Chapter 7 Types of Cavitation
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Convection Heat Transfer
Convection Heat Transfer Heat transfer from a solid to the surrounding fluid Consider fluid motion Recall flow of water in a pipe Thermal Boundary Layer • A temperature profile similar to velocity profile. Temperature of pipe surface is kept constant. At the end of the thermal entry region, the boundary layer extends to the center of the pipe. Therefore, two boundary layers: hydrodynamic boundary layer and a thermal boundary layer. Analytical treatment is beyond the scope of this course. Instead we will use an empirical approach. Drawback of empirical approach: need to collect large amount of data. Reynolds Number: Nusselt Number: it is the dimensionless form of convective heat transfer coefficient. Consider a layer of fluid as shown If the fluid is stationary, then And Dividing Replacing l with a more general term for dimension, called the characteristic dimension, dc, we get hd N ≡ c Nu k Nusselt number is the enhancement in the rate of heat transfer caused by convection over the conduction mode. If NNu =1, then there is no improvement of heat transfer by convection over conduction. On the other hand, if NNu =10, then rate of convective heat transfer is 10 times the rate of heat transfer if the fluid was stagnant. Prandtl Number: It describes the thickness of the hydrodynamic boundary layer compared with the thermal boundary layer. It is the ratio between the molecular diffusivity of momentum to the molecular diffusivity of heat. kinematic viscosity υ N == Pr thermal diffusivity α μcp N = Pr k If NPr =1 then the thickness of the hydrodynamic and thermal boundary layers will be the same. -
Laws of Similarity in Fluid Mechanics 21
Laws of similarity in fluid mechanics B. Weigand1 & V. Simon2 1Institut für Thermodynamik der Luft- und Raumfahrt (ITLR), Universität Stuttgart, Germany. 2Isringhausen GmbH & Co KG, Lemgo, Germany. Abstract All processes, in nature as well as in technical systems, can be described by fundamental equations—the conservation equations. These equations can be derived using conservation princi- ples and have to be solved for the situation under consideration. This can be done without explicitly investigating the dimensions of the quantities involved. However, an important consideration in all equations used in fluid mechanics and thermodynamics is dimensional homogeneity. One can use the idea of dimensional consistency in order to group variables together into dimensionless parameters which are less numerous than the original variables. This method is known as dimen- sional analysis. This paper starts with a discussion on dimensions and about the pi theorem of Buckingham. This theorem relates the number of quantities with dimensions to the number of dimensionless groups needed to describe a situation. After establishing this basic relationship between quantities with dimensions and dimensionless groups, the conservation equations for processes in fluid mechanics (Cauchy and Navier–Stokes equations, continuity equation, energy equation) are explained. By non-dimensionalizing these equations, certain dimensionless groups appear (e.g. Reynolds number, Froude number, Grashof number, Weber number, Prandtl number). The physical significance and importance of these groups are explained and the simplifications of the underlying equations for large or small dimensionless parameters are described. Finally, some examples for selected processes in nature and engineering are given to illustrate the method. 1 Introduction If we compare a small leaf with a large one, or a child with its parents, we have the feeling that a ‘similarity’ of some sort exists. -
The Role of MHD Turbulence in Magnetic Self-Excitation in The
THE ROLE OF MHD TURBULENCE IN MAGNETIC SELF-EXCITATION: A STUDY OF THE MADISON DYNAMO EXPERIMENT by Mark D. Nornberg A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Physics) at the UNIVERSITY OF WISCONSIN–MADISON 2006 °c Copyright by Mark D. Nornberg 2006 All Rights Reserved i For my parents who supported me throughout college and for my wife who supported me throughout graduate school. The rest of my life I dedicate to my daughter Margaret. ii ACKNOWLEDGMENTS I would like to thank my adviser Cary Forest for his guidance and support in the completion of this dissertation. His high expectations and persistence helped drive the work presented in this thesis. I am indebted to him for the many opportunities he provided me to connect with the world-wide dynamo community. I would also like to thank Roch Kendrick for leading the design, construction, and operation of the experiment. He taught me how to do science using nothing but duct tape, Sharpies, and Scotch-Brite. He also raised my appreciation for the artistry of engineer- ing. My thanks also go to the many undergraduate students who assisted in the construction of the experiment, especially Craig Jacobson who performed graduate-level work. My research partner, Erik Spence, deserves particular thanks for his tireless efforts in modeling the experiment. His persnickety emendations were especially appreciated as we entered the publi- cation stage of the experiment. The conversations during our morning commute to the lab will be sorely missed. I never imagined forging such a strong friendship with a colleague, and I hope our families remain close despite great distance. -
1 Fluid Flow Outline Fundamentals of Rheology
Fluid Flow Outline • Fundamentals and applications of rheology • Shear stress and shear rate • Viscosity and types of viscometers • Rheological classification of fluids • Apparent viscosity • Effect of temperature on viscosity • Reynolds number and types of flow • Flow in a pipe • Volumetric and mass flow rate • Friction factor (in straight pipe), friction coefficient (for fittings, expansion, contraction), pressure drop, energy loss • Pumping requirements (overcoming friction, potential energy, kinetic energy, pressure energy differences) 2 Fundamentals of Rheology • Rheology is the science of deformation and flow – The forces involved could be tensile, compressive, shear or bulk (uniform external pressure) • Food rheology is the material science of food – This can involve fluid or semi-solid foods • A rheometer is used to determine rheological properties (how a material flows under different conditions) – Viscometers are a sub-set of rheometers 3 1 Applications of Rheology • Process engineering calculations – Pumping requirements, extrusion, mixing, heat transfer, homogenization, spray coating • Determination of ingredient functionality – Consistency, stickiness etc. • Quality control of ingredients or final product – By measurement of viscosity, compressive strength etc. • Determination of shelf life – By determining changes in texture • Correlations to sensory tests – Mouthfeel 4 Stress and Strain • Stress: Force per unit area (Units: N/m2 or Pa) • Strain: (Change in dimension)/(Original dimension) (Units: None) • Strain rate: Rate -
Chapter 5 Dimensional Analysis and Similarity
Chapter 5 Dimensional Analysis and Similarity Motivation. In this chapter we discuss the planning, presentation, and interpretation of experimental data. We shall try to convince you that such data are best presented in dimensionless form. Experiments which might result in tables of output, or even mul- tiple volumes of tables, might be reduced to a single set of curves—or even a single curve—when suitably nondimensionalized. The technique for doing this is dimensional analysis. Chapter 3 presented gross control-volume balances of mass, momentum, and en- ergy which led to estimates of global parameters: mass flow, force, torque, total heat transfer. Chapter 4 presented infinitesimal balances which led to the basic partial dif- ferential equations of fluid flow and some particular solutions. These two chapters cov- ered analytical techniques, which are limited to fairly simple geometries and well- defined boundary conditions. Probably one-third of fluid-flow problems can be attacked in this analytical or theoretical manner. The other two-thirds of all fluid problems are too complex, both geometrically and physically, to be solved analytically. They must be tested by experiment. Their behav- ior is reported as experimental data. Such data are much more useful if they are ex- pressed in compact, economic form. Graphs are especially useful, since tabulated data cannot be absorbed, nor can the trends and rates of change be observed, by most en- gineering eyes. These are the motivations for dimensional analysis. The technique is traditional in fluid mechanics and is useful in all engineering and physical sciences, with notable uses also seen in the biological and social sciences. -
Cavitation in Valves
VM‐CAV/WP White Paper Cavitation in Valves Table of Contents Introduction. 2 Cavitation Analysis. 2 Cavitation Data. 3 Valve Coefficient Data. 4 Example Application. .. 5 Conclusion & Recommendations . 5 References. 6 Val‐Matic Valve & Mfg. Corp. • www.valmatic.com • [email protected] • PH: 630‐941‐7600 Copyright © 2018 Val‐Matic Valve & Mfg. Corp. Cavitation in Valves INTRODUCTION Cavitation can occur in valves when used in throttling or modulating service. Cavitation is the sudden vaporization and violent condensation of a liquid downstream of the valve due to localized low pressure zones. When flow passes through a throttled valve, a localized low pressure zone forms immediately downstream of the valve. If the localized pressure falls below the vapor pressure of the fluid, the liquid vaporizes (boils) and forms a vapor pocket. As the vapor bubbles flow downstream, the pressure recovers, and the bubbles violently implode causing a popping or rumbling sound similar to tumbling rocks in a pipe. The sound of cavitation in a pipeline is unmistakable. The condensation of the bubbles not only produces a ringing sound, but also creates localized stresses in the pipe walls and valve body that can cause severe pitting. FIGURE 1. Cavitation Cavitation is a common occurrence in shutoff valves during the last few degrees of closure when the supply pressure is greater than about 100 psig. Valves can withstand limited durations of cavitation, but when the valve must be throttled or modulated in cavitating conditions for long periods of time, the life of the valve can be drastically reduced. Therefore, an analysis of flow conditions is needed when a valve is used for flow or pressure control. -
Control Valve Sizing Theory, Cavitation, Flashing Noise, Flashing and Cavitation Valve Pressure Recovery Factor
Control Valve Sizing Theory, Cavitation, Flashing Noise, Flashing and Cavitation Valve Pressure Recovery Factor When a fluid passes through the valve orifice there is a marked increase in velocity. Velocity reaches a maximum and pressure a minimum at the smallest sectional flow area just downstream of the orifice opening. This point of maximum velocity is called the Vena Contracta. Downstream of the Vena Contracta the fluid velocity decelerates and the pressure increases of recovers. The more stream lined valve body designs like butterfly and ball valves exhibit a high degree of pressure recovery where as Globe style valves exhibit a lower degree of pressure recovery because of the Globe geometry the velocity is lower through the vena Contracta. The Valve Pressure Recovery Factor is used to quantify this maximum velocity at the vena Contracta and is derived by testing and published by control valve manufacturers. The Higher the Valve Pressure Recovery Factor number the lower the downstream recovery, so globe style valves have high recovery factors. ISA uses FL to represent the Valve Recovery Factor is valve sizing equations. Flow Through a restriction • As fluid flows through a restriction, the Restriction Vena Contracta fluid’s velocity increases. Flow • The Bernoulli Principle P1 P2 states that as the velocity of a fluid or gas increases, its pressure decreases. Velocity Profile • The Vena Contracta is the point of smallest flow area, highest velocity, and Pressure Profile lowest pressure. Terminology Vapor Pressure Pv The vapor pressure of a fluid is the pressure at which the fluid is in thermodynamic equilibrium with its condensed state. -
Anomalous Viscosity, Resistivity, and Thermal Diffusivity of the Solar
Anomalous Viscosity, Resistivity, and Thermal Diffusivity of the Solar Wind Plasma Mahendra K. Verma Department of Physics, Indian Institute of Technology, Kanpur 208016, India November 12, 2018 Abstract In this paper we have estimated typical anomalous viscosity, re- sistivity, and thermal difffusivity of the solar wind plasma. Since the solar wind is collsionless plasma, we have assumed that the dissipation in the solar wind occurs at proton gyro radius through wave-particle interactions. Using this dissipation length-scale and the dissipation rates calculated using MHD turbulence phenomenology [Verma et al., 1995a], we estimate the viscosity and proton thermal diffusivity. The resistivity and electron’s thermal diffusivity have also been estimated. We find that all our transport quantities are several orders of mag- nitude higher than those calculated earlier using classical transport theories of Braginskii. In this paper we have also estimated the eddy turbulent viscosity. arXiv:chao-dyn/9509002v1 5 Sep 1995 1 1 Introduction The solar wind is a collisionless plasma; the distance travelled by protons between two consecutive Coulomb collisions is approximately 3 AU [Barnes, 1979]. Therefore, the dissipation in the solar wind involves wave-particle interactions rather than particle-particle collisions. For the observational evidence of the wave-particle interactions in the solar wind refer to the review articles by Gurnett [1991], Marsch [1991] and references therein. Due to these reasons for the calculations of transport coefficients in the solar wind, the scales of wave-particle interactions appear more appropriate than those of particle-particle interactions [Braginskii, 1965]. Note that the viscosity in a turbulent fluid is scale dependent. -
Eulerian–Lagrangian Method for Simulation of Cloud Cavitation ∗ Kazuki Maeda , Tim Colonius
Journal of Computational Physics 371 (2018) 994–1017 Contents lists available at ScienceDirect Journal of Computational Physics www.elsevier.com/locate/jcp Eulerian–Lagrangian method for simulation of cloud cavitation ∗ Kazuki Maeda , Tim Colonius Division of Engineering and Applied Science, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA a r t i c l e i n f o a b s t r a c t Article history: We present a coupled Eulerian–Lagrangian method to simulate cloud cavitation in a Received 3 December 2017 compressible liquid. The method is designed to capture the strong, volumetric oscillations Received in revised form 10 April 2018 of each bubble and the bubble-scattered acoustics. The dynamics of the bubbly mixture Accepted 16 May 2018 is formulated using volume-averaged equations of motion. The continuous phase is Available online 18 May 2018 discretized on an Eulerian grid and integrated using a high-order, finite-volume weighted Keywords: essentially non-oscillatory (WENO) scheme, while the gas phase is modeled as spherical, Bubble dynamics Lagrangian point-bubbles at the sub-grid scale, each of whose radial evolution is tracked Cavitation by solving the Keller–Miksis equation. The volume of bubbles is mapped onto the Eulerian Eulerian–Lagrangian method grid as the void fraction by using a regularization (smearing) kernel. In the most general Compressible multiphase flows case, where the bubble distribution is arbitrary, three-dimensional Cartesian grids are used Multiscale modeling for spatial discretization. In order to reduce the computational cost for problems possessing Reduced-order modeling translational or rotational homogeneities, we spatially average the governing equations along the direction of symmetry and discretize the continuous phase on two-dimensional or axi-symmetric grids, respectively. -
A Numerical Algorithm for MHD of Free Surface Flows at Low Magnetic Reynolds Numbers
A Numerical Algorithm for MHD of Free Surface Flows at Low Magnetic Reynolds Numbers Roman Samulyak1, Jian Du2, James Glimm1;2, Zhiliang Xu1 1Computational Science Center, Brookhaven National Laboratory, Upton, NY 11973 2 Department of Applied Mathematics and Statistics, SUNY at Stony Brook, Stony Brook, NY 11794, USA November 7, 2005 Abstract We have developed a numerical algorithm and computational soft- ware for the study of magnetohydrodynamics (MHD) of free surface flows at low magnetic Reynolds numbers. The governing system of equations is a coupled hyperbolic/elliptic system in moving and ge- ometrically complex domains. The numerical algorithm employs the method of front tracking for material interfaces, high resolution hy- perbolic solvers, and the embedded boundary method for the elliptic problem in complex domains. The numerical algorithm has been imple- mented as an MHD extension of FronTier, a hydrodynamic code with free interface support. The code is applicable for numerical simulations of free surface conductive liquids or flows of weakly ionized plasmas. Numerical simulations of the Muon Collider/Neutrino Factory target have been discussed. 1 Introduction Computational magnetohydrodynamics, greatly inspired over the last decades by the magnetic confinement fusion and astrophysics problems, has achieved significant results. However the major research effort has been in the area of highly ionized plasmas. Numerical methods and computational software for MHD of weakly conducting materials such as liquid metals or weakly ionized plasmas have not been developed to such an extent despite the need 1 for fusion research and industrial technologies. Liquid metal MHD, driven by potential applications of flowing liquid metals or electrically conducting liquid salts as coolant in magnetic confinement fusion reactors as well as some industrial problems, has attracted broad theoretical, computational, and experimental studies (see [16, 17, 18] and references therein). -
Aerodynamics of Small Vehicles
Annu. Rev. Fluid Mech. 2003. 35:89–111 doi: 10.1146/annurev.fluid.35.101101.161102 Copyright °c 2003 by Annual Reviews. All rights reserved AERODYNAMICS OF SMALL VEHICLES Thomas J. Mueller1 and James D. DeLaurier2 1Hessert Center for Aerospace Research, Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana 46556; email: [email protected], 2Institute for Aerospace Studies, University of Toronto, Downsview, Ontario, Canada M3H 5T6; email: [email protected] Key Words low Reynolds number, fixed wing, flapping wing, small unmanned vehicles ■ Abstract In this review we describe the aerodynamic problems that must be addressed in order to design a successful small aerial vehicle. The effects of Reynolds number and aspect ratio (AR) on the design and performance of fixed-wing vehicles are described. The boundary-layer behavior on airfoils is especially important in the design of vehicles in this flight regime. The results of a number of experimental boundary-layer studies, including the influence of laminar separation bubbles, are discussed. Several examples of small unmanned aerial vehicles (UAVs) in this regime are described. Also, a brief survey of analytical models for oscillating and flapping-wing propulsion is presented. These range from the earliest examples where quasi-steady, attached flow is assumed, to those that account for the unsteady shed vortex wake as well as flow separation and aeroelastic behavior of a flapping wing. Experiments that complemented the analysis and led to the design of a successful ornithopter are also described. 1. INTRODUCTION Interest in the design and development of small unmanned aerial vehicles (UAVs) has increased dramatically in the past two and a half decades. -
Low Reynolds Number Effects on the Aerodynamics of Unmanned Aerial Vehicles
Low Reynolds Number Effects on the Aerodynamics of Unmanned Aerial Vehicles. P. Lavoie AER1215 Commercial Aviation vs UAV Classical aerodynamics based on inviscid theory Made sense since viscosity effects limited to a very small region near the surface True because inertial forces 2 1 Ul 6-8 Re = = ⇢U (µU/l)− = 10 viscous forces ⌫ ⇠ AER1215 - Lavoie 2 Commercial Aviation vs UAV Lissaman (1983) AER1215 - Lavoie 3 Commercial Aviation vs UAV For most UAV 103 Re 105 As a result, one can expect the flow to remain laminar for a greater extent flow field. This has fundamental implications for the aerodynamics of the flow. AER1215 - Lavoie 4 Low Reynolds Number Effects Lissaman (1983) AER1215 - Lavoie 5 Low Reynolds number effects At high Re, the drag is fairly constant with lift As low to moderate Re, large values of drag are obtained for moderate angles of attack What is going on? How to explain all this? Lissaman (1983) AER1215 - Lavoie 6 The Boundary Layer To understand what is going on, let us investigate the boundary layer a bit more closely. First, governing equations @ (⇢u) @ (⇢v) Mass conservation + =0 @x @y x-momentum conservation @u @u @p @ 4 @u 2 @v @ @v @u ⇢u + ⇢v = + µ + µ + @x @y −@x @x 3 @x − 3 @y @y @x @y ✓ ◆ ✓ ◆ AER1215 - Lavoie 7 Boundary Layer Equations After some blackboard magic… @ (⇢u) @ (⇢v) Mass conservation + =0 @x @y x-momentum conservation @u @u dp @ @u ⇢u + ⇢v = e + µ @x @y − dx @y @y ✓ ◆ AER1215 - Lavoie 8 Flow Separation What is the momentum equation at the surface (assume constant viscosity)? Blackboard magic… Fluid is doing work against the adverse pressure gradient and loses momentum, until it comes to rest and is driven back by the pressure.