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September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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ANS joins others in seeking to discuss SNF/HLW impasse
The American Nuclear Society joined seven other organizations to send a letter to Energy Secretary Christopher Wright on July 8, asking to meet with him to discuss “the restoration of a highly functioning program to meet DOE’s legal responsibility to manage and dispose of the nation’s commercial and legacy defense spent nuclear fuel (SNF) and high-level radioactive waste (HLW).”
Alessandro Petruzzi, Dan G. Cacuci, Francesco D'Auria
Nuclear Science and Engineering | Volume 165 | Number 1 | May 2010 | Pages 45-100
Technical Paper | doi.org/10.13182/NSE09-37C
Articles are hosted by Taylor and Francis Online.
This work presents a paradigm application of a new methodology for simultaneously calibrating (adjusting) model parameters and responses, through assimilation of experimental data, to the benchmark transient thermal-hydraulic experiment IC1, performed at London's Imperial College. Following the description of the experimental setup, the corresponding mathematical model is developed and solved numerically. The sensitivities of typically important responses (e.g., temperatures, pressures) to model parameters are computed by applying both the forward and the adjoint sensitivity analysis procedures. These sensitivities not only identify the most important model parameters but also propagate, within the data assimilation procedure, parameter uncertainties for obtaining predictive best-estimate quantities, with reduced best-estimate uncertainties (i.e., “smaller” values for the variance-covariance matrices). This assimilation procedure also provides a quantitative indication of the degree of agreement between computations and experiments. In particular, the paradigm application presented in this work indicates the path for validating and calibrating thermal-hydraulic computational models used for reactor safety analyses. The concluding remarks highlight several important open issues, the resolution of which would significantly advance the area of predictive best-estimate modeling, while opening new avenues for applications in nuclear reactor engineering and safety.