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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 1 Chapter 15: Computational Fluid Dynamics Eric G. Paterson Department of Mechanical and Nuclear Engineering The Pennsylvania State University Spring 2005

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 2 Introduction Practice of engineering and science has been dramatically altered by the development of Scientific computing Mathematics of numerical analysis The Internet Computational Fluid Dynamics is based upon the logic of applied mathematics provides tools to unlock previously unsolved problems is used in nearly all fields of science and engineering Aerodynamics, acoustics, bio-systems, cosmology, geology, heat transfer, hydrodynamics, river hydraulics, etc…

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 3 Introduction We are in the midst of a new Scientific Revolution as significant as that of the 16th and 17th centuries when Galilean methods of systematic experiments and observation supplanted the logic-based methods of Aristotelian physics Modern tools, i.e., computational mechanics, are enabling scientists and engineers to return to logic-based methods for discovery and invention, research and development, and analysis and design

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 4 Introduction Scientific method Aristotle ( BCE) Greek philosopher, student of Plato Logic and reasoning was the chief instrument of scientific investigation; Posterior Analytics To possess scientific knowledge, we need to know the cause of which we observe Through their senses humans encounter facts or data Through inductive means, principles created which will explain the data Then, from the principles, work back down to the facts Example: Demonstration of the fact (Demonstratio quia) »The planets do not twinkle »What does not twinkle is near the earth »Therefore the planets are near the earth Knowledge of Aristotle’s work lost to Europe during Dark Ages. Preserved by Mesopotamian (modern day Iraq) libraries.

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 5 Introduction Scientific method Galileo Galilei ( ) Formulated the basic law of falling bodies, which he verified by careful measurements. He constructed a telescope with which he studied lunar craters, and discovered four moons revolving around Jupiter. Observation-based experimental methods: required instruments & tools ; e.g., telescope, clocks. Scientific Revolution took place in the sixteenth and seventeenth centuries, its first victories involved the overthrow of Aristotelian physics Convicted of heresy by Catholic Church for belief that the Earth rotates round the sun. In 1992, 350 years after Galileo's death, Pope John Paul II admitted that errors had been made by the theological advisors in the case of Galileo.

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 6 Introduction Mathematics Isaac Newton (1643 – 1727) Laid the foundation (along with Leibniz) for differential and integral calculus It has been claimed that the Principia is the greatest work in the history of the physical sciences. Book I: general dynamics from a mathematical standpoint Book II: treatise on fluid mechanics Book III: devoted to astronomical and physical problems. Newton addressed and resolved a number of issues including the motions of comets and the influence of gravitation. For the first time, he demonstrated that the same laws of motion and gravitation ruled everywhere under a single mathematical law.

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 7 Introduction Fluid Mechanics Faces of Fluid Mechanics : some of the greatest minds of history have tried to solve the mysteries of fluid mechanics ArchimedesDa VinciNewtonLeibnizEuler BernoulliNavierStokesReynoldsPrandtl

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 8 From mid-1800’s to 1960’s, research in fluid mechanics focused upon Analytical methods (Primary focus of ME33) Exact solution to Navier-Stokes equations (~80 known for simple problems, e.g., laminar pipe flow) Approximate methods, e.g., Ideal flow, Boundary layer theory Experimental methods Scale models: wind tunnels, water tunnels, towing-tanks, flumes,... Measurement techniques: pitot probes; hot-wire probes; anemometers; laser-doppler velocimetry; particle-image velocimetry Most man-made systems (e.g., airplane) engineered using build- and-test iteration. 1950’s – present : rise of computational fluid dynamics (CFD) Introduction Fluid Mechanics

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 9 Introduction History of computing Mastodons of computing, Early computer engineers thought that only a few dozen computers required worldwide Applications: cryptography (code breaking), fluid dynamics, artillery firing tables, atomic weapons ENIAC, or Electronic Numerical Integrator Analyzor and Computer, was developed by the Ballistics Research Laboratory in Maryland and was built at the University of Pennsylvania's Moore School of Electrical Engineering and completed in November 1945

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 10 Introduction High-performance computing Top 500 computers in the world compiled: Computers located at major centers connected to researchers via Internet

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 11 Outline CFD Process Model Equations Discretization Grid Generation Boundary Conditions Solve Post-Processing Uncertainty Assessment Examples (note to instructors: this section has been removed. When I taught ME33 at PSU, I showed examples of my personal research) Conclusions FLOWLAB

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 12 Model Equations Most commercial CFD codes solve the continuity, Navier-Stokes, and energy equations Coupled, non-linear, partial differential equations For example, incompressible form

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 13 Discretization Grid Generation Flow field must be treated as a discrete set of points (or volumes) where the governing equations are solved. Many types of grid generation: type is usually related to capability of flow solver. Structured grids Unstructured grids Hybrid grids: some portions of flow field are structured (viscous regions) and others are unstructured Overset (Chimera) grids

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 14 Structured Grids

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 15 Structured Overset Grids Submarine Moving Control Surfaces Artificial Heart Chamber Surface Ship Appendages

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 16 Unstructured Grids Branches in Human Lung Structured-Unstructured Nozzle Grid

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 17 Discretization Algebraic equations To solve NSE, we must convert governing PDE’s to algebraic equations Finite difference methods (FDM) Each term in NSE approximated using Taylor series, e.g., Finite volume methods (FVM) Use CV form of NSE equations on each grid cell ! ME 33 students already know the fundamentals ! Most popular approach, especially for commercial codes Finite element methods (FEM) Solve PDE’s by replacing continuous functions by piecewise approximations defined on polygons, which are referred to as elements. Similar to FDM.

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 18 Boundary Conditions Typical conditions Wall No-slip (u = v = w = 0) Slip (tangential stress = 0, normal velocity = 0) With specified suction or blowing With specified temperature or heat flux Inflow Outflow Interface Condition, e.g., Air-water free surface Symmetry and Periodicity Usually set through the use of a graphical user interface (GUI) – click & set

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 19 Solve Run CFD code on computer 2D and small 3D simulations can be run on desktop computers (e.g., FlowLab) Unsteady 3D simulations still require large parallel computers Monitor Residuals Defined two ways Change in flow variables between iterations Error in discrete algebraic equation

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 20 Uncertainty Assessment Process of estimating errors due to numerics and modeling Numerical errors Iterative non-convergence: monitor residuals Spatial errors: grid studies and Richardson extrapolation Temporal errors: time-step studies and Richardson extrapolation Modeling errors (Turbulence modeling, multi-phase physics, closure of viscous stress tensor for non- Newtonian fluids) Only way to assess is through comparison with benchmark data which includes EFD uncertainty assessment.

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 21 Conclusions Capabilities of Current Technology Complex real-world problems solved using Scientific Computing Commercial software available for certain problems Simulation-based design (i.e., logic-based) is being realized. Ability to study problems that are either expensive, too small, too large, or too dangerous to study in laboratory Very small : nano- and micro-fluidics Very large : cosmology (study of the origin, current state, and future of our Universe) Expensive : engineering prototypes (ships, aircraft) Dangerous : explosions, response to weapons of mass destruction

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 22 Conclusions Limitations of Current Technology For fluid mechanics, many problems not adequately described by Navier-Stokes equations or are beyond current generation computers. Turbulence Multi-phase physics: solid-gas (pollution, soot), liquid-gas (bubbles, cavitation); solid-liquid (sediment transport) Combustion and chemical reactions Non-Newtonian fluids (blood; polymers) Similar modeling challenges in other branches of engineering and the sciences

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 23 Conclusions Because of limitations, need for experimental research is great However, focus has changed From Research based solely upon experimental observations Build and test (although this is still done) To High-fidelity measurements in support of validation and building new computational models. Currently, the best approach to solving engineering problems often uses simulation and experimentation

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 24 FlowLab FlowLab (http://www.flowlab.fluent.com/) Educational software that uses the power of flow visualization through CFD to teach basic fluid mechanics principles in the engineering classroom. Runs Fluent's general purpose CFD code, FLUENT, and pre- processor, GAMBIT, in the background, with a user-friendly, student-specific graphical user interface (GUI) on its front end. Based on ready-to-use exercises, FlowLab eliminates the long learning curve associated with general fluid flow modeling packages, making it easy to deploy as part of the undergraduate or masters-level curriculum. Templates for Problems – in Cengel and Cimbala.

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 25 FlowLab Templates are Nearly self-guided Designed to teach specific lessons Effect of grid resolution Effect of domain size Flow physics and sensitivity to parameters, e.g., diffuser angle, airfoil angle of attack, etc. GUI for post-processing is easy to learn Contours Streamlines Vector Plots

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 26 FlowLab Step 1: Select template from startup menu (Example: Creep Domain template)

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 27 FlowLab Step 2: Read problem overview

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 28 FlowLab Step 3: Select R/L ratio (size of bump to domain size) Click Create Geometry

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 29 FlowLab Step 4: Create Mesh

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 30 FlowLab Step 5: Solve, monitor residuals Select “printer friendly” colors to turn off black background

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Chapter 15: Computational Fluid Dynamics ME33 : Fluid Flow 31 FlowLab Step 6: Post- process Compute C D Plot velocity profile Repeat process with new R/L ratio to learn how domain size impacts solution

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