The fatigue assessment of welded joints requires several input data, which can be subdivided into three categories: geometry, material and loading. The number of input data depends essentially on the complexity of the models employed and on the level of accuracy of the analysis. It is common practice to use safety factors in design to account for the scatter of the input parameters. Nevertheless, overly-conservative factors lead often to unrealistic estimations of fatigue life. This work presents a fracture mechanics-based model for the structural integrity assessment of welded joints under constant amplitude fatigue loading, in which the local geometry at the weld toe and the fatigue crack growth properties are considered statistically distributed. The approach is validated against a large number of experimental data.
EXTENDED ABSTRACT

The National Aeronautics and Space Administration (NASA) operates approximately 300 aging, carbon steel, layered pressure vessels (LPVs) that were designed and manufactured prior to ASME Boiler and Pressure Vessel (B&PV) code requirements. Fitness-for-service assessments and traditional evaluations of these non-code vessels is a challenge due to unique uncertainties that are not present in code vessels, such as missing construction records and the use of proprietary materials in construction. Furthermore, many of the steels used in these non-code vessels are at a risk of cleavage fracture at low temperatures within the operating temperature ranges of the NASA sites where these vessels are installed. Additionally, the stress state in critical regions of the LPVs, such as the longitudinal seam welds and circumferential welds, is uncertain due to weld residual stresses (WRS), geometric discontinuities, and stress concentrations in weld connections. In order to guide non-destructive evaluation (NDE) and assessment of the circumferential welds and account for uncertainties in these non-code LPVs, probabilistic critical initial flaw size (CIFS) and critical crack size (CCS) analyses were performed for eleven locations of interest within the head-to-shell and shell-to-shell circumferential welds of three demonstration LPVs.
EXTENDED ABSTRACT

The USAF Airworthiness Bulletin Risk Identification and Acceptance for Airworthiness Determination defines airworthiness in terms of the probability of aircraft loss per flight hour1 with one important component being the aircraft structure. The probability-of-failure of an aircraft component is challenging to compute due to its small size, typically 10-7 or less. As a result, simplified fracture mechanics models are usually used with a small number of random variables. However, these simplifications may lead to an inaccurate probability-of-failure estimate. To address this issue, an adaptive multiple importance sample method was developed that can compute very low probabilities with significant efficiency. This allows one to consider more realistic fracture mechanics models and a larger number of random variables than has been previously possible. The method is adaptive in that it will adjust to the varying relative importance of the random variables for different applications. Convergence is ensured such that the coefficient of variation is below a user-defined threshold. Results to date show efficiency gains of 5 or 6 orders of magnitude over standard Monte Carlo sampling for typical problems of interest. The methodology will be outlined and demonstrated using aircraft example problems.
EXTENDED ABSTRACT

We present probabilistic fracture mechanics methodologies and applications from an energy industry perspective. Topics include probabilistic fracture mechanics for heavy-duty rotating equipment, including gas turbine rotor disks, probabilistic modeling of forging flaw crack nucleation, modeling of non-destructive inspection capabilities, and probabilistic crack propagation from low-cycle fatigue-initiated cracks. We will present relevant new design and service applications in which reliable risk quantification and minimization are paramount. We will also illustrate how the developed Monte Carlo scheme harnesses the power of high-performance computing, including Graphics Processing Unit (GPU) utilization, to enable a fast computational turn-around time for the millions of individual fatigue crack growth calculations needed to resolve the low-risk requirements.
The presented methods pave the way for a fast and reliable robust risk quantification of power plant components and systems, including probabilistic digital twins, and support power plants’ efficient, reliable operation. These methods are essential for the energy transition, including intermittent renewable energy sources such as wind turbines and photovoltaic systems, where the start-up flexibility of gas turbines is a crucial requirement.
EXTENDED ABSTRACT