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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Advanced Materials & Manufacturing Technology (AMMT): Development of Additive Manufacturing Agnostic Process Parameter Procedure, 316H Stainless Steel Readiness Level Data Sets, and Machine Maintenance Plan

The University of California, Davis is involved in a project to deploy and enhance an artificial intelligence (AI) system for predicting and preventing plasma disruptions on the DIII D tokamak, under the funding from Department of Energy DE-SC0023500 (title: AI/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS)). The overarching goal is to demonstrate that real-time, AI-guided intervention can proactively modify the plasma state to avoid or mitigate disruptions—a critical challenge for the future of fusion energy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sequential Decision Making (SDM) for Mesh Refinement and Model Selection in Multiscale, Multi-Physics Applications

Intelligent automation and decision support are needed to enhance computational efficiency and robustness in multiscale and multi-physics problems, including materials science, manufacturing, and climate and weather modeling. Current scientific computing approaches for enabling decisions by scientists fail to explore the role of learning, reasoning, and probabilistic planning. Often these decisions are not performed in real-time during the computation but are made prior to the start of the computation, which must be interrupted in order to make changes to the prior choices. Such interruptions at different stages of the computation increase the total computing time and the need for a human expert to frequently monitor the results. State of art scientific computing methods consist of rule-based algorithms that cannot automatically adapt to a dynamically changing computing environment. The development of a Sequential Decision Making (SDM) framework will automate scientific computing by optimizing the policies for mesh refinement, time-stepping, model and algorithm selection, resource allocation, and pre and post-processing. Our agent SDM framework for scientific computing will consist of data-driven learning (Classifier), automated reasoning (contextual knowledge), and probabilistic planning (Reinforcement Learning). In this project, we focused on three problems to demonstrate our SDM framework on a set of ordinary and partial differential equations. Classification of Lorenz system regions using Feed-Forward Neural Networks examined learning in the SDM framework. On the other hand, reasoning and planning in the SDM framework were used in two problems: adaptive time-stepping for nonlinear ODEs using on-policy RL algorithms, and adaptive mesh refinement for 2-D PDEs using off-policy RL algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗
NASA NTRS1996

Spectral imaging applications: Remote sensing, environmental monitoring, medicine, military operations, factory automation and manufacturing

This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. The authors discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the Intelligent Missile Seeker (IMS) demonstration project for real-time target/decoy discrimination, and the Thermal InfraRed Imaging Spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.

National Defense↗
DOE OSTI2026

U.S. Department of Energy Competitiveness Improvement Project 2024 Technology Commercialization Awardee: Intelligent Energy Systems

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Intelligent Energy Systems for technology commercialization. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NLR on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed-generation technology option.

17 WIND ENERGY↗
DOE OSTI2023

Preliminary Feasibility of Printing, Microstructure Analysis and Mechanical Performance of a Down Selected Ni Alloy

This report is submitted as completion of a Milestone 3 deliverable under work package ORNL CT-23OR1304051 in support of the Advanced Materials and Manufacturing Technologies (AMMT) program. The AMMT program is aiming at the faster incorporation of new materials and manufacturing technologies into complex nuclear-related systems. An integrated approach, combining advanced characterization, high-throughput and accelerated testing, modeling and simulation including machine learning and artificial intelligence will be employed. While 316H (Fe-16-18Cr-10-14Ni-2-3Mo-0.04-0.1C) has been identified as a key alloy to be integrated into the AMMT accelerated alloy qualification approach due its relevance for many current and future nuclear energy reactors, many other alloys could be considered for the advanced fabrication of innovate high-performance nuclear components. ANL, INL, ORNL and PNNL are collaborating on identifying the most promising alloy candidates relevant for the AMMT program. A selection criteria matrix was established to evaluate the alloys considering their relative importance and technological readiness levels for nuclear energy applications, with a focus on laser powder bed fusion (LPBF). Due to the broad range of potential candidate alloys, ORNL and INL focused on Ni-based alloys, while ANL and PNNL mainly evaluated Fe-based alloys. PNNL previously published materials scorecards reports on several key alloys and this report is providing a broader overview of Ni-based candidate alloys, expending beyond alloys well-known to the nuclear community. Of particular interest are alloys that are currently commercially available in powder form due to the growing demand from other industries that have invested heavily in additive manufacturing. Among these alloys, Haynes 282 (Fe-20Cr-20Co-8Mo) was selected due to its superior strength at high temperature compared to the code-qualified alloy 617 (Ni-20-24Cr-10-15Co-8-10Mo). To assess the integration of this new alloy into the AMMT digital manufacturing framework, we performed the rapid optimization of alloy 282 printing parameters on a Renishaw 250 machine, the fabrication of sufficient materials for extensive characterization and mechanical testing both at ORNL and INL, and added the printing data into the digital platform via the Peregrine software. A detailed analysis of the LPBF 718 alloy was also conducted, with creep specimens being tested at 600-650°C and characterized by advanced electron microscopy. The alloy superior mechanical strength and great printability associated with the extensive database that has already been generated highlight the promising potential of LPBF 718 as a candidate alloy for the AMMT program. INL, ANL and PNNL have generated similar reports and all the information will be compiled into a final M2 milestone report to provide the AMMT leadership team with clear recommendations on the down selection of reactor materials, as well as establish a roadmap for the qualification of these selected alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics

As the demand for low-cost, high-efficiency solar energy technologies grows, metal halide perovskite (MHP) solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conversion efficiencies. However, their poor durability and issues with manufacturing consistency remain significant barriers to commercialization. In this work, we develop deep learning models to support materials characterization and provide insight into features and processes influencing performance. The models are trained using transfer learning of a pretrained model to predict relevant current-voltage (IV) metrics based on different combinations of input electroluminescence (EL) and photoluminescence (PL) images of MHP devices. We examine which image types are most informative in accurately predicting different IV metrics. Additionally, we use explainable artificial intelligence (XAI) techniques to provide insights into specific spatial features in the devices that drive differences in performance. We find that stabilized luminescence images (e.g. those collected after biasing the devices for at least 1 min) are better for predicting metrics of open-circuit voltage (by PL) and short-circuit current (by PL with EL), but that predicting fill factor and overall power output may use the time-evolution of EL images. Based on attribution masks generated by integrated gradients for each device performance metric, we further suggest different loss mechanisms associated with categories of large and small spatial defects. Overall, this case study highlights the potential applicability of XAI methodology for streamlining MHP device analysis and accelerating detailed understanding of the relationships between spatial defects and impacts on performance.

14 SOLAR ENERGY↗
DOE OSTI2022

Autonomous Aerial Power Plant Inspection in GPS-denied Environments

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Materials Characterization, Prediction and Control Project: Summary Report on Data Analytics Framework

This report summarizes the activities performed under the data analytics Vertex in the Materials Characterization, Prediction and Control Project funded under laboratory directed research and development at Pacific Northwest National Laboratory. The data analytics Vertex developed models for associating global or local process parameters, microstructural features, and performance properties of friction-stir-processed 316L stainless steel plates. Statistical, machine learning, and deep learning models, as well as generative artificial intelligence approaches, were used to develop the associations between the process-structure-property data streams. These associations formed the basis for predicting global properties of parts manufactured under different process envelopes, providing a basis for predicting performance using data driven as well as physics-informed and physics-constrained approaches. Additionally, the associations were used to predict local process parameters and microstructural features of the product, predictive relationships that have the potential to form the basis of a control framework that could eventually modulate a friction-stir process to maintain product quality.

316L stainless steel↗
NASA NTRS1980

Advanced automation for space missions: Technical summary

Several representative missions which would require extensive applications of machine intelligence were identified and analyzed. The technologies which must be developed to accomplish these types of missions are discussed. These technologies include man-machine communication, space manufacturing, teleoperators, and robot systems.

Source record↗
DOE OSTI2023

Prioritization of Existing Reactor Materials

The Advanced Materials and Manufacturing Technologies (AMMT) Program is aiming at the faster incorporation of new materials and manufacturing technologies into complex nuclear-related systems. An integrated approach, combining advanced characterization, high-throughput and accelerated testing, modeling and simulation, including machine learning and artificial intelligence, will be employed. Although 316H (Fe–[16–18]Cr–[10–14]Ni–[2–3]Mo–[0.04–0.1]C) has been identified as a key alloy to be integrated into the AMMT accelerated alloy qualification approach because of its relevance for many current and future nuclear energy reactors, many other alloys could be considered for the advanced fabrication of innovative, high-performance nuclear components. Argonne National Laboratory (ANL), Idaho National Laboratory (INL), Oak Ridge National Laboratory (ORNL), and Pacific Northwest National Laboratory (PNNL) are collaborating on identifying the most promising alloy candidates relevant for the AMMT Program. A selection criteria matrix was established to evaluate the alloys considering their relative importance and technological readiness levels for nuclear energy applications, with a focus on laser powder bed fusion (LPBF). Because of the broad range of potential candidate alloys, ORNL and INL focused on nickel-based alloys, and ANL and PNNL mainly evaluated iron-based alloys. PNNL previously published material scorecards reports on several key alloys, and this report provides a broader overview of iron- and nickel-based candidate alloys, expending beyond alloys well-known to the nuclear community.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1987

Management Tools

Manugistics, Inc. (formerly AVYX, Inc.) has introduced a new programming language for IBM and IBM compatible computers called TREES-pls. It is a resource management tool originating from the space shuttle, that can be used in such applications as scheduling, resource allocation project control, information management, and artificial intelligence. Manugistics, Inc. was looking for a flexible tool that can be applied to many problems with minimal adaptation. Among the non-government markets are aerospace, other manufacturing, transportation, health care, food and beverage and professional services.

Source record↗
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
NASA NTRS1987

Development of Moire machine vision

Three dimensional perception is essential to the development of versatile robotics systems in order to handle complex manufacturing tasks in future factories and in providing high accuracy measurements needed in flexible manufacturing and quality control. A program is described which will develop the potential of Moire techniques to provide this capability in vision systems and automated measurements, and demonstrate artificial intelligence (AI) techniques to take advantage of the strengths of Moire sensing. Moire techniques provide a means of optically manipulating the complex visual data in a three dimensional scene into a form which can be easily and quickly analyzed by computers. This type of optical data manipulation provides high productivity through integrated automation, producing a high quality product while reducing computer and mechanical manipulation requirements and thereby the cost and time of production. This nondestructive evaluation is developed to be able to make full field range measurement and three dimensional scene analysis.

Harding, Kevin G.↗
NASA NTRS2001

Experience and Challenges in Implementing Stratospheric Aerosol Gas Experiment on Meteor-3M Platform

Implementation of Stratospheric Aerosol Gas Experiment (SAGE) is a joint science mission between the Rosavioskosmos, also called Russian Aviation and Space Agency (RASA) and the National Aeronautics and Space Administration (NASA). Under the global collaboration agreement established by President Clinton and Yeltsin in 1995 between the United States and Russia, space was one of the major areas identified for joint scientific collaboration. There were several collaborative projects identified under space, earth, human exploration of space and aeronautics. SAGE was one of the key Earth Science instruments selected common to both countries' interests in ozone research. SAGE has a long space heritage, and four earlier versions of this instrument have flown in space for the last 15-year period. It has provided a vital ozone and aerosol data in the mid latitudes and has contributed in the overall ozone depletion research. SAGE II, the fourth instrument has been flying in space on NASA's Earth Radiation Budget Satellite (ERBS) for the last 14 years. Ball Aerospace built the instrument under Langley Research Center's (LaRC) management. SAGE III for Russian Meteor-3M mission is a third generation design with more spectral bands, elaborate data gathering and storage and intelligent terrestrial software. The Russian collaboration required a complete integration of SAGE III on the Russian Meteor-3M satellite and a launch on a Zenit-2 launch vehicle manufactured in Ukraine. The whole complex is scheduled to be launched from Baikonur cosmodrome in early 2001. This cooperative mission has presented a number of management, technical and logistical challenges on both sides. This paper makes an attempt to review and document such experiences.

Habib, Shahid↗
NASA NTRS1983

Application of Kalman filters to robot calibration

This report explores new uses of Kalman filter theory in manufacturing systems (robots in particular). The Kalman filter allows the robot to read its sensors plus external sensors and learn from its experience. In effect, the robot is given primitive intelligence. The study, which is applicable to any type of powered kinematic linkage, focuses on the calibration of a manipulator.

Whitney, D. E.↗
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↗