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The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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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.
Iván Lux and Zoltán Szatmáry
Nuclear Science and Engineering | Volume 89 | Number 2 | February 1985 | Pages 137-149
Technical Paper | doi.org/10.13182/NSE85-A18188
Articles are hosted by Taylor and Francis Online.
Given a number of independent realizations of the k-dimensional random variable x = (x1, x2,…, xk), the components of which may be correlated or independent, each has the same marginal expectation. The question is how the componentwise averages over the realizations are combined to yield an unbiased nearly optimum estimate of the common mean, and how the variance of the mean is to be estimated. An answer is given for the extreme cases of a small number of realizations and of rare events, when the majority of realizations is meaningless and only a small fraction of the samples contributes effectively to the estimate. It is shown how the sample statistics, based on the maximum likelihood estimates, are corrected to yield unbiased estimates. The results can readily be applied in Monte Carlo calculations and in evaluations of experimental data.