English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Full-field simulations used to enhance the predictive capability of classical finite element models require the preparation of high-quality initial digital material representation (DMR) data. Depending on the selected method, such a procedure can be time-consuming and, in subsequent model runs, result in a significant increase in computational time. An unconstrained grain growth algorithm based on the random cellular automata (RCA) method can be used as a potential solution to provide high-quality DMR. However, the most time-consuming part of that approach, the neighbor-search algorithm, should be optimized from an algorithmic point of view before practical application in full-field analysis. Therefore, four neighbor-search algorithms dedicated to the RCA method were developed and benchmarked within the paper for the algorithm's complexity and impact of individual parameters on computational efficiency. The research has shown that significantly reducing RCA simulation time is possible with the properly developed neighbor-search algorithm and problem-specific code optimizations.

Evaluation of the effectiveness of neighbors’ selection algorithms in the random cellular automata model of unconstrained grain growth