Search NASA⌕ Search

DOE OSTI · 3097479

Finite deformation implementation of a mixed-mode single-integral type cohesive zone with reorienting surfaces of separation

Abstract

To model material ductile failure and crack propagation, cohesive zone elements can be embedded along potential fracture paths in a finite element simulation. When damage criteria are met, elements in the mesh decohere, simulating the formation and propagation of a crack. In this paper, we present a novel computational algorithm based on finite deformation theory, essential to modeling crack initiation and growth in solids undergoing large deformations. This new algorithm was formulated within a Lagrangian frame of reference to extend previous cohesive zone algorithms to include modeling crack growth in finite deformation contexts. The local coordinate system, necessary for defining an embedded cohesive zone, is constructed based upon the current configuration and is updated within the nonlinear iteration process, thereby resulting in the convergence of the solution for a growing crack in a large deformation quasi-static setting. The model’s accuracy was demonstrated by comparing finite element model simulation results with the analytic case of a constant surface separation, as shown in the verification examples. The power and efficacy of the algorithm to capture large deformations during crack growth were then demonstrated with a double cantilever beam example case. It indicates that the model can be applied to a variety of physical circumstances for predicting crack initiation and growth with delamination and fracture.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Whetstone, Gavin [Texas A & M Univ., College Station, TX (United States)] (ORCID:0000000190013611), Morris, Gabe [Mississippi State Univ., Starkville, MS (United States)], Zocher, Marvin A. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000168193169), Morgan, Nathaniel [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000276118449), Kim, Yong-Rak [Texas A & M Univ., College Station, TX (United States)], Allen, David H, [Texas A & M Univ., College Station, TX (United States)]. 2026-04-13. Finite deformation implementation of a mixed-mode single-integral type cohesive zone with reorienting surfaces of separation. https://doi.org/10.1177/10812865261437042

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗