An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples, volume 1

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented

Optimising non-destructive examination of newbuilding ship hull structures by developing a data-centric risk and reliability framework based on fracture mechanics

This thesis was previously held under moratorium from 18/11/19 to 18/11/21Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure.Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure

Measurement, Data Interpretation, and Uncertainty Propagation for Fatigue Assessments of Structures

Real behavior of existing structures is usually associated with large uncertainty that is often covered by the use of conservative models and code practices for the evaluation of remaining fatigue lives. In order to make better decisions related to retrofit and replacement of existing bridges, new techniques that are able to quantify fatigue reserve capacity are required. This paper presents a population-based prognosis methodology that takes advantage of in-service behavior measurements using model-based data interpretation. This approach is combined with advanced traffic and fatigue models to refine remaining-fatigue-life predictions. The study of a full-scale bridge demonstrates that this methodology provides less conservative estimations of remaining fatigue lives. In addition, this approach propagates uncertainties associated with finite-element, traffic and fatigue-damage models to quantify their effects on fatigue-damage assessments and shows that traffic models and structural model parameters are the most influential sources of uncertainty

Nondestructive Testing Methods and New Applications

Nondestructive testing enables scientists and engineers to evaluate the integrity of their structures and the properties of their materials or components non-intrusively, and in some instances in real-time fashion. Applying the Nondestructive techniques and modalities offers valuable savings and guarantees the quality of engineered systems and products. This technology can be employed through different modalities that include contact methods such as ultrasonic, eddy current, magnetic particles, and liquid penetrant, in addition to contact-less methods such as in thermography, radiography, and shearography. This book seeks to introduce some of the Nondestructive testing methods from its theoretical fundamentals to its specific applications. Additionally, the text contains several novel implementations of such techniques in different fields, including the assessment of civil structures (concrete) to its application in medicine

Bending moment and efficient fatigue assessment in a Subsea Shuttle Tanker under the effect of waves

The subsea shuttle tanker (SST) is the next-generation autonomous submarine designed to transport liquid CO2 from land/offshore facilities to the smaller fields for injection. Unlike normal shuttle tankers, which are highly weather dependent, the SST can carry out freight operations in all weather conditions because it travels underwater between 40 m and 70 m water depth. The first part of the thesis proposes a fast, efficient and reliable multi-body approach to determine the bending moment response of the SST hull at 40 m and 70 m water depth. The chosen approach is based on the discrete-module-beam bending-based hydroelasticity principle. The flexible hull of the vessel is divided into several multi-body rigid modules. All the hydrodynamic and hydrostatic forces are applied to the center of gravity of each rigid module. The parametric models, like the state-space model system, are used to compute the free-surface memory effect more effectively. The multi-body equation of motion is solved to determines the bending moment response of an interconnected multi-body rigid module. The numerical model is prepared using Matlab Simulink to study the dynamics of the vessel. A convergence study is conducted to select the optimal number of bodies needed to perform this study. The result shows that the lower number of bodies (i.e., three and five bodies) does not have enough points to capture all the wave encounter frequencies, thus underestimating the bending moment. Therefore, seven-body SST is used to carry out a further assessment. The bending moment standard deviation is reduced by approximately 50 % when SST travels at 70 m water depth instead of 40 m. The second part of the thesis presents the fatigue assessment of the SST hull, considering the stiffeners' local details. Two FE models (2D axisymmetric and 3D shell element models) representing the local detail of the flooded-mid body of the SST are prepared to determine the stress concentration factor (SCF). The resultant SCF can be given using the superposition concept by taking the product of the SCF for the individual models. The Rainflow counting method and Palmgren-Miner rule are used to calculate the accumulated fatigue damage and fatigue life. The numerical results show that the impact of long waves has contributed to the most damage to the vessel. The minimum fatigue life at the flooded-mid section is 13 and 19 years for the 40 m and 70 m water depths, respectively. The results also shows that fatigue life due to the change in hydrostatic pressure during dive-in and dive-out is five years

Gaussian process regression for fatigue reliability analysis of offshore wind turbines

The fatigue limit state (FLS) often drives the design of offshore wind turbine (OWT) substructures in European waters. Assessing fatigue damage over the intended design life of an OWT is computationally expensive, primarily as dynamic structural analyses have to be run for a large number of stochastic wind and wave loading conditions. This makes structural reliability assessment for the FLS a challenging task. In addition to evaluating load-induced fatigue damage, simulation-based structural reliability analysis also requires sampling of random variables that model uncertainties in the capacity of OWT structural components. To this aim, we develop and validate a computational framework for OWT fatigue reliability analysis that relies on Gaussian process (GP) regression to build surrogate models of load-induced fatigue damage. We demonstrate that the proposed approach can reduce the computational effort required to evaluate FLS reliability with high accuracy through application to three plausible offshore wind farm sites in Europe. The sensitivity of various goodness-of-fit metrics to different model assumptions is investigated to further reduce the computational effort required to perform GP regression/predictions. The results from this study can provide guidance for practical applications of the proposed framework in OWT projects