Three SRI researchers presented information at the National Energy Technology Laboratory (NETL) 2015 Carbon Capture Technology Meeting.
Energy & green tech publications
Evidence for Generation of Unstable Suprathermal Electron Population in the Auroral F Region
In this work we used a one-dimensional Zakharov simulation to constrain the range of physical mechanisms underlying these observational features.
A Top to Bottom Evaluation of IRI 2007 within the Polar Cap
This analysis demonstrates notable differences between IRI and ionosonde NmF2 diurnal and seasonal behavior over the entire period studied, where good agreement is found during summer periods but otherwise errors in excess of 50% were prevalent, particularly during equinox periods.
Characteristics of an Advanced Carbon Sorbent for CO2 Capture
The adsorption and desorption characteristics on an advanced carbon sorbent for gases present in the flue gas stream, including CO2, N2, O2, Ar, H2O vapor, and impurities NO and SO2 are discussed.
Race for Developing Promising CO2 Capture Technologies Ready for 2020 Deployment: Novel Mixed-Salt Based Solvent Technology for Post Combustion Application
Self-ignition of Hydrogen Releases through Electrostatic Discharge Induced by Entrained Particulates
The potential for particulates entrained in hydrogen releases to generate electrostatic charge and induce electrostatic discharge ignitions was investigated.
Experimental Investigation of Hydrogen Release and Ignition from Fuel Cell Powered Forklifts in Enclosed Spaces
Conducted experiments were devised to assess the utility of modeling approaches used to analyze potential consequences from ignited hydrogen leaks in facilities certified according to existing code language.
Releases from Hydrogen Fuel-Cell Vehicles in Tunnels
To investigate potential consequences, a combined experimental and modeling study has been performed to characterize releases from a hydrogen fuel-cell vehicle inside a tunnel.
Applying Learning Curves to Modeling Future Coal and Gas Power Generation Technologies
To forecast each of these competing technologies under various scenarios of electricity demand, fuel cost, and research investment, we created a Power Technology Futures Model (PTFM) based on “learning curves” methodology.