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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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NECX debut: Shaping the next era of energy
The sold-out inaugural Nuclear Energy Conference & Expo (NECX) got off to a roaring start in Atlanta, Ga., Tuesday morning with an opening plenary that was a live highlight reel discussing the latest industry achievements.
Starting with a lively promo video that left the audience amped up for Entergy’s CEO and NEI chair Drew Marsh, who welcomed everyone to the event, hosted jointly by the American Nuclear Society and the Nuclear Energy Institute. He spoke to a full house of more than 1,300 attendees, promising a blend of science, technology, policy, and advocacy centered around the future of nuclear energy.
Cihang Lu, Zeyun Wu
Nuclear Technology | Volume 208 | Number 1 | January 2022 | Pages 37-48
Technical Paper | doi.org/10.1080/00295450.2021.1874779
Articles are hosted by Taylor and Francis Online.
A one-dimensional (1-D) thermal stratification (TS) model was recently developed in our research group to predict the TS phenomenon in pool-type sodium-cooled fast reactors. This paper performs uncertainty quantification (UQ) of the 1-D TS model to evaluate its performance by considering the aleatoric uncertainties that existed in the model parameters and to identify the plausible sources of the epistemic uncertainties. The Latin hypercube sampling–Monte Carlo method (LHS-MC), which is elaborated with an example in this paper to facilitate its understanding and implementation, is used for the UQ process. The advantages of LHS-MC, including both better stability and better accuracy than the conventional random sampling–Monte Carlo method with fewer realizations, are demonstrated in this paper.
In total, 648 temperature measurements acquired from nine experimental transients performed in a university-scale Thermal Stratification Experimental Facility are used to evaluate the performance of the computational 1-D TS model. The UQ result shows that 77.5% of the experimental data can be predicted by the 1-D TS model within uncertainty ranges, which indicates the good performance of the computational model when the aleatoric uncertainties are correctly captured. The rest 22.5% of the experimental data are found located outside of the uncertainty ranges, which reveals the existence of the epistemic uncertainties caused by the lack of understanding of the TS phenomenon and defects in the 1-D model. The simple jet model currently employed by the 1-D TS model is thought to be one of the attributors to these defects.