Over the last thirty years, much research has been done on the development and application of failure event databases, NDE databases, and materials property databases for pressure vessels and piping, as reported in two recent symposia: (1) ASME 2007 PVP Symposium (in honor of the late Dr. Spencer Bush), San Antonio, Texas, on “Engineering Safety, Applied Mechanics, and Nondestructive Evaluation (NDE).” (2) ASME 2008 PVP Symposium, Chicago, Illinois, on “Failure Prevention via Robust Design and Continuous NDE Monitoring.” The two symposia concluded that those three types of databases, if properly documented and maintained on a worldwide basis, could hold the key to the continued safe and reliable operation of numerous aging structures including nuclear power or petro-chemical processing plants. During the 2008 symposium, four uncertainty categories associated with causing uncertainty in fatigue life estimates were identified, namely, (1) Uncertainty-1 in failure event databases, (2) Uncertainty-2 in NDE databases, (3) Uncertainty-3 in materials property databases, and (4) Uncertainty-M in crack-growth and damage modeling. In this paper, which is one of a series of four to address all those four uncertainty categories, we address Uncertainty-3 in materials property databases by developing a Dataplot-Python-ANLAP (DPA) plug-in, which automates the uncertainty estimation algorithms of material property test data such that those data can be combined with field NDE data by office engineers to speed up the process of probabilistic damage assessment and remaining life estimation. To illustrate this approach, we describe an example application where several mechanical property data sets of a U.S.-made low-carbon steel (A36) and a proprietary high-strength steel (Class 590 MPa) from Japan, are first computed with uncertainty estimates, and then compared with a traditional calculation without uncertainty for deterministic modeling. Significance of the development of computer plug-ins to facilitate data mining of materials property databases and to assist risk-informed analysis is discussed.
- Pressure Vessels and Piping
Dataplot- Python- Anlap ( DPA) Plug-In for High Temperature Mechanical Property Databases to Facilitate Stochastic Modeling of Fire-Structure Interactions
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Fong, JT, & Marcal, PV. "A Dataplot-Python-Anlap (DPA) Plug-In for High Temperature Mechanical Property Databases to Facilitate Stochastic Modeling of Fire-Structure Interactions." Proceedings of the ASME 2009 Pressure Vessels and Piping Conference. Volume 6: Materials and Fabrication, Parts A and B. Prague, Czech Republic. July 26–30, 2009. pp. 1573-1601. ASME. https://doi.org/10.1115/PVP2009-77867
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