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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine Reading at Scale: A Search Engine for Scientific and Academic Research

The Internet, much like our universe, is ever-expanding. Information, in the most varied formats, is continuously added to the point of information overload. Consequently, the ability to navigate this ocean of data is crucial in our day-to-day lives, with familiar tools such as search engines carving a path through this unknown. In the research world, articles on a myriad of topics with distinct complexity levels are published daily, requiring specialized tools to facilitate the access and assessment of the information within. Recent endeavors in artificial intelligence, and in natural language processing in particular, can be seen as potential solutions for breaking information overload and provide enhanced search mechanisms by means of advanced algorithms. As the advent of transformer-based language models contributed to a more comprehensive analysis of both text-encoded intents and true document semantic meaning, there is simultaneously a need for additional computational resources. Information retrieval methods can act as low-complexity, yet reliable, filters to feed heavier algorithms, thus reducing computational requirements substantially. In this work, a new search engine is proposed, addressing machine reading at scale in the context of scientific and academic research. It combines state-of-the-art algorithms for information retrieval and reading comprehension tasks to extract meaningful answers from a corpus of scientific documents. The solution is then tested on two current and relevant topics, cybersecurity and energy, proving that the system is able to perform under distinct knowledge domains while achieving competent performance.

Sousa, Norberto (ORCID:0000000329194817)↗
DOE OSTI2017

Landscape of international event-based biosurveillance

Event-based biosurveillance is a scientific discipline in which diverse sources of data, many of which are available from the Internet, are characterized prospectively to provide information on infectious disease events. Biosurveillance complements traditional public health surveillance to provide both early warning of infectious disease events and situational awareness. The Global Health Security Action Group of the Global Health Security Initiative is developing a biosurveillance capability that integrates and leverages component systems from member nations. This work discusses these biosurveillance systems and identifies needed future studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Distributed Real-time Plume Monitoring for Deep Sea Mineral Extraction​

In the emerging industry of deep-sea mining for minerals and deposits (e.g. polymetallic nodules for nickel, cobalt, copper, and manganese), more data is required to understand the effects of sediment plume generation and predict the distribution of disturbed sediment. There are two main sources of plume generation, the first being at the active mining site where the “collector” directly removes the top layer of the sea floor. The other is the “midwater plume” consisting of unwanted sediment that was collected during extraction that is pumped back into the aphotic zone. The vast majority of plume generation is caused by the collector, causing detrimental and long-lasting impacts on seafloor ecosystems due to the lack of wave activity or strong currents at the sea floor. Therefore, it is crucial to invest in the infrastructure to support the study and constant monitoring over a large area of the sea floor where plume generation is present. Due to the limited number of usable channels and power requirements, current subsea wireless communications technologies are not well suited to instrumenting the large areas of the sea floor needed to monitor plume migration. The scope of this effort is to transition experimental demonstrations of high-bandwidth, full-duplex scalable underwater laser communications to the seafloor in an open ocean environment. Specifically tackling challenges associated with the dynamic nature of the subsea world, including but not limited to, deployment logistics, sustainability, and range. The goal is to enable the internet of underwater things for deep sea industries by broadening the capabilities of subsea communications. By using high-precision laser transmitters, many of the challenges current subsea optical systems face can be circumvented, such as power consumption, interference, and bandwidth limitations. This approach lends itself to wireless interlinking multi-node networks, in series or parallel, facilitating the implementation of a wide array of sensor types. This interlinking allows all the data gathered from the network to be processed through a single hardline uplink to the surface, lowering the complexity required for near real-time data processing. Additionally, the laser control systems produce metadata that can be used to help characterize the water column between the nodes. Combining data from various sensors such as turbidity, temperature, current velocity with metadata such as beam attenuation and deflection can produce a high-resolution model of sea floor conditions around an active mining zone. The resulting near real-time model can be used to optimize location and flow rate of the mining operation to minimize and quantify the environmental impact.

Mons, Ishan↗
DOE OSTI2019

Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming

This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The PHEV used in this investigation is the second-generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route. The objective of the optimizer is to enable future Connected and Automated Vehicles (CAVs) to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile information from Intelligent Transportation Systems (ITS), Internet of Things (IoT), High-definition Mapping, and onboard sensing technologies. Emphasis is placed on runtime minimization to quickly react and plan an optimal mode scheme in highly dynamic road conditions with minimal computational resources. On-road performance of the optimizer paired with automated CD and CS mode selection is evaluated on a fleet of four instrumented Chevrolet Volts in a variety of driving scenarios. Here, the results indicate variable energy savings depending on the drive route and initial battery SOC with potential ranging between 2 to 12% and less than 7 seconds initial optimization for a 24-mile drive cycle.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Epi Archive: Automated Synthesis of Global Notifiable Disease Data

ObjectiveLANL has built software that automatically collects global notifiable disease data, synthesizes the data, and makes it available to humans and computers within the Biosurveillance Ecosystem (BSVE) as a novel data stream. These data have many applications including improving the prediction and early warning of disease events.IntroductionMost countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health.While most nations likely store incident data in a machine-readable format, governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues1.A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language.The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors.LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.MethodsWe conducted a survey of the national notifiable disease reporting systems notating how the data are reported and in what formats. We determined the minimal metadata that is required to contextualize incident counts properly, as well as optional metadata that is commonly found.The development of software to regularly ingest notifiable disease data and make it available involves three or four main steps: scraping, detecting, parsing and persisting.Scraping: we examine website design and determine reporting mechanisms for each country/website, as well as what varies across the reporting mechanisms. We then designed and wrote code to automate the downloading of the data for each country. We store all artifacts presented as files (PDF, XLSX, etc.) in their original form, along with appropriate metadata for parsing and data provenance.Detecting: This step is required when parsing structured non-machine-readable data such as tabular data in PDF files. We combined the Nurminen methodology of PDF table detection with in-house heuristics to find the desired data within PDF reports2.Parsing: We determined what to extract from each dataset and parsed these data into uniform data structures, correctly accommodating the variations in metadata (e.g., time interval definitions) and the various human languages.Persisting: We store the data in the Epi Archive database and make it available on the internet and through the BSVE. The data is persisted into a structured and normalized SQL database.ResultsThe Epi Archive tool currently contains national and/or subnational notifiable disease data from twenty nations. When a user accesses the Epi Archive site, they are prompted with four fields: country, subregion, disease of interest, and date duration. Upon form submission, a time series is generated from the users’ specifications. The generated graph can then be downloaded into a CSV file if a user is interested in performing personal analysis. Additionally, the data from Epi Archive can be reached through a REST API (Representational State Transfer Application Programming Interface).ConclusionsLANL, as part of a currently funded DTRA effort, is automatically and continually collecting global notifiable disease data. While 20 nations are in production, more are being brought online in the near future. These data are already being utilized and will have many applications including improving the prediction and early warning of disease events.References[1] van Panhuis WG, Paul P, Emerson C, et al. A systematic review of barriers to data sharing in public health. BMC Public Health. 2014. 14:1144. doi:10.1186/1471-2458-14-1144[2] Nurminen, Anssi. "Algorithmic extraction of data in tables in PDF documents." (2013).

Khalsa, Hari S.↗
DOE OSTI2023

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Parallel Heuristics for Scalable Community Detection

Community detection has become a fundamental operation in numerous graph-theoretic applications. It is used to reveal natural divisions that exist within real world networks without imposing prior size or cardinality constraints on the set of communities. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to the irregular and inherently sequential nature of the underlying heuristics. In this paper, we present parallelization heuristics for fast community detection using the Louvain method as the serial template. The Louvain method is an iterative heuristic for modularity optimization. Originally developed by Blondel et al. in 2008, the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. However, the method is also inherently sequential, thereby limiting its scalability to problems that can be solved on desktops. Here, we observe certain key properties of this method that present challenges for its parallelization, and consequently propose multiple heuristics that are designed to break the sequential barrier. Our heuristics are agnostic to the underlying parallel architecture. For evaluation purposes, we implemented our heuristics on shared memory (OpenMP) and distributed memory (MapReduce-MPI) machines, and tested them over real world graphs derived from multiple application domains (internet, biological, natural language processing). Experimental results demonstrate the ability of our heuristics to converge to high modularity solutions comparable to those output by the serial algorithm in nearly the same number of iterations, while also drastically reducing time to solution.

clustering, graph algorithms↗
DOE OSTI2015

SBIR Phase II Final Report - Multi-Protocol Energy Management Gateway for Home-Area Networks

Significant amounts of electricity, natural gas, and heating oil are wasted by homeowners due to inefficient operation and inadequate maintenance of heating, ventilation, and air conditioning (HVAC) equipment. Coincident’s work under this award reduces energy waste, saves consumers money, and reduces carbon emissions. It does so in three ways: First, Coincident’s approach replaces the traditional thermostat with a wireless network of sensors and controllers that measure temperature, humidity and occupancy in multiple rooms in the house. The “Internet of Things” is a technology trend holding the promise of ubiquitous inexpensive sensors. The reality, however, is that energy and HVAC monitoring and management is a patchwork of incompatible protocols and expensive proprietary technologies. Coincident’s multi-protocol architecture, developed in part under this award tackles this problem and brings low cost interoperable sensor and control devices to market. Second, the Coincident system eliminates hard-to-program and rigid thermostat schedules and instead provides automatic operation of heating and cooling by combining individual temperature and comfort preferences with energy-saving targets, real-time utility use information, weather data, and room utilization patterns. Energy efficiency technology must be appealing to consumers otherwise it will not be used. The Coincident user interface has engaging features such as remote control from any smart phone or web browser and per-room performance breakdowns. Expected energy savings resulting from more efficient operation of heating and air conditioning equipment are in the range of 10-20%. Third, the Coincident system provides heating and air-conditioning contractors with fine-grained performance data for every residence they support (subject to customer privacy controls). This data is integrated from diverse networks within the residence and includes HVAC performance and fuel use data. This information allows the partner to validate energy savings and identify potential system faults (whether from installation problems or maintenance issues). When combined with professional installation as part of high-efficiency HVAC upgrade, energy savings levels of 20-30% can be achieved. Economic feasibility of energy efficiency technology is one of the key challenges addressed in this award. The Coincident system is engineered to be delivered at a disruptive price point, making the system financially feasible for new and retrofit homes of all types and sizes. The Coincident system is intended to be sold through the HVAC professional—the industry most capable of improving HVAC efficiency. Providing HVAC contractors with detailed home performance data motivates them to sell the product, provides them with maintenance and upgrade revenue opportunities, and therefore delivers customer savings and environmental benefits. Having demonstrated technical and financial feasibility, Coincident has won additional grants and awards, participated in pilot projects, started partnership discussions with several HVAC equipment vendors, and has lined up several large channel partners ready to participate in large pilot rollouts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Performance Monitoring of Residential Hot Water Distribution Systems

Current water distribution systems are designed such that users need to run the water for some time to achieve the desired temperature, wasting energy and water in the process. We developed a wireless sensor network for large-scale, long time-series monitoring of residential water end use. Our system consists of flow meters connected to wireless motes transmitting data to a central manager mote, which in turn posts data to our server via the internet. This project also demonstrates a reliable and flexible data collection system that could be configured for various other forms of end use metering in buildings. The purpose of this study was to determine water and energy use and waste in hot water distribution systems in California residences. We installed meters at every end use point and the water heater in 20 homes and collected 1s flow and temperature data over an 8 month period. For a typical shower and dishwasher events, approximately half the energy is wasted. This relatively low efficiency highlights the importance of further examining the energy and water waste in hot water distribution systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Frequent Subgraph Discovery in Large Attributed Streaming Graphs

The problem of finding frequent subgraphs in large dynamic graphs has so far only consid- ered a dynamic graph as being represented by a series of static snapshots taken at various points in time. This representation of a dynamic graph does not lend itself well to real time processing of real world graphs like social networks or internet traffic which consist of a stream of nodes and edges. In this paper we propose an algorithm that discovers the frequent subgraphs present in a graph represented by a stream of labeled nodes and edges. Our algorithm is efficient and consists of tunable parameters that can be tuned by the user to get interesting patterns from various kinds of graph data. In our model updates to the graph arrive in the form of batches which contain new nodes and edges. Our algorithm con- tinuously reports the frequent subgraphs that are estimated to be found in the entire graph as each batch arrives. We evaluate our system using 5 large dynamic graph datasets: the Hetrec 2011 challenge data, Twitter, DBLP and two synthetic. We evaluate our approach against two popular large graph miners, i.e., SUBDUE and GERM. Our experimental re- sults show that we can find the same frequent subgraphs as a non-incremental approach applied to snapshot graphs, and in less time.

Dynamic graph, Frequent subgraph mining, pattern d↗
DOE OSTI2015

Rapid deployable global sensing hazard alert system

A rapid deployable global sensing hazard alert system and associated methods of operation are provided. An exemplary system includes a central command, a wireless backhaul network, and a remote monitoring unit. The remote monitoring unit can include a positioning system configured to determine a position of the remote monitoring unit based on one or more signals received from one or more satellites located in Low Earth Orbit. The wireless backhaul network can provide bidirectional communication capability independent of cellular telecommunication networks and the Internet. An exemplary method includes instructing at least one of a plurality of remote monitoring units to provide an alert based at least in part on a location of a hazard and a plurality of positions respectively associated with the plurality of remote monitoring units.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

EXAMINING THE ROLE AND RESEARCH CHALLENGES OF SOCIAL MEDIA AS A TOOL FOR NONPROLIFERATION AND ARMS CONTROL TREATY VERIFICATION

Traditional arms control treaty verification activities typically involve a combination of technical measurements via physical and chemical sensors, state declarations, political agreements, and on-site inspections involving international subject matter experts. However, the ubiquity of the internet, and the electronic sharing of data that it enables, has made available a wealth of open source information with the potential to benefit verification efforts. Open source information is already being used by organizations such as the International Atomic Energy Agency to support the verification of state-declared information, prepare inspectors for in-field activities, and to maintain situational awareness . The recent explosion in social media use has opened new doors to exploring the attitudes, moods, and activities around a given topic. Social media platforms, such as Twitter, Facebook, and YouTube, offer an opportunity for individuals, as well as institutions, to participate in a global conversation at minimal cost. Social media data can also provide a more data-rich environment, with text data being augmented with images, videos, and location data. The research described in this paper investigates the utility of applying social media signatures as potential arms control and nonproliferation treaty verification tools and technologies, as determined through a series of case studies. The treaty relevant events that these case studies touch upon include detection of undeclared facilities or activities, determination of unknown events recorded by the International Monitoring System (IMS), and the global media response to the occurrence of an Indian missile launch. The case studies examine how social media can be used to fill an information gap and provide additional confidence to a verification activity. The case studies represent, either directly or through a proxy, instances where social media information may be available that could potentially augment the evaluation of an event. The goal of this paper is to instigate a discussion within the verification community as to where and how social media can be effectively utilized to complement and enhance traditional treaty verification efforts. In addition, this paper seeks to identify areas of future research and development necessary to adapt social media analytic tools and techniques, and to form the seed for social media analytics to aid and inform arms control and nonproliferation policymakers and analysts. While social media analysis (as well as open source analysis as a whole) will not ever be able to replace traditional arms control verification measures, they do supply unique signatures that can augment existing analysis.

nonproliferation, arms control, social media↗
DOE OSTI2015

Personalized professional content recommendation

A personalized content recommendation system includes a client interface configured to automatically monitor a user's information data stream transmitted on the Internet. A hybrid contextual behavioral and collaborative personal interest inference engine resident to a non-transient media generates automatic predictions about the interests of individual users of the system. A database server retains the user's personal interest profile based on a plurality of monitored information. The system also includes a server programmed to filter items in an incoming information stream with the personal interest profile and is further programmed to identify only those items of the incoming information stream that substantially match the personal interest profile.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2014

Introduction to Cyber Technologies exercise environment

Exercise environment for Introduction to Cyber Technologies class. This software is essentially a collection of short scripts, configuration files, and small executables that form the exercise component of the Sandia Cyber Technologies Academy's Introduction to Cyber Technologies class. It builds upon other open-source technologies, such as Debian Linux and minimega, to provide comprehensive Linux and networking exercises that make learning these topics exciting and fun. Sample exercises: a pre-built set of home directories the student must navigate through to learn about privilege escalation, the creation of a virtual network playground designed to teach the student about the resiliency of the Internet, and a two-hour Capture the Flag challenge for the final lesson. There are approximately thirty (30) exercises included for the students to complete as part of the course.

Source record↗
DOE OSTI2015

Stonix, Version 0.x

STONIX is a program for configuring UNIX and Linux computer operating systems. It applies configurations based on the guidance from publicly accessible resources such as: NSA Guides, DISA STIGs, the Center for Internet Security (CIS), USGCB and vendor security documentation. STONIX is written in the Python programming language using the QT4 and PyQT4 libraries to provide a GUI. The code is designed to be easily extensible and customizable.

Source record↗
DOE OSTI2016

The Password Problem

Passwords are an ubiquitous, established part of the Internet today, but they are also a huge security headache. Single sign-on, OAuth, and password managers are some of the solutions to this problem. OAuth is a new, popular method that allows people to use large, common authentication providers for many web applications. However, it comes at the expense of some privacy: OAuth makes users easy to track across websites, applications, and devices. Password managers put the power in the hands of the users, but this vulnerability survey reveals that you have to be extremely careful which program you choose. All in all, password managers are the solution of choice for home users and small organizations, but large companies will probably want to invest in their own SSO solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Current Trends in the Detection of Sociocultural Signatures: Data-Driven Models

The harvesting of behavioral data and their analysis through evidence-based reasoning enable the detection of sociocultural signatures in their context to support situation awareness and decision making. Harvested data are used as training materials from which to infer computational models of sociocultural behaviors or calibrate parameters for such models. Harvested data also serve as evidence input that the models use to provide insights about observed and future behaviors for targets of interest. The harvested data is often the result of assembling diverse data types and aggregating them into a form suitable for analysis. Data need to be analyzed to bring out the categories of content that are relevant to the domain being addressed in order to train or run a model. If, for example, we are modeling the intent of a group to engage in violent behavior using messages that the group has broadcasted, then these messages need to be processed to extract and measure indicators of violent intent. The extracted indicators and the associated measurements (e.g. rates or counts of occurrence) can then be used to train/calibrate and run computational models that assess the propensity for violence expressed in the source message. Ubiquitous access to the Internet, mobile telephony and technologies such as digital photography and digital video have enabled social media application platforms such as Facebook, YouTube, and Twitter that are altering the nature of human social interaction. The fast increasing pace of online social interaction introduces new challenges and opportunities for gathering sociocultural data. Challenges include the development of harvesting and processing techniques tailored to new data environments and formats (e.g. Twitter, Facebook), the integration of social media content with traditional media content, and the protection of personal privacy. As these and other challenges are addressed, a new wealth of behavioral data and data analysis methods becomes available that are shaping social computing as a strongly data-driven experimental discipline with an increasingly stronger impact on the decision-making process of groups and individuals alike. In this chapter, we review current advances and trends in the detection of sociocultural signatures. Specific embodiments of the issues discussed are provided with respect to the assessment of violent intent and sociopolitical contention. We begin by reviewing current approaches to the detection of sociocultural signatures in these domains. Next, we turn to the review of novel data harvesting methods for social media content. Finally, we discuss the application of sociocultural models to social media content, and conclude by commenting on current challenges and future developments.

Sanfilippo, Antonio P.↗
DOE OSTI2016

Expanding geothermal resource utilization through directed research, education, and public outreach: Final Technical Report

The University of Nevada, Reno (UNR) conducts research and outreach activities that will lead to increased utilization of geothermal resources in the western US. The Great Basin Center for Geothermal Energy (GBCGE) is working in partnership with US industry to establish geothermal energy as a sustainable, environmentally sound, economically competitive contributor to energy supply in the western US. Task 1 involves conducting geoscience and engineering research and developing technology to improve the assessment, exploration, and stimulation of geothermal resources. Subtask projects were selected based on peer review of proposals submitted to the GBCGE from Nevada System of High Education (NSHE) institutions for short project development and seed awards intended to develop background and establish viability of approaches for future activities. Task 2 includes project management and organization of workshops periodically requested by DOE and others to satisfy other mission goals of the GBCGE and the DOE geothermal program. GBCGE supports interaction with national and international geothermal organizations, with brochures, presentations, and materials describing GBCGE accomplishments and current research. We continue to maintain and develop an internet-based information system that makes geothermal data and information available to industry, government, and academic stakeholders for exploration and development of geothermal resources. This award also partially supported post-doctoral scholar Drew Siler and research scientist Betsy Littlefield Pace whose effort is included under developing future research projects. Task 2 also focuses on education and outreach through a competitive graduate fellowship program. The budget is for two-year stipends for three graduate students to work collaboratively with GBCGE faculty on Master’s or PhD degrees in geoscience and engineering fields. This grant supported three MS students in full for two years toward the degree and contributed bridge money for four other students to finish their degrees. In total, eight graduate degrees were supported by this grant, either through the faculty seed grants or the fellowship program.

15 GEOTHERMAL ENERGY↗