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When a nuclear plant closes
Theresa Knickerbocker, the mayor of the village of Buchanan, N.Y., where the Indian Point nuclear power plant is located, is not happy. What has gotten Ms. Knickerbocker’s ire up is the fact that Indian Point’s Unit 2 was closed on April 30, and Unit 3 is scheduled to close in 2021. The village, population 2,300, is about 1.3 square miles total, with the Indian Point site comprising 240 acres along the Hudson River, 30 miles upstream of Manhattan. Unit 2 was a 1,028-MWe pressurized water reactor; Unit 3 is a 1,041-MWe PWR.
The nuclear plant provides the revenue for half of Buchanan’s annual $6-million budget, Knickerbocker told Nuclear News. That’s $3 million in tax revenues each year that eventually will go away. How will that revenue be replaced? Where will the replacement power come from?
Maria Pusa, Jaakko Leppänen
Nuclear Science and Engineering | Volume 175 | Number 3 | November 2013 | Pages 250-258
Technical Paper | dx.doi.org/10.13182/NSE12-52
Articles are hosted by Taylor and Francis Online.
The Chebyshev Rational Approximation Method (CRAM) has recently been introduced by the authors to solve burnup equations, and the results have been excellent. This method has been shown to be capable of simultaneously solving an entire burnup system with thousands of nuclides both accurately and efficiently. The method was prompted by an analysis of the spectral properties of burnup matrices, and it can be characterized as the best rational approximation on the negative real axis. The coefficients of the rational approximation are fixed and have been reported for various approximation orders. In addition to these coefficients, implementing the method requires only a linear solver. This paper describes an efficient method for solving the linear systems associated with the CRAM approximation. The introduced direct method is based on sparse Gaussian elimination, where the sparsity pattern of the resulting upper triangular matrix is determined before the numerical elimination phase. The stability of the proposed Gaussian elimination method is discussed based on consideration of the numerical properties of burnup matrices. Suitable algorithms are presented for computing the symbolic factorization and numerical elimination in order to facilitate the implementation of CRAM and its adoption into routine use. The accuracy and efficiency of the described technique are demonstrated by computing the CRAM approximations for a large test case with 1606 nuclides.
