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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.
Haining Zhou, Volkan Seker, Thomas Downar
Nuclear Technology | Volume 206 | Number 6 | June 2020 | Pages 839-861
Technical Paper | doi.org/10.1080/00295450.2020.1746620
Articles are hosted by Taylor and Francis Online.
The paper presents a self-adaptive feature selection algorithm we developed for solving high-dimensional uncertainty quantification problems. The development of the algorithm was motivated and supported by the benchmarking of the Transient Reactor Test (TREAT) transient test 2857. The generalized polynomial chaos expansion scheme was adopted to decompose the response functions. Our algorithm was applied to select the dominant basis from the candidate polynomial basis in a self-adaptive manner by assigning weights to the polynomial basis and adjusting the weights using the least absolute shrinkage and selection operator regularization–estimated coefficients through iterations. The developed algorithm can recognize the significant basis terms in the polynomial expansion of the response functions and therefore build a sparse polynomial expansion using a limited number of samples. The algorithm was implemented and verified through three different TREAT modeling cases. The testing results demonstrated the general stability and prediction performance of our algorithm and provided useful information about the uncertainty mechanism of the TREAT transient test 2857.