2012 Research Project Descriptions
Quantum Imaging by Compressive Sampling for Enhanced Surveillance and Real-Time Monitoring
Division - CSED/CSMD
Project Description - Quantum imaging, which involves manipulation of images stored in quantum mechanical systems, is a rapidly growing field that promises increases in the information capacity of images. This increase in information capacity yields increases in image resolution and information content that is expected to subsequently impact the performance of many image applications. Remote sensing, pattern recognition, position measurements, image amplification, and surveillance are a few applications that benefit from quantum imaging methods. The goal of this project is to develop quantum imaging using compressive sampling. We will develop a quantum light source using four wave mixing in rubidium vapor to act as the active quantum imaging medium. We will also develop a novel detection scheme that combines quantum correlations with compressive sampling techniques in order to increase accuracy and reduce integration time. The eventual goal is a real-time quantum imaging system.
Qualification and Skills Desired - Experience in the use of CW lasers. Knowledge of quantum optics is desirable. Knowledge in programming spatial light modulators is a plus.
Point of Contact - Raphael Pooser, (email@example.com)
Quantum Lightwave Circuits
Research Team - Phil Evans, Ryan Bennink, Duncan Earl, Warren Grice, Travis Humble, Raphael Pooser
Division - CSED/CSMD
Project Description - Quantum Information Science (QIS) is an innovative and enormously promising new field comprising the creation, storage, manipulation, secure transfer, and use of information encoded in the quantum states of light. This project seeks to develop integrated quantum lightwave circuits to advance the capabilities, and accelerate the transfer, of QIS from research to the applications domain. Our approach will take advantage of new techniques for fabricating polarization manipulating and phase modulating circuit elements on silicon-on-insulator (SOI) waveguide platforms. Our goal is to demonstrate dynamic, multi-photon integrated quantum lightwave circuits that outperform their bulky and delicate lab brethren in terms of size, stability, and tunability.
Qualifications Desired - Masters, or Ph.D. degree in physics, optics or electrical engineering required. Previous clean-room and/or optical laboratory experience is necessary. Experience with integrated optical device packaging and characterization are desired. Experience with nano-fabrication tools including e-beam lithography, RIE etching, PECVD, etc., and with optical modeling software such as BeamPROP, OptiBPM, etc., would be advantageous but not required. OPEN TO U.S. CITIZENS ONLY.
Point of Contact - Phil Evans (firstname.lastname@example.org)
Decadal-scale hydro-climatic predictions and impact assessments
Research Team - Principal Investigator: Moetasim Ashfaq CSMD; Co-PIs: Shih-Chieh Kao, ESD; Abdoul A. Oubeidillah, ESD; Gautam Bisht, Valentine Anantharaj, NCCS
Division - Computer Science and Mathematics Division
Project Description - Probabilistic prediction of climate change at the decadal scale, and quantification of their impacts on natural and human systems are two grand scientific challenges, progress on which is critically needed to inform decision-making regarding the management of climate risk and adaptation actions. Due to the policy relevance of near-term projections, 8-10 global modeling groups will undertake decadal-scale prediction experiments of the global climate as part of the Fifth Coupled Model Inter-comparison Project (CMIP5), whose results will be an integral part of the Fifth Assessment Report of the International Panel on Climate Change (IPCC-AR5). While a new generation of General Circulation Models (GCMs) has improved substantially in the simulation of the large-scale response to climate forcing, GCM resolution and accuracy is still inadequate to fully understand the response of regional-to-local-scale processes, particularly those governing hydro-climatic extremes. To improve on the limitations of GCMs, we will develop a hierarchical regional modeling framework to substantially enhance our ability in the quantitative understanding of the decadal-scale climate change at national and sub-national levels, and its potential implications for energy, water resources, and other sectors. This framework will use a suite of Earth system models and statistical techniques to downscale predictions from a multi-model ensemble of IPCC-AR5 GCMs to an ultra-high horizontal resolution of 4 km over the United States and the South Asia. This research will improve the quantitative understanding of nature of interactions between fine-scale hydroclimate processes and large-scale climate forcing, and role of such interactions in the occurrence of high-intensity low-frequency hydro-climate extremes at multi-decadal time scales.
Qualification and Skills Desired - We are using WRF and RegCM4 for dynamic downscaling and VIC for hydrological simulations. We are looking for expertise (or some knowledge) in the application of any of these models and/or the analysis of their output. Skill in the use of any other regional climate model or hydrological model and statistical data analysis technique is also useful. General understanding of North American and/or South Asian climates is required
Point of Contact - Moetasim Ashfaq (email@example.com)
Novel neutron detector for scattering applications.
Research Team - Y. Diawara, L. Crow, J. Hayward, V. Sedov, M. Kocsis and A. Barnett
Division - Instrument & Source Design Division
Project Description - A novel neutron detection technology employing solid materials as neutron converter is proposed here. It retains the desirable performance characteristics of gaseous detectors (direct conversion) and scintillators (no parallax) while offering high count rate capability. Gaseous and scintillator-based detectors are the most widely used for neutron detection technologies. He-3 Gaseous detectors have a number of very attractive features for neutron scattering including large active area, direct conversion process, high dynamic range and low gamma sensitivity. However, the spatial resolution and the parallax errors of these neutron detectors are fundamentally limited respectively by the particle (protons and tritons) range and the conversion volume design; moreover, the He-3 shortage will limit its future use in neutron detection. The proposed program outlines the design of an advanced nanostructure neutron converter and the development of a back-end electronic. The goal is to develop a new neutron detection system offering higher counting rate and better time and position resolution than existing gas neutron detectors, while replacing the He-3 gas.
Qualification and Skills Desired - The position requires expertise in gaseous and solid detectors with emphasis on assembling and testing.
Point of Contact - Yacouba Diawara (firstname.lastname@example.org)
A Scalable Framework for Timely Discovery and Situational Understanding of Cyber Attacks
Research Team - John Goodall, Erik Ferragut, Joel Reed, Chad Steed
Division - Computational Sciences & Engineering Division
Project Description - Rapidly discovering novel and sophisticated cyber attacks from masses of heterogeneous data and providing situational understanding to analysts are ongoing problems in cyber defense. We will meet the challenges of knowledge discovery and extraction from security events in large data sets through the integration of anomaly detection, event classification, real-time information visualization, and a context-aware learning feedback loop between users and algorithms. Security analysts maintain continually evolving mental models that allow them to differentiate malicious and benign traffic. However, current systems do not exploit this knowledge. This effort addresses three pressing challenges in knowledge discovery for cyber defense: (1) incorporation of previously unused domain knowledge, (2) scalability of architecture and algorithms, and (3) timeliness of discovery.
Qualifications and Skills Desired - Probabilistic modeling, active learning, machine learning, data mining, visual analytics, information visualization, human-computer interaction, user modeling. Experience in developing algorithms for distributed architectures (e.g. map reduce) preferred.
Point of Contact - John Goodall (email@example.com)
Lignin as a precursor for high performance energy storage applications
Research Team – Orlando Rios (PI), Alexander Johs
Divisions - Environmental Sciences Division and Materials Science and Technology Division
Project Description - The faculty member will participate in a project to investigate the chemical modification of lignin as a cost-effective and ‘green’ precursor for advanced energy storage materials. The general approach will combine organic chemistry and relevant analytical techniques to obtain and functionalized lignin biopolymers. Lignin biopolymers possess a large amount of relatively easily accessible hydroxyl functional groups, both penolic and aliphatic, that can be used to introduce a variety of modifications. The goal of this 10 week project is to introduce a specific type of modification on different types of lignin, optimize reaction conditions to maximize yield and homogeneity of the functionalized product and to determine the impact of the modification on chemical and physical properties of the lignin biopolymers. Analytical methods will include Fourier Transform Infrared Spectroscopy (FTIR), NMR?, Thermogravimetric Analysis and Mass Spectrometry.
Qualification and Skills Desired - Candidates should have a good understanding of organic chemistry and relevant analytical techniques. This project is a great opportunity for a strong experimentalist with background in macromolecular chemistry, polymer chemistry or a closely related field. Prior experience with lignin-based biopolymers is desirable, but not required.
Point of Contact – Alexander Johs (firstname.lastname@example.org)
Intelligent Advanced Propulsion Systems
Research Team - Andreas Malikopoulos (PI)
Division- Energy & Transportation Science Division
Project Description - Hybrid-Electric Vehicles (HEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) have shown great potential for enhancing fuel economy and reducing emissions compared to vehicles powered only by internal combustion engines (conventional vehicles). The main advantage of these powertrain configurations is the existence of two individual subsystems, thermal (internal combustion engine) and electrical (motor, generator, and battery), that can power the vehicle either separately or in combination. The power management control algorithm in HEVs determines how to split the power demanded by the driver between the thermal and electrical subsystem so that maximum fuel economy and minimum pollutant emissions can be achieved. State-of-the-art power management control algorithms cannot guarantee continuous optimum operation of an HEV for any different driving style.
The research proposed here intends to develop the theoretical framework and algorithms for making a HEV into realizing continuously its most efficient operating point for any different driving style. It is intended to draw from stochastic control theory research in a wide range of areas including reinforcement learning, game theory, agent modeling, and evolutionary computation, and attempt to develop the theory and algorithms to address this problem. The long-term potential benefits of this approach are substantial. True fuel economy of HEVs will be increased while meeting emission standard regulations with respect to any different driving style. Having the HEV always operate at its most efficient operating point is equivalent to achievement of fuel economy and emissions benefits as fully as possible for that particular HEV configuration; for further improvement, new technologies for individual subsystem, e.g., engine, motor, generator, and battery, should be realized.
Qualifications and Skills Desired - In this project, basic research is conducted towards developing the optimization and control algorithms suitable for real-time implementation in HEVs and PHEVs. A strong background in large-scale optimization, nonlinear optimization and convex analysis, dynamic programming and stochastic control is desired.
Point of Contact - Andreas Malikopoulos (email@example.com)
Incorporating Molecular-Scale Mechanisms Stabilizing Soil Organic Carbon into Terrestrial Carbon Cycle Models
Research Team - Melanie A. Mayes (ESD), W. Mac Post (ESD), Haile Ambaye (NScD), Loukas Petridis (BSD), Sindhu Jagadamma (ESD)
Division – Environmental Sciences Division
Project Description - The top meter of soil contains 1500 Pg of carbon (C), twice that of the atmosphere and 3 times that of standing vegetation. Contemporary soil carbon models are based on the prevailing assumption that organic matter composition and transformations largely determines its biochemical decomposition. Recent analytical and experimental advances have demonstrated that molecular structure alone does not control SOM stability: in fact, environmental and biological controls predominate. Consequently, soil C response to climate change is inadequately predicted in many circumstances. The goal of our project is to produce a mechanistic model of organic C (OC) in soils. We hypothesize that attachment of OC compounds at the interface with soil minerals will determine the bioavailability of OC to microbes, and thereby exert control over soil C turnover. The project integrates ORNL world-class neutron scattering and supercomputing facilities. Neutron reflectometry (NR) measurements and molecular dynamics (MD) simulation on supercomputers will synergistically derive information on the molecular-level structure of OC stabilized on model soil minerals. The relationship between attachment and stabilization for common OC compounds (lignin, lipid, sugars, and starch) is determined in batch sorption and long-term incubation experiments using globally representative soils. The turnover of the OC compounds as they cycle through measurable soil pools (dissolved, mineral OC, particulate OC, and microbial biomass) are modeled through the mechanism of enzyme-facilitated microbial degradation. The model framework is developed and validated using published data, followed by application of our coupled sorption and degradation measurements on global soils. The ultimate outcome is a validated, realistic, enzymatic soil C model that is linkable into widely-used global land-surface models used in climate change simulations.
Qualifications and Skills Desired - Expertise or interest in learning about one or more of the following aspects: experimentation or modeling of soil C cycling, understanding and analysis of the role of microbial communities and enzymatic function in soil nutrient cycling, or use of neutron reflectivity or molecular dynamics simulation for investigation of interfaces.
Point of Contact - Melanie Mayes (firstname.lastname@example.org)
Citizen Engagement for Energy Efficient Communities (CoNNECT)
Research Team - Olufemi A. Omitaomu (PI), CSED; Co-PIs: Budhendra L. Bhaduri, Mallikarjun Shankar, CSED; Melissa V. Lapsa, Joshua R. New (ETSD); Michael A. Matheson (CCS).
Division - Computational Sciences & Engineering Division
Project Description - The two main strategies that are presently being promoted for achieving sustainable energy policy are energy efficiency and renewable energy of which the former is relatively easier and cheaper to implement. The focus of energy efficiency has been on residential and commercial consumers since about 40% of the electricity produced in the US is used by these consumers. As a result, there have been some programs that targeted the use of energy efficient technologies and appliances in buildings. However, it is highly unlikely that these programs can scale up to achieve the projected energy saving because they are usually treated as one-time improvements that are not monitored and measured over time. Allowing consumers to easily analyze and share their own energy usage data can lead to an effective and sustainable way of achieving many EERE goals. Taking advantage of the increasing use of smart meters, we propose to develop an improved energy feedback mechanism that informs households in more detail about their consumption pattern so that they can achieve better awareness and control, and motivates them to conserve. Building upon a unique partnership developed among Knoxville area utility and community partners, we propose to develop an integrated multi-partner platform called Citizen eNgagement for eNergy Efficient CommuniTies (CoNNECT) that will: (i) establish a community-based network of stakeholders to facilitate consumer engagement for energy efficiency; (ii) provide a prototype internet-based decision support application to better inform and motivate consumers; and (iii) provide data analysis capabilities that will drive the decision support system for comparative visualization and identification of spatial consumption and carbon emission patterns. This shared resource platform will strongly position ORNL for future programmatic opportunities in large scale utility data analytics and Energy Efficiency and Renewable Energy programs.
Qualifications and Skills Desired - Good background in one or more of statistics, modeling and optimization, machine learning, time series analysis, signal processing, GIS is desired.
Point of Contact - Olufemi A. Omitaomu (email@example.com)
Ultrascale Algorithms for Verification of Security Properties (UVSP)
Research Team - Dr. Stacy Prowell (PI), Dr. Kirk Sayre, Dr. Mark Pleszkoch, Dr. James Horey, Richard Linger, Logan Lamb
Division - Computational Sciences and Engineering Division (CSED)
Point of Contact - Stacy Prowell (firstname.lastname@example.org, (865) 241-8874)
Data-Driven Threat Radar for Local-to-Regional Energy Grid Stability
Research Team - Arjun Shankar, PhD (PI), Justin Beaver, PhD, Supriya Chinthavali, Aleksandar Dimitrovski, PhD, Steven Fernandez, PhD, Joe Hubss, Dude Neergaard, Rangan Sukumar, PhD, Louis Wilder
Project Description - Using real data obtained from the field over the past three years, this project develops a data driven threat radar for the electric grid. The threat radar takes into account distribution outages, transmission outages, and incipient cyber vulnerabilities (announced and predicted). The goal is to combine signals of cyber incursions along threat vectors applicable to electric grid SCADA systems with tracked behaviors in the electric grid. After enabling detections at a fine-grained resolution within a facility - a local cyber-to-SCADA impacts estimation, we couple these estimates with regionally detected grid behaviors of distribution and transmission outages.
Qualifications and Skills Desired - Strong expertise in one or more of the following categories - cyber-security analysis, power-systems/SCADA control, visualization, data-analysis/machine-learning.
Point of Contact - Arjun Shankar (email@example.com)
Unraveling the molecular and biochemical basis of crassulacean acid metabolism (CAM) in Agave for sustainable biofuel production
Research Team - Principal Investigator: Xiaohan Yang, BSD; Co-PIs: David J. Weston, BSD; Stan D. Wullschleger, ESD; Timothy J. Tschaplinski, BSD
Division – Biological Sciences Division
Project Description - As an emerging biofuel crop, Agave has high cellulose and sugar contents, along with high biomass yield. More importantly, it is one of the most water-use efficient plants in the world due to its crassulacean acid metabolism (CAM). The goal of this project is to establish CAM expertise at ORNL and characterize genes regulating CAM physiology. CAM has four phases: phase I, CO2 fixation catalyzed by phosphoenolpyruvate carboxylase (PEPC) and accumulation of malate at night with the stomata open; phase II, CO2 fixation shifted from PEPC fixation to Rubisco fixation after dawn; phase III, Rubisco refixation of CO2 released from malate decarboxylation with the stomata closed in the late morning; and phase IV, direct CO2 fixation by Rubisco with limited stomatal opening in the late afternoon. Two dimensional aspects (diurnal 24-hour time course and developmental difference in CAM between young and mature leaves) will be explored using a systems biology approach integrating physiology, genomics, metabolomics, and computational biology. This research will discover the transcriptional and metabolic networks driving the four CAM phases, set the stage for genetic improvement of Agave to increase the magnitude of CAM expression, and consequently enhance biomass production.
Qualifications and Skills Desired - This research project has multiple aspects and welcomes individuals with broad interests and diverse backgrounds. Experience in at least one of the five areas is desirable: Genomics, molecular biology, cell biology, plant physiology, or genetics.
Point of Contact - Xiaohan Yang (firstname.lastname@example.org)