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Two methodologies to propagate the uncertainties on the nuclide inventory in combined Monte Carlo-spectrum and burn-up calculations are presented, based on sensitivity/uncertainty and random sampling techniques (uncertainty Monte Carlo method). Both enable the assessment of the impact of uncertainties in the nuclear data as well as uncertainties due to the statistical nature of the Monte Carlo neutron transport calculation. The methodologies are implemented in our MCNP-ACAB system, which combines the neutron transport code MCNP-4C and the inventory code ACAB.A high burn-up benchmark problem is used to test the MCNP-ACAB performance in inventory predictions, with no uncertainties. A good agreement is found with the results of other participants.This benchmark problem is also used to assess the impact of nuclear data uncertainties and statistical flux errors in high burn-up applications. A detailed calculation is performed to evaluate the effect of cross-section uncertainties in the inventory prediction, taking into account the temporal evolution of the neutron flux level and spectrum. Very large uncertainties are found at the unusually high burn-up of this exercise (800 MWd/kgHM). To compare the impact of the statistical errors in the calculated flux with respect to the cross uncertainties, a simplified problem is considered, taking a constant neutron flux level and spectrum. It is shown that, provided that the flux statistical deviations in the Monte Carlo transport calculation do not exceed a given value, the effect of the flux errors in the calculated isotopic inventory are negligible (even at very high burn-up) compared to the effect of the large cross-section uncertainties available at present in the data files.

Sustainability is a field of growing interest in Project Management (PM). Literature on Sustainability in PM is abundant at a theoretical level; however, it is necessary to explore hands-on approaches for designing models and practices. The purpose of this study is to introduce management systems as a practical tool for Sustainability in PM. Management system certifications are used as an indicator of the implementation of Sustainability practices, and thus, the impact of Sustainability on the success of projects is analyzed. The methodology for this study includes the analysis of the correspondence between Sustainability and five recognized management system standards (ISO 9001, ISO 14001, ISO 50001, UNE 166002 and OHSAS 18001) and experimental research based on data delivered by CDTI (Center for Industrial Technological Development) including relevant and objective information about R&D&I Projects in the energy sector. This study analyzes the impact of four variables (duration, budget, year of funding and certifications to management systems) on the success of the project. The conclusion is the significant positive impact of having management system certifications on the success of company projects analyzed in the Spanish energy sector, which may be of interest to PM practitioners in order to consider Sustainability as a factor for success.

The estimation of modal parameters of a structure from ambient measurements has attracted the attention of many researchers in the last years. The procedure is now well established and the use of state space models, stochastic system identification methods and stabilization diagrams allows to identify the modes of the structure. In this paper the contribution of each identified mode to the measured vibration is discussed. This modal contribution is computed using the Kalman filter and it is an indicator of the importance of the modes. Also the variation of the modal contribution with the order of the model is studied. This analysis suggests selecting the order for the state space model as the order that includes the modes with higher contribution. The order obtained using this method is compared to those obtained using other well known methods, like Akaike criteria for time series or the singular values of the weighted projection matrix in the Stochastic Subspace Identification method. Finally, both simulated and measured vibration data are used to show the practicability of the derived technique. Finally, it is important to remark that the method can be used with any identification method working in the state space model.

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points.The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used.

This artiele proposes a generalization and improvement on the method of Lenth (1989). The problem is solved by fixing outliers in highly contaminated samples. To do this a scale robust estimator is obtained and its performance is analyzed using computer simulations. The method is extremely simple to use and leads to the same results as the more complex one proposed by Box and Meyer (1986).

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This anicle desc ribes a procedure for the detection of multivariate oU lliers based on the anal ysis 01' certain angular properties 01' the observations. The method is simple. exploratory in nature. and particularly well suited for the detection of concen trated contamination paltems, in which the outl iers appear 10 foml a cluster, separated from ¡he sample. II is shown that it presents good properties for the identification of conta minations on high-dimensional sample spaces and for high contaminati on levels. includ ing some cases in which methods based on robust estimators (the minimum covariance determinan! and minimum volu me ell ipsoid estimators, the Stahel-Donoho estimator, or other recen! proposals ) may fail. The use 01' lhe procedure is illu straled lhrough several examples.