headshot of Tatsuya Sakurahara

Tatsuya Sakurahara

Assistant Professor
tsakurahara@pitt.edu
Mechanical Engineering & Materials Science

overview

Dr. Tatsuya Sakurahara’s research advances risk and reliability analysis to enhance the safety and performance of complex technological systems, particularly nuclear energy systems. The goal of his work is to support risk-informed decision-making throughout the life cycle of these systems—from design and licensing to construction and operation, aimed at enabling the deployment of next-generation nuclear energy systems and other emerging technologies.

His research integrates state-of-the-art simulations, systematic risk modeling, and probabilistic techniques. His expertise spans multiple aspects of risk and reliability analysis, including uncertainty quantification, probabilistic physics-of-failure simulation, human reliability analysis, decision analysis, and AI and machine learning for risk and reliability analysis.

Before joining Pitt, Dr. Sakurahara was a Research Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering (NPRE) at the University of Illinois at Urbana-Champaign (UIUC). Dr. Sakurahara earned his Ph.D. in nuclear engineering from UIUC in 2018, and his M.S. in nuclear engineering and management (2013) and B.S. in systems engineering with a concentration in environment and energy systems (2011) from the University of Tokyo, Japan. Dr. Sakurahara is a recipient of the 2022 George Apostolakis Fellowship from the International Association for Probabilistic Safety Assessment and Management.

about

PhD, Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana Champaign, 2018

MS, Nuclear Engineering and Management, University of Tokyo, 2013

BS, Environment and Energy System Engineering, University of Tokyo, 2011

Shimada, K., Sakurahara, T., Farshadmanesh, P., Reihani, S., & Mohaghegh, Z. (2024). Integration of Level 3 probabilistic risk assessment for nuclear power plants with transportation simulation considering earthquake hazards. Annals of Nuclear Energy, 197, 110243.Elsevier BV. doi: 10.1016/j.anucene.2023.110243.

Cheng, W.C., Beal, J., Sakurahara, T., Reihani, S., Kee, E., & Mohaghegh, Z. (2022). Modeling interconnections of safety and financial performance of nuclear power plants, part 3: Spatiotemporal probabilistic physics-of-failure analysis and its connection to safety and financial performance. Progress in Nuclear Energy, 153, 104382.Elsevier BV. doi: 10.1016/j.pnucene.2022.104382.

Sakurahara, T., Reihani, S., Kee, E., & Mohaghegh, Z. (2020). Global importance measure methodology for integrated probabilistic risk assessment. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 234(2), 377-396.SAGE Publications. doi: 10.1177/1748006x19879316.

Schumock, G., Zhang, S., Farshadmanesh, P., Owens, J.G., Kasza, N., Stearns, J., Sakurahara, T., & Mohaghegh, Z. (2020). Integrated Risk-Informed Design (I-RID) methodological framework and computational application for FLEX equipment storage buildings of Nuclear Power Plants. Progress in Nuclear Energy, 120, 103186.Elsevier BV. doi: 10.1016/j.pnucene.2019.103186.

Sakurahara, T., Schumock, G., Reihani, S., Kee, E., & Mohaghegh, Z. (2019). Simulation-Informed Probabilistic Methodology for Common Cause Failure Analysis. Reliability Engineering & System Safety, 185, 84-99.Elsevier BV. doi: 10.1016/j.ress.2018.12.007.

Sakurahara, T., Mohaghegh, Z., Reihani, S., & Kee, E. (2018). Methodological and Practical Comparison of Integrated Probabilistic Risk Assessment (I-PRA) with the Existing Fire PRA of Nuclear Power Plants. Nuclear Technology, 204(3), 354-377.Informa UK Limited. doi: 10.1080/00295450.2018.1486159.

Alkhatib, S., Sakurahara, T., Reihani, S., Kee, E., Ratte, B., Kaspar, K., Hunt, S., & Mohaghegh, Z. Phenomenological Nondimensional Parameter Decomposition to Enhance the Use of Simulation Modeling in Fire Probabilistic Risk Assessment of Nuclear Power Plants. Journal of Nuclear Engineering, 5(3), 226-245.MDPI AG. doi: 10.3390/jne5030016.