A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm

•CPU Time for XIGA and XFEM based on inverse problem.•Jaya and PSO for crack idetification.•XIGA is much better than XFEM.•The objective of NURBS order for best convergence and fast simulation is provided. In this paper, a creative and intelligent approach based on an inverse problem that accurately...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Theoretical and applied fracture mechanics 2019-10, Vol.103, p.102240, Article 102240
Hauptverfasser: Khatir, Samir, Abdel Wahab, Magd
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•CPU Time for XIGA and XFEM based on inverse problem.•Jaya and PSO for crack idetification.•XIGA is much better than XFEM.•The objective of NURBS order for best convergence and fast simulation is provided. In this paper, a creative and intelligent approach based on an inverse problem that accurately predicts crack location in plate structures is presented. The eXtended Finite Element (XFEM) and the eXtended IsoGeometric Analysis (XIGA) are combined with two optimization techniques, namely Particle Swarm Optimization (PSO) and Jaya algorithm to predict the crack location. The superiority of XIGA is demonstrated by using various NURBS orders to reduce the number of elements, provide fast simulation and achieve best convergence compared with XFEM. Four numerical-optimization techniques are considered in this paper, namely XFEM-Jaya, XIGA-Jaya, XFEM-PSO and XIGA-PSO. In the optimization techniques, the objective function minimizes the difference between the calculated and measured displacements and strains. Convergence studies for various positions of a crack and a hole in plates are performed and the results show that Jaya algorithm significantly performs more accurate and faster than PSO. In addition, theproposedtechniques are validated using experimental data and another numerical-optimization technique, i.e. XFEM coupled with Genetic Algorithm (GA), presented in literature. The comparisons show that XIGA-Jaya performs the best of all considered techniques.
ISSN:0167-8442
1872-7638
DOI:10.1016/j.tafmec.2019.102240