synthetic-models

PRA/reliability models, typically event trees and fault trees, in various @openpra-org supported formats.

https://github.com/openpra-org/synthetic-models

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 5 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (6.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

PRA/reliability models, typically event trees and fault trees, in various @openpra-org supported formats.

Basic Info
  • Host: GitHub
  • Owner: openpra-org
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 77.2 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created 10 months ago · Last pushed 10 months ago
Metadata Files
Readme Citation

README.md

Synthetic PRA Models

DOI

PRA/reliability models, typically event trees and fault trees, in various @openpra-org supported formats.

Schema Validation

Schemas for these models can be found on the submoduled repo @openpra-org/mef-schema. Initialize the submodule using: git submodule update --init --recursive

Additional References

  • E. M. Aras, Enhancement Methodology for Probabilistic Risk Assessment Tools through Diagnostics, Optimization, and Parallel Computing, Doctor of Philosophy, North Carolina State University, Raleigh, North Carolina, 2024. [Online]. Available: https://repository.lib.ncsu.edu/items/bb05f7f5-1cff-4beb-9312-331bc94b0b95
  • E. M. Aras, A. S. Farag, A. Earthperson, and M. A. Diaconeasa, Methodology and Demonstration for Performance Analysis of a Probabilistic Risk Assessment Quantification Engine: SCRAM, in 18th International Probabilistic Safety Assessment and Analysis (PSA 2023), Knoxville, TN: American Nuclear Society, 2023, pp. 452459.
  • E. M. Aras, A. S. Farag, A. Earthperson, and M. A. Diaconeasa, Method of Developing a SCRAM Parallel Engine for Efficient Quantification of Probabilistic Risk Assessment Models, in 18th International Probabilistic Safety Assessment and Analysis (PSA 2023), Knoxville, TN: American Nuclear Society, 2023, pp. 134140.
  • E. M. Aras, A. S. Farag, A. Earthperson, and M. A. Diaconeasa, Benchmark Study of XFTA and SCRAM Fault Tree Solvers Using Synthetically Generated Fault Trees Models, in Volume 9: Mechanics of Solids, Structures, and Fluids; Micro- and Nano-Systems Engineering and Packaging; Safety Engineering, Risk, and Reliability Analysis; Research Posters, Columbus, Ohio, USA: American Society of Mechanical Engineers, Oct. 2022, p. V009T14A016. doi: 10.1115/IMECE2022-95783.
  • A. Farag, S. Wood, A. Earthperson, E. Aras, J. Boyce, and M. Diaconeasa, Evaluating PRA Tools for Accurate and Efficient Quantifications: A Follow-Up Benchmarking Study Including FTREX, in Advanced Reactor Safety (ARS), Las Vegas, NV: American Nuclear Society, 2024, pp. 573582. doi: 10.13182/T130-43377.
  • A. S. Farag, E. M. Aras, A. Earthperson, S. T. Wood, and J. Boyce, Preliminary Benchmarking of SAPHSOLVE, XFTA, and SCRAM using Synthetically Generated Fault Trees with Common Cause Failures, in 18th International Probabilistic Safety Assessment and Analysis (PSA 2023), Knoxville, TN: American Nuclear Society, 2023, pp. 4049.

Owner

  • Name: OpenPRA
  • Login: openpra-org
  • Kind: organization
  • Email: admin@openpra.org

OpenPRA

GitHub Events

Total
  • Release event: 1
  • Push event: 5
  • Create event: 1
Last Year
  • Release event: 1
  • Push event: 5
  • Create event: 1

Dependencies

generator/Dockerfile docker
  • python 3.9-alpine build
generator/requirements.txt pypi
generator/setup.py pypi
  • argparse *
  • setuptools *