Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Plant Health”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27

5G integrated edge computing platform for efficient component monitoring in coal-fired power plants

This project developed a cutting-edge 5G-integrated edge computing framework to enhance operational efficiency and reliability in coal-fired power plants through real-time component monitoring and anomaly detection. The initiative focused on leveraging distributed machine learning, federated learning, and 5G-based dynamic network slicing to support scalable, fault-tolerant monitoring environments to meet the operational requirements in industrial control systems. With a Distributed Edge Computing Service (DECS) orchestration, this project enabled federated learning at edge for condition monitoring and introduced adaptive client selection strategies to minimize communication overhead. Scalable distributed training was achieved using the Horovod framework, thus enhancing performance across edge nodes. In the realm of 5G networking, the project designed and deployed reconfigurable, QoS-aware network slicing tailored for operational technology (OT) environments, integrating software-defined networks to bolster cyber-resilience and enabling dynamic slicing for federated learning workloads. A significant milestone was the development of a virtualized ICS environment with 5G core integration—which allowed elastic and fault tolerant distributed training on real-world datasets such as NASA Bearings, Hydraulic Systems, and TEP. To broaden the impact of the project, a TRL-3 virtualized ICS testbed for research and education was designed. This project engaged several graduate and undergraduate students to conduct research on the cutting-edge technology, and it resulted in one PhD dissertation, one MS thesis, and over 14 peer-reviewed publications. With the support of this project students also participated in national cybersecurity competitions to improve their professional development skills.

20 FOSSIL-FUELED POWER PLANTS↗

Microradiometers Reveal Ocean Health, Climate Change

When NASA researcher Stanford Hooker is in the field, he pays close attention to color. For Hooker, being in the field means being at sea. On one such research trip to the frigid waters of the Arctic, with a Coast Guard icebreaker looming nearby and the snow-crusted ice shelf a few feet away, Hooker leaned over the edge of his small boat and lowered a tethered device into the bright turquoise water, a new product devised by a NASA partner and enabled by a promising technology for oceanographers and atmospheric scientists alike. Color is a function of light. Pure water is clear, but the variation in color observed during a visit to the beach or a flight along a coastline depends on the water s depth and the constituents in it, how far down the light penetrates and how it is absorbed and scattered by dissolved and suspended material. Hooker cares about ocean color because of what it can reveal about the health of the ocean, and in turn, the health of our planet. "The main thing we are interested in is the productivity of the water," Hooker says. The seawater contains phytoplankton, microscopic plants, which are the food base for the ocean s ecosystems. Changes in the water s properties, whether due to natural seasonal effects or human influence, can lead to problems for delicate ecosystems such as coral reefs. Ocean color can inform researchers about the quantities and distribution of phytoplankton and other materials, providing clues as to how the world ocean is changing. NASA s Coastal Zone Color Scanner, launched in 1978, was the first ocean color instrument flown on a spacecraft. Since then, the Agency s ocean color research capabilities have become increasingly sophisticated with the launch of the SeaWiFS instrument in 1997 and the twin MODIS instruments carried into orbit on NASA s Terra (1999) and Aqua (2002) satellites. The technology provides sweeping, global information on ocean color on a scale unattainable by any other means. One issue that arises from satellite observation, however, is that the instruments must be continuously calibrated over time to maintain the quality of the data they gather from orbit. To validate and calibrate the satellites, researchers must also gather data at sea level.

Source record↗

India Is Overtaking China as the World's Largest Emitter of Anthropogenic Sulfur Dioxide

Severe haze is a major public health concern in China and India. Both countries rely heavily on coal for energy, and sulfur dioxide (SO2) emitted from coal-fired power plants and industry is a major pollutant contributing to their air quality problems. Timely, accurate information on SO2 sources is a required input to air quality models for pollution prediction and mitigation. However, such information has been difficult to obtain for these two countries, as fast-paced changes in economy and environmental regulations have often led to unforeseen emission changes. Here we use satellite observations to show that China and India are on opposite trajectories for sulfurous pollution. Since 2007, emissions in China have declined by 75 percent while those in India have increased by 50 percent. With these changes, India is now surpassing China as the world's largest emitter of anthropogenic SO2. This finding, not predicted by emission scenarios, suggests effective SO2 control in China and lack thereof in India. Despite this, haze remains severe in China, indicating the importance of reducing emissions of other pollutants. In India, approximately 33 million people now live in areas with substantial SO2 pollution. Continued growth in emissions will adversely affect more people and further exacerbate morbidity and mortality.

air pollution↗

VEG-05 Tomato Crop Testing on the International Space Station

Production of fresh, nutritious, and tasty produce for astronauts during spaceflight may provide health-promoting, bioavailable nutrients, enhance the dietary experience, and reduce menu fatigue as we move into longer-duration missions. Growing and caring for plants may also reduce the psychological stresses associated with spaceflight and enhance connection to Earth. A diversity of crops will be required to provide nutrition, variety, and resiliency, however requirements for consistent growth under spaceflight environmental conditions remain unclear. VEG-05 is part of a series of experiments with pick-and-eat salad crops to better define best practices for crop production in space. VEG-05 and predecessor experiments VEG-04A and VEG-04B, grew salad crops in the Veggie vegetable production facilities on the International Space Station using different lighting treatments. In VEG-05 we grew ‘Red Robin’ dwarf cherry tomatoes under two different red: blue lighting spectra. Light can impact the growth habit, yield, nutritional composition, microbial levels, and even flavor attributes within crops, and our goal was to assess these characteristics in ‘Red Robin’ tomatoes during VEG-05. Considerable pre-flight verification testing was performed prior to launch in Nov. 2022. Prior to the flight and ground experiments, lighting in both Veggie units on ISS was measured and lights were standardized between flight and ground hardware. VEG-05 flight operations ran between December 2022 and March 2023, with a ground control initially 48 hours delayed. Due to environmental challenges of very low humidity during the first week of the experiment, seed germination was low for both flight and ground plants. The flight experiment ultimately had 4 - 5 plants per treatment out of the planned 6 plants, but the initial ground control had only three plants in one treatment and none in the other, so this ground control was restarted at the beginning of Feb. 2023 and ran through May, with successful growth of all 12 plants. Both flight and ground control ran 100 days, with harvests of fruit at day 83, day 90, and day 100. Flight plants had uneven growth, and following the early drying events, excess water was frequently observed, which led to a variety of plant stress responses including uneven plant growth, excess adventitious root formation, flower and fruit abortion, and visible microbial growth. In total, from the five surviving red-rich lighted plants, only 5 ripe fruit were produced, and from the four surviving blue-rich lighted plants, 10 fruit were produced with only 6 of these ripe by day 100. Because of the small fruit number and the unsatisfactory growth, crew members were not allowed to consume the tomatoes, and all fruit, as well as large branches with leaves, samples of the adventitious roots, two plant rooting pillows from each treatment, microbial sampling swabs, and some water samples were returned for analysis. Because of the small sample sizes and factors affecting growth on the ISS, objectives of assessing light quality effect (red: blue light treatments) will not be achieved. Revised objectives of this study include to compare stressed flight plants with normal ground plants to determine the impact of plant overwatering stress in space on food safety and the plant microbial community, to determine nutrient content changes in fruit and leaves from stressed plants, and to evaluate stress metabolism changes in returned tissue by transcriptomic analysis. Postflight analysis is underway with the following analyses being conducted: A. culturable microbiology and food safety as well as molecular microbial community analysis of 1. ripe fruit, 2. leaves, stems, and adventitious roots, 3. pillow components (roots, wicks, and substrates), 4. swabs, and 5. water samples from root mats before and after growth. B. transcriptomics of leaf tissue and adventitious roots, and C. elemental analysis of leaf tissue. If sufficient tissue remains elemental analyses will also be conducted on fruit. While not generating the desired information on spaceflight growth responses of healthy crops, our team is hopeful that these analyses will shed light on tomato responses to stress in this environment as plant overwatering stress is a mission-relevant condition that could occur in future space crop growth systems. This research was co-funded by the Human Research Program and Space Biology (MTL#1075) in the ILSRA 2015 NRA call.

Gioia D. Massa↗

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗

Environmental evaluation of radioactivity levels and associated radiation hazards in groundwater around the WIPP site

Groundwater may contain radioactive substances which can be dangerous to human health. Concentrations of natural radionuclides polonium (Po), thorium (Th), uranium (U), and radium (Ra) isotopes were measured in groundwater samples collected from different locations in the vicinity of the Waste Isolation Pilot Plant (WIPP) site in Carlsbad, New Mexico. The average values of gross activity concentrations of 210 Po, 228 Th, 238 U, 234 U, 226 Ra and 228 Ra isotopes were determined to be 1.62 Bq L -1 in shallow groundwater and 5.88 Bq L -1 in deep groundwater, respectively. The total radioactivity in deep groundwater was higher than that in shallow groundwater, and most of the radioactivity in the water is from 226 Ra. Furthermore, the effective doses for ingestion of natural radionuclides were about 0.333 mSv y–1 for shallow groundwater and about 1.338 mSv y –1 for deep groundwater samples, which are higher than the World Health Organization (WHO, 2017) guideline level (0.1 mSv y –1 ) for drinking water. Ra dominated the total ingestion dose, contributing 93.06 % and 75.40 % of the total effective doses to the deep and shallow groundwater, respectively. The ingrowth and decay of natural radionuclides suggested that 228 Ra/ 226 Ra ratio can be a useful indicator of the source of radioactive contamination. The radioactivity data obtained from the investigated groundwater samples can be used to establish a baseline for radioactivity levels in groundwater around the WIPP site.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

Comparative assessment of orbital and terrestrial central power plants

Recent studies of the space power system (SPS) are integrated into a total social cost framework developed for terrestrial central electric power systems. Total social costs include the projection of commercial economics to the time frame of interest as well as the federal research, development and demonstration (RD&D) costs, the health impacts, the resources required, the environmental impacts and other social costs. The SPS system is limited to transporting all materials from the earth's surface to geosynchronous orbit. Only silicon photovoltaic is considered as the SPS energy conversion technique. Costs and impacts of the LWR are considered as a reference for nuclear systems, and the low BTU coal gasification with combined cycle gas and steam turbines is considered as a reference for a fossil central electric plant. The ground solar systems considered are solar thermal using the central receiver approach with thermal storage, and solar photovoltaic using the silicon cell with battery storage.

Caputo, R.↗

Continued Environmental Microbiology Monitoring of The International Space Station (ISS) Veggie Unit Used for In-Flight, Crop-Based Food Systems

The International Space Station is a closed environment where rotating sets of Crewmembers live and work. This environment is monitored to ensure occupants’ health and safety during their spaceflight residency by routine Environmental Health System (EHS) collection of microbial samples including air, surface, and water. The microbial samples are collected, enumerated, and analyzed quarterly to monitor on-board system contamination and potential risks to crew health. Quarterly monitoring of the microorganisms in the ISS environment supports crew safety and contributes to a large set of microbial concentration and diversity data. The current in-inflight microbial requirements were developed using this historical data collected by the routine environmental monitoring. These in-flight microbial requirements have been established to maintain the health and safety of the spacecraft environment. This study leverages quarterly operational EHS sampling by collecting additional microbial samples from the surface of the Veggie plant production system on ISS. These samples will yield microbial concentration and diversity that can be compared and analyzed with nominal surface samples from the vehicle. The data collected in this study will aid in the development of requirements for spaceflight-based food production systems. Continued surface sampling of the internal and external surfaces of the Veggie system, along with collaboration from both Johnson Space Center (JSC) & Kennedy Space Center (KSC) scientists studying the microbiome of the veggie-crop systems, will be implemented as part of the future development of crop-based food system requirements for the ISS and beyond.

Christian Mena↗

Spaceflight Biospecimen and Data Sharing in Support of Science Discovery and Exploration

For decades, NASA and international partners have conducted biological experiments in space to understand effects of spaceflight and address potential hazards. To enable spaceflight back to the Moon, and then to Mars and beyond, it is imperative to further understand basic science and health risks associated with spaceflight, along with developing countermeasures. The sending of experiments and organisms into space is a costly endeavor. To maximize scientific return, sharing with the scientific community both space-flown biospecimens and data from completed experiments is essential. New fundamental, applied, and bioinformatic science insights can be gained from specimen and data sharing efforts. Data reuse enables spaceflight health risk modeling, analyzing adverse outcomes across spaceflight hazards, and deep space autonomous support for the flight medical officer. Space-flown biospecimens not required by mission Principal Investigators are regularly archived and made available for scientific request. The largest biorepository of these samples are found within NASA’s Institutional Scientific Collection at Ames Research Center (ISC-ARC), which stores over 32,000 specimens mostly from Shuttle and International Space Station (ISS) missions, but also some ground-based analog samples. The Ames Life Sciences Data Archive manages the ISC-ARC. Tissues are predominantly from mice and rats, though samples are also available from bacteria and quail. Only a handful of other similar collections exist worldwide. Rodent biospecimens exposed to simulated space radiation at Brookhaven National Laboratory are archived under the purview of NASA HRP Space Radiation Element. Microbial collection and analyses from 20 years of routine environmental monitoring of air, surfaces, and water systems of the ISS were performed to ensure a safe environment for astronauts. Samples from the ISC-ARC, space radiation and microbial collections are searchable and requestable through the NASA Life Sciences Data Archive (LSDA). Decades of planetary protection microbial isolates derived from spacecraft bioburden are archived in JPL’s microbial collection. Rodent biospecimens from spaceflight investigations conducted by the Japan Aerospace Exploration Agency (JAXA) are archived and available at the JAXA Biorepository at Tsukuba Space Center. The Russian Institute of Biomedical Problems also has a collection of animal, microbial, cellular, and fungi available for research from ground analog experiments. Several data repositories exist for scientists to utilize. The LSDA is the primary NASA source of life sciences research data and information. It contains decades of spaceflight and ground-analog research involving human, microbial, cellular, plant, and animal subjects. Data is collected from NASA-funded investigations through the Human Research Program and the Space Biology Program. The NASA Lifetime Surveillance of Astronaut Health collects and grants access to clinical and occupational health monitoring data from astronauts, with a list and description of data collected available for request through the LSDA. NASA GeneLab at ARC collects genomic, transcriptomic, proteomic, and metabolomic data from any species. It is a repository and platform for collaborative open-science bioinformatic approaches. JAXA is establishing an ‘omics-based repository in collaboration with the Tohoku Medical Megabank (ToMMo), called the JAXA-ToMMo Integrated Biobank for Space Life Science. Overall, the sharing of these biospecimen and data resources can assist researchers worldwide in understanding spaceflight effects on biology, along with enabling next generation data science applications for space exploration platforms. Websites: https://lsda.jsc.nasa.gov/ ; https://www.nasa.gov/ames/research/space-biosciences/isc-bsp ; https://www.nasa.gov/ames/research/space-biosciences/alsda

Ryan T. Scott↗

Spatial patterns of historical crop yields reveal soil health attributes in US Midwest fields

Abstract Attaining high crop yields and increasing carbon storage in agricultural soils, while avoiding negative environmental impacts on water quality, soil erosion, and biodiversity, requires accurate and precise management of crop inputs and management practices. The long-term analysis of spatial and temporal patterns of crop yields provides insights on how yields vary in a field, with parts of field constantly producing either high yields or low yields and other parts that fluctuate from one year to the next. The concept of yield stability has shown to be informative on how plants translate the effects of environmental conditions (e.g., soil, climate, topography) across the field and over the years in the final yield, and as a valuable layer in developing prescription maps of variable fertilizer rate inputs. Using known relationships between soil health and crop yields, we hypothesize that areas with measured constantly low yield will return low carbon to the soil affecting its heath. On this premises, yield stability zones (YSZ) provide an effective and practical integrative measure of the small-scale variability of soil health on a field relative basis. We tested this hypothesis by measuring various metrics of soil health from commercial farmers’ fields in the north central Midwest of the USA in samples replicated across YSZ, using a soil test suite commonly used by producers and stakeholders active in agricultural carbon credits markets. We found that the use of YSZ allowed us to successfully partition field-relative soil organic carbon (SOC) and soil health metrics into statistically distinct regions. Low and stable (LS) yield zones were statistically lower in normalized SOC when compared to high and stable (HS) and unstable (US) yield zones. The drivers of the yield differences within a field are a series of factors ranging from climate, topography and soil. LS zones occur in areas of compacted soil layers or shallow soils (edge of the field) on steeper slopes. The US zones occurring with high water flow accumulation, were more dependent on topography and rainfall. The differences in the components of the overall soil health score (SHS) between these YSZ increased with sample depth suggesting a deeper topsoil in the US and HS zones, driven by the accumulation of water, nutrients, and carbon downslope. Comparison of the field management provided initial evidence that zero tillage reduces the magnitude of the variance in SOC and soil health metrics between the YSZ.

Fowler, Ames↗

Annual Site Environmental Report: Pantex Plant

The 2022 Annual Site Environmental Report (ASER) summarizes Pantex’s status, data, and efforts for the environmental compliance, protection, and restoration programs. It has been prepared in accordance with DOE O 231.1B, Environment, Safety and Health Reporting, and DOE O 458.1, Radiation Protection of the Public and the Environment. These orders outline the requirements for environmental protection programs at DOE facilities to ensure that programs fully comply with applicable federal, state, and local environmental laws and regulations, executive orders, and DOE policies.

54 ENVIRONMENTAL SCIENCES↗

Modulation of lignin and anthocyanin homeostasis by GTP cyclohydrolase1 in maize

Summary Maize is a key biomass resource with wide agricultural applications. Anthocyanins, potent antioxidants, offer health benefits like reducing oxidative stress. The biosynthesis of anthocyanins competes with that of lignin for shared metabolic precursors, which can lead to trade‐offs in plant growth and feed quality. Higher lignin content can decrease silage digestibility, posing challenges for livestock feed. The maizebrown midrib 6(bm6) mutant, known for reduced lignin, has an unclear genetic basis. Here, we identifyZmGCH1as the candidate gene forbm6through fine mapping. Mutations inZmGCH1shift precursors from lignin to anthocyanin biosynthesis. Furthermore, we show that ZmGCH1 interacts with ZmPEBP15 to modulate chalcone synthase activity, thereby stabilizing the allocation of precursors between lignin and anthocyanin pathways. To evaluate the practical implications of our findings, we introduced thebm6mutation into Zhengdan958 and Xianyu335. In vitro rumen digestion assays confirmed that the introduction of thebm6mutation significantly improved silage digestibility. This discovery not only holds great potential for enhancing silage digestibility but also provides a broader strategy for optimizing maize production to better meet the increasing demands of both the food and livestock feed.

Biotechnology & Applied Microbiology↗

Opportunities for research on Space Station Freedom

NASA has allocated research accommodations on Freedom (equipment, utilities, etc.) to the program offices that sponsor space-based research and development as follows: Space Science and Applications (OSSA)--52 percent, Commercial Programs (OCP)--28 percent, Aeronautics and Space Technology (OAST)--12 percent, and Space Flight (OSF)--8 percent. Most of OSSA's allocation will be used for microgravity and life science experiments; although OSSA's space physics, astrophysics, earth science and applications, and solar system exploration divisions also will use some of this allocation. Other Federal agencies have expressed an interest in using Space Station Freedom. They include the National Institutes of Health (NIH), U.S. Geological Survey, National Science Foundation, National Oceanic and Atmospheric Administration, and U.S. Departments of Agriculture and Energy. Payload interfaces with space station lab support equipment must be simple, and experiment packages must be highly contained. Freedom's research facilities will feature International Standard Payload Racks (ISPR's), experiment racks that are about twice the size of a Spacelab rack. ESA's Columbus lab will feature 20 racks, the U.S. lab will have 12 racks, and the Japanese lab will have 10. Thus, Freedom will have a total of 42 racks versus 8 for Space lab. NASA is considering outfitting some rack space to accommodate small, self-contained payloads similar to the Get-Away-Special canisters and middeck-locker experiment packages flown on Space Shuttle missions. Crew time allotted to experiments on Freedom at permanently occupied capability will average 25 minutes per rack per day, compared to six hours per rack per day on Spacelab missions. Hence, telescience--the remote operation of space-based experiments by researchers on the ground--will play a very important role in space station research. Plans for supporting life sciences research on Freedom focus on the two basic goals of NASA 's space life sciences program: to ensure the health, safety, and productivity of humans in space and to acquire fundamental knowledge of biological processes. Space-based research has already shown that people and plants respond the same way to the microgravity environment: they lose structure. However, the mechanisms by which they respond are different, and researchers do not yet know much about these mechanisms. Life science research accommodations on Freedom will include facilities for experiments designed to address this and other questions, in fields such as gravitational biology, space physiology, and biomedical monitoring and countermeasures research.

Phillips, Robert W.↗

Computer vision-based rock bolt detection in orthomosaic imagery obtained in the Waste Isolation Pilot Plant underground facility

Assessing structural integrity of large underground tunnel facilities is often a time consuming and human-labor intensive task. Thus, research using various modes of sensing and automated detection of key structural components in mines is posed to aid in safety assessments and establishing overall structural health. We propose an approach utilizing off-the-shelf camera and lidar technology fixed to a custom sensing platform, image stitching techniques, fine-tuned object detection models, and specialized model-inference methods to automatically detect, count, and map roof bolts for assessment of structural safety in man-made underground tunnels. Results show a novel workflow for effective object counting in orthomosaic tunnel ceiling images generated from collections in GPS-denied mining environments. Additionally, we demonstrate effective fine-tuning of EfficientDet object detectors utilizing state-of-the-art image augmentation techniques known as the mosaic and mixup transformations. Our work is demonstrated on sensed data and imagery collected from the Department of Energy (DOE) Waste Isolation Pilot Plant (WIPP) where miles of tunnel ceiling must be assessed for structural integrity.

42 ENGINEERING↗

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗