An Artificial-Intelligence and Machine-Learning-Based Methodology to Conduct Seemingly Strain-Controlled Fatigue Test in a Pressurized-Water-Reactor-Test-Loop-Autoclave, While Not Controlling the Strain

Abstract

In general, low cycle fatigue analysis of pressurized water reactor (PWR) components, requires strain-controlled fatigue test data such as using strain versus life (ε–N) curves. Conducting strain-controlled fatigue tests under in-air conditions is not an issue. However, controlling strain in a PWR-test-loop-autoclave is a challenge, since an extensometer cannot be placed in a narrow autoclave (typically used in a high-temperature-pressure PWR-test-loop). This is due to lack of space inside an autoclave that houses the test specimen. In addition, installing a contact-type extensometer in the path of a high-pressure flow can be a challenge. These difficulties of using an extensometer inside an autoclave led us to use an outside-autoclave displacement sensor which measures the displacement of pull-rod-specimen assembly. However, in our study (based on in-air fatigue test data), we found that a pull-rod-controlled based fatigue test can lead to substantial cyclic hardening/softening resulting in substantially different cyclic strain amplitudes and their rates compared to the desired cyclic strain amplitudes and its rates. In this paper, we propose an Artificial-Intelligence and Machine-Learning based technique such as using k-means clustering technique to improve the pull-rod-control based fatigue test method, such that the gage-area strain amplitude and rates can reasonably be achieved. In support of this, we present the fatigue test results for both 316 SS base and 81/182 dissimilar-metal-weld specimens.