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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Summary of Martian Dust Filtering Challenges and Current Filter Development

Traditional air particulate filtering in manned spaceflight (Apollo, Shuttle, ISS, etc.) has used cleanable or replaceable catch filters such as screens and High-Efficiency Particulate Arrestance (HEPA) filters. However, the human mission to Mars architecture will require a new approach. It is Martian dust that is the particulate of concern but the need also applies to particulates generated by crew. The Mars Exploration Program Analysis Group (MEPAG) high-lighted this concern in its Mars Science, Goals, Objectives, Investigations and Priorities document [7], by saying specifically that one high priority investigation will be to "Test ISRU atmospheric processing systems to measure resilience with respect to dust and other environmental challenge performance parameters that are critical to the design of a full-scale system." By stating this as high priority the MEPAG is acknowledging that developing and adequately verifying this capability is critical to success of a human mission to Mars. This architecture will require filtering capabilities that are highly reliable, will not restrict the flow path with clogging, and require little to no maintenance. This paper will summarize why this is the case, the general requirements for developing the technology, and the status of the progress made in this area.

O'Hara, William J., IV↗
NASA NTRS1985

Assessment of metabolic bone diseases by quantitative computed tomography

Advances in the radiologic sciences have permitted the development of numerous noninvasive techniques for measuring the mineral content of bone, with varying degrees of precision, accuracy, and sensitivity. The techniques of standard radiography, radiogrammetry, photodensitometry, Compton scattering, neutron activation analysis, single and dual photon absorptiometry, and quantitative computed tomography (QCT) are described and reviewed in depth. Results from previous cross-sectional and longitudinal QCT investigations are given. They then describe a current investigation in which they studied 269 subjects, including 173 normal women, 34 patients with hyperparathyroidism, 24 patients with steroid-induced osteoporosis, and 38 men with idiopathic osteoporosis. Spinal quantitative computed tomography, radiogrammetry, and single photon absorptiometry were performed, and a spinal fracture index was calculated on all patients. The authors found a disproportionate loss of spinal trabecular mineral compared to appendicular mineral in the men with idiopathic osteoporosis and the patients with steroid-induced osteoporosis. They observed roughly equivalent mineral loss in both the appendicular and axial regions in the hyperparathyroid patients. The appendicular cortical measurements correlated moderately well with each other but less well with spinal trabecular QCT. The spinal fracture index correlated well with QCT and less well with the appendicular measurements. Knowledge of appendicular cortical mineral status is important in its own right but is not a valid predictor of axial trabecular mineral status, which may be disproportionately decreased in certain diseases. Quantitative CT provides a reliable means of assessing the latter region of the skeleton, correlates well with the spinal fracture index (a semiquantitative measurement of end-organ failure), and offers the clinician a sensitive means of following the effects of therapy.

Minerals/analysis↗
NASA NTRS2022

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

cognitive networking↗
NASA NTRS2010

ORDEM2010 and MASTER-2009 Modeled Small Debris Population Comparison

The latest versions of the two premier orbital debris engineering models, NASA s ORDEM2010 and ESA s MASTER-2009, have been publicly released. Both models have gone through significant advancements since inception, and now represent the state-of-the-art in orbital debris knowledge of their respective agencies. The purpose of these models is to provide satellite designers/operators and debris researchers with reliable estimates of the artificial debris environment in near-Earth orbit. The small debris environment within the size range of 1 mm to 1 cm is of particular interest to both human and robotic spacecraft programs. These objects are much more numerous than larger trackable debris but are still large enough to cause significant, if not catastrophic, damage to spacecraft upon impact. They are also small enough to elude routine detection by existing observation systems (radar and telescope). Without reliable detection the modeling of these populations has always coupled theoretical origins with supporting observational data in different degrees. This paper details the 1 mm to 1 cm orbital debris populations of both ORDEM2010 and MASTER-2009; their sources (both known and presumed), current supporting data and theory, and methods of population analysis. Fluxes on spacecraft for chosen orbits are also presented and discussed within the context of each model.

Krisko, Paula H.↗
NASA NTRS2015

Will Astronauts Wash Clothes on the Way to Mars?

Future human space exploration missions will lengthen to years, and keeping crews clothed without a huge resupply burden is an important consideration for habitation systems. A space laundry system could be the solution; however, the resources it uses must be accounted for and must win out over the reliable practice of simply bringing along enough spare underwear. NASA has conducted trade-off studies through its Logistics Reduction Project to compare current space clothing systems, life extension of that clothing, traditional water-based clothes washing, and other sanitizing techniques. The best clothing system depends on the mission and assumptions but, in general, analysis results indicate that washing clothes on space missions will start to pay off as mission durations approach a year.

Ewert, Michael K.↗
NASA NTRS2021

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet [1], outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Cognitive Networking↗
NASA NTRSDate not supplied

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Rachel Dudukovich↗
NASA NTRSDate not supplied

Zero-Trust Architecture for Autonomous Edge Computing

We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.

zero trust↗
NASA NTRSDate not supplied

NAIRAS Model Nowcasting and Forecasting of the Aviation Radiation Environment

The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS) predicts dosimetric quantities for quantifying human radiation exposure and differential/integral flux/fluence quantities for assessing single event effects (SEE) in avionic systems from galactic cosmic rays (GCR), trapped inner belt protons, and solar energetic particle (SEP) events from the Earth’s surface to the space environment. Real-time predictions of the aviation radiation environment are available at NASA Goddard Space Flight Center’s Community Coordinated Modeling Center (CCMC) integrated Space Weather Analysis (iSWA) data feeds and cygnets, and a run-on-request (RoR) capability has also been deployed at CCMC. Recent model improvements include a more accurate atmospheric ionizing radiation transport methodology and more robust and reliable SEP nowcast dose predictions. In addition, preliminary results of SEP dose forecasts are shown by coupling the University of Malaga Solar Energetic Particle (UMASEP) model of integral proton flux forecasts with the NAIRAS model. These model updates and improvements are presented, and results are shown for aircraft, high-latitude balloon, and low-Earth orbit flights during quiescent and solar-geomagnetic disturbed conditions. Model comparisons with flight measurements are also shown.

NAIRAS↗
NASA NTRS1985

Space Station man-machine automation trade-off analysis

The man machine automation tradeoff methodology presented is of four research tasks comprising the autonomous spacecraft system technology (ASST) project. ASST was established to identify and study system level design problems for autonomous spacecraft. Using the Space Station as an example spacecraft system requiring a certain level of autonomous control, a system level, man machine automation tradeoff methodology is presented that: (1) optimizes man machine mixes for different ground and on orbit crew functions subject to cost, safety, weight, power, and reliability constraints, and (2) plots the best incorporation plan for new, emerging technologies by weighing cost, relative availability, reliability, safety, importance to out year missions, and ease of retrofit. A fairly straightforward approach is taken by the methodology to valuing human productivity, it is still sensitive to the important subtleties associated with designing a well integrated, man machine system. These subtleties include considerations such as crew preference to retain certain spacecraft control functions; or valuing human integration/decision capabilities over equivalent hardware/software where appropriate.

Zimmerman, W. F.↗
DOE OSTI2026

Agentic Diagrammatica: Towards Autonomous Symbolic Computation in High Energy Physics

We present Diagrammatica, a symbolic computation extension to the HEPTAPOD agentic framework, which enables LLM agents to plan and execute multi-step theoretical calculations. Symbolic computation poses a distinctive reliability challenge for LLM agents, as correctness is governed by implicit mathematical conventions that are not encoded in a form that can be easily checked in the computational backend. We identify two complementary remedies, tool-constrained computation and targeted knowledge grounding, and pursue the first as the primary architecture. Concretely, we concentrate the agent's action distribution onto tool calls with convention-fixing semantics, in which the agent specifies a compact, human-auditable diagram specification and a trusted backend performs the symbolic or numerical manipulations exactly. The toolkit provides two complementary calculation paths consuming a shared diagram specification: Naive Dimensional Analysis (NDA) for order-of-magnitude rate estimates and Exact Diagrammatic Analysis (EDA) for tree-level symbolic calculations via automatic FeynCalc code generation, both supplemented by automatic Feynman diagram enumeration and a navigable theory knowledge base. The architecture is validated on two benchmarks: (1) an exhaustive catalog of all tree-level, single-vertex $1\to 2$ partial decay widths across scalar, fermion, and vector parents, with complete massless and threshold limits and Standard Model validation; and (2) an NDA sensitivity study of the muon decay multiplicity $μ^+ \to ν_μ\barν_e + n(e^+e^-) + e^-$, determining the maximum observable $n$ at current and planned muon experiments.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗
NASA NTRS1994

Regional climates in the GISS general circulation model: Surface air temperature

One of the more viable research techniques into global climate change for the purpose of understanding the consequent environmental impacts is based on the use of general circulation models (GCMs). However, GCMs are currently unable to reliably predict the regional climate change resulting from global warming, and it is at the regional scale that predictions are required for understanding human and environmental responses. Regional climates in the extratropics are in large part governed by the synoptic-scale circulation and the feasibility of using this interscale relationship is explored to provide a way of moving to grid cell and sub-grid cell scales in the model. The relationships between the daily circulation systems and surface air temperature for points across the continental United States are first developed in a quantitative form using a multivariate index based on principal components analysis (PCA) of the surface circulation. These relationships are then validated by predicting daily temperature using observed circulation and comparing the predicted values with the observed temperatures. The relationships predict surface temperature accurately over the major portion of the country in winter, and for half the country in summer. These relationships are then applied to the surface synoptic circulation of the Goddard Institute for Space Studies (GISS) GCM control run, and a set of surface grid cell temperatures are generated. These temperatures, based on the larger-scale validated circulation, may now be used with greater confidence at the regional scale. The generated temperatures are compared to those of the model and show that the model has regional errors of up to 10 C in individual grid cells.

Hewitson, Bruce↗
NASA NTRS2014

Is a Space Laundry Needed for Exploration?

Future human space exploration missions will lengthen to years, and keeping crews clothed without a huge resupply burden is an important consideration for habitation systems. A space laundry system could be the solution; however, the resources it uses must be accounted for and must win out over the very reliable practice of bringing along enough spare underwear. Through NASA's Logistics Reduction and Repurposing project, trade off studies have been conducted to compare current space clothing systems, life extension of that clothing, traditional water based clothes washing and other sanitizing techniques. The best clothing system of course depends on the mission and assumptions, but in general, analysis results indicate that washing clothes on space missions will start to pay off as mission durations push past a year.

Ewert, Michael K.↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
NASA NTRS2001

Chronic mitral regurgitation and Doppler estimation of left ventricular filling pressures in patients with heart failure

Previous studies relating Doppler parameters and pulmonary capillary wedge pressures (PCWP) typically exclude patients with severe mitral regurgitation (MR). We evaluated the effects of varying degrees of chronic MR on the Doppler estimation of PCWP. PCWP and mitral Doppler profiles were obtained in 88 patients (mean age 55 +/- 8 years) with severe left ventricular (LV) dysfunction (mean ejection fraction 23% +/- 5%). Patients were classified by severity of MR. Patients with severe MR had greater left atrial areas, LV end-diastolic volumes, and mean PCWPs and lower ejection fractions (each P <.01). In patients with mild MR, multiple echocardiographic parameters correlated with PCWP; however, with worsening MR, only deceleration time strongly related to PCWP. From stepwise multivariate analysis, deceleration time was the best independent predictor of PCWP overall, and it was the only predictor in patients with moderate or severe MR. Doppler-derived early mitral deceleration time reliably predicts PCWP in patients with severe LV dysfunction irrespective of degree of MR.

NASA Discipline Cardiopulmonary↗
NASA NTRSDate not supplied

ISRU: Using Biology to Produce Food and Oxygen from Resources on Mars

Any crewed mission to Mars is a “biological” mission. As we move farther away from Earth, we will have to produce the required food, water and nutrient resources to sustain the crew in route or on the surface of other planetary bodies. Critical nutrients, like vitamins, have been shown to degrade in our current food system and represent a risk to long term crew health. One way to address this problem is by developing reliable Biological Life Support Systems (BLSS) that can provide fresh, nutrient-rich foods. Much work in this area will be done as we learn to live and work on the Moon. Systems developed for the Moon should also work on Mars, but will not be designed to take advantage of resources that Mars offers: CO2, more abundant water, and a day length similar to Earth’s. The photosynthetic portion of a BLSS can act as an ISRU element on Mars by providing human life support needs. There is an urgent need to understand how to build components of a fully functional BLSS. One Mars Exploration Program Analysis Group (MEPAG) goal relates to this area.

B. M. Link↗
NASA NTRS1997

Automation-induced monitoring inefficiency: role of display location

Operators can be poor monitors of automation if they are engaged concurrently in other tasks. However, in previous studies of this phenomenon the automated task was always presented in the periphery, away from the primary manual tasks that were centrally displayed. In this study we examined whether centrally locating an automated task would boost monitoring performance during a flight-simulation task consisting of system monitoring, tracking and fuel resource management sub-tasks. Twelve nonpilot subjects were required to perform the tracking and fuel management tasks manually while watching the automated system monitoring task for occasional failures. The automation reliability was constant at 87.5% for six subjects and variable (alternating between 87.5% and 56.25%) for the other six subjects. Each subject completed four 30 min sessions over a period of 2 days. In each automation reliability condition the automation routine was disabled for the last 20 min of the fourth session in order to simulate catastrophic automation failure (0 % reliability). Monitoring for automation failure was inefficient when automation reliability was constant but not when it varied over time, replicating previous results. Furthermore, there was no evidence of resource or speed accuracy trade-off between tasks. Thus, automation-induced failures of monitoring cannot be prevented by centrally locating the automated task.

Automation↗
NASA NTRS2011

Component-Level Electronic-Assembly Repair (CLEAR) Analysis of the Problem Reporting and Corrective Action (PRACA) Database of the International Space Station On-Orbit Electrical Systems

The NASA Constellation Program is investigating and developing technologies to support human exploration of the Moon and Mars. The Component-Level Electronic-Assembly Repair (CLEAR) task is part of the Supportability Project managed by the Exploration Technology Development Program. CLEAR is aimed at enabling a flight crew to diagnose and repair electronic circuits in space yet minimize logistics spares, equipment, and crew time and training. For insight into actual space repair needs, in early 2008 the project examined the operational experience of the International Space Station (ISS) program. CLEAR examined the ISS on-orbit Problem Reporting and Corrective Action database for electrical and electronic system problems. The ISS has higher than predicted reliability yet, as expected, it has persistent problems. A goal was to identify which on-orbit electrical problems could be resolved by a component-level replacement. A further goal was to identify problems that could benefit from the additional diagnostic and test capability that a component-level repair capability could provide. The study indicated that many problems stem from a small set of root causes that also represent distinct component problems. The study also determined that there are certain recurring problems where the current telemetry instrumentation and built-in tests are unable to completely resolve the problem. As a result, the root cause is listed as unknown. Overall, roughly 42 percent of on-orbit electrical problems on ISS could be addressed with a component-level repair. Furthermore, 63 percent of on-orbit electrical problems on ISS could benefit from additional external diagnostic and test capability. These results indicate that in situ component-level repair in combination with diagnostic and test capability can be expected to increase system availability and reduce logistics. The CLEAR approach can increase the flight crew s ability to act decisively to resolve problems while reducing dependency on Earth-supplied logistics for future Constellation Program missions.

Oeftering, Richard C.↗