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.
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.
This document provides an introduction to turbulence modeling methods for computational fluid dynamics simulations. It discusses direct numerical simulation (DNS), large eddy simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) modeling. It explains that DNS resolves all scales of turbulence but is limited to low Reynolds numbers due to computational costs, while LES and RANS attempt to model turbulence to study higher Reynolds number flows. Key aspects of length scales, energy transfer, and the assumptions and limitations of each modeling approach are summarized.
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.
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.
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.
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.
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.
This document provides an introduction to turbulence modeling methods for computational fluid dynamics simulations. It discusses direct numerical simulation (DNS), large eddy simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) modeling. It explains that DNS resolves all scales of turbulence but is limited to low Reynolds numbers due to computational costs, while LES and RANS attempt to model turbulence to study higher Reynolds number flows. Key aspects of length scales, energy transfer, and the assumptions and limitations of each modeling approach are summarized.
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.
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.
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.
Cfd simulation of flow heat and mass transferDr.Qasim Kadhim
This chapter discusses using computational fluid dynamics (CFD) simulation to model flow, heat, and mass transfer in a membrane heat exchanger. A CFD package called FLUENT is used to simulate the heat and moisture transfer. Two methods are presented for modeling moisture transfer across the porous membrane: 1) an effectiveness ratio method that relates air temperature and moisture content using a non-dimensional ratio, and 2) a user-defined function that solves conservation equations for mass transfer across the membrane surface. The CFD model is validated against experimental data and previous research on simpler heat exchanger geometries.
The document discusses different types of meshes used in computational fluid dynamics (CFD) simulations. It explains that meshes are used to discretize spatial domains and store field variable values. Structured meshes include Cartesian, multi-block, and patched block grids, while unstructured meshes include Delaunay triangulations and advancing front methods. Various algorithms for generating meshes are also presented, such as inserting points sequentially or using grid-based, centroid-based, or advancing front approaches. Both benefits and challenges of different meshing methods are summarized.
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 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 various turbulence models that are used in computational fluid dynamics (CFD). It begins by explaining that turbulence models are needed to close the system of mean flow equations since resolving all turbulent fluctuations is computationally infeasible. Common turbulence models discussed include zero-, one-, two-, and seven-equation models. Two-equation k-ε models are described in detail, along with variants like the RNG k-ε and realizable k-ε models that aim to improve on areas where standard k-ε is lacking. Other models covered include the k-ω model, algebraic stress models, and Reynolds stress models.
The lecture was delivered by me for IIChE students chapter on the theme of Student-Industry Interaction at Bharati Vidyapeeth on 8th Feb'14. Foe my blogs kindly refer: https://www.learncax.com/knowledge-base/blog/by-author/ganesh-visavale
This document provides an introduction to computational fluid dynamics (CFD) and the use of ANSYS FLUENT software. It discusses the basics of fluid dynamics and how CFD solves fluid flow problems using numerical methods. It also outlines the steps to set up and solve a problem in ANSYS FLUENT, including pre-processing, defining the physical problem, running the simulation, and analyzing results. The document serves as a brief overview of CFD concepts and capabilities for engineering applications.
This document provides an overview of the ANSYS Fluent tutorial guide:
- The guide contains 12 chapters that walk through tutorials of increasing complexity covering topics such as fluid flow, heat transfer, compressible flow, radiation, and rotating reference frames.
- It assumes the user has a basic understanding of fluid mechanics and CFD concepts and guides them through setting up and solving simulations in ANSYS Fluent.
- Each tutorial contains sections for problem setup, defining models and boundary conditions, obtaining solutions, and examining results to build the user's skills in using ANSYS Fluent for various CFD applications.
Computational fluid dynamics (CFD) is a tool for analyzing systems involving fluid flow, heat transfer and associated phenomena like chemical reactions using computer-based simulations. It involves numerically solving the governing equations of fluid flow to model the flow of liquids and gases. CFD complements experimental and theoretical fluid dynamics by providing a cost-effective means of simulating real flows. It has various applications in aerospace, automotive, turbo machinery, power plants, buildings, environmental engineering, and biomedical areas.
The Powerpoint presentation discusses about the Introduction to CFD and its Applications in various fields as an Introductory topic for Mechanical Engg. Students in General.
This document discusses computational fluid dynamics (CFD) and its application in Ansys. CFD uses physics equations and computer simulations to predict fluid flow, heat transfer, chemical reactions, etc. It helps reduce testing costs and study systems too large for experiments. The CFD process in Ansys involves pre-processing (CAD, meshing), solving the governing equations, and post-processing the results (graphs, contours). Examples demonstrate setting up and solving a CFD problem of air mixing in a tee pipe.
Gas-Solid-Liquid Mixing Systems
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 SELECTION OF EQUIPMENT
5 THREE-PHASE MASS TRANSFER WITH CHEMICAL REACTION
6 STIRRED VESSEL DESIGN
6.1 Agitator Design
6.2 Design for Solids Suspension
6.3 Vessel Design
6.4 Gas-Liquid Mass Transfer Coefficient and Surface Area
7 THREE-PHASE FLUIDIZED BEDS
7.1 Gas and Liquid Hold-Up
7.2 Calculation Procedure
7.3 Bubble Size
7.4 Mass Transfer
7.5 Heat Transfer
7.6 Elutriation
8 SLURRY REACTORS
8.1 Gas Rate
8.2 Mass Transfer
9 NOMENCLATURE
10 BIBLIOGRAPHY
OpenFOAM for beginners: Hands-on trainingJibran Haider
NOTE: A NEW VERSION OF THIS PRESENTATION IS AVAILABLE ON MY RESEARCH GATE ACCOUNT
(https://www.researchgate.net/publication/327594760_OpenFOAM_course_for_beginners_Hands-on_training)
OpenFOAM is an open source Computational Fluid Dynamics software package based on C++ programming language within the context of cell centred Finite Volume Method. It is developed primarily by OpenCFD Ltd and distributed by OpenCFD Ltd and the OpenFOAM Foundation.
Disclaimer:
This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software and owner of the OPENFOAM® and OpenCFD® trade marks. "
This document provides an introduction to fluid mechanics concepts. It defines a fluid, discusses the differences between liquids and gases, and classifies various types of fluid flows according to whether they are viscous or inviscid, internal or external, compressible or incompressible, laminar or turbulent, natural or forced, steady or unsteady, and one-, two-, or three-dimensional. The document also introduces concepts of stress, pressure, systems, control volumes, units, and modeling engineering problems either experimentally or analytically.
Computational fluid dynamics (CFD) is the use of computing to simulate fluid flow, heat transfer, and other related phenomena. CFD works by numerically solving the governing equations of fluid dynamics. It allows for analyzing flows that are difficult to study experimentally. CFD has various applications in fields like aerospace, automotive, biomedical, and power generation. The CFD process involves discretizing the domain, applying initial and boundary conditions, numerically solving the governing equations, and post-processing the results. Common discretization methods are finite volume, finite element, and finite difference methods. CFD provides insight into flows and heat transfer while being faster and cheaper than physical experiments.
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
Based on the information provided, a hydrocarbon system and petroleum refining process type have been selected. The recommended property package is NRTL.
Does this help summarize the recommended package? Let me know if you need any clarification or have additional questions!
Fluid Mechanics Chapter 3. Integral relations for a control volumeAddisu Dagne Zegeye
Introduction, physical laws of fluid mechanics, the Reynolds transport theorem, Conservation of mass equation, Linear momentum equation, Angular momentum equation, Energy equation, Bernoulli equation
This document provides an overview of computational fluid dynamics (CFD) analysis. It discusses the basics of CFD, including its history, concepts, processes, governing equations, examples, applications, and sources of errors. The document was presented by Chaitanya Vudutha, Parimal Nilangekar, Ravindranath Gouni, and Satish Kumar Boppana to Albert Koether. It contains 28 pages covering topics such as laminar and turbulent flow, Newtonian and non-Newtonian fluids, discretization methods, the CFD process, and the Navier-Stokes equations. Applications of CFD include industries like aerospace, automotive, power generation, and meteorology.
This document provides an overview of mass transfer concepts and principles. It begins with an introduction and outlines the topics that will be covered, which include equilibrium fundamentals, molecular diffusion, convective mass transfer, interphase mass transfer, and a conclusion. Each topic is then broken down into further subsections. For example, molecular diffusion covers Fick's law and its various cases. The document provides learning objectives for understanding concepts related to vapor-liquid equilibrium and mass transfer applications in industry. It also includes recommendations for reference books on these topics and notes for students on using the teaching materials.
Dimensional analysis Similarity laws Model laws R A Shah
Rayleigh's method- Theory and Examples
Buckingham Pi Theorem- Theory and Examples
Model and Similitude
Forces on Fluid
Dimensionless Numbers
Model laws
Distorted models
Cfd simulation of flow heat and mass transferDr.Qasim Kadhim
This chapter discusses using computational fluid dynamics (CFD) simulation to model flow, heat, and mass transfer in a membrane heat exchanger. A CFD package called FLUENT is used to simulate the heat and moisture transfer. Two methods are presented for modeling moisture transfer across the porous membrane: 1) an effectiveness ratio method that relates air temperature and moisture content using a non-dimensional ratio, and 2) a user-defined function that solves conservation equations for mass transfer across the membrane surface. The CFD model is validated against experimental data and previous research on simpler heat exchanger geometries.
The document discusses different types of meshes used in computational fluid dynamics (CFD) simulations. It explains that meshes are used to discretize spatial domains and store field variable values. Structured meshes include Cartesian, multi-block, and patched block grids, while unstructured meshes include Delaunay triangulations and advancing front methods. Various algorithms for generating meshes are also presented, such as inserting points sequentially or using grid-based, centroid-based, or advancing front approaches. Both benefits and challenges of different meshing methods are summarized.
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 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 various turbulence models that are used in computational fluid dynamics (CFD). It begins by explaining that turbulence models are needed to close the system of mean flow equations since resolving all turbulent fluctuations is computationally infeasible. Common turbulence models discussed include zero-, one-, two-, and seven-equation models. Two-equation k-ε models are described in detail, along with variants like the RNG k-ε and realizable k-ε models that aim to improve on areas where standard k-ε is lacking. Other models covered include the k-ω model, algebraic stress models, and Reynolds stress models.
The lecture was delivered by me for IIChE students chapter on the theme of Student-Industry Interaction at Bharati Vidyapeeth on 8th Feb'14. Foe my blogs kindly refer: https://www.learncax.com/knowledge-base/blog/by-author/ganesh-visavale
This document provides an introduction to computational fluid dynamics (CFD) and the use of ANSYS FLUENT software. It discusses the basics of fluid dynamics and how CFD solves fluid flow problems using numerical methods. It also outlines the steps to set up and solve a problem in ANSYS FLUENT, including pre-processing, defining the physical problem, running the simulation, and analyzing results. The document serves as a brief overview of CFD concepts and capabilities for engineering applications.
This document provides an overview of the ANSYS Fluent tutorial guide:
- The guide contains 12 chapters that walk through tutorials of increasing complexity covering topics such as fluid flow, heat transfer, compressible flow, radiation, and rotating reference frames.
- It assumes the user has a basic understanding of fluid mechanics and CFD concepts and guides them through setting up and solving simulations in ANSYS Fluent.
- Each tutorial contains sections for problem setup, defining models and boundary conditions, obtaining solutions, and examining results to build the user's skills in using ANSYS Fluent for various CFD applications.
Computational fluid dynamics (CFD) is a tool for analyzing systems involving fluid flow, heat transfer and associated phenomena like chemical reactions using computer-based simulations. It involves numerically solving the governing equations of fluid flow to model the flow of liquids and gases. CFD complements experimental and theoretical fluid dynamics by providing a cost-effective means of simulating real flows. It has various applications in aerospace, automotive, turbo machinery, power plants, buildings, environmental engineering, and biomedical areas.
The Powerpoint presentation discusses about the Introduction to CFD and its Applications in various fields as an Introductory topic for Mechanical Engg. Students in General.
This document discusses computational fluid dynamics (CFD) and its application in Ansys. CFD uses physics equations and computer simulations to predict fluid flow, heat transfer, chemical reactions, etc. It helps reduce testing costs and study systems too large for experiments. The CFD process in Ansys involves pre-processing (CAD, meshing), solving the governing equations, and post-processing the results (graphs, contours). Examples demonstrate setting up and solving a CFD problem of air mixing in a tee pipe.
Gas-Solid-Liquid Mixing Systems
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 SELECTION OF EQUIPMENT
5 THREE-PHASE MASS TRANSFER WITH CHEMICAL REACTION
6 STIRRED VESSEL DESIGN
6.1 Agitator Design
6.2 Design for Solids Suspension
6.3 Vessel Design
6.4 Gas-Liquid Mass Transfer Coefficient and Surface Area
7 THREE-PHASE FLUIDIZED BEDS
7.1 Gas and Liquid Hold-Up
7.2 Calculation Procedure
7.3 Bubble Size
7.4 Mass Transfer
7.5 Heat Transfer
7.6 Elutriation
8 SLURRY REACTORS
8.1 Gas Rate
8.2 Mass Transfer
9 NOMENCLATURE
10 BIBLIOGRAPHY
OpenFOAM for beginners: Hands-on trainingJibran Haider
NOTE: A NEW VERSION OF THIS PRESENTATION IS AVAILABLE ON MY RESEARCH GATE ACCOUNT
(https://www.researchgate.net/publication/327594760_OpenFOAM_course_for_beginners_Hands-on_training)
OpenFOAM is an open source Computational Fluid Dynamics software package based on C++ programming language within the context of cell centred Finite Volume Method. It is developed primarily by OpenCFD Ltd and distributed by OpenCFD Ltd and the OpenFOAM Foundation.
Disclaimer:
This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software and owner of the OPENFOAM® and OpenCFD® trade marks. "
This document provides an introduction to fluid mechanics concepts. It defines a fluid, discusses the differences between liquids and gases, and classifies various types of fluid flows according to whether they are viscous or inviscid, internal or external, compressible or incompressible, laminar or turbulent, natural or forced, steady or unsteady, and one-, two-, or three-dimensional. The document also introduces concepts of stress, pressure, systems, control volumes, units, and modeling engineering problems either experimentally or analytically.
Computational fluid dynamics (CFD) is the use of computing to simulate fluid flow, heat transfer, and other related phenomena. CFD works by numerically solving the governing equations of fluid dynamics. It allows for analyzing flows that are difficult to study experimentally. CFD has various applications in fields like aerospace, automotive, biomedical, and power generation. The CFD process involves discretizing the domain, applying initial and boundary conditions, numerically solving the governing equations, and post-processing the results. Common discretization methods are finite volume, finite element, and finite difference methods. CFD provides insight into flows and heat transfer while being faster and cheaper than physical experiments.
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
Based on the information provided, a hydrocarbon system and petroleum refining process type have been selected. The recommended property package is NRTL.
Does this help summarize the recommended package? Let me know if you need any clarification or have additional questions!
Fluid Mechanics Chapter 3. Integral relations for a control volumeAddisu Dagne Zegeye
Introduction, physical laws of fluid mechanics, the Reynolds transport theorem, Conservation of mass equation, Linear momentum equation, Angular momentum equation, Energy equation, Bernoulli equation
This document provides an overview of computational fluid dynamics (CFD) analysis. It discusses the basics of CFD, including its history, concepts, processes, governing equations, examples, applications, and sources of errors. The document was presented by Chaitanya Vudutha, Parimal Nilangekar, Ravindranath Gouni, and Satish Kumar Boppana to Albert Koether. It contains 28 pages covering topics such as laminar and turbulent flow, Newtonian and non-Newtonian fluids, discretization methods, the CFD process, and the Navier-Stokes equations. Applications of CFD include industries like aerospace, automotive, power generation, and meteorology.
This document provides an overview of mass transfer concepts and principles. It begins with an introduction and outlines the topics that will be covered, which include equilibrium fundamentals, molecular diffusion, convective mass transfer, interphase mass transfer, and a conclusion. Each topic is then broken down into further subsections. For example, molecular diffusion covers Fick's law and its various cases. The document provides learning objectives for understanding concepts related to vapor-liquid equilibrium and mass transfer applications in industry. It also includes recommendations for reference books on these topics and notes for students on using the teaching materials.
Dimensional analysis Similarity laws Model laws R A Shah
Rayleigh's method- Theory and Examples
Buckingham Pi Theorem- Theory and Examples
Model and Similitude
Forces on Fluid
Dimensionless Numbers
Model laws
Distorted models
This document summarizes research into developing novel multi-velocity models for simulating gas-particle flows. It discusses limitations of traditional single-velocity Eulerian models and reviews recent work using kinetic theory and moment methods to derive multi-velocity formulations. The paper examines and compares different multi-velocity models, presents numerical implementations to demonstrate their behavior, and explores how they can overcome issues with single-velocity treatments.
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.
This document provides an introduction and table of contents to the book "Introduction to Transport Phenomena - Momentum, Heat and Mass" by Bodh Raj. The book covers momentum transfer, heat transfer, and mass transfer phenomena across four main sections. It is intended as an introductory text for undergraduate students and includes solved examples and problems for each chapter.
Polymer Molecular weight and its Measurement methods.pptxErozgarProfile2227
- There are several methods to measure the average molecular weight of polymers, including end-group analysis, colligative properties, light scattering, and ultracentrifugation.
- The molecular weight distribution and average molecular weights determine important properties like viscosity and processability.
- Common averages include the number-average molecular weight (Mn), weight-average molecular weight (Mw), and viscosity-average molecular weight (Mv). The ratio of Mw/Mn is called the polydispersity index (PDI).
- Techniques like end-group analysis and colligative properties work best for lower molecular weights, while light scattering and ultracentrifugation can measure wider ranges up to 100,
Polymer molecular weight and it's measurement method.pptxNaiChigi
Polymer molecular weight can be measured using several methods:
- Viscometry measures intrinsic viscosity and uses the Mark-Houwink equation to determine viscosity-average molecular weight.
- Light scattering directly measures weight-average molecular weight by quantifying light scattered by polymer molecules.
- Gel permeation chromatography separates polymers by size into fractions of differing molecular weights using columns packed with porous beads.
This presentation discusses multi-state models and flowgraph models for analyzing longitudinal failure time data in medicine. Multi-state models represent different disease states like healthy, diseased, diseased with complication, and dead. Flowgraph models use graphs to represent possible outcomes and transitions between states. They can model transition probabilities and time spent in each state. The presentation covers Markov and semi-Markov multi-state models, basic flowgraph structures like series and parallel, solving different flowgraph models, and inverting moment generating functions to obtain density functions.
This document provides an overview of the solvent and surfactant models in reservoir simulation. It discusses the objectives and applications of the solvent model, which models miscible displacement processes. It describes the Todd & Longstaff model for representing miscibility and outlines how to treat relative permeability and PVT data. It then discusses the surfactant model, how it models surfactant distribution and its effects on water viscosity, capillary pressure, relative permeability and adsorption.
This document provides an overview of modeling multiphase flows in FLUENT. It describes the different regimes of multiphase flows and examples in each regime. It also summarizes the two main approaches to multiphase modeling - Euler-Lagrange and Euler-Euler. For the Euler-Euler approach, it describes the three models available in FLUENT: the volume of fluid model for immiscible fluids, the mixture model for low solids loading, and the Eulerian model which solves continuity equations for each phase.
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 document summarizes an experiment analyzing biofuels synthesized from corn oil using gas chromatography-mass spectrometry (GC-MS). Two reaction conditions were tested: 1:6/1 wt% and 1:6/3 wt% oil to methanol ratios. GC-MS analysis found glycerin and fatty acid methyl esters in both conditions as expected. Some unique compounds and constitutional isomers were produced depending on the reaction condition. Mass spectra of the main compounds, like glycerin and a fatty acid methyl ester, were similar between conditions. The results were consistent with the proposed reaction for biodiesel synthesis.
This document describes Marco Mazza's research using the Flamelet-Generated Manifold (FGM) method to model turbulent non-premixed flames in OpenFOAM 2.1.1. The FGM method involves generating a manifold of 1D flamelet solutions for different control variables like mixture fraction. These manifolds can then be used in CFD simulations by looking up thermochemical states based on the control variables. Mazza first sets up the simulation of a non-premixed jet flame and analyzes cold flow results. He then incorporates a 1D manifold without combustion before expanding to a 2D manifold with combustion chemistry included. Results are analyzed and compared to experimental data.
Comparative Analysis of Equivalent Material based on MFIDr. Amarjeet Singh
Polymers of the same family show distinct behavior with each other and because of this, the end prediction after molding the part is very difficult. Simulations result does not always match the product. For close substitution in absence of exactly known material composition, the equivalent grade of the same MFI may be used. However, the MFI is a poor indicator of the rheological behavior to be comprehend for accurate simulation. This research analyzes the appropriate parameters for the rheology of polymers, in the same class that are appropriate.
This document discusses liquid membranes for transport of solutes. It begins by introducing the basic components and arrangements of liquid membranes. It then discusses various transport mechanisms that can control solute flux, including solution-diffusion, diffusion regimes, and chemical reaction kinetics. Models are presented for different types of liquid membranes including emulsion, supported, and bulk liquid membranes. Various applications are mentioned for separating metals, biomolecules, and other compounds using these different liquid membrane configurations.
This document provides information for a Chemical Reaction Engineering course, including:
1. The course code, credits, and reference books for CH701 - Chemical Reaction Engineering - II.
2. A request for student feedback and suggestions to improve classroom teaching and learning.
3. An example question asking students to provide examples of natural reactions that are attractive and inspiring.
The document covers various topics that will be discussed in the Chemical Reaction Engineering - II course, including non-ideal reactor behavior, residence time distribution, types of reactors, and reactor design. It emphasizes interactive discussion and questioning to promote learning.
This document discusses upscaling mathematical models for multiphase flow in heterogeneous porous media. It describes how inclusions embedded in porous media can cause non-standard behavior at the macroscale during fluid displacement. The standard upscaling approaches assume local capillary pressure equilibrium but cannot account for effects like fluid trapping in inclusions. The document proposes modifying the upscaled model obtained from asymptotic homogenization to relate the flow equations and effective properties to the heterogeneous properties. It also discusses how heterogeneity, including connectivity, is represented in fine-scale solutions and how this approach may work better for media with long-range channelized features.
The document discusses modeling turbulent premixed and partially premixed combustion. It notes that directly solving transport equations for all reactive species moments is impractical due to the large number of equations and closure terms required. Instead, models track the first two moments of one or two key scalars like mixture fraction and progress variable. The document reviews modeling approaches for lean premixed flames and describes the strained flamelet model and its extension to partially premixed flames. It discusses implementing the model in CFD codes and comparing results.
The document discusses various polymerization techniques including:
(1) Mass or bulk polymerization where the monomer directly polymerizes in bulk in the presence of an initiator.
(2) Solution polymerization where the monomer is dissolved in a solvent before polymerization to overcome heat transfer issues.
(3) Suspension polymerization where the monomer is dispersed as droplets in an aqueous medium using a dispersing agent.
(4) Emulsion polymerization where the water insoluble monomer is dispersed as fine drops in water using emulsifying agents.
This document provides a review of turbulent combustion modeling closures for large eddy simulations (LES), with a focus on models applicable to propulsion applications. It identifies three classes of models that can provide broad-based modeling: flamelet-library/presumed PDF models, linear eddy based (LEM) models, and transported PDF or filtered density function (FDF) based models. The document discusses the fundamental physical assumptions of these models, particularly regarding the presumed size of the turbulent scalar manifold. It also provides novel results from direct numerical simulations testing some assumptions using flames with detailed and reduced chemistry models.
Basic Tutorial on Aspen HYSYS Dynamics - Process control (Tutorial 3)Hamed Hoorijani
This document provides instructions for simulating a dynamic process using Aspen HYSIS software. The process involves cooling methane feed in a two-phase separator and controlling the separator temperature and pressure. It describes:
1) Defining the feed stream and adding process equipment like the separator and cooler to the flowsheet.
2) Adding transfer functions to model temperature distribution and dead time.
3) Adding controllers to regulate separator conditions and cooler duty.
4) Creating a strip chart to monitor key temperatures over time.
5) Simulating the process dynamically and observing the changes in temperatures.
It also provides steps for simulating the process using a cascade control loop configuration and adjusting controller and transfer
Basic Tutorial on Aspen HYSYS Dynamics - Process ControlHamed Hoorijani
This document provides a tutorial for simulating a gas process system in steady state and dynamic mode using Aspen HYSIS. It includes process specifications, operating conditions, equipment details, and controller settings. The tutorial instructs the user to: 1) build the steady state process model and solve it; 2) add PID controllers to control liquid level and pressure; and 3) use the Dynamic Assistant to simulate the dynamic behavior of the system over time.
Tutorial on Aspen Hysys Dynamics - Separator level controllerHamed Hoorijani
This is my first tutorial on Aspen HYSYS - Dynamic mode. It shows how to use dynamic mode to control the liquid level of a separator in aspen Hysys.
you can find the tutorial video on my youtube channel as well.
video of this tutorial on youtube: https://youtu.be/zFETFlE68Gk
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
1. Introduction to Multiphase
ModelsFirst Part
Hamed Hoorijani
M.Sc. Student At the university of Tehran
Linked :linkedin.com/in/hamed-hoorijani/
: Hamed Hoorijani
Hoorijani@ut.ac.ir
Hoorijani.h@gmail.com
July 2019
7. TFM model(Two-Fluid Model)
• The Eulerian multiphase allows for the modeling of multiple separate,
yet interacting phases.
• The phases can be liquids, gases, or solids in nearly any combination.
• An Eulerian treatment is used for each phase, in contrast to the
Eulerian-Lagrangian treatment that is used for the discrete phase
model.
12. TFM Main Equations
Interphase Exchange Coefficients
particulate relaxation time
For the model of Schiller and Naumann
13. • The mixture model is a simplified multiphase model that can be used in different ways.
• It can be used to model multiphase flows where the phases move at different velocities,
but assume local equilibrium over short spatial length scales.
• It can be used to model homogeneous multiphase flows with very strong coupling and
phases moving at the same velocity and lastly, the mixture models are used to calculate
non-Newtonian viscosity.
• The mixture model can model phases (fluid or particulate) by solving the momentum,
continuity, and energy equations for the mixture, the volume fraction equations for the
secondary phases, and algebraic expressions for the relative velocities.
• Typical applications include sedimentation, cyclone separators, particle-laden flows with
low loading, and bubbly flows where the gas volume fraction remains low.
Mixture Model
14. The mixture model is a good substitute for the full Eulerian multiphase model in
several cases:
1. A full multiphase model may not be feasible when there is a wide distribution of
the particulate phase
2. when the interphase laws are unknown or their reliability can be questioned.
A simpler model like the mixture model can perform as well as a full multiphase
model while solving a smaller number of variables than the full multiphase model.
The mixture model allows you to select granular phases and calculates all properties
of the granular phases.
This is applicable for liquid-solid flows
Mixture Model
19. Mixture Main Equations
Granular Properties
Kinetic Viscosity
Collisional Viscosity
Since the concentration of particles is an important factor in the calculation of the
effective viscosity for the mixture, we may use the granular viscosity to get a value for the
viscosity of the suspension. The volume weighted averaged for the viscosity would now
contain shear viscosity arising from particle momentum exchange due to translation
and collision.
Gidaspow
Syamlal
21. VOF Model(Volume of Fraction)
• Appropriate for flow where Immiscible fluids have a clearly defined
interface.
• Shape of the interface is of interest
• Typical problems:
• Jet breakup
• Motion of large bubbles in a liquid
• Motion of liquid after a dam break (shown at right)
• Steady or transient tracking of any liquid-gas interface
• Inappropriate for:
• Flows involving small (compared to a control volume) bubbles
• Bubble columns