Nuclear Safety
NEA/CSNI/R(2020)3
February 2022
www.oecd-nea.org

Requirements for CFD-Grade

Experiments for Nuclear Reactor
Thermal Hydraulics

Nuclear Energy Agency

NEA/CSNI/R(2020)3
Unclassified

English text only
16 février 2022

NUCLEAR ENERGY AGENCYCOMMITTEE ON THE SAFETY OF NUCLEAR INSTALLATIONS

Requirements for CFD-Grade Experiments for Nuclear Reactor Thermal
Hydraulics

This document is available in PDF format only.

JT03489666
OFDE

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2  NEA/CSNI/R(2020)3
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NEA/CSNI/R(2020)3  3

COMMITTEE ON THE SAFETY OF NUCLEAR INSTALLATIONSThe Committee on the Safety of Nuclear Installations (CSNI) is responsible for the Nuclear
Energy Agency (NEA) programmes and activities that support maintaining and advancing
the scientific and technical knowledge base of the safety of nuclear installations.
The Committee constitutes a forum for the exchange of technical information and forcollaboration between organisations, which can contribute, from their respective
backgrounds in research, development and engineering, to its activities. It has regard to theexchange of information between member countries and safety R&D programmes ofvarious sizes in order to keep all member countries involved in and abreast of developments
in technical safety matters.
The Committee reviews the state of knowledge on important topics of nuclear safety
science and techniques and of safety assessments, and ensures that operating experience isappropriately accounted for in its activities. It initiates and conducts programmes identified
by these reviews and assessments in order to confirm safety, overcome discrepancies,
develop improvements and reach consensus on technical issues of common interest. It
promotes the co-ordination of work in different member countries that serve to maintainand enhance competence in nuclear safety matters, including the establishment of joint
undertakings (e.g. joint research and data projects), and assists in the feedback of the results
to participating organisations. The Committee ensures that valuable end-products of thetechnical reviews and analyses are provided to members in a timely manner, and madepublicly available when appropriate, to support broader nuclear safety.
The Committee focuses primarily on the safety aspects of existing power reactors,
other nuclear installations and new power reactors; it also considers the safety implicationsof scientific and technical developments of future reactor technologies and designs.
Further, the scope for the Committee includes human and organisational research activities
and technical developments that affect nuclear safety.

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Acknowledgements
The authors of this document are the principal contributors. They are listed below. Manyother members of the Nuclear Energy Agency (NEA) Committee on the Safety of Nuclear
Installations (CSNI) Working Group on the Analysis and Management of Accidents’ Task
Group for Computational Fluid Dynamics (CFD) application to safety and of the SILENCE
network contributed to discussions and their assistance is gratefully acknowledged.
Main author:
D. Bestion (French Alternative Energies and Atomic Energy Commission [CEA])
Principal contributors:
R. Camy (Électricité de France [EDF])
S. Gupta (Becker Technologies GmbH)C Heib (Institute for Radiological Protection and Nuclear Safety [IRSN])O. Marfaing (French Alternative Energies and Atomic Energy Commission [CEA])
F. Moretti (NINE Nuclear and Industrial Engineering)
T. Skorek (Gesellschaft für Anlagen- und Reaktorsicherheit [GRS])REQUIREMENTS FOR CFD-GRADE EXPERIMENTS FOR NUCLEAR REACTOR THERMAL HYDRAULICS

NEA/CSNI/R(2020)3  5

Table of contents
Acknowledgements ................................................................................................................................ 4
Executive summary ............................................................................................................................... 7
Acronyms and abbreviations ................................................................................................................ 9
1. Introduction ..................................................................................................................................... 11
2. Objectives of computational fluid dynamics code validation ...................................................... 14
3. Objectives of a computational fluid dynamics-grade experiment ............................................... 19
4. Scope of the analysis ........................................................................................................................ 24
Application of single-phase CFD to reactor design: .......................................................................... 24
Application of single-phase CFD to reactor safety ............................................................................ 25
5. Requirements for boundary and initial conditions and physical properties of fluids andsolids ..................................................................................................................................................... 26
Solid properties .................................................................................................................................. 26
Fluid properties .................................................................................................................................. 26
Initial conditions in solids .................................................................................................................. 26
Initial conditions in fluids .................................................................................................................. 27
Fluid boundary conditions on solid walls .......................................................................................... 27
Inlet and outlet fluid boundary conditions ......................................................................................... 27
Inlet fluid boundary conditions .......................................................................................................... 27
Outlet fluid boundary conditions ....................................................................................................... 29
Recommendations .............................................................................................................................. 29
6. Requirements for flow parameters measurement ........................................................................ 30
Recommendations .............................................................................................................................. 31
7. Requirements for measurement uncertainty ................................................................................ 32
Recommendations .............................................................................................................................. 32
8. Feedback from some computational fluid dynamics experiments and benchmarks ................ 34
9. Recommended roadmap for the design of computational fluid dynamics-grade experiments 36
Step 1: Objectives .............................................................................................................................. 36
Step 2: Specifications......................................................................................................................... 37
Step 3: Preliminary CFD calculations................................................................................................ 37
Step 4: Evaluation of expected validation uncertainty....................................................................... 38
Step 5: Building of the test facility and performing the tests............................................................. 38
Step 6: Final check of experiment suitability..................................................................................... 38
10. Specific comments for computational fluid dynamics-grade combined effect tests or
integral effect tests ............................................................................................................................... 39
Step 1: Define objectives and acceptance criteria .............................................................................. 39
Step 2: Specify experimentation and instrumentation ....................................................................... 39
Step 3: Preliminary calculations ........................................................................................................ 39

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6  NEA/CSNI/R(2020)3
Step 4: Evaluate validation uncertainty ............................................................................................. 40
Step 5: Build the test facility and perform the tests ........................................................................... 40
11. Conclusions .................................................................................................................................... 41
References ............................................................................................................................................ 42

FiguresFigure 1. Methodology for solving a complex reactor thermal-hydraulic issue.................................... 15
Figure 2. Illustration of the two methods for taking model uncertainty into account in acomputational fluid dynamics simulation of a reactor issue .................................................. 17
Figure 3. Roadmap for designing and performing a computational fluid dynamics-grade
experiment............................................................................................................................. 36

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NEA/CSNI/R(2020)3  7

Executive summary
The Nuclear Energy Agency (NEA) Committee on the Safety of Nuclear Installations(CSNI) launched an Action Plan on the “Application of Computational Fluid Dynamics(CFD) Codes to Nuclear Reactor Safety (NRS) Problems” in 2003 with mandates toperform the following tasks:


provide a set of guidelines for the application of CFD to NRS problems;

evaluate the existing CFD assessment bases and identify gaps that need to be filled
in order to adequately validate CFD codes for application to NRS problems;summarise the extensions needed to CFD codes for application to two-phase NRS
problems.

It was necessary to carry out a review of uncertainty methods for CFD application to
nuclear reactor safety. The report was issued in 2016 (NEA 2016a).
In parallel, as a spin-off activity, an international workshop was organised every two years
to provide a forum for numerical analysts and experimentalists to exchange information in
the field of NRS-related activities related to CFD validation. The CFD4NRS workshops
started in 2006 and significant progress was reported every two years in modelling,validation and verification. In addition, blind benchmark exercises were also proposed
based on some experimental tests related to safety issues such as thermal fatigue, turbulencein rod bundle flow, hydrogen behaviour in a containment, mixing with buoyancy effects,
and mixing in pressurised water reactor cold leg and downcomer in relation to pressurised
thermal shock. This benchmarking activity highlighted the need to have very well-designed
and instrumented CFD-grade experiments for model validation. Thus, the working groupon CFD application to nuclear safety of the CSNI Working Group on Analysis andManagement of Accidents (WGAMA) decided to establish some requirements for CFDgrade experiments able to properly validate the single-phase CFD tools. The SILENCE
network also supported this initiative and contributed to this work. This document reports
the results of this activity.
When looking for a validation experiment for CFD application to reactor simulation for
either design or safety studies, it appears that available data often suffer from a lack of local
measurements, an insufficient number of measured flow variables, a lack of well-defined
initial and boundary conditions, and a lack of information on results uncertainty.
This report first analyses the gap between available data and what can be called CFD-grade
experimental data. The objectives of the validation are recalled and put in the context of abest estimate plus uncertainty methodology. It then defines the objectives of a CFD-grade
experiment. The scope of this analysis is given with an emphasis on single-phase CFD.
The report then discusses the requirements and gives recommendations for both separate
effect tests and combined effect tests concerning boundary and initial conditions, flow
parameter measurements, measurement uncertainty of quantities of interest, and boundary
and initial conditions in view of minimising the validation uncertainty. The report

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8  NEA/CSNI/R(2020)3
concludes with guidelines and recommendations and proposes a roadmap for the design of
a CFD-grade experiment.
The main outcome of this work is the following:
Clear objectives should be first defined with reference to a reactor application, an analysisof the process to be investigated and a selection of the type of turbulence model that may
be used for the simulation. Then a discussion between experimentalists and the CFD code
practitioners is necessary to define the test section geometry, the initial and boundary
conditions, and the quantities of interest with their locations, so as to define therequirements on the measurement uncertainty. The choice of measurement technique stems
from these specifications.
The acceptance criteria should be defined in accordance with the required accuracy and thesensitivity of the measurements to the uncertainty in the experimental conditions.
Preliminary CFD simulations are necessary to confirm the most appropriate measurement
locations, experimental conditions and overall the interest of the experiment for the physics
to validate. Iterations may be necessary to reach an optimised design of the experiment.
CFD-grade experiments should be able to validate CFD and one important concern is tominimise the validation uncertainty on some selected figures of merit.
It is expected that this document will contribute to building more helpful future
experimental programmes, although the presented guidelines and recommendations stillneed to be complemented and improved.
An important effort should be made in the future to establish practical guidelines for
estimating the experimental uncertainty, which seems to be the weak point in the current
roadmap. This could justify a future activity to write a guide for experimentalists thatgathers all the available knowledge on sources of uncertainty for every type of sensor usedin CFD-grade experiments.

REQUIREMENTS FOR CFD-GRADE EXPERIMENTS FOR NUCLEAR REACTOR THERMAL HYDRAULICS

NEA/CSNI/R(2020)3  9Acronyms and abbreviations
ASME

American Society of Mechanical Engineers

ASN

Autorité de Sûreté Nucléaire (Nuclear Safety Authority, France)

BIC

Boundary and initial condition

BWR

Boiling water reactor

CEA

Commissariat à l'énergie atomique et aux énergies alternatives (French
Alternative Energies and Atomic Energy Commission, France)

CET

Combined effect test

CFD

Computational fluid dynamics

CFD4NRS

Computational fluid dynamics for nuclear reactor safety

CSNI

Committee on the Safety of Nuclear Installations (NEA)

EDF

Électricité de France

FoM

Figure of merit

GRS

Gesellschaft für Anlagen- und Reaktorsicherheit (Association for Plant
and Reactor Safety, Germany)

IAEA

International Atomic Energy Agency

IC

Initial condition

IET

Integral effect test

IRSN

Institut de Radioprotection et de Sûreté Nucléaire (Institute forRadiological Protection and Nuclear Safety, France)

ISP

International standard problems

KAERI

Korea Atomic Energy Research Institute

LES

Large eddy simulation

NEA

Nuclear Energy Agency

NRS

Nuclear reactor safety

OECD

Organisation for Economic Co-operation and Development

PCE

Polynomial chaos expansions

PIRT

Phenomena and Identification Ranking Table

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PSI

Paul Scherrer Institute

PWR

Pressurised water reactor

RANS

Reynolds-Averaged Navier-Stokes

rms

Root mean square

SET

Separate effect test

SILENCE

Significant Light and Heavy Water Reactor Thermal-hydraulicExperiments Network for the Consistent Exploitation of the Data

UMAE

Uncertainty Method based on Accuracy Extrapolation

UQ

Uncertainty quantification

URANS

Unsteady Reynolds-Averaged Navier-Stokes

V&V

Verification and validation

VLES

Very large eddy simulation

WG

Writing group

WGAMA

Working Group on the Analysis and Management of Accidents (NEA)

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1. Introduction
Following the conclusions of the Exploratory Meeting of Experts on the Application ofComputational Fluid Dynamics to Nuclear Reactor Safety (NRS) Problems that took placein Aix-en-Provence in 2002, the Nuclear Energy Agency (NEA) Working Group on the
Analysis and Management of Accidents (WGAMA) initiated activities the following year
in order to promote the use of computational fluid dynamics (CFD) for nuclear safety.
Three separate writing groups (WG) were created. WG1 established the “Best Practice
Guidelines for the Use of CFD in Nuclear Reactor Safety Applications” (NEA, 2007a;2015) with a set of guidelines for a range of single-phase applications of CFD. WG2
produced a document on the “Assessment of CFD Codes for Nuclear Reactor Safety
Problems” (NEA, 2008a; NEA, 2014a) with a compendium of current application areas
and a catalogue of experimental validation data relevant to these applications. A list of the
NRS problems for which CFD analysis is expected to bring real benefits was compiled,
and reviewed critically. Validation data from all available sources have been assembled
and documented. Assessment databases relating to specific NRS issues has been catalogued
separately, and more comprehensively discussed. Gaps in the existing assessment
databases were identified. WG3 treated the “Extension of CFD Codes to Two-Phase Flow
Safety Problems” (NEA., 2006; Bestion, 2010; NEA, 2010; NEA, 2014b). Then a review
of uncertainty methods for CFD application to nuclear reactor thermal hydraulics was
written (NEA, 2016a).
International benchmarks were also organised to test CFD capabilities to address reactor
issues. A first benchmark was based on a mixing Tee experiment from Vattenfall (NEA,
2011a) for investigating thermal fatigue. The second benchmark addressed flow in a rod
bundle with specific influence of spacer grids and was based on a MATIS-H test of the
Korea Atomic Energy Research Institute (KAERI) (NEA, 2013). The third addressed
physical processes (particularly stratification erosion) occurring in a containment following
a postulated severe accident in which there is a significant build-up of hydrogen in thecontainment atmosphere. It is based on a PANDA test of the Paul Scherrer Institute (PSI)(NEA, 2016b). The fourth benchmark was the first uncertainty quantification exercise on arather simple mixing problem in the presence of buoyancy effects. It was based on the
GEMIX mixing layer experiment of the PSI in the presence of density differences. The
ongoing fifth benchmark is the second benchmark with uncertainty evaluation and isdevoted to cold leg mixing processes associated to emergency core cooling system injection
with high density differences. It uses an experiment of Texas A&M University.
The writing groups proposed to extend the work by organising a new series of international
workshops to provide a forum for experimenters and numerical analysts to exchange
information. The first of the workshops, which all specifically focused on the applicationof CFD to NRS issues, took place in Garching, Germany in September 2006 under theacronym CFD4NRS (NEA, 2007b), sponsored jointly by the NEA and the International
Atomic Energy Agency (IAEA). Papers describing CFD simulations were accepted only ifthere was a strong validation component. Most related to the NRS issues highlighted in thispaper, such as pressurised thermal shock, boron dilution, hydrogen distribution, induced

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breaks and thermal striping. Selected papers of each workshop appeared in a special issueof Nuclear Engineering and Design.
The second workshop in the series, XCFD4NRS (NEA, 2008b), took place in Grenoble,
France in 2008. Here, the emphasis was more on new experimental techniques and twophase CFD. CFD4NRS-3 took place in Washington, DC in 2010 (NEA, 2011b), followedby the CFD4NRS-4 workshop in Daejeon, Korea in 2012 (NEA, 2014c). The fifth
workshop took place in Zurich, in 2014 (NEA, 2016c). Another workshop took place inCambridge, Massachusetts, in 2016 and the CFD4NRS-7 workshop took place in Shanghai,
People’s Republic of China, in September 2018. The CFD4NRS workshops are a very
useful addition to the more general conferences aimed at the nuclear technology community
in that they are highly focused on CFD applications to nuclear safety issues and the separate
effect and integral experiments that validate them. The papers reporting experimental
findings must contain data from local measurements of parameters of interest, suitable for
CFD validation, and the use of error bounds on the data are very desirable.
In parallel to the WGAMA activities, the SILENCE (Significant Light and Heavy Water
Reactor Thermal-hydraulic Experiments Network for the Consistent Exploitation of theData) network was created for co-operation among teams of experimentalists managing
significant experimental projects in nuclear reactor thermal hydraulics. Its objectives are:


Optimising the funding available worldwide for experiments, recognising their vitalrole for the design and the safety of existing and coming nuclear power plants.

Co-ordinating the efforts of teams of experimentalists in order to provide support
for international institutions, like the NEA and the IAEA, namely for launching andpossibly organising International Standard Problems (ISPs).



Addressing the scaling issue and providing an agreed view from the side ofexperimentalists. This also implies the design and execution of counterpart tests.



Setting-up a centre of expertise for supporting experimental programmes in“embarking countries” with an interest in the area of large thermal-hydraulicexperiments.



Maintaining, expanding and using the database of experiments already available
from various parts of the world possibly in co-operation with the international
institutions (notably the NEA where the NEA Data Bank is available).

Improving the existing measurement techniques.

One of the activities of the SILENCE network towards fulfilling the above-mentioned
objectives was to organise the “Specialists Workshop on Advanced Instrumentation andMeasurement Techniques for Nuclear Reactor Thermal-Hydraulics (SWINTH)”, which
was held in Italy in 2016. The workshop covered the technology of experimentation in
nuclear thermal hydraulics, including both separate effect and integral test facilities, withcoarse as well as CFD-grade measurements, and not limited to light water-cooled reactor
technology (www.nineeng.org/swinth).
Both the WGAMA CFD Task Group and the SILENCE network identified the need toestablish more detailed guidelines and requirements for future “CFD-grade experiments”
and this paper is an effort in that direction.
In the present report, the role of validation in resolving a thermal-hydraulic issue with CFD
is recalled and the objectives of a CFD-grade experiment are defined. The scope of thisanalysis is given with an emphasis on single-phase CFD.
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NEA/CSNI/R(2020)3  13

The report then proposes a list of requirements for both separate effect tests (SET) andcombined effect tests (CET) about boundary and initial conditions (BIC), flow parametermeasurements, measurement uncertainty of quantities of interest and BIC, with a view ofminimising the validation uncertainty. Finally, the report provides guidelines andrecommendations and proposes a roadmap for the design of a CFD-grade experiment.

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2. Objectives of computational fluid dynamics code validation
Safety demonstration of the nuclear reactors requires the analysis of complex problems
related to accident scenarios. Often, the experiments cannot reproduce at a reasonable cost
the physical situation without any simplification or distortion and the numerical tools
cannot simulate the problem by solving the exact equations. Only reduced scale
experiments are feasible to investigate the phenomena and only an approximate system of
equations may be solved to predict time and/or space averaged parameters with errors due
to imperfections of the closure laws and to numerical errors. Complex methodologies are
therefore necessary to solve a problem, including:a Phenomena and Identification Ranking Table (PIRT) analysis;

a scaling analysis and the selection of scaled integral effect tests (IETs), combined
effect tests (CETs) and separate effect tests (SETs);the selection of a numerical simulation tool;the verification and validation (V&V) of the tool;an analysis of transposition for use of the tool in the intended reactor case (ASN
and IRSN, 2017) (including identification of the geometrical [scale effect] andphysical differences between the validation cases and the scope of utilisation);

an assessment of the ability of the models to remain predictive (or penalising)taking account of the differences identified between the validation range of the tooland the utilisation range, including notably the justification of the transposition of
the adjustments and the uncertainties and assuring the consistency of modelling
choices in the safety studies with the choices adopted for the validation cases;the code application to the safety issue of interest and the use of an uncertainty
method to determine the uncertainty of numerical prediction.

This global approach is illustrated in Figure 1.
Verification answers the question ‘Do we solve the equations correctly?’
Verification of a code allows assessing the correct implementation of all numerical andphysical models in a CFD method. Verification assessment is generally performed in two
steps: 1) to confirm that the software is working as intended; and 2) to verify the calculation
performed by numerical calculations and quantification of numerical errors, if any. For thesecond step, usually simple test cases with analytical solutions or with manufactured
solutions are simulated. It may also include the calculations of experiments to test all the
relevant implementation aspects of a CFD code and the models implemented. This step isconducted by the code developers.

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NEA/CSNI/R(2020)3  15

Figure 1. Methodology for solving a complex reactor thermal-hydraulic issue

Note: UQ: uncertainty quantification; IET: integral effect test; CET: combined effect test; SET: separate effect test.
Source: Adapted from Höhne, T. and S. Kliem, (2020), “Detailed Simulation of the Nominal Flow and Temperature Conditions in a
Pre-Konvoi PWR Using Coupled CFD and Neutron Kinetics”, Fluids 2020, 5, 161, https://doi.org/10.3390/fluids5030161 and courtesyof S. Kliem (HZDR).

Validation answers the question: Do we solve the right equations?
Validation of a code is a process to assess the accuracy of the tested physical models of thecode based on comparisons between computed results and experimental data. The
validation is performed to provide confidence in the ability of a code to predict the valuesof the safety parameters or parameters of interest. It may also be used to estimate a bias on
the specific configuration under concern. The results of a validation may be used todetermine the uncertainty of some constitutive laws of the code. The validation can be
conducted by the code developers and/or by the code users. When it is performed by thecode developers it is called developmental assessment and when it is performed by the codeusers it is called independent assessment. A validation matrix is a set of selected
experimental data for the purpose of extensive and systematic validation of a code within
the limits of a given application scope. The validation matrix usually includes:
basic tests;



SETs;



IETs;



nuclear power plant data;

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SETs are experimental tests that intend to investigate a single physical process, either in
the absence of other processes or in conditions that allow measurements of the effects ofthe process of interest. An SET may be used to validate a physical model independently
from the others.
IETs are experimental tests that intend to simulate the behaviour of a complex system withall interactions between different dynamic phenomena occurring in various systemcomponents. An IET relative to reactor accidental thermal hydraulics can simulate thewhole primary cooling circuit and simulate the accidental scenario through initial andboundary conditions.
SETs may include single effect tests where only one process is investigated and may also
include CETs where a few processes are investigated, implying several physical models
having a significant influence. Although CET is not a standard nomenclature in theCommittee for the Safety of Nuclear Installations’ documents, we use it here to distinguishthem from SETs and IETs. CETs also simulate interactions between various flow and heattransfer processes occurring in various system components without simulating the wholesystem or reactor circuit of interest. It may simulate the flow in several components of areactor, including various geometries and various phenomena. Examples are the ROCOM
or HIBISCUS test facilities used to investigate phenomena in pressurised water reactor cold
legs and pressure vessels in boron dilution or pressurised thermal shock scenarios.
In the validation, the comparison of simulation results with measurements fromexperiments is a key point starting with the metric definition. Depending on the metric,
agreement can appear poor or good. The choice of the metric should be goal oriented.
Quantification of a bias on IET can contribute to uncertainty quantifications. Quantificationof biases on different SETs can be used for calibration if performed.
One may list the following sources of uncertainty of single-phase CFD application (NEA,
2016a):


Initial and boundary conditions.
Uncertainties related to the physical models:o Uncertainties related to the parameters of physical models: wall functions − ifused − to express momentum and energy wall transfers and parameters of
turbulence models (e.g. C1, C2, Cm, Prk and Prɛ of the k-ɛ model) are sourcesof uncertainties.
o Uncertainties related to non-modelled physical processes and uncertainties
related to the form of the models: models may have inherent limitations. For
example, any eddy viscosity model like k-ɛ or k-ω cannot predict a non-isotropic
turbulence nor an inverse-cascade of energy from small turbulence scales to large
ones.







Uncertainties related to the numerical solution: they are related to spatial
discretisation errors, time discretisation errors, iteration errors and round-off errors.
Simplification of the geometry: non-modelled geometrical details may have some
impact on the resulting flow.
Uncertainties due to scaling distortions: when the uncertainty or the accuracy isdetermined in a given range of flow conditions characterised by a geometry andextrapolated to a reactor with a different geometry and different values of somenon-dimensional numbers.
Uncertainty due to physical properties of fluid and solids.

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Uncertainty arising from physical instabilities/chaotic behaviour: non-linear
dynamic systems (like Navier-Stokes equations) can, under certain circumstances,have a chaotic behaviour.
Code validation requires comparison with experiments of various types: SETs, IETs,
possibly including CETs. The determination of code uncertainty also uses comparison todata of the various types to get the basic information for an inverse model uncertainty
quantification. Two types of methods are considered for model uncertainty quantificationin the review of uncertainty methods for CFD application to nuclear reactor thermal
hydraulics (NEA, 2016a): an accuracy extrapolation and an input uncertainty propagation,
as illustrated in Figure 2. Table 1 summarises the characteristics of the methods and shows
what type of experiments (SETs, CETs and IETs) are necessary. One may observe that
input uncertainty propagation methods require SETs to get information on the uncertaintyof the parameters of the models, while extrapolation methods require more prototypicalexperimental data to measure the accuracy of the model in conditions close to the reactor
application. For example, one can validate and determine the uncertainty of the parametersof a turbulence model by calculating a set of experiments with basic flow configurations,such as a boundary layer, a free jet, an impinging jet, a wake, a mixing layer, a gridturbulence decay and so on. This may be useful in a best estimate plus uncertainty approach
using a propagation of input uncertainties approach. One can also determine the code model
accuracy on a specific target value by calculating CET tests that simulate the reactor
situation of interest with a scaled geometry and then propagating by some method theuncertainties determined on the target value to the reactor application. Since these two typesof tests are rather different, they are not treated in the same way for the initial and boundary
conditions, the number and quality of measurements, and the measurement uncertainty.
This has to be taken into account in the requirements for CFD-grade experiments.
Figure 2. Illustration of the two methods for taking model uncertainty into account in acomputational fluid dynamics simulation of a reactor issue

Note: UQ: uncertainty quantification; SET: separate effect test; CET: combined effect test.
Source: Adapted from Höhne, T. and S. Kliem, (2020), “Detailed Simulation of the Nominal Flow and Temperature Conditions in a
Pre-Konvoi PWR Using Coupled CFD and Neutron Kinetics”, Fluids 2020, 5, 161, https://doi.org/10.3390/fluids5030161 and courtesyof S. Kliem (HZDR).

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Table 1. Synthesis of the various methods on uncertainty of computational fluid dynamics

Extrapolation
methodCombined
propagation andextrapolation
methodNon-modelled
processes

Numerical errors

Geometricalsimplifications

Scaling distortions

Propagation
methodAddress the sources of uncertainty

Initial condition andboundary conditions,
physical properties
Physical models

Basic methodology Used methods

Use of integral
Use of separate effect tests oreffect tests
combined effecttests

Monte-Carlo random
sampling

OK

No

OK

OK

OK

Use of metamodels(e.g. PCE)OK

Can use many

Possible

Only a few of themCan use manyNo

Deterministic sampling

Only a few of themCan use manyNo

Extended UMAE

Not
fully

Based on ASME

In principle may address allNo
May depend on how ASME method is extended

Must use

Use of metamodels or not

A few by propagation
Others by extrapolation

Must use

All of them are collectively accounted
for, though not explicitly and individually Must use
addressed

Can useMust use

Note: PCE: Polynomial chaos expansions; UMAE: uncertainty methodology based on accuracy extrapolation; ASME: American
Society of Mechanical Engineers.
Source: Based on NEA, 2016a.

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3. Objectives of a computational fluid dynamics-grade experiment
The American Society of Mechanical Engineers (ASME) has worked on a standard forverification and validation (V&V) and uncertainty quantification (UQ) for computational
fluid dynamics (CFD) and heat transfer application (ASME, 2009).
The ASME VV 20 Standard for Verification and Validation in Computational Fluid
Dynamics and Heat Transfer states that: “The concern of V&V is to assess the accuracy of
a computational simulation.” In current industrial CFD modelling (non-direct numericalsimulation), results come from a solved part of Navier-Stokes equations and from amodelled part of these equations. Verification of correct solving of equations (called
solution verification) can be considered “tractable” even for complex flows and once it isdone, uncertainty arising from physical model uncertainty is a legitimate concern.
Practically, the standard VV 20-2009 affirms that “The ultimate goal of V&V is todetermine the degree to which a model is an accurate representation of the real world”. This
standard is strongly based on the use of experimental data for V&V and consequently for
UQ. With this approach, the ASME places a strong link between V&V and UQ.
Therefore, one may define some requirements for a CFD-grade experiment by considering
the requirements of the ASME standard.
The comparison error E in any validation process is defined as the difference between thesimulation result denoted by S, and the experimental value D:
𝐸 =𝑆−𝐷If we denote T as the true value, then the comparison error can be split into:
𝐸 = 𝑆 − 𝑇 − (𝐷 − 𝑇)
Then, one defines the experimental data error δD and the simulation error δs as follows:𝛿𝐷 = 𝐷 − 𝑇; 𝛿𝑆 = 𝑆 − 𝑇
The simulation error δs has three components: the error due to the physical modelling𝛿𝑚𝑜𝑑𝑒𝑙 ; the numerical solution error 𝛿𝑛𝑢𝑚 produced by the numerical algorithm; and thediscrete mesh used to solve the modelling equations and the input data errors (initialconditions, boundary conditions, properties, etc.) 𝛿𝑖𝑛𝑝𝑢𝑡 . E can be expressed as the overall
result of all the errors coming from the experimental data and the simulation:
𝐸 = 𝛿𝑚𝑜𝑑𝑒𝑙 + ( 𝛿𝑖𝑛𝑝𝑢𝑡 + 𝛿𝑛𝑢𝑚 − 𝛿𝐷 )It is assumed that D is based on an average of individual measurements, and that the error
δD is computed using the ordinary methods of the experimental fluid dynamics, and thesame assumption is valid for the experimental uncertainty 𝑢𝐷 . Therefore, the uncertaintyof the comparison error is given by the expression:222
𝑢𝐸 = √𝑢𝑚𝑜𝑑𝑒𝑙+ 𝑢𝑖𝑛𝑝𝑢𝑡+ 𝑢𝑛𝑢𝑚+ 𝑢𝐷2

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The components of the simulation uncertainty that can be estimated are the numericalsimulation uncertainty 𝑢𝑛𝑢𝑚 , the input data uncertainty 𝑢𝑖𝑛𝑝𝑢𝑡 and the experimental
uncertainty 𝑢𝐷 . However, there is no established method to estimate the physical modelling
uncertainty 𝑢𝑚𝑜𝑑𝑒𝑙 .
To solve this problem, the unknown error 𝛿𝑚𝑜𝑑𝑒𝑙 produced by the modelling is isolated:𝛿𝑚𝑜𝑑𝑒𝑙 = 𝐸 − ( 𝛿𝑖𝑛𝑝𝑢𝑡 + 𝛿𝑛𝑢𝑚 − 𝛿𝐷 )E, its sign and its magnitude are known. Next, the validation uncertainty 𝑢𝑣𝑎𝑙 is defined as
an estimation of the standard deviation of the combination of errors 𝛿𝑖𝑛𝑝𝑢𝑡 + 𝛿𝑛𝑢𝑚 − 𝛿𝐷 ,if these errors are really independent, the combined validation uncertainty is given by theexpression:
2
2𝑢𝑣𝑎𝑙 = √𝑢𝑖𝑛𝑝𝑢𝑡+ 𝑢𝑛𝑢𝑚+ 𝑢𝐷2
2𝑢𝑚𝑜𝑑𝑒𝑙may be given by:
22
𝑢𝐸2 = 𝑢𝑚𝑜𝑑𝑒𝑙+ 𝑢𝑣𝑎𝑙The ASME standard gives solutions to evaluate every term of the comparison error (E) andthe validation uncertainty (𝑢𝑣𝑎𝑙 ).
Propagation methods are mainly used to evaluate code result uncertainties coming from
input parameters. Uncertainties of numerical solutions are given by the solution verification
step. The standard indicates how to use the E and 𝑢𝑣𝑎𝑙 . These quantities give an accuracyof the model used.
2

2
From 𝑢𝑚𝑜𝑑𝑒𝑙 = √𝑢𝐸2 − 𝑢𝑣𝑎𝑙
one can draw the following conclusions:

If 𝑢𝑣𝑎𝑙 is relatively small, the comparison of the code prediction with experimental
data can provide useful and precise information on the quality of the physical model
since 𝑢𝑚𝑜𝑑𝑒𝑙 ≅ 𝑢𝐸 .



If an a priori model uncertainty 𝑢𝑚𝑜𝑑𝑒𝑙 is evaluated or expected, one can calculate
the impact of this model uncertainty on any sensitive parameter of interest (or figureof merit, FoM) by sensitivity tests and compare with the impact of 𝑢𝑣𝑎𝑙 .



If 𝑢𝑚𝑜𝑑𝑒𝑙 < 𝑢𝑣𝑎𝑙 the experiment is not very informative.



If u𝑚𝑜𝑑𝑒𝑙 > 𝑢𝑣𝑎𝑙 the experiment is capable of showing if the expected model
uncertainty is reached.



If |𝐸 |>> 𝑢𝑣𝑎𝑙 the model used induced more error than the validation uncertainty
so the model can be improved or calibrated on the experiment in order to have lessuncertainty on the result.



If |𝐸| < 𝑢𝑣𝑎𝑙 the larger uncertainty is on the validation uncertainty, which implies
that the model accuracy cannot be improved if this uncertainty cannot be reduced.
The standard indicates that in one or the other case, this is not proof that the model
is of good or bad quality, but it gives an indication on it.

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A CFD-grade experiment is an experiment that can be used to validate the physical model,
which means that it provides a relatively low uncertainty of validation 𝑢𝑣𝑎𝑙 and allows agood determination of the model uncertainty 𝑢𝑚𝑜𝑑𝑒𝑙 . Then, using the values of 𝛿𝑚𝑜𝑑𝑒𝑙 formany measured flow parameters, inverse uncertainty quantification methods may, inprinciple, be used to obtain the uncertainty of several model parameters such as C1, C2,
Cµ, Prk and Prɛ of the k-ɛ model. Inverse uncertainty quantification is currently used forsystem codes, but is not yet so common in CFD application. Reported applications may
limit the analysis to one dominant model parameter (e.g. a turbulent Prandtl number for
temperature mixing problems or a turbulent Schmidt number for a boron dilution problem).
Therefore, an experiment that minimises both 𝛿𝑖𝑛𝑝𝑢𝑡 and 𝛿𝐷 also minimises 𝑢𝑣𝑎𝑙 andprovides more information on the accuracy of the model. One can consider that:
A CFD-grade experiment should provide the lowest values of 𝜹𝒊𝒏𝒑𝒖𝒕 and 𝜹𝑫 .
However, the capability for an experiment to provide information on the uncertainty ofmodel parameters may not be the concern of a reactor thermal-hydraulic safety analysis.
This is the case by application of uncertainty extrapolation method. Here, the total
uncertainty of all models together is the target. The final goal is often to compare aparameter of interest to a safety criterion to assess if the reactor is safe in the situation ofinterest. Very often, combined effect tests (CETs) or integral effect tests (IETs) are built torepresent a part of a reactor circuit (e.g. the ROCOM test facility) at reduced scale trying
to simulate the reactor situation of interest by respecting the non-dimensional numbers
characteristic of the dominant phenomena. Due to the large dimensions and the complexityof the reactor components, it is much more difficult than in SETs to measure all of thenecessary boundary and initial conditions and all flow field variables in the region ofinterest with a low uncertainty. Then 𝛿𝑖𝑛𝑝𝑢𝑡 and 𝑢𝑖𝑛𝑝𝑢𝑡 may be rather large. Suchexperiments should at least provide sufficient information to allow quantifying theaccuracy of CFD code predictions for the relevant FoMs (the parameters of interest in thesafety analysis such as some local temperatures, some boron concentrations, thermal
stresses, etc.) and assessing whether such accuracy can support reliable conclusions for thesafety case. In this case, the experiment should target a predetermined code uncertainty forthe selected FoMs. Instead of providing data to allow quantifying the uncertainty on somespecific model parameters, it should help in the determination of the uncertainties on FoMs
prediction, which result from various sources of uncertainty. Minimising 𝛿𝑖𝑛𝑝𝑢𝑡 and 𝛿𝐷remains the objective, but it is applied to any specified FoM. Therefore, one will consider
first the requirements for CFD-grade SET, which may be used in the context of anuncertainty propagation methodology for the reactor application, then one will add somespecific comments for CFD-grade CET or IET, which may be used in the context of an
accuracy extrapolation methodology for the reactor application.
Another important requirement is that the design of the experiment should be done incollaboration with the code user from the beginning of the experimental project. Code users
or safety analysts can then expose the goal of the experiment in terms of model validation(for example, what type of turbulence model is targeted), of target parameters necessary inthe validation process, of uncertainties targeted, etc. On the other hand, CFD code users
can perform pre-calculations to help design the mock-up, in terms of geometrical design,range of pressure and/or temperature reached during the future tests, choice of boundary
conditions, etc.

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A CFD-grade experiment should first be characterised by a fruitful exchange between
experimentalists and code users, from the beginning of the design of the mock-up to
the end of the analysis of its results.
When designing a CFD-grade experiment, it is expected that the domain of interest isclearly defined by:

A fluid volume where phenomena are to be investigated: it is bound by solidboundaries and fluid boundaries. This fluid volume is the volume that will besimulated by the CFD.



Inlet fluid surfaces are defined as surfaces where the fluid enters the fluid volume
of interest and where inlet boundary conditions will be defined for the CFD
simulation.

Outlet fluid surfaces are defined as surfaces where the fluid leaves the fluid volume
of interest and where outlet boundary conditions will be defined for the CFD
simulation.



Solids may be partly integrated to the domain of interest and domain of simulation.
Depending on this, some boundaries of the solid are within the simulation domain(using conjugate heat transfer) and other boundaries will become externalboundaries.

The preliminary specification of fluid and solid volumes of interest and of inlet and outlet
fluid surfaces is of prime importance to select where initial and boundary conditions haveto be known.
A CFD-grade experiment should specify the fluid and solid volume of interest, the inlet
and outlet fluid surfaces, the solid fluid boundaries and the external solid boundaries
in a way that they can be used as input data with the required accuracy.
A general requirement may be to define a priori acceptance criteria before designing anexperiment.
If the only objective is to validate a CFD code on a specific flow configuration, theacceptance criterion may be to minimise the validation uncertainty on specific FoMs.
Examples may include:The objective is to validate a CFD code for singular pressure losses prediction, theacceptance criterion may be that the validation uncertainty related to somepredetermined pressure difference ΔP between two locations in the test section
should not exceed a given percentage X%.

If the objective is to predict the fluid temperature and wall heat transfer in a pressure
vessel with a predetermined space and time resolution (e.g. for pressurised thermal
shock investigation), the acceptance criterion may be that the validation uncertainty
related to a predetermined temperature difference ΔT between emergency corecooling system injection and a region of the annular downcomer should not exceed
a given percentage Y%.

If the objective is to determine a CFD code model parameter uncertainty, the acceptancecriteria will be:measured flow parameters are sufficiently sensitive to the model parameter;measured flow parameters have a minimum validation uncertainty.

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There may be reactor safety issues where the safety criterion is in a reactor component thatwill not be treated by the CFD tool. For example, boron dilution problems and steam line
break transients are usually investigated with a coupling of a CFD tool – used fromintermediate leg of a pressurised water reactor up to entrance of the core – a component
code using a porous 3D model or a sub-channel analysis code for the core, and a systemcode for the rest of the reactor. The objective being to determine the core reactivity change,a requirement may be defined on the accuracy of the predicted temperature field or boron
concentration field at inlet of the core, which will affect the reactivity. In such a case,preliminary simulations should define the requirement on the accuracy of T° or Xboron to
get the required accuracy on the reactivity. Acceptance criteria may then be defined for an
experiment that simulates the mixing in the domain treated by CFD.

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4. Scope of the analysis
CFD stands for computational fluid dynamics. This term could be used for any kind of fluid
dynamic equations solved by a computer. However, in the current practice, CFD is usuallyapplied for 3D (or 2D when some symmetry may be assumed) simulations only. CFD canbe used for 1-phase, 2-phase, multiphase and multicomponent flows. Computational multifluid dynamics was also introduced for multi-fluid approaches.
Here our analysis will be limited to 1-phase possibly multicomponent (water-boron, airsteam-H2, etc.) flow with possible heat and mass transfers within the fluid and with walls,
at first. No chemical reactions are considered here.
Newtonian fluids are considered only.
In components with a lot of solid structures, the porous body approach may sometimes beused; this approach is not considered here. Only CFD in open medium with walls at theboundaries of the simulation domain are considered here, including conjugate heat transfer
with a possible coupling of solid and fluid domains
CFD codes may include various approaches, such as:RANS-URANS approaches: time or ensemble averaging of equations for steady or
slowly varying flows; turbulence is modelled over the entire range of scales.



LES-VLES approaches: space filtered equations: necessarily 3D and transient in
turbulent flows; turbulence is partly resolved (large scales) and partly modelled(small scales).

CFD-grade experiments are considered here for validation of all these approaches that areused for nuclear reactor design and safety assessment. One may list the main applicationsof CFD to reactors.

Application of single-phase CFD to reactor design:prediction of pressure losses: regular and singular pressure losses in any geometry;

prediction of thermal stratification/mixing;

prediction of solid-fluid heat transfer coefficient in nominal condition (predictionof fuel temperature);

prediction of fluid-solid interaction with mechanical efforts on solid structures:vibration, fretting;

optimisation of temperature mixing in a core for critical heat flux investigations;

prediction of flowrate distribution in a multichannel geometry (e.g. lower plenum).

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NEA/CSNI/R(2020)3  25

Application of single-phase CFD to reactor safety
Most issues are related to turbulent mixing problems, including temperature mixing or
mixing of components in a mixture (boron in water, hydrogen in air, etc.):boron dilution;

main steam line break;pressurised thermal shock;

hot-leg temperature heterogeneities;

induced break;

thermal fatigue;hydrogen distribution in containment;

temperature distributions in a spent fuel pool during a loss-of-coolant accident;

special considerations for advanced (including gas-cooled) reactors.

In some mixing problems, density differences induce buoyancy effects, which have asignificant influence on the mixing process.
These problems may be simulated with either RANS or LES models of turbulence or withboth. For steady-state or quasi steady-state flows, one may use RANS. For rather slow andlong transients (boron dilution, pressurised thermal shock, hydrogen distribution, etc.) one
may use URANS. Situations where phenomena of interest are at a small timescale(e.g. thermal fatigue) may require LES, although URANS may be acceptable.

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5. Requirements for boundary and initial conditions and physical propertiesof fluids and solids
Experimentally minimising 𝛿𝑖𝑛𝑝𝑢𝑡 requires accurate knowledge of:

fluid and solid material properties;

initial conditions in solid and fluids in transient problems;boundary conditions in both steady and transient problems (e.g. heat losses).

Solid properties:

Density:ρ(P,T)

Specific heat

Cp(P,T)

Heat conductivity

λ(P,T)



Emissivity (if radiation is playing a role)ɛ

Surface roughness

Ra

Properties of most solids are known. However, in some cases, a specific measurement ofproperties may be needed. Also, the thermal expansion may be taken into account in someproblems with significant boundary deformations.

Fluid properties:

Density (possibly) function of mass concentration:

ρ(P,T) or ρ(P,T, Xj)

Specific heat

Cp(P,T) or Cp(P,T, Xj)

Specific enthalpy
or specific internal energyh(P,T) or h(P,T, Xj)
e(P,T) or e(P,T, Xj)

Heat conductivity

λ(P,T) or λ (P,T, Xj)Dynamic viscosityµ(P,T) or µ(P,T, Xj)



Mass diffusivity

Dj (P,T, Xj)

Fluid properties of most single-component fluids are known. However, multicomponent
fluids may require thermodynamic models to predict all mixture properties.

Initial conditions in solids:Temperature field T(x,y,z) in the whole solid domain.

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Initial conditions in fluids:Temperature field T(x,y,z) in the whole fluid domain;Pressure field P(x,y,z) in the whole fluid domain;Mass concentration fields Xj(x,y,z) in the whole fluid domain;⃗ (𝑥, 𝑦, 𝑧) in the whole fluid domain;
Velocity field 𝑉Turbulent parameters in the fluid domain: k and ɛ, turbulent intensity.

Knowledge of initial conditions is necessary in transient problems. However, complete
knowledge of all instantaneous variables in the whole domain of interest is never possible
and limited information may be sufficient. One may first compare the transit timescale (orconvective timescale defined as the time between fluid inlet and fluid outlet in the domain
of interest) Ttr with the duration of the transient Tend-T0. If the transit timescale is very
short compared to Tend-T0, precise knowledge of the initial state is usually not soimportant.
A pressure has to be prescribed in at least one point of the domain.

Fluid boundary conditions on solid walls
Usually these conditions are:

heat transfer-related: surface temperature or heat flux normal to surface or heattransfer coefficient and wall temperature;

momentum related: wall-fluid slip conditions (no slip, free slip or imposed walltangential velocity) and zero normal velocity or imposed velocity in case of porous
wall;



surface roughness.

Inlet and outlet fluid boundary conditions
The type of boundary conditions in fluids depends on fluid velocity. Some general rulesare:Transported scalars (temperature, mass concentrations, turbulent quantities, etc.)
have to be given at all inlet boundaries where the transporting fluid is entering thevolume of interest.

In subsonic flow conditions (subsonic in the whole domain of interest), the velocity
should be given at inlet and a pressure condition is usually given at outlet. Pressure
at inlet and outlet is also an option. Since pressure is often measured only at walls,
simple assumptions on the pressure field in the outlet surface may be necessary,such as uniform pressure, simple pressure profile or hydrostatic pressure field.

Inlet fluid boundary conditions
Knowledge of mean quantities is, in principle, required in the whole surface of fluid inlet
with a sufficient space resolution. In some cases, the upstream flow is designed so that the
inlet velocity field is very simple:

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

grids tend to homogenise the mean velocity;

a damping chamber followed by a convergent geometry tends to decrease theturbulence intensity.

A symmetry may also decrease the need of measurements:


axi-symmetry reduces the measurement location to a radius;

plane symmetry reduces the measurement location to a transverse chord.

The type of averaging should be adapted to the type of turbulence models that are to be
used. Ensemble averaging is very difficult to obtain in experiments and a time averaging isoften used. In steady boundary conditions, the time averaging duration should be sufficient
to obtain converged results. In transient flow conditions, the time averaging duration should
be sufficiently small to follow the time evolution and sufficiently large to get precise
results.
The velocity profile fixed by the CFD user at the inlet boundary condition cannot take into
account the near wall region profile. In order to allow representative simulations of theexperimental flow at inlet boundary condition, the issue of the hydraulic and thermal
development of the flow must be considered when designing an experimental facility for
CFD validation purposes.
The following quantities have to be known:

local mean velocity vector (time or ensemble averaging);

local mean temperature (if necessary);

local mean mass concentration (if not uniform);

fluctuations:o root mean square (rms) values of velocity components, of temperature and massconcentrations;o Reynolds stress tensor ̅̅̅̅̅̅̅
𝑣′𝑖 𝑣′𝑗;o turbulent heat flux ̅̅̅̅̅̅̅̅̅̅
𝑣′𝑖 𝜃′;
o turbulent mass flux ̅̅̅̅̅̅̅
𝑣′𝑖 𝑋𝑗 ′.



power spectra of fluctuations of velocity components, of temperature and massconcentrations (may be particularly important for LES application);

turbulent dissipation ɛ is a variable of many RANS models, but is practically
impossible to measure. ɛ may be evaluated from the turbulent kinetic energy andthe spectra or from the turbulence intensity and a specified length scale;

quasi 2D flows may need only 1D distribution of all principal variables, but alimited 2D exploration of inlet field is recommended.

The measurement of fluctuations may include rms values, correlations between fluctuations̅̅̅̅̅̅̅
̅̅̅̅̅̅ ̅̅̅̅̅̅̅
(𝑣′
𝑖 𝑣′𝑗 , 𝑣′𝑖 𝜃′, 𝑣′𝑖 𝑋𝑗 ′) and power spectra. If limited to rms values, it is still possible to provide
boundary conditions to some RANS turbulence models such as k-ɛ. When Reynolds
stresses are available, they may provide accurate boundary conditions to Rij-ɛ models. If
spectra are available, they may be used to build instantaneous boundary conditions to LES
models.
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Outlet fluid boundary conditions
In subsonic flow conditions, a pressure condition is usually given at outlet. Since pressure
is often measured only at walls, simple assumptions on the pressure field in the outlet
surface may be necessary, such as uniform pressure or hydrostatic pressure field. The
pressure boundary condition may be a condition on the pressure, on the pressure axial
gradient ∂P/∂n or even on a double derivative of pressure (∂^2 P)/∂n∂t. If assumptions on
the pressure field at outlet are necessary, the assumptions should be justified or evenchecked with some measurements.
“Opening” conditions exist in CFD codes, which authorise backflow at the outlet boundary
Backflow at the outlet condition can be a source of uncertainty due to additional parameters
to be known (backflow temperature and other scalar, etc.). Such backflow may be
influenced by external flow processes that are not simulated and, in general, such boundary
conditions are not recommended.
If backflow at the outlet condition is expected, preliminary CFD calculations can be usefulin order to check if a possible geometrical configuration can avoid the backflow issue. If it
is not possible (for example in case of vortices exiting the fluid domain), precise
information is needed on temperature, mass concentration and velocity of fluid backflow
at the outlet condition and specific conditions that authorise fluid backflow has to be used(“opening” type).
In some cases, the outlet condition can be a mass flow rate imposed (pump imposing themass flow rate after the test section).

Recommendations
Preliminary sensitivity calculations to uncertainty of inlet conditions are recommended
before selecting the measurement technique. Most sensitive fields should be determinedwith special care.
Preliminary sensitivity calculations to outlet conditions are recommended, including theoutlet locations to find the best location that makes boundary conditions easier to handle.

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6. Requirements for flow parameters measurement
Flow parameters have to be measured in the domain of interest particularly where importantphenomena take place. All regions of the flow with shear layers such as boundary layers,
mixing layers, jets, should be well instrumented. The measurements will allow thevalidation of some models of the computational fluid dynamics (CFD) tools that will later
simulate the tests. A large number of measured parameters and a high number ofmeasurement locations will provide better validation capabilities. However, measuring all
principal variables everywhere is never possible or would be too long and expensive.
Moreover, measurement devices are often intrusive, and could modify the downstream
flow. Compromises are necessary following some general principles:An equation model may be precisely validated when all n principal variables are
known. This does not necessarily mean that all variables are measured with thesame density of measuring points. For example, the knowledge of pressure in a few
locations may be sufficient.



If pressure losses are investigated, more detailed pressure measurements arenecessary.



Even if pressure losses are not particularly investigated, measuring irreversible ΔP
losses reflects the turbulent dissipation and contributes to turbulence model
validation.



The mean velocity field in shear layers is of prime importance and should be thefirst priority, since the velocity field strongly influences the mixing of all scalars
and momentum turbulent diffusion is also coupled to scalar turbulent diffusion.

For models other than LES, lack of velocity fluctuation measurement still enables
the global efficiency of a turbulence model to be validated, but the closure laws of
the additional transport equations for k, ɛ, ̅̅̅̅̅̅̅
𝑣′𝑖 𝑣′𝑗 , used in some models will not bevalidated.

For the LES model, the lack of velocity fluctuation measurement does not enableaccurate model validation.



The mean temperature and/or mass concentration fields in shear layers are also ofprime importance when the mixing of all scalars is investigated.



The lack of temperature and/or mass concentration fluctuation measurement still
enables the global efficiency of a turbulence model to be validated, but closure lawsof the additional transport equations for ̅̅̅̅𝜃′2 , ̅̅̅̅̅̅
𝑣′𝑖 𝜃′, ̅̅̅̅̅̅̅
𝑣′𝑖 𝑋𝑗 ′ used in some models will
not be validated.



The lack of frequency spectrum of fluctuations does not enable the LES to be fully
validated.

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

Turbulent dissipation ɛ is a variable of many RANS models, but is practically
impossible to measure. When both the turbulence intensity and the velocity spectra
or turbulent kinetic energy are available, ɛ may be derived from them.

When investigating momentum, energy or mass diffusion processes, themeasurement of the evolution of transverse profiles of mean and fluctuating
quantities along the flow is necessary.

Quasi 2D flows may need only 1D distribution of all principal variables, but alimited 2D exploration is recommended.

Application of invasive measurement techniques has to be carefully analysed. The
intrusive nature of each measurement device has to be evaluated to determine where
each of them has to be placed in co-ordination with the other measurement devices.
In some cases, the intrusive nature of the devices may require performing several
identical experimental tests with only a variation of the location of the device. Even
if the measured quantity is not disturbed, other measurements can be influenced.



Near-wall measurements of velocity, temperature and fluctuations.

The near-wall region is mainly treated with models (single-phase law of the wall withcorrections for roughness) in CFD codes. More precise measurements of the flow
parameters in the near-wall region can validate and help improve these models. In
particular, investigation of the effect of roughness can be useful.

Recommendations
It is recommended to give an objective to the experiment by identifying:the main physical processes of interest;the target in terms of type of turbulence models that may be validated.

The choice of the measured parameters should be made after the objectives are clearly
defined.
The measurement of fluctuations may include root mean square (rms) values, correlations̅̅̅̅̅̅̅
̅̅̅̅̅̅ ̅̅̅̅̅̅̅
between fluctuations (𝑣′𝑖 𝑣′𝑗 , 𝑣′𝑖 𝜃′, 𝑣′𝑖 𝑋𝑗 ′) and power spectra:

If limited to rms values, it is still possible to validate some RANS turbulencemodels such as k-ɛ. It can also provide a partial validation of a LES model.

When Reynolds stresses are available, they may provide accurate boundary
conditions to Rij-ɛ models.



If spectra are available, they may provide information to build boundary conditions
to LES models.

The knowledge of spectra may help validate the turbulent dissipation equation
when it is used.

It is also highly recommended to run preliminary CFD calculations to define the best
measurement locations, taking into account the intrusive nature of the measurement
devices.

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7. Requirements for measurement uncertainty
Providing reliable evaluation of measurement uncertainty is a very difficult task, but ismandatory in a computational fluid dynamics (CFD) code validation perspective and for
determining the model uncertainty. As mentioned above, an experiment that minimises𝛿𝑖𝑛𝑝𝑢𝑡 and 𝛿𝐷 also minimises 𝑢𝑣𝑎𝑙 and provides more information on the accuracy of themodel.

RecommendationsAll measured variables should have a careful uncertainty determination, including
uncertainty linked to the repeatability.



In case of the intrusive measurement method, the impact on the measured flow
parameter should be at least estimated.

Comparison between several local methods measuring the same flow parameter isrecommended.



Repeatability tests should be performed in both steady and transient tests. The goal
is to evaluate uncertainties on the target parameters (if the boundary conditions are
not sufficiently well known), to detect a significant sensitivity to initial conditions
and to identify possible chaotic behaviour.



Local methods (velocity) confronted to integral methods (flowrate) is
recommended. In the presence of heat transfers, integral energy conservation
should be checked to verify the consistency of available measured parameters(velocity, temperature, heat flux).

Preliminary sensitivity calculations of measured variables to uncertainty of a model
parameter of interest (e.g. a turbulent Prandtl number) of field variables within the
CFD domain are recommended to be compared to 𝑢𝐷 . If 𝑢𝐷 is larger than ana priori or expected model uncertainty 𝑢𝑚𝑜𝑑𝑒𝑙 , the experiment will not be very
informative.



A systematic analysis of all possible sources of errors or uncertainty should be madethat may include:o errors due to space resolution that filters space variations;o errors due to imperfect knowledge of the locations of the sensors;o errors due to time resolution that filters time variations (inertia of the sensor);o errors due to non-fully converged time averaging;o error from imperfect signal treatment;o uncertainty due to the limited sensitivity of the sensor;
o bias due to statistical treatment of a sample of discrete measurements;o any other source.

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This is a very difficult and heavy task and very few experimental data are provided with afull evaluation of uncertainties. Some uncertainties may also be provided which are notfully reliable. Therefore, there is a clear need for a guide for experimentalists that gathers
all the available knowledge on sources of uncertainty for every type of sensor used in CFDgrade experiments.

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8. Feedback from some computational fluid dynamics experiments
and benchmarks
Looking at validation experiments for computational fluid dynamics (CFD), it appears thatsome existing data suffer from some weaknesses:lack of local measurements;

insufficient number of measured flow variables;

lack of well-defined initial and boundary conditions;lack of information about experimental uncertainty.

This may be related to insufficient preliminary collaboration between CFD code users andexperimentalists and insufficient specification of the requirements.
Experience gathered in past exercises, benchmarks and any CFD validation may be useful
to avoid errors or limitations in future CFD-grade experiments.
The PSBT (PWR subchannel and bundle tests) and BFBT (BWR full-size fine-mesh bundletest) benchmarks offered a unique possibility of code validation against void fraction
repartition in a pressurised water reactor (PWR) and boiling water reactor (BWR) rod
bundle in high velocity flow. However, a deformed geometry was suspected (not proved),
and consequently the boundary and initial conditions were not very accurate. Moreattention should be paid to the exact knowledge of the solid boundaries in future
experiments.
Several Tee-junction experiments were designed for thermal fatigue investigations. In thiscase, the prediction of large-scale fluctuations is necessary. It appears that this requires very
good knowledge of inlet conditions, not only the mean velocity field, but also thefluctuations. It was suspected that specific eddies created in a tube bend rather far upstream
could influence the characteristics of the large eddies of interest in the mixing zone.
The MATIS-H rod bundle benchmark offered very detailed information of flow in a rod
bundle downstream a spacer grid. Some simulations found a rather high sensitivity to theoutlet boundary conditions and to the position of the outlet boundary. This illustrates theneed to also properly characterise the outlet boundary conditions.
The PANDA test facility was used for a benchmark on stratification of H2 layer erosion bya jet. Some sensitivity to inlet conditions in jet pipe and to initial conditions was found,which illustrates the need of preliminary CFD calculations before a test specification.
GEMIX mixing tests were envisaged for a CFD benchmark including uncertainty
quantification. It was first found that measurements in inlet conditions were not sufficient,although the flow was carefully homogenised by grids. Additional velocity (mean and root
mean square [rms] values) measurements were added. After comparing the calculations
with data in a blind benchmark, a measured peak of turbulence intensity was neverpredicted by any code. After analysis, a possible effect of density and refraction index

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fluctuations on the measurement was suspected. Difficulties arose from the use of brine
together with laser measurement techniques.
A generic problem is encountered when a part of a closed reactor circuit is simulated by
CFD. Boundary conditions of this CFD part are not well known and can be approximated only
by a macroscopic model (1D) or from established flow assumption whereas no establishedflow exists in a reactor. For such reactor applications of CFD, strategies to reduce theuncertainty of the boundary and initial conditions should be established: choice of thedomain of CFD, preliminary CFD on a large domain compared to a smaller domain, etc.

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9. Recommended roadmap for the design of computational fluid
dynamics-grade experiments
A computational fluid dynamics (CFD)-grade experiment should first be characterised bya fruitful exchange between experimentalists and code users, from the beginning of thedesign of the mock-up to the end of the analysis of its results.
A multi-step method can be proposed to design a CFD-grade experiment as illustrated in
Figure 3.
Figure 3. Roadmap for designing and performing a computational fluid dynamics-grade
experiment

Note: PIRT: phenomena identification and ranking table; CFD: computational fluid dynamics; BIC: boundary and initial conditions.

Step 1: Objectives
The first step is to assign clear objectives to the experiment:

Identify the main physical processes of interest. Processes of interest may begeneric processes, like temperature mixing, mixing of a mass concentration in caseof a multicomponent flow, buoyancy driven natural circulation, pressure losses,
heat transfers with solid structures, large-scale flow fluctuations and associatedtime fluctuations of velocities, temperatures and wall heat transfers.
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

Identify the type of turbulence models that may be validated.



If the experiment is devoted to nuclear reactor safety, identify the safety
demonstration methodology that may need the use of this experiment. In particular,
attention must focus on the uncertainty quantification method that may be used ina best estimate plus uncertainty methodology. The planned experiment should also
be put in the framework of a general validation matrix.

Define the figures of merit (FoMs) and acceptance criteria for the experiment.

Step 2: Specifications
At this step, discussions between experimentalists and CFD code users are recommended
to make the basic design of the facility, define the test conditions and select themeasurement techniques. The needs have to be confronted to the experimental
potentialities.
The geometry of the test section and general conditions of the tests to perform are first
defined to answer the needs and to address the physical processes of interest.
The choice of the measured parameters should be made to meet the objectives and toinvestigate the processes of interest.
Then detailed test conditions are to be defined by the following:

specify the fluid and solid volume of interest that will be later simulated by CFD;

specify the inlet and outlet fluid surfaces;

specify the solid fluid boundaries and the external solid boundaries;

define requirements on boundary and initial conditions (BIC), physical properties;

define requirements on measurement uncertainty;

target some type of CFD model, either RANS (eddy viscosity based or Reynolds
stress based) or LES or even hybrid model and define requirements on what tomeasure and where depending on the targeted type of CFD model.

Step 3: Preliminary CFD calculations
When the test section is designed to address the physical processes of interest, preliminary
calculations may be useful:Sensitivity calculations to uncertainty of inlet boundary conditions arerecommended before selecting the measurement technique. Most sensitive fields
should be determined with special care.



Sensitivity calculations to outlet boundary conditions are recommended varying theoutlet locations and possibly the type of outlet conditions.



Calculations to define the best measurement locations in the test section.

Feedback on the specification step is possible if necessary, possibly including the test
section geometry.

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Step 4: Evaluation of expected validation uncertainty
A CFD-grade experiment should provide the lowest values of δinput and δD related to theexhaustive BIC specification, to the physical properties and to the measurement accuracy.
After having defined the FoM based on available measurements, the global validation
uncertainty on this FoM is evaluated.


all measured field variables should have a careful evaluation to determine δD on thefield variable;

all BIC variables should have a careful evaluation to determine 𝑢𝑖𝑛𝑝𝑢𝑡 ;2preliminary calculations are used to evaluate the numerical uncertainty 𝑢𝑛𝑢𝑚;the resulting validation uncertainty on the FoM is determined by:2
2𝑢𝑣𝑎𝑙 = √𝑢𝑖𝑛𝑝𝑢𝑡+ 𝑢𝑛𝑢𝑚+ 𝑢𝐷2

If the acceptance criterion (criteria) is (are) met, one can go to the next step. If it/they is/arenot met, feedback on Step 2 is possible to change the measurement technique, or to add
measurements in the inlet and outlet fluid boundaries, or even to improve the measurement
technique.

Step 5: Building of the test facility and performing the tests
The test facility is built according to specifications and tests are carried out following thespecified test conditions. Repeatability tests are mandatory to check and evaluate
uncertainties on FoM. Then a posteriori verifications may be done after a comparison to
the code simulations if unexplained differences are observed.

Step 6: Final check of experiment suitability
Normally, the defined test conditions are not exactly the same as those observed during theexperiments. Sometimes the differences are significant. The check of actual experimental
uncertainties and a new evaluation of validation uncertainty allows proving the quality ofthe experimental work and eventually gives an indication what can (or needs to) be
improved. Finally, it would provide information, if the experiment met the acceptancecriteria and is suitable for the purpose of validation.

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10. Specific comments for computational fluid dynamics-grade
combined effect tests or integral effect tests
Due to the specific difficulties related to combined effect tests (CETs) and integral effecttests (IETs), there cannot be the same density of instrumentation in all parts of the test
facility as in separate effect tests (SETs). The general principles applicable to CFD-grade
SET experiments also have to be followed in CETs or IETs as far as possible and the sameroadmap must be followed.

Step 1: Define objectives and acceptance criteria
The general objective of the experiment must be clearly identified together with thereference reactor, the scenario and the figures of merit (FoMs) with the expected accuracy(see Chapter 2). The type of CFD model that will be used for the validation and for thereactor application is selected in order to select the appropriate quantities to be measured
in initial conditions and boundary conditions.

Step 2: Specify experimentation and instrumentation
Preliminary CFD calculations have to be performed during the specification step at both
the reactor scale and at the experiment scale of the planned test in order to:Identify the regions of interest where important phenomena affecting the FoM aretaking place. Check that the same phenomena may take place qualitatively andquantitatively in the experiment as in the reactor.



The selection of the nature and the locations of the measured parameters should bemade to make the validation of the models that control the processes of interest
possible.



A sufficient number of measured parameters has to be chosen to avoid
compensation errors.



Attention should be paid to geometrical details that may have an influence, such as
sharp edge or rounded edge connections, obstacles, etc. Check that simplificationof the geometry does not significantly affect the process of interest, as far as thecode model is able to see this effect. This is an a priori verification that may becomplemented by a posteriori verifications (see below).

Step 3: Preliminary calculations
CET also needs preliminary calculations at the experiment scale:Sensitivity tests to the assumed uncertainty on boundary and initial conditions(BIC) corresponding to the planned instrumentation are necessary to check that the
impact on the uncertainty of the FoMs is not too high.

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

An estimation of the total uncertainty of the predicted FoM coming from all sourcesof uncertainty other than physical modelling is necessary to determine if the test
will be useful for the safety analysis. If necessary, some sources of uncertainties(BIC, measurement uncertainty) have to be reduced by additional efforts on the
instrumentation.

Step 4: Evaluate validation uncertainty


If CFD code users intend to use an uncertainty evaluation of the code models for
uncertainty quantification (UQ) of the CFD simulation in a general UQ method, apreliminary estimation of the total uncertainty of the predicted FoM coming fromall sources of uncertainty including physical modelling is necessary to determine if
the test will be useful for the safety analysis.

Step 5: Build the test facility and perform the tests
When the test facility is built and the tests are started, some recommendations are:

An a posteriori check of the physical behaviour in the regions where importantphenomena affecting the FoMs were expected is recommended to confirm thepreliminary analysis. If necessary, more precise experimental investigations inother regions of the test facility may be added.



Possible sensitivity tests by changing some geometrical details that may have an
influence – such as sharp edge or rounded edge connections, obstacles, etc. – may
provide valuable information on the real influence of these details.



Repeatability tests are mandatory.

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11. Conclusions
The design of validation experiments for computational fluid dynamics (CFD) application
to reactor simulation still requires some guidelines to check that they will be able to provide
the expected information on the accuracy of reactor simulations. In an attempt to establish
requirements for CFD-grade experiments, a multi-step roadmap is proposed andrecommendations and guidelines provided. Collaboration between code users andexperimentalists at the beginning of the design is required to lay out clear objectives anddefine the specifications of the test facility and the instrumentation. This is placed in thegeneral framework of a best estimate plus uncertainty approach for safety analysis of somereactor transients. Preliminary calculations are necessary in the design process and theuncertainty of validation must be evaluated to check that the experiment will be informative
enough on some predetermined figure of merit.
This document is expected to help build more useful future experimental programmes,
although the proposed guidelines and recommendations still need to be complemented andimproved.
A significant effort should be made in the future to establish practical guidelines for
estimating the experimental uncertainty, which seems to be the weak point in the current
roadmap. This could justify a future joint activity between the NEA Working Group on the
Analysis and Management of Accidents’ CFD Task Group and the SILENCE network towrite a guide for experimentalists that gathers all the available knowledge on sources of
uncertainty for every type of sensor used in CFD-grade experiments.

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