wind turbine simulations using cpu/gpu heterogeneous...
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Wind Turbine Simulations Using CPU/GPU Heterogeneous ComputingYong Su Jung
Graduate Research AssistantUniversity of Maryland, College Park
James BaederProfessor
University of Maryland, College Park
Contact info.Yong Su Jung (Dept. AE)[email protected]
14th Symposium on Overset Composite Grids and Solution Technology
A heterogeneous solution framework using both CPUs and GPUs has been used tonumerically simulate flow over the NREL Phase VI horizontal-axis wind turbine. An in-houseline-based unstructured flow solver on CPU (HAMSTR) is coupled to an in-house structuredsolver on GPUs (GARFIELD) through a lightweight Python-based framework within anoverset mesh system. The full wind turbine simulations including the nacelle and tower areused to understand blade-tower interference on both upwind and downwind configurationsand the predictions have been compared against experimental data in terms of bladeairloads. The effects of laminar-turbulent transition are also investigated on the blade using𝛾𝛾 − 𝑅𝑅𝑅𝑅𝜃𝜃𝜃𝜃 − 𝑆𝑆𝑆𝑆 transition model, whose inclusion resulted in a more accurate torqueprediction and better resolved the unsteady airloads arising from blade-tower interference. Anormal wind profile model is used to simulate free-stream wind shear for wind turbinesoperating in an atmospheric boundary layer.
• Couple unstructured grid based CPU solver and structured GPU accelerated solver
• Exploit advantages of both multiple mesh paradigms and line-based method
• Apply to full wind turbine simulation for investigating the effect of flow unsteadiness
• Apply laminar-turbulent transition model on wind turbine simulation to study the effect on turbine performance
Abstract
Objectives
NRTEL Phase VI Wind Turbine Overset Grids• Two-bladed horizontal-axis-wind-turbine model
• Rotor diameter of 10.058 m and hub height of 12.19 m
• Tower clearance of 1.401 m
• Operates on upwind/downwind configuration at 72 RPM
• Test wind speed : 7 m/s, 10 m/s, and 20 m/s
Condition Rigid/Teetered Coning angle Blade tip pitchUpwind Rigid 0.0° 3.0°
Downwind Teetered (Avg. 3.0°)
3.4° 3.0°
*Dimensions are based on NASA Ames wind tunnel section
Near body (CPU)
Cylindrical nest (GPU)
Background (GPU)
5.1 millions cell 5.8 millions cell 4.1 millions cell
Coupled CPU-GPU Flow Solver
• HAMSTR: Line Method on Unstructured Grids for turbine blade, nacelle, and tower components
• GARFIELD : GPU Accelerated RANS Solver for background domain
• Communication between HAMSTR and GARFIELD is accomplished with overset grid assembler (TIOGA).
• Data pointers are shared in an Python framework which allow for the flexibility of a familiar scripting language without sacrificing performance.
Laminar-Turbulent Transition Model• 𝜸𝜸 − 𝑹𝑹𝑹𝑹𝜽𝜽𝜽𝜽 − 𝑺𝑺𝑺𝑺 transition model: Local correlation-based transition model for Spalart-Allmaras one equation
turbulence model
V∞(m/s)
Torque[N-m]
Experiment[N-m]
Thrust[N]
Experiment[N]
7 766 (696) 805 1,185 (1,126) 1,15410 1,196 (1,172) 1,340 1,673 (1,665) 1,67520 1,134 (1,084) 1,110 3,278 (3,261) 3,005
• The use of transition model better estimates the torque.
• Turbulent transition onset along with laminar separation bubble was predicted at mid-chord along the span at wind speed of 7 m/s.
Isolated Rotor Computation𝑽𝑽∞= 10 m/s 𝑽𝑽∞= 20 m/s𝑽𝑽∞=7 m/s
• At operating condition of 7m/s, flow is fully attached on the blade.• Separated flow presented on suction side at higher wind speeds.
• Explored CPU/GPU heterogeneous CFD framework for wind turbine simulation
• Observed improvements in turbine performance prediction using 𝛾𝛾 − 𝑅𝑅𝑅𝑅𝜃𝜃𝜃𝜃 − 𝑆𝑆𝑆𝑆 transition model, especially in attached flow condition
• Investigated blade-tower interference for both upwind and downwind configurations at wind speed of 7 m/s
• Studied effect of free-stream wind shear on the rotor performance and turbine wakes
• As a future work, study on optimal load balancing between CPU solver and GPU solver can be performed for a wind farm.
Conclusions
References1. M. Hand et al, “Unsteady Aerodynamics Experiment Phase VI: Wind Tunnel Test Configurations and
Available Data Campaigns,” NREL/TP-500-29955, National Renewable Energy Laboratory, 2001.2. D. Simms et al, “NREL Unsteady Aerodynamics Experiment in the NASA-Ames Wind Tunnel: A Comparison
of Predictions to Measurements,” NREL/TP-500-29494, National Renewable Energy Laboratory, 2001.3. Y. Jung, B. Govindarajan, J. Baeder, “Turbulent and Unsteady Flows on Unstructured Line-Based
Hamiltonian Paths and Strand Grids,” AIAA Journal, Vol. 55, pp.1986-2001, 2017.4. D. Jude and J. Baeder, “Extending a Three-Dimensional GPU RANS Solver for Unsteady Grid Motion and
Free-Wake Coupling,” 54th AIAA SciTech Forum, San Diego, CA, 20165. S. Medida, “Correlation-based Transition Modeling for External Aerodynamic Flows,” University of Maryland
PhD Dissertation, 2014.
Atmospheric Boundary Layer• Free-stream wind shear was described by normal wind profile using power law.
• Circular form low momentum wake zone lifted upward in wind shear.
• Large airload variations of sectional force occur near blade tip.
• Variation of blade airloads according to azimuth angle can induce highly unsteady hub moment.
• Opposite effects between two blades canceled each other in terms of rotor performance.
Low momentum wake zone
Tower shedding
𝑉𝑉 𝑧𝑧 = 𝑉𝑉ℎ𝑢𝑢𝑢𝑢( ⁄𝑧𝑧 𝑧𝑧ℎ𝑢𝑢𝑢𝑢)𝛼𝛼
at 3R at 6R
Full Configuration Computation (Left: upwind, Right: downwind)
decelerated
• Slower axial velocity due to tower blockage effect reduces blade aerodynamic force.
• The region affected by tower extends to half rotor revolution.
• Inner section experience more interaction due to lower tangential velocity
• The rotor was modeled without coning and teetered hub based on the blade being approximately parallel to the tower axis during tower passage.
• Blade shed wake interaction (BWI) was captured in downwind configuration.
• More severe blade-tower interference than upwind configuration, indicated by sharp drop in blade airloads.
• Over 30% reduction in single blade thrust and 50% reduction in torque at 𝜓𝜓 = 180°
Transition Fully turbulence Transition Fully turbulence
Leeward surface Windward surface
Quadrangulated triangles
Strand volume grid
Quad-dominant grid
10 %
Region of effect
Unstructured Grid CPU
Structured Grid GPU
1.2 R2.5 R
Cylindrical nested mesh
Chord-wise pressure distribution (Top: r/R=0.3, Bottom: r/R=0.8)
Iso-surface of vorticity magnitude contourInflow distribution ( ⁄𝑤𝑤 𝑉𝑉∞) at sectional plane of 0.63R
Inflow distribution ( ⁄𝑤𝑤 𝑉𝑉∞) at sectional plane of 0.63R
60 %25 %
15 %
Normal wind profile (𝛼𝛼=0.2)
𝑉𝑉 [m/s]
Sectional force distribution along radial direction
Flowchart showing function calls through Python