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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Development of Human System Integration at NASA

Human Systems Integration seeks to design systems around the capabilities and limitations of the humans which use and interact with the system, ensuring greater efficiency of use, reduced error rates, and less rework in the design, manufacturing and operational deployment of hardware and software. One of the primary goals of HSI is to get the human factors practitioner involved early in the design process. In doing so, the aim is to reduce future budget costs and resources in redesign and training. By the preliminary design phase of a project nearly 80% of the total cost of the project is locked in. Potential design changes recommended by evaluations past this point will have little effect due to lack of funding or a huge cost in terms of resources to make changes. Three key concepts define an effective HSI program. First, systems are comprised of hardware, software, and the human, all of which operate within an environment. Too often, engineers and developers fail to consider the human capacity or requirements as part of the system. This leads to poor task allocation within the system. To promote ideal task allocation, it is critical that the human element be considered early in system development. Poor design, or designs that do not adequately consider the human component, could negatively affect physical or mental performance, as well as, social behavior. Second, successful HSI depends upon integration and collaboration of all the domains that represent acquisition efforts. Too often, these domains exist as independent disciplines due to the location of expertise within the service structure. Proper implementation of HSI through participation would help to integrate these domains and disciplines to leverage and apply their interdependencies to attain an optimal design. Via this process domain interests can be integrated to perform effective HSI through trade-offs and collaboration. This provides a common basis upon which to make knowledgeable decisions. Finally, HSI must be considered early in the requirements development phase of system design and acquisition. This will provide the best opportunity to maximize return on investment (ROI) and system performance. HSI requirements must be developed in conjunction with capability ]based requirements generation through functional. HSI requirements will drive HSI metrics and embed HSI issues within the system design. After a system is designed, implementation of HSI oversights can be very expensive. An HSI program should be included as an integral part of a total system approach to vehicle and habitat development. This would include, but not limited to, workstation design, D&C development, volumetric analysis, training, operations, and human -robotic interaction. HSI is a necessary process for Human Space Flight programs to meet the Agency Human ]System standards and thus mitigate human risks to acceptable levels. NASA has been involved in HSI planning, procedures development, process, and implementation for many years, and has been building several internal and publicly accessible products to facilitate HSI fs inclusion in the NASA Systems Engineering Lifecycle. Some of these products include: NASA STD 3001 Volumes 1 and 2, Human Integration Design Handbook, NASA HSI Implementation Plan, NASA HSI Implementation Plan Templates, NASA HSI Implementation Handbook, and a 2 ]hour short course on HSI delivered as part of the NASA Space and Life Sciences Directorate Academy. These products have been created leveraging industry best practices and lessons learned from other Federal Government agencies.

Whitmore, Mihriban↗
NASA NTRS2023

Chapter 10: IceCube: Submillimeter-Wave Technology Development for Future Science on a CubeSat

This paper provides an overview of the IceCube project, including its payload and CubeSat development and performance in spaceflight. Like other CubeSat missions, IceCube has a goal to miniaturize remote-sensing sensors and to increase the reliability of small satellites. Using small, modular and standardized spacecraft along with miniaturized sensor units, we hope to advance Earth and planetary sciences by forming a space sensor constellation or sending scout-units from a mothership for targeted science investigations. IceCube is a pathfinder at NASA that infuses and integrates small spacecraft technologies to merge it with its larger mission goals. Effective government commercial partnerships have played a key role in meeting the fast-track, lowcost requirements. Early lessons learned from IceCube will benefit the CubeSat community as well as the science investigations that plan to use nano/microsatellites.

Cubesats↗
NASA NTRSDate not supplied

Management of Operations under Visual Flight Rules in UTM for Disaster Response Missions

The disaster response domain has experienced an increased focus in recent years due to the rise in number and scale of events, lessons learned from past experience, and emerging technologies that make possible a more coordinated and effective response. As part of this focus, JAXA and NASA have been collaborating on the integration of manned and unmanned aircraft in support of disaster response operations through integrated testing of their respective mission planning and optimization system (Disaster Relief Aircraft Information Sharing Network, or D-NET) and an automated UAS traffic management system (e.g., UTM). In 2018, JAXA and NASA jointly participated in a large-scale disaster drill in Japan where the integration of systems was successfully demonstrated through real-time data exchanges, visualization, and decision making as part of the coordinated airspace management of a manned helicopter in VFR conditions and unmanned small UAS operating in common areas. This work details a flight test consisting of two flights that were conducted December 2019 near the Chofu Aerodrome in Tokyo, which focused on the evaluation of pilots operating under Visual Flight Rules (VFR) communicating through D-NET and sharing intent and position information within UTM. This work contributes to defining the necessary requirements for digital coordination between manned and unmanned operations. UTM requires the use of operation volumes, which are spatial and temporal volumes that encompass UAS flight trajectories and account for technical performance errors and deviations due to disturbances (e.g., wind). A series of flights, representing different missions, used landmark-based operation volumes and conformance of the aircraft to those operation volumes were tracked within UTM. Experienced disaster response helicopter pilots provided insight on the development of the operation plans and their usability during disaster response operations. Results from the flight test supported the suggested benefits of using landmarks for planning and positional awareness and highlighted the need for future research in advanced visualization capabilities to support operations that consider both system constraints and flight deck/airspace management interaction.

disaster response↗
NASA NTRS1989

Learning from external environments using Soar

Soar, like the previous PRODIGY and Theo, is a problem-solving architecture that attempts to learn from experience; unlike them, it takes a more uniform approach, using a single forward-chaining architecture for planning and execution. Its single learning mechanism, designated 'chunking', is domain-independent. Two developmental approaches have been employed with Soar: the first of these allows the architecture to attempt a problem on its own, while the second involves a degree of external guidance. This learning through guidance is integrated with general problem-solving and autonomous learning, leading to an avoidance of human interaction for simple problems that Soar can solve on its own.

Laird, John E.↗
NASA NTRSDate not supplied

The Lunar Lab Initiative

Analogous to terrestrial Antarctic basecamps at our south pole, space exploration outposts will be a combination of habitation and science-focused assets. The Artemis Exploration Roadmap endeavors to establish a sustained human presence on the lunar south pole starting in 2028. Within the planned Artemis Base Camp, most efforts to date have focused on the habitation assets or a mixture of science and habitation assets . With the recent advent of commercial lunar landing capabilities, the trade space can be further expanded to include dedicated science focused assets that ensure adequate science capability. To that end, the Forge, an innovation team initiative created at the Johnson Space Center (JSC), explored alternative ways to increase the science capabilities within the basecamp. The Forge uses structured brainstorming and facilitation to increase innovation within the assigned team, while executing its process in a rapid turnaround fashion for ideation and design study. As a pilot study for the Forge, a multi-disciplinary team investigated a lunar lab module to augment the lunar surface plans for Artemis. This formulation study evaluated the scientific and operational considerations for this dedicated crew space that would increase the capabilities of lunar surface science operations. The trade space evaluated single lunar lander delivery versus multiple lunar lander delivery for module assembly, alternative structural designs, the science instrument outfitting with mapping to scientific goals to maximize lunar science objectives, and how the lab would be operated. The lab operations options explored a stand-alone facility, as well as a facility integrated into a larger lunar surface complex, while evaluating crew usage and habitability considerations. The resulting alternatives were evaluated by the Forge team with a set of metrics to determine their originality, feasibility, and science performance, and thereby provide a recommendation to the lunar architecture planning team. Lessons learned from the lunar lab study were compiled to improve the process and tools for future Forge studies. Forward work, open issues, and challenges to further refine the design of the various science-focused asset options were also documented. By providing a robust science capability at humanity’s furthest outpost, we can plan for a sustained human lunar presence once the initial lunar surface access capability is well established.

Lunar outpost↗
NASA NTRS2021

Machine Learning Based Path Planning for Improved Rover Navigation

Enhanced AutoNav (ENav), the baseline surface navigation software for NASA’s Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.

Yue, Yisong↗
DOE OSTI2025

Integrating Machine-learning-assisted Computer Vision with RICH System

Developments in artificial intelligence have vastly expanded the capabilities of robots. Currently, the Spallation Neutron Source (SNS) beamlines at Oak Ridge National Lab (ORNL) have robotic sample loaders to increase the efficiency of running experiments. However, they require retraining if anything about the situation changes, e.g., where the samples are, and cannot notice if errors occur. So, the viability of using computer vision and machine learning to enhance these sample loaders’ functionality was investigated. In this project, the RICH system with a Dobot CR3 6-axis robot present at the VULCAN beamline assisted by an Intel Realsense D435i camera, a unique camera that enables convenient translation of 2D pixel coordinates to 3D world points, was programmed to load ceramic crucibles into a thermogravimetric analyzer (TGA) furnace. An algorithm was constructed in Python with three major phases planned: (1) obtaining a sample, (2) moving it to the target location, and then (3) bringing the sample back to its original location once the experiment finished. In the first phase, the algorithm would dynamically detect sample locations using ArUco markers to recognize the samples’ general location and a custom-trained yolov5 object detection model to locate the crucibles’ centers. Afterward, the robot would be directed to pick up samples based on the crucibles’ calculated positions. In the second phase, the robot would move the sample to a secondary point, reorient its grip, and place the sample at the target location. In the final phase, the robot would determine whether the sample was intact and would bring it back to its original place if it was or raise an alarm. Using this algorithm, the robot was able to pick up different types of crucibles at varying positions. These results indicate that integrating machine-learning-assisted computer vision with robotic sample loaders can result in effective autonomous detection of samples.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Institutionalizing Lessons Learned

The NASA Integrated Action Team (NIAT) was formed by the NASA Administrator in March 2000. The purpose of this team was to identify the actions that NASA must take to address systemic findings reported in 4 different anomaly investigations. Team membership represented senior managers from all the field centers and NASA Headquarters. NIAT report addressed 165 findings and developed 17 action plans that are described in five themes: people and teams, technology, risk, formulation rigor, and communications. The NIAT actions present a systems solution for strengthening formulation and implementation of programs and improving the environment for their support. NIAT results included: enhancing success by avoiding failures that could have been prevented through good planning and sound practice; ensuring that prudent risks do not compromise safety; and ensuring that mission risks are objectively assessed, appropriately mitigated and consciously accepted by the program team and customers. Definitions of Faster, Better, Cheaper and Success Criteria were also developed and included as part of the NIAT report. As a result of the NIAT report, program and project management process changes were incorporated into NASA's quality system documentation, including NPG 7120.513, "NASA Program and Project Management Processes and Requirements. This paper describes the NIAT results and the resulting updates to NPG 7120.5 that keep this program and project management description a living process.

McBrayer, Robert O.↗
NASA NTRS1989

CMIF ECLS system test findings

During 1987 three Space Station integrated Environmental Control and Life Support System (ECLSS) tests were conducted at the Marshall Space Flight Center (MSFC) Core Module Integration Facility (CMIF) as part of the MSFC ECLSS Phase II test program. The three tests ranged in duration from 50 to 150 hours and were conducted inside of the CMIF module simulator. The Phase II partial integrated system test configuration consisted of four regenerative air revitalization subsystems and one regenerative water reclamation subsystem. This paper contains a discussion of results and lessons learned from the Phase II test program. The design of the Phase II test configuration and improvements made throughout the program are detailed. Future plans for the MSFC CMIF test program are provided, including an overview of planned improvements for the Phase III program.

Schunk, Richard G.↗
NASA NTRS2000

Marquardt's Mach 4.5 Supercharged Ejector Ramjet (SERJ) High-Performance Aircraft Engine Project: Unfulfilled Aspirations Ca.1970

The Supercharged Ejector Ramjet (SERJ) engine developments of the 1960s, as pursued by The Marquardt Corporation and its associated industry team members, are described. In just three years, engineering work on this combined-cycle powerplant type evolved, from its initial NASA-sponsored reusable space transportation system study status, into a U.S. Air Force/Navy-supported exploratory development program as a candidate 4.5 high-performance military aircraft engine. Bridging a productive transition from the spaceflight to the aviation arena, this case history supports the expectation that fully-integrated airbreathing/rocket propulsion systems hold high promise toward meeting the demanding propulsion requirements of tomorrow's aircraft-like Spaceliner class transportation systems. Lessons to be learned from this "SERJ Story" are offered for consideration by today's advanced space transportation and combined-cycle propulsion researchers and forward-planning communities.

Escher, William J. D.↗
NASA NTRS2006

Promoting Lifelong Ocean Education: Shaping Tomorrow's Earth Stewards and the Science and Technology Workforce

The coming ocean observing systems provide an unprecedented opportunity to change both the public perception of our oceans, and to inspire, captivate and motivate our children, our young adults and even our fellow adults to pursue careers allied with the oceans and to become stewards of our Planet's last unexplored environment. Education plans for the operational component, the Integrated Ocean Observing System (IOOS), and for the research component, Ocean Research Interactive Observatory Networks (ORION), are designed to take advantage of this opportunity. In both cases, community recommendations were developed within the context of the following assumptions: 1. Utilize research on how people learn, especially the four-pronged model of simultaneous learner-centered, knowledge-center, assessment-centered and community-centered learning 2. Strive for maximum impact on national needs in science and technology learning 3. Build on the best of what is already in place 4. Pay special attention to quality, sustainability, and scalability of efforts 5. Use partnerships across federal, state and local government, academia, and industry. Community recommendations for 100s and ORION education have much in common and offer the opportunity to create a coherent education effort allied with ocean observing systems. Both efforts focus on developing the science and technology workforce of the future, and the science and technology literacy of the public within the context of the Earth system and the role of the oceans and Great Lakes in that system. Both also recognize that an organized education infrastructure that supports sustainability and scalability of education efforts is required if ocean observing education efforts are to achieve a small but measurable improvement in either of these areas. Efforts have begun to develop the education infrastructure by beginning to form a community of educators from existing ocean and aquatic education networks and by exploring needs and issues associated with using ocean observing information assets in education. Likewise efforts are underway to address workforce issues by a systematic analysis of current and future workforce and educational needs. These activities will be described as will upcoming opportunities for the community to participate in these efforts.

Meeson, Blanche↗
NASA NTRS2000

NASA's Integrated Space Transportation Plan

Improvements in the safety, reliability and affordability of current and future space transportation systems must be achieved if NASA is to perform its mission and if the U.S. space industry is to reach its full commercial potential. In response to Presidential Policy in 1994, NASA, working with our industrial partners, initiated several efforts including the X-33, X-34, X-37 and Advanced Space Transportation programs with the goal of demonstrating the technologies that could enable these goals. We have learned that emerging technologies will enable the needed advancements but that more development along multiple, competing paths is needed. We have learned that developing requirements diligently and in partnership with industry will allow us to better converge with commercial capabilities. We have learned that commercial markets are not growing as fast as projected earlier, but there are still possibilities in the near-term to pursue alternate paths that can make access to space more robust. The goal of transitioning NASA's space transportation needs to commercial launch vehicles remains the key aim of our efforts and will require additional investment to reduce business and technical risks to acceptable levels.

Cook, Stephen A.↗
DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

The future of self-driving laboratories: from human in the loop interactive AI to gamification

Recent developments in artificial intelligence (AI) and machine learning (ML), implemented through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities for the accelerated discovery and optimization of materials. This paper provides a joint analysis of SDLs from both academic and industry perspectives, highlighting the importance of integrating human intelligence in these systems. It discusses the necessity of careful planning in SDL design across physical, data, and workflow dimensions, including instrumental setup, experimental workflow, data management, and human–SDL interaction. The significance of integrating human input within SDLs, especially as the focus shifts from individual tools and tasks to the creation and management of complex workflows, is emphasized. The paper stresses on the crucial role of reward function design in developing forward-looking workflows and examines the interplay between hardware evolution, ML application across chemical processes, and the influence of reward systems in research. Ultimately, the article advocates for a future where SDLs blend human intuition in hypothesis formulation with AI's precision, speed, and data-handling capabilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2007

Use of Remote Sensing for Decision Support in Africa

Over the past 30 years, the scientific community has learned a great deal about the Earth as an integrated system. Much of this research has been enabled by the development of remote sensing technologies and their operation from space. Decision makers in many nations have begun to make use of remote sensing data for resource management, policy making, and sustainable development planning. This paper makes an attempt to provide a survey of the current state of the requirements and use of remote sensing for sustainable development in Africa. This activity has shown that there are not many climate data ready decision support tools already functioning in Africa. There are, however, endusers with known requirements who could benefit from remote sensing data.

Policelli, Frederick S.↗
NASA NTRSDate not supplied

Developmental and Cryogenic Thermal Vacuum (TVAC) Testing Lessons Learned

Developmental thermal vacuum (TVAC) testing is a critical step in maturing hardware designs and validating performance prior to flight qualification. Unlike qualification or acceptance testing, developmental testing provides flexibility to explore design margins, uncover integration challenges, and refine test approaches before formal verification activities begin. This presentation highlights the value of developmental testing while sharing common pitfalls encountered during developmental TVAC campaigns. Lessons learned from hands-on testing experience, including extensive developmental testing at cryogenic temperatures, will be shared in this presentation. Topics include test planning and preparation, instrumentation strategies, contamination control considerations, troubleshooting unexpected anomalies, and approaches for staying on schedule while meeting test objectives. The audience will gain practical insights and best practices that can improve test efficiency, reduce risk, and enhance the overall success of future developmental TVAC efforts.

Mackenzie Byrnes↗
DOE OSTI2023

Advanced Long-Term Environmental Monitoring Systems (ALTEMIS) Artificial Intelligence Data Management Plan

Across the Department of Energy’s Environmental and Legacy Management sites, complex groundwater plumes exist that will require long-term monitoring to ensure remedial actions that have been put in place remain effective decades into the future. The current monitoring paradigm predominantly consists of groundwater well sampling, whereby samples are collected, concentrations analyzed, and plume anomalies are detected after they have occurred. The Advanced Long Term Environmental Monitoring Systems (ALTEMIS) program is a multi-lab, multi-institution team of researchers that is deploying spatially integrative technologies (i.e., real-time in situ sensor networks), coupled with artificial intelligence and machine learning, to establish a more proactive monitoring paradigm. Within this approach, plume anomalies can be predicted, and corrective actions can be established prior to the occurrence, offering a more cost-effective and robust approach to long-term monitoring. The team has deployed a variety of different in situ sensing technologies at the Savannah River Site’s F-Area Hazardous Waste Management Facility around the F-Area Seepage Basins, which are unlined basins that received 7 billion liters of acidic low-level radioactive waste from the 1950s until the late 1980s. The technologies and techniques that the team is deploying are intended to ensure that the remedial actions that have been taken by the site remain effective decades into the future. Foundational to this approach is a robust, integrated data management and analysis plan to ensure accurate and timely reporting from the variety of sensor systems that are in place. This report will outline the data management plan that has been implemented by the ALTEMIS team at the Savannah River Site and will serve as a blueprint as the technology is translated to new sites across the DOE Complex.

54 ENVIRONMENTAL SCIENCES↗