- LATIN HYPERCUBE SAMPLING UNCERTAINTY ANALYSIS UPDATE
- LATIN HYPERCUBE SAMPLING UNCERTAINTY ANALYSIS SOFTWARE
LATIN HYPERCUBE SAMPLING UNCERTAINTY ANALYSIS SOFTWARE
In this paper we couple the BISON fuel performance code to the DAKOTA uncertainty analysis software to analyze a representative fuel performance problem. In addition, sensitivity analyses can be performed to determine which input parameters have the greatest influence on the outputs. Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in the more » inputs. However, there are associated uncertainties in the input parameters and correlations used to determine the behavior of the fuel and cladding under irradiation. Traditionally, best-estimate results are presented using the correlations with no quantification of the uncertainty in the output metrics of interest. = ,īest-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions. The results highlight the importance of quantifying the uncertainty and sensitivity in fuel performance modeling predictions and the need for additional research into improving the material models that are currently available. The output metrics investigated in this study are the fuel centerline temperature, cladding surface temperature, fission gas released, and fuel rod diameter. Specifically, we demonstrate the use of sampling methods, polynomial chaos expansions, surrogate models, and variance-based decomposition. Utilizing DAKOTA, a variety of statistical analysis techniques are applied to quantify the uncertainty and sensitivity of the output metrics of interest. The input parameters uncertainties are broken into three separate categories including boundary condition uncertainties (e.g., power, coolant flow rate), manufacturing uncertainties (e.g., pellet diameter, cladding more » thickness), and model uncertainties (e.g., fuel thermal conductivity, fuel swelling). The rodlet is representative of a BWR fuel rod. The case studied in this paper is based upon rod 1 from the IFA-432 integral experiment performed at the Halden Reactor in Norway. Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in the inputs. # 5.Best-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions. do.call allows to call function with names as list Rhv <- function(b, Bm, Bh, eta, teta, c, gammaH, gammaHl, gammav, muv, Normally after that I was running: # 3.create the function I know that Pomp package has been updated.Įrror in sobol(vars = list(b = b, Bm = Bm, Bh = Bh, eta = eta, teta =
I now got this message, while it was working perfectly a year ago. GammaH = gammaH, gammaHl = gammaHl, gammav = gammav,
Scalars1 <- sobol(vars=list(b = b, Bm = Bm, Bh = Bh,Įta = eta, teta = teta, c = c, muv = muv,
# 2.do the Latin hypercube sampling method to generate 100 000samples
LATIN HYPERCUBE SAMPLING UNCERTAINTY ANALYSIS UPDATE
Now that I try to update this work, it doesn't work anymore. I combined uncertainty analysis through Latin hypercube sampling (LHS) with the sensitivity analysis through robust Partial rank correlation coefficient (PRCC) method. The aim was to quantify the impact of the variation of each parameter used on the outcome variable (Rhv). I did an uncertainty analysis a year ago using Pomp package.
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