NDTT, RTNDT, and Fracture Toughness: A Study of Their Interrelationships Using a Large Data Base and Computer Models

Abstract

The nil-ductility transition temperature (NDTT) test has been used extensively in naval ferritic steel applications since World War II. The use of the test results in fracture-safe design has extended into other structural steel industries, most notably those covered by the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code. The construction of nuclear reactor pressure vessels, in accordance with the ASME code, requires material specifications that utilize both the NDTT and Charpy V-notch test data; coupling the NDTT and Charpy V-notch data results in a “reference” temperature (RTNDT), which is either greater than or equal to the NDTT. [The RTNDT is defined as being greater than or equal to the NDTT; at RTNDT plus 33°C (60°F), three Charpy V-notch specimen results must exhibit a minimum of 68 J (50 ft · lb) of absorbed energy and 0.89 mm (35 mil) of lateral expansion. The energy requirement generally governs except for irradiated materials.] Interestingly, some materials generally have an RTNDT based upon the NDTT, while others have an RTNDT consistently greater than the NDTT since the RTNDT is governed by the Charpy V-notch data; (that is, the temperature at 68 J (50 ft · lb) minus 33°C (60°F) is greater than the NDTT). For example, the RTNDT for SA533B-1 plate steel is generally set by the Charpy data, but the RTNDT for SA508-2 forging steel (which is similar to SA533B-1) is typically controlled by the measured NDTT. The science of fracture mechanics has also evolved since World War II, but the matching of transition temperature test results (NDTT, and RTNDT) with actual material fracture toughness (stress intensity) values is still uncertain. However, considerable progress in matching Charpy data with fracture toughness values has been made in recent years through the development of a large material property data base, which includes transition temperature and fracture toughness data. The work described in this paper builds on this research, and uses the same data base to examine the relationship between other transition temperature and toughness parameters. Computer modeling using stochastic procedures was employed to develop probability distributions which, by simulating the measurement of NDTT, clarify the interrelationships between NDTT, RTNDT, and fracture toughness. Predictions by the model of NDTT and RTNDT from dynamic fracture toughness data are in excellent agreement with measured values.