Search NASA⌕ Search

DOE OSTI · 1478648

Peridynamics using Irregular Domain Discretization with MOOSE-Based Implementation

Abstract

In this paper, reformulation of classical bond-based peridynamic thermomechanical model for irregular domain decomposition and its MOOSE-based implicit formulation are presented. First, the irregular grid based peridynamic thermomechanical model is formulated and model parameters are derived. Following this, an implicit formulation for the solution of static or quasi-static problems is presented. Some aspects of the MOOSE-based implementation are given. After that, the formulation is verified against benchmark solutions for thermomechanic problems. Crack initiation and propagation in circular (2D) and cylindrical (3D) nuclear fuels at high temperature are studied using irregular grids.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chen, Hailong, Hu, Yile, Spencer, Benjamin W. 2017-11-01. Peridynamics using Irregular Domain Discretization with MOOSE-Based Implementation. https://doi.org/10.1115/imece2017-71527

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↗