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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
DOE OSTI2026

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

Aarrestad, Thea Klaeboe [ETH, Zurich (main)]↗
DOE OSTI2022

Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning

The ability to measure occupants’ thermal state in real time will enable major advances in the control of air conditioning systems. This study proposes predicting occupant thermal state by a combination of infrared thermography, computer vision, and machine learning. The approach (1) uses cheek, nose, and hand temperatures because they are least subject to blockage by hair, glasses, and clothing; (2) measures the distribution of skin temperatures within geometrically defined sub-areas of the face and hand; and (3) uses temperature differences within and between these areas to eliminate the effects of calibration drift that are unavoidable in thermal infrared (TIR) cameras. Two series of tests were conducted, respectively in an outdoor carport and an indoor environmental chamber, collecting a total of 48,422 sets of cheek, nose, and hand skin temperatures using a TIR camera and computer-vision technology, coupled with 715 subjective responses of thermal sensations. To predict occupant thermal state, Random Forest classification models were built using either absolute skin temperatures (the maximum and median temperatures of cheek and hand segments, and the temperature of the central spot on the nose), or intra- and inter-segment temperature differences of cheeks, hands, and nose. These measurements were found to accurately predict occupant thermal state. Using the maximum and median temperatures for cheek and nose, or for cheek and hand, predicts thermal state with an accuracy of 92–96%. In conclusion, using only the intra- and inter-segment temperature differences from cheek and nose is 83% accurate; adding the hand temperature differences increases the accuracy to 96%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Mitigation of spatial nonstationarity with vision transformers

Spatial nonstationarity, the location variance of features’ statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechanical compaction trends, in mineral deposits grades vary due to sedimentation and concentration processes, in hydrology rainfall varies due to the atmosphere and topography interactions, and in metallurgy crystalline structures vary due to differential cooling. Conventional geostatistical modeling workflows rely on the assumption of stationarity to be able to model spatial features for geostatistical inference. Nevertheless, this is often not a realistic assumption when dealing with nonstationary spatial data and this has motivated a variety of nonstationary spatial modeling workflows such as trend and residual decomposition, cosimulation with secondary features, and spatial segmentation and independent modeling over stationary subdomains. The advent of deep learning technologies has enabled new workflows for modeling spatial relationships. However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context. We demonstrate the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance and propose the mitigation of such impacts using self-attention (vision transformer) models. We demonstrate the utility of vision transformers for the mitigation of nonstationarity with relative errors as low as 10%, exceeding the performance of alternative deep learning methods such as convolutional neural networks. We establish best practice by demonstrating the ability of self-attention networks for modeling large-scale spatial relationships in the presence of commonly observed geospatial nonstationarity.

58 GEOSCIENCES↗
DOE OSTI2022

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗
DOE OSTI2014

An inkjet vision measurement technique for high-frequency jetting

Inkjet technology has been used as manufacturing a tool for printed electronics. To increase the productivity, the jetting frequency needs to be increased. When using high-frequency jetting, the printed pattern quality could be non-uniform since the jetting performance characteristics including the jetting speed and droplet volume could vary significantly with increases in jet frequency. Therefore, high-frequency jetting behavior must be evaluated properly for improvement. However, it is difficult to measure high-frequency jetting behavior using previous vision analysis methods, because subsequent droplets are close or even merged. In this paper, we present vision measurement techniques to evaluate the drop formation of high-frequency jetting. The proposed method is based on tracking target droplets such that subsequent droplets can be excluded in the image analysis by focusing on the target droplet. Finally, a frequency sweeping method for jetting speed and droplet volume is presented to understand the overall jetting frequency effects on jetting performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Effect of laser radiation wavelength and reepithelization process on optical quality of eye cornea after laser correction of vision

The optical quality of the eye cornea surface after performing the laser vision correction essentially depends on the characteristic roughness scale (CRS) of the ablated surface, which is mainly determined by the absorption coefficient of the cornea at the laser wavelength. Thus, in the case of using an excimer ArF laser (λ = 193 nm) the absorption coefficient is equal to 39000 cm{sup -1}, the darkening by the dissociation products takes place, and the depth of the roughness relief can be as large as 0.23 mm. Under irradiation with the Er : YAG laser (λ = 2940 nm) the clearing is observed due to the rupture of hydrogen bonds in water, and the relief depth exceeds 1 μm. It is shown that the process of reepithelization that occurs after performing the laser vision correction leads to the improvement of the optical quality of the cornea surface. (interaction of laser radiation with matter)

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2015

Data Fusion for a Vision-Radiological System: a Statistical Calibration Algorithm

Presented here is a fusion system based on simple, low-cost computer vision and radiological sensors for tracking of multiple objects and identifying potential radiological materials being transported or shipped. The main focus of this work is the development of calibration algorithms for characterizing the fused sensor system as a single entity. There is an apparent need for correcting for a scene deviation from the basic inverse distance-squared law governing the detection rates even when evaluating system calibration algorithms. In particular, the computer vision system enables a map of distance-dependence of the sources being tracked, to which the time-dependent radiological data can be incorporated by means of data fusion of the two sensors' output data. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Vision–based Robust $\mathcal{H}$ ∞ Gain Scheduling Longitudinal and Lateral Following Controller for Autonomous Vehicles on Urban Curved Roads

Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2012

Grid Modeling for the SunShot Vision Study

This document describes the use of production cost modeling in the SunShot Vision study, including methods used to create the SunShot Vision scenarios, their implementation in the Gridview model, and assumptions regarding transmission system and operation of each generator type. It also describes challenges and limitations of modeling solar generation technologies in production cost models, andsuggests methods for improving their representation in current models.

14 SOLAR ENERGY↗
DOE OSTI2015

Wind Vision. A New Era for Wind Power in the United States (Executive Summary, Full Report, and Appendices); U.S. Department of Energy (DOE), NREL (National Renewable Energy Laboratory)

The Wind Vision analysis demonstrates the economic value that wind power can bring to the nation, a value exceeding the costs of deployment. Wind’s environmental benefits can address key societal challenges such as climate change, air quality and public health, and water scarcity. Wind deployment can provide U.S. jobs, U.S. manufacturing, and lease and tax revenues in local communities to strengthen and support a transition of the nation’s electricity sector towards a low-carbon U.S. economy. The path needed to achieve 10% wind by 2020, 20% by 2030, and 35% by 2050 requires new tools, priorities, and emphases beyond those forged by the wind industry in growing to 4.5% of current U.S. electricity demand. Consideration of new strategies and updated priorities as identified in the Wind Vision could provide substantial positive outcomes for future generations.

17 WIND ENERGY↗
DOE OSTI2015

Wind Vision: A New Era for Wind Power in the United States

With more than 4.5% of the nation's electricity supplied by wind energy today, the Department of Energy has collaborated with industry, environmental organizations, academic institutions, and national laboratories to develop a renewed Wind Vision, documenting the contributions of wind to date and envisioning a future where wind continues to provide key contributions to the nation’s energy portfolio. Building on and updating the 2008 20% Wind Energy by 2030 report, the new Wind Vision Report quantifies the economic, environmental, and social benefits of a robust wind energy future and the actions that wind stakeholders can take to make it a reality.

17 WIND ENERGY↗
DOE OSTI2015

INL Vision and Strategy 2015

This Laboratory vision and strategy presents INL’s vision and strategy for the Laboratory and is our introduction to a special place dedicated to improving our nation’s energy security future.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Automated Progress Monitoring in Modular Construction Factories Using Computer Vision and Building Information Modeling

Modular construction methods have recently gained interest due to the advantages offered in terms of safety, quality, and productivity for projects. In this method, a significant portion of the construction is performed off-site in factories where modular components are built in different workstations, assembled on the production line, and shipped to the site for installation. Due to the labor-intensive nature of tasks, cycle times in modular construction factories are highly variable, which commonly leads to major bottlenecks and delays in construction projects. To remedy this effect, recent methods rely on sensors such as RFID to monitor the production process, which is reportedly expensive, and intrusive to the work process. Recently, computer vision-based methods have been proposed to track the production process in modular construction factories. However, these methods overlook monitoring the assembly process on the production line. Therefore, this paper presents a method to monitor the assembly process by integrating computer vision-based methods with Building Information Modeling (BIM). The proposed method detects the modular units using object segmentation; superimposes the installation area with the corresponding 2D region using BIM, and identifies the installation of the components using image processing techniques. The proposed method has been validated using surveillance videos captured from a modular construction factory in the US. Successful implementation of the proposed method can lead to timely identification of delays during the assembly process and reduce delays in modular integrated construction projects.

building information modeling↗
DOE OSTI2023

Bottleneck Detection in Modular Construction Factories Using Computer Vision

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2015

VISIONS: Remote Observations of a Spatially-Structured Filamentary Source of Energetic Neutral Atoms near the Polar Cap Boundary During an Auroral Substorm

We report initial results from the VISualizing Ion Outflow via Neutral atom imaging during a Substorm (VISIONS) rocket that flew through and near several regions of enhanced auroral activity and also sensed regions of ion outflow both remotely and directly. The observed neutral atom fluxes were largest at the lower energies and generally higher in the auroral zone than in the polar cap. In this paper, we focus on data from the latter half of the VISIONS trajectory when the rocket traversed the polar cap region. During this period, many of the energetic neutral atom spectra show a peak at 100 electronvolts. Spectra with peaks around 100 electronvolts are also observed in the Electrostatic Ion Analyzer (EIA) data consistent with these ions comprising the source population for the energetic neutral atoms. The EIA observations of this low energy population extend only over a few tens of kilometers. Furthermore, the directionality of the arriving energetic neutral atoms is consistent with either this spatially localized source of energetic ions extending from as low as about 300 kilometers up to above 600 kilometers or a larger source of energetic ions to the southwest.

Polar Cap↗
NASA NTRS2017

Technology for NASA's Planetary Science Vision 2050.

NASAs Planetary Science Division (PSD) initiated and sponsored a very successful community Workshop held from Feb. 27 to Mar. 1, 2017 at NASA Headquarters. The purpose of the Workshop was to develop a vision of planetary science research and exploration for the next three decades until 2050. This abstract summarizes some of the salient technology needs discussed during the three-day workshop and at a technology panel on the final day. It is not meant to be a final report on technology to achieve the science vision for 2050.

Vision↗
DOE OSTI2014

Wind Vision: Updating the DOE 20% Wind Energy by 2030 Report (Poster)

The 20% Wind Energy by 2030 report was developed as part of the Advanced Energy Initiative. Published in 2008, the report was largely based on information collected and analyzed in 2006. Much has changed since then, including shifts in technology, markets, and policy. The industry needs a new, clear, vision for wind power that is shared among stakeholders from the U.S. government, industry, academia, and NGO communities. At WINDPOWER 2013, the U.S. Department of Energy, in partnership with the American Wind Energy Association and the Wind Energy Foundation, launched a project to update the 20% report with new objectives. This conference poster outlines the elements of the new Wind Vision.

17 WIND ENERGY↗