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)jung9053@umd.edu

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