A comprehensive modelling strategy for Horizontal...

STAR Global Conference

7-9th March 2016

A comprehensive modelling strategy for Horizontal

Axis Wind Turbine (HAWT) aerodynamics

J. O’Brien, P. Griffin, T. Young, J. Early

Presenter: Jerry O’Brien

Department of Mechanical, Aeronautical & Biomedical Engineering

University of Limerick, Ireland


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Overview

2

Introduction

Current State of the Art

Trends within numerical research

Experimental Conditions

Introduction

Measurement techniques

Numerical Model

Introduction

Boundary conditions and mesh generation

Results

Introduction

Velocity Deficit

Stall

Turbulence characteristics

Conclusions


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Introduction Blade-wake interactions in wind turbine applications

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Kyoto Agreement, European energy agreements of 2020 and 2050

Larger turbines – Energy harvested is proportional to rotor area

Large turbine – Results in design complications

Flexible composite blades

Fluid-Structure Interaction (FSI) modelling

This presentation will focus on the aerodynamic modelling of the wind turbine structure

Validation of numerical models


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Current state of the art Trends within the literature – Numerical studies

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Simplified – Time and computational expense!

Often one blade is modelled (120o periodic boundary condition)

Tower structure not modelled

Not representative of “real life”

Transient effects from the tower and nacelle on the rotor are ignored


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Current state of the art Trends within the literature – Numerical studies

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Experimental Conditions Introduction

6

Investigation carried out at U of 10m/s (Queen’s University, Belfast)

Tip speed ratio (λ) of 2.54 and 3.87

Two component hot-wire (X-probe)

λ = Ω𝑅 𝑈∞


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Test Section entrance

HAWT model

X-wire

Traverse system (fixed to test section roof)

Optically transparent side walls

Experimental Conditions Measurement techniques

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Sampling rate and time were 1 kHz for 1s respectively

5 data planes were recorded behind the HAWT in the near wake

Detailed data measurement – 1558 data points per plane


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Numerical model Introduction

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Investigate the ability of several turbulence models to predict wake turbulence

SST k- turbulence model (Vorticity Confinement model & curvature correction terms)

Reynolds Stress Transport (RST) turbulence model

All simulations were modelled on ICHEC* HPC cluster (Fionn)

144 and 192 cores respectively

2s of physical time modelled

Time cost: 50hrs and 144hrs respectively

*Irish Centre for High End Computing


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Numerical model Boundary conditions and mesh generation

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Solution domain divided into a stationary and rotational domain

Predetermined rotational velocity placed on rotating region


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Divided the meshing process into two parts;

Meshing process for the rotating region

Meshing process for the wake/stationary region


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Meshing process for rotating region

Drag, Surface Avg. Pressure and Y+ values over the turbine blades

Base Size (mm) No. of Elements Surface Avg.

Pressure (Pa)

Drag (N)

20 6.69x106 -71.50 0.301

30 5.19x106 -71.22 0.281

38 4.28x106 -71.20 0.273

40 3.9x106 -71.20 0.264

60 2.4x106 -71.09 0.210


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Meshing process for wake region

Volume change


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Region No. of Cells Percentage of Total

Cells

Stationary 5.009x106 53.9%

Rotating 4.286x106 46.1%

Total 9.295x106 100%


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Results Introduction

17

Mean velocity deficit and Reynolds stress (u′v′) for both TSR values

The u′v′ component of Reynolds stress is normalized by U2∞

Velocity measurements are normalized by U

Flow is time dependant Experimental results represent a statistical average of the

flow field for a large amount of rotor rotations

To represent the turbulence characteristics, data was taken from a line through the

centre of the wake in both the y and z directions.

Field variance monitors used such that numerical and experimental would both be

statistically averaged results


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Results Velocity deficit λ = 2.54

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SST k- > 5% of exp

RST > 3% of exp

Tower and nacelle have a major

impact of the velocity deficit


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Results Velocity deficit λ = 3.87

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Deficit not a severe as λ = 2.54

Tower and nacelle have a major

impact of the velocity deficit

SST k- > 8.32% of exp

RST > 9.46% of exp

Stall


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Results Stall

20

Secondary structure

Merged 2D downstream

Noted by Yang et al.

(2011) and Whale et al.

(2000)


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Z. Yang, P. Sarkar, H. Hu, An experimental investigation on the wake characteristics of a wind turbine in an atmospheric boundary layer wind, 29th AIAA Applied Aerodynamics Conference 3815 (June) (2011) 1-18.

J. Whale, C. Anderson, R. Bareiss, S. Wagner, An experimental and numerical study of the vortex structure in the wake of a wind turbine, Journal of Wind Engineering 84 (1) (2000) 1-21.

Results Stall

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Results Experimental and numerical Data – TSR 2.54

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Results Experimental and numerical Data – TSR 2.54

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Results Turbulence characteristics – Z_direction

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Turbulence Model Tip Speed Ratio Percentage Error

SST k- turbulence

model

2.54 30.7

3.87 119.7

Reynolds Stress

Transport model

2.54 3.6

3.87 6.73


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Results Turbulence characteristics – Y_direction

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Turbulence Model Tip Speed Ratio Percentage Error

SST k- turbulence

model

2.54 10.66

3.87 162

Reynolds Stress

Transport model

2.54 5.17

3.87 10.46


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Current Conclusions Conclusions

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Both the SST and the RST turbulence models are capable of predicting the mean

velocity characteristics within a HAWT wake. (RST model appeared to correlate

stronger with experimental result)

Both models capable of accurately modelling the onset of stall

Tower structure is a major contributor of turbulence in the wake

Both models had difficultly modelling the fluctuating velocity components of the

flow


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Thank you for your attention

Acknowledgements

27 STAR Global Conference

7-9th March 2016

A comprehensive modelling strategy for Horizontal...

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