Report Final

Project Report on
COMPUTATIONAL FLUID DYNAMICS & IMAGE PROCESSING Submitted by: Name PRATIK BOSE SANDIP KUMAR GUPTA RABINDRA KR. BARANWAL PRASENJIT DEY ANAND KUMAR VIJAY KUMAR PRASAD MANISHANKAR KUMAR Class Roll No. 11 69 42 41 40 44 23 WBUT Roll No. 09148007005 09148005053 09148007006 09148007047 09148007027 09148007007 09148007013

Under Guidance of Prof. Moloy Kumar Banerjee

DEPARTMENT OF MECHANICAL ENGINEERING FUTURE INSTITUTE OF ENGINEERING AND MANAGEMENT KOLKATA-700150, INDIA

~1~

ACKNOWLEDGEMENT
Acknowledgement, although a light sound term, contains the deepest sense of gratitude and delivers and extensive emotional aspect on the minds of the people concerned. An effort of this magnitude always needs contributions from various associates. We are fully aware of the fact that this project would never attain a successful initiation without the kind efforts of Mr. Vijendra Kumar and our beloved guide Dr. M. K. Banerjee, whose fathomless expertise in this field helped us procure a distinct initiation of the project. We owe sincere thanks to our beloved Dr. M. K. Banerjee, Head of The Department, Mechanical Engineering, Future Institute of Engineering and Management for assisting and believing in our dreams for all these years. Our sincere thanks and appreciation goes to all the authorities for Future Institute of Engineering and Management, Kolkata for providing the atmosphere to carry out the work. We are grateful to the authorities of Bio-Technology department of JADAVPUR UNIVERSITY, Jadavpur for allowing us to participate in a symposium based on haemodynamics. Finally we wish to thank all of our friends and our associates who bear with us the pain of long hours of inconvenience during the execution of this project work.

Pratik Bose Sandip Kumar Gupta Rabindra Kumar Baranwal Prasenjit Dey Anand Kumar Vijay Kumar Prasad Manishankar Kumar

Page | 2

ABSTRACT In this project Computational fluid dynamics (CFD) techniques is applied to study the flow characteristics of steady flow through arteries with stenosis & aneurysum. Effects of flow Reynolds number is used to study the variation of key hemodynamic parameters such as pressure drops, flow velocities and shearing stresses on the arterial walls are examined and their significance on the progression of arterial diseases is discussed. Human bloodflow measurement in cardiac, coronary & peripheral arteries can be visualize the bloodflow and can be calculated the velocity magnitude in specific region of artery system. Thus, this study proposes the combination of CT, image processing and CFD analysis of artery bloodflow simulation in normal human system. This method can demonstrate the blood flow direction, and also calculate the velocity magnitude inside the arteries. The processes of this study are segmentation, meshing, solving, and post-processing. In the process of image processing with the help o f 3D image reconstruction application package of SIMPLEWARE® we create a simulation model. It offers a powerful tool to physicians by easing the search for representative images. This study is the important beginning of an applied CFD analysis with medical imaging, CT SCAN & MRI SCAN. CT imaging combines with CFD analysis could be useful in medical field like haemodynamics as a blood flow function evaluation technique. Moreover, the applications of this method could be utilized in a wide range of research topics, especially development of diagnostic and treatment planning techniques. Understanding the behaviour of liquids and gases is crucial to engineers who need to predict and improve the performance of new designs or processes. Engineering organizations around the world rely on ANSYS® FLUENT® software to gain this understanding through computer simulation. CFD simulation is focused on predicting and improving the performance of new designs or processes, reducing time to market, and reducing overall engineering costs.

Keywords : CFD, image processing, Blood Flow, ANSYS®.

Page | 3

Table of contents:

Introduction to CFD Medical Aspects Computed Tomography Scan Magnetic Resonance Imaging DICOM Standard Introduction to ScanIP® Case Study: Blood flow of arteries Introduction to ANSYS® Organization of ANSYS® files Working in ANSYS® Case study 1 Case study 2 Case study 3 References

12-16 16-19 19-24 25-26 27-31 31-32 33-42 43-44 45 46-63 64-67 68-71 72-77 78

Page | 4

List of figures

Fig a: 3D Model of heart to be analyzed in ScanIP ® Fig b: Analysis of stenosed carotid artery Fig c: 3D model of abdominal artery (with aneurysm) showing different arteries Fig d: Geometry with sudden enlargement Fig e: Geometry with sudden contraction Fig f: Artery geometry with stenosis and bifurcation Fig 1: applications of CFD Fig 2: relationship between the degree of Stenosis and Coronary Flow Fig 3: CT scan Fig 4: Glance of CT scan Fig 5a: sequential CT scan Fig 5b: spiral CT scan Fig 6: scanning unit Fig 7: detector unit of CT scan Fig 8: multi-row detector Fig 9: imaging by MRI Fig 10: magnetic horizontal field Fig 11: superconductive MR system Fig 12: DICOM Information Model Fig 13: three software options in SIMPLEWARE® Fig 14: processes in ScanIP® and ScanFE® Fig 15: ScanIP® after loading the Blood_Flow.sip file Fig 15.1: creating a new mask Fig 15.2: XY view after the threshold segmentation Fig 15.3: selecting pixel for floodfill Fig 15.4: initial 3-D structure Fig 15.7: resampling to a cubic spacing Fig 15.8: the result of the smoothing (circled is the area to be removed) Fig 15.9: surface point picking in the 3D view to locate that in 2D view Fig 15.10: (a) before unpainting the connection and (b) after

09 09 10 10 11 11 14-15 18 20 21 22 23 23 24 24 25 26 26 29-30 31 33 34 34 35 36 36 37 38 39 39
Page | 5

Fig 15.5: crop dialog box and cropping position indicators in the 2D view 37

Fig 15.11: view after floodfilling and painting Fig 15.12: configuring the mesh Fig 15.13: screen after smoothing the mesh Fig 16: Main window of ANSYS® Fig 16.1: workbench window Fig 16.2: ANSYS® window with Fluid Flow (FLUENT®) Fig 16.3: selecting properties in Fluid Flow window Fig 16.4: properties menu Fig 16.5: geometry window under Fluid Flow Fig 16.6: dimensioning in design modeller Fig 16.7: creating surfaces from sketches Fig 16.8: mesh generation in meshing Fig 16.9: mapped face meshing Fig 16.10: details of mapped face meshing Fig 16.11: mesh generation Fig 16.12: plots obtaining during calculation Fig 17.0: geometry for case study 1: section with sudden enlargement Fig 17.01: contours of velocity (m/s) for case study 1(Re=200) Fig 17.02: contours of stream function (kg/s) for case study 1(Re=200) Fig 17.03: contours of static pressure (Pa) for case study 1(Re=200) Fig 17.04: wall shear stress (Pa) for case study 1(Re=200) Fig 17.05: axial velocity (m/s) plot for case study 1 (Re=200) Fig 18.01: contours of velocity (m/s) for case study 1 (Re=400) Fig 18.02: contours of stream function (kg/s) for case study 1 (Re=400) Fig 18.03: contours of static pressure (Pa) for case study 1 (Re=400) Fig 18.04: wall shear stress (Pa) for case study 1(Re=400) Fig 18.05: axial velocity (m/s) plot for case study 1 (Re=400) Fig 19.0: geometry for case study 2: section with sudden contraction Fig 19.02: contours of stream function (kg/s) for case study 2(Re=200) Fig 19.03: contours of static pressure (Pa) for case study 2(Re=200) Fig 19.04: wall shear stress (Pa) for case study 2(Re=200) Fig 19.05: axial velocity magnitude (m/s) for case study 2(Re=200) Fig 20.01: contours of stream function (kg/s) for case study 2 (Re=400)

40 41 42 44 46 47 48 48 49 50 50 51 52 52 52 53 64 64 64 65 65 65 66 66 66 67 67 68 68 69 69 69 70
Page | 6

Fig 19.01: contours of velocity magnitude (m/s) for case study 2(Re=200) 68

Fig 20.02: contours of velocity magnitude (m/s) for case study 2 (Re=400) 70 Fig 20.03: (a) & (b) contours of static pressure (Pa) (Re=400) Fig 20.04: plot of wall shear stress (Pa) for case study 2 (Re=400) Fig 20.05: plot of axial velocity (m/s) for case study 2 (Re=400) Fig 21.0: geometry for case study 3 70 71 71 72

Fig 21.01: contours of velocity magnitude (m/s) for case study 3 (Re=200) 72 Fig 21.02: contours of stream function (kg/s) for case study 3 (Re=200) Fig 21.03: contours of static pressure (Pa) for case study 3 (Re=200) Fig 21.04: plot of wall shear stress (Pa) for case study 3 (Re=200) Fig 21.05: contours of X- velocity (m/s) for case study 3 (Re=200) Fig 22.01: contours of velocity magnitude (m/s) for Re=400 Fig 22.02: contours of stream function (kg/s) for Re=400 Fig 22.03: contours of static pressure (Pa) for Re=400 Fig 22.04: plot of wall shear stress (Pa) Vs position (m) for Re=400 Fig 22.05: plot of centreline velocity Vs position fo r Re=400 73 73 74 74 75 75 76 76 77

Page | 7

AIM
To gain some insight about the haemodynamics of blood flow through stenosed artery using different Reynold’s number as input parameter. As part of the study, we consider here variation of velocity magnitude, stream function and pressure profile through the arterial system along with the variation of wall shear stress and centre line velocity distribution.

Page | 8

PROJECT DETAIL OF 7 TH SEMESTER
1) In this part of analysis we used a realistic artery model using CT Scan data under the three different configurations in ScanIP®.

Fig a: 3D Model of heart to be analyzed in ScanIP®

Stenosed artery

Fig b: Analysis of stenosed carotid artery

Page | 9

Fig c: 3D model of abdominal artery (with aneurysm) showing different arteries

2) We studied here three types of geometry which may mimics the human coronary artery system. Initially we consider a sudden enlargement profile, a sudden contraction profile and finally we considered stenosed artery with bifurcation model.

Fig d: Geometry with sudden enlargement

Page | 10

Fig e: Geometry with sudden contraction

Fig f: Artery geometry with stenosis and bifurcation [Note:-All dimensions are in mm.]

PROJECT DETAIL OF 8TH SEMESTER
To analyze the realistic geometry developed by Scan IP using ANSYS® FLUENT®.

Page | 11

INTRODUCTION Computational Fluid Dynamics: Computational fluid dynamics (CFD) 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 a numerical process. The result of CFD analyses is relevant engineering data used in: – Conceptual studies of new designs. – Detailed product development. – Troubleshooting. – Redesign. This is intended as an introductory guide for Computational Fluid Dynamics CFD. Due to its introductory nature, only the basic principles of CFD are introduced here. CFD provides numerical approximation to the equations that govern fluid motion. Application of the CFD to analyze a fluid problem requires the following steps. F irst, the mathematical equations describing the fluid flow are written. These are usually a set of partial differential equations. These equations are then discretized to produce a numerical analogue of the equations. The domain is then divided into small grids or elements. Finally, the initial conditions and the boundary conditions of the specific problem are used to solve these equations. The solution method can be direct or iterative. In addition, certain control parameters are used to control the conver gence, stability, and accuracy of the method. All CFD codes contain three main elements: (1) A pre-processor, which is used to input the problem geometry, generate the grid, to define the flow parameter and the boundary conditions to the code. (2) A flow solver, which is used to solve the governing equations of the flow subject to the conditions provided. There are four different methods used as a flow solver: (i) finite difference method; (ii) finite element method, (iii) finite volume method, and (iv) spectral method. (3) A post processor, which is used to massage the data and show the results in graphical and easy to read format.

CFD-how it works:1) Analysis begins with a mathematical model of a physical problem. 2) Conservation of matter, momentum, and energy must be satisfied throughout the region of interest. 3) Fluid properties are modelled empirically.

Page | 12

4) Simplifying assumptions are made in order to make the problem tractable (e.g., steady-state, incompressible, inviscid, 2-D). 5) Provide appropriate initial and boundary conditions for the problem.

Another method:1) CFD applies numerical methods (called discretization) to develop approximations of the governing equations of fluid mechanics in the fluid region of interest. – Governing differential equations: algebraic. – The collection of cells is called the grid. – The set of algebraic equations are solved numerically (on a computer) for the flow field variables at each node or cell. – System of equations are solved simultaneously to provide solution. 2) The solution is post-processed to extract quantities of interest (e.g. lift, drag, torque, heat transfer, separation, pressure loss, etc.). Note:Domain is discretized into a finite set of control volumes or cells. The discretized domain is called the “grid” or the “mesh.” 1) General conservation (transport) equations for mass, momentum, energy, etc., are discretized into algebraic equations. 2) All equations are solved to render flow field.

Uses of CFD:Numerical simulations of fluid flow (will) enables • architects to design comfortable and safe living environments • Designers of vehicles to improve the aerodynamic characteristics • Chemical engineers to maximize the yield from their equipment • Petroleum engineers to devise optimal oil recovery strategies

Page | 13

• Surgeons to cure arterial diseases (computational hemodynamics) • Meteorologists to forecast the weather and warn of natural disasters • Safety experts to reduce health risks from radiation and other hazards • Military organizations to develop weapons and estimate the damage • CFD practitioners to make big bucks by selling colourful pictures

Examples of CFD applications:Aerodynamic shape design

Page | 14

Fig 1: applications of CFD (source: www.google.com) Steps followed for CFD:1.) Discretization 2.) Design and create the grid Pre-Processor 3.) Create the mesh 4.) Set up the numerical model 5.) Compute the solution 6.) Examine the results Solver Post-Processor

Advantages of CFD:1.) Relatively low cost. – Using physical experiments and tests to get essential engineering data for design can be expensive. – CFD simulations are relatively inexpensive, and costs are likely to decrease as computers become more powerful. 2.) Speed – CFD simulations can be executed in a short period of time. – Quick turnaround means engineering data can be introduced early in the design process.
Page | 15

3.) Ability to simulate real conditions. – Many flow and heat transfer processes cannot be (easily) tested, e.g. hypersonic flow. – CFD provides the ability to theoretically simulate any physical condition. 4.) Ability to simulate ideal conditions. – CFD allows great control over the physical process, and provides the ability to isolate specific phenomena for study. – Example: a heat transfer process can be idealized with adiabatic, constant heat flux, or constant temperature boundaries. 5.) Comprehensive information. – Experiments only permit data to be extracted at a limited number of locations in the system (e.g. pressure and temperature probes, heat flux gauges, LDV, etc.). – CFD allows the analyst to examine a large number of locations in the region of interest, and yields a comprehensive set of flow parameters for examination.

Limitations of CFD:1.) Physical models. – CFD solutions rely upon physical models of real world processes (e.g. turbulence, compressibility, chemistry, multiphase flow, etc.). – The CFD solutions can only be as accurate as the physical models on which they are based. 2.) Numerical errors. – Solving equations on a computer invariably introduces numerical errors. – Round-off error: due to finite word size available on the computer. Round-off errors will always exist (though they can be small in most cases). – Truncation error: due to approximations in the numerical models. Truncation errors will go to zero as the grid is refined. Mesh refinement is one way to deal with truncation error. 3.) Boundary conditions. – As with physical models, the accuracy of the CFD solution is only as good as the initial/boundary conditions provided to the numerical model. – Example: flow in a duct with sudden expansion. If flow is supplied to domain by a pipe, you should use a fully-developed profile for velocity rather than assume uniform conditions.

Page | 16

Medical Aspects: Coronary Artery Disease:It is estimated that more than 16 million Americans have coronary artery disease (CAD) and 8 million have had a myocardial infarction (MI). Every year approximately 1 million will have a new myocardial infarction. Based on data from the Framingham trial nearly 50% of males and 30% of females over the age of 40 will develop coronary artery disease. Coronary artery disease is most commonly due to atherosclerotic occlusion of the coronary arteries. Atherosclerosis is a process that can involve many of the body’s blood vessels with a variety of presentations. When it involves the coronary arteries it results in coronary artery disease, the cerebral arteries; cerebrovascular disease (transient ischemic attack, stroke), the aorta; aortic aneurysms, the ileo- femoral and popliteal arteries; peripheral vascular disease, the mesenteric arteries; intestinal ischemia. Half of all deaths in the developed world and a quarter of deaths in the developing world are due to Cardiovascular Disease which are comprised of hypertension and the diseases caused by atherosclerosis.

Coronary Blood Flow:The heart is an aerobic organ that is dependent for its oxygen supply entirely on coronary perfusion. Under resting condition, the myocardium extracts the maximum amount of oxygen from the blood it receives. The O 2 saturation of blood returning from the coronary sinus to the right atrium has the lowest saturation of any body organ (30%). Interruption of coronary blood flow will result in immediate ischemia. Coronary blood flow is directly dependent upon perfusion pressure and inversely proportional to the resistance of the coronary vessel. Q ∝ Perfusion pressure / Vessel resistance Coronary perfusion occurs in diastole hence diastolic pressure is more important than systolic pressure in determining coronary perfusion. Coronary vessels are divided into epicardial or conductance vessels (R1), pre capillary (R2) and microvascular vessels (R3). The epicardial vessels, the site most commonly affected by atherosclerosis, offer negligible resistance to coronary flow. Resistance to flow occurs in the pre capillary (R2), and microvascular (R3) vessels which are termed resistance vessels. The increase coronary blood flow in response to increase myocardial oxygen demand (MVO 2 ) is achieved by the dilatation of these resistance vessels. Three factors play a key role in modifying vascular tone; the accumulation of local metabolites, endothelial factors and neural tone. The accumulation of adenosine during ischemia is an example of local metabolic factors. The most important endothelial substance mediating vasodilatation is nitric oxide (NO). Other important mediators are bradykinin, endothelium de rived hype rpolarizing factor and prostacyclin. On the other hand, endothelin-1 (ET-1) is a well known vasoconstricting substance. Angiotensin II and thromboxane A2 are other well known endothelium derived constricting factors. Alpha receptor adrenergic stimulation results in coronary vasoconstriction whereas beta 1 receptor stimulation leads to vasodilatation. Coronary arteries suffering from atherosclerosis lose the ability to release the vasodilating substances that allow the increase in coronary perfusion in the face of increased demand. Their coronary reserve is limited by the failure to dilate and reduce

Page | 17

vascular resistance. Furchgott showed that acetylcholine, through the release of NO, results in vasodilation of the coronary vessel. However if the endothelium overlying the vascular smooth vessel was diseased (e.g. by atherosclerosis), the smooth muscle will paradoxically vasoconstrict. Blockage of the epicardial coronary vessels (coronary stenosis) of up to 60% is compensated at rest and maximal exercise by vasodilation of the resistance coronary vessels. Blockage of epicardial coronary vessels in excess of 60% will result, under conditions of increases myocardial oxygen demand, in reduced perfusion and in turn ischemia. Clinically, this translates to effort or exercise induced angina. This is the basis for performing exercise stress testing in patients suspected of having coronary artery disease. When the severity of the blockage is greater than 90%, perfusion is compromised even at rest. Clinically, this may result in resting angina, a critical stage of coronary artery disease. Ischemia is the result of the coronary vessel’s inability to meet the demand of the myocardium it supplies. The imbalance between supply and demand (↑ demand, ↓ supply) results in ischemia. Clinically this presents as chest discomfort and / or shortness of breath.

Fig 2: Relationship between the degree of Stenosis and Coronary Flow Coronary artery disease can present in a variety of ways. The classical presentation is with chest discomfort. Chest discomfort resulting from myocardial ischemia secondary to coronary artery disease is called angina pectoris (squeezing of the chest). Discomfort is diffused and not localized and may radiate down the arms, as low as the umbilicus and up to the lower jaw. This may be associated with shortness of breath (dyspnea). This discomfort is the result of myocardial ischemia however it is one of the last manifestations to appear. Due to the myocardium’s complete reliance
Page | 18

on coronary blood flow for energy supply, within a few beats of coronary occlusion, diastolic and systolic dysfunction set in and the electrocardiogram begins to register abnormalities before the patient begins to experience angina pectoris. This explains why patients may describe associated shortness of breath when they experience angina. The association of both symptoms together indicates that the myocardium fed by the narrowed vessel is sizable.

Signs and Symptoms:The most common signs and symptoms for the aspect of CAD most often seen by the clinical practitioner is angina pectoris. Other symptoms include:  Vague, somewhat troublesome ache or severe, intense pericardial crushing sensation Most commonly sensation is felt beneath the sternum or may radiate to the left shoulder and down the inside of the left arm; straight through to the back, into the throat, jaws, and teeth; occasionally down the inside of the right arm. Sometimes upper abdomen Typically precipitated by physical exertion or stress, persists for a few minutes, and subsides with rest or nitro- glycerine Worse exertion after a meal Worse cold weather, walking into the wind, or first contact with cold air after leaving a warm room Modest increase in heart rate Significant elevation in systolic and diastolic blood pressure, but sometimes hypotension Diffuse apical impulse and more distant heart sounds Paradoxical second heart sound Fourth heart sound

       

Differential Diagnosis:   Peptic ulcer Chronic cholecystitis Esophageal spasm  Reflux esophagitis  Spontaneous pneumothorax  Degenerative and inflammatory lesions of the left shoulder  Thoracic outlet syndrome  Anterior chest wall syndrome  Inflammation of chondrocostal junctions  Intercostal neuritis  Cervical or thoracic spine or disk disease

Page | 19

Innovations for People: Computed Tomography Scan (CT scan):
The initial use of computed tomography (CT) for applications in radiological diagnostics during the seventies sparked a revolution in the field of medical engineering. And even throughout the eighties, a CT examination lost little if any of its special and exclusive character. In the meantime, however, times have changed. Today computed tomography represents a perfectly natural and established technology which has advanced to become an indispensable and integral component of routine work in clinics and medical practices. The initial use of computed tomography (CT) for applications in radiological diagnostics during the seventies sparked a revolution in the field of medical engineering. And even throughout the eighties, a CT examination lost little if any of its special and exclusive character. In the meantime, however, times have changed. Today computed tomography represents a perfectly natural and established technology which has advanced to become an indispensable and integral component of routine work in clinics and medical practices.

Fig 3: CT scan (source: www.google.com) Today, CT is one of the most important methods of radiological diagnosis. It delivers non superimposed, cross-sectional images of the body, which can show smaller contrast differences than conventional X-ray images. This allows better visualization of specific differently structured soft-tissue regions. Since the introduction of spiral CT in the nineties, computed tomography has seen a constant succession of innovations. The development of slipring technology allowed for a continuously rotating gantry – the prerequisite for spiral CT. The first spiral CT scanner was a Siemens SOMATOM Plus system. Today this technology is widely used.

Page | 20

Glance of CT scan:-

3D Shaded Surface Display of the lung, High resolution, spiral CT

Spiral CT, lung, high-resolution, 2 mm, SOMATOM Plus

CT-Angio (MIP) of the renal arteries, ,

Sub second spiral CT, long MPR, ab domen/pelvis, SOMATOM Plus 4

Fig 4: Glance of CT scan (source: www.google.com)

Page | 21

Sequential CT:A cross-sectional image is produced by scanning a transverse slice of the body from different angular positions while the tube and detector rotate 360° around the patient with the table being stationary. The image is reconstructed from the resulting projection data.

Fig 5a: sequential CT scan (source: www.ajronline.org) If the patient moves during the acquisition, the data obtained from the different angular positions are no longer consistent. The image is degraded by motion artifacts and may be of limited diagnostic value. The tomographic technique is suitable only to a limited extent for the diagnosis of anatomical regions with automatism functions such as the heart or the lung.

Spiral CT:Spiral CT is often referred to as “volume scanning“. This implies a clear differentiation from conventional CT and the tomographic technique used there. Spiral CT uses a different scanning principle. Unlike in sequential CT, the patient on the table is moved continuously through the scan field in the z direction while the gantry performs multiple 360° rotations in the same direction. The X-ray thus traces a spiral around the body and produces a data volume. This volume is created from a multitude of three-dimensional picture elements, i.e. voxels. The table movement in the z direction during the acquisition will naturally generate inconsistent sets of data, causing every image reconstructed directly from a volume data set to be degraded by artefacts. However, special reconstruction principles – interpolation techniques which generate a planar set of data for each table position – produce artifact- free images. Thus it is possible to reconstruct individual slices from a large data volume by overlapping reconstructions as often as required .

Page | 22

Fig 5b: spiral CT scan (source: www.ajronline.org) Software applications enable the clinical use of spiral CT even for regions which are subject to involuntary movements.

Setup of a CT System:A CT system comprises several components. These basically include: • The scanning unit, i.e. the gantry, with tube and detector system • The patient table • The image processor for image reconstruction • The console The console represents the man-machine interface and is designed to be multifunctional. It is the control unit for all examination procedures, and is also used to evaluate the examination results. To enhance the workflow, Siemens has developed a double console capable of performing both functions at the same time.

Scanning unit (gantry):A CT scanning system consists of an X-ray unit, which functions as a transmitter, and a data acquisition unit, which functions as a receiver. In commercial CT systems these two components are housed in a ring shaped unit called the gantry.

Fig 6: scanning unit (source: www.google.com)

Page | 23

Detector:The detector system plays a special role in the interaction of the CT components. It converts the incident X-rays of varying intensity to electric signals. These analog signals are amplified by downstream electronic components and converted to digital pulses. Over time, certain materials have proven very effective in the utilization of Xrays. For example, Siemens uses UFC (Ultra Fast Ceramic) Detectors which, due to their excellent material properties, dramatically improve image quality.

Fig 7: detector unit of CT scan (source: www.google.com)

Multi-row Detector:Multi-row detectors utilize radiation delivered from the X-ray tube more efficiently than single row detectors. By simultaneously scanning several slices, the scan time can be reduced significantly or the smallest details can be scanned within practicable scan times. In the adaptive array detectors used by Siemens, the rows inside the detector are very narrow, becoming wider as you move toward its outer edges in the z direction (longitudinal axis of the body). A combination of collimation and electronic interconnection provides considerable flexibility in the selection of slice thicknesses. At the same time the space required by the detector septa, and therefore the unused space, is minimized.

Fig 8: multi-row detector (source: www.google.com)

Page | 24

Magnetic Resonance Imaging (MRI):Clinical Magnetic Resonance Imaging (MRI) uses the magnetic properties of hydrogen and its interaction with both a large external magnetic field and radiowaves to produce highly detailed images of the human body. In this first module, we will discuss some basic principles of magnetism, the magnetic properties of the hydrogen nucleus, and its interaction with the externally applied magnetic field (B0 ).

Fig 9: imaging by MRI (source: www.google.com) In its early days, MRI was known as NMR. This stands for Nuclear Magnetic Resonance. Although the name has changed (primarily due to the negative connotation of the word “nuclear”), the basic principles are the same. We derive our images from the magnetic resonance properties of nuclear particles (specifically hydrogen). In order to perform MRI, we first need a strong magnetic field. The field strength of the magnets used for MR is measured in units of Tesla. One (1) Tesla is equal to 10,000 Gauss. The magnetic field of the earth is approximately 0.5 Gauss. Given that relationship, a 1.0 T magnet has a magnetic field approximate ly 20,000 times stronger than that of the earth. The type of magnets used for MR imaging usually belongs to one of three types; permanent, resistive, and superconductive. A permanent magnet is sometimes referred to as a vertical field magnet. These magnets are constructed of two magnets (one at each pole). The patient lies on a scanning table between these two plates. Advantages of these systems are: 1) Relatively low cost, 2) No electricity or cryogenic liquids are needed to maintain the magnetic field, 3) Their more open design may help alleviate some patient anxiety, 4) Nearly nonexistent fringe field. It should be noted that not all vertical field magnets are permanent magnets. Resistive magnets are constructed from a coil of wire. The more turns to the coil, and the more current in the coil, the higher the magnetic field. These types of magnets are most often designed to produce a horizontal field due to their solenoid design.

Page | 25

Fig 10: magnetic horizontal field (source: www.google.com) Superconducting magnets are the most common. They are made from coils of wire (as are resistive magnets) and thus produce a horizontal field. They use liquid helium to keep the magnet wire at 4 degrees Kelvin where there is no resistance. The current flows through the wire without having to be connected to an external power source. The main advantage of superconducting magnets is their ability to attain field strengths of up to 3 Tesla for clinical imagers and up to 10 Tesla or more for small bore spectroscopy magnets. Below is an example of a superconductive MR system:

Fig 11: superconductive MR system (source: www.google.com)

Page | 26

A Brief Background of the DICOM Standard:The introduction of digital medical image sources in the 1970’s and the use of computers in processing these images after their acquisition led the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) to form a joint committee in order to create a standard method for the transmission of medical images and their associated information. This committee, formed in 1983, published in 1985 the ACR-NEMA Standards Publication No. 300-1985. Prior to this, most devices stored images in a proprietary format and transferred files o f these proprietary formats over a network or on removable media in order to perform image communication. While the initial versions of the ACR-NEMA effort (version 2.0 was published in 1988) created standardized terminology, an information structure, and unsanctioned file encoding, most of the promise of a standard method of communicating digital image information was not realized until the release of version 3.0 of the Standard in 1993. The release of version 3.0 saw a name change, to Digital Imaging and Communications in Medicine (DICOM), and numerous enhancements that delivered on the promise of standardized communications.

The DICOM Standard now specified a network protocol utilizing TCP/IP, defined the operation of Service Classes beyond the simple transfer of data, and created a mechanism for uniquely identifying Information Objects as they are acted upon across the network. DICOM was also structured as a multi-part document in order to facilitate extension of the Standard. Additionally, DICOM defined Information Objects not only for images but also for patients, studies, reports, and other data groupings. Standard was now ready to deliver on its promise not only of permitting the transfer of medical images in a multi- vendor environment, but also facilitating the development and expansion of picture archiving and communication systems (PACS) and interfacing with medical information systems.

Scope of DICOM:The DICOM Standards Committee exists to create and maintain international standards for communication of biomedical diagnostic and therapeutic information in disciplines that use digital images and associated data. The goals of DICOM are to achieve compatibility and to improve workflow efficiency between imaging systems and other information systems in healthcare environments worldwide. DICOM is a cooperative standard. Connectivity works because vendors cooperate in testing via either scheduled public demonstrations, over the Internet, or during private test sessions. Every major diagnostic medical imaging vendor in the world has incorporated the Standard into its product design, and most are actively participating in the enhancement of the Standard. Most of the professional societies throughout the world have supported and are participating in the enhancement of the Standard as well.

Page | 27

DICOM is used or will soon be used by virtually every medical profession that utilizes images within the healthcare industry. These include cardiology, dentistry, endoscopy, mammography, ophthalmology, orthopaedics, pathology, paediatrics, radiation therapy, radiology, surgery, etc. DICOM is even used in veterinary medical imaging applications. DICOM also addresses the integration of information produced by these various specialty applications in the patient’s Electronic Health Record (EHR). It defines the network and media interchange services allowing storage and access to these DICOM objects for EHR systems.

Technology Overview:The DICOM Standard addresses multiple levels of the ISO OSI network model and provides support for the exchange of information on interchange media. DICOM currently defines an upper layer protocol (ULP) that is used over TCP/IP (independent of the physical network), messages, services, information objects and an association negotiation mechanism. These definitions ensure that any two implementations of a compatible set of services and information objects can effectively communicate. Independence from the underlying network technology allows DICOM to be deployed in many functional areas of application, including but not limited to communication within a single site (often using various forms of Ethernet), between sites over leased lines or virtual private networks (VPNs), within a metropolitan area (often using Asynchronous Transfer Mode), across dial- up or other remote access connections (such as by modem, ISDN or DSL), and via satellite (with optimized protocol stacks to account for increased latency). At the application layer, the services and information objects address five primary areas of functionality:  Transmission and persistence of complete objects (such as images, waveforms and documents),  Query and retrieval of such objects,  Performance of specific actions (such as printing images on film),  Workflow management (support of work lists and st tus information) and a  Quality and consistency of image appearance (both for display and print).

Page | 28

Page | 29

Fig 12: DICOM Information Model

Page | 30

Introduction to Scan IP:SIMPLEWARE® provides the world- leading software solution for the conversion of 3D images into CAD, Rapid Prototyped and Finite Element models. SIMPLEWARE® offers three software options for processing and meshing 3D image data. The software is based on a core image processing platform, ScanIP®, with optional bolt-on modules for mesh generation and CAD integration. The relationship between these products is shown in Figure.

Fig 13: three software options in SIMPLEWARE®

Page | 31

ScanIP® as image processing software:ScanIP® offers an extensive selection of image processing tools to assist you in visualising and segmenting regions of interest from any volumetric 3D data (e.g. MRI, CT, Micro-CT, etc.). Segmented images can be exported as STL files for CAD analysis and RP manufacturing.

ScanFE® – mesh generation module:ScanFE® provides a robust approach to the conversion of segmented 3D image data into multi-part volumetric and/or surface meshes. The high quality meshes generated can be directly imported into a range of commercial FE and CFD packages.

ScanCAD® – CAD integration module:ScanCAD® allows the import and interactive positioning of CAD models within image data. The resulting combined models can then be exported as multi part CAD models or, using +ScanFE®, converted automatically into multi-part finite element or CFD meshes.

A typical workflow within SIMPLEWARE® software:Generation of STL/surface models 1. Run ScanIP® to import data and segment different regions of interest. 2. Export segmented data as STL file for CAD analysis or RP production. Generation of volumetric meshes for FE/CFD analysis 1. Run ScanIP® to import data and segment different regions of interest. 2. Run +ScanFE® and import segmented data. 3. Assign mesh parameters, define contact surfaces and define material properties. 4. Export the mesh directly into commercial FE or CFD solvers. Import of CAD data into image 1. Run ScanIP® to import data and segment different regions of interest. 2. Run +ScanCAD® and import segmented data. 3. Import STL and/or CAD files and position within the image data. 4. Export combined data into ScanIP®. 5. In ScanIP®, apply filters and Boolean operations. 6. Export as STL file for CAD analysis or RP production, and/or into +ScanFE® to mesh and export directly into commercial FE or CFD solvers.

Page | 32

Case study: Blood flow of arteries
An MRI of arteries around the heart will be converted in to a 3D model and the inlets and outlets of the model will be defined before it is exported to CFD for blood flow analysis. What we Learn:-

Fig 14: processes in ScanIP® and ScanFE®

Working in Scan IP:Importing the data To open the file:1) Start ScanIP®. 2) Open File Open… Blood_Flow.sip. Once loaded, a screen similar to Figure 15 should be seen. Ensure that the three different 2D views are shown, i.e. XY, XZ and YZ. Use the Slice slider (above each 2D view) to go through the slices. To visualise the position of each view relative to the other, use the Slice position indicator button.

Page | 33

Fig 15: ScanIP® after loading the Blood_Flow.sip file Segmenting the arteries:The aim is to use the threshold and floodfill segmentation tools to create a mask of t he arteries. To use the threshold segmentation:1) Create an empty mask from which to start the threshold. For this: a) Right-click on Masks in the Mask browser toolbox b) Select Create new mask, as shown in Figure 15.1. This will enable you to toggle on the Interactive threshold in the Segmentation toolbox for instant feedback in the active 2D slice.

Fig 15.1: creating a new mask

Page | 34

2) Select Segmentation

Threshold in the Tool selector toolbox.

3) In the Threshold panel a) Tick the box Interactive. b) Move the threshold slider to update the active slice view which will allow you to find the right values to segment the dataset. In this example, values of 110 for the Lower value and 255 for the Upper value seem satisfying. c) In the Mask Operation list, select Replace with mask d) Perform on… All slices. e) Click Apply. The threshold has now created a mask with all the pixels that fall in to the range specified. To create a mask where there is one continuous structure, the FloodFill segmentation tool is used.

Fig 15.2: XY view after the threshold segmentation To use Flood Fill segmentation:1) Select Segmentation FloodFill in the Tool selector toolbox. 2) In the FloodFill panel a) Select Apply… …from active mask. b) In the Mode options, select 3D (local). c) In the Mask Operation list, select Replace with mask. d) Perform on All slices. 3) To apply the FloodFill, left click on a pixel belonging to the arteries, e.g. pixel (121, 341, 37), as shown in Figure 15.3. This will replace the threshold settings with one continuous structure throughout all the slices.

Page | 35

Fig 15.3: selecting pixel for floodfill To visualise the segmented structure:1) In the 3D view, select Fast Preview 2) Click the Refresh button to apply the settings. Figure 15.4 shows the initial 3D structure.

Fig 15.4: initial 3-D structure Cleaning up the model:As seen in the 3D view, there are a lot of artefacts at the top of the image. To remove these, the data is cropped and cleaned up using a combination of unpainting and floodfilling. The structure is also smoothed using a combination of binarising the mask and then Gaussian smoothing. To crop your data:Page | 36

1) Open the Crop dialog box Data Crop… 2) Move the sliders in the Y direction to remove the unwanted areas, e.g. use values of 150 (Lower boundary) and 360 (Upper boundary) as shown in Figure 9 7. 3) Click OK.

Fig 15.5: crop dialog box and cropping position indicators in the 2D view To resample your data:1) Open the Resample dialog box Data Resample… (Figure 15.7). 2) Select the Units: Pixel spacing (mm) to resample the volume. 3) Type values of 0.8 mm and tick Cubic Resampling in order to make the values in Y and Z match the X value. 4) Choose a Linear Background Image Interpolation method, as well as a Linear Mask Interpolation method. 5) Click OK.

Fig 15.7: resampling to a cubic spacing

Page | 37

To smooth your data:1) Select Filter Binarisation filter in the Tool selector toolbox. 2) Click Apply 3) Select Filter Smoothing - Recursive gaussian filter in the Tool selector toolbox. 4) In the Recursive gaussian filter panel: a) Select Apply… …on active mask. b) Type in the values of 1 mm in the Sigma parameters. c) Apply the filter. To see the effect of the last steps, refresh the 3D view.

Fig 15.8: the result of the smoothing (circled is the area to be removed) To locate the connected area:The aim is to find the connecting structures and manually unpaint the connections which will then be removed after a floodfill on the main structure. 1. To find the connection use the 3D view and rotate/zoom until you find it - there is one connection to remove. 2. With the three 2D views and the 3D view showing, point the mouse cursor in the 3D view on the area that is to be removed and press the [P] key. This locates the surface point in the 3D view and in all 2D slices. For further information see Figure 15.9. 3. The Surface Point Picking should locate slice 45 in the XY view as shown in Figure 15.9.

Page | 38

Fig 15.9: surface point picking in the 3D view to locate that pixel in the 2D view We can now remove the connected area with the unpaint option. To remove the connected area:1. Select Segmentation Paint in the Tool selector toolbox. 2. In the Paint panel: a. Choose a small Brush size (1 should be ok), as you only want to unpaint a small area. b. In the Paint option list, select Unpaint. c. Perform on… Active slice. 3. Remove the area highlighted in Figure 15.10a. The island should include pixel (42, 92, 45). The slice should now look like Figure 15.10b.

Fig 15.10: (a) before unpainting the connection and (b) after To use Flood Fill segmentation:1. Select Segmentation FloodFill in the Tool selector toolbox. 2. In the FloodFill panel:
Page | 39

a. Select Apply… …from active mask. b. In the Mode options, select 3D (local). c. In the Mask Operation list, select Replace with mask. d. Perform on All slices. 3. To apply the FloodFill, left click on a pixel belonging to the large artery, e.g. pixel (144, 153, 45). To finish the model, crop the edges to create flat inlet/outlet areas. To crop your data:1. Open the Crop dialog box Data Crop… 2. Move the sliders in the X direction to 10 (Lower boundary), and in the Z direction to 75 (Upper boundary). 3. Click OK. Refresh the 3D view and the model should look like Figure 15.11.

Fig 15.11: view after floodfilling and painting to remove an unwanted structure To save your project:1. Open the Save as dialog box File Save As… 2. Type in a file name, e.g. Blood_Flow-1.sip, and Save the file. We will now export the model to +ScanFE® to generate a volumetric mesh which can be exported to CFD.

Exporting to ScanFE®:To down sample your data:1. Open the Resample dialog box DataBResample… 2. Select the Units: Pixel spacing (mm) to resample the volume. 3. Type values of 2 mm and tick Cubic Resampling in order to make the values in Y and Z match the X value. 4. Choose a Linear Background Image Interpolation method, as well as a Linear Mask Interpolation method. 5. Click OK.
Page | 40

To ensure the model is one continuous structure use again a floodfill on the main structure. To export to ScanFE®:1. Open the ScanFE® Export dialog box File Export Volume ScanFE® 2. In the ScanFE® Export dialog box make sure Use pre-smoothing is unticked. 3. Click Export. 4. Choose a location and type in the file name, for example Blood_Flow-2. 5. Click Save. To import your ScanIP® file:1. Start +ScanFE®. 2. Import the file generated in ScanIP® File 3. Open the file Blood_Flow-2. Import from ScanIP® …

The mesh is now based on the voxels, so purely hexahedral elements are present. To generate a mesh:1. In the Control panel click on the Mesh tab 2. Apply the following setting, as shown in Figure 15.12: a. Mesh type: Smoothed. b. Choose a Minimum quality target of 0.15 by clicking on the spin control. c. Tick Optimize quality and select Lazy (to min. quality). d. Tick Additional smoothing with a Max curvature of 0.5 and Max iterations of 2. e. Use an Interior mesh adaption of 32 x 32 x 32. f. Click Apply.

Fig 15.12: configuring the mesh The resulting mesh should look similar to the one shown in Figure 15.13.

Page | 41

Fig 15.13: screen after smoothing the mesh The mesh will now be produced for the mask and background.

Page | 42

Introduction to ANSYS®
ANSYS® is a general purpose finite element modelling package for numerically solving a wide variety of mechanical problems. These problems include: static/dynamic structural analysis (both linear and non- linear), heat transfer and fluid problems, as well as acoustic and electro- magnetic problems. In general, a finite element solution may be broken into the following three stages. This is a general guideline that can be used for setting up any finite element analysis. 1.) Pre-processing: defining the problem; the major steps in pre-processing are given below: a) Define key-points/lines/areas/volumes b) Define element type and material/geometric properties c) Mesh lines/areas/volumes as required The amount of detail required will depend on the dimensionality of the analysis (i.e. 1D, 2D, axisymmetric, 3D). 2.) Solution: assigning loads, constraints and solving ; here we specify the loads (point or pressure), constraints (translational and rotational) and finally solve the resulting set of equations. 3.) Post-processing: further processing and viewing of the results; in this stage one may wish to see: a) Lists of nodal displacements b) Element forces and moments c) Deflection plots d) Stress contour diagrams

ANSYS® Interface Graphical Interface vs. Command File Coding
There are two methods to use ANSYS®. The first is by means of the graphical user interface or GUI. This method follows the conventions of popular Windows and X Windows based programs. The second is by means of command files. The command file approach has a steeper learning curve for many, but it has the advantage that an entire analysis can be described in a small text file, typically in less than 50 lines of commands. This approach enables easy model modifications and minimal file pace requirements.

Page | 43

Starting the program:Preliminaries:The ANSYS® environment contains two windows: the Main Window and an Output Window. Within the Main Window there are five divisions:(1) Utility Menu: The Utility Menu [A] contains functions that are available throughout the ANSYS® session, such as file controls, selections, graphic controls, and parameters. (2) Input Line: The Input Line [B] shows program prompt messages and allows to type in commands directly. (3) Toolbar: The Toolbar [C] contains push buttons that execute commonly used ANSYS® commands. More push buttons can be made available if desired. (4) Main Menu: The Main Menu [D] contains the primary ANSYS® functions, organized by pre-processor, solution, general postprocessor, and design optimizer. It is from this menu that the vast majority of modelling commands are issued. (5) Graphics Windows: The Graphics Window [E] is where graphics are shown and graphical picking can be made. It is here where the model in its various stages of construction and the ensuing results from the analysis can be viewed.

Fig 16: Main window of ANSYS® .
Page | 44

Organization of ANSYS® Files
A large number of files are created when ANSYS® is run. If ANSYS® is started without specifying a job name, the name of all files created will be File.*, where the * represents various extensions described below. If a job name is specified, say Beam, then the created files will all have the file prefix, Beam again with various extensions: beam.db – database file (binary). This file stores the geometry, boundary conditions, and any solutions. beam.dbb – backup of the database file (binary). beam.e rr – error file (text), Listing of all error and warning messages. beam.out – output of all ANSYS® operations (text). This is what normally scrolls in the output window during ANSYS® session. beam.log – log file or listing of ANSYS® commands (text). Listing of all equivalent ANSYS® command line commands used during the current session. Depending on the operations carried out, other files may have been written. These files may contain, for example, results. It is important to know what to save when, for instance, there is a need to clean up a directory or to move things from the /scratch directory. If the GUI is always used, then only the .db file is required. This file stores the geometry, boundary conditions, and any solutions. Once the ANSYS ® program has started, and the job name has been specified, only the resume command has to be activated to proceed from where the model was last left off. If, however, ANSYS® command files are planned to be used, then only command file and/or the log file have to be stored. The log file contains a complete list of the ANSYS® commands used to get the model to its current stage. That file may be run as is, or edited and rerun as desired. Parameter & Dime nsion Control:The ANSYS® Workbench Environment uses a unique plug- in architecture to maintain compatibility and association with the CAD systems for solid and surface models. This allows the user to make design changes to the CAD model under consideration without having to reapply loads and or supports. The user can either pick the CAD dimension to change directly, or enhance the design iterations with the Parameter Manager. The ANSYS® Parameter Manager in Workbench provides an easy way to set up multiple design scenarios by simply filling out the Parameter Manager spreadsheet and automatically updating the geometry, running multiple solutions and allowing a more efficient simulation.

Page | 45

Working in ANSYS®
Steps followed in ANSYS®:Step 1: Pre-Analysis & Start-up:We expect the viscous boundary layer to grow along the pipe starting at the inlet. It will eventually grow to fill the pipe completely (provided that the pipe is long enough). When this happens, the flow becomes fully developed and there is no variation of the velocity profile in the axial direction, x (see figure below). One can obtain a closed- form solution to the governing equations in the fully-developed region. We will compare the numerical results in the fully developed region with the corresponding analytical results. Start ANSYS® FLUENT®:Prior to opening ANSYS®, create a folder called pipe in a convenient location. We'll use this as the working folder in which files created during the session will be stored. For this simulation FLUENT® will be run within the ANSYS® Workbench Interface. Start ANSYS® workbench: Start> All Programs> ANSYS® 13.0> Workbench The following figure shows the workbench window.

Fig 16.1: workbench window

Page | 46

Step 2: Geometry:Saving:It would be of best interest, to save the project at this point. Click on the "Save As.." button, , which is located on the top of the Workbench Project Page. Save the project as "LaminarPipeFlow" in your working directory. When we save in ANSYS® a file and a folder will be created. For instance if we save as "LaminarPipeFlow", a "LaminarPipeFlow" file and a folder called "LaminarPipeFlow_files" will appear. In order to reopen the ANSYS® files in the future we will need both the ".wbpj" file and the folder. Fluid Flow (FLUENT®) Project Selection:On the left hand side of the workbench window, you will see a toolbox full of various analysis systems. To the right, you see an empty work space. This is the place where you will organize your project. At the bottom of the window, you see messages from ANSYS®. Left click (and hold) on Fluid Flow (FLUENT®), and drag the icon into the empty space in the Project Schematic. Your ANSYS® window should now look comparable to the image below.

Fig 16.2: ANSYS® window with Fluid Flow (FLUENT® ) Since we selected Fluid Flow (FLUENT®), each cell of the system corresponds to a step in the process of performing CFD analysis using FLUENT®. Rename the project to Laminar Pipe. We will work through each step from top down to obtain the solution to our problem. Analysis Type:In the Project Schematic of the Workbench window, right click on Geo metry and select Properties, as shown below.

Page | 47

Fig 16.3: selecting properties in Fluid Flow window The properties menu will then appear to the right of the Workbench window. Under Advance Geometry Options, change the Analysis Type to 2D as shown in the image below.

Fig 16.4: properties menu Launch Design Modeler:In the Project Schematic, double click on Geometry to start preparing the geometry. At this point, a new window, ANSYS® Design Modeler will be opened. You will be asked to select desired length unit. Use the default meter unit and click OK.

Page | 48

Creating a Sketch:Start by creating a sketch on the XY Plane. Under Tree Outline, select XY Plane, then click on Sketching right before Details View. This will bring up the Sketching Toolboxes. Click on the +Z axis on the bottom right corner of the Graphics window to have a normal look of the XY Plane. In the Sketching toolboxes, select Rectangle. In the Graphics window, create a rough Rectangle by clicking once on the origin and then by clicking once somewhere in the positive XY plane. (Make sure that you see a letter P at the origin before you click. The P implies that the cursor is directly over a point of intersection.) At this point you should have something comparable to the image below.

Fig 16.5: geometry window under Fluid Flow Dimensions:At this point the rectangle will be properly dimensioned. Under Sketching Toolboxes, select Dimensions tab, use the default dimensioning tools. Dimension the geometry as shown in the following image. Under the Details View table (located in the lower left corner), set V1=0.1m and set H2=8m, as shown in the image below.

Page | 49

Fig 16.6: dimensioning in design modeler Surface Body Creation:In order to create the surface body, first (Click) Concept > Surface From Sketches as shown in the image below.

Fig 16.7: creating surfaces from sketches
Page | 50

This will create a new surface SurfaceSK1. Under Details View, select Sketch1 as Base Objects and then under Surface body select the thickness to 0.1m and click Apply. Finally click Generate to generate the surface. At this point, you can close the Design Modeler and go back to Workbench Project Page. Save your work thus far in the Workbench Project Page.

Step 3: Mesh:In this section the geometry will be meshed with 500 elements. That is, the pipe will be divided into 100 elements in the axial direction and 5 elements in the radial direction. Launch Mesher:In order to begin the meshing process, go to the Workbench Project Page, then (Double Click) Mesh. Default Mesh:In this section the default mesh will be generated. This can be carried out two ways. The first way is to (Right Click) Mesh > Generate Mesh, as shown in the image below.

Fig 16.8: mesh generation in meshing

Page | 51

Mapped Face Meshing:As can be seen above, the default mesh has irregular elements. We are interested in creating a grid style of mesh that can be mapped to a rectangular domain. This meshing style is called Mapped Face Meshing. In order to incorporate this meshing style (Click) Mesh Control > Mapped Face Meshing as can be seen below.

Fig 16.9: mapped face meshing Now, the Mapped Face Meshing still must be applied to the pipe geometry. In order to do so, first click on the pipe body which should then highlight green. Next, (Click) Apply in the Details of Mapped Face Meshing table, as shown below.

Fig 16.10: details of mapped face meshing Now, generate the mesh by using either method from the "Default Mesh" section above. You should obtain a mesh comparable to the following image.

Fig 16.11: mesh generation
Page | 52

Edge Sizing:The desired mesh has specific number of divisions along the radial and the axial direction. In order to obtain the specified number of divisions Edge Sizing must be used. The divisions along the axial direction will be specified first. Now, an Edge Sizing needs to be inserted. First, (Click) Mesh Control > Sizing as shown below.

Fig 16.12: edge sizing Now, the geometry and the number of divisions need to be specified. First (Click) “Edge Selection Filter”. Then hold down the "Control" button and then click the bottom and top edge of the rectangle. Both sides should highlight green. Next, hit Apply under the Details of Sizing table as shown below.

Fig 16.13: edge sizing parameters Now, change Type to Number of Divisions as shown in the image below.

Page | 53

(a) Then, set Number of Divisions to 100 as shown below.

Fig 16.14: (a) & (b): edge sizing settings At this point, the edge sizing in the radial direction will be specified. Follow the same procedure as for the edge sizing in the axial direction, except select the left and right side instead of the top and bottom and set the number of division to 5.Then, generate the mesh by using either method from the "Default Mesh" section above. You should obtain the following mesh. As it turns out, in the mesh above there are 540 elements, when there should be only 500. Mesh statistics can be found by clicking on Mesh in the Tree and then by expanding Statistics under the Details of Mesh table. In order to get the desired 500 element mesh the Behaviour needs to be changed from Soft to Hard for both Edge Sizing's. In order to carry this out first Expand Mesh in the tree outline then click Edge Sizing and then change Behaviour to Hard under the Details of Edge Sizing table, as shown below.

Page | 54

Fig 16.15: edge sizing parameters Then set the behaviour to Hard for Edge Sizing 2. Next, generate the mesh using either method from the "Default Mesh" section above. You should then obtain the following 500 element mesh.

Create Named Selections:Here, the edges of the geometry will be given names so one can assign boundary conditions in FLUENT® in later steps. The left side of the pipe will be called "Inlet" and the right side will be called "Outlet". The top side of the rectangle will be called "PipeWall" and the bottom side of the rectangle will be called "CenterLine" as shown in the image below.

Fig 16.16: named selection diagram In order to create a named selection first (Click) Edge Selection Filter. Then click on the left side of the rectangle and it should highlight green. Next, right click the left side of the rectangle and choose Create Named Selection as shown below.

Page | 55

Fig 16.17: named selection menu Select the left edge and right click and select Create Named Selection. Enter Inlet and click OK, as shown below.

Now, create named selections for the remaining three sides and name them according to the diagram. Save, Exit & Update:First save the project. Next, close the Mesher window. Then, go to the Workbench Project Page and click the Update Project button.

Step 4: Setup (Physics):Your current Workbench Project Page should look comparable to the following image. Regardless of whether you downloaded the mesh and geometry files or if you created them yourself, you should have checkmarks to the right of Geometry and Mesh.

Page | 56

Fig 16.18: fluid flow window in workbench after meshing Next, the mesh and geometry data need to be read into FLUENT®. To read in the data (Right Click) Setup > Refresh in the Workbench Project Page as shown in the image below.

Fig 16.19: editing setup parameters After you click Update, a question mark should appear to the right of the Setup cell. This indicates that the Setup process has not yet been completed. Launch FLUENT®: Double click on Setup in the Workbench Project Page which will bring up the FLUENT® Launcher. When the FLUENT® Launcher appears change the options to "Double Precision", and then click OK as shown below. The Double Precision option is used to select the double-precision solver. In the doubleprecision solver, each floating point number is represented using 64 bits in contrast to the single precision solver which uses 32 bits. The extra bits increase not only the precision, but also the range of magnitudes that can be represented. The downside of using double precision is that it requires more memory. Twiddle your thumbs a bit while the FLUENT® interface starts up. This is where we'll specify the governing equations and boundary conditions for our boundary-value problem. On the left-hand side of the FLUENT® interface, we see various items listed under Problem Setup. We will work from top to bottom of the Problem Setup items to

Page | 57

setup the physics of our boundary- value problem. On the right hand side, we have the Graphics pane and, below that, the Command pane. Define Solver Properties:In this section the various solver properties will be specified in order to obtain the proper solution for the laminar pipe flow. First, the axisymmetric nature of the geometry must be specified. Under General > Solver > 2D Space select Axisymmetric as shown in the image below.

(a)

Next, the Viscous Model parameters will be specified. In order to open the Viscous Model Options Models > Viscous - Laminar >Edit. . . . By default, the Viscous Model options are set to laminar, so no changes are needed. Click Ca ncel to exit the menu. Now, the Energy Model parameters will be specified. In order to open the Energy Model Options Models > Energy-Off > Edit.. .. For incompressible flow, the energy equation is decoupled from the continuity and momentum equations. We need to solve the energy equation only if we are interested in determining the temperature distribution. We will not deal with temperature in this example. So leave the Energy Equation set to off and click Cancel to exit the menu. Define Material Properties:Now, the properties of the fluid that is being modelled will be specified. The properties of the fluid were specified in the Problem Specification section. In order to create a new fluid (Click) Materials > Fluid > Create/edit. . . as shown in the image below. In the Create/Edit Materials menu set the Density to 1kg/m3 (constant) and set the Viscosity to 2e-3 kg/(ms) (constant) as shown in the image below.

Page | 58

(b) Click Change/Create. Close the window. Define Boundary Conditions:At this point the boundary conditions for the four Named Selections will be specified. The boundary condition for the inlet will be specified first. Inlet Boundary Condition:-

(c) In order to start the process (Click) Boundary Conditions > inlet > Edit. .. as shown in the following image. Note that the Boundary Condition Type should have been automatically set to velocity-inlet. Now, the velocity at the inlet will be specified. In the Velocity Inlet menu set the Velocity Specification Method to Components, and set the Axial Velocity (m/s) to 1 m/s, as shown below.
Page | 59

(d) Then, click OK to close the Velocity Inlet menu. Outlet Boundary Condition:First, select out let in the Boundary Conditions menu, as shown below.

(e) As can be seen in the image above the Type should have been automatically set to pressure- outlet. If the Type is not set to pressure outlet, then set it to pressure-outlet. Now, no further changes are needed for the out let boundary condition.

Page | 60

Save:In order to save your work (Click) File > Save Project as shown in the image below.

(f) Second Orde r Scheme:A second-order discretization scheme will be used to approximate the solution. In order to implement the second order scheme click on Solution Methods then click on Momentum and select power law as shown in the image below.

(g) Set Convergence Criteria:FLUENT® reports a residual for each governing equation being solved. The residual is a measure of how well the current solution satisfies the discrete form of each governing equation. We'll iterate the solution until the residual for each equation falls below 1e-6. In order to specify the residual criteria (Click) Monitors > Residuals > Edit..., as shown in the image below.

Page | 61

(h) Fig 16.20 (a), (b), (c), (d), (e), (f), (g) and (h): setting parameters in FLUENT® window Next, change the residual under Convergence Criterion for continuity, x- velocity, and y-velocity, all to 1e-6. Lastly, click OK to close the Residual Monitors menu.

Page | 62

Execute Calculation:Prior, to running the calculation the maximum number of iterations must be set. To specify the maximum number of iterations click on Run Calculation then set the Number of Iterations to 100, as shown in the image below.

Fig 16.21: run calculation window As a safeguard, save the project now. Now, click on Calculate two times in order to run the calculation. The residuals for each iteration are printed out as well as plotted in the graphics window as they are calculated. After running the calculation, you should obtain the following residual plot.

Fig 16.22: plots obtaining during calculation

Page | 63

Case Study 1(Reynold’s Number=200)
Geometry:-

Fig 17.0: geometry for case study 1: section with sudden enlargement Velocity Magnitude:-

4Fig 17.01: contours of velocity (m/s) for case study 1(Re=200) Stream Function:-

Fig 17.02: contours of stream function (kg/s) for case study 1(Re=200)

Page | 64

Pressure Profile:-

Fig 17.03: contours of static pressure (Pa) for case study 1(Re=200) Wall shear Stress:-

Fig 17.04: wall shear stress (Pa) for case study 1(Re=200) Centre Line Velocity:-

Fig 17.05: axial velocity (m/s) plot for case study 1 (Re=200)

Page | 65

Case Study 1 (Reynold’s Number=400)
Velocity Magnitude:-

Fig 18.01: contours of velocity (m/s) for case study 1 (Re=400) Stream Function:-

Fig 18.02: contours of stream function (kg/s) for case study 1 (Re=400) Pressure Profile:-

Fig 18.03: contours of static pressure (Pa) for case study 1 (Re=400)

Page | 66

Wall shear Stress:-

Fig 18.04: wall shear stress (Pa) for case study 1(Re=400)

Centre Line Velocity:-

Fig 18.05: axial velocity (m/s) plot for case study 1 (Re=400)

Page | 67

Case Study 2 (Reynold’s Number=200)
Geometry:-

Fig 19.0: geometry for case study 2: section with sudden contraction Velocity Magnitude:-

Fig 19.01: contours of velocity magnitude (m/s) for case study 2(Re=200) Stream Function:-

Fig 19.02: contours of stream function (kg/s) for case study 2(Re=200)

Page | 68

Pressure Profile:-

Fig 19.03: contours of static pressure (Pa) for case study 2(Re=200) Wall Shear Stress:-

Fig 19.04: wall shear stress (Pa) for case study 2(Re=200)

Centre Line Velocity:-

Fig 19.05: axial velocity magnitude (m/s) for case study 2(Re=200)

Page | 69

Case Study 2 (Reynold’s Number=400)
Stream Function:-

Fig 20.01: contours of stream function (kg/s) for case study 2 (Re=400) Velocity Magnitude:-

Fig 20.02: contours of velocity magnitude (m/s) for case study 2 (Re=400) Pressure Profile:-

Fig 20.03: (a) & (b) contours of static pressure (Pa) for case study 2 (Re=400)
Page | 70

Wall Shear Stress:-

Fig 20.04: plot of wall shear stress (Pa) for case study 2 (Re=400)

Centre Line Velocity:-

Fig 20.05: plot of axial velocity (m/s) for case study 2 (Re=400)

Page | 71

Case Study 3(Reynold’s Number=200)
Geometry:-

Fig 21.0: geometry for case study 3 This geometry includes contraction, enlargement and two outlets for a single inlet

Velocity Magnitude:-

Fig 21.01: contours of velocity magnitude (m/s) for case study 3 (Re=200)

Page | 72

Stream Function:-

Fig 21.02: contours of stream function (kg/s) for case study 3 (Re=200)

Pressure Profile:-

Fig 21.03: contours of static pressure (Pa) for case study 3 (Re=200)

Page | 73

Wall shear Stress:-

Fig 21.04: plot of wall shear stress (Pa) for case study 3 (Re=200)

Centre Line Velocity:-

Fig 21.05: contours of X- velocity (m/s) for case study 3 (Re=200)

Page | 74

Case Study 3 (Reynold’s Number=400)
Velocity magnitude:-

Fig 22.01: contours of velocity magnitude (m/s) for Re=400 Stream Function:-

Fig 22.02: contours of stream function (kg/s) for Re=400

Page | 75

Pressure Profile:-

Fig 22.03: contours of static pressure (Pa) for Re=400

Wall Shear Stress:-

Fig 22.04: plot of wall shear stress (Pa) Vs position (m) for Re=400

Page | 76

Centreline velocity:

Fig 22.05: plot of centreline velocity vs position for Re=400

Page | 77

References

An Introduction to Computational Fluid Dynamics H.k Versteeg & W. Malalaskera

Step by Step Ct Scan: A Practical Guide for Residents and Technologists D. Karthikeyan, Deepa Chegu

Essentials of Body MRI Brant William E., de Lange Eduard

ScanIP® Lambert M. Surhone, Miriam T. Timpledon, Susan F. Marseken VDM Publishing, 10-Aug-2010

Engineering Analysis with ANSYS® SoftwareTadeusz Stolarski, Y. Nakasone, S. Yoshimoto Elsevier Science, 15-Feb-2007

Page | 78