Investigation on Deep-Array Wake Losses Under Stable Atmospheric Conditions

Transcription

1 Investigation on Deep-Array Wake Losses Under Stable Atmospheric Conditions Yavor Hristov, Mark Zagar, Seonghyeon Hahn, Gregory Oxley Plant Siting and Forecasting Vestas Wind Systems A/S

2 Introduction

3 Introduction Atmpospheric stability How wind flow changes? Surface warms and cools through diurnal and seasonal cycles Air near cold surface becomes cold, heavy Turbulence shuts down, shear increases, atmosphere is stable Features like low-level jet, katabatic flow, appear Situation is not stationary, wind and turbulence profiles undergo significant changes in time Wind speed profile in neutral (classic) and an example of stable conditions

4 Introduction Atmospheric stability Downward momentum transport (replacement of extracted wind energy) blockage Nacelle wind speed data

5 Unstable Stable Introduction Atmospheric stability Correlation between Stability and Wake losses from Production data

6 Lower Order Wake Models Fuga 2 and DWM

7 FUGA 2 Linearized CFD Wake model FUGA2 (March 2014 version) can predict effects of atmospheric stability from Moderately stable over Neutral to Unstable on wake deficit calculations. assumes a horizontally homogeneous ABL suited for flat terrain with a constant roughness length Wake losses in %- Neutral Wake losses in % - Moderately Stable Model Wake losses Fuga2- Neutral 17% Fuga2- Stable 23% SCADA 27%

8 Dynamic Wake Meandering (DWM) Park-level implementation of standalone DWM Time-averaged DWM wake flow field for different U h, TI, turbine types, stability, etc. (lookup table) Validation: Horns Rev 270 deg Neutral LES+ALM SCADA Results for the site in consideration are in preparation!

9 Multi-Scale High-Fidelity Simulations

10 High-Fidelity Modeling VestasFOAM Large-Eddy Simulation Actuator Line Model Coupling Based on SOWFA (NREL) Modifications: parallel architecture of ALM for linear scalability, run-time selectable BCs and turbulence models, wall functions based on local Monin-Obukhov similarity theory for applicability to heterogeneous terrain. Proper turbulence statistics according to class of thermal stratification and surface roughness Geostrophically driven and terrain temperature set from Mesoscale (WRF) simulation

11 High-Fidelity Modeling VestasFOAM Large-Eddy Simulation Actuator Line Model Coupling Models the blade as a single line of distributed forces that can vary in both space and time (unlike an actuator disk)

12 Surface Temperature (K) Mesoscale Simulations-WRF A typical day/night at the site WRF simulated surface temperature during a typical diurnal cycle Used as input forcing to the CFD microscale model Surface heating during the day produces a positive heat flux generating convective conditions. Characterized by low shear and high turbulence. Reverse is true during the nighttime, with surface cooling generating a negative heat flux causing a temperature inversion, high shear and low turbulence Time (hr)

13 Mesoscale Simulations Validation with Mast data Full physics: radiation, surface energy budget, humidity, clouds Real world observed data driven Surface forcing (+ possibly initial and lateral boundary conditions) for CFD Verification baseline for CFD Mast Data Model Data Observed (left) and WRF simulated (right) average vertical temperature gradient for all wind directions. Note frequent stable conditions for N-NE directions.

14 Precursor Simulations Microscale CFD Model Neutral (reference case) Precursor simulation with cyclic boundary conditions used to spin-up turbulence in the computational domain. Turbulence statistics accurate for given surface roughness and thermal stratification desired. Show below is instantaneous wind speed contours (left) and mean wind speed contours (right) for neutral flow conditions. Note mean wind speed contours reduce to the log-law profile as theory predicts.

15 Precursor Simulations Microscale CFD Model Neutral (reference case) Neutral - Instantaneous wind speed at hub height Neutral - Mean wind speed at hub height (10min averaging)

16 Precursor Simulations Diurnal Diurnal cycle begins from neutral conditions at T=15hr Unstable convective conditions progress to stable inversion at approx. T=26hr Unstable region characterized by high free stream turbulence and low shear Stable region characterized by low free stream turbulence and high shear Low-level jet can be seen to develop between m during stable hrs.

17 LES+ ALM Neutral Neutral - Instantaneous wind speed at hub height Neutral - Mean wind speed at hub height (10min averaging)

18 LES+ ALM Stable Stable - Instantaneous wind speed at hub height Stable - Mean wind speed at hub height (10min averaging)

19 High-Fidelity simulation results Neutral vs. Stable Neutral Stable

20 High-Fidelity simulation results Neutral vs. Stable Array 2 Array 1 Array 3 Neutral Stable

21 High-Fidelity simulation results Neutral vs. Stable Production Comparison Normalized Power by WTG1 Neutral = 17.3 corresponds to 33.5 % losses due to wake from N Stable = 14.9 corresponds to 42.3 % losses due to wake from N Array 1 Array 2 Array stable neutral stable neutral stable neutral

22 Conclusions and Future work HFM-Production losses across the park increase in stable flow conditions but it is more qualitative than quantitative result HFM-Massive effect of stability on wake losses on second row of turbines Lower order models (NO Jensen, Fuga2, DWM) are still much more viable for operational work! HFM-Aeroelastic Coupling and further refined wakes needs to be included HFM- Improved turbulence modeling (SGS)

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