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Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Proving DRACO will deliver
The United States is now closer than it has been in over five decades to launching the first nuclear thermal rocket into space, thanks to DRACO—the Demonstration Rocket for Agile Cislunar Orbit.
Travis A. Cunning, Paul Chan, Mahesh D. Pandey, Aniket Pant
Nuclear Technology | Volume 187 | Number 3 | September 2014 | Pages 270-281
Technical Paper | Fuel Cycle and Management | doi.org/10.13182/NT13-126
Articles are hosted by Taylor and Francis Online.
This study employs a novel approach to the prediction of CANDU [Canada deuterium uranium (reactor)] fuel reliability. Probability distributions are fitted to actual fuel manufacturing data sets provided by Cameco Fuel Manufacturing. They are used to form input for two industry-standard fuel performance codes: ELESTRES for the steady-state case and ELOCA for the transient case—a hypothesized 80% reactor outlet header break loss-of-coolant accident. Using a Monte Carlo technique for input generation, 105 independent trials are conducted, and probability distributions are fitted to key model output quantities. Comparing model output against recognized industrial acceptance criteria, no fuel failures are predicted for either case. Output distributions are well removed from failure limit values, implying that margin exists in current fuel manufacturing and design. To validate the results and attempt to reduce the simulation burden of the methodology, two dimensional reduction methods are assessed. Using just 36 trials, both methods are able to produce output distributions that agree strongly with those obtained via the brute-force Monte Carlo method, often to a relative discrepancy of <0.3% when predicting the first statistical moment and to a relative discrepancy of <5% when predicting the second statistical moment. In terms of global sensitivity, pellet density proves to have the greatest impact on fuel performance, with an average sensitivity index of 48.93% on key output quantities. Pellet grain size and dish depth are also significant contributors, at 31.53% and 13.46%, respectively. A traditional “limit of operating envelope” case is also evaluated. This case produces output values that exceed the maximum values observed during the 105 Monte Carlo trials for all output quantities of interest. In many cases the difference between the predictions of the statistical methods and the limit method is very prominent, and the highly conservative nature of the deterministic approach is demonstrated.