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Nuclear Data Statistical Treatment

[+] Author Affiliations
Mohammad Alrwashdeh, Jian Kai Yu, Aniseh Abdalla, Kan Wang

Tsinghua University, Beijing, China

Paper No. ICONE21-16790, pp. V005T11A020; 3 pages
  • 2013 21st International Conference on Nuclear Engineering
  • Volume 5: Fuel Cycle, Radioactive Waste Management and Decommissioning; Reactor Physics and Transport Theory; Nuclear Education, Public Acceptance and Related Issues; Instrumentation and Controls; Fusion Engineering
  • Chengdu, China, July 29–August 2, 2013
  • Conference Sponsors: Nuclear Engineering Division
  • ISBN: 978-0-7918-5582-9
  • Copyright © 2013 by ASME


Several methods have been developed in order to evaluate the best fit for nuclear data parameters, these methods relies on sequence logical steps should be followed to get accurate and reliable results, it subdivided into: (1) the physical model have been used (2) data types (3) statistical methods (4) problems. This paper will discuss the statistical methods used to evaluate the best fit for the nuclear data.

The difficulty in finding a real and reasonable solution to the fitting of data can be made easier by choosing the right fitting method. Different methods will converge differently depending on several parameters like the correct choose of the fitting function and number of fitting parameters.

Here we will discuss the uses of two methods, first is Differential evaluation for nonlinear data to optimize a problem by iterative to improve the solution with regard to the quality.

The second one is nonlinear regression method, this method used a model function is not linear in the parameters, and to estimate the relationships among variables, by capturing the trend in the data by assigning a single function in order to minimize the sum of residuals (Or Chi square) with respect to a set of parameters a = {A1, A2,…..An}.

Copyright © 2013 by ASME
Topics: Fittings



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