Post on 15-Apr-2017
A Comprehensive Validation A Comprehensive Validation Approach Using The RAVEN Code - Approach Using The RAVEN Code - Un innovativo approccio di validazione Un 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 EngineeringDegree in Energy EngineeringDegree in Energy Engineering
OutlineOutline
• RAVEN and the Validation Process
• The Experimental set-ups
• Results
• Future Developments
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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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The Validation AssessmentThe Validation Assessment
Experiment 2 Experiment 3
Experiment 1
Validation
ProcessValidated Design Space
TargetDesign 1
TargetDesign 2
TargetDesign 3
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RELAP5-3D Validation MetricRELAP5-3D Validation Metric
ERROR
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ERROR
A Probabilistic Reading of Experimental DataA Probabilistic Reading of Experimental Data
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The Comparison The Comparison
RELAP7RELAP5-3D
Probabilistic Input
Probabilistic Code Output Probabilistic Experimental Reading
EXPERIMENTS
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RAVEN
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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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
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
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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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
Monte Carlo Sampling Monte Carlo Sampling
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Monte Carlo Response SurfaceMonte Carlo Response Surface
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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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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 Reading
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Code
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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Binning of the Data GeneratedBinning of the Data Generated
• Number of Samples: 8300
• Optimized number of bins: 15
Bin Midpoint Bin Count0.187921 1
0.188897 40.189874 17
0.19085 630.191826 2050.192802 5180.193778 9380.194755 1419
0.195731 16380.196707 16290.197683 1042
0.198659 5410.199636 213
0.200612 640.201588 13
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COUNTS
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
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
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
Comparison with Experimental DataComparison with Experimental Data• It is implemented the Difference of Continuous
Functions Metric, defined as:
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µ (z) = 0.0662σ (z) = 0.0325
Where the best overlapping
position is
Disagreement in the best overlapping position
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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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 Society American 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