Mixed-mode stress intensity factors computation in functionally graded materials using a hypercomplex-variable finite element formulation

Ramirez-Tamayo, Daniel; Balcer, Matthew; Montoya, Arturo; Millwater, Harry

doi:10.1007/s10704-020-00489-5

Cited by 5 publications

(1 citation statement)

References 44 publications

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“…Their usefulness lies in the fact that they operate in higher-dimensional spaces, and are thus able to explain the relationships between the dimensions. As an example, we can mention the use of hypercomplex domains in virtual reality [17,18], acoustic applications [19,20], communication [21,22], image processing [23,24], seismic phenomena [25,26], robotics [27,28], materials [29,30], avionics [31,32], etc.…”

Section: Introductionmentioning

confidence: 99%

The Distributed and Centralized Fusion Filtering Problems of Tessarine Signals from Multi-Sensor Randomly Delayed and Missing Observations under Tk-Properness Conditions

et al. 2021

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This paper addresses the fusion estimation problem in tessarine systems with multi-sensor observations affected by mixed uncertainties when under Tk-properness conditions. Observations from each sensor can be updated, delayed, or contain only noise, and a correlation is assumed between the state and the observation noises. Recursive algorithms for the optimal local linear filter at each sensor as well as both centralized and distributed linear fusion estimators are derived using an innovation approach. The Tk-properness assumption implies a reduction in the dimension of the augmented system, which yields computational savings in the previously mentioned algorithms compared to their counterparts, which are derived from real or widely linear processing. A numerical simulation example illustrates the obtained theoretical results and allows us to visualize, among other aspects, the insignificant difference in the accuracy of both fusion filters, which means that the distributed filter, although suboptimal, is preferable in practice as it implies a lower computational cost.

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