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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 NTRSDate not supplied

Investigation and Evaluation of Advanced Spectrum Management Concepts for Aeronautical Communications

With the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations, there will be an increasing demand for voice and data communications within the National Airspace System (NAS). The continued use of existing VHF and UHF frequency allocations is not a sustainable approach, and as a result, a new spectrum management solution is required to support future mission needs. The proposed spectrum management concepts leverage modern advancements such as artificial intelligence (AI) and big data to dynamically optimize the spectrum utilization based on the predicted communications demand throughout the airspace. This technical investigation considers both air-ground and air-air communications networks, and can be applied to both existing applications such as the air traffic control (ATC) system, as well as future applications, such as the emerging Advanced Air Mobility (AAM). To support the evaluation of the proposed concepts and technologies, a modeling and simulation capability is currently under development and will continue to evolve to support new and advanced airspace applications. It is anticipated that this proposed spectrum concept will better serve the spectrum needs of future NAS applications.

Eric J Knoblock↗
NASA NTRS1996

Immobile Robots: AI in the New Millennium

A new generation of sensor rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biosphere-like life support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. To accomplish this, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves in order to survive decades of autonomous operations. Achieving these large scale modeling and configuration tasks will require a tight coupling between the higher level coordination function provided by symbolic reasoning, and the lower level autonomic processes of adaptive estimation and control. To be economically viable they will need to be programmable purely through high level compositional models. Self modeling and self configuration, coordinating autonomic functions through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous systems architecture that is taking us into the New Millennium.

Williams, Brian C.↗
NASA NTRS2004

Multi-Year Characterization of PSCs Using Solar Occultation Satellite Observations

POAM solar occultation observations from1994 to present are studied for the purpose of determining Type I PSC formation characteristics and winter-long evolution. This information is critical to an improved understanding and predictive capability of stratospheric ozone depletion. Solar occultation satellite observations of these clouds yields more continuous and wide-spread information than can be obtained from aircraft, balloon, or ground-based observations. This multi-winter PSC study is augmented by the use of our Type Ia/Ib discrimination scheme. Recent studies of PSC formation made with POAM observations and simulations during the 1999-2000 Arctic winter have shown characteristics that shed light on the formation mechanisms responsible for Type Ia solid phase PSCs. This study examines PSC observations from many years on a common basis to see if the characteristics observed & cuing the 1999-2000 Arctic winter ai-e observed in other years and if other characteristics can be identified. The results show that Type Ia PSCs form at the beginning of the winter, within several days of the fxst drop in temperature below TNAT, and peak early in the winter. Type Ia PSCs typically outnumber Ib PSCs over the winter, especially at the beginning of the winter. Type Ia and Ib PSC observations continue throughout the winter. Micro-physical models of PSC formation must match these observed characteristics. Some models predict that temperatures must be more 5 K below T-NAT for five days before significant freezing can occur. This is not seen in the POAM observations. Differences in PSC characte ristics between the first two Arctic winters (1 994- 1995 and 1995- 1 996) and later winters also suggest the influence of volcanic perturb ations on PSC formation. Type Ia and Ib PSC characteristics observed by POAM III and SAGE III for the 2002-2003 Arctic winter are compared.

Strawa, A. W.↗
NASA NTRS2014

Probabilistic 21st and 22nd Century Sea-Level Projections at a Global Network of Tide-Gauge Sites

Sea-level rise due to both climate change and non-climatic factors threatens coastal settlements, infrastructure, and ecosystems. Projections of mean global sea-level (GSL) rise provide insufficient information to plan adaptive responses; local decisions require local projections that accommodate different risk tolerances and time frames and that can be linked to storm surge projections. Here we present a global set of local sea-level (LSL) projections to inform decisions on timescales ranging from the coming decades through the 22nd century. We provide complete probability distributions, informed by a combination of expert community assessment, expert elicitation, and process modeling. Between the years 2000 and 2100, we project a very likely (90% probability) GSL rise of 0.5–1.2 m under representative concentration pathway (RCP) 8.5, 0.4–0.9 m under RCP 4.5, and 0.3–0.8 m under RCP 2.6. Site-to-site differences in LSL projections are due to varying non-climatic background uplift or subsidence, oceanographic effects, and spatially variable responses of the geoid and the lithosphere to shrinking land ice. The Antarctic ice sheet (AIS) constitutes a growing share of variance in GSL and LSL projections. In the global average and at many locations, it is the dominant source of variance in late 21st century projections, though at some sites oceanographic processes contribute the largest share throughout the century. LSL rise dramatically reshapes flood risk, greatly increasing the expected number of “1-in-10” and “1-in-100” year events.

sea level↗
NASA NTRS1993

Applications of artificial intelligence 1993: Knowledge-based systems in aerospace and industry; Proceedings of the Meeting, Orlando, FL, Apr. 13-15, 1993

The present volume on applications of artificial intelligence with regard to knowledge-based systems in aerospace and industry discusses machine learning and clustering, expert systems and optimization techniques, monitoring and diagnosis, and automated design and expert systems. Attention is given to the integration of AI reasoning systems and hardware description languages, care-based reasoning, knowledge, retrieval, and training systems, and scheduling and planning. Topics addressed include the preprocessing of remotely sensed data for efficient analysis and classification, autonomous agents as air combat simulation adversaries, intelligent data presentation for real-time spacecraft monitoring, and an integrated reasoner for diagnosis in satellite control. Also discussed are a knowledge-based system for the design of heat exchangers, reuse of design information for model-based diagnosis, automatic compilation of expert systems, and a case-based approach to handling aircraft malfunctions.

Fayyad, Usama M.↗
NASA NTRS1985

A.I.-based real-time support for high performance aircraft operations

Artificial intelligence (AI) based software and hardware concepts are applied to the handling system malfunctions during flight tests. A representation of malfunction procedure logic using Boolean normal forms are presented. The representation facilitates the automation of malfunction procedures and provides easy testing for the embedded rules. It also forms a potential basis for a parallel implementation in logic hardware. The extraction of logic control rules, from dynamic simulation and their adaptive revision after partial failure are examined. It uses a simplified 2-dimensional aircraft model with a controller that adaptively extracts control rules for directional thrust that satisfies a navigational goal without exceeding pre-established position and velocity limits. Failure recovery (rule adjusting) is examined after partial actuator failure. While this experiment was performed with primitive aircraft and mission models, it illustrates an important paradigm and provided complexity extrapolations for the proposed extraction of expertise from simulation, as discussed. The use of relaxation and inexact reasoning in expert systems was also investigated.

Vidal, J. J.↗
NASA NTRS1991

Space Shuttle processing - A case study in artificial intelligence

A scheduling system incorporating AI is described and applied to the automated processing of the Space Shuttle. The unique problem of addressing the temporal, resource, and orbiter-configuration requirements of shuttle processing is described with comparisons to traditional project management for manufacturing processes. The present scheduling system is developed to handle the late inputs and complex programs that characterize shuttle processing by incorporating fixed preemptive scheduling, constraint-based simulated annealing, and the characteristics of an 'anytime' algorithm. The Space-Shuttle processing environment is modeled with 500 activities broken down into 4000 subtasks and with 1600 temporal constraints, 8000 resource constraints, and 3900 state requirements. The algorithm is shown to scale to very large problems and maintain anytime characteristics suggesting that an automated scheduling process is achievable and potentially cost-effective.

Mollikarimi, Cindy↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRSDate not supplied

Development of the Advanced Regolith Ground Operations (ARGO) Test Bed– A Robotic Construction Test Facility with Simulated Lunar Environments

NASA’s Artemis Program is working towards developing a sustained presence on the Moon and eventually Mars. To achieve this goal, robotic excavation, site preparation and construction technologies are under development to establish the capability to construct infrastructure such as launch/landing pads and radiation protection shelters. Technologies must be proven in simulated Lunar conditions prior surface demonstration missions. To that end, the Relevant Additive Construction Technology (REACT) Announcement of Collaboration Opportunities (ACO) project with AI Space Factory and the NASA Kennedy Space Center’s (KSC) Granular Mechanics and Regolith Operations Laboratory (a.k.a. Swamp Works) has developed the Advanced Regolith Ground Operations (ARGO) Test Bed. ARGO includes a ~1.5m x 1.5m x 1.2m (~5x5x4ft) vacuum chamber, cryogenically cooled thermal shroud, a 3-axis robotic positioning system, and a regolith bin. For the REACT project, a pellet extruder and feed hopper have been installed on ARGO to advance the Technology Readiness Level (TRL) of regolith-polymer composite Fused Deposition Modeling (FDM) additive construction systems, processes, and materials. This paper will focus on the design and operational characteristics of the ARGO Test Bed with pellet extruder.

Moon↗
NASA NTRS2003

Simulation-Based Verification of Livingstone Applications

AI software is viewed as a means to give greater autonomy to automated systems, capable of coping with harsh and unpredictable environments in deep space missions. Autonomous systems pose a serious challenge to traditional test-based verification approaches, because of the enormous space of possible situations that they aim to address. Before these systems are put in control of critical applications, appropriate new verification approaches need to be developed. This article describes Livingstone PathFinder (LPF), a verification tool for autonomous diagnosis applications based on NASA's Livingstone model-based diagnosis system. LPF applies state space exploration algorithms to an instrumented testbed, consisting of the Livingstone diagnosis system embedded in a simulated operating environment. The article describes different facets of LPF and reports some experimental results from applying LPF to a Livingstone model of the main propulsion feed subsystem of the X-34 space vehicle. This paper describes Livingstone PathFinder (LPF), a verification tool for autonomous diagnosis applications based on NASA's Livingstone model-based diagnosis system. LPF applies state space exploration algorithms to an instrumented testbed, consisting of the Livingstone diagnosis system embedded in a simulated operating environment. Section 2 provides an overview of Livingstone; Section 3 describes the LPF architecture; Section 4 discusses its applicability; Section 5 reviews some experimental results; Section 6 compares LPF to related verification approaches; Section 7 draws conclusions and discusses some perspectives.

Lindsey, Anthony E.↗
NASA NTRSDate not supplied

Relevant Environment Additive Construction Technology (REACT)

Project Overview This project is focused on developing technologies that enable construction of a Lunar Safe Haven type structure on the moon. A full scale architectural and structural design will be completed based on lunar conditions and Artemis mission needs. A scaled structure will be 3D printed in simulated lunar environments. Technical Approach Polymer bound regolith composite materials will be developed and characterized for lunar additive construction applications. A full scale architectural and structural design will be completed based on the latest scientific data on lunar environments including radiation, meteoroids, thermal conditions, reduced gravity, and moonquakes. The project will culminate with a demonstration of additive construction in simulated lunar vacuum, thermal, regolith, and UV conditions. Results/Summary Preliminary material specifications have been developed and an initial batch has been produced. A first iteration of the architectural and structural designs have been completed. Hardware for 3D printing in simulated lunar conditions is under development. Contributing Partners AI Space Factory – Winner of the NASA 3D Printed Habitat Centennial Challenge Infusion and Transition Plan On-surface construction of infrastructure is a critical capability of the sustainable phase of the Artemis Program and eventual sustainable human presence on Mars. It provides the ability to create protective shelters on-demand using local resources. The 3D-printing technology will achieve TRL 6 in 1g/vacuum tests with thermal/UV exposure in this project. CLPS missions will be pursued for infusion to demonstrate vertical construction with lunar regolith and conditions. Modeling and simulation will be used to define the pilot-scale shelter construction mission for long-term exposure on the lunar surface to validate the system for Artemis operational deployment.

Lunar Construction↗
NASA NTRSDate not supplied

High School Citizen Scientists Use AI/ML to Predict Intra-Ocular Pressure From Gene Expression Data for Spaceflown Mice

Artificial Intelligence (AI) and Machine Learning (ML) have increasingly become pivotal in biological and biomedical research, largely due to the culture of open data sharing and its associated benefits. The methodologies inherent in AI/ML are particularly adept at identifying and forecasting biological phenotypes from the vast amounts of data generated by next-generation sequencing technologies. These techniques offer substantial promise for advancing research in space biosciences and for the development of automated systems for monitoring space health. Nevertheless, there are crucial aspects to consider when training, validating, and testing machine learning models in both biological research and clinical contexts. It is essential that Open Science principles, including data sharing and the availability of open-source code, are complemented by high-quality, publicly accessible training resources. These resources should focus on best practices and include modules based on real-world scientific cases and data to ensure that future AI/ML practitioners gain practical experience with genuine problems. Addressing this knowledge gap, we have designed, developed, and delivered both interactive and self-paced training programs for citizen scientists worldwide, enabling them to utilize AI/ML for space biology research. This initiative was made possible through generous funding from a Transformation to Open Science Training grant. The interactive training sessions, conducted this summer, utilized AI/ML techniques to analyze data from the Open Science Data Repository, specifically targeting the effects of spaceflight on ocular structure and function. The dataset OSD-583, from the Rodent Research 9 mission, provides experimental data detailing the ocular responses of mice subjected to a 35-day spaceflight, compared with ground control counterparts. Using OSD-583 as observational data, our summer training participants applied AI/ML methods to predict intraocular pressure from RNA-seq data and identify the genes most predictive of the observed responses. Further analysis through pathway enrichment and gene set enrichment revealed that these genes are involved in molecular and cellular processes contributing to retinal degeneration.

James Casaletto↗
NASA NTRSDate not supplied

Development of the Advanced Regolith Ground Operations (ARGO) Test Bed – A Robotic Excavation and Construction Test Facility with Simulated Lunar Environments

NASA’s Artemis Program is working towards developing a sustained presence on the Moon and eventually Mars. To achieve this goal, robotic excavation, site preparation and construction technologies are under development to establish the capability to construct infrastructure such as launch/landing pads and radiation protection shelters. Technologies must be proven in simulated Lunar conditions prior to surface demonstration missions. To that end, the Relevant Additive Construction Technology (REACT) Announcement of Collaboration Opportunities (ACO) project with AI Space Factory and the NASA Kennedy Space Center’s (KSC) Granular Mechanics and Regolith Operations Laboratory (a.k.a. Swamp Works) has developed the Advanced Regolith Ground Operations (ARGO) Test Bed. ARGO includes a ~1.5m x 1.5m x 1.2m (~5x5x4ft) vacuum chamber, cryogenically cooled thermal shroud, 3-axis robotic positioning system, and regolith bin. For the REACT project, a pellet extruder, feed hopper, and heated 600mm x 600mm (23.6x23.6in) build plate have been installed on ARGO to advance the Technology Readiness Level (TRL) of regolith-polymer composite Fused Deposition Modeling (FDM) additive construction systems, processes, and materials. This paper will focus on the design and operational characteristics of the ARGO Test Bed with pellet extruder.

regolith↗
NASA NTRSDate not supplied

Development of the Advanced Regolith Ground Operations (ARGO) Test Bed: A Robotic Excavation and Construction Test Facility with Simulated Lunar Environments

NASA’s Artemis Program is working towards developing a sustained presence on the Moon and eventually Mars. To achieve this goal, robotic excavation, site preparation and construction technologies are under development to establish the capability to construct infrastructure such as launch/landing pads and radiation protection shelters. Technologies must be proven in simulated Lunar conditions prior to surface demonstration missions. To that end, the Relevant Additive Construction Technology (REACT) Announcement of Collaboration Opportunities (ACO) project with AI Space Factory and the NASA Kennedy Space Center’s (KSC) Granular Mechanics and Regolith Operations Laboratory (a.k.a. Swamp Works) has developed the Advanced Regolith Ground Operations (ARGO) Test Bed. ARGO includes a ~1.5m x 1.5m x 1.2m (~5x5x4ft) vacuum chamber, cryogenically cooled thermal shroud, 3-axis robotic positioning system, and regolith bin. For the REACT project, a pellet extruder, feed hopper, and heated 600mm x 600mm (23.6x23.6in) build plate have been installed on ARGO to advance the Technology Readiness Level (TRL) of regolith-polymer composite Fused Deposition Modeling (FDM) additive construction systems, processes, and materials. This paper will focus on the design and operational characteristics of the ARGO Test Bed with pellet extruder.

regolith↗
NASA NTRS2022

AMD Radeon e9173 Low Power PCIE GPU Single Event Effects Test Report

The AMD Radeon Embedded e9170 Graphics Processing Unit (GPU), notably the e9173 Peripheral Component Interconnect Express (PCIE) variant, is of interest to Artemis generation programs with requirements for graphics rendering, compute, artificial intelligence (Ai) with a constraints-requiring piece-part procurement and power consumption of less than 50W. In addition to collecting heavy ion data on this device, a secondary purpose of this test campaign was to validate video capture hardware and software workflows used with GPU, microprocessor and system-on-chip device testing. Five (5) test patterns from the NEPP Processor Enclave (NPE) test suite were used with the e9173. The test patterns covered the operating system’s (OS) idle contribution towards the cross section, matrix math using tensorflow-rocm, two artificial intelligence models developed at NASA GSFC, and an industry standard GPU benchmarking application called Mesa GLXGears.

NASA Technical Memorandum (TM) test report for pos↗
NASA NTRS2016

Evaluation of the MICAST #2-12 AI-7wt%Si Sample Directionally Solidified Aboard the International Space Station

The US team of the European led "MIcrostructure Formation in CASTing of Technical Alloys under Diffusive and Magnetically Controlled Convective Conditions" (MICAST) program recently received a third Aluminum - 7wt% silicon alloy that was processed in the microgravity environment aboard the International Space Station. The sample, designated MICAST#2-12, was directionally solidified in the Solidification with Quench Furnace (SQF) at a constant rate of 40micometers/s through an imposed temperature gradient of 31K/cm. Procedures taken to evaluate the state of the sample prior to sectioning for metallographic analysis are reviewed and rational for measuring the microstructural constituents, in particular the primary dendrite arm spacing (Lambda (sub1)), is given. The data are presented, put in context with the earlier samples, and evaluated in view of a relevant theoretical model.

Tewari, Surendra N.↗