A keff search and optimization using noisy differentials

A common problem encountered by the criticality engineer is that of establishing worst-case or optimum conditions for a system subject to certain constraints. This is an essential part of criticality analysis, and the engineer needs not only to be able to assess the possible accident conditions, but also to take account of the more expected variations of the parameters that define a system. A keff search and optimization algorithm has been produced for the Monte Carlo criticality computer code MONK6, to assist the engineer in performing such assessments. It is able to solve certain types of criticality assessment problems more efficiently than by alternative methods, as well as being easier to use. An additional benefit is the calculation of statistical uncertainties on the optimum conditions, which are not easy to determine using existing techniques. Since the amount of extra work involved for each additional parameter is slight, the method becomes more and more economical as the number of parameters increases