Skip to content Skip to main navigation Report an accessibility issue

2012 JDRD Features

Desktop DeviceAs promising as this sounds, Veerle Keppens says efficient thermoelectric materials require the unusual combination of poor thermal conductivity and good electrical conductivity.

“Typically, materials with good electrical conductivity also have good thermal conductivity,” she says.

Electrons alone carry electricity; both electrons and phonons (the atomic vibrations from atoms in a crystal’s lattice structure) carry heat. So, a good thermoelectric material is efficient at scattering phonons, which impedes the transfer of heat from one side of a material to the other while allowing the electrons to pass right on through.

Working in tandem, Keppens’ JDRD team, with PhD student Lindsay VanBebber and an LDRD team led by Olivier Delaire of ORNL’s Materials Science and Technology Division, are examining the influence ferroelectric properties have on lattice dynamics—in other words, how these properties affect the atoms moving within the crystal. Their goal is to gain a better understanding of the microscopic origins of suppressed thermal conductivity.

The two teams will explore how a material’s ferroelectric ability to spontaneously polarize under specific conditions correlates with thermoelectric performance.

Temperature Chart“Ferroelectric material as a whole is neutral; there’s no actual charge,” Keppens says. “But the way the tiny units inside are distributed makes it a little more negative on one end and a little more positive on the other. This can only happen if there’s a lack of a certain symmetry in the structure.

“In a perfect crystal at absolute zero degrees, the atoms occupy well-defined positions. Raise the temperature and the atoms start to move, some differently from others. Because of that you can create peculiar lattice dynamics, depending on the structure and the atoms you put into a material.”

For their study, the JDRD team is growing single crystals of Pb1-xSnxTe (lead, tin, telluride)—a new combination, cousin to lead-telluride materials, foremost in thermoelectric power generation for applications above room temperature. They will use Resonant Ultrasound Spectroscopy (RUS) to measure the crystal’s physical response to ultrasonic signals at varying temperatures. Keppens says RUS should show when structural changes actually occur in the material—information that will add insight into the mechanisms linking lattice dynamics with suppressed thermal conductivity.

JDRD project:
Ferroelectric instabilities in thermoelectric materials
Veerle Keppens, UT Department of Materials Science and Engineering

LDRD project:
Improving energy efficiency in thermoelectric materials by integrating neutron scattering with supercomputing and modeling
Oliver Delaire, ORNL Materials Science and Technology Division

han_studentsMelding a lot of simultaneous and heterogeneous data to develop and validate robust algorithms at scale—together with the learning, as Han says, that comes from where “the rubber meets the road”—allows for projection of likely scenarios for a range of possible decisions. Doing this very quickly provides managers and decision makers with readily available, solid evidence in good time.

In traffic simulation, it is essential to preserve the sequential nature of the data. We all know what this means because we have intimate knowledge at an individual or “microscopic” level of driver-reaction times and behavior in stop-and-go driving and traffic jams. We all can also understand what a successful, overall system promises: safety; security in times of terrorist attacks, mass evacuations, and major accidents; fuel savings; and emission reduction.

Massive heterogeneous real-time traffic data is required to build the foundation for a faster-than-real-time, random traffic simulation system. In addition to being 1,000 times faster than the present state-of-the-practice, likely scenarios must be simulated at least thirty to fifty times each for validation. Soon, highway and on-board wireless sensors will add even more to the data mix and flood.

Clearly, the scale and challenges of the required fusion of data and calculations—and the “sequential” constraint in developing parallel algorithms for petascale computing—is enormous.

Emergency Graduate StudentHan has a large and diverse group of gifted students involved in this project, including PhD candidate Ryan Overton; post-doc Qiang Yang; and undergraduate students Allyson Foster, Clement Oigbokie, and Michael Raup. Two doctoral students—Jianjiang Yang, with extensive background in transportation engineering, and Wei Lu, with training and expertise in computer science—have worked with the computational scientists of the corresponding LDRD project led by James Horey of ORNL’s Computational Sciences and Engineering Division. Together, they have designed a new distributed programming model for optimizing traffic flow and have tested it in Han’s large-scale microscopic traffic simulation system.

At least two publications are in the works on a field theory proposed by Han and his students and UTSim (a Universal Traffic Simulation algorithm based on the field theory) with follow-up funding proposals in process to the Department of Transportation, Department of Energy, and National Science Foundation. Han has recently lectured and met with colleagues on the associated computational challenges of traffic simulation in Berlin and Sarajevo. In order to extend and generalize the research, his students have also traveled on UT’s McClure Travel Scholarship to collect driving data in different countries.

JDRD project:
Distributed computation framework for faster-than-real-time microscopic traffic simulation (Year 2)
Lee Han, UT Department of Civil and Environmental Engineering

LDRD project:
Distributed Computational Framework for Massive Heterogeneous Data Fusion
James Horey, ORNL Computational Sciences and Engineering Division

Lab EquipmentAkin to Metal Organic Frameworks (MOFs) discovered twenty years ago, COFs hit the new materials scene just ten years ago. Both materials have three-dimensional, porous nanostructures built from repeating nodes held together by linking units. With carefully planned construction, each has the potential to trap molecules, such as carbon dioxide or hydrogen, and hold them in the pores for later release. MOFs have metal-ion nodes and organic linkers; COFs are assembled solely from organic building blocks and address a problem MOFs encounter in CO2 capture from flue gas.

“Most MOFs react with water, particularly the acidic water found in flue gas,” Jenkins says, “The metal-ligand bonds are weaker, making it harder for them to hold up in high heat [and] humid and acidic conditions.”

COFs have robust covalent bonds held together by shared electrons, so they are less likely to fall apart under similar conditions. On the down side, Jenkins says, COFs with a special selective affinity for CO2 have as yet to be created.

CloudsThis is the task Jenkins and Custelcean have laid out for their teams. Custelcean will synthesize frameworks and linkers designed to attract carbon dioxide based on their electronic structure. Jenkins’ linkers feature nitrogen-based compounds called tetrazoles (five-member rings with four nitrogen atoms) and triazoles (similar but with three nitrogen atoms). Recent research with porous polymer films suggests tetrazole and triazole compounds improve CO2 selectivity and uptake.

But, synthesizing COFs is not for the faint of heart. “We do combinatorial chemistry,” Jenkins says.

“It’s very much a black-box approach, in that you set up hundreds of reactions on a very small scale, making tiny changes to each, and then observe what you get. If something looks promising, we do it again on a larger scale, testing and scaling-up until we have enough material to do a full gamut of tests.

“If we can functionalize these really small pores that have no metal in them—that are much more stable once they are synthesized—and actually show they interact and bind the CO2 more strongly than with other gases, I would consider that a huge success.”

JDRD project:
Triazole and Tetrazole Linkers for Covalent Organic Frameworks for Carbon Dioxide Capture
David Jenkins, UT Department of Chemistry

LDRD project:
Novel Covalent Organic Frameworks with Tailored Carbon Capture Functionality
Radu Custelcean, ORNL Chemical Sciences Division

i*STATIONIf the various streams of data can be accurately characterized and analyzed in real time and full context—as is the ambitious aim of Qing Cao’s exceptionally promising JDRD project—their “fusion” would allow us to infer patterns of behavior and activity, enabling even smarter, data driven, context-”aware” applications for immediate point-of-use.

Existing systems attempting to exploit the rich semantics of mass heterogeneous data are rudimentary. Cao’s initiative targets head on the challenge of coming up with tools to make sense of the huge amounts of collected data. Essential to achieving success is its attention to scalability and fundamentals by undertaking the first systematic research on programming abstractions for real-time sensor data.

In year one, the JDRD team led by Cao, with PhD students Yanjun Yao and Kefa Lu, demonstrated the feasibility of a distributed client-server model for collecting and processing real-time location information. In year two, their efforts are proceeding from small-scale testbeds to larger-scale communities of volunteer participants. Curriculum and laboratory designs for undergraduate and graduate students are also part of ongoing activities. The project has produced four applications so far—Friend Book, SmartDiary, PhoneCon, and Uno—with two conference papers delivered and another two under submission.

Cao LectureOn the LDRD side of the collaboration is James Horey of ORNL’s Computational Sciences and Engineering Division. For Horey, the complementary effort serves as an opportunity to acquire unique sensor data while sharing his group’s design of a new distributed programming model to express spatiotemporal data fusion. In effect, the JDRD team is extending the original LDRD project to include innovative techniques for location-based sensor networks and data mining of novel data sources.

In addition to several publications, Cao and Horey are jointly pursuing opportunities to leverage the success of their JDRD-LDRD collaboration with next-stage funding from the National Science Foundation, where Cyber-Physical Systems has been designated as an important emerging area of research. Four proposals representing nearly $2 million in potential grants are in the pipeline—three to NSF and one to Google.

JDRD Project:
Distributed computational framework for massive heterogeneous data fusion: A location-centric approach (Year 2)
Qiang He, UT Department of Civil and Environmental Engineering Department

LDRD project:
Distributed Computational Framework for Massive Heterogeneous Data Fusion
James Horey, ORNL Computational Sciences and Engineering Division

Thawed PermafrostEd Perfect has studied the physics of ground freezing and thawing phenomena at different spatial scales since his PhD dissertation work in the early 1980s. He has significant experience with the techniques and mathematics employed in predicting the scale dependency of parameters needed to model freezing and thawing of geomaterials. Lessons learned in a previous JDRD project involving neutron imaging are also proving valuable, enabling both radiography and tomography of thawing permafrost samples to yield better clues to the physical processes involved.

Perfect’s JDRD team member and post-doc Chu-Lin Cheng contributes experience in modeling groundwater flow and surface-water/ground-water interactions, using various numerical packages. The team also includes Ken Christle, a summer intern, who brings an apt set of skills to the research tasks of code and parameter-sensitivity analyses.

LDRD team leader Richard Mills just happens to have completed his undergraduate degree in geology and physics at UT. Today he is a computational scientist in the Computational Earth Sciences Group of the Computer Science and Mathematics Division and in the Earth and Aquatic Sciences Group of the Environmental Sciences Division at ORNL. His team aims to improve the state-of-the-art by integrating detailed models of ground freezing and thawing mechanisms, such as those developed by Perfect’s JDRD research team, into the massively parallel subsurface flow and reactive transport code PFLOTRAN.

Rock SlicesThe collaboration projects led by Perfect and Mills will also enable comparison of simulation results from different software packages—especially those produced by the older STOMP/MarsFlo code with the massively parallel PFLOTRAN. This research promises to yield particular value in the application of neutron imaging to the task of working up to larger scale data from smaller scale data on the continuum of “bench” to field to region. A target dataset is laboratory data generated from the Next-Generation Ecosystem Experiment Arctic project.

Moving beyond the over-simplification of current land surface models, information-rich surface-to-subsurface thermal, hydrologic, and biogeochemical reaction models will be coupled with comprehensive models for land-surface processes using leadership-class supercomputers. The improved code for field-to-regional-scale simulations will be known as CLM-PFLOTRAN (Community Land Model-PFLOTRAN). For UT and ORNL, the anticipated result will be an exciting and completely new initiative in the strategically core research areas of climate change and supercomputing.

JDRD project:
Coupled simulation of hydrologic processes and terrestrial ecosystem and climate feedbacks: Inclusion of soil freezing/thawing and upscaling modules in PFLOTRAN
Ed Perfect, UT Department of Earth and Planetary Sciences

LDRD project:
Coupled simulation of surface-subsurface hydrologic processes and ecosystem and climate feedbacks: from Arctic landscapes to the continental US
Richard Mills, ORNL Computer Science and Mathematics Division