Advancing probabilistic risk assessment of offshore wind turbines on monopiles

Offshore Wind Turbines (OWTs) are a unique type of engineered structure. Their design spans all engineering disciplines, ranging from structural engineering for the substructure and foundation to electrical or mechanical engineering for the generating equipment. Consequently, the different components of an OWT are commonly designed independently using codified standards. Within the OWT design process, financial cost plays an important role as a constraint on decision making, because of the competition between prospective wind farm operators and with other forms of electricity generation. However, the current, independent design process does not allow for a combined assessment of OWT system financial loss. Nor does it allow for quantification of the uncertainties (e.g., wind and wave loading, materials properties) that characterise an OWT’s operations and which may have a strong impact on decision making. This thesis proposes quantifying financial losses associated with an OWT exposed to stochastic wind and wave conditions using a probabilistic risk modelling framework, as a first step towards evaluating Offshore Wind Farm (OWF) resilience. The proposed modelling framework includes a number of novel elements, including the development of site-specific fragility functions (relationships between the likelihood of different levels of damage experienced by an OWT over a range of hazard intensities), which account for uncertainties in both structural capacity and demands. As a further element of novelty, fragility functions are implemented in a closed-form assessment of financial loss, based on a combinatorial system reliability approach, which considers both structural and non-structural components. Two important structural performance objectives (or limit states) are evaluated in this thesis: 1) the Ultimate Limit State (ULS) which assesses the collapse of an OWT due to extreme wind and wave conditions, such as those resulting from hurricanes; and 2) the Fatigue Limit State (FLS), which addresses the cumulative effects of operational loading, i.e., cracks growing over the life of the structure until they threaten its integrity. This latter limit state is assessed using a novel machine learning technique, Gaussian Process (GP) regression, to develop a computationally-efficient surrogate model that emulates the output from computationally-expensive time-domain structural analyses. The consequence of the OWT failing is evaluated by computing annualised financial losses for the full OWT system. This provides a metric which is easily communicable to project stakeholders, and can also be used to compare the relative importance of different components and design strategies. Illustrative applications at case-study sites are presented as a walk-through of the calculation steps in the proposed framework and its various components. The calculation of losses provides a foundation from which a more detailed assessment of OWT and OWF resilience could be developed

Surrogate modelling for reliability assessment of cutting tools

Currently, cutting tool life for machining operations is correlated to process parameters through the widely applied Taylor functions. The latter are valuable expressions in established practice however their generalised nature does not allow accurate prediction of the tool’s service life or optimization of the manufacturing process due to effects of uncertainties in various input variables. These variables should be treated in a stochastic way in order to avoid employment of safety factors for quantification of uncertainty. This paper documents a procedure that allows derivation of analytical expressions for cutting tools performance employing advanced approximation methods and concepts of reliability analysis. Due to the complexity of manufacturing processes surrogate modelling (SM) methods are applied, starting from a few sample points obtained through lab or soft experiments and extending them to models able to predict/estimate the values of control values/indicators as a function of the key design variables, often referred to as limit states

Reliability assessment of cutting tool life based on surrogate approximation methods

A novel reliability estimation approach to the cutting tools based on advanced approximation methods is proposed. Methods such as the stochastic response surface and surrogate modeling are tested, starting from a few sample points obtained through fundamental experiments and extending them to models able to estimate the tool wear as a function of the key process parameters. Subsequently, different reliability analysis methods are employed such as Monte Carlo simulations and first- and second-order reliability methods. In the present study, these reliability analysis methods are assessed for estimating the reliability of cutting tools. The results show that the proposed method is an efficient method for assessing the reliability of the cutting tool based on the minimum number of experimental results. Experimental verification for the case of high-speed turning confirms the findings of the present study for cutting tools under flank wear

Reliability analysis and micromechanics: A coupled approach for composite failure prediction

This work aims at associating two classical approaches for the design of composite materials: first, reliability methods that allow to account for the various uncertainties involved in the composite materials behaviour and lead to a rational estimation of their reliability level; on the other hand, micromechanics that derive macroscopic constitutive laws from micromechanical features. Such approach relies on the introduction of variabilities defined at the microscale and on the investigation of their consequences on the material macroscopic response through an homogenization scheme. Precisely, we propose here a systematic treatment of variability which involves a strong link between micro- and macroscales and provides a more exhaustive analysis of the influence of uncertainties. The paper intends to explain the main steps of such coupling and demonstrate its interests for material engineering, especially for constitutive modelling and composite materials optimization. An application case is developed throughout on the failure of unidirectional carbon fibre-reinforced composites with a comparative analysis between experimental data and simulation results

Local-duality QCD sum rules for strong isospin breaking in the decay constants of heavy-light mesons

We discuss the leptonic decay constants of heavy--light mesons by means of Borel QCD sum~rules in the local-duality (LD) limit of infinitely large Borel mass parameter. In this limit, for an appropriate choice of the invariant structures in the QCD correlation functions, all vacuum-condensate contributions vanish and all nonperturbative effects are contained in only one quantity, the effective threshold. We study properties of the LD effective thresholds in the limits of large heavy-quark mass $m_Q$ and small light-quark mass $m_q$. In the heavy-quark limit, we clarify the role played by the radiative corrections in the effective threshold for reproducing the pQCD expansion of the decay constants of pseudoscalar and vector mesons. We show that the dependence of the meson decay constants on $m_q$ arises predominantly (at the level of 70--80%) from the calculable $m_q$-dependence of the perturbative spectral densities. Making use of the lattice QCD results for the decay constants of nonstrange and strange pseudoscalar and vector heavy mesons, we obtain solid predictions for the decay constants of heavy--light mesons as functions of $m_q$ in the range from a few to 100 MeV and evaluate the corresponding strong isospin-breaking effects: $f_{D^+} - f_{D^0}=(0.96 \pm 0.09) ~ {\rm MeV}$, $f_{D^{*+}} - f_{D^{*0}}= (1.18 \pm 0.35) ~ {\rm MeV}$, $f_{B^0} - f_{B^+}=(1.01 \pm 0.10) ~ {\rm MeV}$, $f_{B^{*0}} - f_{B^{*+}}=(0.89 \pm 0.30) ~ {\rm MeV}$.Comment: 14 pages. Typo corrected: the term describing power corrections in Eq.(1.1), which was lost when updating v2 -> v3, is restored. Notice that this term is also lost in the EPJC version (to be corrected via Erratum

Likely equilibria of stochastic hyperelastic spherical shells and tubes

In large deformations, internally pressurised elastic spherical shells and tubes may undergo a limit-point, or inflation, instability manifested by a rapid transition in which their radii suddenly increase. The possible existence of such an instability depends on the material constitutive model. Here, we revisit this problem in the context of stochastic incompressible hyperelastic materials, and ask the question: what is the probability distribution of stable radially symmetric inflation, such that the internal pressure always increases as the radial stretch increases? For the classic elastic problem, involving isotropic incompressible materials, there is a critical parameter value that strictly separates the cases where inflation instability can occur or not. By contrast, for the stochastic problem, we show that the inherent variability of the probabilistic parameters implies that there is always competition between the two cases. To illustrate this, we draw on published experimental data for rubber, and derive the probability distribution of the corresponding random shear modulus to predict the inflation responses for a spherical shell and a cylindrical tube made of a material characterised by this parameter.Comment: arXiv admin note: text overlap with arXiv:1808.0126

Uncertainty Updating in the Description of Coupled Heat and Moisture Transport in Heterogeneous Materials

To assess the durability of structures, heat and moisture transport need to be analyzed. To provide a reliable estimation of heat and moisture distribution in a certain structure, one needs to include all available information about the loading conditions and material parameters. Moreover, the information should be accompanied by a corresponding evaluation of its credibility. Here, the Bayesian inference is applied to combine different sources of information, so as to provide a more accurate estimation of heat and moisture fields [1]. The procedure is demonstrated on the probabilistic description of heterogeneous material where the uncertainties consist of a particular value of individual material characteristic and spatial fluctuations. As for the heat and moisture transfer, it is modelled in coupled setting [2]