Engineering topics

Listwan, Joseph T.

Publications and source records attributed to Listwan, Joseph T..

2 records

DOE OSTI2021

Location-Dependent Mechanical Property Evaluation on Additively Manufacture Materials

This report summarizes research activities conducted at Argonne National Laboratory in support of the development, qualification and certification of additively manufactured (AM) metallic components to allow for innovative reactor design and licensing for the Transformational Challenge Reactor (TCR). Focus is on the evaluation of high temperature mechanical properties including creep and fatigue of 316L stainless steel manufactured by the laser powder bed fusion process. Creep behavior of AM 316L stainless steel was evaluated for rods printed by either or a combination of both lasers of the dual-laser system of a Concept Laser-M2 printer to examine the consistency across the build plate. Creep tests were conducted at temperatures of 550, 575, 600, and 650°C and stresses between 175 and 300 MPa using ASTM standard-sized specimens. The effect of heat treatments at different temperatures on creep properties of AM 316L SS was examined to understand the processing-microstructure-property relationship. Fatigue properties of AM 316L SS were investigated for two print geometries (rod and plate) and in two build orientations of printed plates. Locations of specimens in the build were carefully tracked such that the testing data can be directly related to location-specific in situ data to establish links between printing process, post-printing treatment, microstructure and mechanical properties. The work is to support the development of a digital platform informed approach to AM component qualification and certification for nuclear applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

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

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.

42 ENGINEERING↗