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High Dose Rate Irradiations of Enduray Vision System for Nuclear Inspections
Higher radiation-hardened video cameras are needed in the operation and remote handling of equipment in nuclear reactor inspection and refueling applications. Vega Wave Systems, Inc. has developed a radiation-hardened vision system for nuclear energy applications. This vision system has been developed under several small business innovative research programs (SBIRs) from the U.S. Department of Energy, and the previous GAIN voucher program at Argonne National Laboratory (ANL) was successful in demonstrating that a prototype of the camera is radiation-hard up to at least 525 kGy (5.25 × 106 rad, Si equivalent) at dose rates of 10 kGy/hr with no measurable degradation in image quality and no measurable radiation-induced noise (RIN). Vega Wave Systems has developed a new version of the camera with more than 3X the resolution using a new design and new components, and this new design requires radiation-hardness qualification for marketplace acceptance. The program described in this report provided high radiation-hardness testing of this redesigned high-resolution vision system using the Argonne Low-Energy Accelerator Facility (LEAF). This report presents the results of irradiation tests performed on Vega Wave System’s redesigned high resolution vision system at a dose rate of 9.3 kGy/hr and up to total doses of 1823 kGy. This was an accumulation of 197 hours of irradiation. The results were excellent, providing proof of the redesigned high-resolution vision system’s immunity to high levels of radiation.
Material sorting using a vision system
A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification. The material sorting system may include an x-ray fluorescence system to perform a classification of the materials in combination with the vision system, whereby the classification efforts of the vision system and x-ray fluorescence system are combined in order to classify and sort the materials.
Photon Irradiation Effects on the Enduray Vision System for Nuclear Inspections
Video cameras with greater radiation hardening are needed for the operation and remote handling of equipment in nuclear reactor inspection and refueling applications. Vega Wave Systems has developed a radiation-hardened vision system for nuclear energy applications. The program described in this report provided radiation-hardness testing of this vision system using the Argonne Low-Energy Accelerator Facility (LEAF). This report presents the results of irradiation tests performed on several of Vega Wave System’s cameras at dose rates ranging from 1 kGy/hr up to 10 kGy/hr and up to total doses of >500 kGy. The results were excellent, providing proof of the camera system’s immunity to radiation effects at all levels tested.
Integration of computer vision system to track the alignment SRF cavities into the test cryostat for PIP-II at Fermilab
PIP-II cryomodules use a computer vision system (H-BCAMs system) to monitor the alignment of SRF cavities and focusing lenses during assembly, testing, and operation. This contribution details the integration of the H-BCAMs into the Spoke Test Cryostat (STC) at Fermilab, which is utilized for cold testing SRF cavities prior to their integration into the string assembly. Thermal and structural finite element analyses were employed to estimate the cavities’ deformations, to be validated during cold testing in the STC using H-BCAMs. Notably, this marks the first instance of H-BCAMs integration into a cryostat and operation within a cryogenic environment.
In Search of Optimum Fresh-Cut Raw Material: Using Computer Vision Systems as a Sensory Screening Tool for Browning-Resistant Romaine Lettuce Accessions
The popularity of ready-to-eat (RTE) salads has prompted novel technology to prolong the shelf life of their ingredients. Fresh-cut romaine lettuce is widely used in RTE salads; however, its tendency to quickly discolor continues to be a challenge for the industry. Selecting the ideal lettuce accessions for use in RTE salads is essential to ensure maximum shelf life, and it is critical to have a practical way to assess and compare the quality of multiple lettuce accessions that are being considered for use in fresh-cut applications. Thus, in this work we aimed to determine whether a computer vision system (CVS) composed of image acquisition, processing, and analysis could be effective to detect visual quality differences among 16 accessions of fresh-cut romaine lettuce during postharvest storage. The CVS involved a post-capturing color correction, effective image segmentation, and calculation of a browning index, which was tested as a predictor of quality and shelf life of fresh-cut romaine lettuce. The results demonstrated that machine vision software can be implemented to replace or supplement the scoring of a trained panel and instrumental quality measurements. Overall visual quality, a key sensory parameter that determines food preferences and consumer behavior, was highly correlated with the browning index, with a Pearson correlation coefficient of −0.85. Other important sensory decision parameters were also strongly or moderately correlated with the browning index, with Pearson correlation coefficients of −0.84 for freshness, 0.79 for off odor, and 0.57 for browning. The ranking of the accessions according to quality acceptability from the sensory evaluation produced a similar pattern to those obtained with the CVS. This study revealed that multiple lettuce accessions can be effectively benchmarked for their performance as fresh-cut sources via a CVS-based method. Future opportunities and challenges in using machine vision image processing to predict consumer preferences for RTE salad greens is also discussed.
Increasing Efficiency of Underground Geologic Mapping by Integrating Mine Vision System FaceCapture™ Data Collection with Datamine 3D Modeling Software
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Radiation Testing for High-Resolution Radiation-Hardened Camera System (Final CRADA Report)
The nuclear energy industry needs higher-image quality and higher rad-hard vision systems for refueling and inspection operations that are required every 12-18 months for all operating commercial reactors. During refueling operations, the serial numbers of fuel assemblies need to be visually verified in a challenging high radiation environment with dose rates on the order of 1kGy/hr. at the top of the core (approximately 10 days after shutdown). Vega Wave Systems has developed and built a radiation hard vision system that: 1. has high radiation hardness, 2. demonstrated best in the industry image quality, 3. has small size and weight, and 4. does not suffer from radiation-induced noise. ANL-1060 (02/13/2023) The proprietary Enduray vision system from Vega Wave Systems, Inc. is designed to withstand more than 400x the radiation level of the Vidicon-based systems, currently the highest radiation tolerant system in use today. The Van De Graaff (VDG) electron accelerator at Argonne National Laboratory is ideally suited for testing of Enduray vision system. VDG provides a broad band (up to 3 MeV) x-ray source that reasonably approximates the energy and spectrum of the gamma radiation expected at the top of a nuclear core 10 days after shutdown (~1kGy/hr.), is easily controlled for variable tests, and will not activate the parts under radiation for ease of timely and close inspection and characterization of the system in case of operational issues.
Radiation Testing for Nuclear Inspection Systems (Final CRADA Report)
Higher-radiation hardened video cameras are needed in the operation and remote handling of equipment in nuclear reactor inspection and refueling applications. Vega Wave Systems has developed a radiation-hardened vision system for Nuclear Energy applications. The high-radiation hardness testing of this vision system was conducted using the Argonne Low-Energy Accelerator Facility (LEAF).
Renewable ammonia for sustainable energy and agriculture: vision and systems engineering opportunities
Synthetic ammonia is essential for agriculture, but its production at present is unsustainable. Ammonia synthesized with hydrogen from renewable-powered electrolysis and nitrogen separated from air has the potential to alleviate these sustainability concerns while also having promise as a low-cost storage medium for intermittent renewable energy. This paper reviews recent research and development on the topic of renewable ammonia production and utilization as fertilizer and as energy storage. We describe our vision for synergistically combining these renewable ammonia applications to improve sustainability. Furthermore, we outline opportunities for systems engineering to play a crucial role in advancing the adoption of renewable ammonia in a manner which is sustainable, economically competitive, and reliable.
A Heterogeneous System for Eagle Detection, Deterrent, and Wildlife Collision Detection for Wind Turbines (Final Technical Report)
This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.
A systems engineering vision for floating offshore wind cost optimization
The U.S. offshore wind resource potential is immense, and often in close proximity to densely-populated coastal load centers. In many U.S. coastal areas, water depths favor the deployment of floating over fixed-bottom offshore wind technology. Floating offshore wind plants have the potential to be cost-competitive with fixed-bottom installations, but because the technology has not yet been deployed at commercial scale, it is not clear when and with what configurations this potential cost parity can be achieved. This article first reviews the state of floating offshore wind technology and deployments to identify key gaps that must be addressed to bring down the overall cost of energy produced. The article then puts forth a long-term vision for a research program and design methodology that may be able to push floating wind plants toward a lower levelized cost of energy than fixed-bottom offshore wind. The method involves a fully integrated systems-engineering and techno-economic design approach to capture the complex interactions between the physics, manufacturing, installation, and operation of floating wind turbines to achieve transformational cost reductions. The approach also envisions multifidelity and uncertainty management strategies to examine the most robust and viable concepts in the design trade-space. To better focus the computational resources, engineering lessons learned from existing offshore wind systems and concept studies are used to develop a set of criteria that can be applied to prefilter candidate technology building blocks that have the greatest cost reduction potential.
AI-Driven Breakthroughs in Energy Systems from Vision to Design
Presentation conducted at the DOE booth at the AI Expo in Washington, DC, May 2024.
Counter and Unmanned Aerial Systems (cUAS/UAS) Vision For The Future
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Counter and Unmanned Aerial Systems (cUAS/UAS) Vision For The Future
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Recycling coins from scrap
A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification determining that the materials have a specified geometric shape. Such a system can sort monetary coins or other valuable metals from other forms of scrap.
Sorting cast and wrought aluminum
A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification determining that the materials are composed of either wrought aluminum or cast aluminum.
Identifying coins from scrap
A system classifies materials utilizing a vision system that implements a machine learning system, such as a neural network, in order to identify or classify each of the materials as either a monetary coin or not a monetary coin, which may then be sorted into separate groups based on such an identification or classification. Such a system can sort monetary coins from other forms of scrap, which may have been produced from a shredding of end of life vehicles.