Abstract
In the framework of Best Estimate Plus Uncertainty methodology, the uncertainties involved in model predictions must be quantified to prove that the investigated design is reasonable and acceptable. The uncertainties in predictions are usually calculated by propagating input uncertainties through the simulation model, which requires knowledge of the model or code input uncertainties, for example, the means, variances, distribution types, etc. However, in best-estimate system thermal-hydraulic codes such as TRACE, some parameters in empirical correlations may have large uncertainties that are unknown to code users, and their uncertainties are therefore simply ignored or described by expert opinion. In this paper, the issue of missing uncertainty information for physical model parameters in the thermal-hydraulic code TRACE is addressed with inverse uncertainty quantification (IUQ), using the steady-state void fraction experimental data in the Organisation for Economic Co-operation and Development/Nuclear Energy Agency PSBT (Pressurized water reactor Sub-channel and Bundle Tests benchmark. The IUQ process is formulated through a Bayesian perspective, which can yield the posterior distributions of the uncertain inputs. A Gaussian process emulator is employed to significantly reduce the computational burden involved in sampling the posteriors using the Markov Chain Monte Carlo method. The posterior distributions are further used in forward uncertainty quantification and sensitivity analysis to quantify the influences of those parameters on the quantities of interest. The results demonstrate the effectiveness of the IUQ framework with a practical nuclear engineering example and show the necessity of quantifying and reducing uncertainty of physical model parameters in future work.
| Original language | English (US) |
|---|---|
| Journal | Nuclear Science and Engineering |
| DOIs | |
| State | Accepted/In press - Jan 1 2018 |
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Keywords
- Gaussian process
- Inverse uncertainty quantification
- physical model parameter uncertainty
- PSBT benchmark
ASJC Scopus subject areas
- Nuclear Energy and Engineering
Cite this
Gaussian Process–Based Inverse Uncertainty Quantification for TRACE Physical Model Parameters Using Steady-State PSBT Benchmark. / Wang, Chen; Wu, Xu; Kozlowski, Tomasz.
In: Nuclear Science and Engineering, 01.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Gaussian Process–Based Inverse Uncertainty Quantification for TRACE Physical Model Parameters Using Steady-State PSBT Benchmark
AU - Wang, Chen
AU - Wu, Xu
AU - Kozlowski, Tomasz
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In the framework of Best Estimate Plus Uncertainty methodology, the uncertainties involved in model predictions must be quantified to prove that the investigated design is reasonable and acceptable. The uncertainties in predictions are usually calculated by propagating input uncertainties through the simulation model, which requires knowledge of the model or code input uncertainties, for example, the means, variances, distribution types, etc. However, in best-estimate system thermal-hydraulic codes such as TRACE, some parameters in empirical correlations may have large uncertainties that are unknown to code users, and their uncertainties are therefore simply ignored or described by expert opinion. In this paper, the issue of missing uncertainty information for physical model parameters in the thermal-hydraulic code TRACE is addressed with inverse uncertainty quantification (IUQ), using the steady-state void fraction experimental data in the Organisation for Economic Co-operation and Development/Nuclear Energy Agency PSBT (Pressurized water reactor Sub-channel and Bundle Tests benchmark. The IUQ process is formulated through a Bayesian perspective, which can yield the posterior distributions of the uncertain inputs. A Gaussian process emulator is employed to significantly reduce the computational burden involved in sampling the posteriors using the Markov Chain Monte Carlo method. The posterior distributions are further used in forward uncertainty quantification and sensitivity analysis to quantify the influences of those parameters on the quantities of interest. The results demonstrate the effectiveness of the IUQ framework with a practical nuclear engineering example and show the necessity of quantifying and reducing uncertainty of physical model parameters in future work.
AB - In the framework of Best Estimate Plus Uncertainty methodology, the uncertainties involved in model predictions must be quantified to prove that the investigated design is reasonable and acceptable. The uncertainties in predictions are usually calculated by propagating input uncertainties through the simulation model, which requires knowledge of the model or code input uncertainties, for example, the means, variances, distribution types, etc. However, in best-estimate system thermal-hydraulic codes such as TRACE, some parameters in empirical correlations may have large uncertainties that are unknown to code users, and their uncertainties are therefore simply ignored or described by expert opinion. In this paper, the issue of missing uncertainty information for physical model parameters in the thermal-hydraulic code TRACE is addressed with inverse uncertainty quantification (IUQ), using the steady-state void fraction experimental data in the Organisation for Economic Co-operation and Development/Nuclear Energy Agency PSBT (Pressurized water reactor Sub-channel and Bundle Tests benchmark. The IUQ process is formulated through a Bayesian perspective, which can yield the posterior distributions of the uncertain inputs. A Gaussian process emulator is employed to significantly reduce the computational burden involved in sampling the posteriors using the Markov Chain Monte Carlo method. The posterior distributions are further used in forward uncertainty quantification and sensitivity analysis to quantify the influences of those parameters on the quantities of interest. The results demonstrate the effectiveness of the IUQ framework with a practical nuclear engineering example and show the necessity of quantifying and reducing uncertainty of physical model parameters in future work.
KW - Gaussian process
KW - Inverse uncertainty quantification
KW - physical model parameter uncertainty
KW - PSBT benchmark
UR - http://www.scopus.com/inward/record.url?scp=85051918828&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051918828&partnerID=8YFLogxK
U2 - 10.1080/00295639.2018.1499279
DO - 10.1080/00295639.2018.1499279
M3 - Article
AN - SCOPUS:85051918828
JO - Nuclear Science and Engineering
JF - Nuclear Science and Engineering
SN - 0029-5639
ER -