Strategies for finding the design point in non-linear finite element reliability analysis

An essential step in FORM, SORM and importance sampling reliability methods is the determination of the so-called design point. This point is the solution of a constrained optimization problem in the outcome space of the random variables, which is commonly solved by an iterative, gradient-based search algorithm. In solving this problem in the context of non-linear finite element reliability analysis, two serious impediments are encountered: (a) for certain material models, the constraint function may have a discontinuous gradient, leading to failure of the search algorithm to converge. (b) The search algorithm may generate trial points too far in the failure domain, where the finite element code fails to produce a result due to lack of numerical convergence. In this paper, remedying strategies are developed for both impediments. The first impediment is addressed by using smooth or smoothed material models, including a smoothed bi-linear model, a Bouc–Wen model and a generalized plasticity model. This is complemented by a proof that sudden elastic unloading does not give rise to gradient discontinuities. The second impediment is addressed by modifying or introducing search algorithms that prevent the trial points from overshooting into the failure domain. Numerical examples are used to demonstrate the two impediments and effectiveness of the proposed remedies.     

Formulae for the average unavailability (MFDT) of a coherent system with periodically tested components

In this paper we establish some approximation-formulae (bounds) for two measures of importance of the components in a coherent system: Barlow-Proschan (Stochastic Process. Appl.3, 153–173, 1975) measure and Natvig (Stochastic Process. Appl.9, 319–330, 1979) measure. A finite time-interval [0, τ] is considered. The components are assumed to have Weibull lifelength distributions.     

A risk influence model applied to North Sea helicopter transport

Offshore helicopter transport represents a quite complex, socio-technical system. A number of factors affect the risk related to this activity, e.g. maintenance, design of helicopter/helideck, and the competence/training of the crew. The present paper presents an overall, holistic risk model, which aims at presenting the total risk picture and the risk influencing factors (RIFs) for helicopter transport. The main objective is to provide a tool for identifying the most effective risk reducing measures. The RIFs both for accident frequency and accident consequence are arranged hierarchically in a type of influence diagram, illustrating the effect of the various RIFs. Accident and incident statistics from North Sea helicopter transport are used in combination with expert judgements to assess the weight (importance) and the present status of the RIFs. The model is also used to provide numerical results.     

Adaptive finite element simulation of ventricular fibrillation dynamics

The paper communicates simulation results (computations and visualizations) for the dynamics of ventricular fibrillation caused by irregular excitation in the frame of the monodomain model with an action potential model due to Aliev–Panfilov for a human 3D geometry. The numerical solution of this challenging multiscale reaction–diffusion problem is attacked by algorithms which are fully adaptive in both space and time (code library KARDOS). The obtained results clearly demonstrate an accurate resolution of the cardiac potential during the excitation and the plateau phases (in the regular cycle) as well as after a reentrant excitation (in the irregular cycle). Supported by the DFG Research Center Matheon “Mathematics for key technologies” in Berlin.     

An approach to maintenance optimization where safety issues are important

The starting point for this paper is a traditional approach to maintenance optimization where an object function is used for optimizing maintenance intervals. The object function reflects maintenance cost, cost of loss of production/services, as well as safety costs, and is based on a classical cost–benefit analysis approach where a value of prevented fatality (VPF) is used to weight the importance of safety. However, the rationale for such an approach could be questioned. What is the meaning of such a VPF figure, and is it sufficient to reflect the importance of safety by calculating the expected fatality loss VPF and potential loss of lives (PLL) as being done in the cost–benefit analyses? Should the VPF be the same number for all type of accidents, or should it be increased in case of multiple fatality accidents to reflect gross accident aversion?In this paper, these issues are discussed. We conclude that we have to see beyond the expected values in situations with high safety impacts. A framework is presented which opens up for a broader decision basis, covering considerations on the potential for gross accidents, the type of uncertainties and lack of knowledge of important risk influencing factors. Decisions with a high safety impact are moved from the maintenance department to the “Safety Board” for a broader discussion. In this way, we avoid that the object function is used in a mechanical way to optimize the maintenance and important safety-related decisions are made implicit and outside the normal arena for safety decisions, e.g. outside the traditional “Safety Board”.A case study from the Norwegian railways is used to illustrate the discussions.     

Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

While forest inventories based on airborne laser scanning data (ALS) using the area based approach (ABA) have reached operational status, methods using the individual tree crown approach (ITC) have basically remained a research issue. One of the main obstacles for operational applications of ITC is biased results often experienced due to segmentation errors. In this article, we propose a new method, called “semi-ITC” that overcomes the main problems related to ITC by imputing ground truth data within crown segments from the nearest neighboring segment. This may be none, one, or several trees. The distances between segments were derived based on a set of explanatory variables using two nonparametric methods, i.e., most similar neighbor inference (MSN) and random forest (RF). RF favored the imputation of common observations in the data set which resulted in significant biases. Main conclusions are therefore based on MSN. The explanatory variables were calculated by means of small footprint ALS and multispectral data. When testing with empirical data the new method compared favorably to the well-known ABA. Another advantage of the new method over the ABA is that it allowed for the modeling of rare tree species. The results of predicting timber volume with the semi-ITC method were unbiased and the root mean squared error (RMSE) on plot level was smaller than the standard deviation of the observed response variables. The relative RMSEs after cross validation using semi-ITC for total volume and volume of the individual species pine, spruce, birch, and aspen on plot level were 17, 38, 40, 101, and 222%, respectively. Due to the unbiasedness of the estimation, this study is a showcase for how to use crown segments resulting from ITC algorithms in a forest inventory context.     

Metabolomics: a valuable tool for stem cell monitoring in regenerative medicine

Metabolomics is a method for investigation of changes in the global metabolite profile of cells. This paper discusses the technical application of the approach, considering metabolite extraction, separation, mass spectrometry and data interpretation. A particular focus is on the application of metabolomics to the study of stem cell physiology in the context of biomaterials and regenerative medicine. Case studies are used to illustrate key points, focusing on the use of metabolomics in the examination of mesenchymal stem cell responses to titania-nanopillared substrata designed for orthopaedic applications.     

Aspects of student housing satisfaction: a quantitative study

The aim of this article is to attain a better understanding of which aspects influence students’ housing satisfaction in Trondheim, Norway. Due to rising student numbers in the last decade in Norway, there is a distinct need for new student housing. It has been stated previously that students prefer specific, often central locations in university cities and that they have become more demanding when it comes to the standard of accommodation. Questions related to how and where to accommodate students have become an issue in both public and professional discussions. This study adds to the knowledge on different aspects that influence student housing satisfaction and thus offers background information for further discussion on the student housing situation in medium-sized university cities. Data were collected through a quantitative survey, which emphasized the following five aspects: (1) Type of tenancy/ownership, (2) The impact of demographic variables, (3) Housing location, (4) Different housing characteristics, and (5) Individual facilities (kitchen/bathroom). The survey data indicate that the most important variables for student residential satisfaction were, first, the type of tenancy/ownership; second, the quality of different housing characteristics; and third, the location. In this study, individual facilities and demographic variables did not have a significant effect on housing satisfaction.