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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Energy Transfer in Aqueous Light Harvesting Antennae Based on Brush-like Inter-Conjugated Polyelectrolyte Complexes

Conjugated polyelectrolytes (CPEs) have the potential to serve as building blocks of artificial light-harvesting systems. This is primarily due to their delocalized electronic states and potential for hierarchical self-assembly. We showed previously that inter-CPE complexes composed of oppositely charged exciton-donor and exciton-acceptor CPEs displayed efficient electronic energy transfer. However, near ionic charge equivalence, complexed CPE chains become net-neutral and thus experience a precipitous drop in aqueous solubility. To increase the stability and to rationally manipulate the phase behavior of inter-CPE complexes, we synthesized a series of highly water-soluble exciton-donor CPEs composed of alternating ionic and polar nonionic fluorene monomers. The nonionic monomer contained oligo(ethyleneglycol) sidechains of variable length. We then formed exciton donor-acceptor complexes and investigated their relative energy transfer efficiencies in the presence of a fixed exciton-acceptor CPE. We find that, even when the polar nonionic sidechains become quite long (nine ethyleneglycol units), the energy transfer efficiency is hardly affected so long as the inter-CPE network retains a net polyelectrolyte charge. However, near the onset of spontaneous phase separation, we observe a clear influence of the length of the oligo(ethyleneglycol) sidechains on the photophysics of the complex. Our results have implications for the use of polyelectrolyte phase separation to produce aqueous light-harvesting soft materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Charge Recombination, Transport Dynamics, and Interfacial Effects in Organic Solar Cells

The need for renewable sources of energy is well known. Conversion of sunlight to electricity using solar cells is one of the most important opportunities for creating renewable energy sources. The research carried out under DE-FG02-08ER46535 focused on the science and technology of “Plastic” solar cells comprised of organic (i.e. carbon based) semiconductors. The Bulk Heterojunction concept involves a phase separated blend of two organic semiconductors each with dimensions in the nano-meter length scale --- one a material that functions as a donor for electrons and the other a material that functions as an acceptor for electrons. The nano-scale inter-penetrating network concept for “Plastic” solar cells was created at UC Santa Barbara. A simple measure of the impact of this concept can be obtained from a Google search which gives 244,000 “hits” for the Bulk Heterojunction solar cell. Research funded through this program focused on four major areas: 1. Interfacial effects in organic photovoltaics, 2. Charge transfer and photogeneration of mobile charge carriers in organic photovoltaics, 3. Transport and recombination of the photogenerated charge carriers in organic photovoltaics, 4. Synthesis of novel organic semiconducting polymers and semiconducting small molecules, including conjugated polyelectrolytes. Following the discovery of ultrafast charge transfer at UC Santa Barbara in 1992, the nano-organic (Bulk Heterojunction) concept was formulated. The need for a morphology comprising two interpenetrating bicontinuous networks was clear: one network to carry the photogenerated electrons (negative charge) to the cathode and one network to carry the photo-generated holes (positive charge) to the anode. This remarkable self-assembled network morphology has now been established using Transmission electron Microscopy (TEM) either in the Phase Contrast mode or via TEM-Tomography. The steps involved in delivering power from a solar cell to an external circuit are the following: • Photo-excitation of the donor (or the acceptor). • Charge transfer with holes in the donor domain and electrons in the acceptor domain. • Sweep-out to electrodes prior to recombination by the internal electric field. • Energy delivered to the external circuit. Each of these four steps was studied in detail using a wide variety of organic semiconductors with different molecular structures. This UC Santa Barbara group was the first to clarify the origin and the mechanism involved in the ultrafast charge transfer process. The ultrafast charge transfer (time scale approximately 100 times faster than the first step in the photo-synthesis of green plants) is the fundamental reason for the potential for high power conversion efficiency of sunlight to electricity from plastic solar cells. The UCSB group was the first to emphasize, clarify and demonstrate the need for sweep-out to electrodes prior to recombination by the internal electric field. The UCSB group was the first to synthesize small molecule organic semiconductors capable of high power conversion efficiencies. The results of this research were published in high impact peer-reviewed journals. Our published papers (40 in number) provide answers to fundamental questions that have been heavily discussed and debated in the field of Bulk Heterojunction Solar Cells; scientific questions that must be resolved before this technology can be ready for commercialization in large scale for production of renewable energy. Of the forty publications listed, nineteen were co-authored by two or more of the PIs, consistent with the multi-investigator approach described in the original proposal. The specific advantages of this “plastic” solar cell technology are the following: a. Manufacturing by low-cost printing technology using soluble organic semiconductors; this approach can be implemented in large scale by roll-to-roll printing on plastic substrates. b. Low energy cost in manufacturing; all steps carried out at room temperature (approx. a factor of ten less than the use of Silicon which requires high temperature processing). c. Low carbon footprint d. Lightweight, flexible and rugged Because of the resolution of many scientific issues, a significant fraction of which were addressed in the research results of DE-FG02-08ER46535, the power conversion efficiencies are improving at an ever increasing rate. During the funding period of DE-FG02-08ER46535, the power conversion efficiencies of plastic solar cells improved from just a few per cent to values greater than 11% with contributions from our group and from researchers all over the world.

14 SOLAR ENERGY↗
DOE OSTI2022

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm 2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Structure and Dynamics of N, N-diethyl-N-methylammonium Triflate Ionic Liquid, Neat and with Water, from Molecular Dynamics Simulations

We investigated by means of molecular dynamics simulations the properties (structure, thermodynamics, ion transport, and dynamics) of the protic ionic liquid N,N-diethyl-N-methyl-ammonium triflate (dema:Tfl) and of selected aqueous mixtures of dema:Tfl. This ionic liquid, a good candidate for a water-free proton exchange membrane, is shown to exhibit high ion mobility and conductivity. For bulk melts in the temperature range of 303-453K, both liquid densities and enthalpies of vaporization are found to decrease roughly linearly with increasing temperature. The radial distribution functions reveal a significant long-range structural correlation. The ammonium cations [dema]+ are found to diffuse slightly faster than the triflate anions [Tfl]-, and both types of ions exhibit enhanced mobility at higher temperatures, leading to higher ionic conductivity of these ionic liquids. Analysis of the dynamics of ion pairing clearly points to the existence of long-lived contact ion pairs in this ionic liquid. We also examined the effects of water on the ionic properties of dema:Tfl-water mixtures. From the structural analysis it was found that water molecules tend to replace counter ions in the coordination shell and hydrogen bond to both ions, thus weakening their mutual association. As water concentration increases, water molecules start to connect with each other and eventually form a large network that percolates through the system. It is also found that water has a strong influence on the ion dynamics in the mixtures. As the concentration of water increases, both translational and rotational motion of [dema]+ and [Tfl]- are significantly enhanced. As a result, higher ionic conductivity is observed with increased hydration level. This work was supported by the US Department of Energy Basic Energy Sciences' Chemical Sciences, Geosciences & Biosciences Division. Pacific Northwest National Laboratory is operated by Battelle for the US Department of Energy.

08 HYDROGEN↗
DOE OSTI2026

Site-decorated model for unconventional frustrated magnets: Ultranarrow phase crossover and two-dimensional spin reversal transition

Here, the site-decorated Ising model is introduced to advance the understanding and experimental realization of the recently discovered one-dimensional (1D) finite-temperature ultranarrow phase crossover in an external magnetic field, while mitigating the geometric complexities of traditional bond-decorated models. The unconventional frustration and physics are clarified by exactly mapping the 1D site-decorated Ising model in a magnetic field onto a zero-field bond-decorated 𝐽 1 −𝐽 2 Ising model with conventional geometrical frustration. Furthermore, although higher-dimensional Ising models in an external field remain unsolved exactly, an exact solution for a spin-reversal transition—driven by an exotic, hidden half-ice, half-fire state induced by site decoration—is derived. This transition, triggered by a slight variation in temperature or magnetic field—without changing its direction—even in the weak-field limit, offers a promising route toward energy-efficient applications such as data storage and processing. The results suggest that site decoration offers an avenue for materials and device design, particularly in systems such as mixed 𝑑−𝑓 compounds, optical lattices, and neural networks, calling for further studies with site-decorated Heisenberg models. In addition, the site-decorated model offers a rigorous test ground for artificial intelligence (AI) in science, as the analytic derivation of the present results was not only validated but also improved by a general-purpose large language model, inspiring the use of AI as scientific discoverer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

A Systems Biology and Pond Culture-based Understanding and Improvement of Metabolic Processes Related to Productivity in Diverse Microalgal Classes for Viable Biofuel Production

The overarching goal of this project was to understand and manipulate fundamental molecular mechanisms involved in maximizing growth rate and lipid accumulation in diverse classes of microalgae under authentic diurnal conditions to enhance production capabilities for biofuels. The research was aimed at filling two critical knowledge gaps by addressing fundamental cellular issues that govern growth and fuel molecule accumulation in conjunction with realistic climate-simulated cultivation: 1) The insufficient understanding of the effect of metabolic topology on cellular carbon partitioning and its regulation with regards to productivity. 2) The understudied effect of the diurnal and cell cycles on cellular metabolism, resource allocation, and resource mobilization under simulated production conditions. To unravel broader as well as lineage-specific regulatory mechanisms that determine productivity, we compared green algae with diatoms. Complementing the systems approach with a pond-based cultivation method under authentic diurnal conditions, we anticipated an understanding of how the effect of diurnal and cell cycles affects growth and lipid productivity. Elucidation of the topology and regulation of relevant metabolic networks in proposed new model organisms was expected to enable improvements in functional capabilities and biosynthetic pathways. The major goals of the project were achieved by the following accomplishments. Genome assemblies and functional annotations for the green algae S. obliquus strain DOE0152Z and Coleastrella sp. strain DOE0202. Development of a metabolic map for the core carbon metabolism for S. obliquus. Analyzed the cellular topology for the core carbon network and proposed differences in core carbon metabolism for the Chlorophyceae versus the Trebouxiophyceae. Discovered the genes for enzymes involved in dicarboxylic acid metabolism and proposed the existence of C4-type metabolism for S. obliquus. Calculated the energy and reducing equivalencies for TAG and isoprenoid biosynthesis based on the core carbon network created in this project. Identified one newly created strain SOM-P004 (UV-Mutagenesis), which performed better at higher culture densities than wild type in LEAPS reactors under simulated environmental conditions. Results obtained within the course of this project were used in 2017 to apply for a Community Science Program (CSP) at the DOE Joint Genome Institute (JGI). The project (JGI Proposal Id: 503423) with the Title “Comparative genomics and germ plasm diversity in Acutodesmus, a green microalgal bioenergy feedstock candidate with potential for breeding and hybridization.” was selected for CSP 2018 for sequencing of up to 30 genomes of the alga A. obliquus (=S. obliquus). Our genome of the strain DOE0152Z serves as a reference for this new CSP project. Characterization of synchronized cultures of Cyclotella cryptica and the implications on productivity. Determination that some cellular pigments respond to light, but others to the cell cycle. Characterization of productivity of C. cryptica in PNNL ponds. Genome sequence and metabolic mapping of Marinichlorella kaistiae KAS603. Demonstration that some green algae store and utilize starch in the cytoplasm. Photorespiratory pathway clarified in T. pseudonana leading to a better understanding of carbon flux pathways in diatoms. Genome sequence determination, annotation, and comparison completed for C. cryptica, identifying potential metabolic differences that lead to high biomass and lipid accumulation ability for this species. Transcriptomic comparison of silicon (Si) and nitrogen (N) limited triacylglycerol accumulation in Cyclotella cryptica.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1998

NASA CORE: Central Operation of Resources for Educators-Educational Materials Catalog

The NASA Central Operation of Resources for Educators (CORE), established in cooperation with Lorain County Joint Vocational School, serves as the worldwide distribution center for NASA-produced educational materials. For a minimal charge, CORE will provide a valuable service to educators unable to visit one of the NASA Educator Resource Centers by making NASA educational audiovisual materials available through its mail order service. Through CORE's distribution network, the public has access to more than 200 videocassette, slide, and CD-ROM programs, chronicling NASA!s state-of-the-art research and technology. Through the use of these curriculum supplement materials, teachers can provide their students with the latest in aerospace information. NASAs educational materials on aeronautics and space provide a springboard for classroom discussion of life science, physical science, astronomy, energy, Earth resources, environment, mathematics, and career education.

Source record↗
DOE OSTI2021

Baylor University Campus-Wide Deep Dive

In January 2020, staff members from the Engagement and Performance Operations Center (EPOC) and the Lonestar Education And Research Network (LEARN) met with researchers and staff at Baylor University for the purpose of a Campus-Wide Deep Dive into research drivers. The goal of this meeting was to help characterize the requirements for five campus research use cases and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support. Profiled scientific use cases included: - Experimental High Energy Physics (HEP) - Proton Computed Tomography (pCT) - Nutrition and Relation to Digestive Microbiome - Baylor University Core Research Facilities - Molecular Quantum-dot Cellular Automata (QCA), and Material Science of Quantum Computing - Modeling and Simulation of Low-Dimensional and Nano-Structured Materials - Computational Fluid Dynamics Material for this event included the written documentation from each of the research areas at Baylor University, documentation about the current state of technology support, and a write-up of the discussion that took place in person. The Case Studies highlighted the ongoing challenges that Baylor University has in supporting a cross-section of established and emerging research use cases. Each Case Study mentioned unique challenges which were summarized into common needs. These included: - Tradeoffs for network/software security, and usability of the resulting infrastructure. Better communication to set expectations and understand realities is required. - Computation use on campus is widespread and healthy. While no major problems were uncovered, upgrades to maintain current usage patterns and encourage growth will be required. - Storage is a critical need for enterprise use cases and research. In particular, a campus wide ‘storage architecture’ to support research use cases (e.g. instruments, data sharing) is required in the 2-5 year time window. - Instrumentation on campus is healthy and expanding. Technology must scale with this in the form of computation and storage. - Working with LEARN to upgrade network capacity (in multiples of 10G, or upgrades to 100G) will be required in the 1-3 year time frame. - Network monitoring and visibility will help to establish external science use cases. - Data sharing via portal systems is not currently a critical need, but growing in scope. EPOC can assist Baylor with options.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science

We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. Furthermore, this allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high-performance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Energy Materials Chemistry Integrating Theory, Experiment and Data Science (Final Report)

The Energy Materials Chemistry Integrating Theory, Experiment and Data Science (EM-CITED) project is a multidisciplinary research effort focused on accelerating discovery of scientific knowledge via incorporation of data science and artificial intelligence in materials chemistry research. The project aims to advance materials chemistry-aware data science to unify theory and experiment knowledge streams. The work resulted in foundational AI frameworks for materials chemistry – Deep Reasoning Networks (DRNets), Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP), and Material-to-Spectrum (Mat2Spec) prediction – as well as a host of strategies for accelerated scientific discoveries through principled incorporation of data science in computational and experimental research.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Efficient Algorithm for Locating and Sizing Series Compensation Devices in Large Transmission Grids: Solutions and Applications (PART II)

In a companion manuscript, we developed a novel optimization method for placement, sizing, and operation of Flexible Alternating Current Transmission System (FACTS) devices to relieve transmission network congestion. Specifically, we addressed FACTS that provide Series Compensation (SC) via modification of line inductance. In this manuscript, this heuristic algorithm and its solutions are explored on a number of test cases: a 30-bus test network and a realistically-sized model of the Polish grid (~2700 nodes and ~3300 lines). The results on the 30-bus network are used to study the general properties of the solutions including non-locality and sparsity. The Polish grid is used as a demonstration of the computational efficiency of the heuristics that leverages sequential linearization of power flow constraints and cutting plane methods that take advantage of the sparse nature of the SC placement solutions. Using these approaches, the algorithm is able to solve an instance of Polish grid in tens of seconds. We explore the utility of the algorithm by analyzing transmission networks congested by (a) uniform load growth, (b) multiple overloaded configurations, and (c) sequential generator retirements

25 ENERGY STORAGE↗
DOE OSTI2020

GLM Observations of the Brightest Lightning in the Americas

Two years of Geostationary Lightning Mapper (GLM) science data are used to document the brightest lighting flashes observed on the Americas continent. The most radiant optical lightning emissions—termed “superbolts”—were first identified by the Vela satellite constellation in the 1970s (Turman, 1977) and are defined in terms of peak optical power. GLM is an integrating sensor that, instead, measures the total optical energy from a lightning pulse. Furthermore, while GLM might not correctly classify short-duration superbolts, its top lightning cases certainly fall in the superbolt category, and the wealth of GLM measurements over its stationary hemispheric field of view provide an unmatched sample of extraordinarily bright lightning. While radiant bolts in excess of 100× the optical energy of typical lightning are ubiquitous across the Americas and result from many types of lightning processes, we find the most radiant cases (>1,000×) are concentrated in the central United States and in the La Plata basin in South America. Coincident Earth Networks Global Lightning Network (ENGLN) observations reveal that these extremely bright emissions usually result from + CG strokes with high peak currents in long horizontal flashes outside of the convective core. Single cases of these megaflashes might produce multiple superbolts over their durations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Quantum Networks for Open Science (QNOS) Workshop

Quantum computing systems currently being developed will have extraordinary capabilities to effectively solve complex problems in computational sciences, communication networks, artificial intelligence, and data processing, and will provide a powerful capability for researchers in almost every scientific discipline. Harnessing the full potential of quantum computing will require an ecosystem with a broad spectrum of quantum technologies. Quantum networks are one of the critical and highly anticipated components of this ecosystem. The combination of quantum computing and quantum networks are crucial to the US Department of Energy’s (DOE) mission to provide scientists with the state-of-the-art computational capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Determining and Unlocking Untapped Demand-Side Management Potential in South Africa: Demand Response at the Grid Edge

The National Renewable Energy Laboratory (NREL), funded through the Climate Technology and Change Network (CTCN), has provided technical assistance to key energy sector entities in South Africa to examine untapped demand-side management potential. NREL partnered with the Council for Scientific and Industrial Research (CSIR) and included key South Africa stakeholders: Eskom, the South African National Energy Development Institute (SANEDI), and the Department of Mineral and Resources and Energy (DMRE), including the CTCN National Designated Entity (NDE), the Department of Science and Innovation (DSI). South Africa is currently experiencing an energy crisis with extensive load shedding. While the load shedding crisis in South Africa is a supply side problem, demand-side management provides opportunities to achieve energy efficiency and to reduce peak demand. DSM also has huge potential to help alleviate load shedding, which is a last resort form of DSM.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Understanding the role of segmentation on process-structure–property predictions made via machine learning

Here, the present study investigated the effect of porosity surface determination methods on performance of machine learning models used to predict the tensile properties of AlSi10Mg processed by laser powder bed fusion from micro-computed tomography data. Machine learning models applied in this work include support vector machines, neural networks, decision trees, and Bayesian classifiers. The effects of isosurface thresholding and local gradient approaches for porosity segmentation, as well as image filtering schemes, on model precision were evaluated for samples produced under differing levels of global energy density.

36 MATERIALS SCIENCE↗
DOE OSTI2013

National Geothermal Data System: Transforming the Discovery, Access, and Analytics of Data for Geothermal Exploration

Compendium of Papers from the 38th Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California February 11-13, 2013 The National Geothermal Data System (NGDS) is a distributed, interoperable network of data collected from state geological surveys across all fifty states and the nation’s leading academic geothermal centers. The system serves as a platform for sharing consistent, reliable, geothermal-relevant technical data with users of all types, while supplying tools relevant for their work. As aggregated data supports new scientific findings, this content-rich linked data ultimately broadens the pool of knowledge available to promote discovery and development of commercial-scale geothermal energy production. Most of the up-front risks associated with geothermal development stem from exploration and characterization of subsurface resources. Wider access to distributed data will, therefore, result in lower costs for geothermal development. NGDS is on track to become fully operational by 2014 and will provide a platform for custom applications for accessing geothermal relevant data in the U.S. and abroad. It is being built on the U.S. Geoscience Information Network (USGIN) data integration framework to promote interoperability across the Earth sciences community. The basic structure of the NGDS employs state-of-the art informatics to advance geothermal knowledge. The following four papers comprising this Open-File Report are a compendium of presentations, from the 38th Annual Workshop on Geothermal Reservoir Engineering, taking place February 11-13, 2013 at Stanford University, Stanford, California. “NGDS Geothermal Data Domain: Assessment of Geothermal Community Data Needs,” outlines the efforts of a set of nationwide data providers to supply data for the NGDS. In particular, data acquisition, delivery, and methodology are discussed. The paper addresses the various types of data and metadata required and why simple links to existing data are insufficient for promoting geothermal exploration. Authors of this paper are Arlene Anderson, US DOE Geothermal Technologies Office, David Blackwell, Southern Methodist University (SMU), Cathy Chickering (SMU), Toni Boyd, Oregon Institute of Technology’s GeoHeat Center, Roland Horne, Stanford University, Matthew MacKenzie, Uberity, Joe Moore, University of Utah, Duane Nickull, Uberity, Stephen Richard, Arizona Geological Survey, and Lisa Shevenell, University of Nevada, Reno. “NGDS User Centered Design: Meeting the Needs of the Geothermal Community,” discusses the user- centered design approach taken in the development of a user interface solution for the NGDS. The development process is research based, highly collaborative, and incorporates state-of-the-art practices to ensure a quality user interface for the widest and greatest utility. Authors of this paper are Harold Blackman, Boise State University, Suzanne Boyd, Anthro-Tech, Kim Patten, Arizona Geological Survey, and Sam Zheng, Siemens Corporate Research. “Fueling Innovation and Adoption by Sharing Data on the DOE Geothermal Data Repository Node on the National Geothermal Data System,” describes the motivation behind the development of the Geothermal Data Repository (GDR) and its role in the NGDS. This includes the benefits of using the GDR to share geothermal data of all types and DOE’s data submission process. Authors of this paper are Jon Weers, National Renewable Energy Laboratory and Arlene Anderson, US DOE Geothermal Technologies Office. Finally, “Developing the NGDS Adoption of CKAN for Domestic & International Data Deployment,” provides an overview of the “Node-In-A-Box” software package designed to provide data consumers with a highly functional interface to access the system, and to ease the burden on data providers who wish to publish data in the system. It is important to note that this software package constitutes a reference implementation and that the NGDS architecture is based on open standards, which means other server software can make resources available, and other client applications can utilize NGDS data. Authors of this paper are Ryan Clark, Arizona Geological Survey (AZGS), Christoph Kuhmuench, Siemens Corporate Research, and Stephen Richard, AZGS.

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

Powering your car with sun light

Representing the Center for Lignocellulose Structure and Formation (CLSF), this document is one of the entries in the Ten Hundred and One Word Challenge and was awarded "Overall Winner." As part of the challenge, the 46 Energy Frontier Research Centers were invited to represent their science in images, cartoons, photos, words and original paintings, but any descriptions or words could only use the 1000 most commonly used words in the English language, with the addition of one word important to each of the EFRCs and the mission of DOE: energy. The mission of the CLSF is to dramatically increase our fundamental knowledge of the formation and physical interactions of bio-polymer networks in plant cell walls to provide a basis for improved methods for converting biomass into fuels.

09 BIOMASS FUELS↗