February 15, 2024
Journal Article

Development and Testing of Coarse-grained Models for Ultrasonic Simulations of Cast Austenitic Stainless Steel

Abstract

Ultrasonic inspection of cast austenitic stainless steel (CASS) in the nuclear industry is particularly challenging because of sound field scatter and attenuation caused by the coarse-grained microstructure. Modeling and simulation are important tools in ultrasonic testing, as they can be used to help address key aspects of inspections, such as developing new probe designs, predicting inspection reliability, and testing phased-array focal laws. However, developing a useful and reliable CASS model is challenging due to the many grain interfaces and crystalline orientations that must be captured. We demonstrate a method of creating a realistic CASS model that is usable in CIVA, a commercially available modeling and simulation software platform. Using polished and chemically etched sections, we generate models of a coarse-grained equiaxed specimen and a columnar specimen. We also test an alternative method of generating a coarse-grained model using Voronoi regions. We qualitatively compare sound field scatter and quantitatively compare sound field attenuation and beam partitioning in simulated sound fields to those of laboratory-measured sound fields. Results show that the Voronoi models perform as well as or better than the models based on actual grain morphology. We also show that model-to-model randomness in Voronoi grain structure can impact the magnitude of a simulated echo response by a factor of two or more. Although CASS models are potentially a good depiction of reality for a given scenario, they should not be considered representative since CASS morphology can change significantly from specimen to specimen or within the same specimen.

Published: February 15, 2024

Citation

Jacob R.E., M.S. Prowant, and C.A. Hutchinson. 2024. Development and Testing of Coarse-grained Models for Ultrasonic Simulations of Cast Austenitic Stainless Steel. Ultrasonics 136. PNNL-SA-184732. doi:10.1016/j.ultras.2023.107157

Research topics