Optimization of System reliability target. Reliability allocation in NPPs based on the genetic algorithm

Reliability allocation is an optimization process of minimizing the total plant costs subject to the overall plant safety goal constraints. Reliability allocation has been applied to determine the reliability characteristics of reactor systems, subsystems, major components and plant procedures that are consistent with a set of top level performance goals: the core melt frequency, acute fatalities and latent fatalities. Reliability allocation ban be performed to improve the design, operation and safety of new and/or existing nuclear plants. However, most approaches have some limitations in satisfying all optimization objectives. Most optimization techniques used in the previous approaches are based on conventional optimization techniques that can not guarantee the global optimum for nonlinear multi-objective problems like reliability allocation. Recently, it has become known that the genetic algorithm is one of the most powerful tools for many optimization problems. The genetic algorithm can provide a global optimal solution with the proper choice of related parameters. It also enables us to provide various constraints and to define complex objective functions. Hence, in this report, the genetic algorithm is applied to the reliability allocation process of NPPs. One of the main problems of reliability allocation is defining realistic objective functions. So, in order to optimize the reliability of the system, the cost for improving and/or degrading the reliability of the system should be included in the reliability allocation process. We used techniques derived from the Value Impact Analysis to define the realistic objective in this report, we optimize the reliability targets of systems when the top level performance goals are set. In the case that the top level performance goals are not fixed, we optimize the reliability targets of systems and the top level performance goals altogether. (Author). 15 refs., 6 tabs., 12 figs