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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 127 records · Page 7

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NASA NTRS1982

Hierarchical modeling for image classification

As part of the California Integrated Remote Sensing System's (CIRSS) San Bernardino County Project, the use of data layers from a geographic information system (GIS) as an integral part of the Landsat image classification process was investigated. Through a hierarchical modeling technique, elevation, aspect, land use, vegetation, and growth management data from the project's data base were used to guide class labeling decisions in a 1976 Landsat MSS land cover classification. A similar model, incorporating 1976-1979 Landsat spectral change data in addition to other data base elements, was used in the classification of a 1979 Landsat image. The resultant Landsat products were integrated as additional layers into the data base for use in growth management, fire hazard, and hydrological modeling.

Likens, W.↗

NASA NTRS1992

Space life sciences strategic plan, 1991

Over the last three decades the life sciences program has significantly contributed to NASA's manned and unmanned exploration of space, while acquiring new knowledge in the fields of space biology and medicine. The national and international events which have led to the development and revision of NASA strategy will significantly affect the future of life sciences programs both in scope and pace. This document serves as the basis for synthesizing the option to be pursued during the next decade, based on the decisions, evolution, and guiding principles of the National Space Policy.

Source record↗

NASA NTRS1996

Downsizing Visualization Platforms: From Crays to Indigos and Beyond

Despite its increasingly negative connotation in other domains, "downsizing" is a positive trend as it applies to the computer hardware necessary to perform scientific visualization. In this talk, we will consider how the visualization of a particular data set, the Digital Terrain Model (DTM) derived from the Viking Orbiter imagery, has been realized in three distinct projects over the past decade. These examples serve to demonstrate how the vast improvements in computational performance both decrease the cost of such visualization efforts, and permit an increasing level of interactivity. We then consider how even today's graphical systems require the visualization designer to make intelligent choices and trade-offs in database rendering. Finally, we discuss how insights from an understanding of human visual perception can guide these design decisions, and suggest new options for visualization hardware and software.

Kaiser, Mary K.↗

NASA NTRS2004

Pancam Imaging of the Mars Exploration Rover Landing Sites in Gusev Crater and Meridiani Planum

The Mars Exploration Rovers carry four Panoramic Camera (Pancam) instruments (two per rover) that have obtained high resolution multispectral and stereoscopic images for studies of the geology, mineralogy, and surface and atmospheric physical properties at both rover landing sites. The Pancams are also providing significant mission support measurements for the rovers, including Sun-finding for rover navigation, hazard identification and digital terrain modeling to help guide long-term rover traverse decisions, high resolution imaging to help guide the selection of in situ sampling targets, and acquisition of education and public outreach imaging products.

Bell, J. F., III↗

NASA NTRS1997

Of red planets and indigo computers: Mars database visualization as an example of platform downsizing

The last decade has witnessed tremendous advancements in the computer hardware and software used to perform scientific visualization. In this paper, we consider how the visualization of a particular data set, the digital terrain model derived from the Viking orbiter imagery, has been realized in four distinct projects over this period. These examples serve to demonstrate how the vast improvements in computational performance both decrease the cost of such visualization efforts and permit an increasing level of interactivity. We then consider how even today's graphical systems require the visualization designer to make intelligent choices and tradeoffs in database rendering. Finally, we discuss how insights gleaned from an understanding of human visual perception can guide these design decisions, and suggest new options for visualization hardware and software.

NASA Center ARC↗

NASA NTRS2006

HALE UAS Concept of Operations. Version 3.0

This document is a system level Concept of Operations (CONOPS) from the perspective of future High Altitude Long Endurance (HALE) Unmanned Aircraft Systems (UAS) service providers and National Airspace System (NAS) users. It describes current systems (existing UAS), describes HALE UAS functions and operations to be performed (via sample missions), and offers insight into the user s environment (i.e., the UAS as a system of systems). It is intended to be a source document for NAS UAS operational requirements, and provides a construct for government agencies to use in guiding their regulatory decisions, architecture requirements, and investment strategies. Although it does not describe the technical capabilities of a specific HALE UAS system (which do, and will vary widely), it is intended to aid in requirements capture and to be used as input to the functional requirements and analysis process. The document provides a basis for development of functional requirements and operational guidelines to achieve unrestricted access into the NAS. This document is an FY06 update to the FY05 Access 5 Project-approved Concept of Operations document previously published in the Public Domain on the Access 5 open website. This version is recommended to be approved for public release also. The updates are a reorganization of materials from the previous version with the addition of an updated set of operational requirements, inclusion of sample mission scenarios, and identification of roles and responsibilities of interfaces within flight phases.

Source record↗

NASA NTRS2013

Trade Space Specification Tool (TSST) for Rapid Mission Architecture (Version 1.2)

Trade Space Specification Tool (TSST) is designed to capture quickly ideas in the early spacecraft and mission architecture design and categorize them into trade space dimensions and options for later analysis. It is implemented as an Eclipse RCP Application, which can be run as a standalone program. Users rapidly create concept items with single clicks on a graphical canvas, and can organize and create linkages between the ideas using drag-and-drop actions within the same graphical view. Various views such as a trade view, rules view, and architecture view are provided to help users to visualize the trade space. This software can identify, explore, and assess aspects of the mission trade space, as well as capture and organize linkages/dependencies between trade space components. The tool supports a user-in-the-loop preliminary logical examination and filtering of trade space options to help identify which paths in the trade space are feasible (and preferred) and what analyses need to be done later with executable models. This tool provides multiple user views of the trade space to guide the analyst/team to facilitate interpretation and communication of the trade space components and linkages, identify gaps in combining and selecting trade space options, and guide user decision-making for which combinations of architectural options should be pursued for further evaluation. This software provides an environment to capture mission trade space elements rapidly and assist users for their architecture analysis. This is primarily focused on mission and spacecraft architecture design, rather than general-purpose design application. In addition, it provides more flexibility to create concepts and organize the ideas. The software is developed as an Eclipse plug-in and potentially can be integrated with other Eclipse-based tools.

Wang, Yeou-Fang↗

NASA NTRS2014

Managing Research in a Risk World

The Office of Chief Medical Officer (OCHMO) owns all human health and performance risks managed by the Human System Risk Board (HSRB). While the HSRB manages the risks, the Human Research Program (HRP) manages the research portion of the overall risk mitigation strategy for these risks. The HSRB manages risks according to a process that identifies and analyzes risks, plans risk mitigation and tracks and reviews the implementation of these strategies according to its decisions pertaining to the OCHMO risk posture. HRP manages risk research work using an architecture that describes evidence-based risks, gaps in our knowledge about characterizing or mitigating the risk, and the tasks needed to produce deliverables to fill the gaps and reduce the risk. A planning schedule reflecting expected research milestones is developed, and as deliverables and new evidence are generated, research progress is tracked via the Path to Risk Reduction (PRR) that reflects a risk's research plan for a design reference mission. HRP's risk research process closely interfaces with the HSRB risk management process. As research progresses, new deliverables and evidence are used by the HSRB in conjunction with other operational and non-research evidence to inform decisions pertaining to the likelihood and consequence of the risk and risk posture. Those decisions in turn guide forward work for research as it contributes to overall risk mitigation strategies. As HRP tracks its research work, it aligns its priorities by assessing the effectiveness of its contributions and maintaining specific core competencies that would be invaluable for future work for exploration missions.

Anton, W.↗

NASA NTRS2016

Advanced Interval Management: A Benefit Analysis

This document is the final report for the NASA Langley Research Center (LaRC)- sponsored task order 'Possible Benefits for Advanced Interval Management Operations.' Under this research project, Architecture Technology Corporation performed an analysis to determine the maximum potential benefit to be gained if specific Advanced Interval Management (AIM) operations were implemented in the National Airspace System (NAS). The motivation for this research is to guide NASA decision-making on which Interval Management (IM) applications offer the most potential benefit and warrant further research.

Timer, Sebastian↗

NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗

NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

Artificial Intelligence↗

NASA NTRS2023

Procedure Automation Rating Matrix

The National Aeronautics and Space Administration (NASA) Advanced Air Mobility (AAM) National Campaign (NC) is researching the means by which future Urban Air Mobility (UAM) aircraft will operate safely in an integrated and scalable airspace architecture. Consistent with this objective, the NASA NC Airspace Procedures team designed a matrix to evaluate UAM instrument flight procedure design, flyability and interoperability of candidate departure, enroute, and approach architectures in live flight or simulation. The Procedure Automation Rating Matrix (PARM) is a multi-dimensional rating scale designed to provide direct feedback from test pilots and operators to airspace procedure designers developing airspace constructs for the integration and scalability of AAM operations in the National Airspace System (NAS). The PARM is assessed using a hierarchical decision tree that guides the operator through a ten-point alpha-numeric rating scale initiated either with or without the use of automation.

National Campaign↗

NASA NTRSDate not supplied

Exploring the Effectiveness of Maneuvering Guidelines for Space Traffic Management

The number of objects in space has been increasing rapidly, and the risk of collision has grown as well. Many spacecraft operators are now receiving multiple collision warnings a day. Despite this, systems to manage space traffic have been limited: there are no broadly agreed upon guidelines or rules governing the response to predicted potential collisions. Instead, spacecraft operators generally determine whether, when, and how to respond to these warnings on a manual and ad hoc basis. Coordination between operators, if it occurs, often requires repeated communication and negotiation. Some space actors have suggested that the space community should develop right of way rules, similar to those in the ground, sea, and air domains, to guide collision response decisions. However, it is unclear whether such rules would be effective, and it’s unknown whether such rules would have an equitable impact across various spacecraft operators. To address these issues, we developed the Virtual Environment for Space Traffic Analysis (VESTA), a software tool that was used to simulate the space environment with a recent catalog of objects obtained from the U.S. Space-Track.org system. We implemented multiple potential right of way rules within this model. The analysis confirmed that the choice of right of way rule makes a meaningful difference in terms of both efficiency and distributional effects in terms of collision avoidance maneuvers. For example, our analysis shows that rules in which the less massive satellite is required to maneuver results in a more equitable distribution of maneuver responsibility among space actors and also requires less fuel mass, compared to a rule in which the more massive satellite maneuvers.

space traffic management↗

NASA NTRS2012

Rotorcraft Diagnostics

Health management (HM) in any engineering systems requires adequate understanding about the system s functioning; a sufficient amount of monitored data; the capability to extract, analyze, and collate information; and the capability to combine understanding and information for HM-related estimation and decision-making. Rotorcraft systems are, in general, highly complex. Obtaining adequate understanding about functioning of such systems is quite difficult, because of the proprietary (restricted access) nature of their designs and dynamic models. Development of an EIM (exact inverse map) solution for rotorcraft requires a process that can overcome the abovementioned difficulties and maximally utilize monitored information for HM facilitation via employing advanced analytic techniques. The goal was to develop a versatile HM solution for rotorcraft for facilitation of the Condition Based Maintenance Plus (CBM+) capabilities. The effort was geared towards developing analytic and reasoning techniques, and proving the ability to embed the required capabilities on a rotorcraft platform, paving the way for implementing the solution on an aircraft-level system for consolidation and reporting. The solution for rotorcraft can he used offboard or embedded directly onto a rotorcraft system. The envisioned solution utilizes available monitored and archived data for real-time fault detection and identification, failure precursor identification, and offline fault detection and diagnostics, health condition forecasting, optimal guided troubleshooting, and maintenance decision support. A variant of the onboard version is a self-contained hardware and software (HW+SW) package that can be embedded on rotorcraft systems. The HM solution comprises components that gather/ingest data and information, perform information/feature extraction, analyze information in conjunction with the dependency/diagnostic model of the target system, facilitate optimal guided troubleshooting, and offer decision support for optimal maintenance.

Haste, Deepak↗

DOE OSTI2023

Embodied Carbon Resource Navigator

The Embodied Carbon Resource Navigator is a guide to help architects, engineers, designers, contractors, and those who want to reduce their embodied carbon impact make more informed design decisions for buildings. The guide is a compilation of embodied carbon resources, organized by experience level to direct the user to the necessary resources.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Climate-aware decision-making: lessons for electric grid infrastructure planning and operations

Abstract Climate change poses significant risks to large-scale infrastructure systems and brings considerable uncertainties that challenge historical planning approaches. Here we focus on how climate awareness might be better incorporated into planning and decision-making in the electric power sector. To do so, we consider lessons from decision science literature where researchers have specifically focused on how to make better decisions under uncertainty. We perform a three-part review: of decision science literature on best practices for decision-making under uncertainty, of industry practice documents to understand how new uncertainties may affect the types of decisions electric utilities are making today, and of literature on electricity sector planning under climate change to assess how lessons from decision science might fit into sector-specific analyses. We discuss how characterizations of uncertainty from the decision science literature might guide approaches in the electricity sector to appropriately capture climate-related uncertainties. We also distill three key ideas from the decision science literature that can be incorporated into electricity sector planning to manage these new uncertainties: robustness, adaptive planning, and multi-stakeholder engagement. We offer example recommendations for how these key ideas might be incorporated into one essential type of planning activity, capacity expansion.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Using stable isotopes to inform water resource management in forested and agricultural ecosystems

Present and future climatic trends are expected to markedly alter water fluxes and stores in the hydrologic cycle. In addition, water demand continues to grow due to increased human use and a growing population. Sustainably managing water resources requires a thorough understanding of water storage and flow in natural, agricultural, and urban ecosystems. Measurements of stable isotopes of water (hydrogen and oxygen) in the water cycle (atmosphere, soils, plants, surface water, and groundwater) can provide information on the transport pathways, sourcing, dynamics, ages, and storage pools of water that is difficult to obtain with other techniques. However, the potential of these techniques for practical questions has not been fully exploited yet. Here, we outline the benefits and limitations of potential applications of stable isotope methods useful to water managers, farmers, and other stakeholders. We also describe several case studies demonstrating how stable isotopes of water can support water management decision-making. Finally, we propose a workflow that guides users through a sequence of decisions required to apply stable isotope methods to examples of water management issues. We call for ongoing dialogue and a stronger connection between water management stakeholders and water stable isotope practitioners to identify the most pressing issues and develop best-practice guidelines to apply these techniques.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗