IEC TR 61400-12-4:2020

Wind energy generation systems - Part 12-4: Numerical site calibration for power performance testing of wind turbines

IEC TR 61400-12-4:2020 summarizes the current state of the art in numerical flow modelling, existing guidelines and past benchmarking experience in numerical model validation and verification. Based on the work undertaken, the document identifies the important technical aspects for using flow simulation over terrain for wind application as well as the existing open issues including recommendations for further validation through benchmarking tests.

General Information

• Status: Published
• Publication Date: 21-Sep-2020
• ICS: 01 - GENERALITIES. TERMINOLOGY. STANDARDIZATION. DOCUMENTATION
• ICS: 27.180 - Wind turbine energy systems
• Technical Committee: TC 88 - Wind energy generation systems
• Drafting Committee: PT 61400-12-4 - TC 88/PT 61400-12-4
• Current Stage: PPUB - Publication issued
• Start Date: 22-Sep-2020
• Completion Date: 23-Sep-2020

Overview

IEC TR 61400-12-4:2020 is an IEC Technical Report that summarizes the state of the art for using numerical flow simulation as a method of numerical site calibration (NSC) for wind turbine power performance testing. Rather than prescribing a normative method, the report reviews numerical modelling approaches, existing verification and validation guidelines, and past benchmarking campaigns. It identifies key technical aspects for applying flow simulation over terrain to derive flow correction factors (FCFs) and documents open issues and recommendations for further validation and uncertainty quantification.

Key topics and technical requirements

• Numerical modelling approaches: overview of linear flow models, RANS (Reynolds-averaged Navier–Stokes), and LES / hybrid RANS–LES methods, with discussion of their applicability to wind energy flows.
• Guidelines and standards for V&V: summaries of existing guidance such as AIAA verification/validation guides, ASME V&V 20, COST Action 732, AIJ and VDI guidance, IEA Task 31, and MEASNET practices.
• Benchmarking and validation tests: review of intercomparisons and experiments (e.g., DEWI round robin, Bolund, New European Wind Atlas campaigns like Perdigão, Alaiz, Østerild, Kassel, and Wakebench) used to evaluate microscale models.
• Important modelling inputs and setup issues:
  • Quality of topographical and roughness input data
  • Computational domain size and boundary conditions
  • Mesh resolution and convergence criteria
  • Representation of atmospheric stability, Coriolis effects, and obstacles
  • Suggested applicability ranges for different model classes for NSC
• Open issues and recommendations:
  • How to derive FCFs robustly from simulation outputs for power curve corrections
  • Handling directional bin transitions and seasonal variability
  • Uncertainty quantification and the need for further benchmarking validation campaigns

Practical applications and who uses this standard

• Wind farm developers and site assessors: to evaluate whether NSC can replace or complement traditional mast-based site calibration, potentially reducing cost and lead time.
• Turbine manufacturers and performance engineers: for assessing turbine power performance under complex terrain or heterogeneous roughness.
• CFD practitioners and modelers: for guidance on best practices, model selection (RANS vs LES), input data needs, and validation expectations.
• Certification bodies and test houses: to understand the current capabilities and limitations of NSC when reviewing power performance measurements.
• Researchers and standards committees: to identify research gaps (uncertainty quantification, benchmarking) and priorities for future standardization.

Related standards and keywords

• Related documents: IEC 61400-12-1 (power performance measurement standard), ASME V&V 20, AIAA V&V guides, MEASNET protocols, IEA Task 31 outputs.
• SEO keywords: IEC TR 61400-12-4, numerical site calibration, NSC, flow correction factors, power performance testing, wind turbine site calibration, CFD for wind energy, RANS, LES, benchmarking, uncertainty quantification.

Standards Content (Sample)

IEC TR 61400-12-4 ®
Edition 1.0 2020-09
TECHNICAL
REPORT
Wind energy generation systems –
Part 12-4: Numerical site calibration for power performance testing of wind
turbines
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IEC TR 61400-12-4 ®
Edition 1.0 2020-09
TECHNICAL
REPORT
Wind energy generation systems –

Part 12-4: Numerical site calibration for power performance testing of wind

turbines
INTERNATIONAL
ELECTROTECHNICAL
COMMISSION
ICS 27.180 ISBN 978-2-8322-8781-1

– 2 – IEC TR 61400-12-4:2020 © IEC 2020
CONTENTS
FOREWORD . 4
INTRODUCTION . 6
1 Scope . 7
2 Normative references . 7
3 Terms, definitions, abbreviations and symbols . 7
3.1 Abbreviations . 7
3.2 Symbols and units. 8
4 Overview of Numerical Flow Simulation Approaches . 10
4.1 Linear Flow Models . 10
4.2 Reynolds-averaged Navier-Stokes (RANS) Models . 11
4.3 Large Eddy Simulation (LES) and Hybrid RANS/LES Models . 12
5 Existing Guidelines for Numerical Flow Modelling Applications . 13
5.1 General . 13
5.2 AIAA (1998) Guide for the Verification and Validation of Computational Fluid
Dynamics Simulations . 14
5.3 Standard for Verification and Validation in Computational Fluid Dynamics
and Heat Transfer – ASME V&V 20-2009 . 14
5.4 COST Action 732 “Quality Assurance of Microscale Meteorological Models” . 15
5.5 Architectural Institute of Japan Guidelines . 16
5.5.1 General . 16
5.5.2 The guidebook for practical applications of CFD to pedestrian wind
environment around buildings [18] . 16
5.5.3 Guidebook of recommendations for loads on buildings 2 [19] . 16
5.6 VDI 3783 Part 9 “Environmental meteorology – prognostic microscale wind
field mode- evaluation of flow around buildings and obstacles” . 16
5.7 International Energy Agency Task 31 Wakebench – Model Evaluation
Protocol for Wind Farm Flow Models . 17
5.8 MEASNET – Evaluation of Site-Specific Wind Conditions . 17
6 Summary of Benchmarking Validation Tests . 17
6.1 General . 17
6.2 DEWI Round Robin on Numerical Flow Simulation in Wind Energy . 17
6.3 Bolund Experiment . 18
6.4 European Wind Energy Association Comparative Resource and Energy Yield
Assessment Procedures I and II (2011, 2013) . 18
6.5 IEA Task 31 Wakebench Experiments . 19
6.6 New European Wind Atlas Experiments [32] . 19
6.6.1 Perdigão (double ridge) . 19
6.6.2 Alaiz (complex terrain with a strong mesoscale component) . 19
6.6.3 Østerild (flow over heterogeneous roughness) . 19
6.6.4 Kassel (flow over forested hill) . 20
6.7 Wind Forecast Improvement Project 2 [34] . 20
6.8 Wind Tunnel Test Validation Data . 20
6.8.1 Compilation of Experimental Data for Validation of Microscale
Dispersion Models [23] . 20
6.8.2 AIJ wind tunnel . 20
6.8.3 Wind tunnel test for flow over hill . 20

7 Important Technical Aspects for Performing Flow Simulations over Terrain for
Wind Energy Applications . 21
7.1 General . 21
7.2 Quality of Topographical Input Data . 21
7.3 Computational Domain . 21
7.4 Boundary Conditions for Computational Domain . 21
7.5 Mesh Parameters . 21
7.6 Convergence Criteria . 21
7.7 Atmospheric Stability . 21
7.8 Coriolis Effects . 22
7.9 Obstacles effects . 22
7.10 Suggestion on Model Range Applicability for NSC . 22
8 Open Issues . 22
8.1 General . 22
8.2 Determination of Flow Correction Factors from Numerical Simulation Results
for Power Curve Testing . 23
8.2.1 General . 23
8.2.2 Correlation check for linear regression . 23
8.2.3 Change in correction between adjacent wind direction bins . 23
8.2.4 Site calibration and power performance measurements in different
seasons . 23
8.3 Uncertainty quantification . 23
8.4 Proposal for Validation Campaign for NSC Procedures . 24
8.4.1 General . 24
8.4.2 Assessment of terrain at the test site . 24
8.4.3 Experimental layout . 24
Bibliography . 26

Table 1 – symbols used in this Technical Report . 8

– 4 – IEC TR 61400-12-4:2020 © IEC 2020
INTERNATIONAL ELECTROTECHNICAL COMMISSION
____________
WIND ENERGY GENERATION SYSTEMS –

Part 12-4: Numerical site calibration for power
performance testing of wind turbines

FOREWORD
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The main task of IEC technical committees is to prepare International Standards. However, a
technical committee may propose the publication of a Technical Report when it has collected
data of a different kind from that which is normally published as an International Standard, for
example "state of the art".
IEC TR 61400-12-4, which is a Technical Report, has been prepared by IEC technical committee
88: Wind energy generation systems.
The text of this Technical Report is based on the following documents:
Draft TR Report on voting
88/729/DTR 88/774/RVDTR
Full information on the voting for the approval of this Technical Report can be found in the
report on voting indicated in the above table.
This document has been drafted in accordance with the ISO/IEC Directives, Part 2.

A list of all parts of the IEC 61400 series, under the general title Wind energy generation
systems, can be found on the IEC website.
Future standards in this series will carry the new general title as cited above. Titles of existing
standards in this series will be updated at the time of the next edition.
The committee has decided that the contents of this document will remain unchanged until the
stability date indicated on the IEC website under "http://webstore.iec.ch" in the data related to
the specific document. At this date, the document will be
• reconfirmed,
• withdrawn,
• replaced by a revised edition, or
• amended.
– 6 – IEC TR 61400-12-4:2020 © IEC 2020
INTRODUCTION
IEC 61400-12-1 [1] is the International Standard for power performance measurements for
electricity producing wind turbines. It specifies that in complex terrain, a site calibration (SC) is
required to find the relation in flow characteristics between the measurement location and the
test turbine. This approach requires – in addition to the permanent measurement mast that is
used to measure the turbine power curve – installing a temporary mast at the location of the
turbine being tested, prior to the turbine installation. The IEC 61400-12-1 approach is frequently
used in industrial practice; however, it has a number of disadvantages:
• additional cost of the second mast and analysis of the site calibration results,
• additional time required for the site calibration in the range of 3 months,
• a site calibration decision has to be made before installing the wind turbine.
Due to these disadvantages, there is interest in the industry to find alternative methods for site
calibration. One alternative is to use numerical simulations to derive flow correction factors
(FCFs), i.e., the relation between wind speed at the wind turbine position and wind speed at the
reference meteorological mast position.
The IEC TC 88 committee, “Wind energy generation systems,” initiated the work on this
document to evaluate the potential application of numerical flow simulations for site calibration,
i.e., numerical site calibration (NSC).
With NSC, the flow correction factors are calculated using numerical simulation of the flow.
Despite eliminating some of the disadvantages mentioned earlier, NSC brings other challenges:
• dependence on simulation models,
• dependence on the setup of these models,
• dependence on the modeler’s expertise,
• uncertainty quantification of the model performance.
The project team (PT 61400-12-4) has outlined the current state of the art in numerical flow
modelling and has summarized existing guidelines and past benchmarking experience of
numerical model validation and verification. Based on the work undertaken, the project team
identified the important technical aspects for using flow simulations over terrain for wind energy
applications as well as the existing open issues including recommendations for further
validation through benchmarking tests. The project team concluded that further work is needed
before a standard for NSC can be issued.

___________
Numbers in square brackets refer to the Bibliography.

WIND ENERGY GENERATION SYSTEMS –

Part 12-4: Numerical site calibration for power
performance testing of wind turbines

1 Scope
This part of IEC 61400, which is a Technical Report, summarizes the current state of the art in
numerical flow modelling, existing guidelines and past benchmarking experience in numerical
model validation and verification. Based on the work undertaken, the document identifies the
important technical aspects for using flow simulation over terrain for wind application as well as
the existing open issues including recommendations for further validation through
benchmarking tests.
2 Normative references
There are no normative references in this document.
3 Terms, definitions, abbreviated terms and symbols
3.1 Terms and definitions
No terms and definitions are listed in this document.
ISO and IEC maintain terminological databases for use in standardization at the following
addresses:
• IEC Electropedia: available at http://www.electropedia.org/
• ISO Online browsing platform: available at http://www.iso.org/obp
3.2 Abbreviated terms
The following abbreviated terms are used in this document.
AIAA American Institute of Aeronautics and Astronautics
ABL atmospheric boundary layer
AEP annual energy production
AIJ Architectural Institute of Japan
ALEX17 Alaiz experiment 2017
ASME American Society of Mechanical Engineers
CEDVAL Compilation and Experimental Data for Validation of Microscale
DispersionModels
CFD computational fluid dynamics
CHT computational heat transfer
COST European Cooperation in Science and Technology
CREYAP Comparative Resource and Energy Yield Assessment Procedures
DES detached eddy simulation
DDES delayed detached eddy simulation
DEWI Deutsches Windenergie-Institut

– 8 – IEC TR 61400-12-4:2020 © IEC 2020
DTU Danish Technical University
EWEA European Wind Energy Association
EWTL Environmental Wind Tunnel Laboratory
FCF flow correction factor
GWh gigawatt-hour
IEA International Energy Agency
IEC International Electrotechnical Commission
LES large eddy simulation
LIDAR light detection and ranging
MEASNET Measuring Network of Wind Energy Institutes
MEP model evaluation protocol
NEWA New European Wind Atlas
NSC numerical site calibration
RANS Reynolds-averaged Navier-Stokes
RNG renormalization group
SC site calibration
SODAR sound detection and ranging
TC technical committee
TR technical report
UQ uncertainty quantification
URANS unsteady Reynolds-averaged Navier-Stokes
V&V verification and validation
VDI Verein Deutscher Ingenieure
WAsP Wind Atlas Analysis and Application Program
WFIP Wind Forecast Improvement Project
WTG wind turbine generator
3.3 Symbols and units
Table 1 shows the symbols used in the text and equations in this document.
Table 1 – Symbols used in this document
Symbol Definition Unit
th m/s
u
i component of filtered wind speed
i
filtered pressure Pa
p
µ molecular viscosity Pa s
turbulence viscosity Pa s
µ
t
Smagorinsky constant -
C
s
κ von Karman constant -
distance to the nearest wall m
d
local filter size m
∆
turbulence length scale m
l
Symbol Definition Unit
turbulence length scale obtained from RANS model m
l
RANS
turbulence length scale obtained from LES model m

l
LES
model constant of DDES model m
f
d
m/s
average component of velocity in the direction i
U
i
m/s
u i
turbulent component of velocity in the direction
i
m
x
space variable in the direction i
i
average pressure Pa
P
ρ density
kg/m
ν
kinematic molecular viscosity
m /s
kg m / s
i
body forces in the direction
F
i
2 2
Reynolds stresses
m /s
uu
i j
Kronecker’s delta -
δ
ij
kinematic turbulence viscosity
m /s
ν
T
2 2
turbulence kinetic energy
m /s
k
turbulence length scale m
L
T
2 3
m /s
P production of k
k
2 3
ε
dissipation rate of turbulence kinetic energy
m /s
RANS turbulence model constant -

C
µ
RANS turbulence model constant -
C
1ε
RANS turbulence model constant -
C
2ε
RANS turbulence model constant -
σ
ε
validation comparison error
E
error due to the modelling assumptions
δ
model
error due to numerical solution of the equations
δ
num
error due to input parameters
δ
input
error in the experimental values
δ
D
validation standard uncertainty
u
val
numerical solution uncertainty
u
num
input parameters uncertainty
u
input
experimental value uncertainty
u
D
r correlation coefficient -
DDES parameter -
γ
d
– 10 – IEC TR 61400-12-4:2020 © IEC 2020
Symbol Definition Unit
modified DDES constant / stepwise function -
A
DDES constant -
A
effective horizontal kinematic viscosity
m /s
K
h
effective vertical kinematic viscosity
m /s
K
v
m/s

u
velocity perturbation components in the direction i
i
pressure perturbation Pa

p
m/s
U horizontal velocity components of the unperturbed flow in the direction j
j
rotor diameter m
D
4 Overview of numerical flow simulation approaches
4.1 Linear flow models
Since the late 1980s, when computing resources were limited, linear wind flow models have
been the standard for wind resource assessment. These models are based on a linearization
of the Navier-Stokes equations, which was originally introduced in reference [2]. They were
designed to be used reliably in neutral atmospheric conditions over terrain with sufficiently
gentle slopes to ensure fully attached flow conditions.

∂u
i
0, for i 1,…,3 (1)
∂x
i
 
  
∂u ∂∂p ∂∂uu
i ii
U =−+ K  + K , for i =1,.,3 and j =1,2 (2)
j hv
 
∂x ∂x ρ ∂∂xx
∂x
j i j j
  3
Here, are the horizontal velocity components of the unperturbed flow, are

Uj =1,2 ui( = 1,…,3)
( )
j i
the velocity perturbation components, and 𝑝𝑝� is the pressure perturbation. K and K are the
h v
effective kinematic viscosities in the horizontal and vertical directions.
Linear models perform reasonably well where the wind is not significantly affected by steep
slopes, flow separation, thermally driven flows, low-level jets, and other dynamic and nonlinear
ABL phenomena.
The Wind Atlas Analysis and Application Program (WAsP) [3] has been the most widely used
amongst the linear models. WAsP procedures may be considered as a transfer function model
linking the wind speeds at the reference with those at the predicted locations. Significant
sources of error could be related to the terrain complexity, massive flow separation, wind
direction changes, and varying atmospheric conditions. The latter include, among others,
channeling effects, blocking effects, and thermally driven flows (e.g., diurnal sea breezes,
downslope winds).
Due to their fast and robust performance, linear models are still used in the wind industry.
= =
4.2 Reynolds-averaged Navier-Stokes (RANS) models
Due to the limitation of the linear models, as mentioned in 4.1, computational fluid dynamics
(CFD) models became more widely used in the wind industry. CFD application to the
atmospheric boundary layer (ABL) has been influenced by both CFD for mechanical engineering
and mesoscale meteorological modelling. CFD considers momentum and mass conservation
equations with four unknown variables: pressure and three velocity components. Other
variables describing the atmospheric state, such as temperature, humidity, and aerosol
concentration, are usually not considered.
Typical CFD for atmospheric flow simulation applications follow the single wind direction
approach representing a sector from the discretized wind rose. Flow simulations for each sector,
considering the effects of orography and roughness, result in speed-up factors.
In the Reynolds-averaged Navier-Stokes (RANS) approach [4], due to the turbulent nature of
the flow, the variables are described with statistical functions divided into average and
fluctuating (turbulent) components (e.g., U U+ u resulting in RANS equations:
i i i
 
∂∂(ρUU) (ρ ))∂(ρU
∂∂p
ii i
=0 and U =−+ ν − ρuu + F for i,, j =1.,3 (3)
 
j i j i
∂x ∂x ∂∂xx ∂x
 
i j ij j
 
In the RANS equations, turbulent correlations uu (also called turbulent fluxes or stresses)
i j
have to be parameterized to close the equation system. The Boussinesq hypothesis is used to
define the relation between turbulent fluxes and the gradients of mean values by introducing
eddy viscosity (first-order closure):

U
∂
∂U 2
j
i
−=uu νδ+ − k  for i, j 1,.,3 (4)
i j T ij
∂∂xx 3

ji

Two basic quantities are introduced to describe the turbulence: the kinematic turbulence
viscosity ν , and the turbulence kinetic energy, k . The kinematic turbulence viscosity depends
T
12/
on the turbulence kinetic energy, k , and the size of the turbulent eddies, L , as ν = kL .
T TT
There are different types of closures, e.g., one- and two-equations models. In the one-equation
model, the turbulence kinetic energy, k , equation is solved:

ν
∂k k ∂∂k
T
U =PC−+  for j=1,.,3 (5)


j k µ
∂x νσ∂∂xx
j T j kj


where P is the production of k due to mean wind velocity gradients. The turbulence length
k
scale, , is deduced from an analytical model, such as a function of the height above the
L
T
ground and sometimes the thermal stability [5].
In the two-equation models ( k - ε , RNG k - ε , k - ω , .), the closure is made through two
transport equations, one for k and one for the turbulence dissipation, ε :


∂∂kk∂ ν
T
UP=-ε+  for j=1,.,3 (6)


j k
∂x ∂∂xxσ
j j kj


=
=
– 12 – IEC TR 61400-12-4:2020 © IEC 2020


∂∂ε ε ∂ ν ε
T
U=CP - C ε+  for j=1,.,3 (7)
( ) 

jk12εε
∂xk ∂∂x σ x
j j  ε  j 

k
The kinematic turbulence viscosity , is given by the closure equation ν = C .
ν
T T µ
ε
Without the steady-state hypothesis used in the RANS model, the equations of motion can be
described with unsteady RANS (URANS).
Compared to the linear model, the RANS steady-state model is able to predict flow detachment
and reattachments in the separation zone in most cases, but the accuracy of the results in this
region is questionable. This limitation is inherent in the statistical nature of the model. The
RANS model is mostly applied under the assumption of neutral stratification, which in many
cases limits its applicability. There are solutions proposed for coping with the neutral
stratification limitation assumption, for example, through modifications of the turbulence closure
based on the Monin-Obukhov similarity theory [6] or by solving energy equations and adding a
buoyancy term to the RANS equations [7]; however, additional validations are needed.
RANS models require significantly more computational resources than linear models. Presently,
they are used for resource assessment and site suitability in complex sites, such as nonflat
terrain, abrupt roughness changes, or forested areas.
4.3 Large eddy simulation (LES) and hybrid RANS/LES models
The idea of large eddy simulation (LES) [8] is to ignore small-scale turbulences by a low-pass
filter and to only solve the turbulence that can be resolved by the grid. The governing equations
for incompressible flow (using the Smagorinsky subgrid scale model [8]) can be written as
follows:
∂ρu
i
= 0 (8)
∂x
i

∂ ρuu 
∂ ρu ( ) ∂u
( ) ji ∂∂p ∂u
j
i
i
+ =- + µµ+ + (9)
( )
t

∂t ∂x ∂∂xx ∂x ∂x

j i j j i


µρ= LS (10)
t s
LC∆ (11)
ss
S = 2S S (12)
ij ij

∂u
∂u
1 j
i
S + (13)
ij

2 ∂∂x x
j i

where u is the i component of the filtered wind velocity, C is the Smagorinsky constant, and
i s
∆ is the local filter size, i, j = 13,., . Further information for other subgrid scale models can be
found in reference [9].
=
=
Unlike RANS, LES does not solve transport equations for subgrid scale parameters, i.e., all
eddies that characterize the flow have to be resolved. As a result, LES is highly dependent on
grid resolution, and choice of the grid is critical. On the other hand, when the grid is fine enough,
LES can resolve the unsteady flow separation that can typically be seen behind a hill or at the
edge of a cliff, and a simulated flow field in such a flow separation region is closer to reality
than RANS [10].
One major problem of LES, however, is the modelling of surface roughness. For example, for a
low-roughness surface, LES requires very fine grids to resolve it, which is too computationally
expensive for an engineering application.
Hybrid RANS/LES approaches have been proposed to overcome this problem. Detached eddy
simulation (DES) [11] is one such approach. In DES, the transport equation of k and ε is
solved, and the length scale, l ,can be calculated by using the following equation:
l = min l , l (14)
( )
RANS LES
where
k
l = (15)
RANS
ε
lC= ∆ (16)
LES s
By using this approach, the RANS model is chosen near the boundary and LES is chosen in the
region far from the boundary. However, Spalart et al. [12] mentioned that the DES model
exhibits an incorrect behavior in the thick boundary layer and proposed a modification called
delayed detached eddy simulation (DDES). In DDES, the length scale, l , can be calculated by
using the following equation [13][14]:
ll= - f max (0,-l l ) (17)
RANS d RANS LES
where
A

f = 1- tanh A γ (18)
( )
dd1


In reference [14], the authors proposed a piecewise function of A instead of a constant to
simulate the flow fields in the atmospheric boundary layer.
5 Existing guidelines for numerical flow modelling applications
5.1 General
Scientific communities, certification organizations, and national engineering associations have
put together guidelines on verification and validation, quality assurance, and evaluation of
numerical flow models. An overview of the most relevant guidelines to NSC is provided here.

– 14 – IEC TR 61400-12-4:2020 © IEC 2020
5.2 AIAA (1998) Guide for the Verification and Validation of Computational Fluid
Dynamics Simulations
The American Institute of Aeronautics and Astronautics (AIAA) guide [15] provides guidelines
for benchmarking the performance of CFD models. It is a matter of performing as many
verification and validation (V&V) tests as possible to gain confidence and credibility on the
model results toward the specific intended use of the model. The high complexity of the models
makes it very difficult to validate the full range of operating conditions. Hence, the main
objective of the validation process is to develop and quantify enough confidence in the
numerical models so that they can be used reliably to predict the variables of interest within
acceptable limits.
The following elements should be considered for the V&V process:
• general description of the model including its intended uses or applications,
• description of the prognostic equations that the model solves,
• description of the hypotheses and approximations,
• description of the model parameterizations,
• atmospheric scales and spatial/temporal resolutions,
• description of the computational grid,
• boundary and initial conditions,
• input data and sources,
• output data in terms of prognostic and diagnostic (derived from prognostic) variables,
• references to published material addressing model evaluation results from this and other
similar models.
5.3 Standard for Verification and Validation in Computational Fluid Dynamics and
Heat Transfer – ASME V&V 20-2009
The American Society of Mechanical Engineers (ASME) standard [16] describes the method for
V&V in computational fluid dynamics and computational heat transfer (CHT). The idea of this
document is that the comparison error, E , between the measured and simulated value can be
written by using an error due to the modelling assumptions, δ , an error due to the numerical
model
solutions, δ , an error due to simulation input parameters, δ , and the error in the
num input
experimental values, , as shown in the following equation:
δ
D
E δ ++δ δδ- (19)
model num input D
Assuming that δ , δ and δ are independent, the uncertainty due to these terms ( u )
num input D val
can be written as:
2 22
uu + u + u (20)
val num input D
where u is the uncertainty due to numerical solutions (i.e., the standard deviation of δ ),
num num
etc.
=
=
The purpose of this document is to propose methods to quantify the uncertainty of the modelling
assumptions and interpretation of this value. Thus, the content of the document is as follows:
• Uncertainty due to numerical solutions can be quantified by using code verification and
solution verification. The code verification can be performed by comparing the numerical
solution with analytical solutions. The solution verification can be performed by systematic
grid refinement.
• Uncertainty due to simulation input can be estimated by using systematic sensitivity analysis
of the input parameters.
• The basic concept to determine the uncertainty in the experimental values.
• The method to quantify uncertainty due to modelling assumptions is described for different
cases.
• The interpretation of uncertainty due to modelling assumptions.
• An example of V&V.
5.4 COST Action 732 "Quality Assurance of Microscale Meteorological Models"
European Cooperation in Science and Technology (COST) action 732 "Quality Assurance of
Microscale Meteorological Models" [17] has been set up to improve and assure the quality of
microscale meteorological models that are applied for predicting wind flow in urban
environments. These best practices are based on previous guidelines devoted to steady RANS
equations for neutrally stratified flow fields.
General guidelines are developed to reduce errors and uncertainties in modelling the physics
and in numerical approaches. Uncertainties in modelling can be related to:
• target variables,
• approximate equations describing the physics of the flow, such as the turbulence models,
• the geometrical representation of the obstacles,
• the definition of the computational domain including the boundary and initial conditions.
Uncertainties in the numerical approach can be related to:
• computational grid,
• time step size,
• numerical approximations (discretization schemes),
• iterative convergence criteria.
The COST action 732 guideline remains general for most of the criteria and encourages
engineers to use best practices to reduce errors. Most parameters depend, to a large extent,
on the details of the application problem and cannot always be precise in the general guidelines.
For instance, no best practice for the choice of the turbulence models is given. Nevertheless,
for some parameters, such as the choice of the computational domain, the guideline gives
precise recommendations: domain size should be defined as a function of the urban size model,
especially the height of the urban canopy. The guideline also gives clear advice on spatial and
time discretization, as well as convergence criteria, to reduce numerical errors.
The guideline encourages the application of numerical sensitivity tests and validation of the
model performance on test cases available in the literature.

– 16 – IEC TR 61400-12-4:2020 © IEC 2020
5.5 Architectural Institute of Japan guidelines
5.5.1 General
Two guidelines for CFD applications were proposed by the Architectural Institute of Japan (AIJ).
One is The guidebook for practical applications of CFD to pedestrian wind environment around
buildings [18] and the other is the Guidebook of recommendations for loads on buildings 2 [19].
Both guidelines contain the best practice for setting the computational conditions and
benchmark procedure. The computational conditions are as follows:
• computational domain extension,
• grid generation and resolution,
• boundary conditions,
• numerical scheme,
• turbulence model,
• convergence of solution.
5.5.2 The guidebook for practical applications of CFD to pedestrian wind
environment around buildings [18]
This guideline summarizes important points for using the CFD technique for appropriate
prediction of the pedestrian wind environment. The variables of interest are mean wind speed
ratio and turbulence kinetic energy. The guideline is based on the results of benchmark tests,
such as flow around a single building and blocks of buildings, and buildings in an actual urban
area and tree canopy. The validation database is available on the AIJ web site [21].
5.5.3 Guidebook of recommendations for loads on buildings 2 [19]
This document provides a practical guide for predicting the design wind loads on a building,
including the effect of terrain using CFD based on reference [20]. According to this guideline,
CFD can be used as an alternative to a wind tunnel test. The variables of interest are peak
pressure coefficient as well as mean wind speed and turbulence kinetic energy. In this guideline,
LES is a recommended technique to predict peak wind loads during strong wind events due to
the reproducibility of the turbulent boundary layer and separation flow around the buildings
requirement. This guide shows requirements for model setup, accuracy verification and
validation, and wind loads design.
5.6 VDI 3783 Part 9 Environmental meteorology – prognostic microscale wind field
mode- evaluation of flow around buildings and obstacles
Verein Deutscher Ingenieure (VDI, German Engineers Association) guidelines describe the
state of the art in science and technology in the Federal Republic of Germany and serve as a
decision-making aid in the preparatory stages of legislation and application of legal regulations
and ordinances.
The purpose of guideline VDI 3783 Part 9 [22] is to evaluate microscale CFD wind field models
over terrain that explicitly resolve the flow field around obstacles in the near-ground ABL. The
purpose of the evaluation procedure given in this VDI guideline is to ensure a high-level quality
of the models. The guideline contains recommendations on convergence criteria and grid
resolution independence. It also provides test cases (including analytic and experimental
reference data) to validate model consistency. The experimental reference data originate from
the CEDVAL database by the Environmental Wind Tunnel Laboratory (EWTL), Meteorological
Institute of University of Hamburg [23]. These data were designed and are suitable for the
evaluation of RANS models. Due to the lack of detailed inflow conditions, they are not applicable
for LES model validation. Validation metrics and passing criteria are provided in the VDI
guideline.
5.7 International Energy Agency Task 31 Wakebench – Model Evaluation Protocol for
Wind Farm Flow Models
A model evaluation protocol (MEP) for wind farm flow models has been developed in the frame
of the International Energy Agency (IEA) Wind Task 31 “Wakebench” [24]. The protocol
addresses a V&V framework for wind farm flow models at the microscale level, typically used
in wind resource/site assessment and wind farm design applications.
The MEP is developed as a basis for a framework for activities related to V&V of wind flow
models. The MEP consists of:
• model qualification by scientific review,
• code and solution verification,
• validation, which includes:
– a building-block validation approach,
– validation data requirements and sources,
– variables of interest,
– setting up and running a model,
– metrics,
– quality acceptance criteria,
• model calibration.
5.8 MEASNET – Evaluation of site-specific wind conditions
Measuring Network of Wind Energy Institutes (MEASNET) is a co-operation of companies that
are engaged in the field of wind energy and want to ensure high-quality measurements, uniform
interpretation of standards and recommendations, and interchangeability of results. The
MEASNET document Evaluation of site specific wind conditions [25] outlines the procedure for
site assessment agreed upon between the MEASNET members. The document describes topics
from required input data to results reporting. The most relevant part for NSC is the spatial
extrapolation guidance that requires numerical modelling of the wind field. MEASNET provides
general recommendations on model verification, model assumptions, model validation, and
sensitivity analysis. MEASNET also recognizes that the uncertainties of flow modelling will both
depend on the topography and site meteorological complexity (vertical and horizontal distance
between the measured and extrapolated position, terrain roughness, stability conditions, and
the used model approximations).
6 Summary of benchmarking validation tests
6.1 General
Clause 6 provides a summary of comparison tests from wind flow modelling validation
campaigns. Some of the projects have been com
...