[1 August 2011] Computational Fluid Dynamics

[2011][Laminar and Turbulent Internal Pipe Flow]

[This

assignment contains two section which is Part A, Laminar Flow Computation and Part B, Turbulent Flow Computation. Fluent software is used to simulate and compute both Laminar Flow and Turbulent Flow.]

UNIVERSITI TUNKU ABDUL RAHMAN SETAPAK, KUALA LUMPURFACULTY OF ENGINEERING AND SCIENCE UEME3112 Fluid Mechanics IIComputational Fluid Dynamics Assignment

Group 25No 1 2 3 Name Yuhvhendrra Shrii Kumar (kumar1120@yahoo.com) Sia Yin Chung Kumaresan A/L Thangavelu Student ID 08UEB05965 09UEB01337 08UEB06548 Course ME Y4S1 ME Y3S2 ME Y3S3

Lecturer: Ms Jaslyn Low Foon Siang Submission Date: 1 August 2011

Table Of Content ContentSummary Aim of the Project Introduction Modelling Work Main Result Conclusion Introduction Brief Description Background and Theory of CFD Objective of the Project Modelling Work Brief Description of the Software Brief Description of Methodology Result and Discussion Major Assumption and Calculation Presentation and Discussion of Data in Table and Figures Comments on Possible Errors Conclusion Summary of Findings Key Numerical Values Interpretation References 8

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Summary Aim of the ProjectComputational Fluid Dynamics (CFD) assignment contain two section which is Part A, Laminar Flow Computation and Part B, Turbulent Flow Computation. Using Fluent software, students required to compute both Laminar Flow and Turbulent Flow.

IntroductionComputational Fluid Dynamics is a powerful way of modelling fluid flow, heat transfer, and related processes for a wide range of important scientific and engineering problems. The cost of doing CFD has decreased dramatically in recent years, and will continue to do so as computers become more and more powerful.

Modelling WorkBrief Description of the Software FLUENT is a computational fluid dynamics (CFD) software package to simulate fluid flow problems. It uses the finite-volume method to solve the governing equations for a fluid. It provides the capability to use different physical models such as incompressible or compressible, inviscid or viscous, laminar or turbulent, etc. Geometry and grid generation is done using GAMBIT which is the pre-processor bundled with FLUENT.

Introduction Brief Description Background and Theory of CFDComputational Fluid Dynamics (CFD) is a computer-based analysis technique used for predicting various physical and chemical phenomena (fluid flow, heat transfer, mass transfer, chemical reactions, phase change, combustion, flow acoustics, to name a few). It works by numerically solving the mathematical equations governing these phenomena. CFD is fast becoming a powerful tool, used in conjunction with conventional design techniques, to analyze engineering problems. Dynamics of fluids are governed by coupled non-linear partial differential equations, which are derived from the basic physical laws of conservation of mass, momentum, and energy. Analytical solutions of such equations are possible only for very simple flow domains with certain assumptions made about the properties of the fluids involved. For conventional design of equipment, devices, and structures used for controlling fluid flow patterns, designers have to rely upon empirical formulae, rules of thumb, and experimentation. However, there are many inherent problems with these conventional design processes. Empirical formulae and rules of thumb are extremely specific to the problem at hand and are not globally usable because of the non-linearity of the governing equations. For example, a rule of thumb for designing an aircraft wing may not be applicable for designing a wing mounted on a racing car, as the upstream flow conditions are completely different for the two configurations. The above reasons make experimentation the leading conventional design technique. However, there are many limitations of experimentation techniques as well: Experimentation needs a prototype to be built. Measurement of flow variables may cause these variables themselves to change, might not be possible at all (in very small or unreachable spaces), and may be expensive. Experimentation may take a long time to set up, sometimes lasts for a very short time, and may be very expensive, as in the case of supersonic wind-tunnel runs. Experimental data has limited detail. All these limitations are overcome by CFD, since it is a numerical simulation technique which does not require a prototype to be built, is not thwarted by measurement capabilities, and can provide extremely detailed data as and when required. Using CFD, you can build a computational model that represents a system or device that you want to study. Then you apply the fluid flow physics to this virtual prototype, and the software provides a prediction of the fluid flow pattern and other physical phenomena. CFD analysis not only complements testing and experimentation, but leads to a substantial saving of time as a large number of options can be tested much before the prototyping stage.

CFD is, thus, a tool for compressing the design and development cycle. The major reasons why using CFD analysis is relevant for engineering applications are summarized below: Visualizing designs - There are many devices and systems that are very difficult to prototype. Often, CFD analysis shows you parts of the system or phenomena happening within the system that would not otherwise be visible through any other means. CFD gives you a means of visualizing and enhanced understanding of your designs. Comprehensive Information - Experiments only permit data to be extracted at a limited number of locations in the system. 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. Making predictions using comprehensive results - As CFD is a tool for predicting what will happen under a given set of circumstances, it can analyze numerous hypothetical options very quickly. You give it variables and it gives you related outcomes. Thus, in a short time, you can predict how your design will perform, and test many variations until you arrive at an optimal result. All of this is done before physical prototyping and testing. The foresight you gain from CFD helps you to design better and faster. Improved design ability - Better and faster design or analysis leads to shorter design cycles. This leads to huge savings in terms of cost and time and the product gets to the market faster. Equipment improvements are built and installed with minimal downtime. Low Cost - Computational simulations are relatively inexpensive when compared to testing. 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. Ability to Simulate Real Conditions - Many flow and heat transfer processes cannot be (easily) tested - for example, hypersonic flow at Mach 20. CFD provides the ability to theoretically simulate any physical condition. Ability to Simulate Ideal Conditions - CFD allows great control over the physical process, and provides the ability to isolate specific phenomena for study. For example, a heat transfer process can be idealized with adiabatic, constant heat flux, or constant temperature boundaries.

Limitations of CFD The exactness of a CFD analysis ultimately depends on the precision of the modelled domain and the power and speed of the computer. Some errors are inevitable, for example, round-off errors introduced by computers. Other errors can be rectified by more accurate modelling of the domain. The limitations of CFD analysis are listed below: Physical models - CFD solutions rely upon physical models of real world processes (e.g. turbulence, compressibility, chemistry, multiphase flow, etc.). The solutions that are obtained through CFD can only be as accurate as the physical models on which they are based. Numerical Errors - Solving equations on a computer invariably introduces numerical errors Round-off error - errors due to finite word size available on the computer Truncation error - error due to approximations in the numerical models Round-off errors will always exist (though they should be small in most cases). Truncation errors will go to zero as the grid is refined - so mesh refinement is one way to deal with truncation error. 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. For example, for a problem of flow in a duct with sudden expansion, if flow is supplied to the domain by a pipe, you should use a fully developed profile for velocity rather than assume uniform conditions

Conservation of Mass/Continuity

Newtons Law/Momentum Equations This is a vector equation. The x component in the rectangular system

The vector form is:

For Incompressible, Newtonian fluid of constant viscosity (Navier-Stokes Equation)

Energy/First Law of Thermodynamics In the rectangular system

For constant property Newtonian fluid

Fluent Equations

Examples of CFD applications

Real Experiment

CFD Simulation

Aerodynamic Shape Design

Smoke plume from an oil fire in Baghdad

CFD simulation

Objective of the Project1. 2. 3. 4. To compute steady laminar flow (Re