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Modelling uncertainty in joints using component mode synthesis and a stochastic reduced basis method
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Joint uncertainty propagation in linear structural dynamics using stochastic reduced basis methods
No full textUncertainties in the properties of joints produce uncertainties in the dynamic response of built-up structures. Line
joints, such as glued or continuously welded joints, have spatially distributed uncertainty and can be modeled by a
discretized random field. Techniques such as Monte Carlo simulation can be applied to estimate the output statistics,
but computational cost can be prohibitive. This paper addresses how uncertainties in joints might be included
straightforwardly in a finite element model, with particular reference to approaches based on fixed-interface (Craig–
Bampton) component mode synthesis and a stochastic reduced basis method with two variants. These methods are
used to determine the output statistics of a structure. Unlike perturbation-based methods, good accuracy can be
achieved even when the coefficients of variation of the input random variables are not small. Undamped as well as
proportionally damped components are considered. Efficient implementations are proposed based on an exact
matrix identity that leads to a significantly lower computational cost if the number of joint degrees of freedom is
sufficiently small compared with the structure’s overall number of degrees of freedom. A numerical example is
presented. The proposed formulation is an efficient and effective implementation of a stochastic reduced basis
projection scheme. It is seen that the method can be up to orders of magnitude faster than direct Monte Carlo
simulation, while providing results of comparable accuracy. Furthermore, the proposed implementation is more
efficient when fewer joints are affected by uncertainty
Analysis of vibrations of systems with spatially correlated uncertainty in joints using a Stochastic Reduced Basis Method
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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