using fluent in design & optimization devendra ghate, amitay isaacs, k sudhakar, a g marathe, p...

Using FLUENT in Design & Optimization

Devendra Ghate, Amitay Isaacs, K Sudhakar, A G Marathe, P M Mujumdar

Centre for Aerospace Systems Design and Engineering

Department of Aerospace Engineering, IIT Bombay

http://www.casde.iitb.ac.in/

FLUENT CFD Conference 2003 2

Outline

CFD in designProblem statementDuct parametrizationFlow solutionResultsConclusion


Using CFD in DesignSimulation Time

CFD is takes huge amounts of time for real life problems Design requires repetitive runs of disciplinary analyses

Integration With optimizer With other disciplinary analyses (e.g. grid generator)

Automation No user interaction should be required for simulation

Gradient Information No commercial CFD solvers provide gradient information Computationally expensive and problematic

( ) to get gradient information for CFD solvers (finite difference, automatic differentiation)


Methodology

Problem Specification

Parametrization

New parameters

Geometry Generation

Grid Generation

CFD problem setup

Flow Solution

Optimization usingSurrogate Models

(RSM, DACE)


Methodology

Problem Specification

Parametrization

New parameters

Geometry Generation

Grid Generation

CFD problem setup

Flow Solution

Optimization usingSurrogate Models

(RSM, DACE)

FLUENT


3-D Duct Design Problem

Entry Exit Location and shape known

Geometry of duct from Entry to Exit ?

Pressure Recovery• Distortion• Swirl


Parametrization

Y

X

Z

XDuct

Centerline

A

X

Control / Design Variables

• Ym, Zm

• AL/3, A2L/3Cross Sectional Area


Parametrization (contd.)

Y

X

Z

XDuct

Centerline

A

X

Control / Design Variables

• Ym, Zm

• AL/3, A2L/3Cross Sectional Area


Typical 3D-Ducts


Grid Generation

Generation of entry and exit sections using GAMBIT

Clustering Parameters

Conversion of file format to CGNS using FLUENT

Mesh file

Generation of structured volume grid using parametrizationGrid parameters

Entry & Exit sections

Conversion of structured grid to unstructured format

Complete grid generation process is automated and does not require human intervention

Complete control over

• Distance of the first cell from the wall

• Clustering

• Number of grid points


Turbulence ModelingRelevance: Time per SolutionFollowing aspects of the flow were of interest:

Boundary layer development Flow Separation (if any) Turbulence Development

Literature Survey Doyle Knight, Smith, Harloff, Loeffer

Circular cross-section S-shaped duct

Baldwin-Lomax model (Algebraic model) Computationally inexpensive than more sophisticated models Known to give non-accurate results for boundary layer separation etc.

k- realizable turbulence model Two equation model Study by Devaki Ravi Kumar & Sujata Bandyopadhyay (FLUENT

Inc.)


Turbulence Modeling (contd.)

Standard k- model Turbulence Viscosity Ratio

exceeding 1,00,000 in 2/3 cells

Realizable k- model Shih et. al. (1994) Cμ is not assumed to be

constant A formulation suggested

for calculating values of C1 & Cμ

Computationally little more expensive than the standard k- model

Total Pressure profile at the exit section (Standard k- model)


Distortion AnalysisDC60 = (PA0 – P60min) /qwhere,

PA0 - average total pressure at the section,

P60min- minimum total pressure in a 600 sector, q - dynamic pressure at the cross section.User Defined Functions (UDF) and scheme files were used to generate this information from the FLUENT case and data file.Iterations may be stopped when the distortion values stabilize at the exit section with reasonable convergence levels.


Parallel Execution

Parallel mode of operation in FLUENT16-noded Linux clusterPortable Batch Systems for schedulingBatch mode operation of FLUENT (-g)Scale up depends on grid size


Results: Total Pressure Profile


Results (contd.)

Mass imbalance: 0.17%Energy imbalance: 0.06%Total pressure drop: 1.42%Various turbulence related quantities of interest at entry and exit sections:

Entry Exit

Turbulent Kinetic Energy

124.24 45.65

Turbulent Viscosity Ratio

5201.54 3288.45

y+ at the cell center of the cells adjacent to boundary throughout the domain is around 18.


Slapping

These are huge benefits as compared to the efforts involved.

Methodology Store the solution in case & data files Open the new case (new grid) with the old data file Setup the problem Solution of (0.61 0.31 1 1) duct slapped on (0.1 0.31 0.1 0.1)

3-decade-fall 6-decade-fall

Without slapping 4996 9462

With slapping 1493 6588

Percentage time saving

70% 30%


Conclusion

Time for simulation has been reduced to around 20% using parallel computation and slapping.

0 20 40 60 80 100

Time (hrs)

Time per CFD Run

Serial Run

Parallel Run

Slapping

Process of geometry & grid generation has been automated requiring no interactive user input

FLUENT has been customized for easy integration into an optimization cycle

CFD analysis module ready for inclusion in optimization for a real life problem


Future Work

Further exploration and improvement of slapping methodologyIdentification and assessment of optimum optimization algorithm

Thank You

http://www.casde.iitb.ac.in/mdo/3d-duct/


Problem Statement

• A diffusing S-shaped duct• Ambient conditions: 11Km altitude• Inlet Boundary Conditions

• Total Pressure: 34500 Pa• Total Temperature: 261.4o K• Hydraulic Diameter: 0.394m• Turbulence Intensity: 5%

• Outlet Boundary Conditions• Static Pressure: 31051 Pa (Calculated for the desired mass flow rate)• Hydraulic Diameter: 0.4702m• Turbulence Intensity: 5%


Duct Parameterization Geometry of the duct is derived from the Mean Flow Line (MFL) MFL is the line joining centroids of

cross-sections along the duct Any cross-section along length of the

duct is normal to MFL

Cross-section area is varied parametrically Cross-section shape in merging area is same as the exit section


MFL Design Variables - 1

Mean flow line (MFL) is considered as a piecewise cubic curve along the length of the duct between the entry section and merging section

x

y(x), z(x)

0 LmLm/2

y(Lm/2), z(Lm/2) specified

Centry

Cmerger

y1, z1

y2, z2

Lm : x-distance between the entry and merger section

y1, y2, z1, z2 : cubic polynomials for y(x) and z(x)


MFL Design Variables - 2• y1(x) = A0 + A1x + A2x2 + A3x3, y2(x) = B0 + B1x + B2x2 + B3x3

• z1(x) = C0 + C1x + C2x2 + C3x3, z2(x) = D0 + D1x + D2x2 + D3x3

• y1(Lm) = y2 (Lm), y1’ (Lm) = y2’ (Lm), y1” (Lm) = y2” (Lm)

• z1(Lm) = z2 (Lm), z1’ (Lm) = z2’ (Lm), z1” (Lm) = z2” (Lm)

• y1’ (Centry) = y2’ (Cmerger) = z1’ (Centry) = z2’ (Cmerger) = 0

• The shape of the MFL is controlled by 2 parameters which control the y and z coordinate of centroid at Lm/2

• y(Lm/2) = y(0) + (y(L) – y(0)) αy 0 < αy < 1

• z(Lm/2) = z(0) + (z(L) – z(0)) αz 0 < αz < 1


Area Design Variables – 1

Cross-section area at any station is interpolated from the entry and exit cross-sections

•A(x) = A(0) + (A(Lm) – A(0)) * β(x)

• corresponding points on entry and exit sections are linearly interpolated to obtain the shape of the intermediate sections and scaled appropriately

• Psection = Pentry + (Pexit - Pentry) * β


Area Design Variables - 2

A0 + A1x + A2x2 + A3x3 0 β < β1

B0 + B1x + B2x2 + B3x3 β1 β β2

C0 + C1x + C2x2 + C3x3 β2< β 1

β =

x

β(x)

0 LmLm/30

1

2Lm/3

β1

β2

β(Lm/3) and β(2Lm/3) is specified

β variation is given by piecewise cubic curve as function of x

using fluent in design & optimization devendra ghate, amitay isaacs, k sudhakar, a g marathe, p...

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