Radial basis functions mesh morphing for the analysis of cracks propagation

N/ACitations
Citations of this article
11Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Damage tolerant design requires the implementation of effective tools for fracture mechanics analysis suitable for complex shaped components. FEM methods are very well consolidated in this field and reliable procedures for the strength assessment of cracked parts are daily used in many industrial fields. Nevertheless the generation of the computational grid of the cracked part and its update after a certain evolution are still a challenging part of the computational workflow. Mesh morphing, that consists in the repositioning of nodal locations without changing the topology of the mesh, can be a meaningful answer to this problem as it allows the mesh updating without the need of rebuilding it from scratch. Fast Radial Basis Functions (RBF) can be used as an effective tool for enabling mesh morphing on very large meshes that are typically used in advanced industrial applications (many millions of nodes). The applicability of this concept is demonstrated in this paper exploiting state of the art tools for FEA (ANSYS Mechanical) and for advanced mesh morphing (RBF Morph ACT Extension). Proposed method is benchmarked using as a reference a circular notched bar with a surface defect. Reliability of fracture parameter extraction on the morphed mesh is first verified using as a reference literature data and ANSYS Mechanical tools based on re-meshing: different crack shapes are achieved using the new geometry as a morphing target. Crack propagation workflow is then demonstrated showing the computed shape evolution for different size and shape of the initial crack.

Cite

CITATION STYLE

APA

Biancolini, M. E., Chiappa, A., Giorgetti, F., Porziani, S., & Rochette, M. (2018). Radial basis functions mesh morphing for the analysis of cracks propagation. In Procedia Structural Integrity (Vol. 8, pp. 433–443). Elsevier B.V. https://doi.org/10.1016/j.prostr.2017.12.043

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free