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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Velocity Structure at the Source Physics Experiment Phase I Site Obtained with the Large-N Array Data

The Source Physics Experiment (SPE) consists of a series of chemical explosions at the Nevada National Security Site. The goal of the SPE is to understand and model seismic-wave generation and propagation from these explosions. To achieve this goal, we need an accurate velocity model of the SPE site. A large-N seismic array deployed at the SPE site during one of the chemical explosions (SPE-5) provides great data for this purpose. The array consists of 996 geophones and covers an area of approximately 2 × 2.5 km. In addition to the SPE-5 explosion, the array recorded 53 large weight drops. Using the large-N seismic array recordings, we perform first-arrival analysis and obtain a 2D P-wave velocity model of the SPE site. We image a sharp transition from high-velocity Cretaceous granite to low-velocity Quaternary alluvium. Other geological units such as the Tertiary volcanic rocks and Paleozoic sedimentary rocks are also clearly shown. The results of this work provide important local geological information and will be incorporated into the larger 3D modeling effort of the SPE program to validate the predictive models developed for the site.

58 GEOSCIENCES↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Enabling a Decentralized Smart Grid Using Autonomous Edge Control Devices

As a large number of distributed devices are connected to the modern smart grid, the traditional centralized connectivity models fail to provide economic value. These models have relied on sending data to the cloud for processing and receiving commands to exert control actions, resulting in an ‘on-demand system’ with high bandwidth, low latency and an overload of data on the cloud. For realizing a decentralized system, there is a strong need to embed intelligence at the ‘edge of the network’. These intelligent devices, capable of sensing, local data processing and exerting control actions, report only actionable information to the cloud, acting as an edge control node. The system can then function autonomously, without constant cloud inputs, tolerating longer delays in communication, making the overall system ultra-low cost. The Global Asset Monitoring, Management and Analytics (GAMMA) Platform is a novel ultralow- cost, secure platform that operates through a Bluetooth based delay tolerant network. It relies on so-called ‘data mules’ to bridge the last mile connectivity gap in an inherently secure way. Due to this model, the platform requires no in-country certifications, does not rely on a dedicated backhaul technology and is immune to technology migration. This architecture also addresses some gaps identified in traditional IoT-based solutions in remote areas and sparse connectivity. A functional unit of the edge computing node has been built, taking into account various constraints like costs, customizations, data storage, cybersecurity and power management. Here, the platform has been built, deployed and has demonstrated distributed smart grid applications like power quality sensing, automated metering infrastructure and utility asset monitoring.

42 ENGINEERING↗
NASA NTRS2012

Spinoff 2011

Topics include: Bioreactors Drive Advances in Tissue Engineering; Tooling Techniques Enhance Medical Imaging; Ventilator Technologies Sustain Critically Injured Patients; Protein Innovations Advance Drug Treatments, Skin Care; Mass Analyzers Facilitate Research on Addiction; Frameworks Coordinate Scientific Data Management; Cameras Improve Navigation for Pilots, Drivers; Integrated Design Tools Reduce Risk, Cost; Advisory Systems Save Time, Fuel for Airlines; Modeling Programs Increase Aircraft Design Safety; Fly-by-Wire Systems Enable Safer, More Efficient Flight; Modified Fittings Enhance Industrial Safety; Simulation Tools Model Icing for Aircraft Design; Information Systems Coordinate Emergency Management; Imaging Systems Provide Maps for U.S. Soldiers; High-Pressure Systems Suppress Fires in Seconds; Alloy-Enhanced Fans Maintain Fresh Air in Tunnels; Control Algorithms Charge Batteries Faster; Software Programs Derive Measurements from Photographs; Retrofits Convert Gas Vehicles into Hybrids; NASA Missions Inspire Online Video Games; Monitors Track Vital Signs for Fitness and Safety; Thermal Components Boost Performance of HVAC Systems; World Wind Tools Reveal Environmental Change; Analyzers Measure Greenhouse Gasses, Airborne Pollutants; Remediation Technologies Eliminate Contaminants; Receivers Gather Data for Climate, Weather Prediction; Coating Processes Boost Performance of Solar Cells; Analyzers Provide Water Security in Space and on Earth; Catalyst Substrates Remove Contaminants, Produce Fuel; Rocket Engine Innovations Advance Clean Energy; Technologies Render Views of Earth for Virtual Navigation; Content Platforms Meet Data Storage, Retrieval Needs; Tools Ensure Reliability of Critical Software; Electronic Handbooks Simplify Process Management; Software Innovations Speed Scientific Computing; Controller Chips Preserve Microprocessor Function; Nanotube Production Devices Expand Research Capabilities; Custom Machines Advance Composite Manufacturing; Polyimide Foams Offer Superior Insulation; Beam Steering Devices Reduce Payload Weight; Models Support Energy-Saving Microwave Technologies; Materials Advance Chemical Propulsion Technology; and High-Temperature Coatings Offer Energy Savings.

Source record↗
NASA NTRS2018

Important Changes to User Access at the NASA CDDIS

The Crustal Dynamics Data Information System (CDDIS) supports data archiving and distribution activities for the space geodesy and geodynamics community. The main objectives of the system are to make space geodesy and geodynamics related data and derived products available in a central archive, to maintain information about the archival of these data, to disseminate these data and information in a timely manner to a global scientific research community, and to provide user based tools for the exploration and use of the archive. Since its inception, the user community has utilized anonymous ftp for accessing and downloading files from the CDDIS archive. Although this protocol allows users to easily automate file downloads, many organizations, data systems, and users have already migrated from ftp or are actively pursuing a move away from the protocol due to problems from a system and security standpoint. Furthermore, U.S. Government agencies have become increasingly concerned about this legacy protocol and ensuring data integrity for the user community have begun recently to disallow the use of the ftp protocol. The CDDIS, operated by NASA GSFC, must therefore address these concerns and provide alternative methods for access to its archive for continued easy and automated download of its contents. This poster will discuss the upcoming changes at CDDIS and provide examples on transitioning from anonymous ftp.

Noll, Carey E.↗
NASA NTRS2018

Use of Open Networks and Delay-Tolerant Protocol to Decrease WAN Latency of EOS near Real-Time Data

Since 1999, NASA's Earth Observing System Data Operations System (EDOS) project at Goddard Space Flight Center (GSFC) has provided high-rate data capture, level zero processing, and product distribution services for a majority of NASA's EOS (Earth Observing System) high-rate missions, including Terra, Aqua, Aura, ICESat, EO-1, SMAP, and OCO-2. EDOS high-rate science and engineering (150-300 Mbps) data-driven capture systems are deployed at 7 worldwide ground stations which are connected via both private (closed) and public (open) wide area networks (WANs) to the centralized EDOS Level Zero Processing Facility (LZPF) located at GSFC, where the data is processed and Level 0 products are distributed to users worldwide. All data transferred over the open networks to GSFC traverse an IPSec tunnel, providing the same level of security as a VPN connection. EDOS produces both time-based and near real-time products (session-based). Near real-time data products are produced from a single ground station contact; time-based products are produced from multiple ground station contacts. EDOS is the primary supplier of EOS Level 0 data to the NASA near real-time user community known as the Land, Atmosphere Near real-time Capability for EOS (LANCE). For the past few years, EDOS has streamlined its systems to reduce WAN latency for near real-time data delivery, including implementing Quality of Service (QoS), expanding closed network bandwidth, adding open network connections with more bandwidth, and implementing a delay-tolerant protocol to mitigate long round-trip times to remote ground stations.

Delay-Tolerant Protocol↗
NASA NTRS2017

Digital Model-Based Engineering: Expectations, Prerequisites, and Challenges of Infusion

Digital model-based engineering (DMbE) is the use of digital artifacts, digital environments, and digital tools in the performance of engineering functions. DMbE is intended to allow an organization to progress from documentation-based engineering methods to digital methods that may provide greater flexibility, agility, and efficiency. The term 'DMbE' was developed as part of an effort by the Model-Based Systems Engineering (MBSE) Infusion Task team to identify what government organizations might expect in the course of moving to or infusing MBSE into their organizations. The Task team was established by the Interagency Working Group on Engineering Complex Systems, an informal collaboration among government systems engineering organizations. This Technical Memorandum (TM) discusses the work of the MBSE Infusion Task team to date. The Task team identified prerequisites, expectations, initial challenges, and recommendations for areas of study to pursue, as well as examples of efforts already in progress. The team identified the following five expectations associated with DMbE infusion, discussed further in this TM: (1) Informed decision making through increased transparency, and greater insight. (2) Enhanced communication. (3) Increased understanding for greater flexibility/adaptability in design. (4) Increased confidence that the capability will perform as expected. (5) Increased efficiency. The team identified the following seven challenges an organization might encounter when looking to infuse DMbE: (1) Assessing value added to the organization. Not all DMbE practices will be applicable to every situation in every organization, and not all implementations will have positive results. (2) Overcoming organizational and cultural hurdles. (3) Adopting contractual practices and technical data management. (4) Redefining configuration management. The DMbE environment changes the range of configuration information to be managed to include performance and design models, database objects, as well as more traditional book-form objects and formats. (5) Developing information technology (IT) infrastructure. Approaches to implementing critical, enabling IT infrastructure capabilities must be flexible, reconfigurable, and updatable. (6) Ensuring security of the single source of truth (7) Potential overreliance on quantitative data over qualitative data. Executable/ computational models and simulations generally incorporate and generate quantitative vice qualitative data. The Task team also developed several recommendations for government, academia, and industry, as discussed in this TM. The Task team recommends continuing beyond this initial work to further develop the means of implementing DMbE and to look for opportunities to collaborate and share best practices.

Hale, J. P.↗
DOE OSTI2025

Secure State Estimation with Asynchronous Measurements for Coordinated Cyber Attack Detection in Active Distribution Systems

Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple communication channels and launch coordinated attacks. Therefore, multi-channel and asynchronous measurements could be harnessed to develop more secure cyber defense strategies. In this paper, a prediction-correction-based multi-rate observer is designed to exploit the value of asynchronous measurements for the detection of coordinated false data injection (FDI) attacks. First, a time-function-dependent prediction-correction strategy is proposed to adjust the sampling interval for each sensor’s measurement. Then, an observer is designed based on the trade-off between estimation error and the optimal period of the most recent sampling instant, with the convergence of estimation error with the maximum permitted sampling interval. Moreover, the conditions for exponential stability are developed using the Lyapunov–Krasovskii functional technique. Next, a coordinated FDI attack detection strategy is developed based on the dual nonlinear minimization problem. The proposed attack detection and secure state estimation strategies are tested on the IEEE 13-node system. Simulation results show that these schemes are effective in enhancing attack detection based on asynchronous measurements or compromised data.

asynchronous measurements↗
DOE OSTI2023

Digital Twins for Nuclear Power Plants and Facilities

The nuclear digital twin (DT) is the virtual representation of a nuclear energy system across its lifecycle. The nuclear DT uses real-time information and other data sources to improve the process of design, licensing, construction, security, O&M, decommissioning, and waste disposal. By leveraging the knowledge base and experience from the past 40 years of LWR operation, the nuclear DT is helping to accelerate the development and deployment of advanced nuclear technology in areas of passive safety, new fuel forms, instrumentation, and reactor control. For the currently operating nuclear fleet, DTs are reducing the operational risks, increasing plant availability, increasing energy capability, and reducing electricity production costs. For advanced fission and fusion reactors, DTs are being used to design for passive safety and built-in security-by-design. Rapidly deployable small modular reactor (SMR) and microreactor designs compatible with modular construction techniques and advanced manufacturing will be the new normal, reducing the need for large capital expenditures and compressing construction schedules. In addition, lower operational and maintenance costs will be realized by reducing the complexity of operations, staffing needs, and maintenance-related activities.

Kropaczek, Dave↗
DOE OSTI2015

Cross section measurements at LANSCE for defense, science and applications

The Los Alamos Neutron Science Center (LANSCE) has three neutron sources that are used for nuclear science measurements. These sources are driven by an 800 MeV proton linear accelerator and cover an energy range from sub-thermal to hundreds of MeV. Research at the facilities is performed under the auspices of a US DOE user program under which research proposals are rated for merit by a program advisory committee and are scheduled based on merit and availability of beam time. A wide variety of instruments is operated at the neutron flight paths at LANSCE including neutron detector arrays, gamma-ray detector arrays, fission fragment detectors, and charged particle detectors. These instruments provide nuclear data for multiple uses that range from increasing knowledge in fundamental science to satisfying data needs for diverse applications such as nuclear energy, global security, and industrial applications. In addition, highlights of recent research related to cross sections measurements are presented, and future research initiatives are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Frontiers and opportunities in bioenergy crop microbiome research networks

Researchers from across the four U.S. Department of Energy Bioenergy Research Centers engaged in a microbiome workshop that focused on identifying challenges and collaboration opportunities to better understand bioenergy-relevant plant–microbe interactions. The virtual workshop included hands-on educational sessions and a keynote address on current best practices in microbiome science and community microbiome standards, as well as breakout sessions aimed at identifying microbiome-related data and measurements that should be prioritized, opportunities for and barriers to integrating plant metabolites to microbiome research, and strategies for more effectively integrating microbiome data and processes into existing models. Based on participant discussion, key findings of the workshop were the need to prioritize scaling data sharing across BRCs and the broader research community and securing collaborative infrastructure in the areas of microbiome-ecosystem modeling and molecular plant-microbe interactions. This workshop review highlights additional main findings from this event, to encourage cross-site and more holistic meta-analyses while promoting wide scientific community engagement across plant microbiome sciences.

09 BIOMASS FUELS↗
DOE OSTI2017

DNA2FACE: An approach to correlating 3D facial structure and DNA

In this paper we introduce the concept of correlating genetic variations in an individual's specific genetic code (DNA) and facial morphology. This is the first step in the research effort to estimate facial appearance from DNA samples, which is gaining momentum within intelligence, law enforcement and national security communities. The dataset for the study consisting of genetic data and 3D facial scans (phenotype) data was obtained through the FaceBase Consortium. The proposed approach has three main steps: phenotype feature extraction from 3D face images, genotype feature extraction from a DNA sample, and genome-wide association analysis to determine genetic variations that contribute to facial structure and appearance. Results indicate that there exist significant correlations between genetic information and facial structure. We have identified 30 single nucleotide polymorphisms (SNPs), i.e. genetic variations, that significantly contribute to facial structure and appearance. We conclude with a preliminary attempt at facial reconstruction from the genetic data and emphasize on the complexity of the problem and the challenges encountered.

Srinivas, Nisha↗
DOE OSTI2025

Privacy Preservation from High-Performance Computing to Autonomous Science [Industrial and Governmental Activities]

High-Performance Computing (HPC) and Leadership-Class Supercomputing are driving forces behind scientific advancements, enabling researchers to tackle complex challenges in physics, chemistry, biology, and engineering. These systems power vast simulations and data analyses, fueling discoveries in fields ranging from materials science to climate modeling. However, their use often involves processing sensitive data—such as proprietary industry simulations, biomedical records, and national security computations—posing significant privacy concerns. In conclusion, this issue is amplified in collaborative environments like Department of Energy (DOE) user facilities, where HPC resources are shared across institutions to foster innovation.

Kotevska, Olivera [Oak Ridge National Laboratory (↗
DOE OSTI2017

pnnl/safekit

Some of the most pressing machine learning applications such as cyber security and object recognition lack enough ground-truth training data to build a classifier. We solve this problem by using unsupervised deep learning techniques to determine when data were anomalous or deviated from the norm. Traditional methods require either specific context dependent expertise to construct models, or prior examples of events and large amounts of well-labeled data to perform machine learning. Some cutting-edge deep learning approaches have been used to characterize complex events, but these have not been applied to the streaming environment and require a great deal of data. We demonstrated the use of an autoencoder and relevant featurization techniques to both learn a feature space and then identify anomalous portions without the aid of labeled data or domain knowledge. In this way, we demonstrated the detection of anomalous features without domain knowledge or tagged examples, and delivered these features to users.

Central, PNNL Developer↗
DOE OSTI2020

Report on the deployment of the National Geothermal Data System 2.0

This reports includes a video description of recent upgrades and changes to the National Geothermal Data System (geothermaldata.org) and a text report of its relevant security upgrades. Improvements include a new operating system, implementation of HTTPS, implementation of a standard firewall, PostgreSQL upgrades, an ESRI ArcGIS server, new registration policies, and a non-public API.

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

Equipment Request for the Belleville Agricultural Research and Education Center

The funding provided by the DOE for this project was used exclusively to purchase research equipment involved with the field development and evaluation of crop production technologies and practices for energy crop production. The new equipment has been placed into service on the SIU farms and has significantly enhanced our research capacity and scope for agronomy and precision ag research to support novel seed traits or crop management strategies for improving the efficiency and productivity of corn and soybeans. More specifically, the precision ag capability of the equipment that was purchased has heightened interest by faculty and associated industry partners to develop collaborative projects. In addition, this equipment has provided SIU with a foundation to be more successful at securing competitive grants in energy crop production and precision ag data management. Furthermore, the enhanced capacity for agronomy research in the southern Illinois region has been realized and will benefit crop producers in this region by learning to improve their operations from our research outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

Advancing Aviation Security

Air travel is a popular mode of transportation. Each year, more than 600 million people board U.S. flights for business or pleasure. As demonstrated on 9/11, a coordinated terrorist strike on commercial airlines has devastating consequences where many lives could be lost and the nation’s economy could be crippled. The Transportation Security Administration (TSA) has worked diligently to enhance security at airports and on flights so that people and cargo can continue to move freely. Today, all passengers and every piece of checked or carry-on luggage are screened before they are permitted on an aircraft. X-ray systems and walk-through metal detectors allow security personnel to identify dangerous metal objects, such as guns and knives. A more complex problem is the detection of explosives, where detection technologies such as multiple-energy X-ray radiography, computed tomography (CT), full body scanners using millimeter wave and X-ray backscatter and other techniques are used to screen for explosives hidden in luggage, in cargo and on the people boarding the aircraft. Scientists and engineers at the Lawrence Livermore National Laboratory (LLNL) are working with the Department of Homeland Security (DHS), Science and Technology Directorate (S&T), Explosives Division (EXD) to help improve aviation security by evaluating and enhancing the performance of next-generation explosives detection technologies. LLNL is providing subject matter expertise to TSA for their multi-billion dollar procurements of explosive detection system (EDS) for the nation’s airports and provides recommendations for process and system improvements. To further improve aviation security, EXD is funding research at LLNL to develop advanced explosives detection technologies that can more accurately discriminate between a wide range of explosives and non-threat materials. One challenge with scanning for explosives is that some nonthreatening materials share similar characteristics with actual threats, leading to false positives or false alarms. When an alarm is generated, security personnel must review the scan images to clear the alarm or manually open and verify the bag’s contents, which can increase labor costs and lead to passenger delays. The Livermore efforts currently focus on CT-based applications. First developed for the medical field as a method for diagnosing disease, X-ray CT has become instrumental in various industrial Non-Destructive Evaluation (NDE) applications. Explosives detection equipment that incorporates CT uses a broad-spectrum X-ray beam to capture projections of an object, in particular, the objects inside a piece of luggage. The CT system then applies complex reconstruction algorithms to the projections to produce a three-dimensional representation of the luggage and its contents. Automated threat-detection algorithms (also referred to as threat-recognition algorithms) further process the images to separate each item and flag potential threats for further review (see Figure 1). Transportation security officers can then examine these images and the relevant data to determine whether further interrogation is needed. LLNL has also performed research on advanced X-ray carry-on bag inspection systems and is helping to better understand the performance of X-ray backscatter and millimeter wave passenger screening systems.

42 ENGINEERING↗
DOE OSTI2019

A Report on the Implementation of Smart Grids in Mexico

As part of Mexico's implementation of its constitutional energy reform and the transformation of its electric power industry, Mexico is addressing the central importance of a modernized grid. The energy sector reform laws establish a Smart Grid Program to make Mexico's power grid capable of meeting the country's lofty clean energy goals while improving efficiencies, maintaining system reliability, and increasing its security. Implementing Smart Grids in Mexico required a compilation of data and information from many sources that document Mexico's progress in implementing a Smart Grid. In this report, various knowledge experts contribute with an assessment on the topics of the legislation that created the sectoral transformation now underway; the state of smart grid technology in Mexico's power sector and the efforts undertaken to date; a review of policy in the power sector describing the development of the Smart Grid Implementation Plan by the Ministry of Energy; a review of Mexico's priorities for smart grid deployment; the mapping of several smart grid measures to the specific smart grid drivers; substantial detail from smart grid experts from NREL and from Swedish smart grid research centers describing smart grid measures to assist Mexico's move to a cleaner, more-efficient grid; institutional policies that are recommended for smart grid growth; discussion of some of the barriers to smart grid deployment, including cybersecurity concerns and consumer receptivity.

24 POWER TRANSMISSION AND DISTRIBUTION↗