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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 811 records · Page 45

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NASA NTRS2005

Automating CapCom Using Mobile Agents and Robotic Assistants

We have developed and tested an advanced EVA communications and computing system to increase astronaut self-reliance and safety, reducing dependence on continuous monitoring and advising from mission control on Earth. This system, called Mobile Agents (MA), is voice controlled and provides information verbally to the astronauts through programs called personal agents. The system partly automates the role of CapCom in Apollo-including monitoring and managing EVA navigation, scheduling, equipment deployment, telemetry, health tracking, and scientific data collection. EVA data are stored automatically in a shared database in the habitat/vehicle and mirrored to a site accessible by a remote science team. The program has been developed iteratively in the context of use, including six years of ethnographic observation of field geology. Our approach is to develop automation that supports the human work practices, allowing people to do what they do well, and to work in ways they are most familiar. Field experiments in Utah have enabled empirically discovering requirements and testing alternative technologies and protocols. This paper reports on the 2004 system configuration, experiments, and results, in which an EVA robotic assistant (ERA) followed geologists approximately 150 m through a winding, narrow canyon. On voice command, the ERA took photographs and panoramas and was directed to move and wait in various locations to serve as a relay on the wireless network. The MA system is applicable to many space work situations that involve creating and navigating from maps (including configuring equipment for local topology), interacting with piloted and unpiloted rovers, adapting to environmental conditions, and remote team collaboration involving people and robots.

Clancey, William J.↗

DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗

NASA NTRS1993

Profile negotiation: An air/ground automation integration concept for managing arrival traffic

NASA Ames Research Center and NASA Langley Research Center conducted a joint simulation study to evaluate a profile negotiation process (PNP) between a time-based air traffic control ATC system and an airplane equipped with a four dimensional flight management system (4D FMS). Prototype procedures were developed to support the functional implementation of this process. The PNP was designed to provide an arrival trajectory solution that satisfies the separation requirements of ATC while remaining as close as possible to the airplane's preferred trajectory. The Transport Systems Research Vehicle cockpit simulator was linked in real-time to the Center/TRACON Automation System (CTAS) for the experiment. Approximately 30 hours of simulation testing were conducted over a three week period. Active airline pilot crews and active Center controller teams participated as test subjects. Results from the experiment indicate the potential for successful incorporation of airplane preferred arrival trajectories in the CTAS automation environment. Controllers were able to consistently and effectively negotiate nominally conflict-free trajectories with pilots flying a 4D-FMS-equipped airplane. The negotiated trajectories were substantially closer to the airplane's preference than would have otherwise been possible without the PNP. Airplane fuel savings relative to baseline CTAS were achieved in the test scenarios. The datalink procedures and clearances developed for this experiment, while providing the necessary functionality, were found to be operationally unacceptable to the pilots. Additional pilot control and understanding of the proposed airplane-preferred trajectory and a simplified clearance procedure were cited as necessary for operational implementation of the concept. From the controllers' perspective, the main concerns were the ability of the 4D airplane to accurately track the negotiated trajectory and the workload required to support the PNP as implemented in this study.

Williams, David H.↗

NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Recession tracking↗

NASA NTRS2003

Reusable Solid Rocket Motor Nozzle Joint 5 Redesign

Torque tension testing of a newly designed Reusable Solid Rocket Motor nozzle bolted assembly was successfully completed. Test results showed that the 3-sigma preload variation was as expected at the required input torque level and the preload relaxation were within the engineering limits. A shim installation technique was demonstrated as a simple process to fill a shear lip gap between nozzle housings in the joint region. A new automated torque system was successfully demonstrated in this test. This torque control tool was found to be very precise and accurate. The bolted assembly performance was further evaluated using the Nozzle Structural Test Bed. Both current socket head cap screw and proposed multiphase alloy bolt configurations were tested. Results indicated that joint skip and bolt bending were significantly reduced with the new multiphase alloy bolt design. This paper summarizes all the test results completed to date.

Lui, R. C.↗

NASA NTRS2017

Validation of a Manually Oscillating Chair for In-The-Field Assessment of Dynamic Visual Acuity on Crewmembers Within Hours of Returning From Long-Duration Spaceflight

Long-duration spaceflight results in sensorimotor adaptations, which cause functional deficits during gravitational transitions, such as landing on a planetary surface after long-duration microgravity exposure. Both the vestibular system and the central nervous system are affected by gravitational transitions. These systems are responsible for coordinating head and eye movements via the vestibulo-ocular reflex (VOR) and go through an adaptation period upon exposure to microgravity. Consequently, they must also re-adapt to Earth's gravitational environment upon landing. This re-adaptation causes decrements in gaze control and dynamic visual acuity, with crewmembers reporting oscillopsia and blurred vision caused by retinal slip, or the inability to keep an image focused on their retina. This is thought to drive motion sickness symptoms experienced by most crewmembers following landing. Retinal slip can be estimated by dynamic visual acuity (DVA); visual acuity while in motion. Previously, DVA has been assessed in the laboratory where subjects walked at 6.4 km/hr on a motorized treadmill. Using this method, Peters et al. (2011) found that DVA is worsened in astronauts by an average of 0.75 eye-chart lines one day after landing. However, it is believed that re-adaptation occurs quickly and that DVA might be worse immediately upon re-exposure to a gravitational environment. Since many crewmembers are unable to walk safely upon landing, it was necessary to develop a method for replicating the vertical head movements associated with walking. In addition, the use of a chair to imitate the head displacement caused by walking isolates eye-head interactions without allowing for trunk and lower-body compensation, as seen with treadmill walking (Mulavara & Bloomberg 2003). Therefore, a modality for assessing DVA in the field within a few hours of landing was developed. In this study, we validated the ability of a manually operated oscillating chair to reproduce the oscillatory frequency of walking on a treadmill. Healthy non-astronaut subjects (n=14) participated in one test session and completed three static (seated) and three dynamic (walking/oscillated) visual acuity tests. DVA was assessed using a motorized treadmill, an automated oscillating chair, and a manually operated chair, both developed in the Neuroscience Laboratory at JSC. The automated chair was motor-driven and set to oscillate vertically at 2 Hz with a vertical displacement of +/- 5 cm to simulate vertical translation while walking. The manually operated chair was oscillated vertically by a test operator to the beat of a metronome at 120 beats/min (2 Hz) and a vertical displacement of approximately +/- 5 cm. As the subject was oscillated, they were asked to discern the direction gap of Landolt-C optotypes of varying sizes and verbally reported the direction while an operator recorded their response using a gamepad. Subjects were outfitted with accelerometers (sampling rate = 128 Hz) on their head, trunk and lumbar spine. A fast Fourier transform was performed on the vertical trunk acceleration to compare the peak and spread of the distribution of oscillation frequencies for each oscillating condition. The spread of the frequency distribution for the manual chair was not significantly different from either the treadmill or the automated chair. However, all three conditions had similar non-zero standard error values, suggesting a variance in head movement frequency which may affect DVA. The average oscillation frequency of the manual chair (1.85 Hz) was significantly different (α=0.05) from that of treadmill walking (2.24 Hz), but not significantly different from that of the automated chair (1.85 Hz) and all three conditions had small standard errors (SEM = 0.04, 0.06, and 0.08 Hz for manual, treadmill, and automated respectively). This implies that both chairs oscillate at a frequency below that of treadmill walking, but are comparable to each other and reproducible across sessions. Additionally, DVA scores did not vary significantly across conditions. The smaller spread values of the oscillating chairs' frequencies indicated mitigation of variation induced by locomotor strategies, which enables better examination of the issue of VOR adaptation. Furthermore, due to the deconditioned state of crewmembers in the initial hours after landing, it is easier to transport a manual bouncing chair into the field and safer to perform a vision test while seated in a chair versus walking on a treadmill. Therefore, the manually oscillating chair has been deemed to meet and exceed the DVA testing capabilities previously obtained by treadmill walking.

Kreutzberg, G. A.↗

NASA NTRS2012

Bioculture System Validation

The Bioculture System first flight will be to validate the performance of the hardware and its automated and manual operational capabilities in the space flight environment of the International Space Station. Biology, Engineering, and Operations tests will be conducted in the Bioculture System fully characterize its automated and manual functions to support cell culturing for short and long durations. No hypothesis-driven research will be conducted with biological sample, and the science leads have all provided their concurrence that none of the data they collect will be considered as proprietary and can be free distributed to the science community. The outcome of the validation flight will be to commission the hardware for use by the science community. This presentation will provide non-proprietary details about the Bioculture System and information about the activities for the first flight.

Bioculture↗

NASA NTRS2006

Fluorescent Applications to Crystallization

By covalently modifying a subpopulation, less than or equal to 1%, of a macromolecule with a fluorescent probe, the labeled material will add to a growing crystal as a microheterogeneous growth unit. Labeling procedures can be readily incorporated into the final stages of purification, and tests with model proteins have shown that labeling u to 5 percent of the protein molecules does not affect the X-ray data quality obtained . The presence of the trace fluorescent label gives a number of advantages. Since the label is covalently attached to the protein molecules, it "tracks" the protein s response to the crystallization conditions. The covalently attached probe will concentrate in the crystal relative to the solution, and under fluorescent illumination crystals show up as bright objects against a darker background. Non-protein structures, such as salt crystals, do not show up under fluorescent illumination. Crystals have the highest protein concentration and are readily observed against less bright precipitated phases, which under white light illumination may obscure the crystals. Automated image analysis to find crystals should be greatly facilitated, without having to first define crystallization drop boundaries as the protein or protein structures is all that shows up. Fluorescence intensity is a faster search parameter, whether visually or by automated methods, than looking for crystalline features. Preliminary tests, using model proteins, indicates that we can use high fluorescence intensity regions, in the absence of clear crystalline features or "hits", as a means for determining potential lead conditions. A working hypothesis is that more rapid amorphous precipitation kinetics may overwhelm and trap more slowly formed ordered assemblies, which subsequently show up as regions of brighter fluorescence intensity. Experiments are now being carried out to test this approach using a wider range, of proteins. The trace fluorescently labeled crystals will also emit with sufficient intensity to aid in the automation of crystal alignment using relatively low cost optics, further increasing throughput at synchrotrons.

Pusey, Marc L.↗

DOE OSTI2023

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

We report advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. Our Machine Learning based Motif Extractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Combining genome-wide association studies and expression quantitative trait nucleotide mapping with molecular and genetic validations to identify transcriptional networks regulating drought tolerance in Populus

Objectives: (i). To deploy a large-scale experimental drought trial for up to 1000 unique genotypes of Populus equipping the sites with controlled irrigation and drought treatments that are fully automated and monitored. FULLY COMPLETED (ii) To test the hypothesis that a suite of traits identified for drought tolerance in P. nigra can be measured in drought and control treatments in the wide germplasm collection of P. trichocarpa. FULLY COMPLETED (iii) To use established and novel GWAS model approaches to identify gene loci linked to drought tolerance traits on interest in P. trichocarpa. FULLY COMPLETED (iv) To undertake comparative analysis of GWAS results for drought tolerance traits in P. nigra and P. trichocarpa. PARTIALLY COMPLETED – remains active (v) Using RNAseq in P. trichocarpa, in droughted and control treatments to identify cis- and trans-regulated eQTN. FULLY COMPLETED (vi) Validate up to 50 cis-QTNs, from network hubs using transient protoplast assays. FULLY COMPLETED (vii) To establish Agrobacterium-based gene editing protocols in Populus. FULLY COMPLETED (viii) To utilize early leads from previous research to investigate at least 6 candidate genes for drought tolerance in Populus. FULLY COMPLETED (ix) To validate up to 20 candidate genes for drought tolerance in P. trichocarpa refined from the long-list tested in the transient assays for cis-acting hub gene targets. PARTIALLY COMPLETED- remains active.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

ASME Code change proposal to implement new universal high temperature constitutive models for Section III, Division 5

This report completes work on a universal high temperature constitutive model suitable for use with the ASME Boiler & Pressure Vessel Code Section III, Division 5 rules for the design by inelastic analysis of Class A nuclear reactor components. The goals of this work are to provide a simple model form that adequately captures the high temperature response of materials and can be applied to any future Code material. Additionally, the report describes an automated process for calibrating a model against test data. The idea is to simplify the effort required to generate a constitutive model for an arbitrary material, provided test data is available. This will accelerate the process of qualifying new Code materials in the future. In addition, the report provides calibrated models and detailed validation comparisons to test data for five currently-qualified or soon-to-be qualified materials: 316H, Grade 91, Alloy 800H, Alloy 617, and Alloy 709. The report surveys the available data for the remaining two ASME Class Materials --- 2.25Cr-1Mo and 304H --- concluding that there is enough data data to generate a model for 2.25Cr-1Mo steel provided some additional sources of non-public data can be included in the test database, but that a dedicated cyclic test campaign would be needed for 304H. Supplemental material includes the full text of an ASME Code change proposal to incorporate the models for the four currently-qualified Class A material, detailed validation comparisons to test data for the five material models, and input files for reference implementations of the constitutive models in the NEML and NEML2 modeling frameworks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1968

Science data systems

Shock tests on Mariner Venus 67 prototype data automation subsystems to evaluate multilayer laminate packaging

Source record↗

NASA NTRS1971

Cytotoxicity assay automation

The design and construction of a system to automatically test HLP antigens are described. Major efforts were made to test and evaluate the performance of such a system, and compare its performance with nonautomatic tissue typing techniques. The system is based on the fluorochromatic cytotoxicity assay. Results show the system will work but is subject to malfunctions after a few samplings, and poses problems in showing correctly the necessary readings.

Levinthal, E. C.↗

NASA NTRS1974

Influence of moisture content on the velocity of ultrasounds in black poplar wood

Correlations among the longitudinal, radial and tangential velocities of ultrasounds in black poplar on the one hand and among these magnitudes and the wood's moisture content on the other hand are presented. Mathematical equations approximating these correlations are given, and the influence of moisture content on the elastic constants of wood is discussed. The possibility of using ultrasonic methods in automating the wood drying process and in testing the quality and homogeneity of wood is pointed out.

Bucar, V.↗

NASA NTRS1972

SLAR image interpretation keys for geographic analysis

A means for side-looking airborne radar (SLAR) imagery to become a more widely used data source in geoscience and agriculture is suggested by providing interpretation keys as an easily implemented interpretation model. Interpretation problems faced by the researcher wishing to employ SLAR are specifically described, and the use of various types of image interpretation keys to overcome these problems is suggested. With examples drawn from agriculture and vegetation mapping, direct and associate dichotomous image interpretation keys are discussed and methods of constructing keys are outlined. Initial testing of the keys, key-based automated decision rules, and the role of the keys in an information system for agriculture are developed.

Coiner, J. C.↗