The document discusses different types of multiphase flows. It defines multiphase flow as any fluid system with two or more distinct phases flowing simultaneously in mixture. Multiphase flows are classified into four main categories: gas-liquid flows, gas-solid flows, liquid-solid flows, and three-phase flows. Each category contains different flow regimes depending on factors like particle size and flow rates. Flow maps are used to characterize different flow patterns that can occur for a given system.
Multiphase flow modeling involves the simultaneous flow of mixtures of different phases, such as gases in liquids or liquids in gases. There are two main approaches to numerically model multiphase flows in ANSYS Fluent: the Euler-Lagrange approach and the Euler-Euler approach. The Discrete Phase Model (DPM) is a multiphase model that tracks the dispersed phase in a Lagrangian reference frame, with the continuous phase modeled using Eulerian methods and the two phases coupled through source terms.
multiphase flow modeling and simulation ,Pouriya Niknam , UNIFIPouriya Niknam
This document discusses modeling and simulation of multiphase flows using computational fluid dynamics (CFD). It begins with definitions of multiphase flow and discusses important types including bubbly, droplet, particle-laden, and annular flows. The document then provides tips on multiphase simulation including choosing appropriate modeling approaches such as Lagrangian, Eulerian, or volume of fluid methods depending on the problem. It concludes with discussions of challenges such as convergence difficulties and appropriate solver settings and techniques to address these challenges.
Multiphase flow involves the simultaneous flow of mixtures of different phases, such as gas bubbles in a liquid or liquid droplets in a gas. It is important to study multiphase flows because they occur in many industrial processes and natural phenomena. Multiphase flows can be classified based on the number of phases, type of phases, size of phases, and interaction between phases. Some common types of multiphase flows include dispersed phase flows with discrete elements of one phase in a continuous medium, separated flows where phases are distinctly separated, gas-liquid flows, gas-solid flows, and liquid-solid flows.
The document provides an overview of advanced modelling options in ANSYS FLUENT, including multiphase flows, reacting flows, and modelling moving parts. It discusses various multiphase models (discrete phase model, Eulerian model, mixture model, VOF model). It also covers reacting flow modelling approaches, pollutant formation models, discrete phase reactions, and surface reactions. Examples of applications are provided for each type of modelling capability.
CFD Modeling of Multiphase Flow. Focus on Liquid-Solid FlowLuis Ram Rojas-Sol
This document provides an outline for a workshop on multiphase flow modeling. The workshop will cover introduction to flow assurance and multiphase flows, modeling slurry flows using computational fluid dynamics (CFD) with a focus on ANSYS tools, and a case study of liquid-particle flow in a horizontal pipe using ANSYS Fluent. There are two main approaches to modeling multiphase flows discussed - semi-analytical mechanistic models and numerical models using either an Eulerian-Lagrangian or Eulerian-Eulerian framework.
Multiphase Flow Performance in Piping SystemsChrisJAlexisJr
Multiphase flow refers to the simultaneous flow of more than one fluid phase. It can be found in various places however it is most prevalent in the petroleum engineering field. This phenomenon brings about a major problem of pressure loss in the petroleum industry and results in a loss in production. Multiphase flow has been studied for years but there are few universally accepted solutions to calculate pressure drop. To accomplish this study, we used peer-review journals and articles in order to determine the flow regimes and characteristics of the different pipe orientations. This allowed us to determine the pressure drop calculations which were best suited for our study. We used a system that was designed with different pipe orientations that are found in the petroleum field and simulated the different flow regimes. Doing so allowed us to perform the calculations using two different pipe sizes; 1 inch and 1.5 inches. The results from the calculations showed that the pressure drop in the small pipe was greater than that of the bigger pipe.
The document discusses different types of multiphase flows. It defines multiphase flow as any fluid system with two or more distinct phases flowing simultaneously in mixture. Multiphase flows are classified into four main categories: gas-liquid flows, gas-solid flows, liquid-solid flows, and three-phase flows. Each category contains different flow regimes depending on factors like particle size and flow rates. Flow maps are used to characterize different flow patterns that can occur for a given system.
Multiphase flow modeling involves the simultaneous flow of mixtures of different phases, such as gases in liquids or liquids in gases. There are two main approaches to numerically model multiphase flows in ANSYS Fluent: the Euler-Lagrange approach and the Euler-Euler approach. The Discrete Phase Model (DPM) is a multiphase model that tracks the dispersed phase in a Lagrangian reference frame, with the continuous phase modeled using Eulerian methods and the two phases coupled through source terms.
multiphase flow modeling and simulation ,Pouriya Niknam , UNIFIPouriya Niknam
This document discusses modeling and simulation of multiphase flows using computational fluid dynamics (CFD). It begins with definitions of multiphase flow and discusses important types including bubbly, droplet, particle-laden, and annular flows. The document then provides tips on multiphase simulation including choosing appropriate modeling approaches such as Lagrangian, Eulerian, or volume of fluid methods depending on the problem. It concludes with discussions of challenges such as convergence difficulties and appropriate solver settings and techniques to address these challenges.
Multiphase flow involves the simultaneous flow of mixtures of different phases, such as gas bubbles in a liquid or liquid droplets in a gas. It is important to study multiphase flows because they occur in many industrial processes and natural phenomena. Multiphase flows can be classified based on the number of phases, type of phases, size of phases, and interaction between phases. Some common types of multiphase flows include dispersed phase flows with discrete elements of one phase in a continuous medium, separated flows where phases are distinctly separated, gas-liquid flows, gas-solid flows, and liquid-solid flows.
The document provides an overview of advanced modelling options in ANSYS FLUENT, including multiphase flows, reacting flows, and modelling moving parts. It discusses various multiphase models (discrete phase model, Eulerian model, mixture model, VOF model). It also covers reacting flow modelling approaches, pollutant formation models, discrete phase reactions, and surface reactions. Examples of applications are provided for each type of modelling capability.
CFD Modeling of Multiphase Flow. Focus on Liquid-Solid FlowLuis Ram Rojas-Sol
This document provides an outline for a workshop on multiphase flow modeling. The workshop will cover introduction to flow assurance and multiphase flows, modeling slurry flows using computational fluid dynamics (CFD) with a focus on ANSYS tools, and a case study of liquid-particle flow in a horizontal pipe using ANSYS Fluent. There are two main approaches to modeling multiphase flows discussed - semi-analytical mechanistic models and numerical models using either an Eulerian-Lagrangian or Eulerian-Eulerian framework.
Multiphase Flow Performance in Piping SystemsChrisJAlexisJr
Multiphase flow refers to the simultaneous flow of more than one fluid phase. It can be found in various places however it is most prevalent in the petroleum engineering field. This phenomenon brings about a major problem of pressure loss in the petroleum industry and results in a loss in production. Multiphase flow has been studied for years but there are few universally accepted solutions to calculate pressure drop. To accomplish this study, we used peer-review journals and articles in order to determine the flow regimes and characteristics of the different pipe orientations. This allowed us to determine the pressure drop calculations which were best suited for our study. We used a system that was designed with different pipe orientations that are found in the petroleum field and simulated the different flow regimes. Doing so allowed us to perform the calculations using two different pipe sizes; 1 inch and 1.5 inches. The results from the calculations showed that the pressure drop in the small pipe was greater than that of the bigger pipe.
Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems that involve fluid flows. CFD uses three-dimensional simulations of fluid flow by solving the Navier-Stokes equations with computational algorithms and systems. It gives a comprehensive flow field view not possible through experimental testing alone. CFD has advantages of low cost, speed, ability to simulate real and ideal conditions, and providing comprehensive flow parameter information. Limitations include reliance on accurate physical models, presence of numerical errors, and accuracy of boundary conditions provided. CFD has applications in aerospace, automotive, HVAC, bio-medical, and other industries. Commercial CFD software packages are available
This document discusses computational fluid dynamics (CFD). CFD uses numerical analysis and algorithms to solve and analyze fluid flow problems. It can be used at various stages of engineering to study designs, develop products, optimize designs, troubleshoot issues, and aid redesign. CFD complements experimental testing by reducing costs and effort required for data acquisition. It involves discretizing the fluid domain, applying boundary conditions, solving equations for conservation of properties, and interpolating results. Turbulence models and discretization methods like finite volume are discussed. The CFD process involves pre-processing the problem, solving it, and post-processing the results.
This document discusses two-phase flow models and compares different pressure drop correlation methods. It begins with an introduction to two-phase flow and important variables like liquid holdup, gas void fraction, and slip velocity. It then describes the different flow patterns or regimes that can occur, including dispersed bubble, stratified smooth, wavy, slug, annular, and spray flows. The document outlines factors that affect flow patterns and discusses how patterns vary between horizontal, upward inclined, and downward inclined pipes. It concludes that selecting the most suitable correlation is key to accurately sizing pipelines for different applications.
The forth lecture in the module Particle Technology, delivered to second year students who have already studied basic fluid mechanics.
Fluid flow in porous media covers the basic streamline and turbulent flow models for pressure drop as a function of flow rate within the media. The Modified Reynolds number determines the degree of turbulence in the fluid. The industrial processes of deep bed (sand) filtration and fluidisation are included.
The document discusses the governing equations for reacting flows, including conservation of mass, momentum, molecular species, and energy. It outlines the continuity, momentum, species transport, and energy equations. The species transport equation accounts for convection, diffusion, and chemical reaction sources. The energy equation considers changes in enthalpy due to convection, diffusion, pressure work, and radiation. Simplifications are discussed under certain assumptions, such as a single diffusion coefficient and negligible pressure work/radiation terms, in which case enthalpy behaves as a passive scalar. Other relationships presented include the equation of state and definitions of specific heat capacity and density.
01 reactive flows - finite-rate formulation for reaction modelingMohammad Jadidi
This document discusses equations governing reacting flows as modeled in ANSYS Fluent. It describes how Fluent solves conservation equations for species mass fractions using a convection-diffusion equation, where the chemical source term Ri accounts for reaction rates. Finite-rate kinetics and turbulence-chemistry interaction models are discussed for determining Ri, including the eddy dissipation model. The Arrhenius equation is also presented for calculating forward reaction rate constants based on pre-exponential factors, temperature exponents, and activation energies specified in the kinetic mechanism.
This document discusses recent trends in computational fluid dynamics (CFD). It begins by defining CFD as using numerical analysis and algorithms to solve fluid flow problems described by partial differential equations. CFD offers advantages over physical experiments by enabling low-cost simulation-based design and analysis of fluid phenomena that are difficult to measure experimentally. The document outlines the basic CFD process of geometry description, model selection, grid generation, solution, and post-processing. It provides examples of CFD applications in aerospace, automotive, biomedical, and other industrial fields to analyze designs. The conclusion discusses iterative solution methods and potential future advances in multidisciplinary and on-demand CFD simulations.
This document discusses using computational fluid dynamics (CFD) to analyze the flow through a gear pump. It provides background on CFD methodology and applications. It then describes the specific problem of simulating flow through an external gear pump using ANSYS Fluent. It details the geometry, mesh, boundary conditions, solver settings and dynamic mesh setup used. The goal is to determine the mass flow rate of oil through the pump.
This document provides an introduction and overview of computational fluid dynamics (CFD) and the course "Introduction to Computational Fluid Dynamics". The key points covered are:
- The course covers numerical methods in CFD, including discretization techniques, accuracy, stability, grids, boundary conditions, and modeling turbulent flow.
- The learning objectives are for students to understand the role of computation in fluid dynamics and gain practical knowledge in setting up and analyzing simple aerodynamic problems using CFD.
- The course contents include introduction to partial differential equations, finite difference methods, grids, the Euler and Navier-Stokes equations, and case studies of best practices in CFD applications. Hands-on lab sessions make up
A fluid is a state of matter in which its molecules move freely and do not bear a constant relationship in space to other molecules.
In physics, fluid flow has all kinds of aspects: steady or unsteady, compressible or incompressible, viscous or non-viscous, and rotational or irrotational to name a few. Some of these characteristics reflect properties of the liquid itself, and others focus on how the fluid is moving.
Fluids are :-
Liquid : blood, i.v. infusions)
Gas : O2 , N2O)
Vapour (transition from liquid to gas) : N2O (under compression in cylinder), volatile inhalational agents (halothane, isoflurane, etc)
Sublimate (transition from solid to gas bypassing liquid state) : Dry ice (solid CO2), iodine
This document discusses compressible flow through nozzles. It introduces concepts like stagnation properties, Mach number, and speed of sound. It then derives relationships for isentropic flow of ideal gases through converging and converging-diverging nozzles. The effects of area changes and back pressure on properties like pressure, temperature, density and mass flow rate are examined for both subsonic and supersonic flow regimes. Nozzle design considerations like shapes needed to achieve desired exit velocities are also covered.
This chapter discusses heat transfer through porous media. It begins by presenting the basic energy equations for a simple case where the solid and fluid phases are in local thermal equilibrium and heat conduction occurs in parallel through the phases. It then discusses extensions to more complex situations, including how the overall thermal conductivity depends on the geometry and properties of the solid and fluid, and the effects of pressure changes and viscous dissipation. The overall thermal conductivity can be estimated using weighted arithmetic, harmonic, or geometric means of the solid and fluid conductivities. More complex models have also been developed to account for factors like anisotropy, Knudsen effects, and material microstructure.
In fluid dynamics, laminar flow is characterized by fluid particles following smooth paths in layers, with each layer moving smoothly past the adjacent layers with little or no mixing. At low velocities, the fluid tends to flow without lateral mixing, and adjacent layers slide past one another like playing cards.
- Reservoirs are classified based on the composition of hydrocarbons present, initial reservoir pressure and temperature, and the pressure and temperature of produced fluids.
- A pressure-temperature diagram is used to classify reservoirs and describe the phase behavior of reservoir fluids, delineating the liquid, gas, and two-phase regions.
- Based on the diagram, reservoirs are classified as oil reservoirs if the temperature is below the critical temperature, and gas reservoirs if above the critical temperature.
The document provides an introduction to fluid dynamics and fluid mechanics. It defines key fluid properties like density, viscosity, pressure and discusses the continuum hypothesis. It also introduces important concepts like the Navier-Stokes equations, Bernoulli's equation, Reynolds number, and divergence. Applications of fluid mechanics in various engineering fields are also highlighted.
The document discusses the history of Ukraine and its relationship with Russia and the West over centuries. It notes that Ukraine was originally part of Kievan Rus along with Russia, but later split between Western Catholic powers like Poland and the growing Russian Orthodox state. This created an East-West divide in Ukraine that continues today. In the 17th century, the Cossack people led an uprising for Ukrainian independence but ultimately allied with Russia, bringing Eastern Ukraine under Russian control while Western Ukraine remained with Poland.
Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems that involve fluid flows. CFD uses three-dimensional simulations of fluid flow by solving the Navier-Stokes equations with computational algorithms and systems. It gives a comprehensive flow field view not possible through experimental testing alone. CFD has advantages of low cost, speed, ability to simulate real and ideal conditions, and providing comprehensive flow parameter information. Limitations include reliance on accurate physical models, presence of numerical errors, and accuracy of boundary conditions provided. CFD has applications in aerospace, automotive, HVAC, bio-medical, and other industries. Commercial CFD software packages are available
This document discusses computational fluid dynamics (CFD). CFD uses numerical analysis and algorithms to solve and analyze fluid flow problems. It can be used at various stages of engineering to study designs, develop products, optimize designs, troubleshoot issues, and aid redesign. CFD complements experimental testing by reducing costs and effort required for data acquisition. It involves discretizing the fluid domain, applying boundary conditions, solving equations for conservation of properties, and interpolating results. Turbulence models and discretization methods like finite volume are discussed. The CFD process involves pre-processing the problem, solving it, and post-processing the results.
This document discusses two-phase flow models and compares different pressure drop correlation methods. It begins with an introduction to two-phase flow and important variables like liquid holdup, gas void fraction, and slip velocity. It then describes the different flow patterns or regimes that can occur, including dispersed bubble, stratified smooth, wavy, slug, annular, and spray flows. The document outlines factors that affect flow patterns and discusses how patterns vary between horizontal, upward inclined, and downward inclined pipes. It concludes that selecting the most suitable correlation is key to accurately sizing pipelines for different applications.
The forth lecture in the module Particle Technology, delivered to second year students who have already studied basic fluid mechanics.
Fluid flow in porous media covers the basic streamline and turbulent flow models for pressure drop as a function of flow rate within the media. The Modified Reynolds number determines the degree of turbulence in the fluid. The industrial processes of deep bed (sand) filtration and fluidisation are included.
The document discusses the governing equations for reacting flows, including conservation of mass, momentum, molecular species, and energy. It outlines the continuity, momentum, species transport, and energy equations. The species transport equation accounts for convection, diffusion, and chemical reaction sources. The energy equation considers changes in enthalpy due to convection, diffusion, pressure work, and radiation. Simplifications are discussed under certain assumptions, such as a single diffusion coefficient and negligible pressure work/radiation terms, in which case enthalpy behaves as a passive scalar. Other relationships presented include the equation of state and definitions of specific heat capacity and density.
01 reactive flows - finite-rate formulation for reaction modelingMohammad Jadidi
This document discusses equations governing reacting flows as modeled in ANSYS Fluent. It describes how Fluent solves conservation equations for species mass fractions using a convection-diffusion equation, where the chemical source term Ri accounts for reaction rates. Finite-rate kinetics and turbulence-chemistry interaction models are discussed for determining Ri, including the eddy dissipation model. The Arrhenius equation is also presented for calculating forward reaction rate constants based on pre-exponential factors, temperature exponents, and activation energies specified in the kinetic mechanism.
This document discusses recent trends in computational fluid dynamics (CFD). It begins by defining CFD as using numerical analysis and algorithms to solve fluid flow problems described by partial differential equations. CFD offers advantages over physical experiments by enabling low-cost simulation-based design and analysis of fluid phenomena that are difficult to measure experimentally. The document outlines the basic CFD process of geometry description, model selection, grid generation, solution, and post-processing. It provides examples of CFD applications in aerospace, automotive, biomedical, and other industrial fields to analyze designs. The conclusion discusses iterative solution methods and potential future advances in multidisciplinary and on-demand CFD simulations.
This document discusses using computational fluid dynamics (CFD) to analyze the flow through a gear pump. It provides background on CFD methodology and applications. It then describes the specific problem of simulating flow through an external gear pump using ANSYS Fluent. It details the geometry, mesh, boundary conditions, solver settings and dynamic mesh setup used. The goal is to determine the mass flow rate of oil through the pump.
This document provides an introduction and overview of computational fluid dynamics (CFD) and the course "Introduction to Computational Fluid Dynamics". The key points covered are:
- The course covers numerical methods in CFD, including discretization techniques, accuracy, stability, grids, boundary conditions, and modeling turbulent flow.
- The learning objectives are for students to understand the role of computation in fluid dynamics and gain practical knowledge in setting up and analyzing simple aerodynamic problems using CFD.
- The course contents include introduction to partial differential equations, finite difference methods, grids, the Euler and Navier-Stokes equations, and case studies of best practices in CFD applications. Hands-on lab sessions make up
A fluid is a state of matter in which its molecules move freely and do not bear a constant relationship in space to other molecules.
In physics, fluid flow has all kinds of aspects: steady or unsteady, compressible or incompressible, viscous or non-viscous, and rotational or irrotational to name a few. Some of these characteristics reflect properties of the liquid itself, and others focus on how the fluid is moving.
Fluids are :-
Liquid : blood, i.v. infusions)
Gas : O2 , N2O)
Vapour (transition from liquid to gas) : N2O (under compression in cylinder), volatile inhalational agents (halothane, isoflurane, etc)
Sublimate (transition from solid to gas bypassing liquid state) : Dry ice (solid CO2), iodine
This document discusses compressible flow through nozzles. It introduces concepts like stagnation properties, Mach number, and speed of sound. It then derives relationships for isentropic flow of ideal gases through converging and converging-diverging nozzles. The effects of area changes and back pressure on properties like pressure, temperature, density and mass flow rate are examined for both subsonic and supersonic flow regimes. Nozzle design considerations like shapes needed to achieve desired exit velocities are also covered.
This chapter discusses heat transfer through porous media. It begins by presenting the basic energy equations for a simple case where the solid and fluid phases are in local thermal equilibrium and heat conduction occurs in parallel through the phases. It then discusses extensions to more complex situations, including how the overall thermal conductivity depends on the geometry and properties of the solid and fluid, and the effects of pressure changes and viscous dissipation. The overall thermal conductivity can be estimated using weighted arithmetic, harmonic, or geometric means of the solid and fluid conductivities. More complex models have also been developed to account for factors like anisotropy, Knudsen effects, and material microstructure.
In fluid dynamics, laminar flow is characterized by fluid particles following smooth paths in layers, with each layer moving smoothly past the adjacent layers with little or no mixing. At low velocities, the fluid tends to flow without lateral mixing, and adjacent layers slide past one another like playing cards.
- Reservoirs are classified based on the composition of hydrocarbons present, initial reservoir pressure and temperature, and the pressure and temperature of produced fluids.
- A pressure-temperature diagram is used to classify reservoirs and describe the phase behavior of reservoir fluids, delineating the liquid, gas, and two-phase regions.
- Based on the diagram, reservoirs are classified as oil reservoirs if the temperature is below the critical temperature, and gas reservoirs if above the critical temperature.
The document provides an introduction to fluid dynamics and fluid mechanics. It defines key fluid properties like density, viscosity, pressure and discusses the continuum hypothesis. It also introduces important concepts like the Navier-Stokes equations, Bernoulli's equation, Reynolds number, and divergence. Applications of fluid mechanics in various engineering fields are also highlighted.
The document discusses the history of Ukraine and its relationship with Russia and the West over centuries. It notes that Ukraine was originally part of Kievan Rus along with Russia, but later split between Western Catholic powers like Poland and the growing Russian Orthodox state. This created an East-West divide in Ukraine that continues today. In the 17th century, the Cossack people led an uprising for Ukrainian independence but ultimately allied with Russia, bringing Eastern Ukraine under Russian control while Western Ukraine remained with Poland.
Large eddy simulation (LES) is a computational fluid dynamics technique that resolves the larger turbulent scales in the fluid flow while modeling the smaller scales. LES aims to directly simulate the larger turbulent scales while parameterizing the effects of smaller scales through a subgrid scale model. LES requires significantly more computational resources than Reynolds-averaged Navier–Stokes (RANS) modeling but provides more detailed turbulent flow information.
This document discusses multiphase flow theory and key concepts. It begins by presenting the pressure drop equation for single-phase and two-phase flows. Key parameters that influence flow patterns like surface tension and gravity are described. Common flow regimes in vertical heated tubes are discussed along with how properties vary radially and change along the tube axis. Common terminology used in multiphase flows like phase fraction, velocity, and area are defined. Advanced relationships involving drift velocity and flux are also presented.
The document discusses well deliverability and pressure drop in oil and gas wells. It explains that pressure drop is affected by properties of the reservoir fluids, production rates, and the mechanical configuration of the wellbore. Pressure loss is highest in the tubing and can be estimated using charts, correlations, or equations that consider fluid properties, flow rates, and well geometry. Matching inflow and outflow pressures gives the stabilized flow rate. The document compares methods for estimating pressure drop in single-phase and multiphase flow.
An increasing number of Consumer and Internet Internet of Things applications require some form of edge computing characterised by low latency, peer-to-peer communication, and mobility. Fog computing has recently emerged as the paradigm to address the needs of edge computing in IoT applications. Fog computing complements Cloud computing to allow the design and implementation of IoT systems that scale better, are more reactive and in which local communication and decision is enabled whenever possible.
This presentation introduces the key concepts behind Fog Computing, compare and contrast it with Cloud Computing and explain how the VORTEX platform enables Fog computing architectures.
Fog computing has emerged as a new paradigm for architecting IoT applications that require greater scalability, performance and security. This talk will motivate the need to Fog Computing and explain what it is and how it differs from other initiatives in Telco such as Mobile/Multiple-Access Edge Computing.
This document discusses choosing an appropriate multiphase flow model. It describes the key multiphase flow models in ANSYS Fluent including the volume of fluid (VOF) model, mixture model, Eulerian model, and discrete phase model (DPM). The document provides guidance on selecting a model based on factors like flow regime, particulate loading, phase coupling, and computational requirements.
CFD Lecture (8/8): CFD in Chemical SystemsAbhishek Jain
Above lecture can be downloaded from www.zeusnumerix.com
The presentation aims at explaining to the user the simulations that happen in the chemical industry. These simulations are characterized by the chemical reactions, mixing of fluids, particle flow etc. The standard NS equations requirement introduction of source terms and special methods for CFD simulations and these have been introduced.
This presentation gives a brief introduction to the concept of coupled CFD-DEM Modeling.
Link to file: https://drive.google.com/open?id=1nO2n49BwhzBtT6NnvpxADG5WsC9uMJ-i
This document contains definitions and concepts related to fluid mechanics. It includes:
- Definitions of key terms like density, viscosity, compressibility, surface tension, and boundary layer thickness.
- Descriptions of different types of fluid flow such as laminar, turbulent, compressible, rotational.
- Explanations of important equations like continuity, Bernoulli's, and Darcy-Weisbach for head loss.
- Discussions of pipe flow concepts such as velocity distribution, friction factors, boundary layer growth, and major/minor head losses.
- Lists of factors that influence properties like frictional resistance in pipes.
In summary, this document provides a comprehensive overview of fundamental fluid mechanics
The document discusses terminal velocity and settling of particles in fluids. It defines the equation for terminal settling velocity as a function of gravitational constant, particle and fluid densities, particle diameter, and drag coefficient. The drag coefficient is related to the particle Reynolds number, which quantifies the ratio of inertial to viscous forces on the particle. Ideal rectangular and circular settling vessels are examined in terms of retention time, critical settling velocity, and fractional particle removal. Continuous thickeners are also discussed, including solid flux analysis, mass balances, and methods for determining cross-sectional area based on underflow concentration and settling tests.
Gas-Particulate Models of Flow through Porous StructuresIJERA Editor
A recently developed general model of gas-particulate flow is sub-classified in this work. The model takes into
account both the Darcy resistance and the Forchheimer effects, and is valid for variable particle number density
and flow through variable porosity media. The form of governing equations is discussed when the particle
relaxation time is small.
The document defines key terms related to fluid mechanics, including density, specific weight, viscosity, compressibility, surface tension, and vapor pressure. It also defines different types of fluid flow such as steady/unsteady, laminar/turbulent, compressible/incompressible, and rotational/irrotational flow. Various equations are presented, including the continuity equation, Bernoulli's equation, and the impulse-momentum equation. Boundary layer concepts are introduced, such as boundary layer thickness, displacement thickness, and momentum thickness. Energy losses in pipes are also discussed, distinguishing between major losses due to friction and minor losses due to pipe fittings.
The document discusses multiple particles settling in a fluid. It describes how the presence of other particles affects each particle's motion and reduces their settling velocity. There are different types of particle settling depending on concentration, including hindered settling which occurs in concentrated suspensions. Equations are provided to model hindered settling velocity and calculate parameters like effective viscosity and flux. Design considerations for wastewater treatment facilities that involve particle settling like thickeners and clarifiers are also covered.
ANALYSIS OF VORTEX INDUCED VIBRATION USING IFSIJCI JOURNAL
Interaction of fluid structure (IFS) is one of the upcoming field in calculation and simulation of multiphysics problems. IFS play an important role in calculating offshore structures deformations caused by the vortex induced loads. The complexity interaction nature of fluid around the solid geometries pose the difficulties in the analysis, but IFS analysis technique overshadow the challenges. In this paper, Analysis is done by considering a cylindrical member which is similar to the part of offshore platform. The IFS analysis is done by using the commercial package ANSYS 14.0. The Vortex induced loads simulation with IFS is purely a mesh dependent, for that we have to simulate many problems for getting optimum grid size. Computational Fluid Dynamics (CFD) analysis of a two dimensional model have been done and the obtained results were validated with the literature findings. CFD analysis is performed on the extruded version of the two dimensional mesh and the results were compared with the previously obtained two dimensional results. Preliminary IFS analysis is done by coupling the structural and fluid solvers together at smaller time steps and the dynamic response of the structural member to the periodically varying Vortex induced vibrations (VIV) loads were observed and studied.
1) Mechanical separation uses forces acting on particles to separate them from fluids. It can be divided into sedimentation, centrifugal separation, filtration, and sieving based on the forces used.
2) Sedimentation separates particles from fluids using gravitational forces. It includes free sedimentation where particles fall independently and hindered sedimentation where particle movement is affected by other particles.
3) The key equation for calculating a particle's terminal falling velocity during gravitational sedimentation balances the gravitational, buoyancy, and drag forces on the particle. It depends on properties of both the particle and fluid.
This document contains a question bank with answers for a fluid mechanics and machineries course. It includes 13 questions and answers about fluid properties, density, viscosity, surface tension, momentum equations, laminar flow, head losses, pumps, and cavitation. The questions are divided into 4 units covering fluid properties, flow through pipes, dimensional analysis, and pumps.
[Harry edmar]hydrodynamics concepts and experimentsEnrique Buenaonda
This chapter develops a coupled fluid-structure model to simulate the interaction between water flow and a flexible fishing net. The model combines a porous media fluid model and a lumped-mass mechanical model. The porous media fluid model uses the Navier-Stokes equations to simulate flow around a rigid net, while the lumped-mass model simulates net deformation. An iterative scheme is used to solve for the steady fluid-net interaction. The model aims to better understand hydrodynamic forces on nets and flow patterns, which has significance for net cage and aquaculture design.
1) The document discusses fluid kinematics, which deals with the motion of fluids without considering the forces that create motion. It covers topics like velocity fields, acceleration fields, control volumes, and flow visualization techniques.
2) There are two main descriptions of fluid motion - Lagrangian, which follows individual particles, and Eulerian, which observes the flow at fixed points in space. Most practical analysis uses the Eulerian description.
3) The Reynolds Transport Theorem allows equations written for a fluid system to be applied to a fixed control volume, which is useful for analyzing forces on objects in a flow. It relates the time rate of change of an extensive property within the control volume to surface fluxes and the property accumulation.
The eighth lecture in the module Particle Technology, delivered to second year students who have already studied basic fluid mechanics. Two phase flow, rheology and Powders covers flow of dispersions of powders in liquids and gases, as well as the storage of powders and why they sometimes do not flow. Equations to predict the pressure drop in pumped systems are provided, for both streamline and turbulent flows.
The document contains lecture notes on hydraulics from Minia University in Egypt. It defines key terms related to fluid mechanics such as density, viscosity, laminar and turbulent flow, compressibility, and surface tension. It also provides the continuity equation and defines different types of fluid flow such as steady, uniform, rotational, and one, two, and three-dimensional flow. The notes conclude by listing the Bernoulli equation and its assumptions.
The document summarizes key concepts in polymer rheology and viscosity relevant to polymer extrusion processes. It defines rheology and viscosity, describes how viscosity is measured using capillary and rotational viscometers, and models like the power law model that are used to characterize viscosity. Key factors that influence viscosity, like temperature, molecular weight, additives, and pressure are also summarized.
A study on evacuation performance of sit type water closet by computational f...combi07
This study was undertaken to study the performance of the type of toilet seat by using CFD numerical methods to obtain the optimum flow rate to reduce water usage. Toilet seat has two main types which is siphon and washdown. The case is the model type of siphon and washdown, using a mixture of water and air as a medium to flush the toilet. The area is considered critical to all cases in the stagnant water inlet and outlet. The analysis result, shows that the type of siphon is better than the washdown for the both case. The comparison also show that (Siphon Type Water closet) second case has better performance than (Washdown Water Closet) the first case.
Rheology is the study of deformation and flow of matter. There are several types of rheological properties including stress, viscosity, viscoelastic modulus, creep, and relaxation times. Rheology is important in manufacturing pharmaceutical dosage forms and applications like ointments, syrups, suspensions, and emulsions where rheological properties influence acceptability, bioavailability, and handling. Materials can exhibit Newtonian, plastic, pseudo-plastic, or dilatant flow depending on the relationship between shear stress and shear rate. Viscometers are used to determine viscosity and classify fluids as Newtonian or non-Newtonian.
The document describes an experiment measuring fluid flow rate. Students measured the volume and time it took for water to pass through a volumetric tank. They then calculated the flow rate, mass flow rate, and weight flow rate. The results showed the relationship between flow rate and time, as well as the slopes between flow rate and mass/weight flow rate. Factors that impact flow rate like viscosity, temperature, and pipe characteristics were also discussed.
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2. Presented by: Mohammad Jadidi 2
Multiphase Flows Choosing a Multiphase Model
The first step in solving any multiphase problem
is to determine which of the regimes described
in Multiphase Flow Regimes best represents
your flow.
As a general guide, there are some parameters
that help to identify the appropriate multiphase
model as follows:
Particulate Loading
Volume Fractions
Superficial and Phase Velocities
Response Time
Stokes Number
Dilute and Dense Flows
Phase Coupling
Other Considerations
Multiphase
Models
Euler-Lagrange
approach
DPM
Euler-Euler
Approach
Eulerian
Model
Mixture
Model
VOF
Model
3. Presented by: Mohammad Jadidi 3
Multiphase Flows Fundamental Definitions: Primary & Secondary phases
Multiphase flow is simultaneous flow of:
Materials with different states or phases (i.e. gas, liquid or
solid).
Materials with different chemical properties but in the
same state or phase (i.e. liquid-liquid systems such as oil
droplets in water).
The primary and secondary phases:
One of the phases is continuous (primary) while the
other(s) (secondary) are dispersed within the continuous
phase.
A diameter has to be assigned for each secondary phase
to calculate its interaction (drag) with the primary phase.
particle size distribution is modeled by
assigning a separate phase for each particle
diameter
NOTE: A secondary phase with a particle size
distribution is modeled by assigning a separate
phase for each particle diameter.
4. 4Presented by: Mohammad Jadidi 4
Multiphase Flows Fundamental Definitions: Volume Fractions
The volume fraction of the dispersed phase is
defined as:
the volume fraction of continuous phase is:
And by definition, the sum if the volume
fractions must be unity
5. 5Presented by: Mohammad Jadidi 5
Multiphase Flows Fundamental Definitions: Particulate Loading
Note: that the word “particle” is used in this discussion to refer to a particle, droplet, or bubble
The material density ratio(ϒ):
Particulate loading (β) :
material density ratio is greater than 1000 for gas-solid flows, about 1
for liquid-solid flows, and less than 0.001 for gas-liquid flows.
Particulate loading has a major impact on phase
interactions. The particulate loading is defined as
the mass density ratio of the dispersed phase (d) to
that of the carrier phase (c).
6. 6Presented by: Mohammad Jadidi 6
Multiphase Flows
Average distance between the individual particles of the
particulate phase can be estimated as follows. (Crowe et al
(1998))
For example, for a gas-particle flow with a
particulate loading of 1, the interparticle
space is about 8; the particle can therefore
be treated as isolated (that is, very low
particulate loading).
Fundamental Definitions: Average distance between the individual particles
𝜅 =
𝛽
𝛾
𝒅 𝒅
𝐿
7. 7Presented by: Mohammad Jadidi 7
Multiphase Flows Fundamental Definitions: Superficial and Phase Velocities
The superficial velocity of each phase is the mass flow rate of that phase divided by
the pipe area A and phase density. The superficial velocity for the dispersed phase is:
The phase velocity is the actual velocity of the phase, and it is related to the
superficial velocity by the volume fraction
In other words, superficial velocity is the velocity of the
phase if the phase occupied the whole pipe area
8. 8Presented by: Mohammad Jadidi 8
Multiphase Flows Fundamental Definitions: Relaxation Time or Particle Response Time
The response time of a particle or droplet is the time
required for a particle to be released from rest to
achieve 63%, (𝒆−𝟏 / 𝒆), of the free stream velocity
When does the particle follow the flow?
Typical relaxation times in process applications
𝜏 𝑝 =
𝜌 𝑑 𝑑 𝑑
2
18𝜇 𝑐
9. 9Presented by: Mohammad Jadidi 9
Multiphase Flows Fundamental Definitions: Stokes Number
Stokes Number (St) is a dimensionless parameter
that describes a particle’s flow in a particular
fluid. Stokes number is determined by the ratio
of the relaxation time of the particle (τp), a
characteristic dimension of the obstacle
obstructing fluid flow (LF) and the fluid’s velocity
(V F):
If St <<1, the particle response time is much less than the
characteristic time associated with the flow field. In this case the
particles will have ample time to respond to changes in flow velocity
and, the particle and fluid velocities will be nearly equal
If St>>1, then the particle will have essentially no time to respond to
the fluid velocity changes and the particle velocity will be little
affected by fluid velocity change
Normalized particle distribution for varying Stokes number
𝜏 𝑝 =
𝜌 𝑑 𝑑 𝑑
2
18𝜇 𝑐
𝜏 𝐹 =
𝐿 𝐹
𝑉𝐹
10. 10Presented by: Mohammad Jadidi 10
Multiphase Flows
Answer:
Snow particles with a low Stokes Number are
carried by the moving fluid. Rain particles
with a high Stokes Number settle onto the
windshield. If there was no resultant fluid
flow, both particles would settle.
Calculation:
A value of 20m/s (≈ 45mph) is used as a model velocity for the
car and resultant airflow. The car is assumed to have a
characteristic dimension of D = 1 m. Air at 0 degrees Celsius has
a dynamic viscosity of 1.71 ∗ 10−5 Ns/m2.
Stokes Number for a raindrop Sk = 584 is calculated from a
diameter of dp = 0.003m = 3mm and a density of ρ = 1000
kg/m3.
Stokes Number Sk = 58 for snow is calculated using the same
diameter and a density of ρ = 100 kg/m3.
Question:
“Why is it that I get more snow on my windshield when my car is
stopped at a light than when it’s moving, but I get more rain on my
windshield when it’s moving than when it’s stopped?”
Fundamental Definitions: Stokes Number-Example
11. 11Presented by: Mohammad Jadidi 11
Multiphase Flows Fundamental Definitions: Dilute and Dense Flows
A dilute flow, is one in which the particle motion is controlled by
the fluid forces (drag and lift)
A dense flow, on the other hand, is one in which the particle
motion is controlled by collisions
In collision-dominated flow the collisions between the
particles control the features of the flow, such as in a
fluidized bed
In a contact dominated flow, the particle motion is
controlled by continuous contact such as in a shear
granular flow
There is a further classification of dense flows: collision-and
contact-dominated.
Dense
flows
Collision-
dominated
flow
Contact
dominated
flow
12. 12Presented by: Mohammad Jadidi 12
Multiphase Flows
One-way-coupled : the fluid carrier
influences the particles via drag and
turbulence, but the particles have no
influence on the fluid carrier
Fundamental Definitions: Phase Coupling
Schematic diagram of coupling
Two-way-coupled: the fluid carrier
influences the particulate phase via drag
and turbulence, but the particles in turn
influence the carrier fluid via reduction in
mean momentum and turbulence
Four-way- couple : there is two-way
coupling plus particle pressure and
viscous stresses due to particles
13. 13Presented by: Mohammad Jadidi 13
Multiphase Flows
Dispersed two-phase flow as a function of the particle volume
fraction and inter-particle spacing
Fundamental Definitions: Phase Coupling
NOTE: Four-way coupling effects become
important when particle volume fraction
exceeds 𝟏𝟎-3
14. 14Presented by: Mohammad Jadidi 14
Multiphase Flows Fundamental Definitions: Weber number
Weber number describes the ratio between deforming inertial forces and stabilizing cohesive forces for liquids
flowing through a fluid medium. For example, the Weber number characterizes the atomizing quality of a spray
and the resulting droplet size.
When a liquid flows through a second fluid phase
(gas or liquid), then the aerodynamic force
FA causes the drops to deform and ultimately
disperse.
The cohesion force FK associated with the surface
tension or interfacial tension ,σ, opposes the
increase in surface area which is caused by the
deformation. The drop is therefore held together by
the surface or interfacial tension.
If the deforming force increases due to a
higher speed or longer process length, the
drops of a spray disperse more easily and
drops of oil in an aqueous environment are
split apart more easily. A high surface or
interfacial tension counteracts this process.
15. 15Presented by: Mohammad Jadidi 15
Multiphase Flows Fundamental Definitions: Weber number
If the deforming force increases due to
a higher speed or longer process
length, the drops of a spray disperse
more easily and drops of oil in an
aqueous environment are split apart
more easily. A high surface or interfacial
tension counteracts this process.
VIDEO: Weber number
17. 17Presented by: Mohammad Jadidi 17
Multiphase Flows Choosing a Multiphase Model
Multiphase Models
Euler-Lagrange
approach
DPM
Euler-Euler Approach
Eulerian
Model
Mixture Model VOF Model
There are two approaches for the numerical calculation of multiphase flows: the Euler-Lagrange approach and the
Euler-Euler approach
18. 18Presented by: Mohammad Jadidi 18
Multiphase Flows
The VOF model is a surface-tracking technique
applied to a fixed Eulerian mesh.
It is designed for two or more immiscible fluids
where the position of the interface between the
fluids is of interest.
In the VOF model, a single set of momentum
equations is shared by the fluids, and the volume
fraction of each of the fluids in each computational
cell is tracked throughout the domain.
Choosing a Multiphase Model-Euler-Euler approach-Volume of Fluid (VOF)
The VOF models require a proper mesh
and numerical advection scheme to
approximate the transport of the scalar
function in an accurate manner avoiding
numerical diffusion
Hydrodynamics and Wave Impact Analysis
19. 19Presented by: Mohammad Jadidi 19
Multiphase Flows Choosing a Multiphase Model-Euler-Euler approach-Volume of Fluid (VOF)
Applications of the VOF model include:
Stratified flows
Free-surface flows
Filling
Sloshing
Motion of large bubbles in a liquid,
Motion of liquid after a dam break,
Prediction of jet breakup (surface tension)
Steady or transient tracking of any liquid-gas
interface.
Sloshing
20. 20Presented by: Mohammad Jadidi 20
Multiphase Flows Choosing a Multiphase Model-Euler-Euler approach-The Mixture Model
The mixture model solves for the mixture
momentum equation and prescribes relative
velocities to describe the dispersed phases.
Applications of the mixture model include:
particle-laden flows with low loading
bubbly flows
sedimentation
and cyclone separators
NOTE: The mixture model can also be used without
relative velocities for the dispersed phases to model
homogeneous multiphase flow.
21. 21Presented by: Mohammad Jadidi 21
Multiphase Flows Choosing a Multiphase Model-Euler-Euler approach-The Eulerian Model
The Eulerian model is the most complex of the multiphase
models in ANSYS Fluent. It solves a set of n momentum
and continuity equations for each phase. In the Eulerian
approach both the dispersed particle phase and
continuous fluid phase are solved using the NS equations.
Coupling is achieved through the pressure and interphase
exchange coefficients.
Applications of the Eulerian multiphase model include:
bubble columns
Risers
particle suspension
fluidized beds
NOTE: It can be used to compute any multiphase flow regime, provided
that an adequate closure relation for the interfacial coupling terms are
provided
22. 22Presented by: Mohammad Jadidi 22
Multiphase Flows
The Lagrangian Discrete Phase Model (DPM) in
ANSYS Fluent follows the Euler-Lagrange approach.
The fluid phase is treated as a continuum by solving
the Navier-Stokes equations
The dispersed phase is solved(Using: the Newton’s
second law) by tracking a large number of particles,
bubbles, or droplets through the calculated flow field.
In DPM individual particles are treated as rigid
spheres (i.e., neglecting particle deformation and
internal flows)
The dispersed phase can exchange momentum, mass,
and energy with the fluid phase.
Choosing a Multiphase Model-Euler-Lagrange Approach-The DPM Model
spray dryers
coal and liquid fuel combustion
some particle-laden flows
Applications of the DPM model include:
23. 23Presented by: Mohammad Jadidi 23
Multiphase Flows
The discrete phase formulation
used by ANSYS Fluent contains
the assumption that the second
phase is sufficiently dilute that
particle-particle interactions and
the effects of the particle volume
fraction on the gas phase are
negligible. In practice, these
issues imply that the discrete
phase must be present at a fairly
low volume fraction, usually less
than 10–12%. Note that the mass
loading of the discrete phase may
greatly exceed 10–12%: you may
solve problems in which the mass
flow of the discrete phase equals
or exceeds that of the continuous
phase.
Choosing a Multiphase Model-Euler-Lagrange Approach-The DPM Model
Representation of the particle streams at the end of the injection (t=0.11 s), image shows the particles
coloured by its velocity magnitude. The particle streams are draw as spheres with proportional size
scaled 50 times more than the real diameter
24. 24Presented by: Mohammad Jadidi 24
Multiphase Flows
For bubbly, droplet, and particle-laden flows in which the phases mix
and/or dispersed-phase volume fractions exceed 10% mixture or
the Eulerian model
For slug flows & stratified/free-surface flows VOF model
For pneumatic transport the mixture model for homogeneous
flow or the Eulerian model for granular flow
For fluidized beds Eulerian model for granular flow
For slurry flows and Hydrotransport the mixture or Eulerian
For sedimentation the Eulerian model
Choosing a Multiphase Model based on the flow regime
The use of the DPM is limited to low volume fractions (less than or equal to 10% ),
unless you are using the dense discrete phase model (DDPM) formulation. In
addition, for the discrete phase model simulation, you can choose many more
advanced combustion models compared to the Eulerian models.
25. 25Presented by: Mohammad Jadidi 25
Multiphase Flows Choosing a Multiphase Model - Mixture Or Eulerian model?
If accuracy is more important than computational effort, the Eulerian model is a
better choice. However, the complexity of the Eulerian model can make it less
computationally stable than the mixture model.
If there is a wide distribution of the dispersed phases (that is, if the particles vary in
size and the largest particles do not separate from the primary flow field), the
mixture model may be preferable (that is, less computationally expensive).
If the dispersed phases are
concentrated just in portions of
the domain, you should use the
Eulerian model instead.
If interphase drag laws that are applicable
to your system are available the Eulerian
model can usually provide more accurate
results than the mixture model.
if the interphase drag laws are
unknown or their applicability to
your system is questionable, the
mixture model may be a better
choice.
If you want to solve a simpler problem,
which requires less computational effort,
the mixture model may be a better
option, since it solves a smaller number of
equations than the Eulerian model.
26. 26Presented by: Mohammad Jadidi 26
Multiphase Flows
For very low loading, the coupling between the phases
is one-way. The DPM , mixture, and Eulerian models can
all handle this type of problem correctly. Since the Eulerian
model is the most expensive, the discrete phase or
mixture model is recommended.
Schematic diagram of coupling
Choosing a Multiphase Model based on Loading and St.
For high loading, there is two-way coupling plus particle
pressure and viscous stresses due to particles (four-way
coupling). Only the Eulerian model will handle this type of
problem correctly
27. 27Presented by: Mohammad Jadidi 27
Multiphase Flows
For intermediate loading, the
coupling is two-way. The DPM,
mixture, and Eulerian models
are all applicable in this case
Schematic diagram of coupling
Which one is better?
Example: For a coal classifier with a characteristic length of 1 m and a
characteristic velocity of 10 m/s, the Stokes number is 0.04 for particles with a
diameter of 30 microns, but 4.0 for particles with a diameter of 300 microns.
Clearly the mixture model will not be applicable to the latter case.
Choosing a Multiphase Model based on Loading and St