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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Performance Analysis of Scientific Computing Workloads on Trusted Execution Environments

Scientific computing sometimes involves computation on sensitive data. Depending on the data and the execution environment, the HPC (high-performance computing) user or data provider may require confidentiality and/or integrity guarantees. To study the applicability of hardware-based trusted execution environments (TEEs) to enable secure scientific computing, we deeply analyze the performance impact of AMD SEV and Intel SGX for diverse HPC benchmarks including traditional scientific computing, machine learning, graph analytics, and emerging scientific computing workloads. We observe three main findings: 1) SEV requires careful memory placement on large scale NUMA machines (1x -3.4x slowdown without and 1x -1.15x slowdown with NUMA aware placement), 2) virtualization - a prerequisite for SEV - results in performance degradation for workloads with irregular memory accesses and large working sets (1x -4x slowdown compared to native execution for graph applications) and 3) SGX is inappropriate for HPC given its limited secure memory size and inflexible programming model (1.2x -126x slowdown over unsecure execution). Finally, we discuss forthcoming new TEE designs and their potential impact on scientific computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

ESH-ASI-2021-004 DOE Annual Letter (redacted markings)

File LLNL2020_DOE_GI_ANNUAL.txt was submitted electronically to the REIRS file submission. The file contains positive and zero doses for 3,308 Lawrence Livermore National Security (LLNS) and subcontractor employees. There were 96 individuals with a collective Total Effective Dose (TED) of 6,886 mrem. The 2020 data follow the DOE Radiation Exposure Monitoring Systems (REMS) format per instructions in the REMS Reporting Guide.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Rock Valley Direct Comparison Relocation Working Group Location Results and Recommendations (Version 4.0)

The Source Physics Experiments (SPE) were designed to improve our physics-based understanding of explosion sources for the purposes of nuclear test monitoring. Phase I consisted of 6 chemical explosions in the Climax Stock Granite of the Nevada National Security Site (NNSS), while Phase II consisted of 4 explosions in a contrasting dry alluvium geology (DAG) in Yucca Flat, providing essential data in various media and emplacement conditions to further modeling efforts. For Phase III, the Rock Valley Direct Comparison (RVDC) seeks to directly compare earthquake and explosion source types. An unusually shallow series of events in 1993 along the Rock Valley Fault Zone in the southeastern portion of the NNSS has been targeted for this direct comparison. Depth ranges for the events, previously estimated to be less than 3 km, is achievable by modern drilling techniques and accessibility to the epicentral locations would require minimal improvements to the infrastructure. The events providing this unique opportunity for direct comparison are the focus of this report.

58 GEOSCIENCES↗
DOE OSTI2023

Oak Ridge National Laboratory Annual Sustainability Report 2023

ORNL, managed under contract by UT-Battelle LLC, is DOE’s largest science and energy laboratory and, as such, executes the widest range of mission capabilities. Diverse expertise spans a broad range of scientific and engineering disciplines, enabling research and science achievements to accelerate the delivery of solutions to the marketplace. ORNL supports DOE’s national missions of scientific discovery, clean energy, and security. To execute these activities, ORNL has grown significantly over 80 years of continuous operations, consisting of facilities with commissioning dates ranging from the 1940s to the present—an extraordinary set of distinctive scientific facilities and equipment. The complexities of such a variety of facilities require teamwork among divisions, a wide variety of conservation projects, and creative strategies to achieve the desired energy and water savings. Such a diverse and unique set of major facilities, totaling over 5.5 million square feet, with 6,000 employees, requires an innovative plan to accomplish advancements in operational efficiencies. ORNL is tasked with the management of an extraordinary set of distinctive scientific facilities and equipment for DOE. ORNL is mission-driven, and its mission has grown substantially over the decades. ORNL’s core research capabilities provide broad science and technology support for DOE in the areas of energy, environment, and national security. Currently, ORNL is a world leader in materials, neutron, and nuclear science and engineering, and in high-performance computing and data analytics. ORNL’s vast portfolio of research facilities must be maintained and carefully upgraded to protect the nation’s investment in scientific analysis. The goal of sustainable and resilient operations is to enable more effective execution of ORNL’s science and technology mission. Sustainable operational practices and enhanced resilience strive for excellent results while remaining diligent in energy conservation, environmental stewardship, asset management, and community engagement. The Sustainable ORNL Program (Sustainable ORNL) Continuous improvements in operational and business processes must be integrated into the fabric of the ORNL culture to maximize the return from the investment made in modernizing facilities and equipment. The Sustainable ORNL program promotes the legacy of system-wide best practices, management commitment, and employee engagement that will lead ORNL into a future of efficient, resilient, and sustainable operations. ORNL leadership and Sustainable ORNL champions receive regular status reports on the progress of each project and focus area (i.e., roadmap) and periodic summary reports. More information can be found at the program’s website. The Sustainable ORNL roadmap structure endorses 15 vital roadmaps. The figure below summarizes the current project assignments and demonstrates that each project contributes to the wellbeing of the whole. Continuous employee engagement and regular status reports confirm the ideals of the program. The roadmap structure is not static; as the science mission advances and the needs of the organization evolve, the Sustainable ORNL roadmap structure elements are modified to align with developing priorities. In 2022, Sustainable ORNL made roadmap changes to better align ORNL to support new federal requirements that have been issued.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Oak Ridge National Laboratory's Strategic Research and Development Insights for Digital Twins

Oak Ridge National Laboratory (ORNL) is pleased to provide our response to the NITRD RFI on Digital Twins Research and Development. Digital twins are virtual representations of physical systems, leveraging real-time data to simulate and predict behaviors. ORNL is advancing digital twin technology across various disciplines, including neutron scattering, networking, science ecosystems, supercomputing, secure facilities, mobility technologies, materials design and discovery, power systems, fusion reactors, biological sciences, and earth observation. These efforts aim to enhance scientific research, operational efficiency, and decision-making processes. ORNL facilities, such as the High Flux Isotope Reactor (HFIR), Grid-C, Spallation Neutron Source (SNS), and Oak Ridge Leadership Computing Facility (OLCF), provide the infrastructure to develop and demonstrate these digital twin technologies. In this document, we lay out key challenges, research gaps, and future opportunities based on our experience with digital twins that aim to serve as useful contributions towards a National Digital Twins R&D Strategic Plan. In the remaining document, we address nine of the thirteen topic areas specified in the RFI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

FORESTR: Finding, Organizing, Representing, Explaining, Summarizing, and Thinning Random forests

Random forests have become popular models used for data driven predictions. As a result, random forests are currently used or being considered for high-consequence mission applications in national security, such as the prediction of yield from optical signals and malware detection. While random forests may provide accurate predictions, the complexity of the algorithm causes a lack of interpretability. Random forests are an ensemble of regression or decision trees. Individual regression and decision trees are interpretable, but ensembles are inherently difficult to interpret due to the compilation of many models. We aim to increase the interpretability of random forests by finding patterns in the ensemble of trees that can be used to “thin” (or remove) trees. As a starting point, in this report, we develop a new distance metric for quantifying the similarity between trees based on their topologies (i.e., shapes). We base the metric on a novel distance metric for graphs that is a proper mathematical distance, is invariant to transformations, has registration between graphs, and computes topological evolutions between graphs. We use the tree distance metric to compute tree statistics such as a “mean tree” and to identify clusters of trees. We apply the developed methodology to a toy dataset and a mission relevant product inspection dataset to demonstrate how the metric can provide insight into random forests. Furthermore, we discuss the limitations of the approach and ideas for future research into how the metric could be used as a thinning tool to develop less complex models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

National Renewable Energy Laboratory, South Table Mountain Campus, Golden, Colorado

The National Renewable Energy Laboratory (NREL) South Table Mountain campus, located in Golden, Colorado, has multiyear datasets from buildings with approximately 1,100,000 sq ft of floor area. These 16 buildings include a large office, nine laboratories, and other education or public assembly facilities such as an education center, a warehouse, quick service restaurants, two site entrance security buildings, and a parking garage—all constructed and improved from 1982 to 2014. These datasets are also available from the NREL data storage and management platform SkySparks. The datasets contain electricity meter data (poser, voltage, and current) with 1-minute resolution and building automation system (BAS) data with resolutions of 5 or 15 minutes. Some subsystems (e.g., lighting) or equipment electricity data have the same intervals as above. Some mechanical equipment state or condition data, such as flow and temperature, are captured with 5- to 15-minute intervals. The types of measurement points (i.e., electrical meter power and zone temperature) and associated data quality across the campus are typical, which helps reduce the unreliability of data quality.

STM, Systems and equipment operational, Energy use↗
NASA NTRS1982

Random digital encryption secure communication system

The design of a secure communication system is described. A product code, formed from two pseudorandom sequences of digital bits, is used to encipher or scramble data prior to transmission. The two pseudorandom sequences are periodically changed at intervals before they have had time to repeat. One of the two sequences is transmitted continuously with the scrambled data for synchronization. In the receiver portion of the system, the incoming signal is compared with one of two locally generated pseudorandom sequences until correspondence between the sequences is obtained. At this time, the two locally generated sequences are formed into a product code which deciphers the data from the incoming signal. Provision is made to ensure synchronization of the transmitting and receiving portions of the system.

Doland, G. D.↗
NASA NTRS2004

Aircraft Detection System Ensures Free-Space Laser Safety

As scientists continue to explore our solar system, there are increasing demands to return greater volumes of data from smaller deep-space probes. Accordingly, NASA is studying advanced strategies based on free-space laser transmissions, which offer secure, high-bandwidth communications using smaller subsystems of much lower power and mass than existing ones. These approaches, however, can pose a danger to pilots in the beam path because the lasers may illuminate aircraft and blind them. Researchers thus are investigating systems that will monitor the surrounding airspace for aircraft that could be affected. This paper presents current methods for safe free space laser propagation.

Smithgall, Brian↗
NASA NTRS2004

Data processing pipeline with transaction-oriented data sharing

This paper makes three contributions to the area of modern science data processing systems. First, the paper describes the science data processing pipeline, developed at the Multi-mission Image Processing Lab of JPL, for transforming raw space data into high quality image data and automating the distribution of data using a high-performance file transaction service. File Exchange Interface is the file transaction service developed MIPL. Second, it presents the FEI component architecture in the are of file transaction management, security,a nd file integrity verfication. Finally, the paper presents the federated model for the FEI service to demonstrate how to create a pool of file trasaction services to support load balancing and service fallover, and simplify service management.

science data processing↗
NASA NTRS2006

Aeronautics and Space Report of the President, Fiscal Year 2006 Activities

The annual 'President's Report' is a good summary of the Government's aerospace activities each year. Mandated by law, it contains information on aerospace activities conducted by 14 Federal departments and agencies. It also contains an executive summary organized by agency, narrative sections organized by subject, as well as extensive appendices containing useful historical data on spacecraft launches, budget figures, key policy documents from the fiscal year, and a glossary.

Space shuttle missions↗
NASA NTRSDate not supplied

The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina

Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.

Nazmus Shams Sazib↗
NASA NTRS2021

NASA Framework for the Ethical Use of Artificial Intelligence (AI)

The NASA Framework for the Ethical Use of Artificial Intelligence (AI) provides six key principles to guide NASA's use of AI. The principles are NASA's AI must be 1. Fair, 2., Explainable and transparent, 3. Accountable, 4. Secure and safe, 5. Human-centric and societally beneficial, and 6. Scientifically and technically robust. The framework describes each ethical AI principle, and then applies that principle to NASA work. The framework also includes a list of questions practitioners should use to guide their AI work. Finally, the framework focuses on concrete, practical considerations for the next five - ten years, while also beginning to lay the foundation for longer-term disruptive change as human-level (or beyond) AI is created.

Artificial Intelligence↗
DOE OSTI2013

Knowledge Management at the Fast Flux Test Facility

One of the goals of the Department of Energy’s Office of Nuclear Energy, initiated under the Fuel Cycle Research and Development Program (FCRD) and continued under the Advanced Reactor Concepts Program (ARC) is to preserve the knowledge that has been gained in the United States on Liquid Metal Reactors (LMRs) that could support the development of an environmentally and economically sound nuclear fuel cycle. The Fast Flux Test Facility (FFTF) is the most recent LMR to operate in the United States, from 1982 to 1992, and was designed as a fully instrumented test reactor with on-line, real time test control and performance monitoring of components and tests installed in the reactor. The 10 years of operation of the FFTF provided a very useful framework for testing the advances in LMR safety technology based on passive safety features that may be of increased importance to new designs after the events at Fukushima. Knowledge preservation at the FFTF is focused on the areas of design, construction, and startup of the reactor, as well as on preserving information obtained from 10 years of successful operating history and extensive irradiation testing of fuels and materials. In order to ensure protection of information at risk, the program to date has sequestered reports, files, tapes, and drawings to allow for secure retrieval. The FFTF knowledge management program includes a disciplined and orderly approach to respond to client’s requests for documents and data in order to minimize the search effort and ensure that future requests for this information can be readily accommodated.

data preservation↗
DOE OSTI2015

International-Aerial Measuring System (I-AMS) Training Program

Since the Fukushima reactor accident in 2011, there has been an increased interest worldwide in developing national capabilities to rapidly map and assess ground contamination resulting from nuclear reactor accidents. The capability to rapidly measure the size of the contaminated area, determine the activity level, and identify the radionuclides can aid emergency managers and decision makers in providing timely protective action recommendations to the public and first responders. The development of an aerial detection capability requires interagency coordination to assemble the radiation experts, detection system operators, and aviation aircrews to conduct the aerial measurements, analyze and interpret the data, and provide technical assessments. The Office of International Emergency Management and Cooperation (IEMC) at the U.S. Department of Energy, National Nuclear Security Administration (DOE/NNSA) sponsors an International - Aerial Measuring System (I-AMS) training program for partner nations to develop and enhance their response to radiological emergencies. An initial series of courses can be conducted in the host country to assist in developing an aerial detection capability. As the capability develops and expands, additional experience can be gained through advanced courses with the opportunity to conduct aerial missions over a broad range of radiation environments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

November Mega-AI Newsletter

Mega AI, an internal investment at Pacific Northwest National Laboratory, aims to develop next-generation artificial intelligence (AI) capabilities unique to the Department of Energy national lab complex to address research gaps in large-scale multimodal representation learning, multitask inferences, and the need for increased generalizability, rapid adaptivity, and usability of AI technologies. In this newsletter, we highlight recent developments in the research community on next-gen AI technologies focusing on massive-scale model development, deployment and evaluation, data and code availability, model interactions, and new features and capabilities that are relevant to Mega AI’s goals and science and security applications.

Volkova, Svitlana↗
DOE OSTI2026

Responsible Adoption of Artificial Intelligence (AI) in Electric Grid Operations

The future of the grid will be powered by AI—or undermined by it. Artificial intelligence is rapidly reshaping grid operations, improving fault detection, forecasting accuracy, and real-time optimization. As AI systems move closer to operational decision loops, however, they introduce new consequence pathways: expanded attack surfaces, model integrity risks, regulatory exposure, and human-automation challenges. This talk presents a consequence-driven framework for deploying AI responsibly in the electric grid. Attendees will gain practical strategies to strengthen resilience, boost reliability, and deploy AI securely — ensuring the grid of the future is not only smarter but safer.

25 - ENERGY STORAGE↗
DOE OSTI2013

LINEBACkER: Bio-inspired Data Reduction Toward Real Time Network Traffic Analysis

Abstract—One essential component of resilient cyber applications is the ability to detect adversaries and protect systems with the same flexibility adversaries will use to achieve their goals. Current detection techniques do not enable this degree of flexibility because most existing applications are built using exact or regular-expression matching to libraries of rule sets. Further, network traffic defies traditional cyber security approaches that focus on limiting access based on the use of passwords and examination of lists of installed or downloaded programs. These approaches do not readily apply to network traffic occurring beyond the access control point, and when the data in question are combined control and payload data of ever increasing speed and volume. Manual analysis of network traffic is not normally possible because of the magnitude of the data that is being exchanged and the length of time that this analysis takes. At the same time, using an exact matching scheme to identify malicious traffic in real time often fails because the lists against which such searches must operate grow too large. In this work, we introduce an alternative method for cyber network detection based on similarity-measuring algorithms for gene sequence analysis. These methods are ideal because they were designed to identify similar but nonidentical sequences. We demonstrate that our method is generally applicable to the problem of network traffic analysis by illustrating its use in two different areas both based on different attributes of network traffic. Our approach provides a logical framework for organizing large collections of network data, prioritizing traffic of interest to human analysts, and makes it possible to discover traffic signatures without the bias introduced by expert-directed signature generation. Pattern recognition on reduced representations of network traffic offers a fast, efficient, and more robust way to detect anomalies.

Teuton, Jeremy R.↗

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