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Strong performances across the board
Craig Piercycpiercy@ans.org
Another year, another stellar performance by America’s nuclear plants. We’ve come to expect high capacity factors, and it’s a credit to the men and women of the profession. They’ve made routine something that was unimaginable not so long ago.
The decadal challenge for the nuclear enterprise now is to maintain this high level of operational excellence for the current fleet, while at the same time ushering in a new generation of technologies at scale. It will be a big job—but one that seems more and more likely with each passing day.
Cihang Lu, Zeyun Wu, Xu Wu
Nuclear Technology | Volume 207 | Number 5 | May 2021 | Pages 692-710
Technical Paper | doi.org/10.1080/00295450.2020.1805259
Articles are hosted by Taylor and Francis Online.
Thermal stratification (TS) is a thermal-fluid phenomenon that can introduce large uncertainties to nuclear reactor safety. The stratified layers caused by TS can lead to temperature oscillations in the reactor core. They can also result in damages to both the reactor vessel and in-vessel components due to the growth of thermal fatigue cracks. More importantly, TS can impede the establishment of natural circulation, which is widely used for passive cooling and ensures the inherent safety of numerous reactor designs. A fast-running one-dimensional (1-D) model was recently developed in our research group to predict the TS phenomenon in pool-type sodium-cooled fast reactors. The efficient 1-D model provided reasonable temperature predictions for the test conditions investigated, but nonnegligible discrepancies between the 1-D predictions and the experimental temperature measurements were observed. These discrepancies are attributed to the model uncertainties (also known as model bias or errors) in the 1-D model and the parameter uncertainties in the input parameters.
In this study, we first recognized through a forward uncertainty analysis that the observed discrepancies between the computational predictions and the experimental temperature measurements could not be explained solely by input uncertainty propagation. We then performed an inverse uncertainty quantification (UQ) study to reduce the model uncertainties of the 1-D model using a modular Bayesian approach based on experimental data. Inverse UQ serves as a data assimilation process to simultaneously minimize the mismatches between the predictions and experimental measurements, while quantifying the associated parameter uncertainties. The solutions of the modular Bayesian approach were in the form of posterior probability density functions, which were explored by rigorous Markov Chain Monte Carlo sampling. Results showed that the quantified parameters obtained from the inverse UQ effectively improved the predictive capability of the 1-D TS model.