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Basics Case studies Conclusions

OpenFOAM and the �ap

P.Schmitt1 K. Doherty 2

1Queens University Belfast, Belfast, United Kingdom

2Aquamarine Power Ltd, Edinburgh, United Kingdom

23rd January 2013, Maynooth

Outline

1 Basics

2 Case studies

3 Conclusions

Computational Fluid Dynamics

Characteristics

Fully viscous, non linear simulation of free surface �ows

Reynolds-averaged Navier�Stokes equations

Overhead with pre and postprocessing

computing time/ HPC facilities

CFD - Challenges

Turbulence modeling

Relevant scales

Mesh motion

Automatized and e�cient mesh generation

transient simulations

wave maker and beaches

solver tuning

CFD - Moving bodies

Updating positionof body

Mesh motion

deformation

Sliding

interfaces

Topology

changes

Overlapping

meshes

CFD - Moving bodies

Mesh motion

deformation

Sliding

interfaces

Topology

changes

Overlapping

meshes

CFD - Moving bodies

Mesh motion

deformation

Sliding

interfaces

Topology

changes

Overlapping

meshes

CFD - Moving bodies

Mesh motion

deformation

Sliding

interfaces

Topology

changes

Overlapping

meshes

Outline

1 Basics

2 Case studies

3 Conclusions

Example case

25th scale tank model

Mesh generation: 30 min

Runtime: 24h on 64 cores

0 2 4 6 8 10

Rotationangle[deg]

Time [s]

CFDExp

15 15.5 16 16.5 17 17.5 18 18.5 19

Rotationangle[deg]

Time [s]

ExpCFD

Detailed design of a WEC

Obtaining statistically signi�cant data

20-50 seastates for 20-30 min time (full scale)

Response of Power Take o� (PTO)

directional heading

water-level

24 hours

Tank testing: 144− 288 sea states/PTO conditionsCFD: 2.7$/8node/hour ⇒ 74650− 149300$

24 hours

Tank testing: 144− 288 sea states/PTO conditionsCFD: 2.7$/8node/hour ⇒ 74650− 149300$

For obtaining statistically relevant data CFD will not replace tanktesting for many years!

Why CFD?

no limitation in tank size and shape

non invasive and easy access to all �eld values like e.g.velocity, pressure

within the limitations of RANS models access to viscous shearforces

Easy and automated variation of any model parameter

and why not?

Time and cost...

Shape variation

Gap variation

Shape variation

Gap variation

Shape variation

Gap variation

Shape variation

Gap variation

Shape variation

Gap variation

Rotation Angle

0 1 2 3 4 5 6 7 8

Rotationangle[deg]

Time [s]

nogapsmall

mediumlargeopen

Moment

0 0.5 1 1.5 2 2.5 3 3.5

ent[Nm]

Time [s]

nogapsmall

mediumlargeopen

Power Output

0 20 40 60 80 100

Lossof

Gapsize [%]

Blockage e�ect

In�uence of tank width

Blockage e�ect

Velocity beside the �ap

Blockage e�ect

Rotation

0 2 4 6 8 10

Rotationangle[deg]

Time [s]

2.3m4.6m5.5m

Impact I

Figure: Surface elevation in front of the �ap before and during impact

event.

Impact I

event.

Impact I

event.

Impact I

event.

Impact I

event.

Impact I

event.

Impact I

event.

Impact I

event.

Impact I

event.

Impact I

event.

Impact II

event.

Impact II

event.

Impact II

event.

Impact II

event.

Impact II

event.

Impact II

event.

Impact II

event.

Impact II

event.

Impact II

event.

Impact II

event.

Shape Variation

Figure: Original design and variations.

Shape Variation

Flow �eld

Figure: Flow �eld around shape variations.

Flow �eld

Conclusions and Outlook

Conclusions

CFD still too expensive to create statistically relevant data

CFD complements experimental tests

Shape variations

Understanding experimental limits

Access to all �eld variables

Viscous e�ects

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