Modeling the Cooling Air Flow in an Electric Generator...• A small generator at Uppsala...
Transcript of Modeling the Cooling Air Flow in an Electric Generator...• A small generator at Uppsala...
Pirooz Moradnia, Chalmers / Applied Mechanics / Fluid Dynamics 1
Modeling the Cooling Air Flow in an Electric Generator
6th OpenFOAM workshop
Pirooz Moradnia, Hakan Nilsson
PennState University, USA
2011-06-14
Pirooz Moradnia, Chalmers / Applied Mechanics / Fluid Dynamics 2
Importance of cooling in generators
• Hydroelectric power generation: ≈ 50% of the electricity generation in Sweden
• Modifications to the existing units→ significant contributions to the total energy production
• Increased power output→ more heat generation
• The two large sources of energy losses in the generators:
- Thermal: electric resistance in the generator coils
- Ventilation: when cooling down the unit
• The stators should be cooled by air flowing through the stator air channels
• Axially cooled generators
Pirooz Moradnia, Chalmers / Applied Mechanics / Fluid Dynamics 3
Experimental test rig
• A small generator at Uppsala University,
Sweden
• 4 cooling channel rows
• 108 cooling channels in each row
- Some channels impossible to access
• Stator outer casing with 12 openings
- 5 openings almost completely blocked
• 12 poles
• Rotational speed: 500 rpm
• Inner radius of the stator: 0.365 m
• Outer radius of the stator: 0.437 m
Pirooz Moradnia, Chalmers / Applied Mechanics / Fluid Dynamics 4
Modelling in OpenFOAM
• Geometry:
- A periodic 1/12 sector
- Generated with blockMesh
- Less air blockage than the experimental rig
• Boundaries:
- No in/outlet→ No prescribed mass flow
- Extra space for the air recirculation
- Mass flow given by the solution
• Solver
- OpenFOAM − 1.5.x
- MRFSimpleFoam (frozen rotor concept)
- LaunderSharmaKE turbulence model
- Y + < 10
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Complete generator model
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Stator cooling channels
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Study of the flow properties
• Inlet flow pattern
→ Smoke visualization vs. Steady computations
• Inlet velocity profiles
→ Traverse system, total pressure tube vs. Steady computations
• Outlet velocity profiles
→ Total pressure rake vs. Steady computations
• Volume flow
→ In/outlet measurements vs. Steady computations
Pirooz Moradnia, Chalmers / Applied Mechanics / Fluid Dynamics 8
Inlet flow pattern
Smoke pen Schematic flow pattern, Experiment Unit vectors of the flow, OpenFOAM
Remarks:
• Radially inward flow near the horizontal stator baffle
• Gradual growth of the axial velocity component farther from the baffle
• Purely axial velocity close to the rotor rings
Pirooz Moradnia, Chalmers / Applied Mechanics / Fluid Dynamics 9
Inlet velocity profiles
Location of the inlet, Experiment & OpenFOAM Comparison of the velocity Profiles
Remarks
• Height of the profiles:
- Experiment 29 mm (due to the angle of attack)
- OpenFOAM 38 mm
• Same behaviour in the experiments and OpenFOAM
• Difference in the magnitudes:
- Less air blockage in OpenFOAM
Pirooz Moradnia, Chalmers / Applied Mechanics / Fluid Dynamics 10
Outlet velocity profiles
Channel outlets, Experiment Available channels Location of the rake in the channels Outlet velocity profiles
Remarks
• 5 (out of 9) channels per row available for measurements
• Same behaviour in the experiments and OpenFOAM
• Difference in the magnitudes:
- Less air blockage in OpenFOAM
- Non-periodicity in the experimental rig (scattered experimental data)
Pirooz Moradnia, Chalmers / Applied Mechanics / Fluid Dynamics 11
Volume flow
• Experiment: ≈ 0.09m3/s
• OpenFOAM: ≈ 0.16m3/s
→≈ 43% difference
• Less air blockage in OpenFOAM
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Conclusions
• Non-periodicity in experiments:
- The largest source of error
• Less air blockage in OpenFOAM:
- More qualitative comparison
• Flow behaviours predicted by OpenFOAM:
- Close to the experimental results
• Better quantitative comparison:
- Unsteady simulations
- Exact geometrical similarity
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Thank you!
Acknowledgements
The work has been financed by SVC (www.svc.nu):
Swedish Energy Agency, ELFORSK, Svenska Kraftnat, 1
Chalmers, LTU, KTH, UU
SNIC (Swedish National Infrastructure for Computing) and C3SE (Chalmers Centre for Com-
putational Science and Engineering) have provided the computational resources.
1Companies involved: CarlBro, E.ON Vattenkraft Sverige, Fortum Generation, Jamtkraft, Jonkoping Energi, Malarenergi, Skelleftea Kraft, Sollefteaforsens,
Statoil Lubricants, Sweco VBB, Sweco Energuide, SweMin, Tekniska Verken i Linkoping, Vattenfall Research and Development, Vattenfall Vattenkraft, VG Power,
Oresundskraft, Waplans and Andritz Inepar Hydro