application of a system thermal-hydraulics code to ...1209171/...simulation methodology for analysis...

Application of a system

thermal-hydraulics code to

development of validation

process for coupled STH-CFD

codes

KASPAR KÖÖP

Doctoral thesis No. 10, June 2018 KTH Royal Institute of Technology Engineering Sciences Department of Physics Stockholm, Sweden

AlbaNova University Center Roslagstullsbacken 21 TRITA-SCI-FOU 2018:10 10691 Stockholm ISBN 978-91-7729-727-7 Sweden Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framlägges till offentlig granskning för avläggande av teknologie doktorexamen i fysik den 7 juni 2018, FB52, AlbaNova universitetscentrum, Stockholm. © Kaspar Kööp, June 2018 Tryck: Universitetsservice US AB

I

ABSTRACT

Generation IV reactors are designed to provide sustainable energy generation,

minimize waste production and excel in safety. Due to lack of operational

experience, ever evolving design and stringent safety requirements, these novel

reactors have to rely heavily on simulations.

Best estimate one-dimensional (1D) system thermal-hydraulics (STH) codes,

originally intended for simulating water-cooled reactor systems with high coolant

mass flow rates, are unable to capture complex three-dimensional (3D) phenomena

in liquid metal cooled pool-type reactors. Computational fluid dynamics (CFD)

codes are capable of resolving the 3D effects, however applying these methods with

high resolution for the whole primary system results in prohibiting computational

cost.

At the same time, there are system components where flow can, with reasonable

accuracy, be approximated with 1D models (e.g. core channels, some heat

exchangers, etc.). One of the proposed solutions in order to achieve adequate

accuracy and affordable computational efficiency in modelling of a Generation IV

reactor is to divide the primary system into 1D and 3D regions and apply coupled

STH and CFD codes on the respective sub-domains.

Successful validation is a prerequisite for application of both, standalone and

coupled STH and CFD codes in design and safety analysis of Generation IV systems.

In this work we develop and apply different aspects of code validation methodology

with an emphasis on (i) STH code analysis in support of validation experiment

design (facility and test conditions), (ii) calibration of uncertain code input

parameters and validation of standalone STH code, (iii) development of an approach

to couple STH and CFD codes.

A considerable part of the thesis work is related to the development of a loop-type,

3 leg, liquid metal experimental facility TALL-3D for code validation. Particular

focus was on identification of test conditions featuring complex feedbacks between

1D-3D phenomena, which can be challenging for the codes. Standalone STH code

(RELAP5) was validated against experimental data. The domain of natural

circulation instabilities in TALL-3D operation parameters was discovered using a

validated STH code and global optimum search algorithms. Then existence of

growing natural circulation oscillations was experimentally confirmed. An

international benchmark was initiated in the framework of EU SESAME project

based on the obtained experimental data.

II

Simulations were performed to define dimensions and location of a new test section

for coolant solidification experiments that would also enhance possibilities for

studying natural circulation instabilities in the future tests.

An approach to automated input calibration and code validation is developed in

order to minimize possible “user effect” in case of multiple uncertain input

parameters (UIPs) and system response quantities (SRQs). These methods were

applied extensively in the development of RELAP5 input models and identification

of the natural circulation instability regions.

Domain overlapping approach to coupling of RELAP5 and Star-CCM+ codes was

proposed and resulted in considerable improvement of the predictive capabilities in

comparison to standalone RELAP5.

III

SAMMANFATTNING

Generation IV-reaktorer är utformade för att möjliggörahållbar energiproduktion,

minimera avfallsproduktion och utmärka sig i säkerhet. På grund av brist på

operativ erfarenhet, ständig utvecklaning av design och höga säkerhetskrav behöver

framtagning av dessa tekniska lösningar baseras på beräkningar och simulationer.

Best estimate termohydraulikkoder för endimensionella (1D) system, ursprungligen

avsedda för att simulera vattenkylda reaktorsystem med höga

kylvätskemassaflöden, kan inte beskriva komplexa tredimensionella (3D) fenomen

i flytande metallkylda bassäng-typ reaktorer.Beräkningsströmningsdynamikkoder

(CFD) kan hantera 3D-effekterna, men tillämpning av dessa metoder skulle

innebära beräkningar med hög upplösning för hela primärsystemet som i sin tur

resulterar i ogynnsamma beräkningskostnader.

Samtidigt finns det systemkomponenter där flöde med rimlig noggrannhet kan

approximeras med 1D-modeller (t ex kärnkanaler, vissa värmeväxlare, etc.). En av

de föreslagna lösningarna för att uppnå tillräcklig noggrannhet och kostnadseffektiv

beräkningseffektivitet vid modellering av en Generation IV-reaktor är att dela det

primära systemet i 1D- och 3D-regioner och tillämpa kopplade STH- och CFD-koder

på respektive deldomäner.

Framgångsrik validering är en förutsättning för tillämpning av både fristående och

kopplade STH- och CFD-koder i design och säkerhetsanalys av Generation IV-

system. I detta arbete utvecklar och tillämpar vi vidare aspekter av

kodvalideringsmetodik med inriktning på (i) STH-kodanalys till stöd för

utformningen av valideringsexperimentet (anläggnings- och testförhållanden), (ii)

kalibrering av kodinmatningsparametrar med osäkerheter, (iii) utveckling av

metoder för att koppla STH- och CFD-koder, iv) validering av fristående STH och

kopplade STH-CFD-koder.

En stor del av avhandlingsarbetet är kopplat till utvecklingen av en experimentell

slinga-typ 3-bens flytande metall anläggning TALL-3D för kodvalidering. Särskild

fokus har varit på identifiering av testförhållanden med komplexa återkopplingar

mellan 1D-3D-fenomen, vilket kan vara utmanande för koderna. Fristående STH-

kod (RELAP5) validerades mot experimentella data. Domänen för de instabiliteter

av naturlig cirkulation i TALL-3D-operationsparametrar upptäcktes med en

validerad STH-kod och globala optimala sökalgoritmer. Därpå bekräftades

experimentellt förekomsten av växande naturliga cirkulationsoscillationer. Ett

internationellt benchmark inleddes inom ramen för EU SESAME-projektet, baserat

på de erhållna experimentella uppgifterna.

IV

Simuleringar utfördes för att definiera dimensioner och plats för en ny provsektion

för experiment om stelning av kylmedel, som också skulle förbättra förutsättningar

för att studera naturliga instabiliteter i framtida provningar.

Ett tillvägagångssätt för automatisk inmatningskalibrering och kodvalidering är

utvecklad för att minimera möjlig "användareffekt" vid flertal osäkra

inmatningsparametrar (UIP) och systemresponsmängder (SRQs). Dessa metoder

användes i stor utsträckning vid utveckling av RELAP5 inmatningsmodellerna och

vid identifiering av de instabilitetsregionerna av naturliga cirkulation.

Domän-överlappande tillvägagångssätt för koppling av RELAP5 och Star-CCM +

koder föreslogs vilket resulterade i avsevärd förbättring av de prediktiva förmågorna

i jämförelse med fristående RELAP5.

V

LIST OF PUBLICATIONS

Included journal publications

I. V.-A. Phung, K. Kööp, D. Grishchenko, Y. Vorobyev, P. Kudinov “Automation

of RELAP5 input calibration and code validation using genetic algorithm”,

Published in Nuclear Engineering and Design, Volume 300, 15 April 2016. —

Contribution: Implementation of the genetic algorithm and RELAP5 input

modification, carried out GA-IDPSA calculations.

II. D. Grishchenko, M. Jeltsov, K. Kööp, A. Karbojian, W. Villanueva, and P.

Kudinov, “The TALL-3D facility design and commissioning tests for validation

of coupled STH and CFD codes,” Published in Nuclear Engineering and Design,

vol. 290, 2015. — Contribution: Analytical support to the experiment design

process, STH simulations, participation in conducting the experiments.

III. K. Kööp, D. Grishchenko, P. Kudinov “Automated calibration and validation

of RELAP5 input model against TALL-3D facility experimental data”,

Submitted to Nuclear Engineering and Design 2018.

IV. K. Kööp, M. Jeltsov, D. Grishchenko, P. Kudinov “Pre-test analysis for

identification of natural circulation instabilities in TALL-3D facility”, Published

in Nuclear Engineering and Design, Volume 314C, 2017.

V. M. Jeltsov, K. Kööp, D. Grishchenko, P. Kudinov “Pre-test analysis of an LBE

solidification experiment in TALL-3D”, Submitted to Nuclear Engineering and

Design 2018. — Contribution: Analytical support with STH simulations for

selection of geometry and location of the solidification test section.

Journal publications not included in the thesis

Yu. B. Vorobyev, P. Kudinov, M. Jeltsov, K. Kööp, and T.V.K. Nhat, “Application of

information technologies (genetic algorithms, neural networks, parallel

calculations) in safety analysis of Nuclear Power Plants,” Proceedings of the Institute

for System Programming of RAS, volume 26, 2014. Issue 2, pp.137-158. —

Contribution: RELAP5 simulation results of TALL-3D facility have been used as

comparative material.

G. Bandini, M. Polidori, A. Gerschenfeld, D. Pialla, S. Li, W. Ma, P. Kudinov, M.

Jeltsov, K. Kööp, K. Huber, X. Cheng, G. Bruzzese, A. G. Class, D. P. Prill, A.

Papukchiev, C. Geffray, R.-J. Macian, and L. Maas, "Assessment of Systems Codes

and Their Coupling with CFD Codes in Thermal-Hydraulic Applications to

Innovative Reactors," Nuclear Engineering and Design, Submitted, 2014. —

VI

Contribution: A review paper where the work of myself and our European partners

is described and discussed.

A. Papukchiev, M. Jeltsov, K. Kööp, P. Kudinov and G. Lerchl, "Comparison of

different coupling CFD–STH approaches for pre-test analysis of a TALL-3D

experiment". Nuclear Engineering and Design 2015. — Contribution: In this paper

different code coupling approaches are compared, ours included.

V.-A. Phung, S. Galushin, S. Raub, A. Goronovski, W. Villanueva, K. Kööp, D.

Grishchenko, P. Kudinov “Characteristics of debris in the lower head of a BWR in

different severe accident scenarios”, Nuclear Engineering and Design, Volume 305,

15 August 2016. — Contribution: I contributed to the paper by running genetic

algorithm calculations connected to MELCOR severe accident simulation code.

Conference publications not included in the thesis

M. Jeltsov, F. Cadinu, W. Villanueva, A. Karbojian, K. Kööp and P. Kudinov, "An

approach to validation of coupled CFD and system thermal-hydraulic codes," 14th

International Topical Meeting on Nuclear Reactor Thermalhydraulics (NURETH-

14), Toronto, Ontario, Canada, September 25-29, 2011

M. Jeltsov, K. Kööp, P. Kudinov, W. Villanueva, "Development of domain

overlapping STH/CFD coupling approach for analysis of heavy liquid metal thermal

hydraulics in TALL-3D experiment," CFD4NRS-4, OECD/NEA and IAEA

Workshop, Daejeon, Korea, September 10-12, 2012

M. Jeltsov, K. Kööp, P. Kudinov, W. Villanueva, "Development of multi-scale

simulation methodology for analysis of heavy liquid metal thermal hydraulics with

coupled STH and CFD codes," NUTHOS-9 conference, Kaohsiung, Taiwan,

September 9-13, 2012

M. Jeltsov, K. Kööp, D. Grishchenko, A. Karbojian, W. Villanueva, P. Kudinov,

"Development of TALL-3D Facility Design for Validation of Coupled STH and CFD

Codes," NUTHOS-9 conference, Kaohsiung, Taiwan, September 9-13, 2012

G. Bandini, E. Bubelis, M. Schikorr, M.H. Stempnievicz, A. Lázaro, K. Tucek, P.

Kudinov, K. Kööp, M. Jeltsov, L. Mansani, "Safety Analysis Results of

Representative DEC Accidental Transients for the ALFRED Reactor," International

Conference on Fast Reactors and Related Fuel Cycles: Safe Technologies and

Sustainable Scenarios (FR13), Paris, France, March 4-7, 2013

M. Jeltsov, K. Kööp, P. Kudinov and W. Villanueva, "Development of a domain

overlapping coupling methodology for STH/CFD analysis of heavy liquid metal

thermal-hydraulics," The 15th International Topical Meeting on Nuclear Reactor

Thermalhydraulics (NURETH-15), NURETH15-466, Pisa, Italy, May 12-15, 2013

VII

A. Papukchiev, M. Jeltsov, C. Geffray, K. Kööp, P. Kudinov, R.-J. Macian, and G.

Lerchl, "Prediction of Complex Thermal-Hydraulic Phenomena Supplemented by

Uncertainty Analysis with Advanced Multiscale Approaches for the TALL-3D T01

Experiment", Proceedings of the 12th International Probabilistic Safety Assessment

and Management Conference (PSAM 12), Honolulu, Hawaii, June 22-27, 2014

I. Mickus, K. Kööp, M. Jeltsov, Y.B. Vorobyev, W. Villanueva, and P. Kudinov, "An

Approach to Physics Based Surrogate Model Development for Application with

IDPSA, " Proceedings of the 12th International Probabilistic Safety Assessment and

Management Conference (PSAM 12), Honolulu, Hawaii, June 22-27, 2014

A. Papukchiev, G. Lerchl, C. Geffray, R.-J. Macián, M. Jeltsov, K. Kööp, and P.

Kudinov, "Coupled 1D-3D Thermal-Hydraulic Simulations of a Liquid Metal

Experiment Supplemented by Uncertainty and Sensitivity Analysis, " Application of

CFD/MCFD Codes to Nuclear Reactor Safety and Design and their Experimental

Validation (CFD4NRS-5), OECD/NEA and IAEA Workshop, Zurich, Switzerland,

September 9-11, 2014

Jeltsov, M., Kööp, K., Villanueva, W., Grishchenko, D., Kudinov, P., 2014.

"Validation of a CFD Code Star-CCM+ for Liquid Lead-Bismuth Eutectic Thermal-

Hydraulics Using TALL-3D Experiment," The 10th International Topical Meeting on

Nuclear Thermal-Hydraulics, Operation and Safety (NUTHOS-10) NUTHOS10-

1269 Okinawa, Japan, December 14-18, 2014. Atomic Energy Society of Japan

Phung, V., Galushin, S., Raub, S., Goronovski, A., Villanueva, W., Kööp, K.,

Grishchenko, D., Kudinov, P., 2015. Prediction of Corium Debris Characteristics in

Lower Plenum of a Nordic BWR in Different Accident Scenarios Using MELCOR

Code, 2015 International Congress on Advances in Nuclear Power Plants (ICAPP),

May 03-06, 2015

A. Papukchiev, C. Geffray, M. Jeltsov, K. Kööp, P. Kudinov, D. Grishchenko,

"Multiscale analysis of forced and natural convection including heat transfer

phenomena in the TALL-3D experimental facility,” The 16th International Topical

Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-16), At Chicago, IL,

USA, August 30-September 4, 2015

I. Mickus, K. Kööp, M. Jeltsov, D. Grishchenko, P. Kudinov, J. Lappalainen,

"Development of TALL-3D test matrix for APROS code validation", The 16th

International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-

16), At Chicago, IL, USA, August 30-September 4, 2015

D. Grishchenko, K. Kööp, M. Jeltsov, I. Mickus, and P. Kudinov “TALL-3D test

series for calibration and validation of coupled thermal-hydraulics codes”, The 17th

VIII

International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-

17) Qujiang Int’l Conference Center, Xi’an, China, September 3 - 8, 2017

M. Jeltsov, K. Kööp and P. Kudinov “Coupled CFD-STH Analysis of Liquid Metal

Flows”, STAR Global Conference 2017, Berlin, Germany, March 6 - 8, 2017

IX

ACKNOWLEDGEMENTS

I would like to thank my supervisor Pavel Kudinov for making this thesis work

possible.

I would also like to thank my co-supervisors, all my colleagues in Nuclear Power

Safety and Nuclear Engineering divisions, my family and friends who have

supported me through it all.

Thank you!

This work has received funding from the 7th Framework Programme European

Commission Project THINS No. FP7-249337 and the Euratom research and training

programme 2014-2018 under the grant agreement No 654935 (SESAME).

XI

TABLE OF CONTENTS

Abstract ....................................................................................................................... I

Sammanfattning........................................................................................................ III

List of publications ..................................................................................................... V

Acknowledgements ................................................................................................... IX

1. Background .......................................................................................................... 1

1.1. Lead-cooled fast reactors .................................................................................. 2

1.2. LFR modelling and simulation ......................................................................... 3

1.3. Validation methodology .................................................................................... 4

1.4. Goals, tasks and thesis structure ...................................................................... 6

1.5. Main achievements ........................................................................................... 6

2. Numerical analysis tools ...................................................................................... 9

2.1. RELAP5 ............................................................................................................. 9

2.2. GA-IDPSA ......................................................................................................... 9

3. Application of GA to RELAP5 input parameter calibration ............................... 11

3.1. CIRCUS-IV facility ........................................................................................... 11

3.2. RELAP5 input calibration ............................................................................... 12

4. TALL-3D experimental facility .......................................................................... 19

4.1. Description of TALL-3D ................................................................................. 20

4.2. Example experimental results ........................................................................ 24

5. RELAP5 TALL-3D model validation against TALL-3D data............................. 29

5.1. Input model development and solution verification ...................................... 29

5.2. model calibration and validation .................................................................... 33

6. Natural circulation Instabilities in TALL-3D facility ........................................ 41

6.1. Searching for instabilities ............................................................................... 41

6.2. Experimental results ....................................................................................... 46

7. Code coupling ..................................................................................................... 49

8. TALL-3D Solidification test section ................................................................... 55

9. Summary ............................................................................................................ 63

Bibliography .............................................................................................................. 65

BACKGROUND | 1

1. BACKGROUND

A large number of nuclear reactors in operation today are classified as Generation II

reactors. These power plants, some as old as 50 years, are associated with aging

technology and production of highly radioactive nuclear waste. Generation III

(developed through the 90’s) and Generation III+ reactors provided evolutionary

improvement to the design of conventional water-cooled nuclear power plants,

reduced the reliance on active core cooling and increased grace time during loss of

coolant accidents by adding passive safety systems [1]. However, issues with nuclear

waste production and storage, proliferation resistance and overall sustainability of

the nuclear fuel remain.

Eight specific goals were defined for Generation IV nuclear energy systems to

rethink the technological approaches instead of incremental, marginal

improvements [2]:

1. Provide sustainable energy generation that meets clear air objectives and

delivers long-term availability of systems and effective fuel utilisation.

2. Minimise and manage nuclear waste and notably reduce the long-term waste

storage burden.

3. Have a clear life-cycle cost advantage over other energy sources.

4. Have a level of financial risk comparable to other energy projects.

5. Operations should excel in safety and reliability.

6. Have a very low likelihood and degree of reactor core damage.

7. Eliminate the need for offsite emergency response.

8. Be the least desirable route for theft of weapons-usable materials and provide

increased physical protection against acts of terrorism.

Based on these goals, Generation IV International Forum1 selected six new nuclear

energy system designs for further research and development [2]:

• LFR (Lead-cooled fast reactor)

• SFR (Sodium-cooled fast reactor)

• MSR (molten salt reactor)

• SCWR (Supercritical-water-cooled reactor)

• GFR (Gas-cooled fast reactor)

• VHTR (Very-high-temperature reactor)

1 The Generation IV International Forum (GIF) is an international endeavour set up to carry out the research and development needed for the next generation nuclear energy systems.

2 | BACKGROUND

Out of these six technologies, LFR systems will be focused on in this thesis work.

1.1. LEAD-COOLED FAST REACTORS

Lead-cooled fast reactors feature a fast neutron spectrum, they operate at high

temperature and low pressure, use either liquid lead or lead-bismuth eutectic (LBE)

alloy as a primary coolant. LFRs are considered as one of the most promising among

the proposed six Generation IV designs (Figure 1).

Lead and lead bismuth alloys have low neutron moderation resulting in a fast

neutron spectrum, which in turn allows for effective burning of minor actinides.

They are also chemically inert and do not release energy in accident condition (as

compared to sodium-water interaction). Since LFRs can be operated at atmospheric

pressures, the loss of coolant accident can be all but eliminated by installing a guard

vessel.

Figure 1: Generic LFR schematics.

High density of the coolants enhances natural circulation development compared to

conventional water-cooled reactors. Paired with simple primary flow path and a low

core pressure drop allow heat removal from the core via natural circulation during

loss of offsite power or loss of flow accidents.

Several LFR designs are under development in the world today [3]. Most notable are

SVBR (Russia) [4], BREST (Russia) [5], SSTAR (USA) [6] and ALFRED (EU) [7].

However, there are technical challenges to overcome in order to make LFR designs

viable commercially. Technology roadmap for Generation IV nuclear energy systems

foresees main research and development activities regarding corrosion and lead

BACKGROUND | 3

chemistry management system, development of instrumentation for the core and

fuel with corresponding handling technology [8].

In addition to these efforts, advanced modelling and simulation is listed as an

important next challenge as design and safety analyses of Generation IV metal

cooled reactors must rely on simulations due to lack of operational experience, ever

evolving design and stringent safety requirements.

1.2. LFR MODELLING AND SIMULATION

Challenges for modelling and simulation of pool-type LFR systems stem from the

original intended use of the codes that are being applied in design and licensing of

these reactors [9]. 1D system thermal-hydraulics (STH) codes like RELAP5 [10] and

TRACE [11] were originally designed to be applied to water-cooled reactor systems

with high (forced) coolant mass flow rates. These codes were extensively

benchmarked against plant data [12] and other code results [13] as well as used in

reactor design and licensing [14].

Parts of the systems (e.g. core, some designs of the heat exchangers, secondary

coolant system, etc.) can be simplified into 1D elements resolved by STH codes.

However, 1D STH codes are unable to capture complex 3D phenomena in pool-type

reactors. Transient thermal stratification and mixing in the pool and in the lower

plenum (e.g. in case of asymmetric circulation) render STH codes unable to provide

adequate modelling of physical phenomena. In addition, during loss of flow or

overcooling transients, solidification of the coolant can occur in the primary system,

which 1D STH codes are unable to simulate.

Computational fluid dynamics (CFD) codes are capable of resolving the 3D effects,

however applying these methods with high resolution for the whole primary system

result in prohibiting computational cost. In addition, the number of calculations

needed to cover a wide range of design and beyond-design-basis accidents further

complicates the issue.

One of the proposed solutions is to divide the primary system into 1D and 3D

regions, apply STH and CFD codes separately on the respective domains and

exchange data between the codes [15]–[19]. Coupling STH and CFD codes allows the

user to achieve necessary accuracy with reduced computational cost, however it also

introduces additional source of uncertainty due to the coupling method (e.g.

selection of which data, when in time and where in space is exchanged between the

codes is a non-trivial problem). To be able to apply advanced computational

methods in design and safety analysis these methods need to be validated.

4 | BACKGROUND

1.3. VALIDATION METHODOLOGY

Successful validation is a prerequisite for application of standalone and coupled STH

and CFD codes in design and safety analysis. Significant progress has been

previously made in the field of code validation methodology development [20]–[22].

The ultimate goal of the validation process is to develop sufficient evidences for a

robust decision on selected specific application (intended use). A successful

validation process is:

a) Connected with the intended use, e.g. through the code acceptance criteria;

b) Systematic and complete, i.e. all uncertainty sources addressed;

c) Iterative, e.g. new data from dedicated validation experiments or from code

application to risk analysis can require changes in the validation process; and

d) Converging.

General validation process (Figure 2 [23], [24]) has three main stages aiming to

reduce (i) numerical uncertainty (outlined in red); (ii) experimental and respective

model input uncertainty (outlined in green); in order to characterize (iii) the model

form uncertainty (outlined in blue).

Figure 2: Iterative approach to the validation process [23], [24].

BACKGROUND | 5

The process of validation starts with defining the criteria for decision on code

adequacy for intended use. This criteria can be expressed for example in terms of

predictive capability maturity model (PCMM) [20]–[22]. The outcome of the

validation process is characterization and (if necessary for improvement of

robustness of supported decisions) reduction of the code (model) uncertainty.

Design of the experiment is an important part of the validation process. For

successful validation, experimental design and supporting analytical activities

should be tightly coupled in order to ensure that:

i. code (model) uncertainty is the dominant contributor to the overall code

prediction uncertainty;

ii. relevant regimes and phenomena can be addressed in the tests;

iii. experimental response is sufficiently sensitive to the possible variations of

initial/boundary conditions;

Convergence criteria for the numerical model should be fulfilled for the relevant

experimental conditions. Code input model development usually requires

calibration of uncertain input parameters using a dedicated set of tests. Lack of

numerical convergence and ad-hock calibration of the uncertain model input

parameters based only on the engineering judgement are among the most

ubiquitous “user effects” in the validation process. Code input models can be often

“tuned” by a user to perform well for a specific transient, while lacking general

predictive capabilities for other transients.

In order to reduce the user effects, systematic sensitivity studies are necessary in

order to objectively identify major contributors to the uncertainty (Figure 2).

Experimental evidences are then used in order to reduce the input and experimental

uncertainties. This process is applied iteratively, to make sure that the initial guesses

of the user on the possible ranges of the uncertain input parameters has no effect on

the calibrated model input. In principle the process of sensitivity analysis and data

collection should be user agnostic and converge to the same conclusion with respect

to the code validity.

At the final stage of the process all sources of uncertainties are propagated through

the physics model to obtain the simulation SRQs with uncertainties. Calculated SRQ

values are then compared to the experimental SRQs (validation dataset). The

disagreement between the simulation and the experiment is quantified by applying

a validation metric operator.

Based on the results, the code performance can be concluded to be adequate or not

adequate for the intended use, or a decision to perform an additional iteration of the

validation process can be made. Each new iteration commonly also involves

improvement of the experiment.

6 | BACKGROUND

1.4. GOALS, TASKS AND THESIS STRUCTURE

The ultimate goal of this work is to provide contribution towards development of

methods, data and tools for qualifying multiscale codes in application to design and

licensing of Generation IV nuclear reactor systems. In this thesis we focus on the

following tasks:

• Development and application of methods for automated input calibration and

code validation in case of multiple uncertain input parameters and system

response quantities (SRQs) to minimise the “user effects” [25]. – Addressed

in Chapter 3 (Paper I). Similar methods were also widely used in this work

for other applications (Chapter 5 and 6).

• Development of an experimental facility TALL-3D, suitable for validation of

standalone and coupled STH and CFD codes in application to metal cooled

systems. Defining test procedures and operational conditions in order to

provide data on physical phenomena of importance for safety and code

validation [26], [27]. – Addressed in Chapters 4 and 8 (Papers II and V).

• Development of standalone system thermal-hydraulics code input model for

simulation of the experimental facility and quantification of uncertainties in

the code predictions using experimental data. – Addressed in Chapter 5

(Paper III).

• Identification of experimental conditions with natural circulation flow

instabilities in TALL 3D facility for STH code benchmarking [28] and

validation. – Addressed in Chapter 6 (Paper IV).

• Development of coupling algorithms for STH and CFD codes based on the

domain overlapping approach and validation of the coupled codes [19]. –

Addressed in Chapter 7.

1.5. MAIN ACHIEVEMENTS

1. An approach to data post-processing was developed and implemented in

order to enable selection of any combination of SRQs in the STH code output

as a fitness function used in the genetic algorithm (GA) of global optimum

search. This feature is necessary for automated calibration of the input and

code validation in case multiple input and output parameters.

2. Contribution to TALL-3D facility development including

a. STH analysis to support facility design that should feature thermal-

hydraulic feedbacks between 1D and 3D components;

b. Identification of operational parameters for dedicated tests on

calibration of measurement equipment, STH codes input calibration

and model validation;

BACKGROUND | 7

c. Carrying out experiments to produce validation grade data for

standalone and coupled code validation.

3. Application of the validation methodology with TALL-3D data using

advanced computational methods for automatic calibration and validation of

STH code RELAP5.

4. Application of different automated approaches to identification of domain of

natural circulation instability and limit cycle oscillations in TALL-3D facility.

Experimental conditions for confirmation of the existence of the instability

region were proposed and experiments carried out.

5. Contribution to development, implementation and validation of the coupling

approach for STH and CFD codes.

6. Analysis in support of design parameters of the new solidification test section

in TALL-3D experimental facility. The aim of the analysis was to enhance

TALL-3D capabilities in studying natural circulation instabilities.

8 | BACKGROUND

NUMERICAL ANALYSIS TOOLS | 9

2. NUMERICAL ANALYSIS TOOLS

2.1. RELAP5

STH code RELAP5 [10] has been extensively utilized as the modelling and

simulation tool in this thesis work. RELAP5 was developed by the Idaho National

Engineering Laboratory (INEL) for the United States Nuclear Regulatory

Commission (NRC) with the goal to provide best estimate analysis of pressurized

water reactors.

RELAP5 features coupled kinetics, one-dimensional heat transfer and two

component hydrodynamics based on six-equation two-fluid model.

In the work related to LFR system analysis, a custom RELAP5/Mod3.3 version with

lead or LBE as working fluids [29] and a heat transfer correlation by

Seban/Shimazaki (Eq. 1) has been used:

𝑁𝑢 = 5 + 0.025 ∙ 𝑃𝑒0.8 (1)

where the Péclet number (𝑃𝑒 = 𝐿𝑢/𝛼) is the ratio of transport rates by convection to

thermal diffusion; 𝐿 is characteristic length, 𝑢 is local flow velocity and 𝛼 is thermal

diffusivity (𝛼 = 𝜆/𝜌𝑐𝑝) where 𝜆 is thermal conductivity, 𝜌 is density and 𝑐𝑝 is specific

heat.

LBE properties used with the RELAP5 executable are shown in Table 1.

Table 1: LBE correlations used in the RELAP5 calculations [30].

Property SI unit Correlation Density kg m-3 𝜌 = 11065 − 1.293 ∙ 𝑇

Sound velocity m s-1 𝑢𝑠 = 1855 − 0.212 ∙ 𝑇

Bulk modulus Pa 𝐵𝑠 = (35.18 − 1.541 ∙ 10−3 ∙ 𝑇 − 9.191 ∙ 10−3 ∙ 𝑇2) ∙ 109

Isobaric specific heat J kg-1 K-1 𝑐𝑝 = 164.8 − 3.94 ∙ 10−2 ∙ 𝑇 + 1.25 ∙ 10−5 ∙ 𝑇2 − 4.56 ∙ 105 ∙ 𝑇−2

Dynamic viscosity Pa s 𝜂 = 4.94 ∙ 10−4 ∙ exp (754.1/T)

Thermal conductivity W m-1 K-1 𝜆 = 3.284 + 1.617 ∙ 10−2 ∙ 𝑇 − 2.305 ∙ 10−6 ∙ 𝑇2

2.2. GA-IDPSA

GA-IDPSA is an Integrated Deterministic Probabilistic Safety Analysis (IDPSA) tool

employing genetic algorithms (GA) to identify global optimums in multidimensional

parameter space [31], [32]. GA-IDPSA is extensively utilized in STH code input

model calibration (Paper I [25] and III) as well as in limit cycle oscillation regime

search (Paper IV [28]) for this thesis work.

10 | NUMERICAL ANALYSIS TOOLS

GA is often used to optimise solutions to engineering [33], economic [34], computer

science [35] and supply chain problems [36]. This wide use of genetic algorithms can

be attributed to their inherent ability to work with discreet as well as continuous

parameters, capability of handling large parameter space and potential for parallel

computing for shortening the analysis time.

GA mimics the natural selection process using a fitness function (FF) to evaluate

genes in a population. Crossover operation on genes with high fitness will produce

new candidates for the next generation in the population. Mutation of a gene will

ensure stochastic randomness in the next generation to avoid getting “trapped” in a

local maximum and cover the parameter space with sufficient samples. Users of GA

need to predetermine the ratio between crossover and mutation, describe the FF and

the parameter space by listing parameters included in the analysis as well as value

ranges.

A schematic of GA-IDPSA workflow used in the thesis work is shown in Figure 3.

Figure 3: Workflow for coupled GA-IDPSA and RELAP5.

APPLICATION OF GA TO RELAP5 INPUT PARAMETER CALIBRATION | 11

3. APPLICATION OF GA TO RELAP5 INPUT PARAMETER

CALIBRATION

Validation of system thermal-hydraulics codes is an important step in application of

these codes to reactor design and safety analysis. This is achieved by comparing

predicted and experimental system response quantities (SRQs) while considering

experimental and modelling uncertainties.

Parameters which are required for the code input but are not measured directly in

the experiment can become an important source of uncertainty in the code

validation process. These parameters can be component dimensions (due to 1D

simplification of the space), local pressure losses, heat transfer coefficients etc.

Quantification of such parameters is often called input calibration. Calibration and

uncertainty quantification become challenging when the number of uncertain input

parameters and SRQs is large and dependencies between them are non-trivial.

The goal of this part of the thesis work is to develop an automated approach to

RELAP5 input calibration and validation. The work is performed using experimental

data on two-phase natural circulation flow instability. The main purpose is to

increase robustness and to reduce the effect of engineering judgment on the outcome

of code validation process.

3.1. CIRCUS-IV FACILITY

CIRCUS-IV is a natural circulation facility designed for investigation of two-phase

flow instabilities in boiling water reactors at low pressure [37]. The facility consists

of a test section (four parallel heated channels with individual bypasses), a heat

exchanger at the top of the facility, a downcomer and a preheater in a buffer vessel

below the downcomer (Figure 4). For the purposes of code calibration and validation

effort, experiments were performed with only one active and three inactive channels.

Three CIRCUS-IV experiments were used in this work, all performed at atmospheric

pressure and 2.5 kW heater power (Table 2). Experimental setup and uncertainties

are discussed in detail in Paper I [25].

Table 2: Test conditions and corresponding regimes.

Test Inlet temperature (°C) Test regime

S-1 89.0 ±1 Single-phase steady state

I-1 92.8 ±1 Two-phase instability

I-2 99.8 ±1 Two-phase instability

12 | APPLICATION OF GA TO RELAP5 INPUT PARAMETER CALIBRATION

Figure 4: CIRCUS-IV facility schematics with indicated pressure (P), temperature

(T) and flow (F) measurement locations.

Characteristic inlet mass flow rates for tests S-1, I-1 and I-2 are shown in Figure 5

where periodic flashing for I-1 and I-2 can be observed.

Figure 5: Inlet mass flow rates in CIRCUS-IV single channel experiments.

3.2. RELAP5 INPUT CALIBRATION

In case of both manual and automatic calibration, system response quantities

(SRQs) need to be defined to evaluate the code performance against experimental

measurements. Given the complex physical phenomena present in CIRCUS-IV

experiments, a combination of a number of different SRQs is considered.


A fitness function that represents the overall quantitative difference between the

experiment and the simulation guides the search for global optimum in case of

automatic code input model calibration (Eq. 2).

𝐹 =

1

𝑁∑

𝑤𝑖

𝐴𝑖

𝑁

𝑖=1

|𝑥𝑖 𝑠𝑖𝑚 − 𝑥𝑖 𝑒𝑥𝑝

𝑥𝑖 𝑒𝑥𝑝| (2)

where 𝐹 is the fitness; 𝑁 is the number of SRQs in the function; 𝑤𝑖 is a weighting

factor for each SRQ; 𝐴𝑖 is normalization factor for each SRQ; 𝑥𝑖 𝑠𝑖𝑚 is the simulated

value or the SRQ; 𝑥𝑖 𝑒𝑥𝑝 is the measured value for the SRQ.

Based on the test setup and the phenomena present in the experiment, several SRQs

were identified as of interest and are listed in Table 3.

Table 3: Parameters in the input calibration FF.

# SRQ Normalization

factor % (Ai)

Weighting factor (Wi) matrix

W1 W2 W3 W4 W5 W6

1 Inlet Channel Flow Rate 2 1 1 1 1 10 10

2 Oscillation Period 2 1 1 1 1 1 10

3 Inlet All Channels Temperature 0.5 1 10 10 10 10 10

4 Inlet Heated Channel Temperature 0.5 1 10 10 10 10 10

5 Inlet Riser Temperature 0.5 1 1 1 3 3 3

6 Middle Riser Temperature 0.5 1 1 1 3 3 3

7 Outlet Riser Temperature 0.5 1 1 1 3 3 3

8 Inlet Downcomer Temperature 0.5 1 1 3 3 3 3

9 Inlet Pressure 0.1 1 1 1 1 1 1

Normalization factors in Table 3 are based on acceptable error ranges for

nodalization qualification [9]. Normalization is needed to objectively compare the

importance of SRQs based on their physical properties.

Weighting factors in the fitness function allow the user to further specify SRQ

importance. To evaluate the sensitivity of the fitness function towards the weights of

individual SRQs, six combinations of weighting factors were tested (W1-W6 in Table

3).

It is important to note that in an ideal case where simulated SRQ value equals the

experimentally measured value (Fitness is zero), the choice of the weight becomes

of negligible importance. Therefore, if the combination of SRQs is possible to achieve

numerically by the code and the number of calculations is great enough to capture

these scenarios, the choice of weights has little effect on the end goal of finding the

optimal solution. In case of numerically unachievable optimal scenario or low

number of calculations, the weights influence the final solution.


Table 4: Uncertain input parameters and ranges for calibration.

# Uncertain input parameter Minimum value

S-1/I-1/I-2

Maximum value

S-1/I-1/I-2

1 Heated channel power [W] 2375 2625

2 Inlet channel heat loss [W] 0 600

3 Heated channel heat loss [W] 0 200

4 Riser heat loss [W] 0 400

5 Upper plenum pipe heat loss [W] 0 400

6 Lower buffer vessel temperature BC* [°C] 85/90/97 95/100/105

7 Upper buffer vessel temperature BC* [°C] 85/90/92 95/100/102

8 Heat exchanger temperature BC* [°C] 65 80

9 Steam dome void fraction [-] 0.5 1.0

10 Inlet forward flow loss coefficient [-] 24 32

* BC stands for boundary condition.

A number of uncertain input parameters were identified in the RELAP5 CIRCUS-IV

model. Some were not measured in the experiment (e.g. heat losses, steam dome

void fraction, flow loss coefficient) and some were measured but with a large

uncertainty (e.g. temperature at the buffer vessels, heater channel power). Uncertain

input parameters and the ranges for global optimum search are shown in Table 4.

Table 5: Normalized differences between experimental and simulated SRQs for test

S-1.

# SRQ

Manual

calibration

[%]

GA W1

40 x 40 [%]

GA W1

80 x 80 [%]

Acceptable

error [%]

1 Inlet Channel Flow Rate -2.184 -2.913 -0.485 ±2.0

2 Inlet All Channels Temperature 0.259 -0.044 0.268 ±0.5

3 Inlet Heated Channel Temperature -0.652 0.116 -0.001 ±0.5

4 Inlet Riser Temperature -0.356 0.032 0.109 ±0.5

5 Middle Riser Temperature -0.320 -0.250 -0.098 ±0.5

6 Outlet Riser Temperature -0.107 -0.302 -0.091 ±0.5

7 Inlet Downcomer Temperature -0.540 -0.244 -0.176 ±0.5

8 Inlet Pressure 0.267 0.296 0.273 ±0.1

Fitness function value (W1) 102.9 79.9 55.7

In case of single-phase steady state test S-1, two genetic algorithm (GA) calculations

were performed with uniform weighting factors (W1 Table 3). Population size and

number of generations were set to 40 and 80 respectively. In both cases the mutation

to crossover ratio was 0.5. The difference between simulation and experiment SRQs

is shown in Table 5 where in addition to GA calculations, a manually calibrated input

[38] is compared to evaluate the performance of automated calibration process.

Calibrated uncertain input parameter values are presented and further discussed in

Paper I [25].


The errors in temperature prediction were found within the acceptable range for

both GA calculations whereas the error in prediction of the inlet flow rate was

acceptable for GA with 80 population × 80 generation and slightly higher than

acceptable for 40 population × 40 generation. The error in prediction of the inlet

pressure was higher than acceptable level but lower than measurement error. Both

calibrated inputs provide smaller values of the fitness function compared to manual

calibration (Table 5).

A single GA calculation with 80 members of population and 80 generations can take

equivalent of ~80 days of serial computations, while GA run with 40 population

members and 40 generations takes 4 times less time. Therefore, results obtained

with GA 40 members × 40 generations are considered as optimal in terms of

computational cost versus reduction of the fitness function value.

Six GA-IDPSA calculations were set up and carried out using the provided uncertain

input parameters with ranges and weighted fitness functions with SRQs for both I-1

and I-2 tests. Population size and number of generations was set to 40 with mutation

to crossover ratio 0.5. The resulting most optimal scenarios for each six cases were

compared to manual calibration of the input (see Figure 6 and Figure 7). Note that

in Figure 6 the fitness for each optimal scenario was re-evaluated using W1

conditions for ease of comparison.

Figure 6: Fitness comparison of calibrated inputs (W – weight, M – manual).

Values for optimal scenarios were re-calculated using W1 weights for comparison.

For test I-1, all inputs calibrated with GA performed quantitatively better than the

manually calibrated input. Out of the six weighting factor combinations all but W5

managed to capture the oscillation period (Figure 7).

For test I-2, calibrated inputs with W1 and W6 resulted in fitness values comparable

to the manual calibration. Qualitatively incorrect oscillation patterns were predicted

with the inputs using W2, W3, W4 and W5 (Figure 7).


Analysis done with manually calibrated input and using GA calibration with W5

(where inlet flow rate weight was increased) provided more accurate results for the

maximum flow rate but overestimated the period.

Figure 7: Comparison of experimental (E), manual (M) and GA calibrated inputs.

Predicted SRQ values were in general agreement with each other and with

experimental data. The differences were generally within the ranges of the

measurement errors and experimental uncertainties. The best input calibrated by

GA provided slightly better or equivalent results to those obtained with manual

calibration for majority of the SRQs.

Ranges for uncertain input parameter calibration, calibrated values and ranges for

code validation are further discussed in Paper I [25].

Individual SRQs are most commonly used for comparison between code prediction

and experiment in validation. However, it is important to note that good agreement

in individual SRQs does not necessarily mean the code is capable of successfully

predicting different SRQs simultaneously. Results shown in Figure 8 indicate that

the maximum inlet flow rate and the oscillation period are not predicted by the code

simultaneously.

While in case of calibration the GA fitness function was set to minimize the

difference between the simulation and experiment, in case of uncertainty

propagation the goal of the optimization is to identify the boundaries of the SRQ

response given the uncertain input parameter ranges identified in the calibration

process. To evaluate the efficiency of GA, results obtained with random sampling of

the same uncertain input parameter space are also presented in Figure 8.

It is evident that GA is able to identify the edges of uncertainty domain more

efficiently compared to random sampling. However, random sampling provides data

which can be directly used for assessment of probabilistic characteristics.


Figure 8: Maximum inlet flow rate and period for tests I-1 and I-2. Blue and green

symbols correspond to the maximum (positive) and minimum (negative)

difference between experimental and predicted values of the maximum flow rate

respectively. Purple symbols denote random sampling and yellow symbols

calibrated (W6) input results.

It is recommended to carry out multiple input calibration calculations with varying

initial ranges and weighting factors to show that the results of calibration and

validation do not depend on initial selection of uncertainty ranges.

CONTRIBUTION TO PAPER I [25]: Development and implementation of the

methodology. GA-IDPSA calculations were prepared and executed. A complex

system of RELAP5 results post-processing in MATLAB for evaluating the fitness

function and importing calculated values back into GA-IDPSA was developed, tested

and executed by the author.

TALL-3D EXPERIMENTAL FACILITY | 19

4. TALL-3D EXPERIMENTAL FACILITY

The main design goal for the TALL-3D facility is to provide experimental data on

thermal-hydraulics phenomena for validation of stand-alone and coupled System

Thermal-Hydraulics (STH) and Computational Fluid Dynamics (CFD) codes [39].

To achieve this goal, the facility has to provide [21], [40]:

• mutual feedbacks between 1D phenomena resolved by STH and 3D

phenomena resolved by CFD;

• a possibility to isolate subsections of the facility with well-defined boundary

conditions to provide separate effect validation of standalone codes;

• multiple measurement points and operation regimes to provide sufficient

number of constraints for uncertain input parameter calibration.

For validation of standalone STH codes, the following phenomena should be

appropriately simulated, and the experiment instrumented accordingly:

• Drag;

• Steady state heat transfer;

• Transient heat transfer;

• Stability of natural circulation;

• Thermal inertia of the loop sections;

• Heat losses as a function of temperature.

For validation of standalone CFD codes, the list of phenomena of interest:

• Free jet flow;

• Jet impingement on a surface;

• Jet induced recirculation flow in a pool;

• Thermal stratification;

• Mixing;

• Thermal inertia of the structures;

• Turbulence.

In support of the experimental facility design numerous STH code calculations were

performed to identify most promising combination of system dimensions and

operational conditions. This work was done iteratively with constant input from

other members of the TALL-3D development team and with the help of CFD

calculations.

20 | TALL-3D EXPERIMENTAL FACILITY

4.1. DESCRIPTION OF TALL-3D

TALL-3D thermal hydraulic loop facility is shown in Figure 9 [39].

Figure 9: Schematics of the TALL-3D experimental facility with main components.


The primary side working fluid of TALL-3D is liquid lead-bismuth eutectic (LBE)

[41], whereas secondary side is operated using Dowtherm RP coolant [39]. Heat is

transferred between the primary and secondary side via a counter-current heat

exchanger located in the top of the right-most leg of the loop (generally referred to

as the heat exchanger leg or simply HX-leg). Secondary heat exchanger consisting of

a radiator and a fan is used to remove the heat from the secondary loop.

The pin-type main heater of the TALL-3D facility is located in the bottom of the left-

most primary leg, which is also referred to as the main heater leg (MH-leg). In

addition to the main heater, the primary side can be heated using the pool-type test

section heater in the lower part of the middle leg, also known as the 3D test section

leg (or 3D-leg). Flow in the primary circuit can be forced using an Electric Permanent

Magnet (EPM) pump located in the heat exchanger leg. In natural circulation, LBE

flow is driven by the difference in coolant density in the primary legs, which can be

created by the heaters and heat exchanger. Main parameters of the facility are shown

in Table 6.

The facility has been instrumented extensively with more than 500 measurement

and control data channels. The loop can be effectively divided into several

subsections for separate effect input calibration and section by section code

validation. In-flow thermocouples and pressure transducers provide measurements

of inlet and outlet LBE temperatures and pressures, so that transient boundary

conditions can be provided for each subsection. LBE flow is measured in the HX-leg

and 3D-leg with Coriolis flow meters, whereas flow in MH-leg is estimated from the

mass balance.

Table 6: TALL-3D facility parameters.

Parameter Value

Total facility height 6967 mm

Primary side height 5830 mm

Primary side width 1480 mm

Loop pipe inner diameter 27.8 mm

Main heater power 27 kW

3D test section heater power 15 kW

Test section inner height 200 mm Test section inner diameter 300 mm Test section inlet pipe inner diameter 17 mm

The primary side of the TALL-3D facility can be modelled using one-dimensional

flow model used in an STH code. The outlet temperature of the pool-type test

section, however, can be affected by three-dimensional transient flow phenomena

such as mixing of thermally stratified pool [39]. In steady state conditions, the test


section outlet temperature can be determined using heat balance. Thus, STH codes

are expected to predict the flow in the primary loop in steady state operation, as long

as the overall heat balance and pressure drop over the test section can be properly

modelled.

The 3D test section is a cylindrical pool with an inner height of 200 mm and inner

diameter of 300 mm (Figure 10). The pool can be fully mixed (uniform temperature

distribution) or thermally stratified. The upper two–thirds of the test section are

equipped with two rope heaters with adjustable power (7.5 kW each) coiled around

the circumference. Activation of the heaters can drive the development of thermal

stratification in the pool. A circular inner plate is installed in front of the outlet of

the test section to divert the inlet jet laterally, thereby facilitating mixing of the pool.

In case of low flow rate, the inlet jet might have insufficient momentum to penetrate

into and mix the stratified layer. Thus, temperature distribution in the pool depends

on the heater power and flow conditions.

In total 154 thermocouples are installed in the test section to measure temperature

on the walls and in the bulk of the pool. Instantaneous temperature at the outlet of

the test section affects development of the transient natural circulation in the loop.

Figure 10: 3D test section schematics and photo showing the test section without

insulation (pre-assembly).

The experimental uncertainty of temperature measurement is ±1 K for TCs in the

test section and ±2 K for TCs in the rest of the loop. Several TC offset tests with high

flow rate were performed to reduce the experimental uncertainty in the temperature

readings. The methodology and results are described in a conference paper [42]. The


accuracy of the Coriolis flow meter at nominal flow is 0.1% and can be as high as 3%

at 0.005 kg/s. The uncertainty of the differential pressure transducers is ±40 Pa for

DP1-4 groups and ±162 Pa for the DP5 group over the EPM pump section [39].

Figure 11: Images from the TALL-3D facility construction process.

Pre-test simulations of TALL-3D transients demonstrated an important effect of

heat losses and thermal inertia on the flow and temperature characteristics,

especially in natural circulation regimes. Special attention in the design was devoted

to the estimation of the effects of the heat losses through insulation and thermal

inertia of the metal structures of the loop. The ambient air temperature necessary

for modelling of the heat losses is measured at 3 different elevations.


4.2. EXAMPLE EXPERIMENTAL RESULTS

First series of commissioning tests were carried out in order to verify the existence

of the feedbacks between the 3D phenomena in the test section and system loop

behaviour predicted in the pre-design and pre-test analyses.

A representative forced to natural circulation transient results are shown in Figure

12 and Figure 13. The transient was initiated from a forced circulation steady state.

At time zero the EPM pump was tripped while power in the main heater and in the

3D test section heater were kept constant. After a transition period of approximately

25 minutes, natural circulation steady state was established in the loop.

Figure 12: Transient T01.03 LBE mass flow rates.

The initial and final steady state conditions are summarized in Table 7. Transient

initiating pump trip is followed by the abrupt decrease of the flow rates in all legs

with faster flow redevelopment in the main heater leg. Flow reversal is observed in

the test section leg for about 240 s (Figure 12). During this period LBE temperature

in the main heater leg decreases (see Figure 13) and reduced flow rates in the test

section leg allow heat up and development of thermal stratification in the 3D pool

(Figure 14).


Figure 13: Transient T01.03 LBE in-flow temperatures.

Table 7: T01.03 transient initial and final steady state conditions.

Parameter Forced circulation steady state

Natural circulation steady state

MH electric power [W] 4972 4972

Test section electric power [W] 5078 5078

HX LBE mass flow rate [kg/s] 4.793 0.674

Test section LBE mass flow rate [kg/s] 1.827 0.337

Test section inlet LBE temperature [°C] 262 227

Test section outlet LBE temperature [°C] 279 341

MH section inlet LBE temperature [°C] 262 227 MH section outlet LBE temperature [°C] 274 328 HX section inlet LBE temperature [°C] 277 323 HX section outlet LBE temperature [°C] 262 237

At about 280 seconds after transient initiation, buoyancy force in the 3D leg becomes

larger than that in the main heater leg. The flow in the 3D leg accelerates in the

vertical direction filling the leg with hot LBE (exceeding 380◦C) accumulated in the

test section. The flow acceleration leads to partial mixing of the pool and reduction

of the test section outlet temperature, while the flow rate is reduced (to almost

stagnation), and temperature increases in the main heater leg. This leads to main

heater outlet temperatures rising up to 460°C and test section outlet temperatures

dropping down to 340°C. Such periodic flow oscillations continue for another 20


min with gradually decaying amplitudes and eventually a steady state natural

circulation is established in the loop.

LBE temperatures measured in the 3D test section pool are shown in Figure 14.

During the transient the flow in the test section pool was fairly symmetric as the

maximum temperature difference between symmetrically located TCs did not

exceed four degrees.

Figure 14: LBE temperatures measured at different locations in the 3D test section

pool.

From these initial experimental tests, it can be concluded that the facility features

strong mutual feedbacks between 1D and 3D phenomena and therefore provides

necessary data for validation of the coupled codes. Extensive instrumentation

present in the facility provides a possibility to isolate sections of the primary loop to

provide separate effect and integral validation of standalone STH and CFD codes

with well-defined boundary conditions for each section.

The TALL-3D facility was built and first operated under EU project THINS. The list

of transient experiments performed for the THINS project is shown in Table 8. In

continuation to THINS project, several more transients were conducted under EU

project SESAME (see Table 9).


Table 8: THINS project TALL-3D transient experiments.

Name

Initial steady state Final steady state

Oil

in

let

tem

per

atu

re

HX

ma

ss f

low

ra

te

MH

po

wer

TS

po

wer

HX

ma

ss f

low

ra

te

MH

po

wer

TS

po

wer

kg/s kW kW kg/s kW kW °C T01.08 4.1 2.6 4.8 0.6 2.6 4.8 65 T01.09 4.3 2.6 4.8 0.6 2.6 4.8 61 T01.10 3.3 3.2 4.0 0.6 3.2 4.0 85 T02.03 4.3 6.3 - 0.5 2.8 4.0 95 T02.04 4.2 2.1 - 0.6 2.1 5.2 145 T02.06 4.2 1.7 - 0.5 1.7 5.2 145 T03.01 0.5 2.3 - 0.5 2.3 4.8 140 T06.01 0.6 2.6 4.8 4.3 2.6 4.8 61 T09.01 0.6 3.2 4.0 0.4 - 4.0 85 T11.02 0.5 2.8 4.0 0.5 1.8 4.9 95

Table 9: SESAME project TALL-3D transient experiments.

Name


Oil

in

let

tem

per

atu

re

HX

ma

ss f

low

ra

te

MH

po

wer

TS

po

wer

HX

ma

ss f

low

ra

te

MH

po

wer

TS

po

wer

kg/s kW kW kg/s kW kW °C TG03.S301.01 4.6 3.2 5.5 0.5 3.2 5.5 62 TG03.S301.02 4.6 2.5 4.9 0.5 2.5 4.9 63 TG03.S301.03 4.7 0.7 10.3 * 0.7 10.3 126 TG03.S301.04 3.3 3.2 4.0 0.5 3.2 4.0 86 TG03.S301.05 3.3 3.2 4.2 0.5 3.2 4.2 86 TG03.S301.06 4.8 1.1 10.6 0.6 1.1 10.6 116 TG03.S301.07 4.8 0.5 5.6 0.4 0.5 5.6 133 TG03.S302.01 4.3 2.3 - 0.5 2.3 4.9 141 TG03.S306.01 0.5 2.5 4.8 4.6 2.5 4.8 63 TG03.S307.01 0.5 3.2 5.5 4.6 8.6 - 62 TG03.S310.01 4.6 8.6 - 0.5 8.6 - 62 TG03.S311.01 0.5 3.2 4.0 0.5 0.6 6.7 86


CONTRIBUTION TO PAPER II [39]: Conducted all pre-test RELAP5 STH

simulations needed for selection of facility dimensions, operational regimes and

requirements to positioning of instrumentation. Participation in operating the

facility during the experiments. Contributed to the development of operational

safety procedures.

RELAP5 TALL-3D MODEL VALIDATION AGAINST TALL-3D DATA | 29

5. RELAP5 TALL-3D MODEL VALIDATION AGAINST TALL-3D

DATA

To be able to apply coupled STH-CFD codes to analysis of TALL-3D experimental

facility both standalone and coupled codes need to be validated. The goal of RELAP5

TALL-3D model validation is therefore to evaluate the capability of the model for

use in coupled STH-CFD simulations.

5.1. INPUT MODEL DEVELOPMENT AND SOLUTION

VERIFICATION

Figure 15: Illustration of TALL-3D RELAP5 model nodalization with main heater

heat structure (1), 3D test section heater heat structure (2), expansion tank time

dependent volume (3), secondary side inlet time dependent volume (4), secondary

side outlet time dependent volume (5), EPM pump heat structure (6).

A RELAP5 input model for TALL-3D described in the paper [28] is used in this work.

The model is mainly composed of pipe structures with node size of ~10 cm. Heat

structures represent pipe walls and insulation layer. Additional heat structures are

used for the main and 3D test section heaters, main valves and flanges in the primary

side. Time-dependent volumes are used to represent the expansion tank at the top

of the facility as well as the inlet and outlet of the secondary side. Constant inlet

30 | RELAP5 TALL-3D MODEL VALIDATION AGAINST TALL-3D DATA

temperature and flow rate are assumed for the coolant in the secondary side of the

primary heat exchanger.

Space-time convergence of the STH model was evaluated using an analytic inlet

temperature peak propagated through the main heater leg at a constant mass flow

rate of 0.4 kg/s. The peak amplitude and period were selected in a conservative way

to cover representative experimental values seen during THINS project

experimental campaign [39]. The inlet temperature peak and the response of the

model is shown in Figure 16.

Figure 16: Temperature peak propagation through main heater leg model with

various node sizes.

20 cm long nodes in the main heater leg pipe structures result in approximately

3.5 % reduction in temperature peak absolute value when compared to relatively

small, 2 cm long nodes. However, it must be noted that heat transfer in RELAP5 is

modelled only in radial direction and no axial conduction is simulated. This means

that cases with low flow rates, when conductive heat transfer dominates over

convective in the experiment, the simulation will not be able to predict axial heat

diffusion in the loop, unless numerical diffusion can partially compensate for the

lack of the physical model. Thus an “optimal” grid resolution should be used in

simulations. It is a common misunderstanding that continuous reduction of node

size would improve the accuracy of the prediction in STH codes and is in detail

described in [9].

Similar study was conducted for time convergence where the time step was varied

between 10-2 seconds and 1 second. RELAP5 automatically reduces the time step

when Courant limit is reached and no difference in the result was observed. Based


on the results of the space and time convergence studies it is decided to use time step

of 0.1 sec (to account for larger mass flow rates) and node size of 0.1 m for the

following analysis.

Any STH code input model can contain uncertain input parameters (UIPs). These

can be flow loss coefficients, simplified representation of 3D structures in the 1D

model, temperature dependent material properties or other parameters that are not

directly measured in the experiment. The importance of a specific uncertain

parameter and its influence on system response quantities (SRQs) can be identified

with a sensitivity study. A large number of SRQs were used in this work due to

inherent complexity of the feedbacks between the primary loop and test section

phenomena.

In this work an extended Morris method [43] implemented in DAKOTA code [44] is

used for sensitivity study. In total 22 SRQs with 18 UIPs were analysed including

forced and natural circulation steady state mass flow rates for all three primary legs

as well as initial and final steady state temperatures for the inlet and outlet of the

two heated and one cooled section. In addition, more complex system response

quantities were used such as duration of negative mass flow rate in the 3D-leg during

forced to natural circulation transition, minimum mass flow rate in the 3D-leg and

maximum temperatures at the outlets of the heated sections. Uncertain input

parameters and the respective ranges used in the analysis are show in Table 10.

Table 10: Uncertain input parameters and ranges used in the sensitivity study.

UIP Range Main heater power ± 5% TS heater power ± 5% Initial mass flow rate ± 3% Secondary mass flow rate ± 10% Secondary inlet temp ± 2 ºC Gap size ± 5 mm Pump heating power ± 100 W Flange HS length ± 10 mm Valve HS length ± 35 mm Pump bare metal HSL ± 10% Pool Top/Bottom HSL ± 10% Gap conductivity coeff. ± 10% HX wall conductivity coeff. ± 10% T-junction k-loss straight ± 20% T-junction k-loss bend ± 20% EPM pump k-loss ± 20% Pump bare metal capacity coeff. ± 10% Pool Top/Bottom capacity coeff. ± 10%


The geometry of the pool-type section in the facility is such that heat losses occur not

only in the radial direction, but also in the axial direction from the pool top and

bottom sides. This cannot be modelled explicitly in RELAP5 since the code only

resolves radial heat transfer in heat structures but can be compensated in the input

model by increasing a “virtual” heat structure area connected to pipe nodes at the

top and bottom of the pool. A sensitivity study using test section leg natural

circulation mass flow rate as a system response quantity shows the importance of

Pool Top/Bottom HSL parameter in such modelling technique (Figure 17). In Figure

17 and Figure 18, parameters in the legend are sorted in descending order of Morris

modified mean (µ) value which is the mean elementary effect. The standard

deviation of the elementary effects is displayed on the y-axis and is indicative of non-

linearity in the response.

Figure 17: Sensitivity study results for test section leg natural circulation mass flow

rate SRQ.

The 3D test section pool virtual heat structure size is the fourth most influential

model uncertain parameter for natural circulation mass flow rate prediction after

heater powers and local flow loss in the pump channel (Figure 7).

An example of the results for test section maximum outlet temperature SRQ is

shown in Figure 18. It is apparent that the heat exchanger modelling has a large

effect on the temperatures in the primary side. RELAP5-Mod3.3 used in this work

does not have Dowtherm RP fluid properties implemented and as such only LBE or

water can be used as a working fluid. Using LBE in modelling of the secondary side

is unfeasible, as temperatures would need to drop below solidification point of


126 ºC. Using water however has its own complications related to heat transfer

efficiency of the heat exchanger. In the current model, a combination of water as

working fluid and heat exchanger wall conductivity to simulate the efficiency was

used. Calibration of the heat exchanger and other sections is discussed in more detail

in the next chapter.

Figure 18: Sensitivity study results for test section maximum outlet temperature

SRQ.

It is important to note that the results of the sensitivity study are dependent on the

selection of ranges for the uncertain input parameters (UIP). Lack of knowledge

increases ranges for UIPs, sensitivity study in turn tells if this lack of knowledge

dominates the model output or not. Ranges of the parameters that dominate the

model output uncertainty must be deduced from the experiment in the model input

calibration process. The calibration can be used to assess both UIP range and its

mean. If calibration changes the ranges, SA results can also change. Therefore,

calibration and SA are a part of an iterative process that should be repeated until

further changes in the outcome become marginal for the task at hand (e.g. screening,

reduction of uncertainty, model design) Figure 2.

5.2. MODEL CALIBRATION AND VALIDATION

An automated calibration approach, aiming to minimize user effect using divide and

conquer method [25], was selected in this work. The full facility code input model

was divided into 10 sections (see Figure 19), each individual section having inlet and


outlet temperature, pressure drop over the section as well as mass flow rate in the

corresponding leg measured in the experiment.

Calibration of the model was done using experimental data from four experiments

performed in SESAME project. The experiments were selected based on the quality

of the steady state natural circulation data (e.g. minimal drift in facility

temperatures) and are described in Table 11 and representative system response

curves are shown in Figure 20.

Figure 19: RELAP5 input model schematic showing: (1) main heater riser, (2) right

bottom bend, (3) top left corner, (4) HX flow meter, (5) bottom T-junction, (6)

EPM pump, (7) top T-junction, (8) main heater, (9) heat exchanger and (10) 3D

test section.

Sections shown in Figure 19 are numbered and their inputs calibrated in order of

complexity starting with the main heater riser section. It is a straight pipe with no

valves, flanges or other complex structures. The dimensions as well as material

properties of the stainless-steel pipe and the surrounding insulation are well known

and constant over the section. The only uncertainty lies in the geometry of the rope-

type pre-heaters coiled around the pipe, creating a gap between the stainless-steel

pipe and the insulation. In the input model heat structures, the gap between the

insulation and steel pipe is represented by a material with properties between air

and insulation. The thickness of this material is predefined at maximum thickness


of the pre-heater, but the temperature dependent conductivity is calibrated to match

the temperature drop over the section.

Table 11: Calibration experiments.

# Name


Oil

in

let

tem

pe

ratu

re

HX

ma

ss

flo

w r

ate

MH

po

wer

TS

po

we

r

HX

ma

ss

flo

wra

te

MH

po

wer

TS

po

we

r

kg/s kW kW kg/s kW kW °C

1 TG03.S301.02 4.5 2.6 4.9 0.5 2.6 4.9 62

2 TG03.S301.04 3.3 3.2 4.0 0.5 3.2 4.0 86

3 TG03.S302.01 4.4 2.4 0 0.5 2.4 4.4 141

4 TG03.S306.01 0.5 2.6 4.9 4.6 2.6 4.9 63

Figure 20: Representative parameters of the four experiments used in model

calibration.

In addition to gap conductivity, flange and valve heat structure lengths, 3D test

section pool wall heat structure length, heat exchanger wall conductivity coefficient


and heat loss magnitude through EPM pump un-insulated steel pipe wall are used

as uncertain parameters in the input model.

Lumping all uncertainties from thermal losses into a single parameter for each

section in the facility allows the user to run an automated global optimum search to

identify calibrated parameter values for each given experimental condition. This

method results in a range for any given uncertain parameter based on optimum

values for all experimental conditions used in the calibration. With a large numbers

of available calibration experiments probability distributions can be assigned to the

uncertain parameter value ranges. In this work, uniform distribution is assumed for

all ranges as the number of used experiments was small.

To evaluate the performance of the calibrated RELAP5 TALL-3D input model, a

validation experiment was selected, and model system response quantities

compared to the experimental values. A forced to natural circulation transient

TG03.S301.01 from the SESAME project experimental campaign was selected as the

validation experiment (see Table 12). The selected transient is used as a benchmark

for coupled code qualification and it is therefore necessary to first evaluate single

STH code capability to capture system behaviour.

Table 12: Validation experiment.

# Name

Initial steady state Final steady state O

il i

nle

t

tem

pe

ratu

re

HX

ma

ss

flo

w r

ate

MH

po

wer

TS

po

we

r

HX

ma

ss

flo

wra

te

MH

po

wer

TS

po

we

r

kg/s kW kW kg/s kW kW °C

1 TG03.S301.01 4.7 3.2 5.6 0.6 3.2 5.6 62

Using uncertain parameter ranges from model calibration phase as an input and

Wilks formula [45] for sample size determination, 93 RELAP5 TALL-3D input

models were created providing 95/95 confidence. To form the bounding upper and

lower time-series, all maximum and minimum values for all 93 results of each SRQ

were considered.

The resulting upper and lower values for system response quantities are shown with

comparison to the experimental values in Figure 21. It is important to emphasize

that the upper and lower bounds shown in Figure 21 do not represent any specific

transient but are an overall maximum or minimum value for all 93 transients.

Therefore, the shape of the bound is not indicative of the model behaviour.


Figure 21: Comparison of experimental system response quantities and results of

the uncertainty study with variable heat exchanger wall conductivity coefficient.

Figure 21 suggests that the SRQs are sensitive to the suggested ranges resulting in

up to 40 ºC difference between the upper and lower bounds. This sensitivity can be

attributed to section 9 (heat exchanger) modelling as described in section 5.1. To

check the hypothesis, an additional uncertainty propagation calculation was

performed keeping the heat exchanger wall conductivity coefficient constant to


optimal value found for this specific validation transient. The resulting system

response upper and lower bounds can be seen in Figure 22.

Figure 22: Comparison of experimental system response quantities and results of

the uncertainty study with fixed heat exchanger wall conductivity coefficient.

After removing the uncertainty due to the heat exchanger modelling from the

analysis, the resulting difference in system response is in a more reasonable 10 ºC

range. Apparent deficiencies in 1D modelling of a 3D component are also more


visible now compared to previous analysis results. STH code underestimation of 3D

test section inlet temperature and overestimation of the corresponding outlet

temperature during the first 10 minutes is due to inherent inability to simulate three-

dimensional mixing in the pool. Furthermore, absolute peak values in main heater

outlet temperature are also underestimated during oscillatory period between 10

and 25 minutes of the transient. This can be attributed to nodalization effect as

described in solution verification section.

Even though the number of calibration experiments used was small, propagating the

uncertainty through the code resulted in a large uncertainty bound in the output

SRQs. This is partially due to the cumulative nature of the effect of UIPs (e.g. several

UIPs contributing to the total uncertainty in heat losses in a given primary side leg).

These UIPs could in principle be combined into a single UIP for each leg reducing

the overall uncertainty range. However, in conditions where position of heat losses

or their distribution over the leg is important (e.g. during natural circulation

conditions) this simplification can lead to unphysical results.

A large number of calibration experiments would allow probability distribution

functions to be quantified for each UIP range allowing in turn for quantification of

the confidence in given ranges. This would be easier to implement in smaller scale

experimental facilities.

Deficiencies in STH codes (e.g. absence of Dowtherm RP fluid) can require custom

modelling approaches that contribute to the overall uncertainty in the input model.

The results of the uncertainty propagation confirm the hypothesis that 1D system

codes can capture LBE loop-type systems in steady states but have difficulties in

describing transients with significant influence of 3D phenomena (e.g. transition

from thermal mixing to stratification). Given the intended use of the RELAP5 input

model as a part of coupled STH-CFD code, where 3D test section is simulated with

CFD and corresponding section thermal-hydraulics in RELAP5 are corrected, the

model performance is considered adequate.

NATURAL CIRCULATION INSTABILITIES IN TALL-3D FACILITY | 41

6. NATURAL CIRCULATION INSTABILITIES IN TALL-3D

FACILITY

Pre-test analysis is vital for proper choice of experimental conditions at which the

experimental data is most useful for code validation and benchmarking. The goal of

this work is to identify optimal conditions in TALL-3D facility that can be used for

validation of standalone STH and coupled STH-CFD codes.

Previous tests in TALL-3D facility indicate oscillatory flow behaviour that can

develop between the two competing hot legs [39]. However, it was not immediately

clear if periodic flow and temperature oscillations in TALL-3D facility can last for a

long time without change in their amplitude and frequency.

Phenomena of periodic flow oscillations under steady boundary conditions are of

interest due to the importance of natural circulation instability for safety of

Generation IV reactors [46]. Furthermore, the presence of periodic phenomena

(especially limit cycle oscillations) provides challenging data for code validation

because of relatively long transient times and sensitivity of the transient

characteristics (such as flow recovery from stagnation, the period of the oscillations,

etc.) to the modelling of thermal and local flow losses.

Timing on the flow redistribution between different flow paths in the facility during

natural circulation instabilities can be highly sensitive to the modelling of thermal

and flow losses making it a challenging test for STH code benchmarking.

Experimental exploration and identification of long term natural circulation

instabilities is a very difficult task, given the number of tests which would be needed

as well as practical limitations in operation of the facility.

The objective of this work is to investigate the possibility of existence of long term

natural circulation instabilities in TALL-3D facility and to identify optimal test

conditions suitable for standalone STH validation, considering operational limits of

the facility.

6.1. SEARCHING FOR INSTABILITIES

TALL-3D RELAP5 model described in chapter 5.1 is used for searching for the

conditions at which transition from forced to natural circulation can result in a

periodic flow instability and limit cycle oscillations.

In all of the simulations, the power of the main heater and 3D test section heater are

constant throughout the transient. The transient is initiated by tripping the EPM

pump. Secondary side heat removal is adjusted for every simulation based on the

42 | NATURAL CIRCULATION INSTABILITIES IN TALL-3D FACILITY

combined electrical power of the two heaters. All transient calculations are

performed for 15 000 seconds to capture system behaviours with long period.

The mass flow rates through the two heated legs in natural steady state are

dependent on the balance of heat input and heat losses to the ambient air. We are

looking for the combinations of the 3D test section and main heater powers that will

result in natural circulation flow instability and limit cycle oscillations. A heat

balance over the 3D test section can be predicted by RELAP5 for steady state

conditions, thus it is expected that the prediction will be more reliable when the mass

flow rate changes in 3D test section leg are negligible.

In a general case, when the number of free parameters is large, and the range of the

parameter values significant, such search might take significant amount of

computational time. To reduce the number of simulations on one hand and to find

more details about the optimal heater powers for development of natural circulation

instabilities on the other hand, a two-step procedure is applied.

First, a search for the test conditions where oscillatory flow behaviour can be

expected within wider ranges of the input parameters is conducted. A global

optimum search tool GA-NPO based on the genetic algorithm [31] (more details in

section 2.2) is applied to identify the regions in the input parameter space where

oscillations can occur. Then, a grid study is performed to calculate system response

near the identified instable domain.

Two objectives were defined for the GA search process:

i. finding scenarios with limit cycle oscillation that might be challenging for

prediction by STH codes and,

ii. minimizing 3D effects to enable separate effect STH validation.

Respectively, the FF was defined as the amplitude of flow oscillations in the MH leg

divided by the minimum mass flow rate in the MH leg. This means the fitness value

is larger in case of oscillatory behaviour at low MH leg mass flow rates and smaller

if oscillatory behaviour occurs at high MH leg mass flow rates. The latter indicating

possible flow regime change in the 3D test section, an operating regime not

accurately resolved by STH codes.

The mass flow rate amplitude was calculated by evaluating the difference between

the maximum and minimum flow rates during a 5000 seconds period. To eliminate

the influence of oscillations occurring at the start of the transient and to focus on

identification of quasi periodic, long term oscillatory flow behaviour, the FF was

evaluated after 10000 seconds from the initiation of the transient. As for the

parameter space, both heater powers were varied between 500 and 7500 W.


Obtained fitness function values are shown in Figure 23, where calculations marked

with black dots represent cases that did not successfully finish due to flow stagnation

and consequently critically high temperatures in the heated sections. As can be seen

in the figure, the cases that have largest values of FF (corresponding to the larger

amplitudes of periodic flow instabilities) where found at the lower boundary of the

main heater power (500 W) and at relatively high 3D test section heater power (6220

W). This also explains the sampling density being much higher in the region with

lower main heater power. The parameter space is reasonably covered also in the

regions where oscillations do not occur as a result of stochastic element in the GA

search process.

Figure 23: GA-NPO results for an oscillatory behaviour search in the MH leg. Black

filled circles represent failed calculations (due to reaching temperatures outside of

the ranges for calculation of material properties), coloured circles represent

successful calculations where colour denotes the value of the scenario FF.

A grid study was conducted in order to further investigate the parameter space in

the vicinity of flow instabilities region. Oscillatory behaviour of the mass flow rate in

the main heater leg was found to occur only at relatively low main heater powers,

therefore the main heater power is sampled from 200 to 1500 watts with a step of

25 watts and the 3D test section heater power is sampled from 3250 to 15000 watts

with a step of 250 watts, increasing the maximum value compared to GA search.

In total 2544 calculations were performed to study the oscillatory behaviour region

in addition to 448 calculations that were needed by the genetic algorithm analysis to

identify the region of interest in the first place.


Figure 24: Applying the fitness function used in the GA-NPO search to the

scenarios simulated for the grid study for a better comparison. Black circles

represent failed calculations, coloured circles represent successful calculations

where colour denotes the value of the scenario FF.

Figure 25: MH leg mass flow rates [kg/s] for selection of cases in the grid study.

Red cross indicates a failed calculation.


To visualize the results, the same fitness function is used for the grid study as was

used in the global optimum search calculations. The results are shown in Figure 24.

Cases marked with black dots resulted in LBE temperature going out of the range of

the material property tables in the code (stagnation of the flow in the MH leg).

Several oscillatory flow regimes are present in this parameter space as can be seen

in Figure 25. Scenarios with very low MH power result in constant negative flow in

the MH leg (left blue area in Figure 24). Increasing the MH power first leads to

stagnation of flow (black area in Figure 24) which leads to the numerical failures.

Further increase of the MH power will result in development of oscillatory behaviour

(light blue to red area in Figure 24). Transition to fast decaying oscillations is

observed at even higher MH power (right blue area in Figure 24).

Decay ratio (DR) of the mass flow rate oscillations was used to identify occurrence

of limit cycle oscillations (LCO). For each simulation, mass flow rate in the MH leg

was evaluated and last two oscillation peaks automatically identified. DR was

defined as a ratio:

𝐷𝑅 =

�̇�𝑝𝑒𝑎𝑘(𝑁) − �̇�𝑎𝑣𝑔

�̇�𝑝𝑒𝑎𝑘(𝑁 − 1) − �̇�𝑎𝑣𝑔 (3)

where �̇�𝑝𝑒𝑎𝑘(𝑁) and �̇�𝑝𝑒𝑎𝑘(𝑁 − 1) are values of the flow rate at the last and the

previous peak of the flow rate respectively; �̇�𝑎𝑣𝑔 is an average mass flow rate and

was estimated using a linear fit to capture long term increasing or decreasing flow

rate trends. DR values larger / smaller than 1 indicate growing / decaying amplitude

of the oscillations respectively. The LCO condition is reached when the DR is equal

to unity.

An example of the simulation with DR closest to unity (with 800 W in the main

heater and 12000 W in the 3D test section heater) is shown in Figure 26. At the end

of the transient, the evaluated DR is equal to 1.001. It can be seen from the figure

that after the initial period, the system is close to a limit cycle. This is further

visualized in lower right graph in Figure 26 where the mass flow rates of the two

competing hot legs are plotted against each other.

The parameters that drive the oscillatory behaviour of the mass flow rate are the

balance between the power-to-heated volume ratios in the hot legs, the residence

time of the LBE in a given heated volume and the time it takes for the heated LBE to

pass through the riser of each leg. After initial pump trip at time zero, the mass flow

rate in the MH leg drops and becomes negative. Loop behaviour at this time is driven

by the 3D test section heater. After ~15 minutes, the flow in the MH leg recovers and

an oscillatory behaviour develops in the loop.


Figure 26: A scenario with DR closest to unity.

Relatively high power in the 3D test section results in the LBE flow circulating

mainly between the 3D leg and the HX leg. As the flow in the MH leg nearly

stagnates, the average LBE temperature in the MH leg rises increasing the buoyancy

force. Once the hydrostatic pressure at the inlet of MH leg falls below the pressure

at the inlet to 3D leg, the mass flow in the MH leg accelerates moving the hot LBE

from the main heater leg to the heat exchanger leg. At the same time, MH leg is filled

with cold LBE at the inlet. This results in temporary decrease in the buoyancy force

acting on the LBE in the MH leg. This result in the next cycle of near stagnation of

the flow in the main heater leg.

As seen in Figure 26, the main heater section outlet temperature oscillates with an

amplitude of 100°C while 3D test section and heat exchanger outlet temperatures

remain relatively constant (oscillation amplitude of 5°C).

6.2. EXPERIMENTAL RESULTS

Experimental validation of possible long term natural circulation instabilities in

TALL-3D was done with an experiment using MH heater at 755 W and 3D test

section heater at 10400 W. Secondary side mass flow rate was set to 1 kg/s (54%

pump power) and oil temperature at the inlet of the HX at 126°C. Initial steady state

was achieved with 4.76 kg/s LBE mass flow rate in the heat exchanger leg. At time

zero the EPM pump was stopped and natural circulation developed.


Experimental results for mass flow rates and temperatures of LBE in the primary

system are shown in Figure 27 and Figure 28. It is clear from the results that natural

circulation instabilities can be achieved experimentally in TALL-3D facility.

Figure 27: Mass flow rates in the three legs during verification experiment.

Figure 28: LBE temperatures at key locations during verification experiment.

This work related to natural instability region identification in the TALL-3D

experimental facility resulted in an international benchmark in EU project SESAME.

CODE COUPLING | 49

7. CODE COUPLING

As discussed in the background, the main motivation behind coupling 1D STH and

3D CFD codes is to achieve the required accuracy with affordable computational

effort. Recent works on code coupling in the field of nuclear safety include [16], [47]–

[52].

In general, coupling methodologies can be divided into two main approaches [53]:

• Domain overlapping – STH resolves the whole facility, CFD resolves the

3D domain and STH solution is corrected according to CFD solution.

• Domain decomposition – STH resolves only 1D domain of the facility,

CFD resolves the 3D domain and boundary conditions are exchanged

between the codes.

The illustration of the two approaches with TALL-3D facility example can be seen in

Figure 29.

Figure 29: Domain overlapping and decomposition coupling approaches.

In this work, the two codes used for coupling were Star-CCM+ (CFD) and RELAP5

(STH). The lack of access to source code for both codes was a restriction in

developing an efficient coupling algorithm as information exchange between

RELAP5 and Star-CCM+ had to be executed out of random access memory (using

text files).

50 | CODE COUPLING

To choose a coupling approach several aspects of the specific coupling application

need to be considered. The amount and type of variables that are to be exchanged

need to be identified, the locations of 1D-3D boundaries need to be defined and the

frequency of the data exchange must be evaluated.

TALL-3D facility was designed to provide clear locations for the information to be

exchanged between the codes. The boundaries of the 1D and 3D domains (3D test

section inlet and outlet) are instrumented with the in-flow thermocouples and

pressure transducers. The information to be exchanged is temperature and mass

flow rate of LBE as well as pressure drop over the section.

A representative TALL-3D experiment features forced to natural flow transient with

a period of time during which mass flow rates in the legs fluctuate driven by

buoyancy. A relatively long time (~10000 seconds) is usually necessary to reach a

steady state natural circulation. To avoid code stability issues at low flow rates and

discontinuity in the STH solution, the domain overlapping approach was selected

for application to TALL-3D facility.

Star-CCM+ and RELAP5 were coupled using a built-in java macro capability of Star-

CCM+. This enables easy access to Star-CCM+ variables and execution of external

scripts.

The algorithm composes of the following steps (see also Figure 30):

1. A common steady state is achieved in both STH and CFD codes.

2. Coupling time step starts with STH calculation providing boundary

conditions (flow rate and inlet temperature) to CFD.

3. CFD calculates the same coupling time step.

4. Difference in STH and CFD solutions is evaluated against a convergence

criterion (e.g. a difference between outlet temperatures in STH and CFD).

5. Based on the difference, correction terms are calculated for STH in order to

achieve the same solution for the outlet parameters as provided by CFD.

6. If needed, STH calculates a new solution for the same coupling time step until

convergence criterion or maximum iteration limit is reached. If convergence

is not reached, coupling time step should be decreased. If number of

iterations to achieve the convergence is small, the coupling time step can be

increased to increase overall computational efficiency.

7. Return to step 2.

STH model is composed of the whole facility including valves, heaters as well as EPM

pump. Therefore, change in mass flow rate in the system (e.g. pump trip) or changes

in the 3D test section inlet temperature (defined by the loop behaviour) will be

resolved by the STH model. In order to reflect these changes in the CFD boundary

conditions during the same coupling time step, it is important to initiate coupling

CODE COUPLING | 51

time step with a single STH calculation. When coupling was initiated with a CFD

calculation, the boundary conditions had to be extrapolated from previous time

steps. This method failed to capture a rapid mass flow rate change during the

coupling time step.

Figure 30: STH-CFD coupling flowchart.

Internal STH and CFD time steps can be equal or smaller than the coupling time step

(see Figure 31). The selection of the coupling time step is governed by the timescales

of the phenomena in the simulation. STH solution not converging to the CFD

solution is one indication that the coupling time step should be reduced.

Figure 31: Time steps in coupled codes.

52 | CODE COUPLING

The temperature correction is done with a set of “virtual heaters” connected to each

STH node (shown as red elements in Figure 32) of the 3D test section model. The

virtual heaters are constructed of a material with negligible heat capacity and high

conductivity to minimize the time it takes for the heaters to change the LBE

temperature in the nodes.

Figure 32: Exchange of data between STH and CFD.

Coupling algorithm calculates the temperature difference for each node in STH and

corresponding volume in CFD. It then evaluates how much additional energy should

be provided to (or removed from) each node to match the temperature distribution

in the CFD solution.

Correcting the temperature distribution in the test section has an effect on the

hydrostatic component of the pressure drop over the 3D test section. In case

additional pressure drop correction is needed (e.g. large local flow losses due to jet

interactions with the flow deflection plate at high flow rates), k-loss coefficient in the

test section pool outlet junction is evaluated from CFD simulation and corrected in

STH.

The algorithm can resolve both forward and reverse flows automatically. When the

algorithm identifies flow reversal in the STH solution, the inlet and outlet

boundaries are changed in CFD and STH.

To evaluate the performance of the developed coupled RELAP5 - Star-CCM+ code,

a forced to natural circulation transient was simulated with standalone RELAP5 and

with coupled RELAP5 – Star-CCM+. The results were compared to the experiment

and are shown in Figure 33 and Figure 34.

CODE COUPLING | 53

Figure 33: Comparison of standalone STH, coupled STH-CFD and experimental

mass flow rate in the 3D leg.

Figure 34: Comparison of standalone STH, coupled STH-CFD and experimental

LBE temperatures at the inlet and outlet of the test section.

These initial results indicate significant improvement of the RELAP5 solution when

coupled with Star-CCM+.

CONTRIBUTION TO CODE COUPLING: Developed RELAP5 inputs, carried

out all RELAP5 calculations and contributed significantly to the overall development

of the coupling methodology.

54 | CODE COUPLING

TALL-3D SOLIDIFICATION TEST SECTION | 55

8. TALL-3D SOLIDIFICATION TEST SECTION

Coolant solidification is a phenomenon of potential safety importance for liquid

metal cooled fast reactors. Solidification can affect local heat transfer and lead to

partial or complete blockage of coolant flow path. This in turn might lead to failure

of decay heat removal systems.

Figure 35: Possible solidification location in an LFR heat exchanger during loss of

flow and overcooling accident.

Prediction of possible outcomes for scenarios with complex interactions between

local physical phenomena of solidification and system scale natural circulation

behaviour is subject to modelling uncertainty. Development and validation of

adequate models requires validation grade experimental data [54].

A modification to the TALL-3D experimental facility is envisioned under EU project

SESAME, adding a solidification test section (STS) to the system where active

cooling of the test section walls would allow for local solidification of LBE. When

changing an existing experimental configuration, the influence of the change upon

the whole system behaviour has to be investigated. Prior to the implementation of

solidification test section, TALL-3D was mainly used for providing validation data

for standalone STH and CFD codes. An important property of the facility has been

the “competition” between the two hot legs resulting in flow instabilities at natural

circulation conditions. During TALL-3D initial designing phase it was foreseen that

the short-term flow instabilities in the facility could be amplified by increasing the

volume of the main heater leg. In the pre-STS configuration, LBE volume in the 3D-

leg is approximately four times that of the main heater leg. Assuming equal (or less

56 | TALL-3D SOLIDIFICATION TEST SECTION

asymmetric) configuration of the two hot legs a more pronounced parallel channel

instabilities can be present [55].

TALL-3D system analysis with solidification test section was performed using the

RELAP5 model of the facility. RELAP5, being a 1D STH code, does not provide

options to simulate solidification and all calculations were performed with no

cooling of the STS focusing on the natural circulation instabilities of the system. The

goal of the analysis is to verify that increasing the main heater leg volume by adding

an additional test section will result in an increased instability in the facility at

natural circulation conditions.

The effect of nodalization in the model input was analysed using simulations with 1,

3 and 6 nodes for STS with dimensions H=300 mm, D=92.1 mm. The dimensions

correspond to 11 times increase of the flow area and respective added volume of ~1.8

litres. Increasing the number of nodes increases the amplitude of the flow

oscillations as shown in Figure 36 (reduced numerical diffusion). There is only a

relatively small quantitative difference between the results obtained with 3 and 6

nodes (10 and 5 cm node height, respectively). The onset of instability is predicted

in both cases, only the amplitude is slightly under-predicted with 3 nodes. However,

mesh with 6 nodes will be used in the following analysis.

Figure 36: Results of the RELAP5 TALL-3D STS nodalization study.

Several cases of forced to natural circulation transient with constant power of 2.5

kW in both heaters were simulated (see Table 13). In each case the location of the

STS was in the main heater leg just above the heater.


Table 13: RELAP5 simulations with varying STS diameter.

Simulation STS Diameter

[mm]

Added Volume

[l]

5x flow area 62.3 0.5

15x flow area 107.9 1.8

25x flow area 139.3 3.0

99x flow area 277.2 12.3

Figure 37: Mass flow rate in the main heater leg for different STS configurations

with varying diameter.

Figure 38: Main heater outlet temperature for different STS configurations with

varying diameter.

Figure 37 and Figure 38 show that a STS volume of about 1.8 – 3.0 litres would be

sufficient to create a possibility for instable natural flow circulation when main


heater and 3D test section heater powers are equal. The results in the figures are

compared to the original design (indicated ORIGINAL) without the added volume

(using only standard TALL-3D piping with volume of 0.125 litres).

First series of pre-test simulations were performed with STS positioned above the

main heater in the main heater leg. Further analysis was conducted with STS

positioned at the top of the main heater leg, just below the expansion tank Figure 39.

Results (Figure 40) show that higher position for the STS pool would result in more

stable flow conditions than lower position.

Figure 39: Possible future STS locations in TALL-3D facility.

Figure 40: Mass flow rates in all three primary legs with STS positioned at high and

low elevations.


Based on the results of RELAP5 and Star-CCM+ pre-test analysis (CFD analysis is

described in detail in Paper V), requirements for instrumentation, safety and

manufacturing, the final configuration of the TALL-3D facility with STS is defined

(Figure 41).

Figure 41: TALL-3D facility with proposed solidification test section.


TALL-3D solidification test section (Figure 42) is a rectangular vessel made of

stainless steel with inlet at the bottom and outlet at the top (nominal flow direction).

The left side of the STS has a rectangular flange to which different instrumentation

can be connected. The top and right side are equipped with active water cooling. The

pool inner dimensions are 200x200x52 mm. The inlet and outlet pipes adjacent to

the test section have rectangular profile with inner dimensions of 27x52 mm. For the

connection to the TALL-3D piping those are further reduced to circular cross-section

with inner diameter of 27.8 mm. The wall thickness around the solidification section

is 7 mm. The front and back walls are additionally equipped with stiffeners to

minimize bulging.

Figure 42: STS design with FBG probes.

Three sets of instrumentation have been chosen for the measurement of the

solidification front and characterization of the flow in the STS pool:

• Thermocouples for temperature measurement outside and inside the pool,

• Fiber Bragg gratings (FBGs) for temperature measurement inside the pool,

• Ultrasound Doppler Velocimetry (UDVs) for measurement of the LBE

velocity and location of the solidus front.


CONTRIBUTION TO PAPER V: TALL-3D facility with various STS designs was

analysed. The dimensions of the test section, positioning of the section in the loop

and possible feedbacks during natural circulation were investigated; most promising

parameters for facility modification proposed.

SUMMARY | 63

9. SUMMARY

The ultimate goal of this thesis work is to provide contributions towards

development of methods, data and tools for qualifying multiscale codes and facilitate

their application to design and safety analysis of Generation IV metal cooled

reactors.

An approach to data post-processing was developed and implemented in order to

enable selection of a large number of SRQs in the STH code output as a fitness

function in the global optimum search. This feature was instrumental for automated

calibration of the input model and code validation reducing the “user effect” on the

resulting uncertainty estimation. Complex system of data processing and exchange

between STH code RELAP5, post-processing scripts in MATLAB and global

optimum search tool GA-IDPSA was developed and applied to all code calibration

and validation activities presented in the thesis.

The developed search methodology was instrumental in identification of natural

instability region in TALL-3D facility operational parameter space. Experimental

verification of said instability region resulted in an international benchmark test in

EU project SESAME for support of code qualification activities.

Contributions to TALL-3D facility development include RELAP5 analysis to support

facility design featuring thermal-hydraulic feedbacks between 1D and 3D

components, identification of operational parameters for dedicated tests on

calibration of measurement equipment as well as carrying out experiments to

produce validation grade data for standalone and coupled code validation. Data

produced at the TALL-3D facility has been used by a number of European

institutions for code qualification purposes. In addition, TALL-3D facility RELAP5

model was calibrated and validated for application with coupled STH-CFD code.

Contributions to coupled code development include RELAP5 TALL-3D input model

development for data exchange and an implementation of the algorithm, for STH

solution corrections according to CFD simulation. Coupled code methodology

development resulted in RELAP5-Star-CCM+ coupled code which improved

considerably standalone RELAP5 solution during forced to natural circulation

transient.

In support of TALL-3D facility modifications to include solidification test section,

RELAP5 model was used to evaluate the natural circulation instabilities at equal

powers of the main and 3D test section heaters. STS dimensions and location

selection were based on STH code analysis allowing more symmetric instabilities in

natural circulation regime in the TALL-3D facility in the future. Flow reversals,

64 | SUMMARY

induced by the symmetric instabilities, in both hot legs will increase the difficulty of

accurate code prediction and therefore increase the value of experimental data.

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