A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm. (October 2019)
- Record Type:
- Journal Article
- Title:
- A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm. (October 2019)
- Main Title:
- A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm
- Authors:
- Khatir, Samir
Abdel Wahab, Magd - Abstract:
- Highlights: 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. Abstract: 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, the proposed techniques 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 consideredHighlights: 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. Abstract: 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, the proposed techniques 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. … (more)
- Is Part Of:
- Theoretical and applied fracture mechanics. Volume 103(2019)
- Journal:
- Theoretical and applied fracture mechanics
- Issue:
- Volume 103(2019)
- Issue Display:
- Volume 103, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 103
- Issue:
- 2019
- Issue Sort Value:
- 2019-0103-2019-0000
- Page Start:
- Page End:
- Publication Date:
- 2019-10
- Subjects:
- XIGA -- XFEM -- PSO -- Jaya algorithm -- Inverse problem -- Crack identification -- Plate structures
Fracture mechanics -- Periodicals
620.1126 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01678442 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.tafmec.2019.102240 ↗
- Languages:
- English
- ISSNs:
- 0167-8442
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8814.551850
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11808.xml