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Division Spotlight
Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
Meeting Spotlight
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2025)
February 3–6, 2025
Amelia Island, FL|Omni Amelia Island Resort
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
US, Korea sign MOU for nuclear cooperation
The U.S. departments of Energy and State have signed a memorandum of understanding with the Republic of Korea’s ministries of Trade, Industry and Energy and of Foreign Affairs for the two nations to partner on nuclear exports and cooperation.
Federico Di Rocco, Dan G. Cacuci, Madalina C. Badea
Nuclear Science and Engineering | Volume 185 | Number 3 | March 2017 | Pages 549-603
Technical Paper | doi.org/10.1080/00295639.2017.1279943
Articles are hosted by Taylor and Francis Online.
This paper provides the results of the adjoint sensitivity model developed in the accompanying Part I for a natural draft counter-flow cooling tower. The selected responses are (1) outlet air temperature, (2) outlet water temperature, (3) outlet water mass flow rate, (4) air outlet relative humidity, and (5) air mass flow rate. Explicit expressions for the best-estimate nominal values of the model parameters and responses are also provided, together with the best-estimate reduced standard deviations of the predicted model parameters and responses. The results stemming from this work show that the PM_CMPS procedure reduces the predicted standard deviations of all responses and model parameters.