2010 Research Project Descriptions

1. Advanced Energy

Research Project 1a: Design of Evanescent-Wave Power Transfer for Stationary and Moving Hybrid Electric Vehicles

Research Team: Matthew B. Scudiere, John W. McKeever, Burak Ozpineci, Cliff P. White, Timothy S. Bigelow, John B. Caughman, Philip M. Ryan

Division: Energy & Transportation Science Division

Project Description: This initiative seeks to support Concepts for transferring energy to vehicles with the potential to mitigate the current vehicular requirement for on-board storage of energy. Wireless power transfer is a topic of great interest for Plugin Hybrid Electric Vehicles [PHEV]. To date it’s been mostly a laboratory development effort with a few field deployable systems. Most wireless power transfer systems to date operate in the MHz range and transfer only a few hundred watts at best. The goal of this project is to develop wireless power transfer in the 5+ kW range for efficient charging of PHEVs in a cost effective and efficient manner. This is accomplished with a loosely coupled resonant air core transformer operating in the commercially viable range of 20 kHz. We are in the process of completing a full scale laboratory unit to test various configurations for battery charging on PHEVs. We are most interested in applicants that have familiarity and experience in the laboratory working with power electronics and control circuits. Also, data analysis and reporting are an important part of the effort.

Qualifications and Skills Desired: Power electronics systems, both theory and practical laboratory experience, data analysis, and report generation.

Point of Contact: Mitchell Olszewski olszewskim@ornl.gov

Research Project 1b: Development of Cermet High-Level Waste Forms

Research Team: Robert T. Jubin¹, Scott W. Aaron¹, Emory D. Collins¹, David W. DePaoli¹, Guillermo D DelCul¹, Leslie Kevin Felker¹, Douglas B Kothe³, Bradley D Patton¹, Stewart L Voit², Robert M Wham^¹

¹ Nuclear Science and Technology Division,
² Materials Science and Technology Division
³ Computer Science and Mathematics Division

Project Description: The successful re-expansion of nuclear energy is dependent on the development of waste disposal options that minimize repository use and maximize efficiency and public safety. We are developing advanced cermet waste form concepts for the optimal storage of high-level wastes that will be generated in next-generation nuclear fuel cycles. Cermets, which consist of ceramic phases dispersed in continuous metal matrices, provide a unique opportunity to tailor waste forms that take optimal advantage of the properties of both metallic and oxide materials. In this project, we are exploring the development of cermets consisting of ceramic materials that sequester the short-lived, but highly heat generating 137Cs/Ba and 90Sr/Y components of spent fuel along with metal alloys that enable isolation of long-lived fission products such as 99Tc, noble metal components, transition metals and cladding materials. The proposed waste form will provide significant cost benefit by improving the heat-handling capability of the waste, enabling increased repository capacity. Proof of concept will be established through experimentation and preliminary modeling studies to elucidate the long-term performance of such a cermet waste form.

Qualifications and Skills Desired: Nuclear waste management, repository modeling, nuclear waste form performance testing, chemical/nuclear engineering, ceramic formulation/testing, materials engineering

Point of Contact: Robert Jubin, jubinrt@ornl.gov

Research Project 1c: Enabling Technologies for Highly Efficient Engines

Research Team: Jim Szybist, Jim Parks, Jim Conklin, Charles Finney, Dean Edwards, and Ronald Graves

Division: Energy and Transportation Science Division

Project Description: The goal of this project is to increase the real-world fuel economy gasoline engines by increasing the engine system thermal efficiency under part-load conditions. While the project as a whole encompasses several approaches to increased efficiency, the visiting faculty will be focused on investigating is thermoelectric devices. Thermoelectric devices can recover waste heat in automotive exhaust and convert it to usable electricity in a vehicle, thereby increasing overall system efficiency. The effort will include performing experiments on a bench-scale thermoelectric heat recovery apparatus that can both simulate automotive exhaust or be used with real engine exhaust. In addition, there will be a modeling aspect to the project to determine the potential of thermoelectric device to improve real-world fuel economy.

Qualifications and Skills Desired: Thermal energy recovery systems, thermoelectrics

Point of Contact: Jim Szybist, szybistjp@ornl.gov

2. Climate Change Impact

Research Project 2a: Understanding Climate Change Impacts

Research Team: Principal Investigator: Auroop R Ganguly, CSED; Co-PIs: Thomas Wilbanks, ESD; David Erickson, Marcia Branstetter, CSMD; Karsten Steinhaeuser, Shih-Chieh Kao, Olufemi Omitaomu, Esther Parish, Raju Vatsavai, Alexander Sorokine, CSED

Project Description: The IPCC AR4 (IPCC, 2007) has resulted in a wider acceptance of climate change. However, climate modelers struggle to develop precise predictions of extreme events. In addition, the most significant knowledge gap relevant for policymakers and stakeholders remains the inability to produce credible estimates of local to regional scale climate extremes and change impacts. Uncertainties in process studies, climate models, and associated spatio-temporal downscaling strategies, may be quantified and reduced by statistical evaluations. A similar treatment for extreme events may require novel statistical approaches and improved downscaling. Climate change projections are based on future scenarios, for which quantitative assessments, let alone reduction, of uncertainties may be difficult. Regional impacts need to account for additional uncertainties in the estimates of anticipatory risks and damages, whether on the environment, infrastructures, economy or society. The cascading uncertainties from scenarios, to models, to downscaling, and finally to impacts, make costly decisions difficult to justify. This problem grows acute if credible attributions need to be made to causal drivers or policy impacts. This project proposes a comprehensive treatment for uncertainty in the context of climate change related extreme events and impacts at local to regional scales. New capabilities will be developed to assess and reduce uncertainties, which will not only improve climate process models, but also produce credible information for better decisions and integrated assessments.

Qualifications and Skills Desired: Motivation and fundamentals are more important than specific qualifications or skills. The following are examples of relevant disciplines and skill sets: Civil and Environmental Engineering; Climate Sciences; Hydrology and Water Resources; Statistics; GIS. In addition, mathematicians (e.g., computational statisticians), physicists (e.g., nonlinear dynamicians) and/or computer scientists (e.g., data miners) are strongly encouraged to consider joining the project team. In the current year of the project we hope to have a strong emphasis on mathematical and computational methods for uncertainty assessments of climate change and climate extremes, especially as these relate to multimodel ensembles.

Point of Contact: Auroop R. Ganguly, gangulyar@ornl.gov

3. Energy Storage

Research Project 3a: Predictive System Simulation Capability for Evaluating Safety and Performance of Batteries

Research Team: Sreekanth Pannala, Adrian Sabau, Hassina Bilheux, Jagjit Nanda, Bill Shelton, and John Turner

Division: Computer Science and Mathematics Division

Project Description: The batteries of the future require higher energy/power densities (e.g. to displace the gasoline engine), lower cost, longer life and lower foot print (e.g. store solar energy) while retaining safety. These improvements will be primarily the result of new materials, device architectures, and processing techniques. There are significant safety concerns associated with increase in the performance as the electrodes pack more energy and are in closer proximity to each other (e.g. 3D architectures). In this project, we are developing a predictive system-level (macroscopic) 3D petascale simulation tool based on rigorous averaging procedures to model batteries. This detailed simulation capability will model the coupled multiphysics phenomena (charge and thermal transport; electrochemical reactions; mechanical stresses) across the porous 3D structure of the electrodes (cathodes and anodes) and the solid or liquid electrolyte system while including the nanoscale effects through closures based on resolved quantities. The simulation tool will be validated both at the full-cell level as well as at the secondary particle-level and give an unprecedented capability to study the interplay between the particle-scale effects to the overall performance and the safety of the batteries. This tool fills a critical void in the simulation space and gives a unique capability to model batteries.

Qualifications and Skills Desired: This is an integrated project which includes both simulations and experiments. We are looking for expertise either in reactive porous media simulations or expertise in modeling electrochemical reactions or ability to characterize batteries using Raman spectroscopy or neutron imaging.

Point of Contact: Sreekanth Pannala, pannalas@ornl.gov

4. General

Research Project 4a: Novel Zeolitic Carbon Support for Catalytic Bioethanol Production

Research Team: De-en Jiang (PI, CSD), Michelle Kidder (CSD), Zili Wu (CSD/CNMS), Steve Overbury (CSD/CNMS), Sheng Dai (CSD/CNMS), Jane Howe (MSTD)

Project Description:One of the key challenges in the thermochemical conversion of biomass to ethanol is controlling the catalytic transformation of biomass-derived syngas (CO + H2) to obtain high ethanol selectivity with high CO conversion. The most promising catalysts for conversion of syngas to ethanol are based on Rh. Recently, it has been shown that confinement of the Rh catalyst inside carbon nanotubes greatly enhance yield and selectivity, although the origin of this enhancement is unclear. By understanding and controlling confinement effects, significant advances could be made in ethanol formation and other reactions such as Fischer-Tropsch synthesis and formation of longer chain alcohols. The overarching goal of this project is to understand and control the confinement effects on catalysis by transition-metal nanoclusters confined in porous supports. We will pursue three specific aims. First, how can we achieve and control the confinement where the monodisperse catalytic particles are confined in a well defined porous environment? Second, how does the confinement by the porosity of the support affect activity and selectivity of the metal nanoclusters to catalytically convert syngas to ethanol? Third, how can we control the catalyst’s performance to achieve desirable targets of activity and selectivity of syngas to ethanol? This project explores both computational and experimental efforts. The computational efforts are directed toward examining the reaction details, understanding the confinement effect, and screening parameters such size and doping elements for both the carbon nanosystem and the catalytic nanocluster. The experimental efforts are employed to synthesize novel carbon supports, establish a protocol to reliably load metal nanoclusters inside carbon pores, realize and characterize the computationally optimized catalytic nanosystems, and test them for conversion of syngas to ethanol. The knowledge generated from this project will help achieve high-yield ethanol formation from syngas and benefit other energy-relevant reactions, thereby attracting applied funding sources such as DOE-EERE Biomass Program and the joint DOE-USDA program on biofuels.

Qualifications and Skills Desired:For the experimental component, experience with inorganic synthesis and heterogeneous catalysis is needed; for the computational component, experience with thermodynamic analysis or density functional theory computations is necessary.

Point of Contact: De-en Jiang, jiangd@ornl.gov

5. National Security

Research Project 5a: Multi-Photon Entangled States for Quantum Information Science

Research Team: Warren P. Grice, Ryan S. Bennink, Philip G. Evans, and Travis S. Humble

Division: Computational Sciences and Engineering Division

Project Description: Quantum Information Science (QIS) is a new kind of information technology with the potential to improve communication security and perform complex calculations by exploiting heretofore experimentally inaccessible features of quantum systems. An important resource in QIS protocols is entanglement, whereby the properties of individual particles are intimately related, even when the particles are spatially separated. In optical approaches to QIS, information is encoded into photonic degrees of freedom and it is relatively straightforward to generate pairs of entangled photons. However, any QIS protocols of significance require entanglement on a larger scale, i.e., three or more photons. The handful of multi-photon (≥ 3) entangled state demonstrations have been hampered by low generation rates and poor entanglement fidelity and are generally considered impractical for real QIS systems. We propose to overcome this two- photon barrier with the generation of state-of-the-art multi-photon states with better entanglement fidelity and count rates that are higher by several orders of magnitude. Our approach will significantly improve upon previous works by optimizing photon sources not only in brightness, but also in the spatial and spectral properties of the emitted photons.

Qualifications and Skills Desired: Experience in experimental optics preferred.

Point of Contact: Warren Grice, gricew@ornl.gov

6. Neutron Sciences

Research Project 6a: Spin Dynamics of the Mutliferroic Phase of CuFeO2

Research Team: Randy Fishman (MSTD), Feng Ye (NSSD), and Jaime Fernandez-Baca (NSSD)

Division: Materials Sciences and Technology Division

Project Description: Due to the strong coupling between the electric polarization and the magnetization, multiferroic materials hold tremendous technological promise in the magnetic storage industry based on the ability to manipulate magnetic bits with electric currents. This project combines theoretical modeling with elastic and inelastic neutron-scattering measurements to study the multiferroic phase of a typical ferroelectric material, Al-doped CuFeO2. Comparing the observed spin excitations with theoretical predictions will allow us to characterize the multiferroic state and the microscopic interactions responsible for the ferroelectric behavior in this material. Specifically, this project will involve the development and implementation of computer codes to analyze the inelastic neutron-scattering spectrum and compare the predictions with experimental measurements.

Qualifications and skills desired: Programming skills in Fortan, a Ph.D. in Physics or Materials Sciences.

Point of contact: Randy Fishman (fishmanrs@ornl.gov)

7. Science for Extreme Environment

Research Project 7a: Multiphase Self-organized Interfaces for Polymer Photovoltaic Technologies"

Research Team: S. MichaelKilbey (CNMS),William Heller(CSD), Kunlun Hong (CNMS), John Ankner (NSSD), and Robert W. Shaw (CSD)

Project Description: Organic photovoltaics (OPVs) have several advantages over traditional semiconductor photovoltaic materials including ease of processing, light weight, flexibility and lower cost. However, OPVs face key fundamental challenges, particularly poor long-term stability of the organic material under exposure to environmental conditions, including UV light and thermal cycling, that degrade the performance of the solar cell. The response of OPV materials (OPVMs) to long-term exposure to common environmental conditions must be better understood to predict and improve the durability of organic solar devices. Thus, the overarching goal of this project is to understand the effects of environmental aging due to light and thermal cycling on OPVMs, with a specific focus on the morphological changes that result in performance degradation, such as decreased charge mobility. The materials will be physically characterized within the optical characterization portion of this overall project through the use of near-field scanning optical microscopy with contrast derived from fluorescence emission. Other researchers will use a combined experimental approach that includes electrical performance, spectroscopy, and neutron scattering, a capability unique to ORNL, to provide unique and new information about the morphology of OPVMs and to understand the structural consequences of environmental exposure of these materials. A summer faculty participant will interact with other staff conducting these additional techniques to promote project synergy. This research addresses the key aspect of OPV stability as a key roadblock to the technology.

Qualifications and Skills Desired: The optical spectroscopy portion of this project requires a chemist or physicist who has experience in laser-excited spectroscopy using a fluorescence microscope platform. Concurrent atomic force microscope imaging will be performed. Quantification skills for nanosecond emission lifetimes using single photon counting apparatus are necessary. Prior laser experience and training (including laser safety) would be a benefit. He/she should also be adept at sample preparation, including sectioning of hybrid, multi-phase organic materials.

Point of Contact: William Heller – hellerwt@ornl.gov

Research Project 7b.: Materials Behavior Underlying the Electrochemical Performance of Advanced Batteries

Research Team: Sheng Dai, Nancy Dudney, Karren More, Ed Hagaman, Bob Shaw, De-en Jiang, Andrew Payzant , Claus Daniel, and Edgar Lara-Curzio (MSTD)

Project Description: This work undertakes two research thrusts aimed at developing underlying knowledge of basic materials behavior that governs lithium battery electrochemical performance and lifetime. Specific objectives include (1) dynamic characterization of the initial development of the solid electrolyte interphase (SEI) in terms of morphology and molecular composition at a heretofore unattained level of resolution, thus demonstrating the ability to fundamentally relate these characteristics to energetics and kinetic factors, and (2) development of an understanding of the evolution of stress states and mechanical behavior of electrodes and the SEI in order to directly connect structure and materials processing routes to the factors that make major contributions to a lithium battery’s durability (lifetime) and safety. In order to accomplish these goals, it will be necessary to (1) tailor advanced, in-situ characterization tools for effective use with battery material systems that utilize ORNL’s world-class capabilities in electron microscopy, molecular spectroscopies (e.g., nuclear magnetic resonance, electron spin resonance, vibrational spectroscopies), X-ray diffraction, and mechanical behavior, (2) establish the necessary suite of instruments to conduct standard electrochemical characterization of battery cells (or half cells) in order to relate in-situ microscopy, molecular spectroscopies, x-ray, and mechanical observations and measurements to macroscopic current-voltage performance, and (3) develop processing routes to synthesize model systems that facilitate analysis of the results in terms of thermodynamic, kinetic, and stress factors.

Qualifications and Skills Desired: electrochemistry or physical chemistry

Point of Contact: Sheng Dai, Email: dais@ornl.gov

8. Sustainable BioEnergy

Research Project 8a: Spatiotemporal Data Mining Framework for Monitoring Biomass at Regional and Global Scales (Newly Added Project)

Research Team: Principal Investigator: Ranga Raju Vatsavai, CSED; Co-PIs: Auroop Ganguly, Forrest Hoffman, Thomas Paul Karnowski, Christopher T Symons, Tristram O West.

Project Description: With recent government emphasis on biofuel development for reducing dependency on foreign oil and reducing carbon emissions from energy production and consumption (e.g., EERE's Office of Biomass Programs, Biomass Multi-Year Program Plan), the landscape of the United States and many other countries is going to change dramatically in coming years. However, biomass monitoring (changes over time) over large geographic regions using remote sensing images poses several challenges. Conventional techniques are either inadequate or do not scale well for continuous biomass monitoring over large geographic regions. In this research we are developing a spatiotemporal data mining framework that exploit the subtle multidimensional signals through the joint use of high temporal resolution (MODIS) data and moderate- and fine-spatial resolution satellite images to enable the extraction of multi-temporal biomass change information, including crop types and their conditions. Some of the techniques we are developing are semi-supervised learning, Gaussian Process learning, clustering, and change detection. We are addressing scalability issues using modern computing infrastructure, especially distributed and cloud computing.

Qualifications and Skills Desired: Motivation and desire to work in the area of spatiotemporal data mining. Good background in one or more of statistics, machine learning, spatial and time series analysis, distributed and cloud computing, GIS, Remote Sensing is desired.

Point of Contact: Ranga Raju Vatsavai, vatsavairr@ornl.gov

9. Systems Biology

Research project 9a: Discovery and characterization of nanoarchaeal systems from terrestrial and deep sea high temperature environments

Research team: Mircea Podar, James H. Campbell

Division: Biosciences

Project Description. The archaea Nanoarchaeum equitans and Ignicoccus hospitalis engage in one of the simplest and most efficient symbiotic relationships discovered so far. However, the mechanisms by which they recognize each other, establish a physical cell contact and regulate the flux of metabolites are unknown. A project is underway to develop a cross-disciplinary, experimental and computational platform to study cellular and molecular mechanisms that enable interspecific interactions between the two organisms. Based on complete genomic sequences we are conducting integrated gene expression, proteomic and metabolic profiling of the two organisms during different stages of their association. The specificity of the interaction will be further dissected using comparative genomics, gene expression and proteomics with Ignicoccus species that are not symbiotic with N. equitans. We will also analyze environmental samples for the presence of novel species of nanoarchaea, we will establish enrichments in the laboratory directed towards isolation and characterization of novel members of these hyperthermophilic group of organisms. We are especially looking for applicants to participate in this discovery part of the project.

Qualifications and Skills Desired: Microbiological techniques, basic molecular biology (DNA isolation, PCR, basic sequence analysis)

Point of Contact: Mircea Podar, podarm@ornl.gov

10. Ultrascale

Research Project 10a: Wavelength-Division Multiplexed Quantum Communication Network

Research Team: Ryan S. Bennink, D. Earl, P. Evans, W. Grice, T. Humble, and M. Neergaard

Division: Computational Sciences and Engineering Division

Project Description: Quantum Communication (QC) is a next-generation technology for transferring information encoded in the quantum states of particles. Applications of QC include ultra-secure communication and quantum computing, a potentially revolutionary form of computing. Deployment of QC has been held back by questions of practicality, and the fact that present systems offer no networking capability. We propose development of an entangled photon QC testbed at ORNL to explore implementation issues and to advance the capabilities of this nascent technology. Our approach will add network functionality to QC by using spectrally entangled photons and commercially available wavelength-division multiplexing technology to provide quantum communication resources to multiple users simultaneously. In addition, the completed testbed will give ORNL a unique capability for long-term quantum communication technology research and development.

Qualifications and Skills Desired: Experience in experimental optics preferred.

Point of Contact: Ryan Bennink, benninkrs@ornl.gov

Research Project 10b: Automating Ultrascale Ontology-Based Semi-Structured Data Analysis for Cybersecurity

Principal Investigators: Louis P. Wilder, Erik M. Ferragut

Project Description: The massive data produced by cybersecurity systems quickly overwhelms human operators. We are pursuing a knowledge-based approach to efficiently represent complex semi-structured data for (1) automated probabilistic modeling, (2) semi-automated response and control, and (3) presentation to human operators. Briefly, we seek to identify macro-events comprised of low level log entries, informed by a human-generated ontology, and used to automated a thorough, deep, probabilistic analysis. We are seeking research assistance in ontology development, tailoring ontology development methodologies, and aligning the ontology with existing standards (e.g., common vulnerability enumeration).

Qualifications and Skills Desired: Experience with ontologies, ontology development, and semi-structured data analysis required. Interests in probability modeling (e.g., Bayesian Networks), cybersecurity, and visualization desired. Willingness to implement prototype ideas.

Point of Contact: Louis Wilder, wilderlp@ornl.gov

Research Project 10c: Massively Parallel Algorithms for Scalable Exascale Data Analysis

Research Team: Erik Ferragut (CSED), Sudharshan Vazhkudai (CSM), Stuart Campbell (NSSD), Mark Hagen (NSSD), Stephen Miller (NSSD), Christopher Griffin (Applied Research Lab, PSU)

Project Description: The goal of this project is to develop scalable algorithms for interactive analysis of moderate size (GB ~ TB) measurement/observational data that require petascale/exascale computational resources. We will use formal computational complexity analysis methods to prove the scalability of our algorithms. Specifically, we will propose solutions to the data analysis problems of two applications: 1) the training of Support Vector Machines, a problem fundamental to many data mining applications, and 2) the parameter estimation problem faced by neutron scientists. These two applications share the common mathematical and algorithmic challenge: scalable constrained optimization. To the best of our knowledge, very little progress has been made on the first problem and there are no systematic approaches to the second problem. The outcome of this project will help accelerate scientific discovery by providing scalable data analysis tools.

Qualification and Skills Desired: The position requires expertise in high performance computing and scalable optimization algorithm design. Knowledge in data mining and scientific data analysis is a plus.

Point of Contact: Yu (Cathy) Jiao, jiaoy@ornl.gov

Research Project 10d: Soft-Error Resilience for Future-Generation High-Performance Computing Systems

Research Team: Christian Engelmann, Sudharshan S. Vazhkudai

Research Division: CSMD

Project Description: This project aims at developing a soft error resilience strategy for future-generation high-performance computing (HPC) systems. Soft errors are becoming the predominant source of interruptions in large-scale HPC systems. Double-error detection (DED) events that normally occur in a memory module with single-error correction (SEC) error correcting code (ECC) once within 1-2 million hours of operation can cause an error rate of 10-20 hours in a system with 100,000 modules. Moreover, vendors have warned that silent data corruption (SDC), i.e., undetected bit flips, are becoming a problem as well. This project targets two different solutions aiming at alleviating the issue of soft errors in large-scale HPC systems: (1) checkpoint storage virtualization to significantly improve checkpoint/restart times, and (2) software dual-modular redundancy (DMR) to eliminate rollback/recovery in HPC. The checkpoint storage virtualization aggregates a variety of back-end resources, such as flash, memory, or both, and uses them in conjunction with traditional parallel file systems. Applications are able to use it in a seamless fashion through the standard file system interface with high read/write throughput. The core concept of the DMR technology relies on software-level replication of computational processes using the sate-machine replication approach and on process cloning technology for fast recovery.

Qualifications and Skills Desired: Distributed storage system, parallel I/O, user-space file system (FUSE), aggregated storage, caching, storage and I/O virtualization, LUSTRE parallel file system, SSD, performance analysis of storage systems, active replication, state-machine replication, process group communication, process cloning/migration, message logging, performance analysis of fault tolerance protocols, communications libraries, MPI

Point of Contact: Christian Engelmann, engelmannc@ornl.gov

Research Project 10e: Automating Ultrascale Ontology-Based Semi-Structured Data Analysis for Cybersecurity (NEWLY ADDED PROJECT)

Research Team: Louis P. Wilder, Erik M. Ferragut