The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The basic design principle is: Everything is non-linear. All physics models are non-linear from which the linearizations are made as special cases. This is a work in progress. Thus, if you share the vision, contribute and join the community.
Summary.A Bayesian sequential experimental design for fatigue testing based on D-optimality and a non-linear continuous damage model was implemented. The model has two asymptotes for the number of cycles to failure: the fatigue limit and the ultimate tensile strength. With the introduction and stochastic handling of these asymptotes, the D-optimal design accounts naturally for the whole range of reasonable testing levels. Sequential design ensures that all the available data is used efficiently while choosing the next test level.
Summary. This article presents a natural frequency analysis performed with JuliaFEM -an open-source finite element method program. The results are compared with the analysis results pruduced with a commercial software. The comparison shows that the calculation results between the two programs do not differ significantly.
This arcicle describes the use of intelligent algorithms for analysingfield measurement data. The main focus is on describing the generalworkflow and practical issues when the amount of data is ''big''and typical data analysis methods for small data cannot be used. Whenthe amount of data is tens of terabytes, it is no longer fitting tocomputer memory. Data visualization is also challenging: visualizationtools can only render a small fraction of data to computer screenand visual inspecting of the whole dataset is not meaningful at all. Thedata is simply too big. Thus, new approaches to study data are neededwhere the data is processed automatically in calculation clusterswithout manual human work. The basic idea of data mining is to graduallyreduce the amount of data by various techniques, as long as the finaldata contains only information relevant to the research question and insuch a compact form that its viewing from the human point of viewis rational use of time.
JuliaFEM is an open source nite element method solver written in the Julia language. This paper presents an implementation of two common model reduction methods: the Guyan reduction and the Craig-Bampton method. The goal was to implement these algorithms to the JuliaFEM platform and demonstrate that the code works correctly. This paper rst describes the JuliaFEM concept briey after which it presents the theory of model reduction, and nally, it demonstrates the implemented functions in an example model. This paper includes instructions for using the implemented algorithms, and reference the code itself in GitHub. The reduced stiness and mass matrices give the same results in both static and dynamic analyses as the original matrices, which proves that the code works correctly. The code runs smoothly on relatively large model of 12.6 million degrees of freedom. In future, damping could be included in the dynamic condensation now that it has been shown to work.
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