This repository includes synthetic models in SAPHSOLVE JSON format. The goals of including these models are to:
- Serve as test cases for various PRA tools, including Open-PRA.
- Familiarize users with different modeling approaches and formats.
- Provide a reference for PRA model exchange.
- And more.
The repository also maintains a schema for the models to give users valuable insights.
SAPHSOLVE is the quantification engine of legacy PRA tool SAPHIRE. SAPHSOLVE input and output format are detailed in a report published by Idaho National Laboratory.
Models are generated leveraging fault tree generator developed under SCRAM
Currently, the generated models are fault tree models. The configurations are listed below. Although 14 options are available for customizing fault trees, the following three arguments are preferred to create fault trees in different formats:
- The number of basic events
- Maximum probability for basic events
- Minimum probability for basic events
| # | Arguments | Value |
|---|---|---|
| 1 | Fault tree name | Autogenerated |
| 2 | The root gate name | root |
| 3 | The seed of the random number generator | 123 |
| 4 | The number of basic events | 100:50:5000 |
| 5 | The number of house events | 0 |
| 6 | The number of CCF groups | 0 |
| 7 | The average number of gate arguments | 3.0 |
| 8 | The weights of gate types [AND, OR, K/N, NOT, XOR] | [1.0, 1.0, 1.0, 0.0, 0.0] |
| 9 | Percentage of common basic events per gate | 0.3 |
| 10 | Percentage of common gates per gate | 0.1 |
| 11 | The avg. number of parents for common basic events | 2 |
| 12 | The avg. number of parents for common gates | 2 |
| 13 | Maximum probability for basic events | 0.05 |
| 14 | Minimum probability for basic events | 0.01 |
These models can be used to test quantification engines. Additionally, they enable the creation of a verification platform between quantification engines, allowing developers or practitioners to cross-check their results. Moreover, these models serve as a foundation for benchmarking efforts for any quantification tool.
- E. M. Aras, A. S. Amin Aly Farag, S. T. Wood, and J. T. Boyce, “Refining Processing Engines from SAPHIRE: Initialization of Fault Tree/Event Tree Solver,” Idaho National Laboratory (INL), Idaho Falls, ID (United States), INL/RPT-23-75066-Rev000, Oct. 2023. doi: 10.2172/2203095.
- S. Wood, J. Boyce, E. Aras, A. Farag, and M. Diaconeasa, “Advancing SAPHIRE: Transitioning from Legacy to State-of-Art Excellence,” in Advanced Reactor Safety (ARS), Las Vegas, NV: American Nuclear Society, 2024, pp. 532–541. doi: 10.13182/T130-43357.
- E. Aras, S. Wood, J. Boyce, A. Farag, and M. Diaconeasa, “Enhancing the SAPHIRE Solve Engine: Initial Progress and Efforts,” in Advanced Reactor Safety (ARS), Las Vegas, NV: American Nuclear Society, 2024, pp. 542–551. doi: 10.13182/T130-43361.
- 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
- 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. 573–582. doi: 10.13182/T130-43377.
- E. M. Aras, S. T. Wood, A. S. A. A. Farag, and J. T. Boyce, “Diagnostics and Strategic Plan for Advancing the SAPHIRE Engine,” Idaho National Laboratory, Idaho Falls, ID, INL/COM-23-74428, Aug. 2023. [Online]. Available: https://inldigitallibrary.inl.gov/sites/sti/sti/Sort_67446.pdf
- M. Hamza, A. Tezbasaran, E. Aras, A. S. Farag, and M. A. Diaconeasa, “Model Exchange Methodology Between Probabilistic Risk Assessment Tools: SAPHIRE and CAFTA Case Study,” in 18th International Probabilistic Safety Assessment and Analysis (PSA 2023), Knoxville, TN: American Nuclear Society, 2023, pp. 150–158.