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DOE OSTI2024

Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors

The opioid overdose crisis is a global health challenge. Fentanyl, an exceedingly potent synthetic opioid, has emerged as a leading contributor to the surge in opioid-related overdose deaths. The surge in overdose fatalities, particularly due to illicitly manufactured fentanyl and its contamination of street drugs, emphasizes the urgency for drug-testing technologies that can quickly and accurately identify fentanyl from other drugs and quantify trace amounts of fentanyl. In this paper, gold nanoparticle (AuNP)-decorated single-walled carbon nanotube (SWCNT)-based field-effect transistors (FETs) are utilized for machine learning-assisted identification of fentanyl from codeine, hydrocodone, and morphine. The unique sensing performance of fentanyl led to use machine learning approaches for accurate identification of fentanyl. Employing linear discriminant analysis (LDA) with a leave-one-out cross-validation approach, a validation accuracy of 91.2% is achieved. Meanwhile, density functional theory (DFT) calculations reveal the factors that contributed to the enhanced sensitivity of the Au-SWCNT FET sensor toward fentanyl as well as the underlying sensing mechanism. Finally, fentanyl antibodies are introduced to the Au-SWCNT FET sensor as specific receptors, expanding the linear range of the sensor in the lower concentration range, and enabling ultrasensitive detection of fentanyl with a limit of detection at 10.8 fg mL –1 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

The impact of tritium phytoremediation on plant health as measured by fluorescence

Phytoremediation, using plants for soil, sediment, or water contaminant clean-up, is an established technology dependent on plant health. Tritium (3H), a radioactive isotope of hydrogen that is generally found in the environment as tritiated water (HTO), is a low-level beta emitter with a half-life of 12.32 years. Chlorophyll fluorescence (CF) for monitoring risk assessment of tritium to plant health was conducted at the Tritium Irrigation Facility (TIF) located on the US Department of Energy's Savannah River Site (SRS) near Aiken, SC. Two fluorometers were evaluated in conjunction with phytoremediation at the 25 –acre TIF where tritiated groundwater is being spray–irrigated on a mixed coniferous/deciduous forested watershed as a means of reducing tritium release to a nearby stream that serves as a tributary to the Savannah River. Tritium activity in irrigated water averaged 104 + 42 pCi mL -1 during the 2003 project. Fluorescence parameters measured by the two fluorometers were well correlated with each other (p < 0.0001). Tritium in water respired from oak leaves ranged up to 1845.13 pCi ml -1 and 2138.22 pCi ml -1 in pine needles. Trees in both the test and control sites were approximately 15 years old. In this report we demonstrated that fluorescence parameters provide an effective way to estimate the impact of HTO on plant health in a noninvasive, extremely rapid, and cost-effective manner. In the current study applying fluorometry, plants within the TIF phytoremediation site exposed to the site tritiated water were not significantly impacted by the tritium phytoremediation based on CF parameters as compared to the control, a nascent non-irrigated site.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2023

Investigating the Impacts of Outdoor Lighting (Final Report)

This report documents the research associated with the Depart of Energy supported project “Investigating the Health Impact of Outdoor Lighting.” This project was developed to consider how alternative solid state lighting options, particularly the spectral power distribution, effected the hormone melatonin which ultimately could lead to potential health issues. The project also provides an opportunity to gather other secondary data with respect to visual performance in a controlled environment. These tasks were developed to consider how alternative solid state lighting applications impacted performance. This report is a complete documentation of the tasks undertaken as part of this project. The report includes a literature review and consideration of the background data which are part of this field of study as well as a documentation of the methods developed as part of the project. The melatonin results, the primary dependent variable are included in the attached peer-review paper that has been recent published (Gibbons et al., 2022). Our discussion of these results are included in that publication. Other results from this project will be documented in one or more future publications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Linking Pharmacogenomic Information on Drug Safety and Efficacy with Ethnic Minority Populations

Numerous prescription drugs’ labeling contains pharmacogenomic (PGx) information to aid health providers and patients in the safe and effective use of drugs. However, clinical studies for such PGx biomarkers and related drug doses are generally not conducted in diverse ethnic populations. Thus, it is urgently important to incorporate PGx information with genetic characteristics of racial and ethnic minority populations and utilize it to promote minority health. In this project a bioinformatics approach was developed to enhance the collection of PGx information related to ethnic minorities to pave the way toward understanding the population-wide utility of PGx information. To address this challenge, we first gathered PGx information from drug labels. Second, we extracted data on the allele frequency information of genetic variants in ethnic minority groups from public resources. Then, we collected published research articles on PGx biomarkers and related drugs for reference. Finally, the data were integrated and formatted to build a new PGx database containing information on known drugs and biomarkers for ethnic minority groups. This database provides scientific information needed to evaluate available PGx information to enhance drug dose selection and drug safety for ethnic minority populations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

When to vaccinate for seasonal influenza: check the peak forecast

Background Seasonal influenza infects 5-20% of people every year in the United States, resulting in hospitalizations, deaths, and adverse economic impacts. To mitigate these impacts, influenza vaccines are developed and distributed annually; however, growing evidence suggests that vaccine effectiveness (VE) wanes over the course of a flu season. Delaying influenza vaccination for older adults has attracted attention as a potential public health strategy. However, given the uncertainties in seasonal peak, vaccine effectiveness, and waning rates, postponing vaccination could also lead to increased morbidity, motivating an evaluation of a range of potential scenarios. The aim of this study was to investigate favorable age group-specific vaccination schedules that could lead to the greatest disease burden reduction. Methods We systematically investigated a broad range of vaccination start times for five age groups under six combinations of initial effectiveness and waning rates, based on influenza cases and vaccine uptake data from 10 influenza seasons. We defined the most favorable vaccination schedule as the one that resulted in the greatest reduction in disease burden. Results In scenarios with fast waning, all age groups benefit from delaying vaccination regardless of initial VE and peak timing. In scenarios with slower waning, results are mixed. For the ≥65 group, high initial VE and slow waning suggests that in early-peaking seasons, early vaccination most effectively reduces disease burden, while in late-peaking seasons delaying vaccination is most effective. For the ≥65 group in medium and low initial VE, and slow waning scenarios, delaying vaccination appears to prevent the greatest number of cases, regardless of whether the season peaks early or late. Conclusion The most favorable vaccination schedule is sensitive to changes in initial VE, waning rate, and peak timing. Given estimates of these quantities from statistical and immunological models and observations, our methods can inform vaccination recommendations in order to most effectively reduce the annual disease burden caused by seasonal influenza. Specifically, accurate peak timing forecasts for the upcoming season have the potential to guide decisions on when to vaccinate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Multiscale modeling and nonlinear model predictive control for flue gas desulfurization

The primary source of sulfur dioxide (SO 2 ) emissions is flue gas from fossil fuels-based power plants. SO 2 emissions are known to not only cause health issues, but also have an adverse effect on the environment in various ways. Several Flue Gas Desulfurization (FGD) technologies have been incorporated in power plants. The most popular technology is Wet FGD, where a limestone slurry is used to absorb SO 2 from the flue gas. A detailed droplet scale model describing the instantaneous and finite rate chemistry is developed. The ill-posed Differential-Algebraic Equation (DAE) droplet model is reformulated to a well-posed index-1 DAE through index reduction. The droplet model is integrated with the bulk phase by incorporating gas-liquid mass transfer, and an oxidation reactor model to simulate the dynamic operation of the counter-current spray scrubber. As a result, the model has a well-conditioned Jacobian and overcomes the modeling challenges of previous works and enables numerical solution without requiring carefully selected initialization or specialized solution procedures. The model is successfully validated using power plant measurements, and nonlinear model predictive control (NMPC) studies are demonstrated to optimize recycle stream flowrates to minimize pumping costs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Progress and Prospective of Nitrogen-Based Alternative Fuels

Alternative fuels are essential to enable the transition to a sustainable and environmentally friendly energy supply. Synthetic fuels derived from renewable energies can act as energy storage media, thus mitigating the effects of fossil fuels on environment and health. Their economic viability, environmental impact, and compatibility with current infrastructure and technologies are fuel and power source specific. Nitrogen-based fuels pose one possible synthetic fuel pathway. In this paper, we discuss the progress and current research on utilization of nitrogen-based fuels in power applications, covering the complete fuel cycle. We cover the production, distribution, and storage of nitrogen-based fuels. We assess much of the existing literature on the reactions involved in the ammonia to nitrogen atom pathway in nitrogen-based fuel combustion. Furthermore, we discuss nitrogen-based fuel applications ranging from combustion engines to gas turbines, as well as their exploitation by suggested end-uses. Thereby, we evaluate the potential opportunities and challenges of expanding the role of nitrogen-based molecules in the energy sector, outlining their use as energy carriers in relevant fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Enhancing the solubility of SARS-CoV-2 inhibitors to increase future prospects for clinical development

SARS-CoV-2 poses an ongoing threat to human health as variants continue to emerge. Several effective vaccines are available, but a diminishing number of Americans receive the updated vaccines (only 22% received the 2023 update). Public hesitancy towards vaccines and common occurrence of “breakthrough” infections (i.e., infections of vaccinated individuals) highlight the need for alternative methods to reduce viral transmission. SARS-CoV-2 enters cells by fusing its envelope with the target cell membrane in a process mediated by the viral spike protein, S. The S protein operates via a Class I fusion mechanism in which fusion between the viral envelope and host cell membrane is mediated by structural rearrangements of the S trimer. We previously reported lipopeptides derived from the C-terminal heptad repeat (HRC) domain of SARS-CoV-2 S that potently inhibit fusion by SARS-CoV-2, both in vitro and in vivo. These lipopeptides bear an attached cholesterol unit to anchor them in the membrane. Here, to improve prospects for experimental development and future clinical utility, we employed structure-guided design to incorporate charged residues at specific sites in the peptide to enhance aqueous solubility. This effort resulted in two new, potent lipopeptide inhibitors.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Demographic drivers of gut microbiome diversity

Abstract The gut microbiome plays a central role in orchestrating metabolic, immune, and neurological functions essential for human health. While extensive research has explored the effects of diseases and pathological conditions on gut microbiome composition, the influence of demographic factors remains underexplored, limiting our understanding of microbiome variations in disease states. This study addresses this gap by investigating the impact of demographic variables, including age, sex, and geography, on gut microbiome diversity in healthy individuals. Using the American Gut Project’s extensive dataset and the QIIME2 bioinformatics pipeline, we conducted a comprehensive analysis of microbial profiles across diverse demographic groups. Our results revealed significant age-related shifts in microbial richness and composition, and geographic location strongly influenced phylogenetic diversity. In contrast, sex exhibited limited impact on microbial diversity within healthy BMI ranges. These findings highlight the critical role of demographic factors in shaping gut microbiome diversity, providing a foundational framework to better contextualize disease-related microbiome variations and advance personalized healthcare approaches.

Biotechnology & Applied Microbiology↗

DOE OSTI2026

Facilitating Data Collection of Maintenance Events to Populate the Hydrogen Component Reliability Database (HyCReD)

The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety and reliability for hydrogen facilities by implementing component reliability data taxonomies that support hydrogen infrastructure failure rate analysis. The project aims to quantify failure rates of hydrogen components through high-quality data collection and analysis on root causes and maintenance needed. HyCReD provides a common database for cataloging hydrogen component failures which exists for reliability research in many other mature industries [2]. The database fills a gap for the hydrogen community by providing a scientifically rigorous approach to quantitative risk assessment (QRA), prognostic health management (PHM), and reliability-centered maintenance (RCM) analysis. High level results will be aggregated and anonymized to protect company sensitive information; detailed results will be used to help address issues of hydrogen components. These advanced analytics will support accelerated deployment of hydrogen infrastructure by enabling better: design and safety of projects (safety codes and standards development), infrastructure reliability and cost (component failure rates, maintenance protocols), and component R&D needs (robust supply chain). A key to a successful HyCReD implementation is facilitating the ease of reporting and data quality in the database that can be used for analysis. Maintenance data was a previously identified gap in initial efforts to populate and validate the database taxonomies [3]. Collection of maintenance data will be instrumental in identifying failure modes and rates, identifying incipient component failures or reduced performance, cataloging best practices for maintenance routines and methods for prognostic health management, and quantifying the risk and effect of different failure modes. Several key priorities are identified for streamlined data collection to achieve quality and detailed failure data: Applicability, Ease of Use, Accessibility, and Information Security. The HyCReD team has now begun deployment of the database to several companies and groups that have signed non-disclosure agreements to facilitate the data collection of failures in industry hydrogen refueling station infrastructure. This paper will provide an update into the process of HyCReD deployment including the development of a coding guide for facility personnel to reference and ensure data quality and consistency from one station to another as well as implementation of contextually dependent data fields of system taxonomy and formatted entries to provide ease of use. The goal is to communicate the lessons learned from the roll-out to technicians and engineers in the field, and the addition of need for high level of security to protect all stakeholders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Soil metagenomics umbrella narrative

Implementing accessible, authentic research experiences in introductory courses is challenging, particularly at institutions serving diverse student populations. To address this gap, we developed and deployed a Course-based Undergraduate Research Experience (CURE) focused on plant-microbe interactions in General Biology II at Northeastern Illinois University (NEIU), a minority-serving institution with a diverse student body. Students grew sugar beets (Beta vulgaris), extracted DNA from the rhizoplane, and used the Department of Energy Systems Biology Knowledgebase (KBase) for bioinformatic analysis to compare microbial relative abundance in fertilized versus unfertilized soil. Over five semesters, the CURE engaged 103 students and leveraged the intuitive KBase platform to make complex sequencing data accessible. Pre/post-course survey data revealed significant increases in student self-assessed research skills, including the ability to explain results and determine the types of data to collect. Furthermore, students reported significant gains in confidence related to experimental design and hypothesis development, alongside a strong increase in familiarity with KBase. Informal faculty feedback indicated high student engagement and appreciation for the real-world connections (e.g. food systems, agriculture, and health). This scalable, low-cost model effectively integrates data science tools into the foundational curriculum, demonstrating a potent strategy for boosting research skills and broadening participation in authentic scientific inquiry among diverse undergraduate students.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Model bias in simulating major chemical components of PM 2.5 in China

High concentrations of PM 2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm) in China have caused severe visibility degradation. Accurate simulations of PM 2.5 and its chemical components are essential for evaluating the effectiveness of pollution control strategies and the health and climate impacts of air pollution. In this study, we compared the GEOS-Chem model simulations with comprehensive datasets for organic aerosol (OA), sulfate, nitrate, and ammonium in China. Model results are evaluated spatially and temporally against observations. The new OA scheme with a simplified secondary organic aerosol (SOA) parameterization significantly improves the OA simulations in polluted urban areas, highlighting the important contributions of anthropogenic SOA from semivolatile and intermediate-volatility organic compounds. The model underestimates sulfate and overestimates nitrate for most of the sites throughout the year. More significant underestimation of sulfate occurs in winter, while the overestimation of nitrate is extremely large in summer. The model is unable to capture some of the main features in the diurnal pattern of the PM 2.5 chemical components, suggesting inaccuracies in the presented processes. Potential model adjustments that may lead to a better representation of the boundary layer height, the precursor emissions, hydroxyl radical concentrations, the heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate have been tested in the sensitivity analysis. The results show that uncertainties in chemistry perhaps dominate the model biases. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide, hydroxyl radical, and aerosol liquid water are essential for the improvement of the sulfate simulation. The update of the heterogeneous uptake coefficient of nitrogen dioxide significantly reduces the modeled concentrations of nitrate. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The possible bias in the chemical production and the wet deposition of nitrate cannot fully explain the model overestimation of nitrate, suggesting issues related to the atmospheric removal of nitric acid and nitrate. A better understanding of the atmospheric nitrogen budget, in particular, the role of the photolysis of particulate nitrate, is needed for future model developments. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Panel Session 26: Risk-Based Approaches to Environmental Remediation and Adaptive Site Management

This panel discussed the needs and importance of developing risk-based approaches to environmental remediation that will enable complex sites to be adaptively managed by applying cost-effective remedies that are protective of human health and the environment. The discussion emphasized risk-based approaches within decision processes and subsequent incorporation into regulatory decision documents. Remedy approaches, remediation exit strategies, and long-term monitoring plans that support risk-based end-states for difficult-to-remediate sites were discussed Panelists with presentations: Risk-Informed Approaches to Environmental Remediation and Adaptive Site Management (Vicky Freedman); ITRC Adaptive Site Management Perspective (Michael Truex); Optimization During Operations and Maintenance Phase at the Hanford Site 200-ZP-1 Groundwater Operable Unit (Emerald Laija, Kate Amrhein, Michael Truex); Adaptive Management: EPA Overview (Benjamin Simes); Decommissioning and Environmental Remediation Section Division of Nuclear Fuel Cycle and Waste Technology (Horst Monken Fernandes)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Human Mobility to Parks Under the COVID-19 Pandemic and Wildfire Seasons in the Western and Central United States

In 2020, people's health suffered a great crisis under the dual effects of the COVID-19 pandemic and the extensive, severe wildfires in the western and central United States. Parks, including city, national, and cultural parks, offer a unique opportunity for people to maintain their recreation behaviors following the social distancing protocols during the pandemic. However, massive forest wildfires in western and central US, producing harmful toxic gases and smoke, pose significant threats to human health and affect their recreation behaviors and mobility to parks. In this study, we employed the geographically and temporally weighted regression (GTWR) Models to investigate how COVID-19 and wildfires jointly shaped human mobility to parks, regarding the number of visits per capita, dwell time, and travel distance to parks, during June - September 2020. We detected strong correlations between visitations and COVID-19 incidence in southern Montana, western Wyoming, Colorado, and Utah before August. However, the pattern was weakened over time, indicating the decreasing trend of the degree of concern regarding the pandemic. Moreover, more park visits and lower dwell time were found in parks further away from wildfires and less air pollution in Washington, Oregon, California, Colorado, and New Mexico, during the wildfire season, suggesting the potential avoidance of wildfires when visiting parks. This study provides important insights on people's responses in recreation and social behaviors when facing multiple severe crises that impact their health and wellbeing, which could support the preparation and mitigation of the health impacts from future pandemics and natural hazards.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Applying Non-Energy Impacts from Other Jurisdictions in Cost-Benefit Analyses of Energy Efficiency Programs: Resources for States for Utility Customer-Funded Programs

Avoided energy and capacity costs are the primary yardstick utilities use to determine which energy efficiency programs are cost-effective for their customers. But sometimes "non-energy impacts" — not commonly recognized as directly associated with energy generation, transmission and distribution — represent substantial benefits, such as improving comfort, air quality and public health. Considering whether and how to include non-energy impacts is an important part of cost-benefit analyses for these programs. This report offers practical considerations for deciding which non-energy impacts to include and how to apply values or methods from other jurisdictions. Researchers reviewed studies quantifying non-energy impacts used in 30 states and applied a five-point system to indicate transferability of a value or method from each study for 16 categories of non-energy impacts: Water resource costs and benefits; Other fuels costs and benefits; Avoided environmental compliance costs; Environmental impacts; Productivity; Health and safety; Asset value; Energy and/or capacity price suppression effects; Avoided costs of compliance with Renewable Portfolio Standard requirements; Avoided credit and collection costs; Avoided ancillary services; Comfort; Economic development and job impacts; Public health impacts; Energy security impacts; Increased reliability. The U.S. Department of Energy’s Building Technologies Office supported this work.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Geographical Insights into Suicide Mortality Through Spatial Machine Learning

Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗

DOE OSTI2022

Dietary Strategies to Modulate the Health Condition and Immune Responses in Gilthead Seabream (Sparus aurata) Juveniles Following Intestinal Inflammation

Several feed additives have proved to be beneficial in eliciting fish health. Β-glucans and curcumin are compounds with immunomodulatory capacities known to increase growth performance, stimulate immunity, improve general health, and enhance disease resistance in fish. The present study aimed to evaluate the effects of dietary Phaeodactylum tricornutum extracts rich in β-glucans and curcumin on gilthead seabream health status prior to and following an intestinal inflammatory stimulus. Three experimental diets were formulated: a practical commercial-type diet (CTRL), a CTRL diet supplemented with 1% microalgae-derived β-glucans extract (BG), and a CTRL diet supplemented with 0.2% of curcumin (CUR). After 30 days of the feeding trial, fish were sampled and subjected to an oral administration of 1% dextran sodium sulphate (DSS) to induce intestinal inflammation. Four groups were considered: a group of fish continued to be fed on the CTRL diet while the remaining groups were exposed to DSS, including CTRL-D (CTRL + DSS), BG-D (BG + DSS), and CUR-D (CUR + DSS), for 6 days. Growth, plasma and gut humoral immunity, liver and gut oxidative stress biomarkers, and intestinal gene expression were evaluated. No significant differences were found in growth after 30 days of feeding; however, seabream fed BG had decreased anti-protease activity and nitric oxide concentration in plasma while those fed CUR had increased mRNA levels of the tnfα, csf1r, and hep genes compared to those fed CTRL. After the inflammatory stimulus, hematocrit was enhanced in fish fed BG-D and CUR-D while red blood cell counts increased in those fed CTRL-D. Superoxide dismutase activity decreased in the intestine of all DSS groups while lipid peroxidation increased in the gut of fish fed CTRL-D and BG-D compared to CTRL. Moreover, the mRNA expression levels of csfr1 and sod decreased in fish fed CTRL-D and BG-D compared to CTRL, respectively. Despite the mild intestinal inflammatory condition induced by DSS, CUR was able to partially ameliorate its effects, improving the hematological profile and assisting against the oxidative stress.

Teixeira, Carla↗

DOE OSTI2021

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Microreactors, a class of modular reactors with net power output of less than 20 MWth, have innovative applications in nuclear and nonnuclear industries due to their portability, reliability, resilience, and high capacity factors. In order to operate microreactors on a wider scale, it is essential to bring down maintenance life-cycle costs while ensuring the integrity of operating such systems. Autonomous operations in microreactors using augmented digital-twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. Structural health monitoring (SHM) is a key component of nuclear DT frameworks. Artificial neural networks can be beneficial to detect degradation in the nuclear safety systems, such as piping equipment systems, by monitoring the sensor data obtained from the plant and its corresponding structures, systems and components. In this report, an SHM methodology is presented which uses convolutional neural networks to determine degraded locations and their corresponding degradation-severity levels at various locations of nuclear piping equipment systems. A simple pipe system, subjected to seismic loads, is selected to design the post-hazard SHM framework. The effectiveness of the proposed SHM methodology is demonstrated by obtaining high accuracy in detecting degraded locations as well as the severity levels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗