Transcatheter patent foramen ovale closure: a treatment for refractory hypoxaemia.

Right-to-left intracardiac shunt through a patent foramen ovale sometimes induces a significant arterial oxygen desaturation. We describe two cases of severe hypoxia due to a patent foramen ovale, treated successfully by transcatheter closure of the intracardiac shunt. One case occurred after implantation of a cardiac assist device, the other patient presented with a platypnoeaorthodeoxia syndrome. Clinical considerations are outlined

Identification and quantification of multivariate interval uncertainty in Finite Element models

© 2016 Elsevier B.V. The objective of this work is to develop and validate a methodology for the identification and quantification of multivariate interval uncertainty in finite element models. The principal idea is to find a solution to an inverse problem, where the variability on the output side of the model is known from measurement data, but the multivariate uncertainty on the input parameters is unknown. For this purpose, the uncertain simulation results set created by propagating interval uncertainty through the model is represented by its convex hull. The same concept is used to model the uncertainty in the measurements. A metric to describe the discrepancy between these convex hulls is defined based on the difference between their volumes and their mutual intersection. By minimisation of this metric, the interval uncertainty on the input side of the model is identified. It is further shown how the procedure can be optimised with respect to output quantity selection. Validation of the methodology is done using simulated measurement data in two case studies. Numerically exact identification of multiple, coupled parameters having interval uncertainty is possible following the proposed methodology. Furthermore, the robustness of the method with respect to the analyst's initial estimate of the input uncertainty is illustrated. The method presented in this work in se is generic, but for the examples in this paper, it is specifically applied to dynamic models, using eigenfrequencies as output quantities, as commonly applied in modal updating procedures.publisher: Elsevier articletitle: Identification and quantification of multivariate interval uncertainty in finite element models journaltitle: Computer Methods in Applied Mechanics and Engineering articlelink: http://dx.doi.org/10.1016/j.cma.2016.11.023 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved.status: publishe

Robust uncertainty quantification in structural dynamics under scarse experimental modal data: A Bayesian-interval approach

The accurate prediction of the dynamic behaviour of a complex component or system is often difficult due to uncertainty or scatter on the physical parameters in the underlying numerical models. Over the past years, several non-deterministic techniques have been developed to account for these model inaccuracies, supporting an objective assessment of the effect of these uncertainties on the dynamic behaviour. Still, also these methods require a realistic quantification of the scatter in the uncertain model properties in order to have any predictive value. In practice, this information is typically inferred from experiments. This uncertainty quantification is especially challenging in case only fragmentative or scarce experimental data are available, as is often the case when using modal data sets. This work therefore studies the application of these limited data sets for this purpose, and focuses more specifically on the quantification of interval uncertainty based on limited information on experimentally obtained eigenfrequencies. The interval approach, which is deemed to be the most robust against data insufficiency, typically starts from bounding the data using the extreme values in the limited data set. This intuitive approach, while of course representing the experiments, in general yields highly unconservative interval estimates, as the extreme realisations are typically not present in the limited data set. This work introduces a completely new approach for quantifying the bounds on the dynamic properties under scarce modal data. It is based on considering a complete set of parametrized probability density functions to determine likelihood functions, which can then be used in a Bayesian framework. To illustrate the practical applicability of the proposed techniques, the methodology is applied to the well-known DLR AIRMOD test case where in a first step, the bounds on the experimental eigenfrequencies are estimated. Then, based on a calibrated finite element model of the structure, bounds on the frequency response functions are estimated. It is illustrated that the method allows for a largely objective estimation of conservative interval bounds under scarce data.status: Published onlin

Influence of measurement data metrics on the identification of Interval Fields for the representation of spatial variability in finite element models

status: publishe