Oct 25 2012
Three Cornell research teams have received National Science Foundation (NSF) support from a new program that rewards high-risk, high-reward interdisciplinary projects.
NSF's INSPIRE (Integrated NSF Support Promoting Interdisciplinary Research and Education) program funds research teams from "intellectually distinct" disciplines, in the hope of breaking down traditional intellectual boundaries to lead to new discoveries.
Mukund Vengalattore, assistant professor of physics, and Sunil Bhave, associate professor of electrical and computer engineering, received $800,000 over four years to develop a novel optomechanical system that works with atomic-level sensitivity.
Physicist Vengalattore studies nanokelvin ultracold atomic gases for controlling matter in quantum domains. Engineer Bhave designs and fabricates microelectromechanical resonators for radio, microwave and optical systems.
Using optics as a medium, the researchers plan to make a hybrid quantum system that has the precision and sensitivity of one of Vengalattore's ultracold gases and the capabilities of one of Bhave's resonators.
"We have the ability to control the quantum properties of an ultracold gas, but these systems are extremely fragile," Vengalattore said. "In contrast, [Bhave] has something very robust but hard to control. ... The missing ingredient is the conduit, and the conduit is light."
That's because light interacts strongly with both atoms and with the mechanical device and is also something that can be switched on and off, he added.
Potential applications of such hybrid systems include quantum sensor technologies for force and field sensing, as well as macroscopic tests of quantum mechanical phenomena such as entanglement and quantum measurement, according to Vengalattore.
Lang Tong, the Irwin and Joan Jacobs Professor in Engineering, and Shanjun Li, assistant professor of applied economics and management, have received a four-year, $700,000 grant to study of the engineering and economic challenges of a sustainable pathway to an electric vehicle-based transportation system.
Tong, who studies the smart grid for future energy and power systems, aims to develop new technologies for the large-scale charging of electric vehicles. In particular, his group proposes a new architecture of "network-switched charging" to enable charging of a large number of electric vehicles in public facilities. The idea is to achieve engineering and economic efficiency of such large-scale charging by fully utilizing renewable energy, such as rooftop solar generation, and taking advantage of flexible schedules of individual customers.
Li will study the economic side of the equation, including such factors as electricity prices and the effects of government subsidies or other incentives to align the market with infrastructure needs.
"Both charging stations and electric vehicles are new technologies," Li said. "The technology must be adopted in large scale and at low cost so that investors will have incentive to invest in the infrastructure."
In Asia in particular, electric vehicles are the best means to a more sustainable living environment, Tong said. In Beijing and other mega-cities in Asia, for example, the lack of private garages makes it essential for parking facilities to be equipped with efficient charging capabilities, while providing convenient and economic service for electric vehicle owners, Tong said.
Jim Dai, professor of operations research and information engineering, is co-principal investigator on an INSPIRE grant with principal investigator Bill Lin of the University of California-San Diego and co-PI Jun Xu of Georgia Tech. Their three-year, $750,000 project aims to deliver an analytical framework for solving emerging networking problems. The goal is a unifying stochastic processing network that draws from the semiconductor manufacturing industry and virtualized data center networking.
Dai's contribution will be to lead the development of a new mathematical framework called stochastic processing calculus, which would provide a mathematical foundation for analysis of performance guarantees and boundaries in stochastic processing networks.