How to apply Online for Kendriya Vidyalayas 2017-18 session

The admission process for the Academic Session 2017-18 will has started from 08.02.2017 8:00 am onwards end on 10.03.2017 at 11.59 pm . The admission process will be done through online registration.

The website for online registration is http://darpan.kvs.gov.in

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Here is a step by step process for online application:

  • Go to : http://darpan.kvs.gov.in
  • To apply to a school, click on the link “Apply Online” tab.
  • Search for a school you wish to apply by simply clicking on the search button. You can search for a school on different parameters, like:
  • State
  • Name of school
  • Fill the application form which is displayed on the screen. Instructions to fill the form are also available for your convenience.After submission of application, a Permanent Registration Number and a login name will be given to you. Use these details next time you want to visit the site. You can visit the site to check for vacant seats or to view the admission status.

The video link below describes the entire process in steps click here: https://www.youtube.com/watch?v=yN7eU6qzipU

For any further queries please comment, I’ll be happy to reply.

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Multiphase Flow Modeling using CFD

What is a Phase ?
Thermodynamically a phase refers to states of matter which we generally classified as solid, liquid and gaseous” (Clayton Crowe; Handbook of Multiphase flow modeling).
Before we enter into any further discussion it is very important to understand what exactly do we mean by phase.

Multiphase flows

Multiphase flows

From modeling point of view there is a slight modification from the definition. An obvious definition of “phase” is thermodynamic state (gas, liquid, solid). However it is possible to define different phases for computational purpose although the thermodynamic states are not different (V.V.Ranade; Computational Flow Modeling For Chemical Reactor Engineering)
Examples:

  • dispersed gas
  • solid flows with wide distribution of particle sizes usually define multiple phases representing solid phase.

Sometimes it is useful to treat thermodynamically different phases as single phase for computational purposes.

Example : Gas-liquid-solid slurry reactor, if the solid particles are fine enough to essentially follow liquid flow it will be convenient to treat the liquid-solid mixture as slurry phase and model the 3 phase system and as 2 phase system (gas slurry).

Thus from the computational point of view we have different or modified definitions of phase compared to thermodynamic state. Basically this comes from the point of view when we say that within computational modeling, phases are treated as different if they have different flow behavior or different velocity profiles. So if two state of matter have different velocity profile they are considered as different phases even if thermodynamically they are of same phase.

Example: Oil and water, even though they are thermodynamically a single phase (i.e. liquid) but since they show different flow behavior with a fluid domain they are considered as 2 different phases.

Now since we have clearly understood the different phases i.e. computationally how phase are treated compared to thermodynamic state of matter we shall now conclude with the multiphase flow definition.

Multiphase flow: as the name suggests it involves the simultaneous flow of mixture of phases such as gas (like bubbles) in a liquid , or liquid  (like droplets) in gases and similar such flows.

Figure:

Even a jet of water falling into a mass of water can be considered as multiphase as the water jet is interacting with air surrounding it.

Why study Multiphase Flow ?

Multiphase flows are found in many industrial applications like chemical reactors and process flow industry and many other applications wherever there are multiple phases present. Also naturally occurring phenomena like rivers and cloud formation involve multiple phases.

In practice most of the applications will involve more than 1 phase. In such applications, in order to model them it is difficult to isolate single phase from this multiple phases flow involved. Therefore to resolve or study such type of flows knowledge of multiphase flow physics is extremely important in order to carry out any type of experiment or simulation on such applications. Thus multiphase flow becomes a specialized domain within fluid mechanics itself and needs separate models or separate treatment in order to simulate them.

Classifications of Multiphase Flows:

Multiphase flow is a very broad branch of study but these flows are classified into different categories which are mainly based upon number of phases and their types.

  • Dispersed Flows:

In such flows one of the phases is in the form of discrete elements. Their is no connection between individual discrete phase elements which may be in the form of particles or droplets.

Example : droplets in gas, bubbles in liquid, solid particles within gas.

  • Separated flows:

In such types of flows the 2 phases involved are separated by a distinct line of contact. This basically means that they can travel from one location to another in the same phase and remain in the same medium.

Example : annular flow with a liquid layer along the pipe walls and a gaseous inner core.

  • Gas-liquid Flows:

In such flows one phase is gaseous and another is in liquid state, and can be in different forms like bubbly flow and annular flow.

  • Gas-Solid flows:

In such type of flows we generally have gas with suspended solid particles. Granular flows are also among this where particulate-particulate and particulate-wall interactions are more important than the forces due to the interstitial gas.

  • Liquid-Solid Flows:

In these types of flows solid particles are carried by liquid, also called as slurry flows.

  • Three phase flows:

This is the most complex type in multiphase flows encountered in many engineering problems.  As the number of phases increases the modeling becomes complex. For example, bubbles in a slurry flow gives rise to 3-phase flowing together. This is an emerging topic for research and computational modeling.

Industrial examples:

  1. Solid drying systems
  2. Droplet separation
  3. Packed bed reactors
  4. Foam
  5. Packed columns
  6. Centrifugal extractors
  7. Mist eliminators
  8. Sedimentation
  9. Cyclone separators
  10. Mixers
  11. bubble columns
  12. Fluidized bed systems
  13. Particulate systems
  14. Precipitators
  15. Dust collection systems
  16. Solid suspensions

Bubble column reactors:

When we say a multiphase flow is present, the predominantly present phase is referred to as basically continuous phase and the secondary medium is defined as the phase which is present in lesser extent, or occupies lesser volume. Thus one phase is primary that occupies most volume and rest of the phase are defined as secondary phase that occupies less volume.

Now we will see in detail bubble column reactors. These are the equipments used in chemical industry for gas-liquid reactions. They are built of vertical columns of cylindrical forms. Gas is introduced at the bottom of column which causes turbulent stream  and thus provide required stirring action for reactive gas exchange. The CFD modeling of this is predominantly done, an example is as shown in the figure below, with 2 phases as gas & liquid.

Bubble column reactor

Bubble column reactor

Stirred tank reactor:

This is defined basically to carry out chemical reaction. It contains a cylindrical or other shape tank having a central rotary element that causes stirring between different phases hence referred as stirred tank reactor. The impeller stirs the reagent which leads to proper mixing of different phases.

Stirred tank reactor

Stirred tank reactor

Fluidized bed reactor:

These basically have solid fluid mixtures ultimately behaving as a fluid, shown in fig. Below figure shows example of volume distribution of a CFD result within a fluidized bed reactor. There is an insertion of pressurized fluid through the particulate medium. Fluid is purged or forced through the bottom packed particulate medium that cause particulates to act as suspension. So the entire system has ability to provide high levels of contacts between gases and solids.

Fluidized bed reactor

Fluidized bed reactor

Applications:

  1. Chemical reaction
  2. Solid mixing
  3. Enhanced heat transfer
  4. Drying
  5. Heat treatment
  6. Coating

Governing Equations of Fluid Flow:

Continuity equation:

mass

Momentum equations:

mo

Energy equation:

e

  • Multiphase flows – Modelling approaches:
    • Lagrangian specification – Here the observer follows an individual fluid parcel as it moves through space and time. Equations are composed by using this fundamental concept.
    • Eulerian specification – It focuses on specific locations in the space through which the fluid flows as time passes.
The modelling equations are composed keeping in mind the Eulerian and Lagrangian framework, we model the continuous phase by Eulerian method and depending upon the complexity of the flow we consider if the dispersed/ secondary phase can be modeled by either Eulerian or Lagrangian framework. Multiphase flow can be modeled mainly by three different approaches listed below.
    • Eulerian – Lagrangian Approach: Utilises Eulerian framework for the continuous phase and Lagrangian framework for dispersed phases
    • Eulerian – Eulerian Approach: Utilises Eulerian framework for both the phases
    • Volume of Fluid Approach: Eulerian framework for both the phases with specialised interface treatment.
This can be explained in detail as follows.
  • Eulerian-Lagrangian approach:
Let us imagine a vast continuum represented by blue colour as the continuous phase, in the figure shown below and small particle spheres as the dispersed/ secondary phase. The discrete particle or the secondary phase in its motion is affected by the continuous phase and at times also affects the motion of continuous phase.
By Eulerian-Lagrangian (E-L) approach, it means that the Eulerian framework governing equation is used for the continuous phase and the dispersed phase trajectories are solved using Lagrangian framework.
As they coexist there is a interface coupling between continuous and the dispersed phase i.e. as the dispersed phase particles moves in the continuous phase, due to drag, lift and various other forces there is exchange of momentum and energy between the two. This exchange takes place through coupling that can be one-way or two-way i.e the continuous phase can influence the dispersed phase flow or even the dispersed phase can influence the flow of continuous phase. The exchange of momentum and energy exists between the fluid (continuous phase) and the particle (dispersed phase) and is considered while modelling using the E-L framework.
The trajectories of the dispersed phase particles are solved not using the conventional Navier-Stoke’s equation but the equations of motions i.e. the Lagrangian framework. The E-L approach is however valid for simulating dispersed multiphase flows containing a low (<10%) volume fraction of dispersed phase. For higher volume fractions of dispersed phase one may necessarily have to use Eulerian-Eulerian approach. Also due to the lower volume fraction of the dispersed phase the volume displacement of the primary phase is ignored while modeling with this (E-L) approach.
e.g. gas-liquid flow in bubble column reactors
  • Eulerian-Eulerian approach:
In Eulerian-Eulerian (E-E) approach both the dispersed particle phase and continuous fluid phase are solved using the governing equations i.e. the Eulerian approach. The Lagrangian framework is not applied for dispersed phase. This can be explained using the figure given below that shows the distribution of continuous phase fluid (blue) and the dispersed phase particles (pink spheres).
Here the control volume is used to define phase velocities i.e. both the phases are modelled using the Eulerian framework of governing equations and solved within the defined control volume to obtain the phase velocities. The volume fractions of both the phases are also solved at these control volumes. As mentioned earlier there is an exchange of momentum and energy between the two phases & this two-way coupling is solved using volume average equations for the dispersed phase. The E-E approach is valid for denser (>10%) volume fractions of dispersed phase.
e.g. fluidized bed reactors, bubble column reactors, multiphase stirred reactors, etc.
  • Volume of Fluid approach:
The volume of fluid method is particularly applicable for stratified or separated flows where the dispersed phase is well separated from the continuous phase with a distinct interface (figure below). Here, a single set of governing equation is solved for both phases using combined mixture properties. The mixture properties are obtained by using the volume fraction of each phase. The weighted mixture properties i.e. density, viscosity, specific heat etc. are of the mixture and not of the individual phase.
 
To obtain the location or position of the interface the volumetric forces are modelled using the interface tracking techniques.
e.g. interfacial phenomena like wall adhesion, surface tension, etc.

Free Webinar: Introduction to Reacting Flow Modeling using CFD (29th Apr’13)

Chemical reactions are very common in industry as well as nature. They are used as a crucial tool to enhance and control mixing, heat transfer, material formation and deposition, pollutant release, production processes etc. Any type of reacting flow involves strong coupling between reaction chemistry and flow physics. To design better reaction processes it is critical to simulate the reacting flow processing using CFD models in order to come up with productive and efficient process design as well as rectify existing reactor design.

This webinar will provide an introduction to fundamentals of modeling reacting flows and also give a glimpse to complex chemistry models within commercial CFD software.

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Content:

  1. What is reacting flow
  2. How chemistry and flow physics are coupled in reacting flows
  3. Fundamentals of reacting flow
  4. Introduction to CFD for reacting flows
  5. Applications of reacting flow modeling
  6. Overview of basic CFD models for reacting flows

To Register :

 http://www.learncax.com/training/webinar/registrations.html

TERMS AND CONDITIONS:  Please refer our website: www.learncax.com

Contact us:

LearnCAx (CCTech-Pune)
1 Akshay Residency, 50 Anand Park,
Aundh, Pune: 411007. India.

email: info@learncax.com

Grid Generation for CFD Simulation: Insights with Demo

Knowing about the grid generation process as discussed in our earlier blog, Grid Generation for CFD Simulation: Introduction,  we have seen what is a mesh, how a mesh is created using a software, what are mesh types etc. However once a mesh is created the challenge remains to assure that a given mesh is good & would generate  accurate results. So there are some terminologies associated with mesh quality & let us know them.

Mesh quality:

As a term “mesh quality” is a combination of various parameters which together decide how appropriate the mesh is, in order to give accurate CFD results, as is well known that a better the mesh quality, better the results.

In general, as a thumb rule compared to a tetrahedral mesh, hexahedral mesh will always give more accurate results because the mesh elements are always aligned with the flow direction, but it is not possible to mesh the entire geometry using hexahedral mesh. Hence what generally is practised is within regions of importance like curved geometry or regions of predominant flow phenomena we make hexahedral mesh/ prism mesh in order to capture flow aligned phenomena and for those regions away or exiting etc, wherein it is not so important to capture the exact flow phenomena that accurately, we can use tetrahedral type of elements. Thus we use mixing of such elements to generate our mesh which is also having accurate results without having large amount of mesh elements.

For judging the quality of mesh we have three criterion:

  1. Mesh skewness: It is a qualitative or quantitative parameter that decides whether the mesh is having a large amount of skewness or it is an ideal triangle or a square.
  2. Mesh smoothness: It describes as to how smooth the mesh is, how is its transition whether it is an abrupt or in a smooth manner.
  3. Mesh aspect ratio: It is the ratio of the largest dimensional length of an element to the smallest length.

Let us understand them further.

Mesh Skewness:

This determines how much the mesh cell/ elements differ from an ideal mesh cell or element. So if we take the case of a 2-D analysis, than an ideal mesh element will be a square & a triangle i.e. an equilateral triangle and in case of 3-dimensional analysis it will be a cube when using hexahedron and for a tetrahedron will be a perfect equilateral tetrahedron.

For a hexahedron element, skewness is obtained as follows:

For each of 6 faces the angle between the face normal & the vector which is defined by the centre of the hexahedron and the centre of the face will be calculated.

The maximum value thus obtained is normalized so that “0” corresponds to a bad element with high skewness and “1” corresponds to a perfect cube. This is the mathematical method for calculating the skewness but in short it means how closely the element is close to the cube. So for the case with a perfect cube it will be “1” & if its deviating from it by a large amount it will be near to “0”.

For Triangular elements:

Skewness is defined as the ratio of area of element & area of an ideal equilateral triangle.  For a quadrilateral  element skewness is defined/ obtained by connecting the mid-points of each side  with mid-points of opposite side and finding the angle between them.

Within software the calculation is a mathematical process, but in short by definition skewness means how close our element is to the ideal element.

Mesh Aspect Ratio:

It is the ratio of the longest edge length to the shortest edge length. As in figure it shows a perfect aspect ratio (one in ideal situation), but if we stretch the square the longest edge becomes very large compared to the shortest edge. So the aspect ratio is increasing and that means that it may not give the best accurate results. Also for a triangle an equilateral triangle has the perfect aspect ratio, but as we stretch, it will give a bad aspect ratio.

For Quadrilateral:

The vector for each 4 quadrilateral nodes form a parallelogram. The area of each parallelogram is divided by each component vector to give 8 possible aspect ratios & the minimum of this aspect ratio is taken as aspect ratio for quadrilateral element.

For Hexahedral: 

It is defined by the size of minimum element edge divided by the size of maximum edge.

For Tri & Tetra:

ANSYS ICEM CFD does it by calculating the ratio between the radii of an inscribed sphere to a circumscribed sphere for each element.

For triangular elements, this generation is done using circles & for tetrahedron elements with spheres.

These values are scaled so that an aspect ratio of “1” is perfectly regular, & an aspect ratio of “0” indicates that the element has zero volume. So as in the figure above, if the triangle is stretched too much it will end-up with zero volume & thereby forming the worst cell/ element.

Mesh Smoothness:

It determines how the size of the mesh element is changing; because within the entire CFD space we cannot have elements having a single size. We have to change the size in order to control our CFD domain mesh count. Hence we use different size of mesh elements in order to fill the space.

So a smooth mesh size change means, that the element adjusts to a particular cell must have a size, that is equivalent or approximately increasingly by a ratio of 1.2 or 20%.

Whereas in the image above this triangle is very large compared to small triangle; (>20%) hence the cell size is changing abruptly and not a smooth change/ transition. A smooth transition is required to capture the change of CFD results, hence we need to make sure that there is always a smooth transition.

Mesh Density:

It means as to how closely or coarsely mesh elements are packed or placed. As we have to vary the mesh size in the different regions of CFD space, for example in regions where the flow is very close to wall, we have to define mesh so that the triangle and quadrilateral and other shapes are packed together closely in order to capture the boundary layer, but away from the walls/ surfaces we can have elements that are larger or coarsely spaced.

You can see in figure that near the surface we have defined fine mesh, but away from it we have a coarse mesh. This change from a closely packed mesh to a coarse mesh is called change in mesh density.

In regions of flow gradients the mesh density should be high enough to capture all the flow variables or changes. However for regions away from walls, where the flow gradients are not present we can have low mesh density i.e. a coarse mesh.

Meshing guidelines:

To summarize the best practices for meshing can be as follows:

  • Important flow features should be resolved
  • Cell aspect ratio should be near one where flow is multi-dimensional
  • Change in cell/element size should be smooth
  • Ideally change in grid spacing should be around 1.2 times
  • More  the cell count higher the accuracy but more CPU time to solve

However the cell counts can be limited without sacrificing on the accuracy using a non-uniform grid , with varying mesh density and prism layer/ boundary layer mesh.

Software Demo:

A below video show a demo of the mesh formation in the ANSYS ICEM CFD software.

Computer Aided Geometric Design

LearnCAx introduces a six week course that enables you to master the geometrical and mathematical techniques used in CAD software by programming them yourself. The course consists of a number of conventional lectures which includes fundamental, core and advanced topics. Computer Aided Geometric Design (CAGD) underlies applications from computer animation and special eff ects, to advanced modelling software for industrial design and architecture, to rapid prototyping machines that print 3-D objects in plastic, and many others. Geometric models represent the shapes and spatial relationships of the environment that is being studied, permitting a much deeper analysis than would be possible otherwise.
CAGD is the basis for modern design in most branches of industry, from naval, aeronautics to textile industry. The course aims to lay foundations of geometric and mathematical concepts which are needed to understand 3D modelling used in design software like AutoCAD, SolidWorks etc. The course focuses on algorithms & programming those algorithms step by step. The programming language used is C++ but they can be replicated in any language.

Ideal for:

  • Engineering Graduates and Post-Graduates (B.E / B.Tech / M.E. / M. Tech in Aerospace, Automobile, Mechanical, Computer Science, Industrial Production, Chemical, Petro-chemical, Bio-chemical and Bio-medical).
  • Third Year or Final Year Engineering Students.
  • Final Year B.E Students who are aspiring for higher education abroad
  • Working Professionals in Design, Manufacturing and Service Industry

Course Pre-requisites
Course has no prerequisites but participants having knowledge of C++ would get extra advantage.

Course Content
This course will introduce you to fundamentals of CAGD and will progress towards detailed algorithms using programming. The course provides knowledge of geometry and mathematical fundamentals and how to use various algorithms while solving CAD related problems.

Matrix and Vector Algebra 

  • Matrix
  • Operation on Matrices
  • Systems of Linear equations
  • Vectors
  • Operation on Vectors

Transformation 

  • 2D Transformations
  • 3D Transformation
  • Projections

Differential Geometry

  • Implicit and explicit representation
  • Arc Length
  • Tangent Vector and Tangent Line
  • Normal Vector and Curvature
  • Binormal vector and Torsion

Parametric Polynomial Curve 

  • Ferguson Curves
  • Hermite Coons

Bezier Curves 

  • Goals of mathematical curve de nition
  • Introduction to Bezier
  • Construction and Properties
  • Moving Control Points
  • De Casteljau’s Algorithm
  • Derivatives of a Bezier Curve
  • Subdividing a Bezier Curve
  • Degree Elevation of a Bezier Curve
  • Continuity Conditions
  • Parametric Continuity
  • Geometric Continuity

B-Spline Curves 

  • Introduction
  • Basis Function
  • Knot Vectors
  • Control Points
  • Construction and Properties
  • Open And Close Curves
  • Moving Control Points
  • Modifying Knots
  • Algorithms: De Boor Algorithm
  • Knot Insertion
  • Subdividing a B-Spline Curve

Surfaces 

  • Basic Concepts of Surfaces
  • Bezier Surfaces
  • NURBS Surfaces

Intersections 

  • Intersection in 2D-Linear Components and Linear Components
  • Linear Components and Quadratic curves
  • Linear Components and Polynomial curves
  • Intersection in 3D-Linear Components and Planar Components
  • Linear Components and Quadric surfaces
  • Linear Components and Polynomial Surfaces

Projections 

  • Projection of a point on Line
  • Projection of a point on Plane
  • Projection of a point on Curve
  • Projection of a curve on surface
  • Projection of a surface on surface

Demo Videos:

Determinant of a Matrix

Visualizing Linear Equations

Transpose of a Matrix

Course benefits:

The course aims to help participants get conversant with complex techniques of geometric designs and the applications. The conceptual foundation is the key to solve unassuming challenges faced in industrial application. This courses bridges the gap between theory and practice, as all the concepts and algorithms that you learn from video would be implemented in an application using C++ programming language. The development environment would be Visual Studio 2010 on windows, but the programs would run on other platforms too. Participants of this course would be provided a Visualization tool called “Vis3D” to see results of their algorithms in 2D and 3D.
In addition to all the benefits of an online course, the Learning Management System (LMS) tries to simulate as close as possible to class environment. Participants not only take courses, give tests and submit assignments but also discuss their problem with experience faculty at their convenience.

Course Format
The course consists of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Total duration of the course would be six weeks. There will be approximately two to three hours of video content per week. There would be a problem set and programming assignment each week and there would also be a final exam at the end of the course before certificate.

Learning Support 

 

LearnCAx faculties mentor the particpants and are available to answer the queries raised. All possible efforts are made to every query and in special cases we also do conduct skype sessions to resolve the queries in time and facilitate learning process. LearnCAx faculty team comes from with a background of research and training and have vast industrial experience in computational domain.

Other Courses
OpenGL
Geometric Algorithms

CFD Courses:

  • CFD Modeling Using ANSYS ICEM CFD & ANSYS Fluent
    CFD Modeling Using STAR-CCM+ & STAR-CD
    Multiphase Flow Modeling Using ANSYS Fluent

Contact Us
LearnCAx (Initiative by Centre for Computational Technologies Pvt. Ltd)
1 Akshay Residency, 50 Anand Park, Aundh, Pune: 411007. India
Ph: +91 20 40098382, M: +91-8380097760

email: info@learncax.com, www.learncax.com

FREE Webinar: Application of CFD for Electronic Thermal Management

Application of CFD for Electronic Thermal Management on 19th Mar’13

Simulations have become an integral part of the product design process within electronic industry. Thermal management has always been a concern within any electronic assembly as electronic components dissipate heat. But with ever reducing size of electronic products and increased need to accommodate higher power, thermal management is now one of the key tasks of any design engineer dealing with electronic product design.
In order to develop highly efficient products along with superior thermal performance, simulation is utilized as a smart tool to optimize a design. CFD simulation is used within the electronic industry to predict air flow and heat transfer at system level as well as competent level. Usage of CFD simulation within the product development cycle results in efficient design and greater innovation within shorter product development time.
This webinar will introduce you to application of CFD within the electronic industry. It will highlight areas within electronic product design, where CFD simulations are being increasingly applied for studying thermal performance. Using a demonstration simulation, we will show how CFD is applied for ensuring product thermal management within electronic industry.

thermal

A typical cabinet cooling CFD simulation

Webinar topics:
1. What is CFD?
2. Thermal management of electronic products
3. Role of simulations within electronic industry
4. How does simulation help for studying thermal performance of electronic products?
5. CFD applied at system, board and component level
6. CFD Simulation for thermal analysis of electronic cabinet
7. Software tools for thermal simulation of electronic products

• Register Now: http://www.learncax.com/training/webinar/registrations.html

The webinar will be held on 19th March’13 in three sessions i.e. 10 am, 3 pm & 8 pm. Interested participants need to register on the link given above to confirm their registration.

TERMS AND CONDITIONS:  

LearnCAx solely reserves the right to grant or forbid the participation of the candidate subject to availability of seats or otherwise. Please refer our website: http://www.learncax.com

Contact us:

LearnCAx (CCTech-Pune)
1 Akshay Residency, 50 Anand Park, Aundh, Pune: 411007. India.
http://www.learncax.com

CFD Workshop at MIT- Pune: Basics & Applications in Chemical Engineering

Recently LearnCAx conducted a CFD workshop in Chemical Engineering Department, MIT Alandi Pune for the third & final year students on 07th Feb’13.

About LearnCAx
LearnCAx is the education division of CCTech (Centre for Computational Technologies Pvt. Ltd). CCTech was founded in 2006 by alumni of IIT, Bombay. CCTech executes open-ended industrial and research projects in association with educational institutes, industry and research establishments to explore
new horizons. Over the last 6 years, CCTech has created a brand name in CFD and CAD Training. With the quality of training and support, our training has become one of the best choice for academia and industry professionals. With core focus on research in Computer Aided Technologies, the company strengthens
the bridge between the industry and academia, by developing industry focused courses for Computer Aided Design & Computational Fluid Dynamics (CAD & CFD). With a motto of providing quality education in the domain of computer aided technologies, CCTech under the brand name of LearnCAx has launched online training modules. With the basic aim of making CAx training easier and accessible to student and professor community, our online training model helps participants learn complex technologies with ease at a time and place of their choice. In our online learning environment, the LearnCAx faculty team acts as mentor to participants and provides constant support by means of our unique learning management system. Our trainers have exhaustive practical experience in a variety of industries, including aerospace, oil gas, power, nuclear and sustainable energy as well as commanding software usage and implementation knowledge. Over 700 students and 100 corporate professionals have undertaken various classroom training courses in last 6.5 years. Over 100 students have already been benefitted through our online training program over last 2 years from across the globe.

Prerequisites for participation
The workshop has no pre-requisites but participants having knowledge of fluid mechanics and numerical methods would have extra advantage.

Workshop Schedule
This workshop is intended for people who have had little or no experience with
ANSYS ICEM CFD and ANSYS Fluent. The one-day workshop will cover the
following topics:

  1. Introduction to CFD
  2. Fluid mechanics & CFD
  3. CFD Industrial Application & Process Flow
  4. Introduction to ANSYS ICEM-CFD, Work-Flow, Capabilities & Strengths (Software Demo)

  5. Introduction to ANSYS FLUENT, Work-Flow, Capabilities & Strengths (Software Demo)
  6. Application of CFD in Chemical Industry: Introduction to Reactive flow & Multiphase flow modeling

  7. Sample Problem / CFD Case Study
  8. Quiz: Computational Fluid Dynamics Basics
  9. Question & Answer Session

Key Speakers:

Ganesh Visavale
Manager: Centre for Computational Technologies Pvt. Ltd., Pune

Swapnil Dindorkar
Senior CFD Engineer: Centre for Computational Technologies Pvt. Ltd., Pune

A Few snaps:

DSC06326

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DSC06320 DSC06322 DSC06323

Winners of Quiz:

  1. Aniket Chitte (3rd year Chem Engg)
  2. Shrushti Rawat (3rd year Chem Engg)
  3. Shivani Shinde (3rd year Chem Engg)
  4. Priyanka Jadhav (3rd year Chem Engg)
  5. Smita Goswami (B.Tech Chem Engg)

All the winners got 30% discount on our CFD courses & an attractive LearnCAx T-shirt.

Institute & Corporate Training:

insti&Corporate

For queries contact:

LearnCAx (Initiative by Centre for Computational Technologies Pvt. Ltd)

1 Akshay Residency, 50 Anand Park, Aundh, Pune: 411007. India
Ph: +91 20 40098382, M: +91-8380097760
email: info@learncax.com, http://www.learncax.com

Grid Generation for CFD Simulation: Introduction

Having already understood the CFD process with a few examples in the earlier blog, Understanding CFD Simulation Process with Examples ,let-us further try to understand and have greater insights into the meshing process in the pre-processing step of CFD simulations.

Simulation is a broad subject area that is currently used to analyse or design products and processes. Within simulations we can analyse different aspects like structural, as well as thermal and fluid flow analysis, however the basis for all simulation softwares and studies is the mesh, also called a grid. So before we start any simulation study to get any result the first step followed is creating a mesh or a CFD CAD model. Therefore meshing is an extremely critical part of CFD process and without understanding it we cannot proceed to solve or even expect any relevant results. Also the accuracy of the CFD simulation results is directly linked to meshing. the better the mesh in quality, the more accurate results can be expected. Almost 50% of the CFD simulation time of any CFD engineer is involved into meshing or mesh generation and hence it is a critical part of the CFD simulation process that needs thorough understanding of the meshing/ grid generation steps.

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The CFD Simulation Process:

Pre-processing: Mesh generation is a combination of CAD model generation as well as CAD clean-up and termed as pre-processing in the entire CFD simulation process. Pre-processing means, before we move ahead to the solver we need to process CAD model in order to fit into the solver or provide the solver with the correct information.

Within pre-processing we create the geometry, and perform clean-up so that unwanted parts if any are deleted or modified. We generate the mesh, & specify the boundary conditions (B.C), then this entire meshed CAD model is exported to a CFD solver along with B.C. A few CAD models are shown below with their meshing.

Solver: In the solver we import the meshed CAD model and solve the CFD problem specifying additional models like the physics, numerical computation methods etc.

Post-processing: Extracting results out of the CFD model that has been solved, is called as post-processing. Here we analyze and try to understand the results using various color, contour plots, lines, contour data, graphs etc. We can extract data like heat transfer coefficient, drag, lift in the analysis of product design and generate good analysis report.

For more details about the CFD simulation process please refer to : Understanding CFD Simulation Process with Examples

Mesh Generation:

Mesh or grid is defined as a discrete cell or elements into which the domain/ model is divided. All the flow variables & any other variables are solved at centres of these discrete cells. This entire process of breaking up a physical domain into smaller sub-domains (elements/ cells) is called as mesh generation.

Why we need a Mesh ?

Basically a mesh is required because by physics or mathematical theory we are solving the variables like flow and heat transfer and any other variable at these cell centres or nodes.  Also the theoretical methods that are used for any CFD study like the fine difference method (FDM), finite element method (FEM) and the finite volume method (FVM) actually solve the variable at these discrete cells/ nodes.

There are different types of grid/ cell shapes e.g. triangles, quadrilateral, tetrahedra, hexahedra etc, that will we shall see in detail. for e.g. the image below shows a glimpse of a CFD random mesh between two circles, the domain of analysis. We have filled the space (torus) by many small triangles called as cells or elements. A grid or mesh is a collection of all these triangles.

torus

Here the point that needs to be noted is that the mesh composed of many triangles are always connected with each other, however they never intersect with each other.

The domain inputs that would be needed for solving the equation are:

  1. Appropriate shape & size of domain in which the equation will be solved.
  2. The domain needs to be discretized as it will be given to the solver for solving equation in discretized form.
  3. Domain & boundary tags (boundary conditions); making sense of physics.
  4. Write output file needed for the solver.

Below shows a pre-processing example of a fin-tube heat exchanger.

FinTube

Meshing of a fin tube heat exchanger

The typical steps involved during pre-processing can be summarized as:

  1. Creating the shape & size of domain, g

    enerally supplied through CAD data.

  2. Standard CAD software – Used for complex geometries and involves repair operation as the result of import/export issues.

  3. Pre-processing tool like ANSYS ICEM-CFD – For simple geometries. Almost negligible repair operation.

  4. Dividing the domain in small cells – Also called as mesh generation, grid generation, domain discretization.

  5. Various methods like structured multiblock, Cartesian, unstructured methods

  6. Putting tags on the boundaries and domain

  7. Boundary or surface tags – Inlet, outlet, wall etc.

  8. Domain tags – Solid, fluid etc.

  9. Exporting mesh to various solver

  10. Writing the mesh in specific solver format

Meshing Terminologies:

  1. Cells & element types: The cells & elements shapes should be supported by the used solver. Typical cell shapes supported by commercial solvers (ANSYS Fluent, ANSYS CFX) are of following types:
  • For 2-D elements: 2d-cell
  • For 3-D elements:

3d-cells

Pre-processing software, ANSYS ICEM CFD:

ANSYS ICEM CFD is a complete and standalone preprocessing software. We can do CAD model creation, CAD repair as well as mesh generation within ICEM CFD. Typical tasks that CFD engineer performs is:

  1. Geometry creation
  2. Import repair
  3. Mesh generation
  4. Multiblock Structure Hexa
  5. Unstructured hexahedral
  6. Unstructured tetra
  7. Cartesian with H-grid refinement
  8. Hybrid meshes
  9. Quadrilateral and triangular surface meshes
  10. Boundary conditions
  11. Creating tags for boundary conditions
  12. Exporting mesh
  13. Exporting mesh for different solvers

A typical GUI of ANSYS ICEM CFD is as shown in figure below:

ICEM-GUI

GUI of ANSYS ICEM CFD

Types of Grid / Mesh:

The typical types of grid/ mesh used for a commercial software for academic or industrial applications are:

  1. Cartesian 
  2. Structured
  3. Unstructured
  4. Hybrid

The mesh generally may not always consist of a single type of elements viz., triangles or rectangular blocks etc. Sometimes we need to use different meshing techniques to shape the model in order to capture the shape of a particular CAD model. The mesh elements therefore at the same time may consist of cartesian, structured, unstructured & hybrid mesh. Within structured grid we have mono-block & multi-block type of mesh. Mono-block & multi-block meshing are the processes we follow to get a structured type of mesh. Within unstructured grid, we have triangular  tetrahedron & with Hybrid grid we have a mix of above discussed elements.

grid-types

1. Cartesian Grid:

Cartesian grid means that the entire set of discrete cells that we will be using in order to fill the CFD space are aligned along the orthogonal axis, i.e. alignment along the x, y & z axis. eg. as below:

CartesianMesh

This facilitates the use of square or rectangles/ cubes, the most simple form of mesh but it does not capture the shape exactly & hence used rarely. Also the result obtained with this will not be that accurate & typically used for very simple type of geometries.

2. Structured Grid:

Here a single block is used to cover the entire geometry. Also used for simple shapes, where a single block is sufficient for covering the CAD model.

e.g. a cylinder

monoBlock

In a Structured Mesh, elements are aligned in a specific manner or they follow a structured pattern & hence the name structured mesh. Conversely, if the elements are arranged in a random fashion or haphazardly they are called un-structured mesh.

2.1. Structured Mono-Block Grid: 

For preparing a structured mesh a single bounding block is used to cover the entire geometry. Also used for simple shapes where a single block is sufficient for actually covering the entire CAD model.

In order to generate mesh for the cylinder (example below) we can cover the cylinder with a bounding box that can be a single bounding box & then generate the mesh for entire cylinder, called a mono-block since a single bounding block is used for generating mesh.

Within structured mesh the other method we use to mesh a complex geometry is called a multi-block. Let us say, if suppose in the core of cylinder we want to generate a finer mesh or a different pattern compared to the outer region, thus to seperate this we at times use more than one bounding box (figure below). Similarly for a complex geometry like the catalytic converter, we cannot cover the shape by a single bounding box so we generate diffrent bounding box for diffrent shapes within the same CFD space & then combining these blocks we make a single structured mesh. Since there are multiple blocks involved in the mesh generation process this is called multi-block structured mesh.

MultiBlock

3. Unstructured Grid: It is the entire discrete cells we generate within the CFD space are randomly spaced & do not follow any single pattern & hence the name un-structured mesh. The types of unstructured mesh are shown in figure below:

Triangular/ Tetrahedron

Tetra

Quadrilateral/ Hexahedron:

hexa

4. Hybrid Grid:

Most of the complex  meshing process used to study complex geometries as shown in figure below. The top part of the image has squares and triangles i.e. tetrahedrons & hexahedrons in volume. Since the shape/ geometry is complex we cannot capture the shape profile by using a single element type, hence we use multiple types so we use triangles, prisms, wedges as well as hexahderon. Similarly for volume mesh of a sphere along the sphere surface we use tetrahedron and inside we use hexahedron. Thus combination of different shapes gives us a hybrid mesh.

Hybrid

Coming up next, Grid Generation for CFD Simulations: Tips & Tricks

Understanding CFD Simulation Process with Examples

Having already explained the background & evolution of Computational Fluid Dynamics (CFD) in the earlier blog Introduction to CFD, let us now try to understand the CFD simulation process with a few examples.

Computational Fluid Dynamics (CFD):

Defining CFDCFD is the science of predicting fluid flow, heat transfer, mass transfer, chemical reactions, and related phenomena by solving the mathematical equations which govern these processes using  numerical methods (i.e., on a computer). Thus it provides a qualitative and quantitative prediction of fluid flows by means of:

  • mathematical modeling (partial differential equations)
  • numerical methods (discretization and solution techniques)
  • software tools (solvers, pre- and postprocessing utilities)

Simulation Process:

To understand the simulation process and the steps involved in it let us consider an example of a flow through a pipe bend.  The figure below gives series of the steps that would be involved in its analysis. For a fluid flow through a pipe bend we have the geometry built up, segregated into smaller fragments/ segments, called a mesh. With this mesh we actually define our probe-points where we want the analysis to be done. We then define the boundary conditions to get a unique solution solving it with a computer. The results obtained gives us a lot of  data along these probe points that are then post-processed with visualization tools to analyse the results.

Thus CFD process in overall is a 3 step procedure:

  1. Pre Processing: This step consist of defining a geometry to define our domain of interest. The domain of interest is then divided into segments, called as mesh generation step and the problem is set-up defining the boundary conditions. Gridgen, CFD-GEOM, or ANSYS Workbench Environment & Modules, ANSYS ICEM CFD, TGrid etc., are some of the popular pre-processing softwares.
  2. Solver : Once the problem is set-up defining the boundary conditions we solve it with the software on the computer, (can also be done by hand-calculations, but would take long time). We have different popular commercial softwares available for this like Star-CD and Star CCM+ (CD-Adapco), FLUENT and  CFX (ANSYS, Inc), GASP (Aerosoft, Inc), CFD++ (Metacomp Technologies) etc. Also there are free to use softwares like OpenFOAM, CFL3D, Typhon, OVERFLOW, Wind-US  etc, all with different capabilities. These softwares are capable of solving the equations at every probe-point defined during the mesh generation step and also we can include additional models as required by the physics. The numerical methods are also defined at the this stage and we solve the whole problem.
  3. Post-processing: Once we get the results as values at our probe points we analyse them by means of color plots, contour plots, appropriate graphical representations & can generate reports. Tecplot 360, EnSight, FieldView, ParaView, ANSYS CFD-Post etc., are some of the popular post processing softwares.

How CFD Works ?

Now let us try to analyze a real life problem, with 2 examples discussed below.

  • Test Case: Fin-Tube Heat Exchanger
A Fin-Tube Heat Exchanger
The above image is of a fin-tube heat exchanger typically used for transferring heat in radiators  in automobiles or in household applications (in cold countries where room heating is essential). In this we have cold/ hot fluid  being poured through these tubes and other fluid (air/water) flowing over the tubes.
Domain of Interest: Area between two fins
Now looking at the geometry, it can be seen that the fin tube heat exchanger is a cascade of a large number of fins attached to the tubes and thus seems to be a complex problem. However in CFD analysis of the fintube heat exchanger, the problem can be simplified to a great extent by identifying our domain of interest and considering only a small section of the it (as above).  Simplifying assumptions are made in order to make the problem tractable (e.g., in this case: steady-state, incompressible, inviscid, two-dimensional). Also for solving the problem we consider the conservation of mass, momentum and energy as is required in the study. In pre-processing step we take the geometry and divide it into smaller fragments  as in figure below, called meshing or the grid generation step.

4

\oint \rho \phi \vec v \cdot d\vec A = \oint \Gamma_{\phi} \nabla \phi \cdot d\vec A + \int_V S_{\phi} dV \\ \\  \sum_{f}^{N_{faces}} \rho_f \vec V_f \phi_f \cdot \vec{A_f} = \sum_{f} ^{N_{faces}} \Gamma_\phi (\nabla \phi)_n \cdot \vec{A_f} + S_{\phi} V \\  \mbox{~} \\
Thus as the geometry is discretized so are the equations (as above) at each cell which on solving gives us the values that is obtained in the form of colorful contour plots using the visualization techniques that can give us a very good insight to locate the hot-spots, recirculation, & dead zones. So its not only the qualitative depiction of values that we generate but also the quantitative  data  (from figure we can see temperature varying from 464 K to 361 K)  that can help us analyze the overall flow phenomena. Also if you see the regions between the fins as in figure below, it is clear that there is rapid fall in temperature from the base (hot region) to the top as is typically encountered with flue gases.

side-between

  • Water flow over a tube bank

Consider the analysis of a cascade of tubes placed inside a domain across which a fluid (water) is flowing. The objective here is to compute average pressure drop and heat transfer per tube row. So it is a physical system in which we have a complicated setup of several cascade of tubes. To start with, we shall not proceed to solve the real problem but would try to simplify it taking a section for analysis, set-up the problem, first gain confidence over it and then if the computational resources are available and if time permits, proceed to implement on the real problem.

cascade

Assumptions

  • flow is two-dimensional, laminar, incompressible
  • flow approaching tube bank is steady with a known velocity
  • body forces due to gravity are negligible
  • flow is translationally periodic (i.e. geometry repeats itself)

As can be seen from the discretized geometry above, we have a fine mesh size at near the wall of the tubes and coarser at the other regions, so as to resolve the boundary layer flow at these regions. We have a whole system being modeled as we set up the problem on a software  for example, Fluent as in figure below. In the solver we import the mesh, select the appropriate solver methodology, define operating conditions (no-slip, Qw or Tw at walls), initialize and iterate to get a converged solution.

fluent
Once the problem is solved we can have results as in figure  below. It shows contours of temperature around the tubes. As seen, the regions near the tube wall have re-circulation zones as a result of which there is a heat built-up (red color) also shown by color variations. So we see that we can have a very nice depiction of real life situation through a simple 2-D analysis.
result
Also you can analyse a case wherein you have flow over a single tube with a obstruction-free region ahead, as in figure below. The figure shows the analysis for a typical phenomena called vortex shedding typically encountered in cases of flow around tubes because of which a familiar process called the von Karman vortices are generated. The tube encounters a lot of lift force that is sinusoidal in nature. The plot of time versus lift force clearly shows a sinusoidal nature in an analysis performed in ANSYS CFX.
vel-vortex
vortex

Thus with a few real life problems we have tried to understand the CFD simulation process.

Coming up next : Why CFD project as your MS thesis can help you in long term ?

Introduction to CFD

Fluid Flow

Fluid dynamics, actually is the study of fluid under motion, governed with a certain set of conservation equations,  wherein things are conserved, with reference to mass, momentum & energy.

If these three quantities i.e. mass, momentum & energy are solved entirely we can define any fluid flow.  The conservation laws are formulated in the form of equations which we try to solve and that’s what simulation is all about.

The aim of any fluid flow analysis is primarily two fold:

  1. to know the effect of flow on the boundaries
  2. flow visualization

Examples:

  1. Drag on a car
  2. Heat transfer rate in heat exchangers
  3. Heat sink performance
  4. Pressure rise across pump
  5. Temperature distribution in a room

Flow visualization allows us to find the nature of  flow , ie. if  there is a flow separation into primary & secondary flows, find out recirculation zones and is any widely used in heating ventilation & air-conditioning (HVAC) studies. Flow visualization is a very important aspect wherein CFD scores over many other studies.

We had people across the centuries who had greatly contributed to fluid mechanics. Archimedes discovered the phenomena called as buoyancy effect, on the basis of which we can now do natural convection studies, that is still a challenging task in CFD. Newton has a great contribution in the field of mathematics & fluid mechanics and to what we study in CFD today. Also we had people who contributed to mathematical modeling like Leibniz, Bernoulli, Euler, Navier & Stokes about whom we shall frequently come across. In the last 2 centuries the study of fluid mechanics had been primarily mathematical that has helped many mathematician to contribute. We had Reynolds who with his number, the Reynolds number helped us determine whether the inertial forces will dominate the viscous forces and cause the flow to be turbulent. Prandtl in early twentieth century with his boundary layer theory is  helped in predicting more accurate the drag on aerofoils, also contributed in heat transfer  with the Prandtl number, i.e. a relative measure of velocity boundary layer & thermal boundary layer. Taylor has contributed with his  series expansion dealing with numerical methods especially in finite difference method. So we had may researchers contributing to fluid mechanics & its solutions, mathematical models & formulating the models across the centuries. We are now living in a time wherein the software firms/ packages have exploited all this work with the advancement of computational speed.

Where is fluid flow …?

Fluid flows encountered in everyday life include:

  1. meteorological phenomena (rain, wind, hurricanes, floods, fires)
  2. environmental hazards (air pollution, transport of contaminants)
  3. heating, ventilation and air conditioning of buildings, cars etc.
  4. combustion in automobile engines and other propulsion systems
  5. interaction of various objects with the surrounding air/water
  6. complex flows in furnaces, heat exchangers, chemical reactors etc.
  7. processes in human body (blood flow, breathing, drinking . . . ) and so on and so forth.

Fluid flow process occur almost everywhere in the universe, in fact its difficult to find a process that does not involve a flow of fluid. Even if you do not find exactly a fluid flowing you would surely find heat flow existing over in the universe.  More specifically on our earth we have weather & climate changes which are very much function of fluid flow, driven by heat transfer & pressures, of-course also assisted by the earth’s rotation revolution around the sun.

We have fluid flow and heat transfer application for as high as an aircraft, as low as in sea, in surface ships, under the sea in submarines and locomotives.

We have the fluid flow in recreational activities as well to reduce the drag so as to increase the fuel efficiency and the performance of cars/ bike riders & for water sports.

Overall we deal with a very beautiful phenomena of fluid dynamics !

Methods of Solving Fluid Flow:

  1. Analytical Fluid Dynamics (AFD)

Before the high speed computers of today arrived, the usual process followed by researchers were using manual/ hand calculations with some approximations. With the made assumptions, approximations  they generally made a free body diagram of it simplifying the complex 3-D geometry into simple 1-D or 2-D analysis and ended up doing an integration of these equations. Setting up the constraints in the form of initial and boundary conditions to find constants of integration and ultimately getting values at discrete points, where they could end-up having  results getting plots, force variation across a plate (e.g. fig below).D=\rho b [\int_0^\delta U(u_0 - U) dy]_{x=L}

where D, is the drag on the flat plate.

Now for the simple case as above of a flow over flat plate the AFD, might be easy to apply but not in cases of complex problem as of a heat sink for example. However, its always not just the complex geometries but also more often we are limited in defining the exact physics in terms of concerned mathematical equations.

We have the general transport equation given as below:

The quantity “phi” with different values represent different equations. To solve the general transport equation on a real geometry is a challenge and if we do not want face this one can have another approach, with experimental studies i.e. of experimental fluid dynamics (EFD).

Experimental Fluid Dynamics (EFD):

Here we have a scale down model often based on engineering dimensional analysis, wherein we create a prototype model, perform appropriate experiments under conditions that would reflect exactly what would happen in reality. Further we introduce a lot of probe points for data collection thereby introducing  disturbances in the flow itself. Also its not so always easy to make an exact prototype of the real problem and there are problems with cost and feasibility too.

So we can see that both methods; the AFD & EFD have some limitations, AFD with respect to complex geometries and physics capturing and EFD with issues like time, cost & feasiblility.

That’s why we have CFD wherein we have the mathematical model i.e. the physics clearly defined, and these physics equations that are usually the partial differential equations, are solved through numerical methods.  With the assumptions that we can neglect some higher order terms we can use computer to solve them we end-up having a CFD result, a result that is numerical solution of our physics. The advantages of this are many like we have a low cost simulation, we are able to do a lot of analysis within a short period of time as we don’t have to actually make a physical model of our study. With short variation in some parameters we can study the problem using the high speed computer can take about hours or days time to obtain the results. So in brief, with CFD approach we can have a low cost solution, in short time and a comprehensive information as compared to any other approach.

 

Having pointed out the advantages of CFD over the AFD and the EFD, its important to know the limitations of  CFD too. CFD results can never be 100% correct as those depend on the following things:

  1. the input data may involve too much guessing or imprecision
  2. the mathematical model of the problem at hand may be inadequate
  3. the accuracy of the results is limited by the available computing power

Also although CFD does not replace the measurements completely but the amount of experimentation and the overall cost can be significantly reduced.

Coming up next:  CFD Simulation Process