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CRATE v0.7.0
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@BernardoFerreira BernardoFerreira released this 15 Dec 17:38
· 46 commits to master since this release
v0.7.0
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Description

This new release version accounts for a significant improvement of the clustering related code OOP design and brings 2 main improvements: (1) the generalization of the CRVE class as a composition of cluster-reduced material phases (CRMP) and (2) the introduction of adaptive cluster-reduced material phases and associated online clustering adaptivity.

New Features and Improvements

  • Massive clustering OOP refactoring. The code structure associated with the Cluster-Reduced Representative Volume Element (CRVE) has been massively refactored with significant changes in the underlying classes and methods. The CRVE has a main class containing all the required public methods and is composed by one or more Cluster-Reduced Material Phases (CRMP).

  • Cluster-Reduced Material Phases. A new abstract class (CRMP) has been implemented allowing that each material phase is treated in a truly independent manner. This results in a significant increase of CRATE's flexibility, given that all the clustering procedures can now be prescribed independently for each material phase (clustering algorithm, clustering features, clustering parameters, etc.). The most basic type of CRMP is coined Static Cluster-Reduced Material Phase (SCRMP), meaning that the base clustering computed in the offline stage is kept constant during the solution procedure.

  • Clustering adaptivity. Besides the above improvements, CRATE has now the first clustering adaptivity scheme in the world! A coined Adaptive Cluster-Reduced Material Phase (ACRMP) has been implemented as a new abstract class and a particular type of ACRMP, coined Hierarchical Agglomerative Adaptive Cluster-Reduced Material Phase (HAACRMP), has been derived. This ACRMP performs the clustering adaptivity by resorting to an hierarchical agglomerative clustering algorithm and to the underlying linkage matrix. Although it behaves as expected and shows that the idea is feasible, this approach is severely limited due to prohibitive memory requirements associated with the computation of the distance matrix of the base clustering dataset (around 50000 voxels). In addition, a naive non-spatial adaptivity criterion has been developed and implemented in order to select the adaptivity target clusters.

  • New keywords. Associated with the new features and improvements is a set of new keywords whose specification is documented in CRATE's input data file in the sections Cluster Analysis Scheme, Clustering Adaptivity Frequency and Clustering Adaptivity Output. The first allows a completely general prescription of each material phase clustering procedures, including the definition of base and/or adaptive clustering schemes (clustering algorithms and clustering features) and clustering adaptivity parameters. The second controls the frequency of the clustering adaptivity analysis of each adaptive material phase relative to the macroscale loading incrementation. Finally, the last switches on the output of data to the '.screen' file associated to each clustering adaptivity step (execution of adaptivity procedures, number of clusters and execution time table).

Bug Fixes

None

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