Bayesian Uncertainty Quantification of Physical Models in Thermal-Hydraulics System Codes

Bayesian Uncertainty Quantification of Physical Models in Thermal-Hydraulics System Codes

PhD Public Defense (Open Session)

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Damar Wicaksono

February 23, 2018
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  1. 1.

    WIR SCHAFFEN WISSEN – HEUTE FÜR MORGEN Bayesian Uncertainty Quantification

    of Physical Models in Thermal-Hydraulics System Codes Damar Wicaksono (Thesis Directors: Prof. A. Pautz & Mr. O. Zerkak) PhD Public Defense, Paul Scherrer Institut, Villigen-PSI, 23.02.2018
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 2 / 53) Safety Analysis of

    LWR under LBLOCA: Show max. clad temperature < safety limit Cold Leg Hot Leg Gas Phase Liquid Phase Double-ended Guillotine Break Emergency Injection Two-phase hydraulics Heat conduction through solids Reactor Core Reactor Pressure Vessel Clad Temperature Time Safety Limit
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 3 / 53) Forward Uncertainty Quantification:

    Uncertain Inputs ⇒ Uncertain Outputs Forward Model (Code) : ↦ Uncertain Inputs (random variables) Uncertain Outputs Statistical Analysis of ``Quantities of Interest’’ Decision Making • Material properties • Initial conditions • Boundary conditions • Physical model parameters Clad Temperature Safety Limit Safe/Fail, Accept/Reject, etc. Monte Carlo simulation (multiple code runs) e.g., PCT
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 4 / 53) Physical Model Parameters:

    post-Critical Heat Flux (CHF) Flow Regimes pre-CHF (Steam Convection) Dispersed Flow Film Boiling (DFFB) Interpolatory (Inverted Slug) Inverted Annular Film Boiling (IAFB) Transition Boiling pre-CHF (Nucleate Boiling) റ + ⋅ റ റ + = − റ fint + റ fw + α റ + Γint റ + ⋅ റ = Γint = Gas , (Liquid) + 2 2 + ⋅ αi ( + + 2 2 ) റ = int + w + + α റ ⋅ റ − Γint ℎ ′ + (−റ fint +റ fw ) ⋅ റ Mass Momentum Energy Closure Laws Parametric models
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 5 / 53) Origin of Uncertainty

    in Physical Model Parameters FLECHT-SEASET Westinghouse, USA Excerpt from the TRACE Code Theory Manual: • “…the approximate value of the coefficient in Eq. (4-119) was determined from data comparisons with FLECHT-SEASET high flooding rate reflood data…” (pp. 164) • “In TRACE, the above interfacial drag coefficient has been reduced by a factor of ¾ to better match FLECHT-SEASET high flooding rate reflood data, so…” (pp. 166) No statement of uncertainty on these parameters
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 6 / 53) Research Objectives Given

    experimental data from a Separate Effect Test Facility (SETF), develop a methodology to: quantify the uncertainty of physical model parameters in a TH System Code to be propagated within statistical uncertainty analysis framework. Calibration Pre-calibration uncertainties Post-calibration uncertainties Experimental Data from SETF
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 7 / 53) Scope of Research

    (1/2): Statistical Framework • Methods are non-intrusive • Methods require less assumptions about the underlying model • Results conform with probabilistic framework for UQ • Methods tend to be expensive (require numerous code runs) = 102 − 103 1. Global Sensitivity Analysis 2. Metamodeling 3. Bayesian Calibration 4. Uncertainty Propagation Prior uncertainties Posterior uncertainties = 102 − 104 = 103 − 106 = 102 − 103 Reduced parameter sets
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 8 / 53) Scope of Research

    (2/2): FEBA Separate Effect Test Facility (SETF) FEBA Reflood Tests were conducted at Kfz Karlsruhe (KIT) during 1980s for investigating bottom reflood using rod simulators (NiCr) 4.1 [m] Three types of measurements were taken: • Clad temperature (8 axial locations) • Pressure drop (4 axial segments) • Liquid carryover Main analyses are based on Test No. 216: • inlet = 3.8 cm. s−1 • sys = 4.1 bar • inlet = 312 K • Power = 120% ANS Decay Curve
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 9 / 53) Scope of Research

    (2/2): TRACE Model of FEBA SETF Powered Heater Rods Housing/Enclosure Spacer grid Inlet flow Upper plenum Backpressure • (4) Experimental boundary conditions (sys , inlet , etc.) • (9) Material properties (, , etc.) • (2) Spacer grid model (HTCenh. , Δ) • (10) Post-CHF closure relations (IAFB wall HTC, DFFB interfacial drag, etc.) • (1) Quench temperature • (1) Transition boiling HTC Model parameters Controllable inputs 27 Parameters are required to specify the model: With flat independent uncertainties, either in linear or log scales
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 10 / 53) Statistical Framework (1/4):

    Global Sensitivity Analysis 1. Global Sensitivity Analysis Gaussian Process Metamodeling Bayesian Calibration Uncertainty Propagation How to approximate the input/output of the forward model? How to make the inference (quantification)? Is the quantified uncertainty useful? 27 initial parameters • (~ minutes/run) • (~102 MBytes/run) How to select the important parameters? Clad Temperature [K] Pressure Drop [bar] Time [s] Liquid Carryover [kg] Propagation based on 1’000 samples Identify the least influential parameters, and exclude them Nominal parameter
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 11 / 53) Sensitivity Analysis Δ

    Δ ≡ ቤ ≅ 1, , … , , + Δ , ⋯ , , − ( ) Δ Base point Questions on robustness: • Weigh heavily on the region around a single base point • Assumption on linearity across input parameter space Perturbed points
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 12 / 53) Global Sensitivity Analysis:

    The Morris Screening and Sobol’ Total-Effect Elementary effect : Perturbation of one parameter at a time ≡ + Δ ⋅ − () Δ Grid size ≡ ~ ~ Sobol’ total-effect index for : Expectation Variance
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 13 / 53) Uncertainty Propagation using

    Influential vs. Non-Influential Parameters Clad Temperature [K] Pressure Drop [bar] Time [s] Liquid Carryover [kg] 12 Influential 15 Non-influential Parameter subsets Uncertainty propagation using 2 parameter subsets and 500 Monte Carlo samples 12 parameters are influential: (4) (8) Boundary conditions Closure laws and spacer grid
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 14 / 53) Statistical Framework (2/4):

    Metamodeling 1. Global Sensitivity Analysis 2. Metamodeling How to select the important parameters? How to approximate the input/output of a computer model? 27 initial parameters 12 influential parameters • ~ min /run • ~102 MB /run Construct a metamodel for fast and efficient approximation Metamodel: “a model of a model” or a surrogate
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 15 / 53) Prior Data Posterior

    Gaussian Process (GP) Metamodel , ∗ ≡ Cov , (∗) ()~ (), 2 , ∗ Probability distribution of functions mean function process variance correlation (kernel) function Gaussian Process Gaussian process is a Gaussian with continuous variates: ∈ ℝ; ∈ ℝD Application in Regression
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 16 / 53) Constructing GP Metamodel:

    Training and Testing Testing Design of Experiment Training Runs Testing Runs መ () Model fitting Training Inputs Testing Inputs Prediction of testing runs by the metamodel
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 17 / 53) Dealing with Multivariate

    Output: Principal Component Analysis (PCA) Output of the TRACE model of FEBA is highly multivariate. Output Type # of outputs P = Δz × Δt Clad Temperature 8 × 10′000 = 80′000 Pressure Drop 4 × 10′000 = 40′000 Liquid Carryover 1 × 10′000 = 10′000 Dimension reduction by Principal Component Analysis (PCA) ′ = ⋅ ⋅ = ⋅ SVD Concatenate and centered outputs Eigenvectors (principal components) PC scores model ; , = ത model , + ෍ =1 ⋅ , PC scores, predicted by GP metamodel the mean of output Prediction of multivariate output = = =
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 18 / 53) Principal Component Analysis

    (PCA): Intuition PCA decorrelates variables, preserves variance, and re-allocate the variance in descending order Original basis Principal components basis 2nd coordinate is redundant Only 1st PC is used
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 19 / 53) Principal Component Analysis

    (PCA): Liquid Carryover Output Etc. Liquid Carryover kg 1 realization is a curve Time s
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 20 / 53) × Principal Component

    Analysis (PCA): Liquid Carryover Output Centered Output ′ PC Scores Principal Components × × curves = ′ 1 curve per row Projection in new basis new basis
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 21 / 53) × model ;

    = ത model + ෍ =1 ⋅ Principal Component Analysis (PCA): Liquid Carryover Output Centered Output ′ PC Scores Principal Components × × curves = ′ mean 1 curve per row Projection in new basis new basis
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 22 / 53) Truncated × model

    ; ≈ ത model + ෍ =1 ⋅ PCA – Truncation at : Liquid Carryover Output × × curves Truncated = mean 1 curve per row Projection in new basis new basis Centered Output ′ PC Scores Principal Components
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 23 / 53) PCA – Principal

    Components: Liquid Carryover Output Truncated × Principal Components = Etc. 1st PC (~% variance) 2nd PC (~. % variance)
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 24 / 53) PCA – Reconstruction:

    Liquid Carryover Output 2.1 Realization 15 Realization 1801 0.7 Mean 1st PC model ; ≈ ത model + ෍ =1 ⋅
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 25 / 53) Principal Component Analysis

    (PCA): Clad Temperature Output 1 realization is an “image” , Time s Axial Location m 4 0 0 700 Temperature K Etc.
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 26 / 53) PCA – Principal

    Components: Clad Temperature Output Truncated × Principal Components = Etc. 1st PC (~% variance) 2nd PC (~% variance)
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 27 / 53) PCA – Reconstruction:

    Clad Temperature Output model ; , ≈ ത model , + ෍ =1 ⋅ , 1.2 Realization 1803 (reconstructed) Mean 1st PC Realization 1803 (original)
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 28 / 53) × PC –

    GP Metamodel: Summary Centered Output ′ PC Scores Principal Components × × runs PC is used to construct any new realizations Metamodel is used to predict model ; , = ത model , + ෍ =1 ⋅ , 1 curve per row Truncated Truncated
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 29 / 53) Constructing GP Metamodel:

    Training and Testing Testing Design of Experiment Training Runs Testing Runs መ () Model fitting Different factors involved in the construction of GP Metamodel Training Inputs Testing Inputs
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 30 / 53) Testing the Metamodel

    against Testing Samples Metamodel predictive performance is assessed by comparison against large independent test data (i.e. actual TRACE runs) YTRACE,Test − Y TRACE,Test Q YTRACE,Test − Y GP,Test Q • Dimension reduction error • Due to smaller to reconstruct the full output space X-axis: Y-axis: • Dimension reduction error and GP error • Due to (also) miss-prediction of PC scores Both are in terms of RMSE YTRACE,Test − Y TRACE,Test Q YTRACE,Test − Y GP,Test Q
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 31 / 53) Testing the Metamodel

    against Testing Samples (Ntest = 5′000) Clad Temperature [K] Pressure Drop [Pa] Liquid Carryover [kg] GP PC RMSE Testing Samples 22.4 [K] 77.95 [Pa] 0.27 [kg] 254. [K] 9′200 [Pa] 30.4 [kg] (< %) (< . %) (< . %) Metamodel predictive performance is acceptable for each output (Ntrain = 1′920) YTRACE,Test − Y TRACE,Test Q YTRACE,Test − Y TRACE,Test Q YTRACE,Test − Y TRACE,Test Q YTRACE,Test − Y GP,Test Q YTRACE,Test − Y GP,Test Q YTRACE,Test − Y GP,Test Q
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 32 / 53) Statistical Framework (3/4):

    Bayesian Calibration 1. Global Sensitivity Analysis 2. Metamodeling 3. Bayesian Calibration How to select the important parameters? How to approximate the input/output of a computer model? How to make the uncertainty quantification? 27 initial parameters 12 influential parameters • ~ min /run • ~102 MB /run Wide, independent prior uncertainties • ~ s /run • ~102 MB Use experimental data to constrain the prior
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 33 / 53) Bayes’ Theorem Bayesian

    Calibration, Inverse Quantification: Uncertain (Inputs + Data) ⇒ Uncertain Inputs Updated Forward Model : , ↦ Uncertain model parameters (posterior) Likelihood | , Experimental Data from SETF Additional sources of uncertainty |, Uncertain model parameters (prior) Controllable inputs (w.r.t experiment) Probability of observing data given parameters , = | , × ׬ | , ×
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 34 / 53) Normal Likelihood exp|

    ~ ෤ + , Σ + > > > + Σ + exp 2 GP metamodel Principal Comp. truncation Model bias measurement Multiple sources of variance Likelihood exp , = M ; , + , + Model prediction Model bias Measurement error Experimental Data 6 calibration schemes were investigated: • Considering different types of data • With or without model bias term • Excluding a model parameter GP GP Gaussian given unknown
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 35 / 53) Posterior Formulation and

    Computation Bayes’ Theorem exp = exp} × ׬ exp} × Posterior Likelihood Prior Uncertainty on is fully specified by exp , the posterior pdf. Markov Chain Monte Carlo (MCMC) Simulation How exp is used? • Uncertainty propagation (integration of a function under the posterior pdf) • Characterization of parameter uncertainty, e.g., moments (integration of the pdf over the parameter space)
  36. 36.

    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 36 / 53) = න ,

    ~ ~ ≈ 1 ෍ =1 x, Monte Carlo Simulation Revisited = න ≈ 1 ෍ =1 i Forward Model (Code) : ↦ Summary of output e.g., the mean Random inputs Random output Summary of inputs e.g., the mean How to sample from ? • For (integrable) univariate distribution: Inverse transform sampling • For multivariate normal distribution: Cholesky decomposition • For generic multivariate distribution: Markov Chain (Monte Carlo) realizations/samples from
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 37 / 53) Markov Chain Monte

    Carlo: A Generic Sampling Approach 1 1 # of iterations (104) # of iterations (104) 2 Frequency 2 Frequency The Metropolis-Hastings (MH) Algorithm to sample from any density : 1.Start at a random value of 2.Perturb current value new 3.Accept or reject based on: = new old Over many realizations, the resulting Markov Chain is representative samples Bivariate logistics distribution
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 38 / 53) Prior uncertainty of

    the model parameters gridHT iafbWHT dffbWHT dffbVIHT iafbIntDr dffbIntDr dffbWDr tQuench • Diagonal panels: univariate marginal PDFs • Off-diagonal panels: pairwise correlation plots (bright color = concentrated samples)
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 39 / 53) The posterior uncertainty

    based on pressure drop data only (≈ samples) gridHT iafbWHT dffbWHT dffbVIHT iafbIntDr dffbIntDr dffbWDr tQuench
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 40 / 53) The posterior uncertainty

    based on pressure drop data only (≈ samples) gridHT iafbWHT dffbWHT dffbVIHT iafbIntDr dffbIntDr dffbWDr tQuench Interfacial drag of the inverted annular flow regime
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 41 / 53) Posterior on all

    types of data (≈ samples) gridHT iafbWHT dffbWHT dffbVIHT iafbIntDr dffbIntDr dffbWDr tQuench
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 42 / 53) Posterior on all

    types of data (≈ samples) gridHT iafbWHT dffbWHT dffbVIHT iafbIntDr dffbIntDr dffbWDr tQuench Vapor-Interface HTC of the DFFB Spacer grid HTC enhancement Interfacial drag of the DFFB Wall HTC of the DFFB
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 43 / 53) Posterior on all

    types of data (≈ samples) gridHT iafbWHT dffbWHT dffbVIHT iafbIntDr dffbIntDr dffbWDr tQuench Vapor-Interface HTC of the DFFB Interfacial drag of the DFFB
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 44 / 53) Time [s] Posterior

    Samples are Correlated (i.e., a set of “collectively-fitted” values) Clad Temperature [K] Middle assembly Top assembly Uncertainty propagation on FEBA Test. No. 216 (the calibration data) based on 1’000 Monte Carlo samples. Prior Posterior, Correlated Posterior, Independent Exp. Data
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 45 / 53) Statistical Framework (4/4):

    Posterior Uncertainty Propagation 1. Global Sensitivity Analysis 2. Metamodeling 3. Bayesian Calibration How to select important parameters to be inferred? How to approximate the input/output of a computer model? How to make the uncertainty quantification? How good is the quantified uncertainty? 27 initial parameters 12 influential parameters • ~ min /run • ~102 MB /run Wide, independent prior uncertainties • ~ s /run • ~102 MB Narrower, correlated posterior uncertainties 4. Uncertainty Propagation Wide prior prediction uncertainties Propagate posterior uncertainties for different experimental conditions
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 46 / 53) Comparing Different Calibration

    Results: Informativeness Two scores to summarize and compare results of uncertainty propagation using different posterior samples w.r.t the prior InfY = 1 − 1 2 UUBpost. − LUBpost. UUBprior − LUBprior LUB: Lower Uncertainty Bound (2.5th prediction percentile) (97.5th prediction percentile) UUB: Upper Uncertainty Bound Informativeness for output InfY = 0.5 InfY = 1.0 Posterior prediction uncertainty is equal to that of the prior No posterior prediction uncertainty
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 47 / 53) Comparing Different Calibration

    Results: Calibration Score Two scores to summarize and compare results of uncertainty propagation using different posterior samples w.r.t the prior Calibration Score for output CalY = 0.0 CalY = 1.0 Experimental data falls outside the uncertainty band Experimental data matches the reference value exactly CalY = yexp The height of the experimental data in the information triangle : Reference value (50th prediction percentile; median)
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 48 / 53) Posterior Prediction Uncertainty

    in Terms of Calibration Score and Informativeness Informativeness [-] sys = 4.1 bar inlet = 3.8 cm. s−1 Calibration Scheme All Parameters, Correlated All Parameters, Independent Calibration Score [-] Prior Posterior, Correlated Posterior, Independent Exp. Data
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 49 / 53) Effects of Experimental

    Conditions on Posterior Prediction Uncertainty sys = 4.1 bar inlet = 3.8 cm. s−1 Calibration Scheme All Parameters, Correlated All Parameters, Independent Informativeness [-] Calibration Score [-] Informativeness [-] Calibration Score [-]
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 50 / 53) Removing a Highly

    Correlated Influential Parameter from the Calibration sys = 4.1 bar inlet = 3.8 cm. s−1 Calibration Scheme All Parameters, Independent All Parameters, Correlated Excl. dffbVIHT, Independent Excl. dffbVIHT, Correlated Calibration Score [-] Informativeness [-] dffbVIHT dffbIntDr Vapor-Interface HTC of the DFFB Interfacial drag of the DFFB
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 51 / 53) Effects of Experimental

    Conditions on Posterior Prediction Uncertainty Calibration Scheme All Parameters, Independent All Parameters, Correlated Excl. dffbVIHT, Independent Excl. dffbVIHT, Correlated sys = 4.1 bar inlet = 3.8 cm. s−1 Calibration Score [-] Calibration Score [-] Informativeness [-] Informativeness [-]
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 52 / 53) Conclusion Consolidation, implementation,

    and application of tools based on statistical framework for quantifying the physical model parameters in the TRACE code Motivation: Objectives: Contribution: Given data from a separate effect test facility, develop a methodology to systematically quantify the uncertainty of the parameters in the TRACE code Uncertainty in physical model parameters are often derived mainly based on expert-judgment and on a particular experimental data
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 53 / 53) Contributions: Demonstration of

    the Methodology on the FEBA TRACE Model 1. Global Sensitivity Analysis 2. Metamodeling 3. Bayesian Calibration How to select important model parameters? How to approximate the input/output of a computer model? How to make the quantification? 27 initial parameters 12 influential parameters • ~ min /run • ~102 MB /run Wide, independent prior uncertainties • ~ s /run • ~102 MB Narrower, correlated posterior uncertainties 4. Uncertainty Propagation Narrower posterior prediction uncertainties for all exp. conditions Wide prior prediction uncertainties How good is the quantified uncertainty?
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    http://www.psi.ch/stars 2018.02.23/STARS/WD41 - ( 54 / 53) Upcoming Challenges Bayesian

    Calibration Uncertainty Propagation 216 214 How to summarize generic correlation structure of the posterior useful for downstream analysis? • Calibration was only done based on one dataset. Error structure might differ • Sequential calibration against other SETF remains open question ACHILLES RBHT 220 222 223 218 FEBA
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    Wir schaffen Wissen – heute für morgen Thank you for

    your attention. My sincere gratitude to: • Prof. A. Pautz • Mr. O. Zerkak • Dr. G. Perret • Mr. Ph. Jacquemoud • Dr. M. Hursin • Dr. D. Rochman • Dr. I. Clifford • Mr. H. Ferroukhi • Other members of STARS The jury members: • Dr. J. Baccou • Prof. R. Houdré • Prof. B. Sudret • Dr. W. Zwermann Additional acknowledgments: •Swiss Federal Nuclear Safety Inspectorate (ENSI) •Swiss Federal Office of Energy (BFE) 1.“Global Sensitivity Analysis of Transient Code Output applied to a Reflood Experiment Model using TRACE Code,” NSE, vol. 184, no. 6, 2016. 2.“Bayesian Calibration of Thermal-Hydraulics Model with Time-Dependent Output,” NUTHOS-11, 2016. 3.“A Methodology for Global Sensitivity Analysis of Transient Code Output applied to Reflood Experiment Model using TRACE,” NURETH-16, 2015. 4.“Sensitivity Analysis of Bottom Reflood Simulation using the Morris Screening Method,” NUTHOS-10, 2014. 5.“Exploring Variability in Reflood Simulation Results: an Application of Functional Data Analysis,” NUTHOS-10, 2014.