Improvement of plant reliability based on combination of prediction and inspection of wall thinning due to FAC

Abstract Improvement of plant reliability based on reliability-centered-maintenance (RCM) is going to be undertaken in nuclear power plants (NPPs). RCM is supported by three types of maintenances: risk-based maintenance (RBM); time-based maintenance (TBM); and condition-based maintenance (CBM). RBM is supported by suitable combinations of prediction, inspection and maintenance of elemental defects in stages of their propagations. Especially the combination of prediction and inspection is one of the key issues to promote RBM. As an example of RBM, the fusion of the prediction and inspection related to wall thinning due to flow-accelerated corrosion (FAC) has been introduced. Early prediction of FAC occurrence and its propagation should be confirmed throughout the entire plant systems which should be accomplished by inspections at the target locations followed by timely application of suitable countermeasures such as water chemistry improvements. At the same time, transparency and traceability as well as accuracy are strongly required for the prediction. A one dimensional FAC code based on analysis of materials and water interaction was applied for evaluation of FAC risk as well as FAC occurrence and propagation. The details of FAC mechanism have been reported along with the expected accuracy of prediction to prepare for ensuring their transparency and traceability. Based on prediction results, primary points for inspections will be determined. Unfortunately, it is still difficult to evaluate some key parameters in the plants, e.g., local flow turbulence. From the inspections, accumulated data will be applied to confirm the accuracy of the code, to tune some uncertainties of the key data for prediction, and then, to increase their accuracy. The synergetic effects of prediction and inspection on application of effective and suitable countermeasures are expected. In the paper, the procedures for the combination of prediction and inspection are introduced.