# Open Source Software: SR2ML: Pioneering Safety and Reliability in Nuclear Plant Management

Canonical: https://abierto.us/opportunities/6bd414fb0d774ef6af3a5f16ca38293c

- Notice type: Special notice
- Status: Closed. Deadline was March 15, 2026 at 11:00 AM EDT
- Department: Department of Energy
- Contracting office: Battelle Energy Alliance–doe CNTR (899050)
- NAICS: 221113 Nuclear Electric Power Generation
- Product or service code: 4470 Nuclear Reactors
- Place of performance: Idaho Falls, Idaho
- County: Bonneville County (FIPS 16019). https://abierto.us/counties/bonneville-county-id-16019
- City: Idaho Falls. https://abierto.us/cities/idaho-falls-id-1639700
- First posted: October 31, 2024
- Last posted: October 31, 2024
- SAM.gov: https://sam.gov/workspace/contract/opp/6bd414fb0d774ef6af3a5f16ca38293c/view

## Description

**Open Source Software:**

**SR2ML:** Pioneering Safety and Reliability in Nuclear Plant Management In an industry where safety and efficiency are paramount, SR2ML (SafetyRiskReliabilityModelLibrary) emerges as a transformative software package designed to interface seamlessly with the RAVEN code developed by INL. This powerful toolset enables static and dynamic risk analysis, offering unparalleled insights into system reliability and operational guidelines to enhance the long-term viability of the U.S. reactor fleet.

As the nuclear power sector strives to remain competitive, reducing Operation and Maintenance (O&M) costs while ensuring safety and reliability has become a critical challenge. Traditional approaches to balance these aspects over decades of operation have laid the groundwork for innovative solutions. SR2ML represents a leap forward, combining classical and cutting-edge models to address these challenges head-on, facilitating a new era of optimized plant management.

SR2ML provides a comprehensive suite of safety and reliability analysis models, including classical reliability models like Fault-Trees and Markov and advanced components aging models. These models are designed for integration into the RAVEN ensemble for dynamic system reliability analysis and can interface with system analysis codes for detailed failure and accident progression evaluations.

Through machine learning and quantitative methods, SR2ML empowers operators with dynamic behavior emulation and decision-making tools, driving down O&M costs while enhancing plant safety and efficiency.

**Advantages Optimized Plant Operations:** Enables data-driven decision-making for preventive maintenance and component refurbishment, minimizing O&M costs.

**Advanced Risk Analysis:** Integrates classical and innovative models for comprehensive safety and economic risk assessments.

**Dynamic System Modeling:** Offers deterministic and stochastic models to predict system and component behavior accurately.

**Cost-Effective Maintenance Strategies:** Identifies optimal operational guidelines to balance reliability, safety, and cost-efficiency.

**Seamless Integration:** Designed to work with RAVEN and LOGOS for a unified analysis platform, enhancing decision-making processes.

**Applications Nuclear Plant Management:** Streamlining O&M strategies to enhance reliability and safety while reducing costs.

**Risk Assessment:** Conducting detailed risk analysis to inform strategic decision-making regarding plant operations.

**System Reliability Analysis:** Employing dynamic analysis to predict and mitigate potential system failures.

**Economic Optimization:** Integrating economic models to prioritize actions that maximize plant availability and profitability. Discover how SR2ML can transform your nuclear plant operations. Download now and learn how integrating SR2ML into your management strategy can lead to safer, more efficient, cost-effective plant operations. INL’s Technology Deployment department focuses exclusively on licensing intellectual property and partnering with industry collaborators capable of commercializing our innovations.

Our goal is to commercialize the technologies developed by INL researchers. We do not engage in purchasing, manufacturing, procurement decisions, or providing funding. Additionally, this is not a call for external services to assist in the development of this technology.

## Publications

- October 31, 2024: Special notice, due March 15, 2026 at 11:00 AM EDT. Notice 6bd414fb0d774ef6af3a5f16ca38293c. https://sam.gov/workspace/contract/opp/6bd414fb0d774ef6af3a5f16ca38293c/view

## Points of contact

- Andrew Rankin, andrew.rankin@inl.gov

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