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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Expanding NREL's Advanced Research on Integrated Energy Systems (ARIES) Capabilities

NREL's Integrated Energy Pathways vision represents a transformed, future integrated energy system that is more affordable, clean, secure, and resilient than today. Key to this vision is the unique Advanced Research on Integrated Energy Systems (ARIES) research platform. With a focus on advancing modern grid infrastructure, making investments in energy efficiency building technology research, and innovating battery storage and much more, the unique ARIES research platform can be used to accelerate the integration of new technologies into a modern grid.

advanced research↗
DOE OSTI2021

Circular Economy for Energy Materials: Designing to Reduce, Reuse, and Upcycle for a More Sustainable Planet

Our global economy is on the cusp of a dramatic transformation away from a traditional linear approach - making, using, and landfill disposing of goods - to a more circular one where end of life is considered at the onset and the concepts of reducing, reusing, and chemically recycling or upcycling of goods becomes the new norm. Conventional/mechanical recycling is applicable to some materials but is underused and ineffective for a true circular economy vision. This vision ensures raw materials are used once to produce a product; and, at end of life, the product is chemically recycled back to its core building blocks to enable an infinite loop of product manufacture, use, deconstruction, and remanufacture.

CEEM↗
DOE OSTI2021

A Community Roadmap for Scientific Workflows Research and Development

The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a steep learning curve. To address some of these challenges and lay the groundwork for transforming workflows research and development, the WorkflowsRI and ExaWorks projects partnered to bring the international workflows community together. This paper reports on discussions and findings from two virtual “Workflows Community Summits” (January and April, 2021). The overarching goals of these workshops were to develop a view of the state of the art, identify crucial research challenges in the workflows community, articulate a vision for potential community efforts, and discuss technical approaches for realizing this vision. To this end, participants identified six broad themes: FAIR computational workflows; AI workflows; exascale challenges; APIs, interoperability, reuse, and standards; training and education; and building a workflows community. We summarize discussions and recommendations for each of these themes.

Ferreira Da Silva, Rafael↗
DOE OSTI2022

An Autonomous Robot for Shell and Tube Heat Exchanger Inspection

Shell and tube heat exchangers (STHEs) are critical to energy conversion efficiency of power plants. Eddy current examination is a way to evaluate working conditions of these tubes. However, the current testing apparatus requires human to manually insert an eddy current testing (ECT) probe into and extract it out of individual tubes, and meanwhile monitor measurement results for diagnosis. It is a time-consuming and labor-intensive procedure even for an experienced technician. To tackle this challenge, in this work, we developed a robot enabled ECT system for autonomous inspection of STHEs. The robotic platform employs Mecanum wheeled chassis for high mobility, machine vision to locate tube bundle and tube inlets, a rotational Cartesian mechanism to operate at planes with all possible inclinations, and a task-specific mechanism for ECT probe delivery. Machine vision locates tube bundle and tube inlets by an April tag detection algorithm and a Circle Hough Transform (CHT) algorithm, respectively. Assisted by a guidance cone, the ECT probe is continuously fed into the tubes with a fill factor of 0.819. During this process, the eddy current data are automatically collected and real-time analyzed by convolutional neural networks (CNN), showing accuracy of nearly 100% for identifying defective and non-defective tubes and 85% for four types of defective tubes and non-defective tubes.

autonomy, deep learning, eddy current testing, hea↗
DOE OSTI2023

Rifle-like camera for long distance face recognition

An improved long-range facial recognition system is provided. The facial recognition system includes an integrated, rifle-like mounting platform with modular and interchangeable components, including a machine-vision camera, a facial recognition module, and an aiming scope. The mounting platform includes an elongated frame, a hand grip, a shoulder stock, and an underbody support attachment for an optional bipod. The mounting platform provides an intuitive, shoulder-operated support structure for stabilizing the machine-vision camera and reducing vibrations that otherwise inhibit long distance imaging.

Bolme, David S.↗
DOE OSTI2023

Real-time process monitoring for direct ink write additive manufacturing

Direct ink write (DIW) printing of reactive resins presents a unique challenge due to the time-dependent nature of the rheological and chemical properties of the ink. As a result, careful print optimization or process control is important to obtain consistent, high quality prints. The present invention uses a flow-through characterization cell for in situ chemical monitoring of a resin ink during DIW printing. Additionally, in-line extrusion force monitoring can be combined with off-line post inspection using machine vision. By combining in-line spectroscopy and force monitoring, it is possible to follow reaction kinetics (for example, curing of a reactive resin) and viscosity changes during printing, which can be used for a closed-loop process control. Additionally, the capability of machine vision to automatically identify and quantify print artifacts can be incorporated on the printing line to enable real-time, AI-assisted quality control of the printed products. Together, these techniques can form the building blocks of an optimized process control strategy when complex reactive ink must be used to produce printed hardware.

Cook, Adam W.↗
DOE OSTI2024

Building Automation

In this chapter, we focus on both need-based and vision-based control innovation for building automation for HVAC systems. After providing a brief background that describes different HVAC architectures and products, we describe the control challenges and opportunities from a needs-based point of view, at the equipment level, the systems level, and the building level. At the equipment level, increased use of continuously variable actuation such as variable speed compressors and fans, requires application of robust multivariable control that must consider various types of nonlinear behavior across increasingly larger operating envelopes. The objective at this level is to achieve robust and energy efficient operation. At the system level, coupled and interactive dynamics among subsystems are increasingly important to consider, and functional integration of these subsystems, along with set points and operating schedules are designed to minimize energy consumption and improve occupant comfort. At the building level, issues such as integration among other building systems, such as the building envelope and the electric grid, offer opportunities to develop new types of demand response and grid-interactive behaviors and technologies. At the higher levels, system dynamics and robust stability are less of an issue, similar to process control applications, while optimization and integration are more important, although dynamic stability is increasing an issue at the higher levels too. From a vision-driven innovation point of view, we describe three areas that will require sustained research efforts and will eventually impact building automation: Digital Twins, Model Predictive Control, and Grid Interactive Buildings.

Bortoff, Scott↗
DOE OSTI2024

A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models

Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.

artificial intelligence↗
DOE OSTI2024

National Reactor Innovation Center Annual Report

The National Reactor Innovation Center (NRIC), established in August 2019, is a national United States (U.S.) Department of Energy (DOE) program. NRIC’s mission is to partner with industry and national laboratories to bridge the gap between the concept, demonstration, and commercialization of advanced nuclear technology. NRIC accomplishes this through building or enhancing existing DOE infrastructure to support the testing of components and systems that are key to successfully deploying advanced nuclear technology. NRIC works to inspire stakeholders and the public, empower innovators, and deliver successful outcomes through efficient collaboration and coordination with partners. NRIC’s vision is that by 2028, NRIC will be partnered with industry and accelerating the demonstration and deployment of advanced nuclear technology using DOE national laboratory infrastructure and expertise. NRIC will establish four new experimental facilities and two large reactor test beds for integrated technology demonstrations and experimentation by 2028 and complete two advanced nuclear technology tests by 2030. Achieving this vision will enable urgently needed abundant and affordable clean energy both domestically and internationally. NRIC’s success will inspire our nation and the global community to embrace the promising contribution of innovative nuclear reactor technologies to the clean energy economy and re-establish the U.S. as the global leader in advanced nuclear energy. NRIC is tasked with expediting the development of advanced nuclear energy technologies by bringing together private-sector technology developers and the world-class capabilities of the DOE national laboratory system. Through this program, the U.S. private sector is given access to the physical infrastructure available at DOE national laboratories to test and demonstrate their reactor concepts. NRIC works closely with the Gateway for Accelerated Innovation in Nuclear (GAIN),; which is the DOE-Nuclear Energy (NE) program that grantings access to technical, regulatory, and financial support for commercializing nuclear energy. As observed in Figure 1, NRIC builds upon these new reactor concepts and technology successes to effectively strengthen U.S. nuclear leadership.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

The 200 Gbps Challenge: Imagining HL-LHC analysis facilities

The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a "Grand Challenge" format, constructing a series of increasingly large, complex, and realistic exercises to show the vision of HL-LHC analysis. Recently, the focus has been demonstrating the IRIS-HEP analysis infrastructure at scale and evaluating technology readiness for production. As a part of the Analysis Grand Challenge activities, the institute executed a "200 Gbps Challenge", aiming to show sustained data rates into the event processing of multiple analysis pipelines. The challenge integrated teams internal and external to the institute, including operations and facilities, analysis software tools, innovative data delivery and management services, and scalable analysis infrastructure. The challenge showcases the prototypes - including software, services, and facilities - built to process around 200 TB of data in both the CMS NanoAOD and ATLAS PHYSLITE data formats with test pipelines. The teams were able to sustain the 200 Gbps target across multiple pipelines. The pipelines focusing on event rate were able to process at over 30 MHz. These target rates are demanding; the activity revealed considerations for future testing at this scale and changes necessary for physicists to work at this scale in the future. The 200 Gbps Challenge has established a baseline on today's facilities, setting the stage for the next exercise at twice the scale.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Interoperability Maturity Roadmap Methodology, V1.3

The Interoperability Roadmap Methodology document supports the U.S. Department of Energy (DOE) Grid Modernization Laboratory Consortium (GMLC) Interoperability Project’s efforts to advance interoperability as an essential component for the successful and more seamless integration of intelligent, communicating technologies into the electric power system. This proposed methodology supports the strategic approach to advance interoperability described in the GMLC Interoperability Project’s Interoperability Strategic Vision: Enabling an Interactive Grid. That document proposes engaging an ecosystem of businesses interested in integrating smart technology with the electric power system to develop an interoperability roadmap using a methodical approach. This roadmap methodology is to be applied to technology integration ecosystems (e.g., electric vehicles, photovoltaic inverters, or automated buildings), using tools that measure the state of interoperability today, to identify the gaps and challenges that can be overcome to improve interoperability, and thereby, simplify the integration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1967

Flight research program. VI.

Correlation between heart rate, landing error and field of view for binocular and monocular sphere of vision of jet pilots

VISUAL FIELD↗
NASA NTRS2002

Stereo transparency and the disparity gradient limit

Several studies (Vision Research 15 (1975) 583; Perception 9 (1980) 671) have shown that binocular fusion is limited by the disparity gradient (disparity/distance) separating image points, rather than by their absolute disparity values. Points separated by a gradient >1 appear diplopic. These results are sometimes interpreted as a constraint on human stereo matching, rather than a constraint on fusion. Here we have used psychophysical measurements on stereo transparency to show that human stereo matching is not constrained by a gradient of 1. We created transparent surfaces composed of many pairs of dots, in which each member of a pair was assigned a disparity equal and opposite to the disparity of the other member. For example, each pair could be composed of one dot with a crossed disparity of 6' and the other with uncrossed disparity of 6', vertically separated by a parametrically varied distance. When the vertical separation between the paired dots was small, the disparity gradient for each pair was very steep. Nevertheless, these opponent-disparity dot pairs produced a striking appearance of two transparent surfaces for disparity gradients ranging between 0.5 and 3. The apparent depth separating the two transparent planes was correctly matched to an equivalent disparity defined by two opaque surfaces. A test target presented between the two transparent planes was easily detected, indicating robust segregation of the disparities associated with the paired dots into two transparent surfaces with few mismatches in the target plane. Our simulations using the Tsai-Victor model show that the response profiles produced by scaled disparity-energy mechanisms can account for many of our results on the transparency generated by steep gradients.

NASA Discipline Space Human Factors↗
NASA NTRS2007

Visual End-Effector Position Error Compensation for Planetary Robotics

This paper describes a vision-guided manipulation algorithm that improves arm end-effector positioning to subpixel accuracy and meets the highly restrictive imaging and computational constraints of a planetary robotic flight system. Analytical, simulation-based, and experimental analyses of the algorithm's effectiveness and sensitivity to camera and arm model error is presented along with results on several prototype research systems and 'ground-in-the-loop' technology experiments on the Mars Exploration Rover (MER) vehicles. A computationally efficient and robust subpixel end-effector fiducial detector that is instrumental to the algorithm's ability to achieve high accuracy is also described along with its validation results on MER data.

vision-guided manipulation algorithm↗
DOE OSTI2021

Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

In this work, we propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we present a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel DataBase, demonstrating the flexibility of the technique.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Time-Resolved Geometric Feature Tracking Elucidates Laser-Induced Keyhole Dynamics

In this report, during laser melting of metals, localized metal evaporation resulting in the formation of a keyhole shaped cavity can occur if processing conditions are chosen with high power density. An unstable keyhole can have deleterious effects in certain applications (e.g., laser powder bed fusion) as it increases the likelihood of producing defects such as porosity. In this work, we propose a pipeline that enables complete segmentation and extraction of various geometric features in keyholing conditions. In situ synchrotron high-speed X-ray visualization at the Advanced Photon Source provides large datasets of experimental images with a high spatio-temporal resolution across a range of laser parameters for Ti-6Al- 4 V. Computer vision image processing techniques were used to extract time-resolved quantitative geometric features (e.g., depth, width, front wall angle) throughout keyhole evolution which were subsequently analyzed to understand the relationship between the variation of local keyhole geometry and processing conditions. This analysis is the first to employ a data-driven approach to further our understanding of the keyholing process regime.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Coaxial color channel focus evaluation to estimate standoff height in directed energy deposition additive manufacturing

Directed energy deposition (DED) is an additive manufacturing process that is being rapidly adopted by industry and is well suited for the fabrication of complex components in a variety of metal alloys. In laser cladding systems such as DED, powder is blown in a stream to a metal substrate coincident with a laser necessary to deposit molten metal with 3D spatial control. The focus of both the laser and the powder stream are crucial, and best deposition occurs at a predetermined standoff height between the build surface and the print head. Generally, no monitoring of this distance is implemented in commercial DED systems. Due to potential over or under building, the standoff height often changes over time but tends to self-correct. However, inexpensive and minimally intrusive methods to identify optimal standoff are required to provide real-time control to maintain the optimal distance. The present work explores the quantification of the focus of the three-color channels of a coaxial camera to determine the standoff height. An experiment was performed in which a 254 mm wall is built and the standoff height, initially 5.0 mm below the optimal position, was then intentionally increased every 25.4 mm of wall length by an amount of 1.0 mm to a final position 7.0 mm above optimal. Computer vision is demonstrated to monitor the amount of focus in each color band and estimate standoff distance. Finally, a response can be calculated in under 40 ms using simple hardware and can work in most laser-based DED systems.

36 MATERIALS SCIENCE↗
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↗