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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Moon to Mars In Situ Resource Utilization (ISRU) Status Update

In 2017, NASA initiated the Artemis program to send astronauts back to the lunar surface, create a sustainable human lunar exploration program, and lead the first human exploration mission to the Mars surface. While much of NASA’s plans for the Artemis program currently focus on the Human Lunar Return and the ability for astronauts to explore the lunar surface for limited durations each year, the longer-term vision for the Artemis program is to enable sustained human exploration and commercial operations in cis-lunar space and the lunar surface. An important aspect of achieving this long-term vision, is to better understand and characterize the resources on the Moon and Mars and learn how to extract and use these resources. Known as In Situ Resource Utilization (ISRU), the identification, mapping, extraction, and processing of space resources has the potential to greatly reduce the cost and risk of human exploration. These are achieved by reducing what needs to be delivered from Earth and the dependency on these supplies, lowering costs through commercial operations, and expanding infrastructure for safer and more capable exploration and surface operations. To guide development of ISRU technologies and systems on the ground and demonstrate these capabilities on the Moon and Mars, the NASA Space Technology Mission Directorate (STMD) created and released the ISRU Envisioned Future Priorities (EFP) strategic plan in 2022 and updated it in 2023. While lunar ISRU technology development had already started, these publicly released strategic plans have been used to guide and prioritize technology development, and assess the progress in achieving the vision. Since the release of the ISRU EFP, there have been several significant activities/events that have occurred with respect to human lunar exploration, surface infrastructure, and ISRU. This presentation will provide an overview and status of on-going technology and system development activities, an update of ISRU into the Artemis campaign, an update on ISRU-related activities.

NASA↗
DOE OSTI2020

Session 097B - Panel: Pathway to End State at the East Tennessee Technology Park in Oak Ridge, TN (2/4) (R1.16b)

This panel was to focus on the 'Pathway to End State at the East Tennessee Technology Park (ETTP)'. It was to discuss the DoE's long-term vision for the ETTP site and the process for converting the site into a multi-use industrial park, historic preservation and national park, and conservation area. The Panelists were to discuss the challenges in executing a cleanup project of this magnitude, the waste management, and the monitoring efforts that supported the achievement of the Oak Ridge Office of Environmental Management (OREM) Vision 2020. This session integrated with panel sessions 97A, 97C, and 97D. Panelists with presentations: Beginning with the End in Mind (Kevin Ironside); Project Execution (Daniel Macias); Waste Management (John Wrapp); Environmental Remediation (Samantha Pack); Waste Management (Josh Pemberton); DOE's Long-Range Vision (Laura Wilkerson)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Session 097A - Panel: Accomplishments and Update of the East Tennessee Technology Park in Oak Ridge, TN (1/4) (R1.16a)

This panel was deferred until 2021. The session was designed to highlight and celebrate the accomplishment of Vision 2020 at US DoE's East Tennessee Technology Park (ETTP) in Oak Ridge, Tennessee. Vision 2020 is the overarching strategy and guidance that have formed the foundation of success in closing one of the largest cleanup site in the DOE Complex. The session was supposed to feature representatives from the DOE Oak Ridge Office of Environmental Management (OREM) and its contractor, UCOR, in exploring the partnerships and performance excellence that have made the difference in achieving success. OREM's Vision 2020 is a collaboration with entities that have made a difference in its success: economic developers, workforce developers, the University of Tennessee, and community partners. The vigorous Oak Ridge small business contracting community has also been instrumental in helping achieve success. This session plans to celebrate the entire process, its numerous players, and the spirit of accomplishment as the Oak Ridge community comes to realize the promise of 30 years of continuous cleanup excellence. This session integrated with panel sessions 97B, 97C and 97D. Panelists with presentations: Beyond Cleanup: Achieving Risk Reduction through Partnerships and Performance Excellence (Kenneth Rueter); Strategic Approach to Achieve End States at the East Tennessee Technology Park (Jay Mullis)

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS2006

Acceleration of Stereo Correlation in Verilog

To speed up vision processing in low speed, low power devices, embedding FPGA hardware is becoming an effective way to add processing capability. FPGAs offer the ability to flexibly add parallel and/or deeply pipelined computation to embedded processors without adding significantly to the mass and power requirements of an embedded system. This paper will discuss the JPL stereo vision system, and describe how a portion of that system was accelerated by using custom FPGA hardware to process the computationally intensive portions of JPL stereo. The architecture described takes full advantage of the ability of an FPGA to use many small computation elements in parallel. This resulted in a 16 times speedup in real hardware over using a simple linear processor to compute image correlation and disparity.

machine vision↗
DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗
DOE OSTI2023

In situ characterization of material extrusion printing by near-infrared spectroscopy

Material extrusion printing of reactive resins and inks present a unique challenge due to the time-dependent nature of the rheological and chemical properties they possess. As a result, careful print optimization or process control is important to obtain consistent, high quality prints via additive manufacturing. Here, we present the design and use of a near-infrared (NIR) flow through cell for in situ chemical monitoring of reactive resins during printing. Differences between in situ and off-line benchtop measurements are presented and highlight the need for in-line monitoring capability. Additionally, in-line extrusion force monitoring and off-line post inspection using machine vision is demonstrated. By combining NIR and extrusion force monitoring, it is possible to follow cure reaction kinetics and viscosity changes during printing. When combined with machine vision, the ability to automatically identify and quantify print artifacts can be incorporated on the printing line to enable real-time, artificial intelligence-assisted quality control of both process and product. Together, these techniques form the building blocks of an optimized closed-loop process control strategy when complex reactive inks must be used to produce printed hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Advancements in reflected target nonintrusive assessment (ReTNA) for large optical surface measurement

Reflected computer vision targets are a powerful tool for measurement of mirror surface shape, with several important advantages over traditional fringe deflectometry methods. This method was first presented in 2021 and has undergone significant improvement and demonstration since. We describe a new baseline system using reflected computer vision targets, and present results from a large-scale measurement campaign conducted on both commercial heliostats and test mirrors in the laboratory. Calibration of the measurement system with photogrammetry allows for accurate measurement without careful control of target shape or camera position. Overall, the results show that a baseline setup using this method achieves measurement uncertainties in the slope error root-mean-square less than ±0.11 milliradian due to a series of repeatability conditions, varying sample position, rotation, lighting, camera settings, and system rebuild and recalibration. We present a detailed description of the setup, the results generated by this measurement tool, repeated measurement results, and the strengths and limitations of this metrology system.

14 SOLAR ENERGY↗
DOE OSTI2023

A survey of techniques for optimizing transformer inference

Recent years have seen a phenomenal rise in the performance and applications of transformer neural networks. The family of transformer networks, including Bidirectional Encoder Representations from Transformer (BERT), Generative Pretrained Transformer (GPT) and Vision Transformer (ViT), have shown their effectiveness across Natural Language Processing (NLP) and Computer Vision (CV) domains. Transformer-based networks such as ChatGPT have impacted the lives of common men. However, the quest for high predictive performance has led to an exponential increase in transformers' memory and compute footprint. Researchers have proposed techniques to optimize transformer inference at all levels of abstraction. Further, this paper presents a comprehensive survey of techniques for optimizing the inference phase of transformer networks. We survey techniques such as knowledge distillation, pruning, quantization, neural architecture search and lightweight network design at the algorithmic level. We further review hardware-level optimization techniques and the design of novel hardware accelerators for transformers. We summarize the quantitative results on the number of parameters/FLOPs and the accuracy of several models/techniques to showcase the tradeoff exercised by them. We also outline future directions in this rapidly evolving field of research. We believe that this survey will educate both novice and seasoned researchers and also spark a plethora of research efforts in this field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Block segmentation in feature space for realtime object detection in high granularity images

Computer vision has applications in object detection, image recognition and classification, and object tracking. One of the challenges of computer vision is the presence of useful information at multiple distance scales. Filtering techniques may sacrifice details at small scales in order to prioritize the analysis of large-scale features of the image. We present a strategy for coarse-graining multidimensional data while maintaining fine-grained detail for subsequent analysis. The algorithm is based on fixed-size block segmentation in the feature space. We apply this strategy to solve the long-standing challenge of detecting particle trajectories at the Large Hadron Collider in real time.

Computer vision↗
NASA NTRS2000

Visual detection of spatial contrast patterns: evaluation of five simple models

The ModelFest Phase One dataset is a collection of luminance contrast thresholds for 43 two-dimensional monochromatic spatial patterns confined to an area of approximately two by two degrees. These data were collected by a collaboration among twelve laboratories, and were designed to provide a common database for calibration and testing of spatial vision models. Here I report fits of the ModelFest data with five models: Peak Contrast, Contrast Energy, Generalized Energy, a Gabor Channels model, and a Discrete Cosine Transform model. The Gabor Channels model provides the best fit, though the other, simpler models, with the exception of Peak Contrast, provide remarkably good fits as well. Though there are clear individual differences, regularities in the data suggest the possibility of constructing a standard observer for spatial vision. c2000 Optical Society of America.

NASA Discipline Space Human Factors↗
NASA NTRS2006

Attenuating Stereo Pixel-Locking via Affine Window Adaptation

For real-time stereo vision systems, the standard method for estimating sub-pixel stereo disparity given an initial integer disparity map involves fitting parabolas to a matching cost function aggregated over rectangular windows. This results in a phenomenon known as 'pixel-locking,' which produces artificially-peaked histograms of sub-pixel disparity. These peaks correspond to the introduction of erroneous ripples or waves in the 3D reconstruction of truly Rat surfaces. Since stereo vision is a common input modality for autonomous vehicles, these inaccuracies can pose a problem for safe, reliable navigation. This paper proposes a new method for sub-pixel stereo disparity estimation, based on ideas from Lucas-Kanade tracking and optical flow, which substantially reduces the pixel-locking effect. In addition, it has the ability to correct much larger initial disparity errors than previous approaches and is more general as it applies not only to the ground plane.

pixel locking effect↗
NASA NTRS2007

Overview of Key Saturn Probe Mission Trades

Ongoing studies, performed at NASA/JPL over the past two years in support of NASA's SSE Roadmap activities, proved the feasibility of a NF class Saturn probe mission. I. This proposed mission could also provide a good opportunity for international collaboration with the proposed Cosmic Vision KRONOS mission: a) With ESA contributed probes (descent modules) on a NASA lead mission; b) Early 2017 launch could be a good programmatic option for ESA-CV/NASA-NF. II. A number of mission architectures could be suitable for this mission: a) Probe Relay based architecture with short flight time (approx. 6.3-7 years); b) DTE probe telecom based architecture with long flight time (-11 years), and low probe data rate, but with the probes decoupled from the carrier, allowing for polar trajectories I orbiter. This option may need technology development for telecom; c) Orbiter would likely impact mission cost over flyby, but would provide significantly higher science return. The Saturn probes mission is expected to be identified in NASA's New Frontiers AO. Thus, further studies are recommended to refine the most suitable architecture. International collaboration is started through the KRONOS proposal work; further collaborated studies will follow once KRONOS is selected in October under ESA's Cosmic Vision Program.

probes↗
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 study, 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 algorithm, respectively. Assisted by a guiding cone, the ECT probe is continuously fed into the tubes with a fill factor of 0.819. In conclusion, during this process, the eddy current data are automatically collected and real-time analyzed by convolutional neural networks, showing accuracy of nearly 100% for identifying defective and nondefective tubes and 85% for four types of defective tubes and nondefective tubes.

42 ENGINEERING↗
DOE OSTI2024

The Cauchy Problem for Boltzmann Bi-linear Systems: The Mixing of Monatomic and Polyatomic Gases

Abstract From a unified vision of vector valued solutions in weighted Banach spaces, this paper establishes the existence and uniqueness for space homogeneous Boltzmann bi-linear systems with conservative collisional forms arising in complex gas dynamical structures. This broader vision is directly applied to dilute multi-component gas mixtures composed of both monatomic and polyatomic gases. Such models can be viewed as extensions of scalar Boltzmann binary elastic flows, as much as monatomic gas mixtures with disparate masses and single polyatomic gases, providing a unified approach for vector valued solutions in weighted Banach spaces. Novel aspects of this work include developing the extension of a general ODE theory in vector valued weighted Banach spaces, precise lower bounds for the collision frequency in terms of the weighted Banach norm, energy identities, angular or compact manifold averaging lemmas which provide coerciveness resulting into global in time stability, a new combinatorics estimate for p -binomial forms producing sharper estimates for the k -moments of bi-linear collisional forms. These techniques enable the Cauchy problem improvement that resolves the model with initial data corresponding to strictly positive and bounded initial vector valued mass and total energy, in addition to only a $$2^+$$ 2 + moment determined by the hard potential rates discrepancy, a result comparable in generality to the classical Cauchy theory of the scalar homogeneous Boltzmann equation.

Physics↗
DOE OSTI2023

Engineering the Future through Synthetic Biology

The last two decades have witnessed tremendous progress in synthetic biology. Despite the technological advances, the maturing field has yet to transition from fundamental study to translational practice. In this perspective article, I discuss my vision to enable this transition. Here, with this vision, our next generation will solve global problems through synthetic biology.

42 ENGINEERING↗
DOE OSTI2024

The activity of antimicrobial peptoids against multidrug-resistant ocular pathogens

Background: Ocular infections caused by antibiotic-resistant pathogens can result in partial or complete vision loss. The development of pan-resistant microbial strains poses a significant challenge for clinicians as there are limited antimicrobial options available. Synthetic peptoids, which are sequence-specific oligo-N-substituted glycines, offer potential as alternative antimicrobial agents to target multidrug-resistant bacteria. Methods: The antimicrobial activity of synthesised peptoids against multidrug-resistant (MDR) ocular pathogens was evaluated using the microbroth dilution method. Hemolytic propensity was assessed using mammalian erythrocytes. Peptoids were also incubated with proteolytic enzymes, after which their minimum inhibitory activity against bacteria was re-evaluated. Results: Several alkylated and brominated peptoids showed good inhibitory activity against multidrug-resistant Pseudomonas aeruginosa strains at concentrations of ≤15 μg mL -1 (≤12 µM). Similarly, most brominated compounds inhibited the growth of methicillin-resistant Staphylococcus aureus at 1.9 to 15 μg mL -1 (12 µM). The N-terminally alkylated peptoids caused less toxicity to erythrocytes. The peptoid denoted as TM5 had a high therapeutic index, being non-toxic to either erythrocytes or corneal epithelial cells, even at 15 to 22 times its MIC. Additionally, the peptoids were resistant to protease activity. Conclusions: Peptoids studied here demonstrated potent activity against various multidrug-resistant ocular pathogens. Their properties make them promising candidates for controlling vision-related morbidity associated with eye infections by antibiotic-resistant strains.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗
DOE OSTI2021

Closing transdisciplinary collaboration gaps of food-energy-water nexus research

The nexus of food, energy, and water (FEW) systems is key to ensuring global sustainability in the face of climate change, population growth, and urbanization. To address FEW resources inequity among different regions and countries, transdisciplinary research networking becomes increasingly important for tackling this intractable, complex grand challenge. In contrast to interdisciplinary or multidisciplinary research, transdisciplinary research emphasizes the interactions of scientific, cognitive, and social factors from a top-down view. Intellectual and strategic integrations entail weaving scientific, socioeconomic, and political perspectives together into a new convergence model and fostering a shared vision and comparable assessment, though these goals might not be realistically achievable in the short term. This article summarizes major barriers to transdisciplinary research on FEW nexus grand challenges and possible solutions to be implemented at multiple levels of distinct social systems. Implementation of the solutions relies on not only top-down incentives of governments but also bottom-up initiatives of academic communities and individual researchers. The relevance of shared interests and visions between the research communities and the public is emphasized.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗