Statistical analysis of ECC bypass data using a nonlinear constrained maximum likelihood estimation technique

Abstract Recently much attention has been focused on the problem of developing models which describe the partial penetration of ECC water into the lower plenum of a pressurized water reactor (PWR) during a postulated loss-of-coolant accident (LOCA). To date model parameters have been estimated using inappropriate classical statistical techniques which assume that only one of the experimental variables is subject to error and that it is functionally related to a set of independent experimental variables which are not subject to error. A nonlinear constrained maximum likelihood estimation (MLE) technique is discussed and illustrated which incorporates several nonstandard features, including errors in all variables, into the estimation procedure. This technique places the statistical analysis of ECC bypass data on a solid theoretical foundation. The technique is illustrated by sample calculations of estimates of the parameters in a modified form of the Wallis flooding correlation.