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- 저자 : Dean Price (검색결과 3 건)
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Dean Price,Andrew Maile,Joshua Peterson-Droogh,Derreck Blight 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.3
Large-scale reactor simulation often requires the use of Monte Carlo calculation techniques to estimateimportant reactor parameters. One drawback of these Monte Carlo calculation techniques is they inevitablyresult in some uncertainty in calculated quantities. The present study includes parametric uncertaintyquantification (UQ) and sensitivity analysis (SA) on the Advanced Test Reactor Critical (ATRC)facility housed at Idaho National Laboratory (INL) and addresses some complications due to Monte Carlouncertainty when performing these analyses. This approach for UQ/SA includes consideration of MonteCarlo code uncertainty in computed sensitivities, consideration of uncertainty from directly measuredparameters and a comparison of results obtained from brute-force Monte Carlo UQ versus UQ obtainedfrom a surrogate model. These methodologies are applied to the uncertainty and sensitivity of keff for twosets of uncertain parameters involving fuel plate geometry and fuel plate composition. Results indicate that the less computationally-expensive method for uncertainty quantification involvinga linear surrogate model provides accurate estimations for keff uncertainty and the Monte Carlo uncertaintyin calculated keff values can have a large effect on computed linear model parameters for parameterswith low influence on keff.
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Performing linear regression with responses calculated using Monte Carlo transport codes
Dean Price,Brendan Kochunas 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.5
In many of the complex systems modeled in the field of nuclear engineering, it is often useful to uselinear regression-based analyses to analyze relationships between model parameters and responses ofinterests. In cases where the response of interest is calculated by a simulation which uses Monte Carlomethods, there will be some uncertainty in the responses. Further, the reduction of this uncertaintyincreases the time necessary to run each calculation. This paper presents some discussion on how theMonte Carlo error in the response of interest influences the error in computed linear regression coefficients. A mathematical justification is given that shows that when performing linear regression inthese scenarios, the error in regression coefficients can be largely independent of the Monte Carlo errorin each individual calculation. This condition is only true if the total number of calculations are scaled tohave a constant total time, or amount of work, for all calculations. An application with a simple pin cellmodel is used to demonstrate these observations in a practical problem.
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Radaideh, Majdi I.,Price, Dean,Kozlowski, Tomasz Korean Nuclear Society 2020 Nuclear Engineering and Technology Vol.52 No.6
This work presents two different methods for quantifying and propagating the uncertainty associated with fuel composition at end of life for cask criticality calculations. The first approach, the computational approach uses parametric uncertainty including those associated with nuclear data, fuel geometry, material composition, and plant operation to perform forward depletion on Monte-Carlo sampled inputs. These uncertainties are based on experimental and prior experience in criticality safety. The second approach, the data-driven approach relies on using radiochemcial assay data to derive code bias information. The code bias data is used to perturb the isotopic inventory in the data-driven approach. For both approaches, the uncertainty in k<sub>eff</sub> for the cask is propagated by performing forward criticality calculations on sampled inputs using the distributions obtained from each approach. It is found that the data driven approach yielded a higher uncertainty than the computational approach by about 500 pcm. An exploration is also done to see if considering correlation between isotopes at end of life affects k<sub>eff</sub> uncertainty, and the results demonstrate an effect of about 100 pcm.
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