Uncertainty Quantification Implementation for Quantitative Risk Assessments of Hydrogen Systems
By: Dusty Brooks, Benjamin Schroeder and Brian Ehrhart
References
- Adams, B. et al. (2022) Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 User’s Manual, s.l.: Sandia National Laboratories, SAND2022-6171.
- Ehrhart, B. D., Hecht, E. S. and Schroeder, B. B. (2023) Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+) Version 5.1 Technical Reference Manual, s.l.: Sandia National Laboratories, SAND2023-14224.
- Glover, A. and Brooks, D. (2023) Risk Analysis of a Hydrogen Generation Facility near a Nuclear Power Plant, s.l.: Sandia National Laboratories, SAND2023-07884. Available at: 10.2172/2430381
- Groth, K., LaChance, J. and Harris, A. P. (2012) Early-Stage Quantitative Risk Assessment to Support Development of Codes and Standard Requirements for Indoor Fueling of Hydrogen Vehicles, s.l.: Sandia National Laboratories, SAND2012-10150. Available at: 10.2172/1057251
- Helton, J. and Davis, F. (2001) Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems, s.l.: Sandia National Laboratories, SAND2001-0417. Available at: 10.2172/806696
- Kaplan, S. and Garrick, B. (1981) ‘On the quantitative definition of risk’, Risk Analysis, 1, pp. 11–27. Available at: 10.1111/j.1539-6924.1981.tb01350.x
- LaChance, J., Houf, W., Middleton, B. and Fleur, L. (2009) Analyses to Support Development of Risk-Informed Separation Distances for Hydrogen Codes and Standards, s.l.: Sandia National Laboratories, SAND2009-0874. Available at: 10.2172/983689
- Saltelli, A. et al. (2008) Global Sensitivity Analysis: The Primer. Chichester, England: Wiley. Available at: 10.1002/9780470725184
- Sandia National Laboratories (2024) Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+), Version 5.1. Available at:
https://hyram.sandia.gov - Schroeder, B. and Brooks, D. (2024) Uncertainty Quantification and Sensitivity Analysis for Quantitative Risk Assessments of Hydrogen Infrastructure, s.l.: Sandia National Laboratories, SAND2024-09878. Available at: 10.2172/2463031
- Sobol’, I. (1993) ‘Sensitivity estimates for nonlinear mathematical models’, Mathematical Modeling & Computational Esperiment (English Translation), 1, pp. 407–414.
- Sobol’, I. (2001) ‘Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates’, Mathematics and Computers in Simulation, 55(1–3), pp. 271–280. Available at: 10.1016/S0378-4754(00)00270-6
- Swiler, L. et al. (2020) ‘A survey of constrained Gaussian process regression: approaches and implementation challenges’, Journal of Machine Learning for Modeling and Computing, 1(2), pp. 119–156. Available at: 10.1615/JMachLearnModelComput.2020035155
- Swiler, L. P. et al. (2021) Sensitivity Analysis Comparisons on Geologic Case Studies: An International Collaboration, s.l.: Sandia National Laboratories, SAND2021-11053. Available at: 10.2172/1822591
- Tchouvelev, A. V. (2008) Knowledge gaps in hydrogen safety: a white paper., s.l.: International Energy Agency Hydrogen Implementing Agreement Task 19.
- Virtanen, P. et al. (2020) ‘SciPy 1.0: fundamental algorithms for scientific computing in Python’, Nature Methods, 17(3), pp. 361–272. Available at: 10.1038/s41592-019-0686-2
Language: English
Page range: 317 - 330
Submitted on: May 6, 2026
Accepted on: Oct 15, 2026
Published on: Oct 30, 2026
Published by: KIT Scientific Publishing
In partnership with: Paradigm Publishing Services
© 2026 Dusty Brooks, Benjamin Schroeder, Brian Ehrhart, published by KIT Scientific Publishing
This work is licensed under the Creative Commons Attribution 4.0 License.