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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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NASA NTRS1995

The effect of lossy image compression on image classification

We have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. We also examined the effect of compression on spatial pattern detection using a neural network.

Paola, Justin D.↗

NASA NTRS1998

The Impact of NPG 7120.5A Upon Training and Development

NASA Procedures and Guidance 7120.5A for Program and Project Management Processes and Requirements should have minimal effect upon current Agency training and development programs - mainly because the new directive simply formalizes what we have been teaching and learning in the NASA Program/Project Management Initiative all along. A frequent complaint we get from the 8,000 or so graduates of our PPMI courses over the years, however, deals with resistance to what they may have learned in the classroom or training site. Brimming with new ideas, these young men and women often run up against an entrenched program or project manager who insists that things be done "the old way," too often perceived as "the NASA way" or even "the Goddard way." Management was all too often in the eyes of the manager; now we're all reading from the same book, 7120.5A, Still, there is no single method or "one size fits all" approach to project management in NASA. While each Center is responsible for developing policies, processes and procedures to comply with the new NPG, individual program and project managers will still need to tailor their requirements to the specific needs of the project, consistent with the size, complexity, risk and criticality of the project. Under NPG 7120.5A, the results of such tailoring are to be documented in agreements among managers, directors, Enterprise Associate Administrators and the Administrator.

Hoffman, Edward J.↗

NASA NTRS1998

Characteristics of Forests in Western Sayani Mountains, Siberia from SAR Data

This paper investigated the possibility of using spaceborne radar data to map forest types and logging in the mountainous Western Sayani area in Siberia. L and C band HH, HV, and VV polarized images from the Shuttle Imaging Radar-C instrument were used in the study. Techniques to reduce topographic effects in the radar images were investigated. These included radiometric correction using illumination angle inferred from a digital elevation model, and reducing apparent effects of topography through band ratios. Forest classification was performed after terrain correction utilizing typical supervised techniques and principal component analyses. An ancillary data set of local elevations was also used to improve the forest classification. Map accuracy for each technique was estimated for training sites based on Russian forestry maps, satellite imagery and field measurements. The results indicate that it is necessary to correct for topography when attempting to classify forests in mountainous terrain. Radiometric correction based on a DEM (Digital Elevation Model) improved classification results but required reducing the SAR (Synthetic Aperture Radar) resolution to match the DEM. Using ratios of SAR channels that include cross-polarization improved classification and

Ranson, K. Jon↗

NASA NTRS2005

Searching for Life with Rovers: Exploration Methods & Science Results from the 2004 Field Campaign of the "Life in the Atacama" Project and Applications to Future Mars Missions

The Life In The Atacama (LITA) project develops and field tests a long-range, solarpowered, automated rover platform (Zo ) and a science payload assembled to search for microbial life in the Atacama desert. Life is barely detectable over most of the driest desert on Earth. Its unique geological, climatic, and biological evolution have created a unique training site for designing and testing exploration strategies and life detection methods for the robotic search for life on Mars.

Cabrol, N. A.a↗

NASA NTRS2015

Expanding the Operational Use of Total Lightning Ahead of GOES-R

NASA's Short‐term Prediction Research and Transition Center (SPoRT) has been transitioning real‐time total lightning observations from ground‐based lightning mapping arrays since 2003. This initial effort was with the local Weather Forecast Offices (WFO) that could use the North Alabama Lightning Mapping Array (NALMA). These early collaborations established a strong interest in the use of total lightning for WFO operations. In particular the focus started with warning decision support, but has since expanded to include impact‐based decision support and lightning safety. SPoRT has used its experience to establish connections with new lightning mapping arrays as they become available. The GOES‐R / JPSS Visiting Scientist Program has enabled SPoRT to conduct visits to new partners and expand the number of operational users with access to total lightning observations. In early 2014, SPoRT conducted the most recent visiting scientist trips to meet with forecast offices that will used the Colorado, Houston, and Langmuir Lab (New Mexico) lightning mapping arrays. In addition, SPoRT met with the corresponding Center Weather Service Units (CWSUs) to expand collaborations with the aviation community. These visits were an opportunity to learn about the forecast needs of each office visited as well as to provide on‐site training for the use of total lightning, setting the stage for a real‐time assessment during May‐July 2014. With five lightning mapping arrays covering multiple geographic locations, the 2014 assessment has demonstrated numerous uses of total lightning in varying situations. Several highlights include a much broader use of total lightning for impact‐based decision support ranging from airport weather warnings, supporting fire crews, and protecting large outdoor events. The inclusion of the CWSUs has broadened the operational scope of total lightning, demonstrating how these data can support air traffic management, particularly in the Terminal Radar Approach Control Facilities (TRACON) region around an airport. These collaborations continue to demonstrate, from the operational perspective, the utility of total lightning and the importance of continued training and preparation in advance of the Geostationary Lightning Mapper.

GLM↗

NASA NTRS2011

Software Certification - Coding, Code, and Coders

We describe a certification approach for software development that has been adopted at our organization. JPL develops robotic spacecraft for the exploration of the solar system. The flight software that controls these spacecraft is considered to be mission critical. We argue that the goal of a software certification process cannot be the development of "perfect" software, i.e., software that can be formally proven to be correct under all imaginable and unimaginable circumstances. More realistically, the goal is to guarantee a software development process that is conducted by knowledgeable engineers, who follow generally accepted procedures to control known risks, while meeting agreed upon standards of workmanship. We target three specific issues that must be addressed in such a certification procedure: the coding process, the code that is developed, and the skills of the coders. The coding process is driven by standards (e.g., a coding standard) and tools. The code is mechanically checked against the standard with the help of state-of-the-art static source code analyzers. The coders, finally, are certified in on-site training courses that include formal exams.

training↗

NASA NTRSDate not supplied

Testing the Exploration Conops (Excon) Mockup Suit in Lunar Analog Environments in 2022

Understanding how to effectively train for Extravehicular Activities (EVAs) for Artemis missions is critical. Developing high-fidelity simulation environments is important for Artemis mission preparation. Because the actual Lunar exploration environment cannot be fully replicated on Earth, it is paramount to determine where and how to properly train the Artemis team. Tasked by the new Extravehicular Activity and Human Surface Mobility Program(EHP), a team of stakeholders from engineering, flight operations, human health and performance, astromaterials and science, and the crew office worked together to perform EVAs in 3 simulated lunar environments. The overall focus for this test series was developing the capability to perform Artemis simulated EVAs in high-fidelity, full-scale environments. This test series was broken into 3 distinct tests titled after the EHP integrated test team: Joint EVA Test Team (JETT). The test locations are planned to serve as Artemis training sites and were selected because of their relevance to the expected Artemis Lunar terrain. JETT1was conducted near Kilbourne Holeby El Paso, Texas and focused on hardware development and checkout. JETT2 was conducted in the Icelandic Highlands and began the transition towards EVA concept operations(con-ops), risks and technology.JETT3 was conducted near SP Crater by Flagstaff Arizona and focused on simulating the Artemis III mission with a simulated Houston based Flight Control Team (FCT) and a Science Mission Directorate (SMD) science team. All three JETT tests utilized the Excon mockup space suit. The Excon mockup suit is a light-weight, unpressurized Exploration Extravehicular Mobility Unit (xEMU)simulator. While it cannot replicate the feel of working within a pressurized suit, it does introduce similar volume constraints and some of the mobility programing to simulate the user experience in the xEMU. Overall, the JETT testing was able to create a simulated Lunar EVA and have two subjects perform full scale operations in line with Artemis III mission expectations. Future work is planned to continue to improve the simulation quality of Lunar EVA simulations.

spacesuit↗

NASA NTRSDate not supplied

Testing the Exploration ConOps (ExCon) Mockup Suit in Lunar Analog Environments in 2022

Understanding how to effectively train for Extravehicular Activities (EVAs) for Artemis missions is critical. Developing high-fidelity simulation environments is important for Artemis mission preparation. Because the actual Lunar exploration environment cannot be fully replicated on Earth, it is paramount to determine where and how to properly train the Artemis team. The overall focus for this test series was developing the capability to perform Artemis simulated EVAs in high-fidelity, full-scale environments. This test series was broken into three distinct tests titled after the EVA & Human Surface Mobility (HSM) Program (EHP) integrated test team: Joint EVA & HSM Test Team (JETT). The test locations are planned to serve as Artemis training sites and were selected because of their relevance to the expected Artemis Lunar terrain. JETT1 was conducted near Kilbourne Hole by El Paso, Texas and focused on hardware development and checkout. JETT2 was conducted in the Icelandic Highlands and began the transition towards EVA concept of operations (con-ops), risks and technology. JETT3 was conducted near SP Crater by Flagstaff, Arizona and focused on simulating the Artemis III mission including a Houston based Flight Control Team (FCT) and a Science Mission Directorate (SMD) science team. All three JETT tests utilized the Exploration Concept of Operations (ExCon) mockup space suit. The ExCon mockup suit is a lightweight, unpressurized Exploration Extravehicular Mobility Unit (xEMU) simulator. While it cannot replicate the feel of working within a pressurized suit, it does introduce similar volume constraints and some of the mobility programing to simulate the user experience in the xEMU. Overall, the JETT testing was able to create a simulated Lunar EVA and have two subjects perform full scale operations in line with Artemis III mission expectations. Future work is planned to continue to improve the simulation quality of Lunar EVA simulations.

spacesuit↗

NASA NTRSDate not supplied

Testing the Exploration Conops (Excon) Mockup Suit in Lunar Analog Environments in 2022

Understanding how to effectively train for Extravehicular Activities (EVAs) for Artemis missions is critical. Developing high-fidelity simulation environments is important for Artemis mission preparation. Because the actual Lunar exploration environment cannot be fully replicated on Earth, it is paramount to determine where and how to properly train the Artemis team. The overall focus for this test series was developing the capability to perform Artemis simulated EVAs in high-fidelity, full-scale environments. This test series was broken into three distinct tests titled after the EVA & Human Surface Mobility (HSM) Program (EHP) integrated test team: Joint EVA & HSM Test Team (JETT). The test locations are planned to serve as Artemis training sites and were selected because of their relevance to the expected Artemis Lunar terrain. JETT1 was conducted near Kilbourne Hole by El Paso, Texas and focused on hardware development and checkout. JETT2 was conducted in the Icelandic Highlands and began the transition towards EVA concept of operations (con-ops), risks and technology. JETT3 was conducted near SP Crater by Flagstaff, Arizona and focused on simulating the Artemis III mission including a Houston based Flight Control Team (FCT) and a Science Mission Directorate (SMD) science team. All three JETT tests utilized the Exploration Concept of Operations (ExCon) mockup space suit. The ExCon mockup suit is a lightweight, unpressurized Exploration Extravehicular Mobility Unit (xEMU) simulator. While it cannot replicate the feel of working within a pressurized suit, it does introduce similar volume constraints and some of the mobility programing to simulate the user experience in the xEMU. Overall, the JETT testing was able to create a simulated Lunar EVA and have two subjects perform full scale operations in line with Artemis III mission expectations. Future work is planned to continue to improve the simulation quality of Lunar EVA simulations.

spacesuit↗

DOE OSTI2025

Enhancing Security and Resiliency in Operational Technology Environments Through Network Slicing and Federated Learning

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.

Rodiles Delgado, Brian G↗

DOE OSTI2021

ESPC ESA Training Series: Site-Specific/Stand-Alone Contract Vehicle

This webinar provides instructions and resources for financing distributed energy projects (e.g., on–site renewable energy, storage, and combined heat and power) using an energy savings performance contract (ESPC) energy sales agreement (ESA) through the site–specific/stand–alone contract vehicle. The site–specific/stand–alone contract vehicle is a procurement process using a request for proposal (RFP). Topics covered during the webinar include forming a strong project team, validating the feasibility of the distributed energy project at a site, acquisition planning, RFP development, procurement, proposal evaluation and contract award, and the construction and performance period. This webinar also provides an overview of the resources available in the ESPC ESA Toolkit, such as checklists and templates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Site 300 Small Firearms Training Facility Project Soil Sampling and Analysis Plan

Lawrence Livermore National Laboratory's (LLNL) Project Management Office (PMO) is planning to execute a construction project at the Small Firearms Training Facility within LLNL's Experimental Test Site, Site 300 (S300). The proposed project will include extensive dirt work from a removal of a berm, the installation of a cinder block wall, and the replacement of a canopy and a fence with an access gate. This Sampling and Analysis Plan (SAP) outlines the procedures to collect environmental samples for the management and/or disposition of excess soil generated from the project site and geotechnical samples for building pad and pavement designs. This SAP has been prepared and is organized to be consistent with LLNL's Soil Screening and Management Plan (SSMP) (LLNL, 2021), as well as the U.S. Environmental Protection Agency's (EPA) Data Quality Objectives (DQO) programs (EPA, 2006).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1977

Nevada Test Site craters used for astronaut training

Craters produced by chemical and nuclear explosives at the Nevada Test Site were used to train astronauts before their lunar missions. The craters have characteristics suitable for reconnaissance-type field investigations. The Schooner test produced a crater about 300 m across and excavated more than 72 m of stratigraphic section deposited in a fairly regular fashion so that systematic observations yield systematic results. Other features common on the moon, such as secondary craters and glass-coated rocks, are present at Schooner crater. Smaller explosive tests on Buckboard Mesa excavated rocks from three horizontal alteration zones within basalt flows so that the original sequence of the zones could be determined. One crater illustrated the characteristics of craters formed across vertical boundaries between rock units. Although the exercises at the Nevada Test Site were only a small part of the training of the astronauts, voice transcripts of Apollo missions 14, 16, and 17 show that the exercises contributed to astronaut performance on the moon.

Moore, H. J.↗

DOE OSTI2022

How Generalizable is a Machine-Learning Approach for Modeling Hub-Height Turbulence Intensity?

Hub-height turbulence intensity is essential for a variety of wind energy applications. However, simulating it is a challenging task. Simple analytical models have been proposed in the literature, but they all come with significant limitations. Even state-of-the-art numerical weather prediction models, such as the Weather Research and Forecasting model, currently struggle to predict hub-height turbulence intensity. Here, we propose a machine-learning-based approach to predict hub-height turbulence intensity from other hub-height and ground-level atmospheric measurements, using observations from the Perdigao field campaign and the Southern Great Plains atmospheric observatory. We consider a random forest regression model, which we validate first at the site used for training and then under a more robust round-robin approach, and compare its performance to a multivariate linear regression. The random forest successfully outperforms the linear regression in modeling hub-height turbulence intensity, with a normalized root-mean-square error as low as 0.014 when using 30-minute average data. In order to achieve such low root-mean-square error values, the knowledge of hub-height turbulence kinetic energy (which can instead be modeled in the Weather Research and Forecasting model) is needed. Interestingly, we find that the performance of the random forest generalizes well when considering a round-robin validation (i.e., when the algorithm is trained at one site such as Perdigao or Southern Great Plains) and then applied to model hub-height turbulence intensity at the other location.

17 WIND ENERGY↗

NASA NTRSDate not supplied

Volcanic Field Sites for Artemis Testing and Training

The Artemis Program will reestablish human presence on the Moon and lead to a new era of scientific discovery and exploration. Led by the National Aeronautics and Space Administration (NASA), the Artemis Program is a collaboration of space agencies and companies around the world. An integrated effort between various disciplines of science, engineering, and mission operations is currently developing methods, facilities, and analog field locations to train astronauts and test hardware and concepts of operations. These efforts aim to best prepare for the next steps of human exploration on the lunar surface and beyond. Numerous terrestrial volcanic field sites were evaluated and selected for their unique roles in helping to prepare for the lunar surface mission phases. This effort heavily leveraged the comprehensive academic research conducted at these field sites, as well as the tremendous Apollo heritage. The currently selected volcanic field sites include the San Francisco Volcanic Field in Northern Arizona, the Potrillo Volcanic Field in southern New Mexico, the highlands of Iceland, and the Southwestern Nevada Volcanic Field. Within each of these volcanic field sites numerous specific testing and training locations are being further developed utilizing the analogous terrain and unique features in these regions. Recent Artemis testing and training events have been conducted at a number of these volcanic field sites by both a dedicated Artemis Geology Training Team and a Joint Extra Vehicular Activity (EVA) Testing Team. This presentation will highlight the selected sites as well as the objectives and accomplishments of some of the recent field-testing events and training courses. Additionally, we continually strive to pursue additional sites, locations, data sets, collaborations, and partnerships in this endeavor and welcome knowledge transfer and community input.

Trevor Graff↗

DOE OSTI2021

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large‐scale environmental information associated with convective events is obtained from the long‐term constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid‐latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE‐based triggers in terms of the F 1 score metric, widely used to estimate the performance of ML methods. The site‐specific ML trigger functions can achieve, respectively, 91% and 93% F 1 scores at SGP and MAO. The unified trigger also has a 91% F 1 score, with virtually no degradation from the site‐specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE‐based triggers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site require training a computationally intensive GAN model to thousands of dimensions. We develop a highly optimized implementation that scales to {\herogpucount} NVIDIA Volta GPUs. We develop a hierarchical scheme based on a multi-player game-theoretic approach for exploiting domain parallelism, map discriminators and generators to multiple GPUs, and employ efficient communication schemes to ensure training stability and convergence. Our implementation scales to {\heronodecount} nodes on the Summit supercomputer with a \weakscalingeff\% scaling efficiency, achieving peak and sustained half-precision rates of {\heropeakperf} PF/s and {\herosustainedperf} PF/s.

physics informed ML, machine learning, generative ↗

DOE OSTI2020

Machine learning defect properties in Cd-based chalcogenides

Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.

density functional theory↗