Research
Research Overview
Our research focuses on advancing the theoretical and methodological foundation of risk and reliability analysis for complex engineering systems, with a particular emphasis on energy systems.
Why is this field important?
Systematic risk and reliability analysis can help predict and prevent future failures, enhance safety and security, and improve the overall performance of complex engineering systems.
Advancing risk and reliability analysis is crucial to achieving a carbon-free energy paradigm while ensuring the safety, security, and resilience of critical energy systems. This includes the deployment of advanced nuclear reactors and the development of integrated energy systems.
The fundamental principles of risk and reliability analysis extend beyond energy sectors, including but not limited to, civil infrastructure, aerospace, industrial processes, and healthcare. Academic research aimed at advancing risk and reliability analysis can contribute to enhancing decision-making across broader sectors and improving system outcomes throughout the entire lifecycle.
How do we approach?
We model complex engineering systems by combining:
- Systems modeling to analyze functional interrelationships among critical systems and their components.
- Phenomenological and mechanistic modeling and simulation to analyze the underlying phenomena that impact system performance (e.g., physics, human performance, and organizational processes).
- Statistical computation to quantify model inputs and outputs while accounting for uncertainties in the behavior of complex systems.
- AI and machine learning techniques are incorporated to efficiently handle large volumes of data and computational demands. This allows us to balance the resolution of risk and reliability models with scalability and practicality.
Research Areas
- Probabilistic risk assessment (PRA)
- Risk assessment of integrated and multi-network energy systems
- Uncertainty analysis in nuclear power risk assessment
- Probabilistic programming for modeling and simulation of complex cyber-physical-human systems
- Reliability and maintainability analysis
- Risk-benefit analysis and risk-informed decision-making
Current Projects
Probabilistic Risk Assessment for Integrated Energy Systems with Data Centers
Develops a probabilistic risk assessment (PRA) framework for integrated energy systems consisting of multiple interconnected energy networks, with a focus on the emerging risks from hyperscale data center loads. Advancing the modeling of Multi-Energy Coupling Components (MECC) that can induce inter-network dependencies and cascading events.
Advancing Understanding and Modeling of Deep Uncertainty in Nuclear Power Risk Assessment
Supported by the 2026 ORAU Ralph E. Powe Junior Faculty Enhancement Award.
Combines existing probabilistic risk assessment approaches with non-probabilistic uncertainty analysis theories and methods to analyze deep uncertainty (e.g., model and completeness uncertainty that is difficult to quantify using probabilities) in nuclear risk assessment and management.
Combining GIS Spatial Analysis and PRA Concepts to Support Advanced Reactor Siting
Develops a GIS-based decision-support platform, integrated with PRA concepts and techniques, to support siting decisions for advanced reactors deployed as fleets across a geographical region. Addresses the portfolio-level and sequential nature of siting small-modular reactors or microreactors.