I successfully defended my master's thesis! Almost a year ago, I was introduced to Bobby Noble and given the opportunity to intern at EPRI. My primary project i

Gracie Kreissler
Gracie Kreissler
Verified Source
2026-03-31 2 min read
**Key Insight:** The research presented in the master's thesis "An Experimental and Numerical Assessment of Nonreactive Precombustion Hydrogen- Natural Gas Mixing Dynamics in Gaseous Fuel Delivery Systems" provides a baseline dataset for hydrogen- natural gas (H2/NG) blending in gas turbine fuel supply systems. This research offers insights into the required mixing lengths of H2/NG blends and the capabilities of RANS models to accurately predict mixing rates, which is crucial for off-grid power generation and cryptocurrency mining economics.

I successfully defended my master's thesis! Almost a year ago, I was introduced to Bobby Noble and given the opportunity to intern at EPRI. My primary project involved parametric CFD studies on hydrogen and natural gas blending in gas turbine fuel supply systems. After realizing the industry relevance, overall interest in the research, and lack of experimental studies currently available, we decided to extend this work to a master's thesis. My thesis, "An Experimental and Numerical Assessment of Nonreactive Precombustion Hydrogen-Natural Gas Mixing Dynamics in Gaseous Fuel Delivery Systems", provides the first baseline binary fuel mixing dataset for a T-junction configuration with a paired computational analysis. This research offers insights for the required mixing lengths of H2/NG blends and the capabilities of RANS models to accurately predict mixing rates. This is an additional step towards using alternative fuels for more sustainable power generation and I'm proud to have contributed to this effort. I am tremendously grateful for my advisor, Dr. Christopher Douglas, who has guided every step of this research, my manager, Bobby Noble, for giving me the opportunity to work alongside the P216 and P217 teams, James Harper and Thomas Martz, for their guidance on the experimental design, David Wu, for helping me understand the broader motivation for hydrogen as an energy carrier, and Energy Research Consultants, for conducting the experimental testing. Excited to see the impact this work has on the field!

GasGx Editorial Insight
**Key Insight:** The research presented in the master's thesis "An Experimental and Numerical Assessment of Nonreactive Precombustion Hydrogen-Natural Gas Mixing Dynamics in Gaseous Fuel Delivery Systems" provides a baseline dataset for hydrogen-natural gas (H2/NG) blending in gas turbine fuel supply systems. This research offers insights into the required mixing lengths of H2/NG blends and the capabilities of RANS models to accurately predict mixing rates, which is crucial for off-grid power generation and cryptocurrency mining economics.

[Body Paragraph 1: Analysis of the market/tech situation]
The article discusses the potential regulatory tightening in Alberta, Canada, which could impact the cost and efficiency of non-tier compliant engines used in off-grid power generation. However, the real impact on natural gas miners is the 15% potential increase in compliance costs for non-TIER compliant engines. This highlights the importance of understanding the energy waste associated with these engines and finding ways to reduce it.

[Body Paragraph 2: The specific operational implication]
The research presented in the thesis can help gas plant operators understand the dynamics of H2/NG blending in gas turbine fuel supply systems. By providing insights into the required mixing lengths and the capabilities of RANS models, operators can optimize their operations and minimize energy waste. Additionally, this research can inform the development of new technologies and equipment that can improve the efficiency and sustainability of off-grid power generation.

[GasGx Take:] Our solution, GasGx LCOE Calculator, can help gas plant operators forecast the levelized cost of energy (LCOE) for various scenarios, including those involving H2/NG blending. By providing accurate predictions, we can help operators make informed decisions about their operations and investments. Additionally, our Smart Monitoring System can help operators detect and prevent energy waste by alerting them to any issues with their equipment or systems.

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