and highlighting the significance of the added mass and history term in certain fast switching valve applications. A general approximate model for arbitrary valve geometries is then proposed with offset in the analytic model terms. The coefficients in this general model are determined based on CFD analyses, which are evaluated throughout the movement range of the moving member on an example valve geometry. The proposed model is compared to complete unsteady CFD simulations and found to generally predict the opposing fluid force well and gives accurate predictions under certain conditions. The proposed model is suitable for valve designers who need a computationally inexpensive fluid force model suitable for optimization routines or efficient dynamic models.
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Звіти організацій з теми "Fluid Dynamic Modeling"

1

Rokkam, Ram. Computational fluid dynamic modeling of fluidized-bed polymerization reactors. Office of Scientific and Technical Information (OSTI), January 2012. http://dx.doi.org/10.2172/1082969.

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2

Lyczkowski, R. W., J. X. Bouillard, J. Ding, S. L. Chang, and S. W. Burge. State-of-the-art review of computational fluid dynamics modeling for fluid-solids systems. Office of Scientific and Technical Information (OSTI), May 1994. http://dx.doi.org/10.2172/34291.

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3

Giljarhus, Knut Erik Teigen. Disc Golf Trajectory Modelling Combining Computational Fluid Dynamics and Rigid Body Dynamics. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317502.

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4

Wix, S. D., J. K. Cole, and J. A. Koski. Modeling fires in adjacent ship compartments with computational fluid dynamics. Office of Scientific and Technical Information (OSTI), May 1998. http://dx.doi.org/10.2172/645528.

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5

Sahu, Jubaraj, Harris L. Edge, Karen R. Heavey, and Earl N. Ferry. Computational Fluid Dynamics Modeling of Multi-body Missile Aerodynamic Interference. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada354107.

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6

Rakowski, Cynthia L., William A. Perkins, Marshall C. Richmond, and John A. Serkowski. Computational Fluid Dynamics Modeling of the John Day Dam Tailrace. Office of Scientific and Technical Information (OSTI), July 2010. http://dx.doi.org/10.2172/1033088.

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Tossas, Luis A. Martinez, and Stefano Leonardi. Wind Turbine Modeling for Computational Fluid Dynamics: December 2010 - December 2012. Office of Scientific and Technical Information (OSTI), July 2013. http://dx.doi.org/10.2172/1089598.

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Voigt. An Unsolicited Proposal for Modeling, Identification, and Active Control of Fluid Dynamics. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada252051.

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9

Martin, R., J. Bernardin, L. Parietti, and B. Dennison. National Ignition Facility computational fluid dynamics modeling and light fixture case studies. Office of Scientific and Technical Information (OSTI), February 1998. http://dx.doi.org/10.2172/576114.

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Rodriguez, Salvador. Computational Fluid Dynamics and Heat Transfer Modeling of a Dimpled Heat Exchanger. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1893993.

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