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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 235 records · Page 13

All sources NASA NTRS DOE OSTI

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 OSTI2025

Modeling Small Modular Reactors in GCAM

This study uses the Global Change Analysis Model (GCAM) to examine the critical role and future of nuclear energy in the global energy system, with a focus on the United States. We also analyzed the U.S. government’s new strategic vision for the nuclear energy sector.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Robotic Systems integration for cleanroom SRF assembly at Fermilab

Superconducting Radio Frequency (SRF) cryomodule assembly is a critical process that demands the highest levels of contamination control in a cleanroom to ensure product quality and reliability. Within the cleanroom environment, human-driven activities remain the major source of uncontrolled particulate. Moreover, the precise manual alignment of components often exceeds expected timeframes, increasing the risk of particulate migration into the critical inner space of the SRF resonant cavity, and causing extended discomfort for workers. Automation comes as a natural solution to address these challenges. The goal of this activity is to set up and integrate a robotic system to support cavity coupler installation in the cleanroom environment. Initially, remote control of a 6-axis collaborative robot arm will be implemented as an introductory step towards automation in the cleanroom. Subsequently, the system will be further enhanced by integrating manipulation capabilities through an appropriate end-effector, and computer vision technology to enable accurate pose estimation. This comprehensive approach aims to achieve a fully automated and adaptable assembly process in the partially structured cleanroom environment, thereby enhancing efficiency and mitigating existing challenges for better product quality and workers safety.

42 ENGINEERING↗

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 OSTI2022

Driver Distraction Behavior Detection using a Vision Transformer Model based on Transfer Learning Strategy

Driver distraction behavior is one of the critical factors in traffic accidents. Thus, advanced driver state detection system has become the focus in the field of intelligent vehicle. However, in practical applications, insufficient samples of driving distraction behaviors bring great challenges to training a personalized behavior distraction detection model for a specific driver. To this end, a novel transformer model based on a transfer learning strategy is proposed in this paper to accurately recognize driver distraction behavior. Inspired by the effect of the transformer network in visual recognition, we firstly present a transformer behavior distraction detection system to identify the behavior categories that cause driver distraction. Then, for the specific driving dataset in practical application scenarios, the transfer learning strategy is introduced into the driver distraction detection model to further train the general transformer network. The effectiveness of the transformer based on the transfer learning strategy is validated compared with other traditional deep learning methods. The results show that the proposed detection method has better generalization ability and higher accuracy.

Fang, Zhenwu↗

DOE OSTI2023

Ultra: Underwater Laser Telecommunications & Remote Access (Final Report)

High speed wireless communication has proven elusive in subsea environments due to the inherent bandwidth limitations of acoustics and range limitations of other transmission modalities. A truly connected subsea system necessitates a high-speed, resilient architecture that can enable the integration of new sensor technologies and edge analytics and allow closed-loop monitoring and control of subsea operations for integrity monitoring and optimization. Like terrestrial Internet of Things applications, the realization of this “digital subsea” vision requires the application of high speed, point-to-point wireless technologies to complement rather than replace “hard-wired” communications such as optical fiber or acoustic systems. This work addresses the development of ULTRA (Underwater LASER Telemetry and Remote Access), an ultra-long range underwater laser communications system for use in critical points of the subsea communications architecture to increase reliability, operational flexibility, and reduce communication system maintenance associated with physical subsea connections.

02 PETROLEUM↗

DOE OSTI2023

Model metamers reveal divergent invariances between biological and artificial neural networks

Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated ‘model metamers’, stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from late model stages, suggesting differences between model and human invariances. Targeted model changes improved human recognizability of model metamers but did not eliminate the overall human–model discrepancy. The human recognizability of a model’s metamers was well predicted by their recognizability by other models, suggesting that models contain idiosyncratic invariances in addition to those required by the task. Metamer recognizability dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Advanced Collision Detection and Site Monitoring for Avian and Bat Species for Offshore Wind Energy (Final Technical Report)

This final technical report summarizes the outcomes from a project that aimed to design, build, and test a persistent and autonomous monitoring system for avian and bat collisions with offshore wind turbines blades and structures. The system comprises four primary sensor modules: 1) on-blade sensor modules for collision detection and dual-vision image capture on each blade with both visible light and near-infrared imagers; 2) additional on-blade collision sensors mounted further from the root; 3) a nacelle-mounted unit including a 360º camera and ultrasonic microphone array; and, 4) an on-blade, high-performance infrared camera module. Primary targeted outcomes were high sensitivity for the detection of blade strikes from bats and small birds, and automatically captured visual confirmation of the striking object; these features are critical for monitoring offshore wind turbine installations, where ground-based methods are not viable. In addition, local recording will provide a long-term sensor recording database. Following laboratory validation, field testing was conducted on an operational wind turbine in collaboration with the National Wind Technology Center at the NREL Flatirons campus over two planned field tests.

17 WIND ENERGY↗

DOE OSTI2024

Light: An Extrinsic Factor Influencing Animal-based Research

Abstract Light is an environmental factor that is extrinsic to animals themselves and that exerts a profound influence on the regulation of circadian, neurohormonal, metabolic, and neurobehavioral systems of all animals, including research animals. These widespread biologic effects of light are mediated by distinct photoreceptors—rods and cones that comprise the conventional visual system and melanopsin-containing intrinsically photosensitive retinal ganglion cells (ipRGCs) of the nonvisual system that interact with the rods and cones. The rods and cones of the visual system, along with the ipRGCs of the nonvisual system, are species distinct in terms of opsins and opsin concentrations and interact with one another to provide vision and regulate circadian rhythms of neurohormonal and neurobehavioral responses to light. Here, we review a brief history of lighting technologies, the nature of light and circadian rhythms, our present understanding of mammalian photoreception, and current industry practices and standards. We also consider the implications of light for vivarium measurement, production, and technological application and provide simple recommendations on artificial lighting for use by regulatory authorities, lighting manufacturers, designers, engineers, researchers, and research animal care staff that ensure best practices for optimizing animal health and well-being and, ultimately, improving scientific outcomes.

Veterinary Sciences↗

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

Representing the Future Role of Hydropower and Pumped Storage Hydropower (PSH) in Electricity Planning Tools

Existing tools for long-term electric sector planning struggle to represent hydropower's nuanced site-specific technical and operating characteristics, which depend on technical specifications as well as water management practices and regulations. As a result, long-term planning models and tools insufficiently characterize hydropower value and incentives, and they cannot fully represent the role hydropower can play in a future electricity system that could include a high penetration of variable wind and solar generation, battery storage, and other low-carbon technologies. This presentation demonstrates the culmination of a multi-year effort to enhance hydropower representations in electricity planning models at the National Renewable Energy Laboratory (NREL), as part of the U.S. Department of Energy (USDOE) HydroWIRES Initiative. New modeling techniques are demonstrated using the NREL Regional Energy Deployment System (ReEDS), an open-access electric sector capacity expansion model used extensively in a wide range of technology deployment and integration analysis, including the 2016 USDOE Hydropower Vision. ReEDS uses a least-cost optimization approach to understand investment and operation of electricity generation, storage, and transmission technologies under future scenarios of electricity technology innovation, demand, policy, and other sectoral drivers. ReEDS was modified to better represent value and opportunities for both pumped storage hydropower (PSH) and hydropower systems without pumping. We incorporated a new national closed-loop PSH resource and cost assessment to explore new PSH deployment opportunities and added plant-level data to better represent the existing PSH fleet. New upgrade pathways enable opportunities for enhanced hydropower flexibility by adding pumps, upgrading dispatchability, increasing capacity, or increasing energy availability. The model was also modified to better represent the value of long-duration energy storage beyond diurnal time scales, allowing both hydropower and PSH to better balance energy supply and demand variations in high-renewable systems. These new features are demonstrated under reference and high-renewable futures and a range of sensitivity scenarios to understand which hydropower and PSH deployment and upgrade opportunities are the most attractive. These scenarios indicate potential for new closed-loop PSH deployment and for hydropower flexibility improvements to have important impacts on long-term electricity system emissions and economic outcomes. Increasing flexibility of the existing hydropower fleet can reduce the need to invest in new flexible grid technologies and help achieve decarbonization goals. Systems with sufficient energy storage could also be valuable for balancing seasonal differences in renewable energy availability, particularly from solar energy. The methods developed for ReEDS and subsequent scenario results reveal important considerations for future hydropower and grid system planning, and all data and code is freely available in a public code repository for use throughout the hydropower industry.

capacity expansion↗

DOE OSTI2021

A METHODOLOGY TO SUPPORT THE DEVELOPMENT OF A NEW STATE VISION FOR THE UNITED STATES NUCLEAR INDUSTRY

A new strategy in the way in which the United States nuclear power plants (NPPs) are operated, maintained, and supported is needed. One such strategy is to transform the NPP operating model through a business-driven approach that leverages technology to enable new capabilities that improve performance and reduce cost. This paper presents a methodology for developing an achievable yet transformative new state vision that ensures continued safe and efficient operations of the United States NPP fleet. This work builds on existing guidance and leverages previous research to comprehensively address both bottom-up (i.e., utility needs) and top-down (i.e., first principles) considerations important for developing a new state vision. The proposed methodology is intended to provide industry-wide guidance for developing a new state vision that leverages both the selected vendor’s capabilities in a way that meets the utility’s modernization goals while ensuring state-of-the-art systems engineering and human factors engineering principles are applied that promote overall plant safety, performance, and efficiency.

99 GENERAL AND MISCELLANEOUS↗

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 OSTI2022

Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management

Transportation accounts for a significant portion of total global energy consumption. Excessive energy consumption usually occurs in urban traffic environments with congestion and travel delays. With the advancement of remote sensing and computer vision technologies, real-time traffic conditions can be monitored. Therefore, sustainable transportation management strategies can be developed to optimize the overall energy and environment performance and reduce congestion and emissions. This work presents a smart traffic monitoring system based on remote camera sensors. Real-time and historical traffic conditions at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) were monitored and analyzed to develop optimal transportation management strategies for sustainability. Computer vision algorithms were developed and applied to process the real-time camera data to obtain complete traffic information across the ORNL campus. Weeks of historical data were collected and processed to analyze the traffic and identify bottlenecks. The proposed traffic monitoring and management approach can be applied and extended to benefit other campuses or urban areas.

Shao, Yunli↗

DOE OSTI2020

Addressing Human and Organizational Factors in Nuclear Industry Modernization: An Operationally Focused Approach to Process and Methodology

Utility owners and operators of commercial nuclear power plants in the United States (U.S.) are and will be modernizing their nuclear power plants by performing a digital transformation involving design of an integrated set of systems that together enable a technology centric operating plant. The Plant Modernization Pathway of the U.S. Department of Energy Light Water Reactor Sustainability Program has a strategic action plan that lays the groundwork for a digital transformation of the nuclear industry. The model for this transformation is an advanced concept of operations, with an end point vision, “To achieve the maximum aggregate benefit enabled by this digital transformation.” To achieve this, the digital infrastructure for a nuclear plant must be designed as an integrated set of systems that together enable a technology centric operating model. The digital transformation process obviously needs to involve technology considerations and systems engineering, but it also needs to include human and organizational expertise. Thus, human and organizational factors, including sociotechnical systems methods and techniques (e.g., Cognitive Systems Engineering, Systems Theoretic Accident Modeling and Processes, human systems integration, and Macroergonomics) need to be considered for digital transformation projects in order to effectively integrate human and organizational expertise efforts into the new work system that results from nuclear power plant digital modernization. That is, the work system is the basic unit of sociotechnical systems analysis and contains three components: personnel, technical, and organization and management. These components should be jointly optimized with respect to the interdependence of systems performance criteria of effectiveness, efficiency and safety. Joint optimization can be achieved through the application of three human and organization functions: knowledge representation, knowledge elicitation, and cross-functional integration. This report provides a strategic framework for effective integration of human and organizational expertise within nuclear power plant digital modernization efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Foveal vision reduces neural resources in agent-based game learning

Efficient processing of information is crucial for the optimization of neural resources in both biological and artificial visual systems. In this paper, we study the efficiency that may be obtained via the use of a fovea. Using biologically-motivated agents, we study visual information processing, learning, and decision making in a controlled artificial environment, namely the Atari Pong video game. We compare the resources necessary to play Pong between agents with and without a fovea. Our study shows that a fovea can significantly reduce the neural resources, in the form of number of neurons, number of synapses, and number of computations, while at the same time maintaining performance at playing Pong. To our knowledge, this is the first study in which an agent must simultaneously optimize its visual system, along with its decision making and action generation capabilities. That is, the visual system is integral to a complete agent.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

An in-situ view cell system for investigating swelling behavior of elastomers upon high-pressure hydrogen exposure

The transition to hydrogen as a clean and efficient energy carrier is impeded by challenges in the compatibility of hydrogen with materials used within hydrogen infrastructure. Elastomers, crucial in sealing components, often exhibit premature failures in high-pressure hydrogen environments due to excessive swelling. This study employs an innovative in-situ view cell system to assess the swelling behavior of hydrogenated nitrile butadiene rubber (HNBR) under various hydrogen conditions. The system, designed to withstand pressures up to 96.5 MPa, incorporates Digital Image Correlation (DIC) for strain measurements and volume estimation. Results reveal non-linear volume increases during depressurization, challenging conventional assumptions. Furthermore, investigations into peak hydrogen pressures and pressure-holding scenarios during decompression highlight complex swelling trends. The introduction of a novel computer vision (CV) method enhances precision in volume estimation, overcoming DIC limitations. The study provides insights into mitigating elastomer swelling, crucial for developing robust materials to support future hydrogen-driven energy systems.

Elastomer↗