Creep strain prediction in power plant material via ANN modeling of nonlinear ultrasonic test results

Abstract

Reliable and accurate prediction of the creep life of power plant components is crucial for both economic and safety reasons. Existing prediction models, based on creep test data, can be complex and time-consuming. Nonlinear ultrasonic (NLU) is a widely accepted non-destructive testing (NDT) technique for evaluating damage progression in crept specimens. The information from NLU measurements alone is insufficient to forecast the life of any component. In real-time applications, intelligent NDT protocols are needed to enable fast and accurate life prediction of such components. A methodology for creep life prediction using artificial neural networks (ANN) has been introduced based on NLU test results of crept P92 steel specimens. The technique involved creep tests of P92 specimens exposed to a temperature of 625SUPERSCRIPT ZEROC with applied stress ranging from 120MPa to 160MPa, NLU measurements at each step load, and prediction of creep life of the material with an ANN trained with creep strain and NLU test data. The technique involves prediction from previously generated historical data, thus saving both cost and time of conducting continuous experiments. This approach for ANN modeling of NLU data can be considered a reliable, time-saving, and effective technique for assessing creep damage progression in power plant components.