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Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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2024 ANS Annual Conference
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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.
Luv Sharma, Tunc Aldemir, Robert Parker
Nuclear Technology | Volume 169 | Number 1 | January 2010 | Pages 18-33
Technical Paper | Reactor Safety | doi.org/10.13182/NT10-A9340
Articles are hosted by Taylor and Francis Online.
In the simulation of nuclear plant behavior through system codes, there are often uncertainties associated with the large number of model parameters required as code inputs. The use of the Taguchi method is investigated for the importance ranking of uncertainties when a single metric is used to characterize system performance. The proposed procedure is illustrated on a simplified boiling water reactor (BWR) model to determine the dominant parameters affecting the maximum limit cycle amplitude (MLCA) in BWRs. A reduced-order BWR model is used for the analysis. A regression model is also generated to predict the MLCA as a function of the parameter values in their assumed uncertainty regions. The results indicate that (a) 7 out of the 11 parameters (factors) under consideration have a significant impact on the MLCA, (b) a linear regression model can be constructed to predict the MLCA with 88% confidence, (c) higher-order effects of the control factors are negligible, and, (d) cross effects between the factors are negligible compared to their individual effects. The results also indicate that the use of the Taguchi method leads to a 99.4% reduction in the computational effort over a full factorial experiment design. The use of the Taguchi method is not proposed to replace the well-established conventional methods for sensitivity and uncertainty analysis but rather to assist them in the selection of the parameters that may require more detailed analysis.