This document discusses using the RAVEN code to perform a comprehensive validation approach for nuclear reactor simulation codes. RAVEN implements uncertainty quantification and sensitivity analysis techniques to validate code predictions against experimental data from tests like the Semiscale and NACIE experiments. The validation process involves running probabilistic simulations with RAVEN, comparing output to experimental measurements using metrics, and performing sensitivity analysis to identify influential input parameters. The approach aims to define validated design spaces for nuclear reactor codes.

1. A Comprehensive ValidationA Comprehensive Validation
Approach Using The RAVEN Code -Approach Using The RAVEN Code -
Un innovativo approccio di validazioneUn innovativo approccio di validazione
utilizzando il codice RAVENutilizzando il codice RAVEN
Candidate: Rinaldi Ivan, 1325668Candidate: Rinaldi Ivan, 1325668
Supervisor: Prof. Gianfranco CarusoSupervisor: Prof. Gianfranco Caruso
Co Supervisor: Fabio Giannetti PhDCo Supervisor: Fabio Giannetti PhD
Faculty of Industrial and Civil EngineeringFaculty of Industrial and Civil Engineering
Degree in Energy EngineeringDegree in Energy Engineering

2. OutlineOutline
• RAVEN and the Validation Process
• The Experimental set-ups
• Results
• Future Developments
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3. What is RAVEN?What is RAVEN?
• RAVEN (Reactor Analysis and Virtual control
ENvironment) is a code being developed at the
Idaho National Laboratories, USA.
• RAVEN was born as a PRA code, and it is now
being coupled with many codes (thermo hydraulic,
fuel performance, neutronics) and its uses are
increasing exponentially
• Our use of RAVEN implements numerical
algorithms in order to supply uncertainty
quantification and sensitivity analysis capabilities
• These techniques may be employed in order to
fulfill what is called a “validation assessment”
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4. The Validation AssessmentThe Validation Assessment
Experiment 2 Experiment 3
Experiment 1
Validation
Process
Validated Design Space
Target
Design 1
Target
Design 2
Target
Design 3
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5. The Old WayThe Old Way
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6. RELAP5-3D Validation MetricRELAP5-3D Validation Metric
E
R
R
O
R
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E
R
R
O
R

7. A Probabilistic Reading of Experimental DataA Probabilistic Reading of Experimental Data
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8. The ComparisonThe Comparison
RELAP7
RELAP5-3D
Probabilistic Input
Probabilistic Code Output Probabilistic Experimental Reading
EXPERIMENTS
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RAVEN

9. The Semiscale ExperimentThe Semiscale Experiment
• It is presented a scaled model of a
pressurized water reactor (PWR)
(Semiscale Mod-2A facility by EG&G
Idaho).
• The Mod-2A test facility is a full
height, 1/1705 power-to-volume
scaled model of a primary system of a
PWR.
• The main objective of the experiment
was to investigate natural circulation
heat rejection. The model presented
examined in particular steady-state
single-phase natural circulation
behavior.
• For such experiment a single-loop
configuration was employed. The
pump was replaced with a spool
piece containing an orifice that
simulated the hydraulic resistance of
a locked pump rotor.
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10. The NACIE ExperimentThe NACIE Experiment
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• Lead-Bismuth
Natural Circulation
Test Facility
• Components:
– Electrically heated
core
– Secondary Water
Heat Exchanger
– Argon Gas Inlet and
Outlet Valves

11. Input Space Probabilistic RepresentationInput Space Probabilistic Representation
• Two input variables were considered
subject to uncertainty:
– Pressure Primary Loop
– Core Power
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More computational
power is yet needed
to take into
consideration more
variables

12. Validation Objectives (Figure of Merits)Validation Objectives (Figure of Merits)
• For the Semiscale experiment, the natural circulation
analysis, the output variables (figures of merit) that
should be compared are:
– Mass Flow Rate
– Cold Leg Temperature
– Hot Leg Temperature
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13. Uncertainties on Figure of MeritsUncertainties on Figure of Merits
• The uncertainties on the figure of merits are connected to the type of
measurements and not specific to a particular detector and location
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Semiscale MOD-2A NACIE

14. Monte Carlo SamplingMonte Carlo Sampling
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15. Monte Carlo Response SurfaceMonte Carlo Response Surface
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16. Sensitivity AnalysisSensitivity Analysis
• In the range examined the response of the system is
dominated by the core power or argon mass flow rate
• The dependence from the pressure is minimal (vertical
distortion)
• The linear interpolation using the sensitivity coefficients is
almost exact
Watts
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17. Comparison of the Output to the ExperimentsComparison of the Output to the Experiments
• The code output is represented by a number of points with or
without probabilistic weights
• The experimental output has most likely an analytical
distribution
• How to perform a comparison:
– Translate the code output in a pseudo analytical formulation
(binning)
– Define a proper metric in the probabilistic space to assess
similarity/dissimilarity
Probabilistic Code Output
Probabilistic Experimental Readin
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Code

18. BinningBinning
• The goal is to achieve a numerical representation of a
probability density function representing sets of points in the
output space
• The less distorting representation is generate by the binning
(histogram)
• The number of bins and its boundaries should be chosen to
regularize the function without altering its meaning
• Several algorithms are under implementation
– Square root:
– Sturge’s Formula:
– Rice rule:
– Doane formula (to be implemented)
– Scott normal reference rule (to be implemented)
– Freedman-Diaconis’ formula (to be implemented)
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19. Binning of the Data GeneratedBinning of the Data Generated
• Number of
Samples: 8300
• Optimized
number of bins:
15
Bin Midpoint Bin Count
0.187921 1
0.188897 4
0.189874 17
0.19085 63
0.191826 205
0.192802 518
0.193778 938
0.194755 1419
0.195731 1638
0.196707 1629
0.197683 1042
0.198659 541
0.199636 213
0.200612 64
0.201588 13
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C
O
U
N
T
S

20. Comparison with Experimental DataComparison with Experimental Data
• Having the distributions of the code outputs and the
experimental data, one can proceed to compare the
various distributions with different metrics
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*Cumulative Distribution Function *Probability Distribution Function

21. Comparison with Experimental DataComparison with Experimental Data
• It is implemented the Minkowski L1 Metric, defined
as the area between the CDF from the simulation
and the empirical distribution
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d(E,R) = 3.029 K
Lower Value
Means Higher
Agreement

22. Comparison with Experimental DataComparison with Experimental Data
• It is implemented the PDF Area Metric, defined as
the common area of the PDFs from the simulation
and the empirical distribution
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I(x) = 0.2704 = 27%
Higher Percentage
Means Higher
Agreement

23. Comparison with Experimental DataComparison with Experimental Data
• It is implemented the Difference of Continuous
Functions Metric, defined as:
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µ (z) = 0.0662
σ (z) = 0.0325
Where the best
overlapping
position is
Disagreement in the best
overlapping position

24. ConclusionsConclusions
• PRA in nuclear field includes some extremely
thorough procedures regarding code applicability,
therefore RAVEN is becoming a validation go-to-
code
• Many data mining capabilities are still under
development, such as multidimensional
distributions, which will take into consideration
more dimensions (such as time dependent
transients)
• More and extra accurate validation metrics
• Moreover, more experiments will be tested in the
future, in both RELAP5-3D (with the NACIE-UP
European project) and in RELAP7.
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25. Work PublishedWork Published
• Technical Report, E. Negretti, C. Parisi, F. Giannetti, I. Rinaldi, G.
Caruso, Feasibility Analysis and Uncertainty Quantification for a "Fast-
Running" Chain of Codes for the NPP Accident Management, Joint
Program ENEA-MSE on Nuclear Safety and Generation IV ReactorsNuclear Safety and Generation IV Reactors.
• PSA, Fabio Giannetti, Ivan Rinaldi, Andrea Alfonsi, Ivan Di Piazza,
Gianfranco Caruso, A Comprehensive Validation Approach Using The
RAVEN Code applied to RELAP5-3D for LBE.
• ANS, Ivan Rinaldi, Andrea Alfonsi, Joshua Cogliatti, Cristian Rabiti,
Fabio Giannetti, Gianfranco Caruso, A Comprehensive Validation
Approach Using The RAVEN Code, American Nuclear SocietyAmerican Nuclear Society
Conference.Conference.
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Work in ProgressWork in Progress
• Journal , Ivan Rinaldi, Andrea Alfonsi, Joshua Cogliatti, Cristian Rabiti,
Fabio Giannetti, Gianfranco Caruso, “A comprehensive Validation Approach
Using The RAVEN Code” Probabilistic Engineering Mechanics JournalProbabilistic Engineering Mechanics Journal
• Journal , Ivan Rinaldi, Fabio Giannetti, Andrea Alfonsi, Cristian Rabiti,
Gianfranco Caruso, The RAVEN procedure applied to the NACIE
experiment, Nuclear Science and Engineering JournalNuclear Science and Engineering Journal