Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.2%) to scientific vocabulary
Repository
Structural Wind-borne debris Impact Risk assessment Library
Basic Info
Statistics
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
Wind-borne debris is a significant contributor to structural damage during high-intensity wind events, but existing methods for estimating debris impact loads on structures are relatively limited. These limitations stem from inherent uncertainties and lack of knowledge regarding the characterization of combined wind and debris loads, as well as a lack of computational modeling strategies for representing wind-borne debris impacts and their effects on structures.
The Structural Wind-borne debris Impact Risk assessment Library (SWIRL) is a physics-based fluid-structure-debris modeling framework intended to be coupled with OpenSees, and which was originally developed to investigate and quantify of the extent to which wind-borne debris impact contributes to structural damage and collapse. Within this modeling framework, flying debris is represented through discrete realizations of debris trajectories and impacts, with the nonlinear transient dynamic behavior of the structure of interest modeled using OpenSees. Collisions between debris and the structure are resolved through a penalty-based contact enforcement strategy. Parametric vortex models are used to represent the wind field and to determine wind pressures acting on both the structure and the debris.
The core functionality of SWIRL is written in C++, with a fully supported Python API. SWIRL is compatible with OpenSeesPy, and can be used in Python workflows that can be run using quoFEM. Several simple examples are provided to demonstrate how SWIRL may be utilized to conduct a variety of risk assessment analyses.
For additional details and resources, please refer to the documentation pages.
Owner
- Name: Brian Giffin
- Login: bdgiffin
- Kind: user
- Location: Davis, CA
- Company: UC Davis
- Repositories: 1
- Profile: https://github.com/bdgiffin
I'm a computational mechanics code developer at LLNL, with an emphasis in solid mechanics.
Citation (CITATION)
abstract: <p>Pre-release version of the SWIRL library.</p> authors: - affiliation: Oklahoma State University family-names: Brian Doran Giffin cff-version: 1.2.0 date-released: '2024-12-02' doi: 10.5281/zenodo.14261045 license: - cc-by-4.0 repository-code: https://github.com/bdgiffin/SWIRL/tree/v0.1-alpha title: 'bdgiffin/SWIRL: SWIRL v0.1-alpha' type: software version: v0.1-alpha
GitHub Events
Total
- Release event: 1
- Watch event: 3
- Member event: 1
- Push event: 32
- Fork event: 2
- Create event: 4
Last Year
- Release event: 1
- Watch event: 3
- Member event: 1
- Push event: 32
- Fork event: 2
- Create event: 4