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Fluent Lecture Dr. Thomas J. Barber www.engr.uconn.edu/~barbertj.

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Presentation on theme: "Fluent Lecture Dr. Thomas J. Barber www.engr.uconn.edu/~barbertj."— Presentation transcript:

1 Fluent Lecture Dr. Thomas J. Barber

2 Outline Background Issues Codes, Flow Modeling, and Reduced Equation Forms Numerical Methods: Discretize, Griding, Accuracy, Error Data Structure, Grids Turbulence Fluent

3 CFD Connection to Other Solution Approaches CFD (numerical) approach is most closely related to experimental approach, i.e.  can arbitrarily select physical parameters (tunnel conditions)  output is in form of discrete or point data  results have to be interpreted (corrected) for errors in simulation.

4 Background Limiting Factors - I  Computer size: –Moore’s law: First postulated by Intel CEO George Moore. Observation that logic density of silicon integrated circuits has closely followed curve: Bits per sq. in.(and MIPS) doubles power of computing (speed and reduced size), thereby quadrupling computing power every 24 months. Calculations per second per year for $1000.

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6 Outline Background Issues Codes, Flow Modeling, and Reduced Equation Forms Numerical Methods: Discretize, Griding, Accuracy, Error Data Structure, Grids Turbulence Fluent

7 What is a CFD code? Geometry Definition Computational Grid and Domain Definition Boundary Conditions Discretization Approach Solution Approach Performance Analysis Solution Display Preprocessing Processing Postprocessing Converts chosen physics into discretized forms and solves over chosen physical domain Computer Usage Strategy Solution Assessment

8 Problem Formulation Equations of Motion Conservation of mass (continuity)= particle identity Conservation of linear momentum= Newton’s law Conservation of energy = 1st law of thermo (E) 2nd law of thermo (S) Any others????? Most General Form: Navier-Stokes Equations Written in differential or integral (control volume) form. Dependent variables typically averaged over some time scale, shorter than the mean flow unsteadiness (Reynolds-averaged Navier-Stokes - RANS equations).

9 Reduced Forms of Governing Equations Critical issue: modeling viscous and turbulent flow behavior

10 Complex Aircraft Analysis, Circa 1968 B with space shuttle Enterprise What is different with these aircraft from normal operation?

11 Reduced Forms of Governing Equations Euler Equations Coupled system of 5 nonlinear first order PDE’s Describes conservation mass, momentum, energy Describes wave propagation (convective) phenomena Full Potential Equation Single nonlinear second order PDE Describes conservation mass, energy Conservation of momentum not fully satisfied in presence of shocks P:otential Flow Equation Single linear second order PDE Describes conservation mass, energy Describes incompressible flow Conservation of momentum not fully satisfied in presence of shocks Navier-Stokes Equations Coupled system of 5 nonlinear second order PDE’s Describes conservation mass, momentum, energy Describes wave propagation phenomena damped by viscosity Neglect viscosity & heat conduction Isentropic, irrotational flows Neglect compressibility More Physics (More complex equations ) More Geometry (More complex grid generation) (More grid points)

12 Outline Background Issues Codes, Flow Modeling, and Reduced Equation Forms Numerical Methods: Discretize, Griding, Accuracy, Error Data Structure, Grids Turbulence Fluent

13 Finite Difference Finite Volume Finite Element All based on discretization approaches P.D.E. Lu=f Discretize System of Linear Algebraic Eqns UpUp

14 Breakup Continuous Domain into a Finite Number of Locations Boundary Condition B. C.

15 Discretization & Order of Accuracy Taylor Series Expansion Polynomial Function [Power Series] Accuracy Dependent on Mesh Size and Variable Gradients f x fifi f i+1 f i+2 f i+3 x i+1 x i+2 x i+3 xixi xx

16 Discretization Example Derivative approximation proportional to polynomial order Order of accuracy: mesh spacing, derivative magnitude –only reasonable if product is small

17 Numerical Error Sources - I Truncation error –Finite polynomial effect –Diffusion: acts like artificial viscosity & damps out disturbances –Dispersion: introduces new frequencies to input disturbance –Effect is pronounced near shocks Exact Diffusion Dispersion

18 Numerical Error Sources - II at t=0 at t=400 Traveling linear wave model problem

19 Numerical Error Sources - III at t=400

20 Numerical Error Sources - IV at t=400

21 Time-Accurate vs. Time-Marching Time-marching: steady-state solution from unsteady equations –Intermediate solution has no meaning Time-accurate: time-dependent, valid at any time step

22 Numerical Properties of Method Stability –Tendency of error in solution of algebraic equations to decay –Implies numerical solution goes to exact solution of discretized equations Convergence –Solution of approximate equations approaches exact set of algebraic eqns. –Solutions of algebraic eqns. approaches exact solution of P.D.E.’s as  x  t  0 Exact Solution U Governing P.D.E.’s L(U) System of Algebraic Equations Approximate Solution u Discretization Consistency Convergence as  x  t  0

23 How good are the results? Assess the calculation for –Grid independence –Convergence (mathematical): residuals as measure of how well the finite difference equation is satisfied. Look for location of maximum errors Look for non-monotonicity

24 How good are the results? Convergence (physical): Check conserved properties: mass (for internal flows), atom balance (for chemistry), total enthalpy, e.g.

25 Outline Background Issues Codes, Flow Modeling, and Reduced Equation Forms Numerical Methods: Discretize, Griding, Accuracy, Error Data Structure, Grids Turbulence Fluent

26 2-D Problem Setup Structured Grid / Data Unstructured Data / Structured Grid i,j+1 i-1,j i,j i,j-1 i+1,j X, i Y, j U i,j X Y U

27 2-D Problem Setup Semi - Structured Grid / Unstructured Data Unstructured Data / Unstructured Grid X Y U X Y U

28 Grid Generation Transformation to a new coordinate system Transformation to a stretched grid

29 Grid Generation - Generic Topologies Block-structured O + H More complicated grids can be constructed by combining the basic grid topologies - cylinder in a duct Overset or Chimera Cartesian + Polar Both take advantage of natural symmetries of the geometry

30 Grid Generation - Generic Topologies Cartesian-stepwise More complicated grids can be constructed taking advantage of simple elements Unstructured-hybrid DimensionUnstructuredStructured 2Dtriangularquadrilateral 3Dtetrahedrahexahedra

31 Outline Background Issues Codes, Flow Modeling, and Reduced Equation Forms Numerical Methods: Discretize, Griding, Accuracy, Error Data Structure, Grids Turbulence Fluent

32 Viscosity and Turbulence

33 Laminar Turbulent Steady Unsteady

34 Viscosity and Turbulence Properties Averaged Over Time Scale Much Smaller Than Global Unsteadiness

35 Viscosity and Turbulence Laminar viscosity modeled by algebraic law: Sutherland Turbulent viscosity modeled by 1 or 2 Eqn. Models –Realizable k-  model is most reliable k=turbulence kinetic energy  = turbulence dissipation –Model near wall behavior by: Wall integration; more mesh near wall, y+  1- 2 Wall functions: less mesh, algebraic wall model, y+  30-50

36 Outline Background Issues Codes, Flow Modeling, and Reduced Equation Forms Numerical Methods: Discretize, Griding, Accuracy, Error Data Structure, Grids Turbulence Fluent

37 Finite Volume Basic conservation laws of fluid dynamics are expressed in terms of mass, momentum and energy in control volume form. F.V. method: on each cell, conservation laws are applied at a discrete point of the cell [node]. –Cell centered –Corner centered Piecewise constant interpolation Piecewise linear Interpolation

38 2D Steady Flux Equation Finite-difference: centered in space scheme W E N S  X i-1,j i,j+1 i,j-1

39 Steady Governing Equations Start with generalized RANS equations  = transport coeff.  /  = diffusivity

40 Fluent Solution Method Simple Scheme SIMPLE: Semi-Implicit Method for Pressure Linked Equations

41 Fluent Solution Method Simple Scheme Solution algorithm: Staggered grid; convected on different grid from pressure. Avoids wavy velocity solutions

42 Fluent Solution Method Simple Scheme CV for u-eqn. Two sets of indices or one and one staggered at half-cell

43 Fluent Solution Method Simple Scheme CV for v-eqn.

44 Fluent Solution Method Simple Scheme CV for p-eqn.

45 Fluent Solution Method Simple Scheme 5-point computational molecules for linearized system using geographical not index notation

46 Fluent Solution Method Simple Scheme – Multidimensional Model 2-D and 3-D computational molecules using geographical not index notation

47 Fluent Operational Procedures Generate Geometry Generate Computational Grid Set Boundary Conditions Set Flow Models: Equation of State, Laminar or Turbulent, etc. Set Convergence Criteria or Number of Iterations Set Solver Method and Solve Check Solution Quality Parameters: Residuals, etc. Post-process: Line Plots, Contour Plots Export Data for Further Post-processing

48 Suggested Fluent Development Path Read FlowLab FAQ notes [Barber Web site] Run FlowLab to familiarize yourself with GUI, solution process and post-processing Read Cornell University training notes [Handout] Develop a relevant validation-qualification process, i.e. compare with known analyses or data –Developing laminar flow in straight pipe –Developing turbulent flow in a straight pipe [if appropriate] –Convection process –Convergent-divergent nozzle flow –….


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