Offshore wind turbines intend to take a rapidly growing share in the electric mix. The design, installation, and exploitation of these industrial assets are regulated by international standards, providing generic guidelines. Constantly, new projects reach unexploited wind resources, pushing back installation limits. Therefore, turbines are increasingly subject to uncertain environmental conditions, making long-term investment decisions riskier (at the design or end-of-life stage). Fortunately, numerical models of wind turbines enable to perform accurate multi-physics simulations of such systems when interacting with their environment. The challenge is then to propagate the input environmental uncertainties through these models and to analyze the distribution of output variables of interest. Since each call of such a numerical model can be costly, the estimation of statistical output quantities of interest (e.g., the mean value, the variance) has to be done with a restricted number of simulations. To do so, the present paper uses the kernel herding method as a sampling technique to perform Bayesian quadrature and estimate the fatigue damage. It is known from the literature that this method guarantees fast and accurate convergence together with providing relevant properties regarding subsampling and parallelization. Here, one numerically strengthens this fact by applying it to a real use case of an offshore wind turbine operating in Teesside, UK. Numerical comparison with crude and quasi-Monte Carlo sampling demonstrates the benefits one can expect from such a method. Finally, a new Python package has been developed and documented to provide quick open access to this uncertainty propagation method.

In the present study, fuzzy uncertainty and reliability analysis of aeroelastic aircraft wings are investigated. The uncertain air speed and structural parameters are represented by fuzzy triangular membership functions. These uncertainties are propagated through the wing model using a fuzzy interval approach, and the uncertain flutter speed is obtained as a fuzzy variable. Further, the reliability of the wing flutter is based on the interference area in the pyramid shape defined by the fuzzy flutter speed and air speed. The ratio between the safe region volume and the total volume of the pyramid gives the reliability value. Two different examples are considered—a typical wing section, and a clean wing—and the results are given for various wind speed conditions. The results show that the approach considered is a low-cost but suitable method to estimate the reliability of the wing flutter speed in the presence of uncertainties.

The most important applications of many computer vision systems are based on robust features extraction, matching and tracking. Due to their extraction techniques, image features locations accuracy is heavily dependent on the variation in intensity within their neighbourhoods, from which their uncertainties are estimated. In the present work, a robust L∞ optimisation solution for monocular motion estimation systems has been presented. The uncertainty estimation techniques based on SIFT derivative approach and its propagation through the eight-point algorithm, singular value decomposition SVD and the triangulation algorithm have proved an improvement to the global motion estimation. Using monocular systems makes the motion estimation challenging due to the absolute scale ambiguity caused by projective effects. For this, we propose robust tools to estimate both the trajectory of a moving object and the unknown absolute scale ratio between consecutive image pairs. Experimental evaluations showed that robust convex optimisation with the L∞ norm under uncertain data and the Robust Least Squares clearly outperform classical methods based on Least Squares and Levenberg-Marquardt algorithms.

We consider multiscale systems for which only a fine-scalemodel describing the evolution of individuals (atoms,molecules, bacteria, agents) is given, while we are interested in theevolution of the population density on coarse space and timescales. Typically, this evolution is described by a coarseFokker-Planck equation. In this paper, we consider a numerical procedure to compute the solution ofthis Fokker-Planck equation directly on the coarse level, based on the estimation of the unknown parameters (drift and diffusion) using only appropriately chosen realizations of the fine-scale,individual-basedsystem. As these parameters might be space- and time-dependent, theestimation is performed in every spatial discretization point and atevery time step. If the fine-scale model is stochastic, the estimationprocedure introduces noise on the coarse level.We investigate stability conditions for this procedure in thepresence of this noise and present ananalysis of the propagation of the estimation error in the numericalsolution of the coarse Fokker-Planck equation.

A non-intrusive stochastic finite-element method is proposed for uncertainty propagation through mechanical systems with uncertain input described by random variables. A polynomial chaos expansion (PCE) of the random response is used. Each PCE coefficient is cast as a multi-dimensional integral when using a projection scheme. Common simulation schemes, e.g. Monte Carlo Sampling (MCS) or Latin Hypercube Sampling (LHS), may be used to estimate these integrals, at a low convergence rate though. As an alternative, quasi-Monte Carlo (QMC) methods, which make use of quasi-random sequences, are proposed to provide rapidly converging estimates. The Sobol' sequence is more specifically used in this paper. The accuracy of the QMC approach is illustrated by the case study of a truss structure with random member properties (Young's modulus and cross section) and random loading. It is shown that QMC outperforms MCS and LHS techniques for moment, sensitivity and reliability analyses.

An examination of the economic impacts of reduced milk production associated with Johne's disease on Johne's-positive and Johne's-negative dairy operations indicated that, if Johne's disease had not existed in US dairy cows in 1996, then the economic surplus of Johne's-negative operations would have been $600 million±$530 million lower, while the economic surplus of Johne's-positive operations would have been higher by $28 million±$79 million, which was not significantly different from zero. The data available for projecting changes in surplus were not sufficiently precise to allow an exact statement on whether Johne's-positive operations would have been better or worse off economically, in terms of the value received for producing more milk if they had not been affected by Johne's disease. The changes in producer surplus, based upon eliminating specific epidemiological risk factors for Johne's disease, were disaggregated between Johne's-positive dairy operations exposed to the risk factor and all other US dairy operations. Eliminating the risk factor of having any cows not born on the operation would have had a significant positive effect on the economic surplus of Johne's-positive operations that had any cows not born on the operation.

The goal of this study was to measure the economic impacts of reduced milk production associated with papillomatous digital dermatitis (PDD) in dairy cows in the USA, and of specific risk factors for PDD, in 1996. The method applied was an economic-welfare analysis of producer and consumer surplus, with the GUM Workbench used to analyse uncertainties in the measurements. Reduced milk production associated with PDD was found to reduce consumer surplus by $750 million±$580 million, and to increase the economic surplus of producers by $560 million±$470 million, with a net economic loss of $190 million±$130 million. An examination of the economic effects of specific epidemiologic risk factors for PDD showed that having dairy cows that were not born on the operation had important economic consequences associated with the disease, as did the type of land to which dairy cows had access during the winter months and the type of flooring on which cows walked. Washing hoof-trimming equipment between cows was an important biosecurity measure that was associated with reduced PDD. The epidemiologic model used also implicated hoof trimmers who trimmed cattle hooves on other operations as having an important economic impact associated with this disease, although this finding may have been erroneous.

An examination of the economic effects of epidemiologic risk factors for Johne's disease identified regional and herd size differences as having the greatest impact. Having dairy cows that were not born on the operation was the most important factor over which individual producers had the most immediate control. Economic consequences associated with using multiple-cow-maternity housing and multiple-preweaned-calf housing were not statistically significant. Economic welfare analysis was applied, and the GUM Workbench was used to analyse uncertainties in the estimates of the economic impacts.

Accurately assessing the economic impacts of diseases and other factors that affect milk production requires that the demand for milk be taken into account. Because demand for milk in the USA is relatively inelastic (i.e., consumers generally purchase a somewhat fixed amount over a given time frame, regardless of fluctuations in price), consumers tend to reap much of the benefit of enhanced production. An examination of the economic impacts of Johne's disease indicated that reduced milk production, associated with the determination of dairy operations as Johne's-positive, reduced consumer surplus by $770 million ± $690 million, and resulted in a total loss of $200 million ± $160 million to the US economy in 1996. Most of the economic surplus lost by consumers was transferred to producers, whose economic surplus increased by $570 million ± $550 million as a result of the reduced milk production associated with Johne's disease. Uncertainty analysis showed that the estimated reduction in milk production on Johne's-positive dairy operations accounted for most of the uncertainty in the economic-impact estimates. If Johne's disease had not been present on US dairy operations, then an additional 580 million ±460 million kg of milk would have been produced, but the price would have fallen by 1·1±1·0 cents/kg, and the total value of the milk would have decreased by $580 million ± $560 million.