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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Vogtle-3 shuts down for valve issue
One of the new Vogtle units in Georgia was shut down unexpectedly on Monday last week for a valve issue that has since been investigated and repaired. According to multiple local news outlets, Georgia Power reported on July 17 that Unit 3 was back in service.
Southern Company spokesperson Jacob Hawkins confirmed that Vogtle-3 went off line at 9:25 p.m. local time on July 8 “due to lowering water levels in the steam generators caused by a valve issue on one of the three main feedwater pumps.”
Yang Liu, Shanbin Shi, Yalan Qian, Xiaodong Sun (Univ of Michigan), Nam Dinh (NCSU)
Proceedings | Advances in Thermal Hydraulics 2018 | Orlando, FL, November 11-15, 2018 | Pages 1028-1040
Multiphase computational fluid dynamics (MCFD) is a promising tool to predict fully turbulent gas-liquid two-phase flows with high resolution. As a complex model, extensive validation and uncertainty quantification are required for an M-CFD solver before it can be trusted for large-scale industrial applications. In this paper, the inverse uncertainty quantification based on Bayesian inference is performed to quantify the uncertainty of the turbulence model in STAR-CCM+. As an inverse approach, the Bayesian approach requires experimental measurements to conduct the inference. In this work, high-resolution turbulence data measured by particle image velocimetry are used. The turbulence model with standard wall function and bubble-induced turbulence is considered. Supported by the PIV data, the uncertainties of the coefficients in the model are quantified, based on which the uncertainties of the solver predictions are evaluated. The Bayesian inference is conducted with the Markov Chain Monte Carlo (MCMC) method, based on a surrogate model constructed with Gaussian Process. It is found that the uncertainty of the turbulent kinetic energy is consistent with the measured data. However, it is also found that the liquid velocity is overestimated in the bulk flow region and underestimated in the near wall flow region compared to the measurement data. Such moderate discrepancies between the solver predictions and measurements require a more comprehensive evaluation that takes all relevant closure relations into consideration.