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RAVEN: Comprehensive Uncertainty Quantification and Optimization for Complex Systems

RAVEN is a versatile framework designed for uncertainty quantification, risk assessment, and optimization of complex systems, integrating advanced statistical, data analysis, and AI techniques for enhanced system understanding and performance.

Overview: Developed in 2012 under the Risk Informed Safety Margin Characterization (RISMC) pathway of the Light Water Reactor Sustainability (LWRS) program, RAVEN (Risk Analysis and Virtual ENvironment) addresses the need for modern risk evaluation in complex systems like nuclear power plants. It caters to the increasing demand for sophisticated tools to analyze systems' responses under varied conditions, especially considering the uncertainties inherent in these systems. RAVEN's design philosophy aims to be user-friendly for engineers and scientists while offering a modular structure for easy expansion by developers. This balance makes RAVEN a powerful tool for a wide range of applications in different sectors.

Description: RAVEN operates by perturbing system responses using various strategies (Monte-Carlo, Latin Hypercube, etc.) and analyzing the data through classical and advanced data mining approaches. It works in tandem with other INL software (like RELAP5-3D, MAAP5, BISON) to model complex physical systems. RAVEN's strength lies in its ability to manage parallel dispatching of simulations and its use of AI algorithms to create surrogate models for complex analysis tasks. This multifaceted approach allows for thorough uncertainty quantification, reliability analysis, and parametric studies, making it a comprehensive tool for risk assessment and optimization in complex environments.

Benefits:          

• Versatile Analysis Capabilities: Supports various analysis methods, including regression, risk assessment, and data mining.
• Integration with Third-party Software: Enhances existing physical models with advanced analytical capabilities.
• Advanced AI Utilization: Employs AI for surrogate modeling and complex system analysis.
• Parallel Computing Support: Efficiently manages computational resources for large-scale analyses.
• Customizable Workflow: Offers flexibility in analysis flow construction to suit specific project needs.

Applications:    

• Risk assessment and safety margin characterization in nuclear power plants.
• System response analysis in multi-physics environments.
• Optimization and data analysis in engineering and scientific research.
• Uncertainty quantification in complex physical and engineering systems.
• Advanced modeling and simulation in various industries, including energy, manufacturing, and research.

Access: https://github.com/idaholab/raven  

IP Status: Copyright asserted on software titled “Risk Analysis and Virtual ENvironment: RAVEN,” BEA Docket No. CW-14-01.

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