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Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
2021 ANS Winter Meeting and Technology Expo
November 30–December 3, 2021
Washington, DC|Washington Hilton
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Study: New U.K. nuclear likely to be lower carbon source than solar or wind
A recent study of life cycle carbon emissions at the United Kingdom’s Hinkley Point C nuclear plant finds that the facility, now under construction in Somerset, England, is likely to produce less CO2 over its lifetime than either solar or wind power.
According to the 70-page analysis—prepared by environmental consultancy Ricardo Energy & Environment for NNB Generation Company HPC Limited, the holding company for the Hinkley Point project—lifetime emissions from Hinkley Point C are likely to be about 5.5g CO2e per kWh. That amount also holds for the proposed Sizewell C plant, the study concludes. (The two 1,630-MWe EPRs at Hinkley Point C are currently scheduled to begin commercial operation in 2026 and 2027.)
D. Pun-Quach, P. Sermer, F. M. Hoppe, O. Nainer, B. Phan
Nuclear Technology | Volume 181 | Number 1 | January 2013 | Pages 170-183
Technical Paper | Special Issue on the 14th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-14) / Reactor Safety | dx.doi.org/10.13182/NT13-A15765
Articles are hosted by Taylor and Francis Online.
This paper presents a best estimate plus uncertainty (BEPU) methodology applied to dryout, or critical channel power (CCP), modeling based on a Monte Carlo approach. This method involves the identification of the sources of uncertainty and the development of error models for the characterization and separation of epistemic and aleatory uncertainties associated with the CCP parameter. Furthermore, the proposed method facilitates the use of actual operational data leading to improvements over traditional methods, such as sensitivity analysis, which assume parametric models that may not accurately capture the possible complex statistical structures in the system input and responses.