Session: 13-01: Hydrogen and Fuel Cells

Paper Number: 157798

157798 - Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring 

Abstract: 

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower and help decarbonize electricity grids, industry, and transportation. While promising, Hydrogen is buoyant, highly flammable, and in the presence of oxygen, it can form explosive mixtures. Therefore, the development of hydrogen as a primary energy carrier needed for our decarbonization goals must be coupled with understanding how to manage it safely. An essential piece of safely managing hydrogen is understanding dispersion or leak scenarios to evaluate and mitigate the safety risks associated with unintentional, but plausible, hydrogen dispersions. An increased understanding of dispersion behavior, and having tools to model dispersions, can help assess how hydrogen would disperse in different facilities, conditions, and environments influencing emergency response plans and safety measures and identifying potential issues with materials and design systems that can withstand the challenges posed by hydrogen. In this study, the modeling environment extends beyond closed spaces (e.g., storage rooms, vehicles, etc.) to modeling hydrogen dispersion in an outdoor, uncontrolled environment. Using the Hydrogen Research Facility under Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory’s (NREL) Flatirons campus as a test bench, the study examines the feasibility, useability, and value of using computational fluid dynamics (CFD) techniques to model hydrogen dispersion in an uncontrollable environment.

The ARIES facility was chosen because controlled hydrogen releases can be performed at a rate of 27 kg-H₂/hr. Site-specific atmospheric and weather condition data such as wind speed and temperature were used as inputs to the model. A subset of the weather conditions experienced during daytime hours without precipitation over the course of three months between October 2023 to December 2023 was collected; using established data clustering techniques, a total of 100 condition sets (e.g., wind speed, temperature, ground factor, Power low coefficient) were chosen.

The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. Wind conditions are found to significantly impact the release behavior, including the hydrogen cloud’s direction and concentrations. At low wind speeds (below 1 Mph), hydrogen forms a cloud and at higher wind speeds (2 to 20 Mph) hydrogen plume stretches in the direction of wind momentum. From the simulation dataset various explainable basic statistical quantities like minimum, maximum, standard deviation, mean, and coefficient of variance of hydrogen concentration were also collected across wind scenarios. Clustering techniques were also used to group regions with similar statistical behavior to maximize detection and estimate optimal sensor locations.  Based on the hydrogen concentrations analysis obtained, a sensor placement is proposed and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The developed model framework can suggest the sensor placement with various sensitivity range requirements of the sensor i.e., ppm range used for detection. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions.

Hydrogen holds great promise, but ensuring safety in its production, storage, and use is paramount. Therefore, studies that improve our understanding of hydrogen dispersion scenarios at open hydrogen facilities are critical in addition to informing sensor placement strategies, safety guidelines, leak detection, and quantifying emissions.

Presenting Author: Munjal Shah National Renewable Energy Laboratory (NREL)

Presenting Author Biography: Dr. Munjal Shah is a Postdoctoral Researcher in the Thermal Energy Systems Group at the National Renewable Energy Laboratory (NREL). He earned his Ph.D. from the University of Buffalo, where his research focused on the aerodynamics of insect flight and the design of planetary exploration vehicles. His expertise includes computational fluid dynamics (CFD), finite element modeling, and machine learning. Dr. Shah currently leads thermal and mechanical modeling efforts for particle-based concentrated solar power (CSP) receivers, aiming to accelerate industry decarbonization. He has also contributed to the development of sulfur-based thermal energy systems for industrial process heat (IPH) applications, as well as prototype pumped thermal energy storage (PTES) and particle-based heat exchanger systems. Dr. Shah actively participates in CSP and thermal energy storage (TES) research, leveraging high-performance computing for advanced fluid and thermal modeling. He also leads projects funded by the Hydrogen Fuel Technologies Office (HFTO) and the Advanced Research Projects Agency-Energy (ARPA-E), focused on developing and deploying hydrogen sensor safety technologies for hydrogen storage facilities.