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Nuclear Installations Safety
Devoted specifically to the safety of nuclear installations and the health and safety of the public, this division seeks a better understanding of the role of safety in the design, construction and operation of nuclear installation facilities. The division also promotes engineering and scientific technology advancement associated with the safety of such facilities.
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2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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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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New laws offer nuclear industry incentives for existing power plant uprates
This year, the U.S. nuclear industry received a much-needed economic boost that could help preserve operating nuclear power plants and incentivize upgrades that extend their lifespan and power output.
Signed into law in 2022, the Inflation Reduction Act offers production tax credits (PTCs) for existing nuclear power plants and either PTCs or investment tax credits (ITCs) for new carbon-free generation. These credits could make power uprates—increasing the maximum power level at which a commercial plant may operate—a much more appealing option for utilities.
A. Hoefer, G. Dirksen, J. Eyink, E.-M. Pauli
Nuclear Science and Engineering | Volume 166 | Number 3 | November 2010 | Pages 202-217
Technical Paper | doi.org/10.13182/NSE10-09
Articles are hosted by Taylor and Francis Online.
In a level-2 probabilistic safety analysis (PSA), two types of uncertainty have to be taken into account: the uncertainty related to random variation (variability) and the uncertainty related to limited knowledge (ignorance). We present a consistent treatment of these two types of uncertainty within a Bayesian framework. This framework allows us to translate both types of uncertainty in the basic parameters into branch probability distributions of the PSA accident progression event tree (APET). This, in turn, results in probability distributions for the different release categories. A generic Monte Carlo algorithm for drawing random samples from branch probability distributions is presented, offering the possibility to directly include information in terms of empirical data. To provide an illustrative example, the developed methods are applied to a specific APET question, related to the temperature-induced rupture of the reactor coolant system in case of a high pressure accident scenario. Although this paper addresses level-2 PSA, the proposed framework is presented in a general form to be applicable to other PSA problems.