Ultra High Pressure Hydrogen Studies

Hydrogen has been called the fuel of the future, and as it’s non- renewable counterparts become scarce the economic viability of hydrogen gains traction. The potential of hydrogen is marked by its high mass specific energy density and wide applicability as a fuel in fuel cell vehicles and homes. However hydrogen’s volume must be reduced via pressurization or liquefaction in order to make it more transportable and volume efficient. Currently the vast majority of industrially produced hydrogen comes from steam reforming of natural gas. This practice yields low-pressure gas which must then be compressed at considerable cost and uses fossil fuels as a feedstock leaving behind harmful CO and CO2 gases as a by-product. The second method used by industry to produce hydrogen gas is low pressure electrolysis. In comparison the electrolysis of water at low pressure can produce pure hydrogen and oxygen gas with no harmful by-products using only water as a feedstock, but it will still need to be compressed before use. Multiple theoretical works agree that high pressure electrolysis could reduce the energy losses due to product gas compression. However these works openly admit that their projected gains are purely theoretical and ignore the practical limitations and resistances of a real life high pressure system. The goal of this work is to experimentally confirm the proposed thermodynamic gains of ultra-high pressure electrolysis in alkaline solution and characterize the behavior of a real life high pressure system.

Accelerating self-consistent field convergence with the augmented Roothaan-Hall energy function.

Based on Pulay's direct inversion iterative subspace (DIIS) approach, we present a method to accelerate self-consistent field (SCF) convergence. In this method, the quadratic augmented Roothaan-Hall (ARH) energy function, proposed recently by Høst and co-workers [J. Chem. Phys. 129, 124106 (2008)], is used as the object of minimization for obtaining the linear coefficients of Fock matrices within DIIS. This differs from the traditional DIIS of Pulay, which uses an object function derived from the commutator of the density and Fock matrices. Our results show that the present algorithm, abbreviated ADIIS, is more robust and efficient than the energy-DIIS (EDIIS) approach. In particular, several examples demonstrate that the combination of ADIIS and DIIS ("ADIIS+DIIS") is highly reliable and efficient in accelerating SCF convergence.

An energy-efficient data delivery scheme for delay-sensitive traffic in wireless sensor networks

We propose a novel data-delivery method for delay-sensitive traffic that significantly reduces the energy consumption in wireless sensor networks without reducing the number of packets that meet end-to-end real-time deadlines. The proposed method, referred to as SensiQoS, leverages the spatial and temporal correlation between the data generated by events in a sensor network and realizes energy savings through application-specific in-network aggregation of the data. SensiQoS maximizes energy savings by adaptively waiting for packets from upstream nodes to perform in-network processing without missing the real-time deadline for the data packets. SensiQoS is a distributed packet scheduling scheme, where nodes make localized decisions on when to schedule a packet for transmission to meet its end-to-end real-time deadline and to which neighbor they should forward the packet to save energy. We also present a localized algorithm for nodes to adapt to network traffic to maximize energy savings in the network. Simulation results show that SensiQoS improves the energy savings in sensor networks where events are sensed by multiple nodes, and spatial and/or temporal correlation exists among the data packets. Energy savings due to SensiQoS increase with increase in the density of the sensor nodes and the size of the sensed events. © 2010 Harshavardhan Sabbineni and Krishnendu Chakrabarty.