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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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All sources NASA NTRS DOE OSTI

DOE OSTI2026

Automation of Laser Plasma Focused Ion Beam Microscopy for Next-Gen Energy Materials

Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Shaping the Future of Self-Driving Autonomous Laboratories Workshop

The "Shaping the Future of Self-Driving Autonomous Laboratories" workshop, held in Denver on November 7-8, 2024, brought together leading experts from materials science and computing to address the growing need to revolutionize scientific research through AI-driven autonomous laboratories. The workshop identified critical challenges, including the integration of heterogeneous data, development of AI systems that understand fundamental physical principles, and comprehensive safety protocols. Key recommendations emerged around developing universal laboratory equipment interfaces, implementing automated metadata collection systems, and creating hybrid AI approaches that combine data-driven learning with scientific principles. The workshop emphasized maintaining human oversight while leveraging automation, transforming scientific education to prepare the next generation of researchers, and establishing a national consortium leveraging DOE facilities as anchors for broader collaboration with academia and industry. Participants stressed the urgency of addressing the growing disconnect between human decision-making timescales and modern instrumentation capabilities, highlighting the need for strategic automation while preserving essential human insight and oversight in the research process.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Application of Artificial Intelligence/Machine Learning to Operations Research

This report examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on operations research, private industry, and government sectors, highlighting their applications in automating processes, enhancing decision-making, and optimizing complex systems. AI/ML technologies have revolutionized industries through predictive maintenance, supply chain optimization, and autonomous systems, while also advancing public safety and defense operations. However, challenges such as data integrity, model transparency, and the need for human oversight persist, particularly in high-consequence environments. The report emphasizes the critical role of explainable AI (XAI) and human-computer interaction models like Human-in-the-Loop (HITL) and Human-on-the-Loop (HOTL) in fostering trust and accountability. Balancing automation with ethical responsibility and transparency is essential for the continued successful integration of AI/ML into operational and strategic decision-making frameworks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Exploring Large Language Model Agents in Cybersecurity: A Literature Review with Experiments

The accelerated development and integration of large language model (LLM) agents have led researchers and developers to explore their effectiveness in cybersecurity, specifically with penetration testing (pentesting). Recent research efforts have attempted to use LLM agents to automate the process of pentesting because of the cost and time requirements that are required to perform a manual review. However, not all of the tools perform as expected. This paper reviews some of the newest and most popular autonomous pentesting frameworks, highlighting the capabilities and limitations of each one with the goal of providing the components needed to successfully and effectively build an autonomous pentesting agent in the future.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1995

A study of space-rated connectors using a robot end-effector

The main research activities have been directed toward the study of the Robot Operated Materials Processing System (ROMPS), developed at GSFC under a flight project to investigate commercially promising in-space material processes and to design reflyable robot automated systems to be used in the above processes for low-cost operations. The research activities can be divided into two phases. Phase 1 dealt with testing of ROMPS robot mechanical interfaces and compliant device using a Stewart Platform testbed and Phase 2 with computer simulation study of the ROMPS robot control system. This report provides a summary of the results obtained in Phase 1 and Phase 2.

Nguyen, Charles C.↗

NASA NTRS2002

Workload-Matched Adaptive Automation Support of Air Traffic Controller Information Processing Stages

Adaptive automation (AA) has been explored as a solution to the problems associated with human-automation interaction in supervisory control environments. However, research has focused on the performance effects of dynamic control allocations of early stage sensory and information acquisition functions. The present research compares the effects of AA to the entire range of information processing stages of human operators, such as air traffic controllers. The results provide evidence that the effectiveness of AA is dependent on the stage of task performance (human-machine system information processing) that is flexibly automated. The results suggest that humans are better able to adapt to AA when applied to lower-level sensory and psychomotor functions, such as information acquisition and action implementation, as compared to AA applied to cognitive (analysis and decision-making) tasks. The results also provide support for the use of AA, as compared to completely manual control. These results are discussed in terms of implications for AA design for aviation.

Kaber, David B.↗

NASA NTRS1986

Automation software for a materials testing laboratory

A comprehensive software system for automating much of the experimental process has recently been completed at the Lewis Research Center's high-temperature fatigue and structures laboratory. The system was designed to support experiment definition and conduct, results analysis and archiving, and report generation activities. This was accomplished through the design and construction of several software systems, as well as through the use of several commercially available software products, all operating on a local, distributed minicomputer system. Experimental capabilities currently supported in an automated fashion include both isothermal and thermomechanical fatigue and deformation testing capabilities. The future growth and expansion of this system will be directed toward providing multiaxial test control, enhanced thermomechanical test control, and higher test frequency (hundreds of hertz).

Mcgaw, Michael A.↗

DOE OSTI2022

Using Information Automation and Human Technology Integration to Implement Integrated Operations for Nuclear

The purpose of the research effort described in this report is to develop and demonstrate an approach to the design and implementation of advanced, automated systems intended to increase operational efficiencies at nuclear power plants. In particular, we describe methods for considering human-technology integration (HTI) issues throughout the various phases of system design, test, and implementation and how these considerations help promote effective design. This research project will develop planning tools and comprehensive guidance on how HTI principles and methods, in combination with information automation technologies, can enable effective data integration and coordination for full nuclear plant modernization. Specifically, this research project will develop an approach to automate the mapping of data from plant systems and processes to application needs, thereby significantly reducing the amount of human workload currently required for the execution of these tasks. In addition to developing an automated solution as a replacement for these tasks, this research will also analyze digitalization’s effectiveness in reducing operational costs of compliance related activities. Compliance activities are estimated to account for as much as 50% of operations and maintenance (i.e., non-fuel and non-capital) costs of plant operation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

FAIRmaterials: Ontology Tools with Data FAIRification in Development

The bilingual FAIRmaterials package simplifies the creation and visualization of materials and data science ontologies. FAIRmaterials, available in the Python and R languages, addresses the complexities associated with traditional ontology editors based on manual user input such as Protege with an intuitive workflow and easy-to-use templates, making it accessible to users both experienced and inexperienced with ontologies. The FAIRmaterials package is its ability to programatically convert simple and structured CSV inputs into rich, well-defined ontologies. This capability is designed to support the findability, accessibility, interoperability, and reusability (FAIR) of research data and serve as a tool in the process of data FAIRification. Its additional features, such as automated ontology merging, static visualizations, and comprehensive documentation for outputs extend its utility, making it a valuable tool for any researcher engaged in knowledge management.

Bradley, Alexander Harding [Case Western Reserve U↗

NASA NTRS1993

ADP Analysis project for the Human Resources Management Division

The ADP (Automated Data Processing) Analysis Project was conducted for the Human Resources Management Division (HRMD) of NASA's Langley Research Center. The three major areas of work in the project were computer support, automated inventory analysis, and an ADP study for the Division. The goal of the computer support work was to determine automation needs of Division personnel and help them solve computing problems. The goal of automated inventory analysis was to find a way to analyze installed software and usage on a Macintosh. Finally, the ADP functional systems study for the Division was designed to assess future HRMD needs concerning ADP organization and activities.

Tureman, Robert L., Jr.↗

NASA NTRS1987

Electro-optical flight deflection measurement system

This paper describes an electrooptical flight-deflection measurement system (FDMS) developed for use on highly-maneuverable-aircraft-technology (HiMAT) RPV flight research program. The FDMS provides in-flight measurements of the aircraft structural deflections and magnetic-tape recordings for automated data processing. The capabilities and limitations, requirements for installation on an aircraft, analytical considerations, and typical flight data acquired from the HiMAT research program are examined. The flight data indicate that the background light is the major obstacle to acquiring high-quality data and that the relationship between the target displacement and the output of the FDMS is nonlinear; however, the nonlinear effects can be minimized with judicious planning of the installation of the FDMS on the aircraft. Excellent flight deflection data were obtained from both the HiMAT and X-29 A flight research program with very little data lost as a result of encounters with severe background light.

Deangelis, V. Michael↗

NASA NTRS2003

Protein Crystal Growth

In order to rapidly and efficiently grow crystals, tools were needed to automatically identify and analyze the growing process of protein crystals. To meet this need, Diversified Scientific, Inc. (DSI), with the support of a Small Business Innovation Research (SBIR) contract from NASA s Marshall Space Flight Center, developed CrystalScore(trademark), the first automated image acquisition, analysis, and archiving system designed specifically for the macromolecular crystal growing community. It offers automated hardware control, image and data archiving, image processing, a searchable database, and surface plotting of experimental data. CrystalScore is currently being used by numerous pharmaceutical companies and academic and nonprofit research centers. DSI, located in Birmingham, Alabama, was awarded the patent Method for acquiring, storing, and analyzing crystal images on March 4, 2003. Another DSI product made possible by Marshall SBIR funding is VaporPro(trademark), a unique, comprehensive system that allows for the automated control of vapor diffusion for crystallization experiments.

Source record↗

NASA NTRS1997

Overview of Recent Flight Flutter Testing Research at NASA Dryden

In response to the concerns of the aeroelastic community, NASA Dryden Flight Research Center, Edwards, California, is conducting research into improving the flight flutter (including aeroservoelasticity) test process with more accurate and automated techniques for stability boundary prediction. The important elements of this effort so far include the following: (1) excitation mechanisms for enhanced vibration data to reduce uncertainty levels in stability estimates; (2) investigation of a variety of frequency, time, and wavelet analysis techniques for signal processing, stability estimation, and nonlinear identification; and (3) robust flutter boundary prediction to substantially reduce the test matrix for flutter clearance. These are critical research topics addressing the concerns of a recent AGARD Specialists' Meeting on Advanced Aeroservoelastic Testing and Data Analysis. This paper addresses these items using flight test data from the F/A-18 Systems Research Aircraft and the F/A-18 High Alpha Research Vehicle.

Brenner, Martin J.↗

DOE OSTI2023

Invertible neural networks for real-time control of extrusion additive manufacturing

Material extrusion additive manufacturing (AM) has enabled an elegant fabrication pathway for a vast material library. Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing. To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization. Here, in this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes. Furthermore, the presented approach can be generalized to provide real-time monitoring and optimization pathways for increasingly complex AM environments.

36 MATERIALS SCIENCE↗

NASA NTRS1989

NASA Space Engineering Research Center for Utilization of Local Planetary Resources

Progress toward the goal of exploiting extraterrestrial resources for space missions is documented. Some areas of research included are as follows: Propellant and propulsion optimization; Automation of propellant processing with quantitative simulation; Ore reduction through chlorination and free radical production; Characterization of lunar ilmenite and its simulants; Carbothermal reduction of ilmenite with special reference to microgravity chemical reactor design; Gaseous carbonyl extraction and purification of ferrous metals; Overall energy management; and Information management for space processing.

Ramohalli, Kumar↗

NASA NTRS2005

3DRT-MPASS

Data from all current JPL missions are stored in files called SPICE kernels. At present, animators who want to use data from these kernels have to either read through the kernels looking for the desired data, or write programs themselves to retrieve information about all the needed objects for their animations. In this project, methods of automating the process of importing the data from the SPICE kernels were researched. In particular, tools were developed for creating basic scenes in Maya, a 3D computer graphics software package, from SPICE kernels.

Lickly, Ben↗

NASA NTRS2019

Uncertainty Quantification of a Rotorcraft Conceptual Sizing Toolsuite

A computational framework to support the quantification of system uncertainties and sensitivities for rotorcraft applications is presented using the NASA Design and Analysis of Rotorcraft (NDARC) conceptual sizing tool. A 90 passenger conceptual tiltrotor configuration was used for case demonstration in the modeling of uncertainties in NDARCs emission module. A non-intrusive forward propagation uncertainty quantification approach was applied to ensemble simulations using a Monte Carlo methodology with stratified Latin hypercube sampling. An off-the-shelf software, DAKOTA, which supports trade studies and design space exploration, including optimization, surrogate modeling and uncertainty analysis was used to address the research goals. A toolsuite was further developed incorporating DAKOTA with automated design processes and methods using function wrappers to execute program routines including support for data post-processing. Uncertainties in rotorcraft emissions modeling using the Average Temperature Response metric for a set mission profile were studied. It was shown that for the current study, using the base-line best estimate modeling parameters for the Average Temperature Response metric, NDARC under-estimates the effects of emissions when compared with results from Monte Carlo simulations. A global sensitivity analysis was further undertaken to quantify the contribution of the various emission species on output sensitivity, hence uncertainty. The work demonstrates that the developed toolsuite is robust and will support the quantification of system uncertainties and sensitivities in future rotorcraft design efforts.

Rotorcraft↗