Automatic shape optimisation in hydraulic machinery using ...web.student.chalmers.se/.../JakobSimaderSlidesOFW5.pdf · Hydraulic Machinery 23.06.2010 1 | 27 Automatic shape optimisation

Universität Stuttgart

Institute of Fluid Mechanics andHydraulic Machinery

23.06.2010 1 | 27

Automatic shape optimisation in hydraulic

machinery using OpenFOAM®

5th OpenFOAM® Workshop, 21-24 June 2010, Gothenburg

Jakob Simader

Andreas Ruopp

Ralf Eisinger

Albert Ruprecht

Institut of Fluid Mechanics and Hydraulic Machinery


www.ihs.uni-stuttgart.de



23.06.2010 5th OpenFOAM® Workshop, 21-25 June 2010, Gothenburg 2 | 25

Contents

1. Motivation

2. Workflow:

1. Parameter check

2. Grid generation and Grid conversion

3. CFD and simulated boundaries

4. convergence check and evaluation

3. Optimisation results

4. Conclusions and outlook



23.06.2010 5th OpenFOAM® Workshop, 21-25 June 2010, Gothenburg

Motivation

• Automatic shape optimisation

reducing calculation time

• Robust calculation shemes

• Low costs

• Low manpower

• Multi design criterias (part load,

BEP, overload)

• Multi parameter setup

3 | 25




Sequential workflow

Perl inte

rface

Optimiser

Parameter check

CFD simulation

Objective

function

optimised design

“Setup” configuration

design

parameters

Convergence check

Sequential workflow

Grid generation

Grid conversion

4 | 25




Advantages:

• Massive parallel cluster nodes

available

• Fast design studies possible

• Calculation of many designs at the

same time

• No license costs using

OpenFOAM®

• Available node number on cluster

is the only limitation

Parameter check

Grid generation

Grid conversion

CFD simulation

Convergence check

Level 1

Level 2

Parallel workflow

Multilevel parallel run on cluster nodes

5 | 25




Optimisation:

• Genetic algorithm (recombination)

• Simplex algorithm

• Stochastic algorithm

CFD and grid:

• OpenFOAM®

• Grid size between 80.000 and 100.000 nodes

• SST model

Used quality function:

• pressure recovery factor

Optimisation and CFD

2

in

inout

v

pp2c

p

6 | 25




First check

Second check

Parameter check

2 checks:

• Limited depth

• No intersection of cross sections:

– Determinate of the four bounding

vectors of each tube segment

must have same sign

Parameter check

)dc(

)cb()ba(

detsign

detsigndetsign

a

b

c

d

7 | 25




Grid generation

Definition of cross sections

• Width

• Height

• Radius

Grid generation

Grid conversion

Width

Height

Radius

8 | 25




Grid generation

Definition of position of cross sections

• Position of middle point of cut (x,y)

• Angle of normal vector

6 parameters for one cut

In total 48 parameters for 8

cross sections

Grid generator build for in house

CFD-code

converter needed for OpenFOAM®

data files

Grid generation

Grid conversion

9 | 25




Inlet velocities

Draft tube flow high sensitive to

inlet flow field

CFD simulation

• Measured velocity profiles for francis turbine:

– part load

– BEP

– full load

• One run for uniform velocity distribution (c = 6 m/s) without any circumferential component

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Convergence check

Criteria:

• Number of max. Iterations

• cp(i = max) 1

• cp(i) cp(i+1)

• ĉp cp(i = max)

check for pressure quantities

therefore Runtime output of:

- Abs. mass flow ave. of p

- Abs. mass flow ave. of ptot

Ensuring a good convergence behaviour

Convergence check

11 | 25




Optimisation setup

• 48 Parameter setup

• Parallel setup with up to 30 individuals

• CFD-Solver OpenFOAM®

• Calculation time approximately 24 hours

• Single design criteria:

– uniform velocity

– Part load, BEP and over load (8 segments)

– Detailed elbow discretisation (12 segments, but still 48 Parameter)

• Multi design criteria:

– Averaged cp of part load, optimum and over load (1/3 each)

– Weighted cp of part load, optimum and overload

(part load: 30%, optimum: 50%, over load: 20%

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Optimisation setup













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Single criteria results: uniform velocity

cp = 0.781

Optimised geometry:

• Area distribution fits common conventions

• smooth tube geometry, except bottom shape

→ detailed view of bottom shape

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cp = 0.736

Hand smoothed geometry:

• Smaller pressure recovery

• The contraction after the elbow has positive

effect on the secondary flow phenomena

15 | 25





Cutting plane

Secondary flow fills

up separated region

16 | 25




Single criteria results: optimum

cp = 0. 870

Optimised geometry:

• Similar shape to the one with uniform velocity

• Also: smooth tube geometry, except bottom

shape

17 | 25




Single criteria results: optimum

• Evolution of cp along optimisation run

18 | 25




Single criteria results: optimum detailed elbow

cp = 0. 892

Optimised geometry:

• smooth tube geometry, except bottom shape

→ “doing the right thing might be a bit wrong,

better than the wrong thing right”

19 | 25




Single criteria results: optimum detailed elbow

20 | 25




Single criteria results: comparison

cp (over load) = 0.757

cp (optimum) = 0.870

cp (part load) = 0.300

Optimised geometries:

• Very smooth shape for part load conditions

• Different shape for different inlet flow

→ optimising an averaged pressure recovery




21 | 25




Optimisation setup













22 | 25




weightedaveraged

cp (averaged) = 0.607




cp (weighted) = 0.696




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Optimisation effort summary

Single Weighted

Ttotal (<24 h) (<24h)

No.indiv. 2700 1902

No.of died indiv. 871 510

No.of calc. Indiv. 1829 1392

No.of best Indiv. 233 112

No.of nodes per Indiv 1 1

No.of cpu„s per node 4 4

• All runs on 24 nodes on xeon cluster / 2xQuadcore 2.8Ghz

• Time consumption not tuned yet

24 | 25




Conclusions

• Introduced optimisation scheme is applicable for high numbers

of parameters

• Fast calculation time due to parallel setup

• Multi design criteria optimisation

Outlook• „Multi-generation‟ optimiser

• Faster parallel „perl‟ algorithm to reduce calculation time

25 | 25

Documents

Automatic shape optimisation in hydraulic machinery using ...web.student.chalmers.se/.../JakobSimaderSlidesOFW5.pdf · Hydraulic Machinery 23.06.2010 1 | 27 Automatic shape optimisation