Nuclear Safety
NEA/CSNI/R(2020)7
October 2022
www.oecd-nea.org

Cold Leg Mixing Computational

Fluid Dynamics – Uncertainty
Quantification (CLM-UQ)
Benchmark Results

NEA/CSNI/R(2020)7  1
Nuclear Energy Agency

NEA/CSNI/R(2020)7
Unclassified

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14 October 2022

NUCLEAR ENERGY AGENCYCOMMITTEE ON THE SAFETY OF NUCLEAR INSTALLATIONS

Cold leg mixing computational fluid dynamics – uncertainty quantification(CLM-UQ) benchmark results

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2 | NEA/CSNI/R(2020)7
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COMMITTEE ON THE SAFETY OF NUCLEAR INSTALLATIONSThe Committee on the Safety of Nuclear Installations (CSNI) addresses Nuclear Energy
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activities and technical developments that affect nuclear safety.

4 | NEA/CSNI/R(2020)7

Acknowledgements
The Nuclear Energy Agency (NEA) Committee on the Safety of Nuclear Installations(CSNI) and the Working Group on the Analysis and Management of Accidents(WGAMA), wish to acknowledge the significant contributions of those individuals who
had a key role in the preparation of the summary report, and those who had a leadershiprole in the conduct and success of the NEA benchmark on “Cold leg mixing
computational fluid dynamics – uncertainty quantification (CLM-UQ)”. Additional
thanks are extended to D. Bestion, G. Zigh, P. Freydier, P. Ruyer, in support of the CFD
Task Group activities.

Leading authors
Rodolfo VAGHETTO

Texas A&M University

Yassin HASSAN

Texas A&M University

Contributors
Satoshi ABE

JAEADominique BESTION

CEA

Romain CAMYEDF

Andrey DEULINROSATOM

Fabien DUVALIRSNMarc FORESTIER

IRSNPhilippe FREYDIER

EDF

Luka GOLIBRODO

GIDROPRESS

Yassin A. HASSAN

Texas A&M University

Jungwoo KIM

SEOULTECH

Jonathan LAI

Texas A&M University

Erwan LE COUPANEC

EDF

Daniel OREA

Texas A&M University

Yago RIVERA DURAN

UPVALPierre RUYER

IRSN

Dmitrii SVESHNIKOVOKBM Afrikantov

Rodolfo VAGHETTO

Texas A&M University

NEA/CSNI/R(2020)7 | 5

Daniele VIVALDI

IRSN

Vasilii VOLKOVGIDROPRESSNEA Secretariat support to the group
Jeong NAM

NEA

Nils SANDBERG

NEA

6 | NEA/CSNI/R(2020)7

Table of contents
Executive summary ...................................................................................................................... 8
List of abbreviations and acronyms.......................................................................................... 10
1. Introduction ............................................................................................................................ 11
2. Background ............................................................................................................................. 12
3. Experimental facility description .......................................................................................... 14
4. Test conditions and measurement locations......................................................................... 17
Fluid properties ......................................................................................................................... 17
Measurement locations ............................................................................................................. 19
5. Experimental methods and measurement techniques......................................................... 21
Particle image velocimetry ....................................................................................................... 21
Laser induced fluorescence....................................................................................................... 22
LIF calibration .......................................................................................................................... 22
Experimental method ................................................................................................................ 24
6. Uncertainty analysis ............................................................................................................... 28
PIV uncertainty ......................................................................................................................... 28
LIF error sources ...................................................................................................................... 29
7. Participants and submitted results ....................................................................................... 32
8. Benchmark results.................................................................................................................. 34
Cold leg results – Streamwise time-averaged velocity component (CL1 Location) ................ 34
Cold leg results – Time-averaged concentrations ..................................................................... 35
Downcomer results – Streamwise time-averaged velocity component (DC1 Location) .......... 38
Downcomer results – Streamwise time-averaged velocity component (DC2 Location) .......... 41
9. Synthesis and conclusion........................................................................................................ 44
References ................................................................................................................................... 45

TablesTable 4.1. Dimensions of vessels and cold leg. ............................................................................ 15
Table 5.1. Fluid properties of sampled mixtures (OT). ................................................................ 17
Table 5.2. OT and BT fluid properties. ........................................................................................ 19
Table 6.1. Equipment and instrumentation specification ............................................................. 27
Table 7.1. Values used for OT and BT uncertainty analysis ........................................................ 29
Table 9.1. Time-averaged measured quantities ............................................................................ 34

NEA/CSNI/R(2020)7 | 7

FiguresFigure 1.1. Vessel and Cold Leg Geometry ................................................................................... 8
Figure 4.1. Experimental facility and instrumentation. ................................................................ 14
Figure 4.2. Vessel and cold leg geometry. ................................................................................... 15
Figure 4.3. Cold leg vessel nozzle. ............................................................................................... 16
Figure 5.1. Density vs. refractive index of solutions. ................................................................... 18
Figure 5.2. Viscosity of fluid mixtures......................................................................................... 18
Figure 5.3. Visualisation windows relative to the valve centerline .............................................. 19
Figure 6.1. OT and BT LIF 5-point calibration curve from window average intensity. .............. 23
Figure 6.2. OT LIF 5-point local calibration curve for a single point in point in space. .............. 24
Figure 6.3. Camera setup for cold leg window. ........................................................................... 25
Figure 6.4. Cold Leg Visualisation and Filter Effect ................................................................... 26
Figure 6.5. Camera system for simultaneous PIV measurements. ............................................... 26
Figure 7.1. Background intensity distribution. ............................................................................. 30
Figure 7.2. Normalised intensity along horizontal profile. .......................................................... 30
Figure 7.3. Normalised intensity along vertical profile................................................................ 31
Table 8.1.Submitted simulation results ....................................................................................... 34
Figure 9.1. Y-position vs. U (cm/s) 5-10 second time-averaged U – CL1 ................................... 34
Figure 9.2. Y-position vs. U (cm/s) 10-15 second time-averaged U – CL1 ................................. 35
Figure 9.3. Y-position vs. U (cm/s) 15-22.24 second time-averaged U – CL1 ............................ 35
Figure 9.4. Y-position vs. concentration (%) 5-10 second time-averaged C – CL1 .................... 36
Figure 9.5. Y-position vs. concentration (%) 10-15 second time-averaged C – CL1 .................. 36
Figure 9.6. Y-position vs. concentration (%) 15-22.24 second time-averaged C – CL1 ............. 37
Figure 9.7. V (cm/s) vs. X-position 10.5-15.5 second time-averaged V – DC1 .......................... 38
Figure 9.8. V (cm/s) vs. X-position 15.5-20.5 second time-averaged V – DC1 .......................... 38
Figure 9.9. V (cm/s) vs. X-position 20.5-25.5 second time-averaged V – DC1 .......................... 39
Figure 9.10. V (cm/s) vs. X-position 25.5-30.5 second time-averaged V – DC1 ........................ 39
Figure 9.11. V (cm/s) vs. X-position 30.5-35.5 second time-averaged V – DC1 ........................ 40
Figure 9.12. V (cm/s) vs. X-position 35.5-41.538 second time-averaged V – DC1 .................... 40
Figure 9.13. V (cm/s) vs. X-position 10.5-15.5 second time-averaged V – DC2 ........................ 41
Figure 9.14. V (cm/s) vs. X-position 15.5-20.5 second time-averaged V – DC2 ........................ 41
Figure 9.15. V (cm/s) vs. X-position 20.5-25.5 second time-averaged V – DC2 ........................ 42
Figure 9.16. V (cm/s) vs. X-position 25.5-30.5 second time-averaged V – DC2 ........................ 42
Figure 9.17. V (cm/s) vs. X-position 30.5-35.5 second time-averaged V – DC2 ........................ 43
Figure 9.18. V (cm/s) vs. X-position 35.5-41.538 second time-averaged V – DC2 .................... 43

8 | NEA/CSNI/R(2020)7

Executive summary
The cold leg mixing computational fluid dynamics (CFD) – uncertainty quantification(CLM-UQ) benchmark is the fifth benchmark concerning CFD applications to nuclear
reactor safety to be organised in the framework of the Nuclear Energy Agency (NEA)
Working Group on Accident Management and Analysis (WGAMA).
The main objective of this benchmark is to go a step further in the application of single
phase CFD to nuclear safety issues involving mixing problems in the presence of
buoyancy effects, like pressurised thermal shocks (PTS).
The experiments were performed at Texas A&M University (TAMU). The test facility
was designed to simulate mixing phenomena of cold water and hot water in a horizontal
cold leg of a pressurised water reactor (PWR), as encountered in some accidental
situations like the PTS. The experimental test facility, as shown in Figure 1.1, consistsof two large transparent vessels connected through a horizontal acrylic pipe. The vesselon the right represents the reactor pressure vessel of a PWR, and is initially filled withlow density water (representing hot water). The vessel on the left is initially filled withheavier water. At time zero, a knife valve in the horizontal leg is rapidly opened and cold
water (here represented by the heavier liquid in the left vessel) enters the cold leg and isallowed to flow into the pressure vessel.
Figure 1.1. Vessel and cold leg geometry

Source: Orea et al., 2019.

Velocities were measured in the two regions of interest (in the cold leg and in thedowncomer). Concentration (and thus water density) was measured in the cold leg.
The benchmark exercise started with 36 participants from 11 countries and 22
institutions. In the end, 19 participants (grouped in 11 teams) from 6 countries and 10
institutions submitted CFD calculation results. The results of the cold leg mixing
benchmark were presented at the third meeting that took place at the UIC-P Espace
Congrès in Paris (France) on 26 June 2019.
Four teams used the same code (CFX), while each of the other teams used a different
CFD code, meaning a total of eight different CFD codes were used. Eight teams used
Large Eddy Simulation (LES), while three teams used a Reynolds Averaged Navier-NEA/CSNI/R(2020)7 | 9

Stokes (RANS)/Unsteady RANS simulation (URANS) approach as CFD techniques forturbulence modelling.
The number of nodes in the CFD mesh varies between about 200 000 (for the coarsest
RANS approach) to about half a billion (for the finest LES). Five teams performed ananalysis of uncertainties.
Results obtained in the horizontal leg show that:•All participants predict a stratification of concentrations (and thus density) in thehorizontal leg, but a systematic bias is observed for the vertical concentration
profile; in all calculation results, the vertical position of this stratification is lower
than the measured position and out of the experimental uncertainty range.

•

Most participants predict the correct general shape of the velocity profile as afunction of vertical position; the vertical position of the inflexion point(corresponding to zero horizontal velocity at the boundary between the two fluidlayers) is globally well predicted.

•

There is a significant scattering between participants’ results concerning thevelocity magnitude in the horizontal leg; most participants predict a velocity
magnitude higher than the measured value and out of the experimental
uncertainty range; a few are close to the experimental results and one participant
predicts a lower magnitude of velocity.

The fact that calculation results give the correct vertical position for the velocity
inflexion point, but not for the concentration stratification level, is rather surprising andshould be further investigated.
In the downcomer, the comparison of velocity profiles between the inner and outer wallshows significant scattering of calculation results. The difference with experimental
results is quite large, except for the LES computations with the largest number of cells,
especially at the beginning of the test; this scattering gets lower at later stages of thetransient (after 30 s). The difference concerns:•

the vertical flowrate along the measured line, which is related to the predictionof the span width (azimuthal direction along the annulus) of the falling jet;•the velocity profile shape through the annulus, which directly impacts theprediction capability of the CFD for the gradients along the outer wall of thedowncomer, this region being of primary relevance for the corresponding safety
application.

A second report, dealing with “open phase” post-test calculations and including further
physical analysis of the results, will be issued later.

10 | NEA/CSNI/R(2020)7

List of abbreviations and acronyms
BT

Blind test

CEA

Commissariat à l’Énergie atomique et aux énergies alternatives(Alternative Energies and Atomic Energy Commission, France)

CFD

Computational fluid dynamics

CLM-UQ

Cold leg mixing computational fluid dynamics – uncertainty
quantification

CSNI

Committee on the Safety of Nuclear Installations (NEA)

DPIV

Digital particle image velocimetry

EDF

Électricité de France

HH

Height of Heavy (fluid)

HL

Height of Light (vessel)

IRSN

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

JAEA

Japan Atomic Energy Agency

LES

Large Eddy Simulation

LIF

Laser induced fluorescence

NEA

Nuclear Energy Agency

OT

Open test

PIV

Particle image velocimetry

PTS

Pressurised thermal shock

PWR

Pressurised water reactor

RANS

Reynolds Averaged Navier-Stokes

RI

Refractive index

RPC

Robust phase correlation

TAMU

Texas A&M University

URANS

Unsteady RANS simulation

WGAMA

Working Group on Accident Management and Analysis (NEA)NEA/CSNI/R(2020)7 | 11

1. Introduction
The experiments were performed at Texas A&M University (TAMU). The test facility
is designed to simulate mixing phenomena of cold water and hot water in a horizontal
cold leg of a PWR, as encountered in some accidental situations.
The benchmark exercise started with 36 participants from 11 countries and 22
institutions. In the end, 19 participants from 6 countries and 10 institutions submitted
CFD calculation results.
The experimental facility, the test conditions and the measurements are presented below.
The calculation results obtained by the 19 participants are then presented and comparedto the experimental results.
A second report, dealing with “open phase” post-test calculations and including further
physical analysis of the results, will be issued later.

12 | NEA/CSNI/R(2020)7

2. Background
Computer codes for analysis of the thermal hydraulics of the primary circuit andassociated safety systems under postulated accident conditions have traditionally been
based on one-dimensional flow models that are fitted and validated against experiments.
Since the early 2000s, single phase computational fluid dynamics has been increasingly
used for design and safety analysis related to specific aspects of reactor thermal
hydraulics. The NEA Working Group for the Analysis and Management of Accidents(WGAMA) has been working over the past 15 years to evaluate the potential of CFD
methods to bring additional insight and knowledge, and increased precision, to thermal
hydraulics safety analysis. A list of safety issues where CFD may bring additional
benefits was established, a best practice guideline was produced, and uncertainty
quantification for CFD methods was addressed. In addition, a series of benchmarks wasorganised in order to evaluate the precision and predictive power of CFD methods fordifferent specific scenarios.
One of the scenarios where CFD has the potential to add crucial new knowledge
compared to traditional 1D methods is pressurised thermal shock (PTS). This scenario
was selected for the current benchmark exercise because of its safety relevance, andbecause it represents a case which can be handled by standard CFD codes while at thesame time being challenging in terms of geometry and boundary conditions.
More specifically, the scenario for the current benchmark is the following: two tanks areconnected by a straight pipe which represents a cold leg and is equipped with a knife
valve. One of the tanks represents the pressure vessel, with an annual downcomer
installed, while the other tank represents the safety injection tank. At time zero, the knife
valve is rapidly opened and cold water (here represented by a heavier liquid) enters thecold leg and is allowed to flow into the pressure vessel. The safety concern is that when
the cold leg water enters the pressure vessel, thermal stresses may induce crack
initiation/growth, which, in turn could challenge the integrity of the vessel if it happens
at elevated pressure.
The conditions of the current benchmark can be compared to reactor conditions asfollows: the density difference between the two liquids corresponds roughly to what one
would have in a real reactor, and the geometry represents well that of the cold leg andthe downcomer despite scaling distortion (approximately a factor of ten). In particular,
the actual connection between the two was precisely modelled after a real reactor
component. On the other hand, the initial and boundary conditions of the experiment are
rather idealised in the sense that initially stagnant heavy fluid is allowed to mix withstagnant light fluid in the cold leg. In an actual safety analysis, one must consider the
possibility of much more complex situations with highly dynamic flows usually under
two-phase conditions. However, the current scenario can be seen as one possible worstcase scenario, since more turbulent situations than the one studied here will lead to more
mixing between cold and hot water, and thereby to lower thermal stresses in thedowncomer.
From a modelling perspective, the following aspects of the current benchmark are worth
pointing out. First, if a benchmark is to be successful, the initial and boundary conditions
need to be well defined. This was repeatedly found in previous benchmarks, and it has
been emphasised that measurements in the test section may be highly sensitive not only

NEA/CSNI/R(2020)7 | 13

to the inlet conditions but also to outlet conditions. For that reason, much attention wasgiven in the current benchmark to make sure that the fluids were completely stagnant at
the beginning of the test. Also the influence of the lifting of the knife valve was carefully
evaluated. Second, it has been previously found that the density difference between
heavy and light fluids may impact measurements that rely on optical instruments if therefractive indices also differ. For that reason, efforts were made to find two liquids withdifferent density but similar refractive indices. This has allowed unprecedented precision
to be obtained in the current particle image velocimetry (PIV) measurements of a mixing
scenario. Third, the comparatively complex geometry and the time dependence of thecurrent transient scenario present certain challenges. Computationally, the scenario ischallenging but clearly within reach for current state-of-the-art CFD calculations.
However, to compare calculations with measurements in a meaningful way still presents
several difficulties. Averaging times and statistics need to be judiciously selected inorder to distinguish between random fluctuations and systematic trends and account forthe capabilities of the different methods for resolving turbulence. These aspects were
carefully discussed as part of the current benchmark exercise, before deciding on a setof output data to be used as figures of merit.

14 | NEA/CSNI/R(2020)7

3. Experimental facility description
The experimental test facility consists of two large transparent vessels connected througha horizontal acrylic pipe. One of the vessels is designed to incorporate specific features
and the geometry of a typical reactor vessel. The nozzle is constructed to realistically
represent the fluid domain of the cold leg nozzle of a PWR vessel. An overview of theexperimental facility with the instrumentation installed is provided in Figure 3.1.
Figure 3.1. Experimental facility and instrumentation

Note: blue solution represents the cold/heavier water; yellow solution represents the hot/lighter water.
Source: Orea et al., 2019.

The vessel on the left is used as a tank for the solution simulating the cold/heavy water
injection (blue solution in Figure 3.1), and is initially filled to a predetermined height.
This vessel is referred to as the heavy vessel due to the heavier density fluid. The vesselon the right represents the reactor vessel. This is made by two acrylic cylinders, theexternal cylinder representing the vessel wall and the internal cylinder representing thereactor barrel. The annular region between these cylinders represents the downcomer ofthe reactor, which is initially filled with a surrogate solution to the hot water (yellow
solution in Figure 3.1). This vessel is referred to as the light vessel because of the lower
density fluid. An isolation valve is installed within the connecting horizontal leg to keep
the two solutions isolated during test preparation. The isolation valve is attached to aspring lever mechanism to achieve consistent and repetitive opening times. Attached to
the valve is a switch that is used to trigger the cameras to begin recording when theisolation valve is open. Figure 3.2 and Table 3.1 provide the main dimensions of theheavy, light vessels and the leg that connects the two vessels.

NEA/CSNI/R(2020)7 | 15

Figure 3.2. Vessel and cold leg geometry

Source: Orea et al., 2019.

Table 3.1. Dimensions of vessels and cold leg

Heavy
Side
VesselSymbolDescription

IDHV

Inner Diameter of Heavy Vessel

34.29

HLC-HBLeg Centerline to Bottom of Heavy Vessel

10.9

IDLC-HB Inner Diameter of Cold Leg

5.31

LL

Total Length

130.81

LLL

Length of Left Section

39.37

LLR

Length of Right Section

91.44

DPT

Distance of Pressure Taps from Valve Midplane

15.98

IDLV

Inner Diameter of Outer Cylinder (Vessel)

34.29

ODLV

Outer Diameter of Inner Cylinder (Barrel)

30.48

HLC-LB

Cold Leg Centerline to Bottom Plate

80.33

HL

Cold Leg Centerline toTop Lid

27.94

Cold Leg
Light Side
VesselDimension (cm)Source: Orea et al., 2019.

It was proposed to implement a reactor-like geometry of the cold leg and downcomer
junction using a conical shape and a rounded edge. The top image of Figure 3.3 shows
the nozzle sitting flush against the vessel wall, while the bottom image shows the nozzle
dimensions required to achieve the best possible fit.

16 | NEA/CSNI/R(2020)7
Figure 3.3. Cold leg vessel nozzle

Source: Orea et al., 2019.

NEA/CSNI/R(2020)7 | 17

4. Test conditions and measurement locations
Two tests (identified as open test [OT] and blind test [BT]) were conducted under
different test conditions. The OT was executed using two solutions with a target density
difference of 100 kg/m3. The BT was executed using two solutions with a target density
difference of 200 kg/m3.

Fluid properties
To confirm that the refractive index remained constant during mixing of the selected
solutions, the refractive index of a solution generated by the mixing of the two surrogatesat different ratios was measured. Density, viscosity, and index of refraction of the parent
solutions and intermediate mixtures were measured and reported in s. One can observethat the index of refraction is stable while the two solutions mix. This condition isimportant when applying the flow visualisation and measurements techniques. Densities
were calculated by measuring the weight of a 10 ml sample taken from a batch of solution
maintained at a given temperature. The 10 ml volume is extracted using an electronic
pipette. The weight of the sample taken is measured with an analytical balance accuracy.
The sample is transferred to a glass beaker and placed on the plate of scale.
Measurements were performed on solutions in thermal equilibrium with the laboratoryenvironment temperature at an average of 21°C. Viscosity was measured using acylindrical system coupled with a double-couette to increase the surface area, yielding
enhanced accuracy. The accuracy the instrumentation can be found in Table 4.1.
Viscosity was measured at an ambient temperature of 20.5ºC. Figure 4.1 shows thecalculated densities of the surrogate fluids and the associated refractive index. Figure 4.2
presents the viscosity of the mixtures for both OT and BT. It can be seen that viscosity
is a linear function of the mixture density. This function is displayed in the figure.

Table 4.1. Fluid properties of sampled mixtures (OT)

Source: Orea et al., 2019.

18 | NEA/CSNI/R(2020)7
Figure 4.1. Density vs. refractive index of solutions

Source: Orea et al., 2019.

Figure 4.2. Viscosity of fluid mixtures

Source: Orea et al., 2019.

All tests were performed with both solutions at a constant ambient temperature of 21°C
±0.3. Figure 4.2 shows the liquid level labelled as Height of Heavy fluid (HH) and theHeight of Light vessel labelled (HL). The OT and BT targeted test conditions wereNEA/CSNI/R(2020)7 | 19

discussed during the benchmark definition and are summarised in Table 4.2. The heightof the heavy fluid is calculated from the densities of the surrogate solutions in order toequalise the pressure at the centreline across the isolation valve at the start of the test.
𝜌𝜌𝐿𝐿
𝐻𝐻𝐻𝐻 =𝐻𝐻
𝜌𝜌𝐻𝐻 𝐿𝐿
Where HL is fixed at 27.9 cm.

Table 4.2. OT and BT fluid properties

Source: Orea et al., 2019.
Note: These boundary conditions were the ones measured at the test start and are within the uncertainty ofthe boundary conditions measured during the benchmark definition.

Measurement locations
The location and dimensions of the two measurement/visualisation windows, together
with the reference co-ordinate systems are shown in figure 4.3. The two windows are
symmetric across their individual axes.

20 | NEA/CSNI/R(2020)7
Figure 4.3. Visualisation windows relative to the valve centreline

Source: Orea et al., 2019.

In addition to the velocity and concentration, the pressure difference was measured
across the isolation valve and recorded for the duration of the experiment. Figure 4.2
shows the location of the pressure taps.

NEA/CSNI/R(2020)7 | 21

5. Experimental methods and measurement techniques
The experimental facility is designed to simulate the buoyancy-driven flow between coldand hot water typical of cold water injection events taking place in the cold leg and thedowncomer. The facility produces high-fidelity experimental data in a prototypicalgeometry to test uncertainty quantification methods in conditions closer to real reactor
applications. To simulate the mixing phenomena of hot and cold water but also maintainlow operating pressures and temperatures for practical reasons, two surrogate solutionsof different densities were carefully chosen and prepared to represent the cold and hot
fluids. The cold fluid is simulated using a solution of water and salt. A solution of waterand ethanol was used to simulate the hot fluid. Concentrations of salt and ethanol inwater were selected to achieve the desired density difference between the heavy and lightsolutions. The properties of the solutions (density, viscosity and index or refraction) were
measured and recorded before the test.
Local variations in the refractive index (RI) in the flow cause potential issues whenattempting to apply optical measurement techniques such as PIV and laser induced
fluorescence (LIF). This issue can be resolved by RI matching. It was possible to findsurrogate solutions of specific concentrations that at a given density difference will resultin matching refractive indexes. As discussed in the introduction, previous tests identified
a combination of ethanol and sodium chloride (NaCl) to be the most suitable forexperiments of a larger scale and it was therefore also chosen for this study. High
resolution measurements of velocity and concentration fields were performed using
specialised laser-based techniques.

Particle image velocimetry
PIV is a non-intrusive, laser-based optical measurement technique to quantify thevelocity vector fields of a fluid. The displacement of fluid elements is captured by using
highly reflective, low density micro seeding particles that follow the fluid flow without
impacting the flow characteristics. A laser sheet is used to illuminate the test section andparticles. A series of images are captured using a high resolution camera at a desired
frequency. Images captured are processed using in-house codes and a velocity vector
field is constructed. These tracer particles scattered the laser light back to the PIV
cameras as they passed through the laser sheets at the regions of interest.
Velocity vectors were retrieved by processing image pairs using PIV analysis software(PRANA) (Nguyen et al., 2017; Eckstein and Vlachos, 2009). All PIV images capturedby the 12-bit depth cameras were processed by the advance multi-pass, multi-grid PIVprocessing algorithms based on the robust phase correlation (RPC) algorithms
implemented in the PRANA codes by Virginia Tech. A three-step processing wasapplied. The initial interrogation window was a 64x64 pixel area, followed by twointerrogation areas of 32x32 pixels. All passes had a 50% window overlap. Velocity
vectors were calculated from the correlation map with a Gaussian peak fit for sub-pixel
accuracy (Raffel et al., 2007). Each pass contained statistical validation of erroneous
vectors. A median filter (Westerweel, 1994) was applied, standard deviations of theneighbouring vectors were used to filter out spurious vectors, and blanks were filled byvelocity interpolation.

22 | NEA/CSNI/R(2020)7
The PIV measurement areas had 37x80 instantaneous velocity vectors in the cold leg
region and 20X80 instantaneous velocity vectors in the downcomer region. The spacingof adjacent vectors at the cold leg was 0.155 cm. The spacing of the adjacent vectors at
the downcomer was 0.105 cm.

Laser induced fluorescence
LIF is a laser-based measurement technique used to measure the temperature orconcentration of fluids. A soluble tracer dye is uniformly mixed with the fluid. A laser
sheet illuminates the test section, causing the particles to fluoresce. A series of images
are captured using a high resolution camera. Concentration distributions can beconstructed by converting the camera signal intensity into physical concentration fromthe images captured and processed using in-house codes. For this experiment,
Rhodamine 6G (absorption peak at 532 nm and emission peak at 552 nm) was used as
the tracer dye. This fluorescent dye was selected due to its negligible response totemperature changes, which makes it suitable for concentration measurements.

LIF calibration
The intensity of the light emitted by the dye was correlated to the concentration of thesolutions by performing a five-point in situ calibration. Rhodamine 6G was dissolvent
to a parent heavy solution with the same concentration used during the test. From thisparent solution (100% heavy solution calibration point), three additional solutions wereprepared by mixing it with an amount of light solutions. These solutions were used forthe 75%, 50%, and 25% calibration points. One additional point was included to accountfor pure light solutions (0% calibration point). To apply the best possible calibration to
the captured images, two calibration methods were attempted.
The first method involved using a single mean intensity value from a captured imaged
for each calibration point. The average intensity is calculated by summing each pixel
intensity and dividing by the total number of number of pixels.
𝑁𝑁

1
𝐼𝐼𝑎𝑎𝑎𝑎𝑎𝑎 = � 𝐼𝐼𝑝𝑝𝑁𝑁
𝑝𝑝=1

Where I is the signal intensity, N is the total number of pixels, and p is the pixel index.
By calculating the mean intensity for five known concentrations, a linear curve can be
estimated to then apply to the open test and blind test. These plots can be seen in
Figure 5.1 for the open test and blind test calibrations.

NEA/CSNI/R(2020)7 | 23

Figure 4.1. OT and BT LIF 5-point calibration curve from window average intensity

Source: Orea et al., 2019.

On the plots, the slope represents the increasing intensity as the mixture ratio approaches
100% water-salt. The result is a linear expression for calculating the concentration.
𝐶𝐶𝑂𝑂𝑂𝑂 = 0.0409 ∗ 𝐼𝐼(𝑖𝑖, 𝑗𝑗) − 6.484
𝐶𝐶𝐵𝐵𝐵𝐵 = 0.0489 ∗ 𝐼𝐼(𝑖𝑖, 𝑗𝑗) − 9.138

The second method involved a local pixel intensity averaging from each calibration
point. An 8 by 8-pixel area is averaged for the entire image size. This was applied to five
known concentrations. A second-degree polynomial fit was then applied to eachco-ordinate for all concentrations from 0-100% mixture ratio.
𝐶𝐶(𝑋𝑋, 𝑌𝑌) = 𝐴𝐴(𝑋𝑋, 𝑌𝑌) ∗ 𝐼𝐼(𝑖𝑖, 𝑗𝑗)2 + 𝐵𝐵(𝑋𝑋, 𝑌𝑌) ∗ 𝐼𝐼(𝑖𝑖, 𝑗𝑗) + 𝐶𝐶(𝑋𝑋, 𝑌𝑌)

Where C is the concentration at the point, I is the intensity at the point, i and j are pixel
indexes, and A, B, C are a two-dimensional coefficient matrices created by curve fitting.
This process was repeated for open tests and blind tests. An example of the polynomial
fit at the centre point of an image with varying intensities is shown in Figure 4.2.

24 | NEA/CSNI/R(2020)7
Figure 4.2. OT LIF 5-point local calibration curve for a single point in point in space

Source: Orea et al., 2019.

Based on the calibration methods applied, the second polynomial curve was used to
calculate fluid concentrations.

Experimental method
PIV and LIF techniques were used to capture the concurrent velocity and concentration
fields in the cold leg region and PIV was used to capture the velocity vector field in thedowncomer region (Díaz, Castanedo and Palomar, n.d.; Odier, Chen and Ecke, 2014; Xu
and Chen, 2012). The solutions were uniformly seeded with silver-coated hollow glass
spheres (mean diameter of 16 µm and a density of 1.6 g cm−3) to perform the PIV
measurements. The PIV and LIF systems consisted of a laser source with appropriate
optics to create two 1-mm laser sheets at the regions of interest (Figure 4.2). Three digital
CMOS cameras with a full resolution of 1280 x 800 pixels were used. Two cameras were
installed to record PIV and LIF data at the cold leg, and one camera was used to record
PIV data at the downcomer. Camera settings were optimised based on the visualisation
window and the flow observations performed during shakedown tests (1 280 x 600
resolution at the leg and 1 280 x 320 at the downcomer). Fluorescent dye (Rhodamine
6G) was homogeneously mixed with the heavy solution to perform concentration
measurements using LIF. This preparation was completed earlier enough from the test
initiation to allow the solutions to reach thermal equilibrium with the laboratoryenvironment. Two thermocouple probes were utilised to monitor and measure thetemperature of the heavy and light solutions respectively, before and during theexperiments. Pressure across the isolation valve was verified at the beginning of eachtest and monitored and recorded during the duration of the tests using a high accuracy
differential pressure transducer.
With the isolation valve closed, the light solution was transferred into the light vessel tobe filled up to the level of the top lid. The heavy solution was then transferred into theheavy vessel up to a level estimated to balance the hydrostatic pressure across the centre
line of the isolation valve. Air pockets formed during the fluid transfer were carefully
removed before each test. The experiment was initiated by releasing the springmechanism connected to the isolation valve which lifted the guillotine valve andNEA/CSNI/R(2020)7 | 25

triggered the visualisation systems. Camera recording start times and durations were
optimised based on the observed arrival and transition times of the heavy fluid front
during previous shakedown tests. The LIF camera was set next to the PIV camera at asmall angle as seen in Figure 5.3. The frame of the LIF images contained that of the PIV
images.
Figure 5.3. Camera setup for cold leg window

Source: Orea et al., 2019.

To avoid interference between the two techniques, the LIF camera was fitted with a
590 nm band pass filter that allowed for the Rhodamine-6G emission wavelength to pass
and reflected the laser light scattered from the particles. Similarly, the PIV camera wasfitted with a 532 nm band pass filter that allowed laser light scattered from the particles
to pass but reflected fluorescent light. Figure 5.4 shows two images with and without theappropriate light filters. Figure 5.5 shows the camera system being applied to the cold
leg and downcomer for PIV only.

26 | NEA/CSNI/R(2020)7
Figure 5.4. Cold leg visualisation and filter effectNote: a) Image without 552 nm light filter. b) Image with 552 nm light filter.
Source: Orea et al., 2019.

Figure 5.5. Camera system for simultaneous PIV measurements

Source: Orea et al., 2019.

A full list of the equipment and instrumentation used during experiments can be foundin Table 5.1.

NEA/CSNI/R(2020)7 | 27

Table 5.1. Equipment and instrumentation specification

Source: Orea et al., 2019.

28 | NEA/CSNI/R(2020)7

6. Uncertainty analysis
PIV uncertainty
PIV uncertainty analysis employs the method applied in (Sciacchitano, 2019).
Calculated velocity can be expressed as,
𝑈𝑈 = 𝛼𝛼 �∆𝑋𝑋� + 𝛿𝛿𝛿𝛿
∆𝑡𝑡

where ∆X is the displacement of particle images, ∆t is the time interval of successive
images, α is the magnification factor identified through a calibration, and δU is theuncertainty factor of the flow field. The uncertainty of the measured velocity can becalculated by propagating the uncertainties of alpha, position, time and δU,

where,𝜎𝜎𝑈𝑈 = ��∆𝑋𝑋 2 2𝛼𝛼 2 2𝛼𝛼∆𝑋𝑋 2 22� 𝜎𝜎𝛼𝛼 + � � 𝜎𝜎∆𝑋𝑋+ � 2 � 𝜎𝜎∆𝑡𝑡+ 𝜎𝜎𝛿𝛿𝛿𝛿
∆𝑡𝑡
∆𝑡𝑡
∆𝑡𝑡

•

α: magnification factor estimated by in situ calibration;•σ∆x: displacement uncertainty determined by the performance of the PRANA
code;

•

∆t : time interval uncertainty determined by camera settings;•σδU : uncertainty factors of flow field estimated by particle density and diameter.

The magnification factor α is estimated by in situ calibration. The conversion from pixels
to milometers was performed by taking calibration images of targets with known
physical dimensions. Images captured during calibration gave correlated pixels to aphysical distance of αCL = 0.0962 mm/pix for the cold leg OT and BT. Downcomer
calibration images gave αDC = 0.0655 mm/pix for OT and BT. Image and lens distortions
create slightly varying magnification factors. This results in σα,CL = 0.000617 mm/pix
and σα,DC = 0.000276 mm/pix for the for OT and BT, respectively.
The uncertainty from the vectors calculated is determined by the performance of the
PRANA code which has been thoroughly studied (Timmins et al., 2012; Wilson andSmith, 2013). These studies have reported an overall σ∆x uncertainty of approximately
0.1 pixels. The spurious and erroneous vectors were evaluated for all time-resolved PIV
velocity vectors. For all tests, OT and BT, less than 1.5% of vectors were erroneous.

NEA/CSNI/R(2020)7 | 29

Table 6.1. Values used for OT and BT uncertainty analysis

Source: Orea et al., 2019.

The time interval between frames recorded by the high-speed camera used during theexperiment is ∆t. The cameras recorded images at a rate of: fOT = 250 Hz andfBT = 500 Hz. Subsequently, ∆tOT = 0.004 s and ∆tBT = 0.002 s. The uncertainty given by
the camera settings is 1% which corresponds to σt,OT = 0.04 ms and σt,BT = 0.02 ms.
The value σδU can be considered negligible as it describes the behaviour of the particles
in the fluid. As mentioned in the section describing the PIV methodology, the particle’s
diameter and density are sufficiently small and low to properly follow the fluid flow.
Overall, uncertainty analysis for the OT and BT was performed assuming the maximum
amount of error. Note that the same approach is taken for the vertical V components, anduncertainties can be calculated. The maximum errors for PIV measurements for OT and
BT were calculated to be,
𝜎𝜎U CL,max = 0.0027 m/s

LIF error sources

𝜎𝜎U DC,max = 0.0031 m/s

LIF uncertainties are less commonly studied as they are difficult to quantify. In this case,an in situ calibration was performed using five points as previously discussed in themethodology section. After thorough investigation, it was observed that one of the main
contributors to error in the LIF data was the volume of dye homogeneously mixed duringcalibration. Systematic errors that affect to LIF measurements include background light,
laser power fluctuations, spatial variation of light intensity, and camera sensor saturation.
In order to measure qualitatively the background and laser light, 10 000 images weretaken of the facility without any fluorescent dye. Figure 6.1 shows recorded background
intensities were small. This error can be eliminated by subtracting background images.

30 | NEA/CSNI/R(2020)7
Figure 6.1. Background intensity distribution

The laser power distribution also contributes to measurement error as damaged opticallenses and/or a fluctuating lasers will create irregularities in the laser sheet. In addition,
small scratches on wall surfaces, micro-bubble formation, and debris will obstruct thelaser sheet and cast shadows in the test section. Figure 6.2 and Figure 6.3 demonstrate
the effects of laser power fluctuations and spatial distributions on captured images.
Figure 6.2. Normalised intensity along horizontal profile

As seen in Figure 6.2, the laser light intensity reaches a maximum near the centre of theframe, and tails off as it moves towards the edges of the frame.

NEA/CSNI/R(2020)7 | 31

Figure 6.3. Normalised intensity along vertical profile

The laser spatial distribution is also affected in the vertical direction as shown in Figure
6.3. Laser light entering from the top of the visualisation window will be attenuated anddecrease in intensity as it traverses the plane. The camera sensor saturation was tested
by increasing the concentration of Rhodamine-6G in small increments. To avoid
saturating the camera sensor, the amount of Rhodamine-6G injected was set atapproximately 65% of the maximum allowed before complete saturation.
The error sources posed a challenge when attempting to quantify into a factor. Therefore,
all experiments were performed in situ. In addition, local calibration aided in minimisingthese effects. By knowing the target concentration during the calibration and thecalculated concentration based on a local point-by-point method, the relative error canbe calculated with the following expression (Gavelli and Kiger, 1998).
𝜖𝜖 =�𝐶𝐶𝑜𝑜 − 𝐶𝐶𝑝𝑝 �
∗ 100
𝐶𝐶𝑜𝑜

Where Co is the known concentration, and Cp is the calculated concentration using the
local method. Applying this to a sample set of 5 000 images of a known 50% water-salt
calibration produced an average relative error of OT = 3.64 % and BT = 4.62 %.
Additional information on the experimental test facility, test methods, and uncertainty
quantification is available in (Orea et al., 2019).

32 | NEA/CSNI/R(2020)7

7. Participants and submitted results
Table 7.1 shows the list of participants that submitted simulation results to thebenchmark organisers. The table includes a generic description of the computational
tools (CFD software), methods adopted and nodalisation of the geometry, and specifies
whether uncertainty quantification was conducted.
The different acronyms refer to the following models for turbulent tensor resolution:•

LES: Large Eddy Simulation (high fidelity, direct resolution of large scale ofmotion, only small scales are modelled, high computational cost, unsteadysimulation);•

RANS: Reynolds Averaged Navier-Stokes simulation (industrial use, modellingof the entire flow field, low computational cost, steady state simulation);•

URANS: Unsteady RANS simulation (same as above, unsteady simulation).

Note that time dependent instabilities captured by LES and URANS are different. The
resolved flow field from LES takes into account the actual turbulent fluctuations(random). The one from URANS does not contain such information, being
representative of coherent fluctuation (periodic) only. Please also note that some LES
computations have been made with relatively low nodalisation, similar to other RANS
or URANS computations.

Table 7.1. Submitted simulation results

NEA/CSNI/R(2020)7 | 33

34 | NEA/CSNI/R(2020)7

8. Benchmark results
Selected velocities and concentrations at identified locations have been included in thebenchmark exercise. These include time-averaged streamwise velocities andconcentrations.
Table 8.1. Time-averaged measured quantities

Location

Quantity

Time intervals (s)

CL1

U(y)

[5-10], [10-15],[15-22]

C(y)
DC1
DC2

V(x)

[10.5-15.5], [15.5-20.5], [20.5-25.5],[25.5-30.5], [30.5-35.5], [35.5-41.5]

The experimental results are plotted (black curves) with the experimental uncertainty range.
The CFD results obtained by all 11 participants are plotted on the same graphs.
Please note that the data presented here are only roughly analysed in the synthesis andconclusion section following their presentation and discussions at the meeting on 26 June
2019. Deeper analysis will be presented in a second report.

Cold leg results – Streamwise time-averaged velocity component (CL1 Location)
The experimental time-averaged velocity component, U, along the probe line CL1 is plottedand compared with the simulation results for five-second intervals.
Figure 8.1. Y-position vs. U (cm/s) 5-10 second time-averaged U – CL1

NEA/CSNI/R(2020)7 | 35

Figure 8.2. Y-position vs. U (cm/s) 10-15 second time-averaged U – CL1

Figure 8.3. Y-position vs. U (cm/s) 15-22.24 second time-averaged U – CL1

Cold leg results – Time-averaged concentrations
The experimental time-averaged concentration, C (0% = pure light solution, 100% = pure
heavy solution), is plotted and compared with the simulation results for five-second
intervals.

36 | NEA/CSNI/R(2020)7
Figure 8.4. Y-position vs. concentration (%) 5-10 second time-averaged C – CL1

Figure 8.5. Y-position vs. concentration (%) 10-15 second time-averaged C – CL1

NEA/CSNI/R(2020)7 | 37

Figure 8.6. Y-position vs. concentration (%) 15-22.24 second time-averaged C – CL1

38 | NEA/CSNI/R(2020)7

Downcomer results – Streamwise time-averaged velocity component (DC1 Location)
The experimental time-averaged velocity component, V, along the probe line DC1 isplotted and compared with the simulation results for five-second intervals.
Figure 8.7. V (cm/s) vs. X-position 10.5-15.5 second time-averaged V – DC1

Figure8.8. V (cm/s) vs. X-position 15.5-20.5 second time-averaged V – DC1

NEA/CSNI/R(2020)7 | 39

Figure 8.9. V (cm/s) vs. X-position 20.5-25.5 second time-averaged V – DC1

Figure 8.10. V (cm/s) vs. X-position 25.5-30.5 second time-averaged V – DC1

40 | NEA/CSNI/R(2020)7
Figure 8.11. V (cm/s) vs. X-position 30.5-35.5 second time-averaged V – DC1.

Figure 8.12. V (cm/s) vs. X-position 35.5-41.538 second time-averaged V – DC1

NEA/CSNI/R(2020)7 | 41

Downcomer results – Streamwise time-averaged velocity component (DC2 Location)
The experimental time-averaged velocity component, V, is plotted and compared with thesimulation results for five-second intervals.
Figure 8.13. V (cm/s) vs. X-position 10.5-15.5 second time-averaged V – DC2

Figure 8.14. V (cm/s) vs. X-position 15.5-20.5 second time-averaged V – DC2

42 | NEA/CSNI/R(2020)7
Figure 8.15. V (cm/s) vs. X-position 20.5-25.5 second time-averaged V – DC2

Figure 8.16. V (cm/s) vs. X-position 25.5-30.5 second time-averaged V – DC2

NEA/CSNI/R(2020)7 | 43

Figure 8.17. V (cm/s) vs. X-position 30.5-35.5 second time-averaged V – DC2

Figure 8.18. V (cm/s) vs. X-position 35.5-41.538 second time-averaged V – DC2.

44 | NEA/CSNI/R(2020)7

9. Synthesis and conclusion
The results of the cold leg mixing benchmark were presented at the third meeting of the WGAMA CFD
task group, which took place in Paris, France, on 26 June 2019. In all, 11 teams from 6 countries and 10
institutions submitted results.
Four teams used the same computer code (CFX), while the other remaining teams used eight other different
CFD codes. The Large Eddy Simulation was used by eight teams. Three other teams used RANS/URANS
techniques for turbulence modelling.
The number of nodes in the CFD mesh varies between about 200 000 (for the coarsest RANS approach)to about half a billion (for the finest LES). Five teams performed an analysis of uncertainties.
The results obtained in the horizontal leg show that:•All participants predict a stratification of concentrations in the horizontal leg, but asystematic bias is observed for the vertical concentration profile; in all calculation
results, the vertical position of this stratification is lower than the measured positionand out of the experimental uncertainty range, see Figure 9.4. Most participantspredict the correct general shape of the velocity profile as a function of the vertical
position; the vertical position of the inflexion point (corresponding to zero
horizontal velocity at the boundary between the two fluid layers) is globally well
predicted (see Figure 9.1).

•

There is significant scattering between participants’ results concerning the velocity
magnitude in the horizontal leg; most participants predict a velocity magnitude thatis higher than the measured value and out of the experimental uncertainty range; a
few are close to the experimental results and one participant predicts a lower
magnitude of velocity (see Figure 9.3).

The fact that the calculation results give the correct vertical position for the velocity
inflexion point, but not for the concentration stratification level, is surprising and should
be further investigated. The discrepancy may be due to diffusion. The selection of theSchmidt number tended to match the code predictions well with the experimental data (Laiand Merzari, 2019).
In the downcomer, the comparison of velocity profiles between the inner and outer walls
shows significant scattering of calculation results (see e.g. Figure 9.8). The difference withexperimental results is quite large, except for the LES computations with the largest numberof cells, especially at the beginning of the test; this scattering is lower at later stages of thetransient (after 30 s). The difference concerns:•

the vertical flowrate along the measured line, which is related to the prediction of
the span width (azimuthal direction along the annulus) of the falling jet;•the velocity profile shape through the annulus, which directly affects the prediction
capability of the CFD for the gradients along the outer wall of the downcomer, thisregion being of primary relevance for the corresponding safety application.

NEA/CSNI/R(2020)7 | 45

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