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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Enabling Real-time Multi-messenger Astrophysics Discoveries with Deep Learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

E A Huerta↗
NASA NTRS1975

Man-machine interactive imaging and data processing using high-speed digital mass storage

The role of vision in teleoperation has been recognized as an important element in the man-machine control loop. In most applications of remote manipulation, direct vision cannot be used. To overcome this handicap, the human operator's control capabilities are augmented by a television system. This medium provides a practical and useful link between workspace and the control station from which the operator perform his tasks. Human performance deteriorates when the images are degraded as a result of instrumental and transmission limitations. Image enhancement is used to bring out selected qualities in a picture to increase the perception of the observer. A general purpose digital computer, an extensive special purpose software system is used to perform an almost unlimited repertoire of processing operations.

Alsberg, H.↗
DOE OSTI2024

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

domain of applicability↗
DOE OSTI2025

Advancing 3D surface imaging: single-axis structured light illumination plenoptic camera with machine learning integration

Structured light illumination (SLI) is a configurable 3D surface imaging modality that can function largely independently of surface texture. At the same time, machine learning (ML) approaches are providing new ways to capture relevant information from SLI patterns, avoiding the need to develop advanced computer vision algorithms. By projecting an optical pattern onto a surface and measuring the apparent distortion of that pattern, one can determine surface topography from a single image. Common realizations of SLI 3D imaging use off-axis SLI to allow for parallax-based determination of depth; however, in constrained geometries, the ability to make single-axis measurements can be of major benefit. While plenoptic imaging (PI) cameras have long been developed for the purpose of single-axis 3D imaging, they are generally reliant on the surface texture of the measured object, thus making them unreliable in certain experimental conditions. Therefore, we present a single-axis 3D SLI plenoptic camera, which combines the single-axis benefits of PI technology while using coaxial SLI to maintain indifference to surface conditions. We also present a study of the camera capabilities paired with the development of several algorithms, including traditional feature tracking methods as well as ML methods, which are found to enhance resolution and range. We report depth sensitivity down to 0.2% $\frac{dz}{z_0}$. The single-axis SLI 3D plenoptic camera demonstrates potential applicability for in-situ topographical measurements under a wide range of conditions including, but not limited to, objects without trackable surface texture, high temperatures, and constrained geometry environments.

Imaging systems↗
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
NASA NTRS1991

Welding technology transfer task/laser based weld joint tracking system for compressor girth welds

Sensors to control and monitor welding operations are currently being developed at Marshall Space Flight Center. The laser based weld bead profiler/torch rotation sensor was modified to provide a weld joint tracking system for compressor girth welds. The tracking system features a precision laser based vision sensor, automated two-axis machine motion, and an industrial PC controller. The system benefits are elimination of weld repairs caused by joint tracking errors which reduces manufacturing costs and increases production output, simplification of tooling, and free costly manufacturing floor space.

Looney, Alan↗
NASA NTRS2017

NASA Centennial Challenge: Three Dimensional (3D) Printed Habitat, Phase 2

The NASA Centennial Challenges: 3D-Printed Habitat Challenge seeks to develop the fundamental technologies necessary to manufacture an off-world habitat using mission recycled materials and/or local indigenous materials. The vision is that autonomous habitat manufacturing machines will someday be deployed to the Moon or Mars to construct shelters for human habitation. NASA and Bradley University, are holding a new US$ 2.5 million competition to design and build a 3-D printed habitat for deep space exploration, including the agency's journey to Mars. The multi-phase 3-D Printed Habitat Challenge, part of NASA's Centennial Challenges program, is designed to advance the additive construction technology needed to create sustainable housing solutions for Earth and beyond. The first phase of the competition ran through Sept. 27, 2015. This phase, a design competition, called on participants to develop state-of-the-art architectural concepts that take advantage of the unique capabilities 3-D printing offers. The top 3 prizes with a prize purse of $40,000 were awarded at the 2015 World Maker Faire in New York. The second phase of the competition is called the Structural Member Competition and it is divided into three levels happening in the spring and summer of 2017. The Compression Test Competition (Level 1) focuses on the fabrication technologies needed to manufacture structural components from a combination of indigenous materials and recyclables, or indigenous materials alone. For Level 1, teams will develop 3D printable materials, build a 3D printing machine, and print two specimens: a truncated cone and a cylinder. The Level 2 Beam Member Competition is the second of three sub-competitions within the overall Structural Member Competition. For Level 2, teams will print a beam that will be tested. The Level 3 Head to Head Competition is the third of three sub-competitions within the overall Structural Member Competition. For Level 3, teams will develop 3D printable materials, use a 3D printing machine, and print three compression specimens of the elected material, three flexural specimens of the elected material, and one dome structure. Tests conducted on the specimens and the dome structure will determine Level 3 scores and awards. On Earth these same habitat manufacturing capabilities could be used to produce housing wherever affordable housing is needed and access to conventional building materials and skills is limited. Terrestrially, it is envisioned that local indigenous materials (dirt, clay, sand, etc.) could be combined with readily available recyclable materials and used to construct semi-permanent shelters against environmental elements for human habitation. The goal of the 3D-Printed Habitat Challenge is to foster the development of new technologies necessary to additively manufacture a habitat using local indigenous materials with, or without, recyclable materials. This paper will summarize the Level 2 results of this NASA Centennial Challenge competition and it will discuss related technology advancement.

3D printing↗
NASA NTRSDate not supplied

Impact Ice Microstructure Segmentation Using Transfer Learned Model

A process of using machine learning to segment impact ice microstructure is presented and analyzed. The segmentation was conducted with the goal of obtaining average grain size estimations. The model was trained on a set of micrographs of impact ice grown at NASA Glenn’s Icing Research Tunnel. The model leveraged a model pre-trained on a large set of micrographs of various materials as a starting point. Post-processing of the segmented images was done to connect broken boundaries. An automatic method of determining grain size following an ASTM standard was implemented. Segmentation results using different training sets as well as different encoder and decoder pairs are presented. Calculated sizes are compared to manual grain size measurement methods. Results show promise in accuracy as well as a possible improvement in repeatability and consistency. Next steps for improving the model are suggested.

Machine learning↗
NASA NTRS2021

Future Model-Based Systems Engineering Vision and Strategy Bridge for NASA

A vision for the future of model-based systems engineering (MBSE) at NASA in 2029 and a strategy bridge towards that future are presented. Strategic thinking and leading change concepts were used to analyze reports and presentations on global trends and visionary thinking about the future of systems and digital engineering. The context, strategic time horizon, stakeholders, strategic challenges, strategic advantages, driving forces, and opportunities were considered. The analysis resulted in a future vision of MBSE that shows what NASA systems engineers and digital machines will do to perform rapid, extraordinary, and unprecedented missions. The NASA systems engineer, in this future vision, works with a global project team in a virtual and collaborative environment, engineers the system, and uses digital approaches as the routine and default way of working. The digital machines provide data-driven and automated mission designs; have a backbone of program and project management, systems engineering, and product life-cycle management; and are a knowledge-sharing infrastructure. The NASA systems engineer and the systems engineering team are envisioned to use digital machines to plan and perform rapid exploration missions, develop a digital twin that lasts across the life cycle, and develop enduring and adaptable systems. NASA has an engineering enterprise and a life-cycle management framework that endure, adapt, and respond. A strategy bridge based on the Baldrige Criteria for Performance Excellence Framework and lessons learned from a recent MBSE initiative illuminates a way forward from today to this desired future. The bridge lays out a strategy for leaders and recommends investments of today for immediate benefits and for benefits in 2029.

model-based systems engineering, digital engineeri↗
NASA NTRS1991

Human factors issues in the use of night vision devices

An account is given of the critical human factors that arise in field data on the differences between night vision displays and unaided day vision. Attention is given to the findings of empirical studies of performance on rotorcraft-flight-relevant perceptual tasks in which depth and distance perception are critical factors. Suggestions are made for man-machine-critical component design modifications in current night vision systems.

Kaiser, Mary K.↗
DOE OSTI2025

Popnet : computer vision based deep learning model for forecasting gridded population

Here, this study introduces Popnet, a deep learning model for forecasting 1 km-gridded populations, integrating U-Net, ConvLSTM, a Spatial Autocorrelation module and deep ensemble methods. Using spatial variables and population data from 2000 to 2020, Popnet predicts South Korea’s population trends by age groups (under 14, 15-64 and over 65) up to 2040. In validation, it outperforms traditional machine learning and state-of-the-art computer vision models. The output of this model discovered significant polarisation: population growth in urban areas, especially the capital region, and severe depopulation in rural areas. Popnet is a robust tool for offering significant insights to policymakers and related stakeholders about the detailed future population, which allows them to establish detailed, localised planning and resource allocations.

computer vision↗
DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗
DOE OSTI2026

Regulation compliant AI for fusion: explainable image-based feedback control of divertor detachment in DIII-D tokamak

While artificial intelligence (AI) has been promising for fusion control, its inherent black-box nature will make compliant implementation in regulatory environments a challenge. This study implements and validates a real-time AI-enabled linear and interpretable control system for successful divertor detachment control with the DIII-D lower divertor camera. Using D 2 gas, we demonstrate successful feedback divertor detachment control with a mean absolute difference of 2% from the target for both detachment and reattachment. This automatic training and linear processing framework can be extended to any image-based diagnostic for future fusion reactors.

computer vision↗
DOE OSTI2025

Defect Detection Model Development for Large Scale Thermoplastic Printing

Large-format additive manufacturing (LFAM) offers several advantages, including high throughput, cost-effective pellet-fed extrusion, and the capability to produce large-scale structures. The main pain points of LFAM include start and stops during the printing process, warpage, long layer times that lead to bead freezing, and bead separation due to shrinkage. These issues can lead to overfill, underfill and buildup of material in different sections of a print. This can lead to hidden defects embedded within the printed layers, or even ultimate failure of the printed structure. This ensures these defects can only be identified through nondestructive testing (NDT) inspection methods after printing, which can be timely and costly. Aligned Vision work specializes in 2D projectors with visual inspection systems and machine learning. Traditionally system is used for composite layup and layup inspections. In this work we used the LFAM system at Oak Ridge National Laboratory to create defect rich samples. The Aligned Vision inspection system then performed in-situ monitoring of the print process after each part was printed. This in-situ vision inspection system was used to develop a layer-by-layer inspection model that looks for overfill, underfill, and the buildup of defects using only a camera-based vision system. This leads to the assurance of high-quality production components.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2024

Interpreting and Accelerating Transformers for Jet Tagging

Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based on the particle of origin, known as jet tagging. In this work, we study the interpretatbility and prospects for acceleration of Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging performance. We analyzing ParT's attention maps and particle-pair correlations in the eta-phi plane, revealing intriguing features, such as a binary attention pattern that identifies critical substructure in jets. These insights enhance our understanding of the model's internal workings and learning process and hint at ways to improve its efficiency. Along these lines, we also explore low-rank attention, attention alternatives, and dynamic quantization to accelerate transformers for jet tagging. With quantization, we achieve a 50% reduction in model size and a 10% increase in inference speed without compromising accuracy. These combined efforts enhance both the performance and the interpretability of transformers in high-energy physics, opening avenues for more efficient and physics-driven model designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1987

Some vision algorithms

The ability to determine the contents of a scene by a machine has obvious applications. There are reasons for seeking horizontal, vertical and diagonal line segments of varying lengths in order to interpret the content of a scene. Such line segments aid in determining edges that determine the boundaries of objects within the scene. Algorithms for each of the above as well as for data compacting are presented.

Johnson, Gordon G.↗