By C. R. Weisbin (auth.), Jeffery Lewins, Martin Becker (eds.)
Read or Download Advances in Nuclear Science and Technology: Volume 14 Sensitivity and Uncertainty Analysis of Reactor Performance Parameters PDF
Best nuclear books
The advance of nuclear guns in the course of the ny undertaking is likely one of the most vital clinical occasions of the 20 th century. This publication, ready by way of a proficient instructor of physics, explores the demanding situations that confronted the contributors of the long island undertaking. In doing so it supplies a transparent advent to fission guns on the point of an upper-level undergraduate physics pupil.
- Safe Decommissioning for Nuclear Activities : proceedings of an International Conference on Safe Decommissioning for Nuclear Activities, held in Berlin, 14-18 October 2002
- Perspectives on Nuclear Medicine for Molecular Diagnosis and Integrated Therapy
- Nuclear Reactor Thermal-Hydraulics Vol 2 [7th Intl meeting]
- Nuclear Physics (Addison-Wesley Series in Nuclear Science and Engineering)
Additional resources for Advances in Nuclear Science and Technology: Volume 14 Sensitivity and Uncertainty Analysis of Reactor Performance Parameters
Again, most existing fitting codes do not permit use of non-diagonal weighting matrices, so the resulting inverse least-squares matrix is not generally a good approximation to the parameter uncertainty matrix (Equation 28 holds only if V is valid) . 41 UNCERTAINTY IN NUCLEAR DATA In curve or physical model fitting the A. elements give the sensitivity of the model output valu~alr at the ith value of some independent variable, to the parameter value b a . The equation (27) is much more general, and applies to any set of observations of quantities that can be represented by a sequence of linear equations in terms of a lesser number of parameters.
Note that the problem chosen was explicitly linear, that the minimum variance solution was boldly assumed to be the one desired, and that no assumptions at all were made concerning the shapes of the density functions for Yl and Y2 • b. A General Least-Squares Formulation. ) be considered, and let the associated variance-covari~nce matrix be (Vij). Suppose the physical quantities Yi that underlie the observations can be represented in terms of the parameter vector (b ) through the (possibly implicit) relation a G[ y (b) i b ] = o.
A la ob a + • • . (24) R. W. •. , (b na ) is to be sought. In this equation, the Yoi are the values of Yi computed from Equation (23), using the estimated parameter vector (boa)' The matrix (Aia) of derivatives is called the sensitivity or sometimes the design matrix, and initially is calculated at (boa)' The iterated solution is to be obtained by minia weighted quadratic form of the vector of residuals corresponding to the second approximation above. m~z~ng (25) Using the linearization of Equation (24) permits Equation (25) to be cast in terms of parameter increments (S ) _ - a (cb a ) and the reduced observation vector (~i) = (y.