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MesoWake (2014-2017)

Unified mesoscale to wind turbine wake downscaling based on an open-source model chain

MesoWake is a project sponsored by the European Commission's within an FP7 International Outgoing Marie Curie Fellowship granted to Javier Sanz Rodrigo, Senior Researcher at the National Renewable Energy Center of Spain (CENER). The outgoing phase of this fellowship, from August 2014 to July 2016, was hosted by the National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy. The reintegration phase is back to CENER until July 2017. The project counts with the National Center for Atmospheric Research (NCAR) and the Barcelona Supercomputing Center (BSC) as scientific partners.

The objective is to contribute to the development of an open-source model chain that can bridge the gap between mesoscale meteorological processes and microscale wind farm models.

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Monin Obukhov

Data Provider: 

The data is based on the Monin Obukhov similarity theory for atmospheric surface layer flows

Data accesibility: 

The test case is offered to participants of the IEA Task 31 Wakebench. In the future it will be open for public access.

Site Description: 

Monin Obukhov (M-O) similarity theory (Monin and Obukhov, 1954) sets the point of departure of modern micrometeorology (Foken, 2006). It is valid in the surface layer, i.e. approximately in the first 10% of the ABL, where Coriolis effects are negligible compared to friction, and under stationary and horizontally homogeneous conditions with no radiation.

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Monin Obukhov Neutral

Scope

The benchmark is open to participants of the Wakebench project using surface layer models. This is the first element of the building-block approach so it should be mandatory if you intend to participate in other test cases down the line.

The benchmark consists on empty domain (flat terrain) simulations of the neutral surface boundary layer in steady-state conditions. 

Objectives

Demonstrate that the flow model, when run in M-O conditions, is able to reproduce the analytical expressions of the profiles predicted by the theory for neutral conditions. At the same time, it will be possible to check the compatibility of the wall treatment with the flow model for a range of surface roughness conditions.

Data Accessibility

The benchmark is offered to participants of the IEA Task 31 Wakebench. In the future it will be open for public access.

Input data

The conditions for simulating the M-O profiles in neutral conditions are:

  • von karman constant: κ = 0.4

  • Roughness length: z0 = [0.0002, 0.03, 0.4] m

  • Obukhov length: L = ∞

  • Use dry air with a density ρ = 1.225 kg/m3 and dynamic viscosity μ = 1.73e-5 kg/ms

Validation data

The validation data will consist on normalized M-O profiles obtained from analytical functions.

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Monin Obukhov Stratified

Scope

The benchmark is open to participants of the Wakebench project using surface layer models with stratification. This is the first element of the building-block approach so it should be mandatory if you intend to participate in other test cases down the line where thermal stratification is present.

The benchmark consists on empty domain (flat terrain) simulations of the thermally stratified surface boundary layer in steady-state conditions. 

Objectives

Demonstrate that the flow model, when run in M-O conditions, is able to reproduce the analytical expressions of the profiles predicted by the theory for stratified flow. At the same time, it will be possible to check the compatibility of the wall treatment with the flow model for a range of heat flux conditions.

Data Accessibility

The benchmark is offered to participants of the IEA Task 31 Wakebench. In the future it will be open for public access.

Input data

The conditions for simulating the M-O profiles in stratified conditions are:

  • von karman constant: κ = 0.4

  • Roughness length: z0 = 0.03 m.

  • Obukhov length: L = [-100, ∞, 100] m

  • Use dry air with a density ρ = 1.225 kg/m3 and dynamic viscosity μ = 1.73e-5 kg/ms.

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NEWA (2015-2019)

The New European Wind Atlas (NEWA) project will develop a new reference methodology for wind resource assessment and wind turbine site suitability based on a mesoscale to microscale mode-chain. This new methodology will produce a more reliable wind characterization than current models, leading to a significant reduction of uncertainties on wind energy production and wind conditions that affect the design of wind turbines.

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NEWA Meso-Micro Challenge for Wind Resource Assessment

Background 

This challenge is organized in the context of the New European Wind Atlas (NEWA) project, whose overarching goal is to produce a seamless high resolution wind atlas for Europe. The wind atlas methodology will be based on a mesoscale to microscale (meso-micro) model-chain, validated with dedicated experiments as well as other observational databases from public and private sources. Wind resource assessment is related to the development of wind farms and implies the prediction of long-term wind statistics, notably the annual energy prediction (AEP).

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NEWA Meso-Micro Challenge: Phase 1

Scope

This first phase will be the basis to establish an assessment process for meso-micro wind resource assessment methodologies. To this end, initial datasets from two sites in horizontally homogeneous  conditions are proposed:

  • Cabauw, onshore, and
  • Fino1 offshore

such that single-column models can be used cost-effectively as proxy for 3D RANS models. This will provide a more efficient approach to test statistical methodologies that can be later applied to heterogeneous sites in 3D.

The results of this benchmark will be anonymous, unless decided otherwise by the participants, both in terms of the name of the participant and the name of the model, to facilitate participation from industry. Of course, it is expected that a subset of modelers will decide to publish their results with a detailed intercomparison in conferences and scientific journals.

Data Accessibility

Data from Cabauw and Fino1 are provided here reformatted to a common standard based on the original data from, respectively, KNMI's CESAR and BSH'x FINO official web repositories. 

Objectives

The objectives of the challenge apply in this first phase as follows:

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NEWA Meso-Micro Challenge: Phase 2

Scope

Brief description about the context (project, general goals, etc)

Data Accessibility

Brief description about the accessibility of the data

Objectives

Specific goals of the benchmark

Input data

Description about the boundary/initial conditions, constants, etc

Data should usually be stored in the Restricted folder (restricted to benchmark members).

Validation data

Description about the target experimental data.

Data should usually be stored in the Managers folder (restricted to Admins, Managers team members and specifically assigned users).

Model runs

Definition of simulations: model set-up, requirements, etc

Output data

Variables (files) required to perform the validation

Remarks

Further information about the experiment. Literature survey

Terms and Conditions

NDA for accessing the benchmark data.

Data should usually be stored in the Public folder (public if benchmark is public, else restricted).

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Norrekaer Enge

Data Provider: 

The data has been extracted from www.winddata.com; [1] site = norre

Data accesibility: 

The test case is offered to all participants of the IEA Task 31 Wakebench.

Site Description: 

The wind farm statistics have been measured on a coastal Wind Farm at Nørrekær Enge, Denmark (Højstrup et al.,  1993). The wind farm is located in a flat, homogeneous terrain characterized by grassland. The wind farm contains 42 Nordtank NTK 300F 330 [kW] wind turbines, stall controlled with 300 kW rated power. The rotor diameter is 28 m and the hub height is 31 m. The turbines are arranged in 6 rows, each with 7 turbines as shown in Figure 1. Furthermore two 58 m mast, equipped with meteorological sensors in 7 levels, located SW of and inside the wind farm are available.

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Norrekaer Enge Data Qualification

Scope

The benchmark is open to participants of Wakebench who want to qualify wind farm measurements for wake model validation.

Objectives

The main objective is to qualify wind farm measurements before data analysis can be performed. The qualification includes an analysis of the wind farm surroundings to identify potential terrain effects and obstacles, which can influence the local flow conditions. The data qualification includes basic quality screening, identification of outliers, and qualification of power values for each wind turbine. This process is used to eliminate sequences where the wind turbines have been stopped or been in an idling mode, start sequence, stop sequence or failure mode.  The data qualification analysis includes to definition of quality-checked references of wind speed and direction.

Data Accessibility

The benchmark is offered to all participants of the IEA Task 31 Wakebench.

Input data

Approximately one year of 10-minute statistics for power and wind measurements recorded in a 42 x 300 kW wind farm have been made available for this benchmark.

The recordings are available in three different formats:

1)       Stored in a MySQL database made accessible through the Internet;
2)       Stored in MS-EXCEL tables or
3)       Stored in a MS-DBASE database.

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Norrekaer Enge Power Deficit 1

Scope

The benchmark is open to participants of Wakebench who want to analyze wind farm measurements for wake model validation.

Objectives

Determination of power deficit between pairs of turbines in the wind farm as function of flow direction.

Data Accessibility

The benchmark is offered to all participants of the IEA Task 31 Wakebench.

Input data

Approximately one year of 10-minute statistics for power and wind measurements recorded in a 42 x 300 kW wind farm have been made available for this benchmark.

Validation data

  1. Determine the power deficit between a pair of wind turbines with 6.3 D spacing for a 20 deg inflow sector. The deficit between turbines A2 and A1 is determined for the inflow sector 155-175 deg for a 5° moving window and wind speed interval of 6 – 12 m/s with reference to M1. Power deficit = 1 - Power(A2)/Power(A1)

Model runs

Not applicable 

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Norrekaer Enge Power Deficit 2

Scope

The benchmark is open to participants of Wakebench who want to analyze wind farm measurements for wake model validation. 

Objectives

is to determine power deficit along straight rows of turbines in the wind farm. The turbines in flow sector 165 deg  has a constant spacing of 6.3D, while the spacing in direction 257 deg is constant and equal to 8.2D except for a large gap of 26.7D where speed recovery is to be expected.

Data Accessibility

The benchmark is offered to all participants of the IEA Task 31 Wakebench.

Input data

Approximately one year of 10-minute statistics for power and wind measurements recorded in a 42 x 300 kW wind farm have been made available for this benchmark. The following inflow conditions will be considered:

  • Wind speed interval: 9 – 11 m/s;
  • Turbulence intensity: all
  • Flow sectors: 155-175º and 247-267º

Validation data

  1. Power deficit along 6 distinct rows (A, B, C, D, E & F) determined as function of spacing for a direction of 165º with reference to M1.
  2. Power deficit along 7 distinct rows (A1-F1, A2-F2, A3-F3, A4-F4, A5-F5, A6-F6 & A6-F6) determined as function of internal spacing for a direction 257º with reference to M1.

Power deficit = 1 - Power(A2)/Power(A1)

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NTUA18_VGs

Scope

Tests concern the flow past VGs on an airfoil at Re = 0.87e6. Test details are given in the relevant Test Case. The input data are the airfoils and VG geometries as well as the test conditions. )

Data Accessibility

Data are freely available to everyone under a Creative Commons Attribution 4.0 International License.

Objectives

The objective of this benchmark is to validate different VG modelling approaches against experimental data.

Input data

Tests concern the flow past VGs on an airfoil at Re = 0.87e6. Test details are given in the relevant Test Case. The input data are the airfoils and VG geometries as well as the test conditions.

Validation data

The validation data are:

  1. Pressure distribution along the wing chord for the examined incidence range for the cases with and without VGs

  2. Force and drag coefficient polars for the cases with and without VGs

  3. Velocity, vorticity and Re stress data on three planes normal to the flow and five planes normal to the wing span for the case without VGs, as shown in Figure 1, for the α = 10° case.

  4. Velocity, vorticity and Re stress data on three planes normal to the flow for the case with VGs as shown in Figure 2, for the α = 10° case.

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