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DOE OSTI2023

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↗

DOE OSTI2020

Challenges and Solutions in Operation and Lay-Up of the Modular Caustic- Side Solvent Extraction Unit - 20078

The Modular Caustic-Side Solvent Extraction (CSSX) Unit (MCU) was designed and constructed to provide interim salt waste processing so that the Liquid Waste Disposition Program (LWDP) could continue waste removal and tank closure until start-up of the Salt Waste Processing Facility (SWPF). MCU processes Clarified Salt Solution (CSS), or salt solution (SS) that has undergone actinide removal/filtration, to produce two waste streams: the cesium laden Strip Effluent (SE), which is incorporated into glass at the Defense Waste Processing Facility (DWPF), and the cesium depleted Decontaminated SS (DSS), which is incorporated into grout at the Saltstone Production Facility (SPF). MCU completed start-up in 2008 with an initial operating life of 3 years and design life of 5 years. Prior to reaching the end of the design life, multiple critical components were upgraded and/or replaced to mitigate risks associated with any delays in SWPF start-up. During the remainder of operation (until June 2019), MCU was challenged to maximize processing with minimal additional modifications to repair/improve aging infrastructure, while maintaining low levels of risk to workers and the environment. Notable challenges during the extended operating life of the facility involved both mechanical and operational issues. Two of the most significant issues were the increased frequency of Process Vessel Ventilation (PVV) High Efficiency Particulate Air (HEPA) filter change-outs and the biofouling of the SE coalescer (SEC). The major impacts of both issues were that replacement of these components 1) resulted in high exposure to workers, and 2) required substantial downtime, thus hindering achievement of processing goals. PVV HEPA filter replacements are required based on dose rate and differential pressure (dP) limits. After introducing a higher-curie feed to MCU, the operating time between change outs was reduced by approximately half. SEC replacements are required based on dP limits. After restarting from an extended outage, the operating life of the SEC was significantly reduced by more than half due to biofouling. As a result of extensive troubleshooting and process improvements, the facility was able to recover performance and extend the operating life of the PVV HEPA filters and the SEC media. Through troubleshooting both of these issues, the facility was also able to learn from prior experience and adapt in order to minimize down time and maintain throughput. Although the facility took advantage of short periods of downtime to perform nonintrusive troubleshooting and minor corrective/preventative maintenance activities, longer outages were periodically required for corrective maintenance involving process cell entry. During these outages, remote tools and specialized shield plates were employed to minimize dose to workers. Mock-ups of non-routine maintenance activities were critical to early identification of potential issues and improvements, which shortened outage duration. Consistent with the facility's operational strategy, evolutions needed for MCU layup were identified and sequenced to ensure safe conditions within the facility, meet pre-defined criteria for lay-up configuration, and protect workers, while minimizing the impact to SWPF integration. De-inventory and flushing of process areas was prioritized so that the total residual inventory was reduced as efficiently as possible. This paper discusses lessons learned and best practices from troubleshooting unique issues, performing complex maintenance activities, and implementing a layup strategy that best supports the overall system mission. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind

Turbine and plant upsizing are major trends in offshore wind deployment, although the quantitative impact on project costs has not been well-characterized. The uncertain value of continued wind turbine and project growth limits the ability of the supply chain to prepare for future technology trends, leading to challenges in the realization of larger projects. This analysis explores the levelized cost of energy impacts of turbine ratings between 6 and 20 MW and plant capacities between 250 and 2,500 MW for fixed-bottom offshore wind using techno-economic cost models for foundation, electrical, installation, and operation and maintenance costs, along with annual energy production. We consider a nominal set of technology assumptions for all scenarios to isolate economies of size and scale without additional benefits from decreasing turbine capital costs, quantity discounts for larger projects, or optimized technology solutions. These results indicate that using a 20-MW wind turbine in a 2,500-MW power plant array can reduce the levelized cost of energy by over 23% relative to the global average turbine and plant size installed in 2019; primarily because of reductions in the balance-of-system and operation and maintenance costs. We also identify improved installation vessels, optimized export systems, and novel operation and maintenance strategies as additional cost reduction opportunities. These results suggest that upsizing represents a significant cost reduction opportunity for offshore wind energy and will continue to be a main factor in shaping the future of the sector.

17 WIND ENERGY↗

DOE OSTI2022

Modeling interconnections of safety and financial performance of nuclear power plants, part 3: Spatiotemporal probabilistic physics-of-failure analysis and its connection to safety and financial performance

Here, this paper is a byproduct of a line of research by the authors to analyze interrelationships of safety and financial performance of nuclear power plants (NPPs). The result of this line of research is summarized in three parts: Part 1 covers a categorical review of relevant literature and the theoretical bases that support the methodological developments in Part 2. Part 2 introduces an Integrated Enterprise Risk Management (I-ERM) methodological framework to quantify the interconnections of safety and financial performance with a focus on operation and maintenance (O&M) of NPPs. Part 2 has also demonstrated the applicability and values of the I-ERM methodology through an NPP case study. This paper is Part 3, where detailed development and implementation of one of the I-ERM modules, i.e., probabilistic physics-of-failure (PPoF) analysis, and its connection with safety and financial performance is reported. In this article, the physical failure modeling for hardware components is advanced by incorporating finite element analysis (FEA) into PPoF analysis and coupling the FEA-based PPoF with the maintenance performance through a renewal process model. This article covers two scientific contributions: (i) first-of-its-kind incorporation of FEA into the PPoF model of thermal fatigue for NPP components; and (ii) advancing the interface between the PPoF analysis and the renewal process model in order to deal with spatiotemporal FEA outputs and to efficiently estimate the physical transition rates even when the PPoF outputs are dominated by success data. Through the incorporation of FEA, the resolution of the PPoF analysis is enhanced as spatiotemporal conditions such as stress and temperature can be considered explicitly instead of relying on simplified assumptions or analytical models with reduced spatiotemporal dimensions. To demonstrate an application of the FEA-based PPoF analysis and its coupling with maintenance through the renewal process model, a case study is conducted using excess letdown elbow piping in the chemical and volume control system of a Pressurized Water Reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors

On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. Here, we develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Field Experience Detecting PV Underperformance in Real Time Using Existing Instrumentation

Maintenance at large-scale photovoltaic plants employs a mix of preventative and corrective maintenance practices. Large outages, such as an inverter tripping offline, are often easy to detect. More subtle sub-inverter faults and failures can accumulate and go unnoticed for months or years. A software-based fault detection method has been developed to analyze commonly measured data from large-scale PV plants for more timely detection of subtle underperformance. The method has been demonstrated on eight datasets from large-scale plants with high accuracy of detection. Results are validated using aerial infrared scanning. String outages are detected with a true positive rate of 73 percent and tracker issues are detected with a true positive rate of 88 percent. The developed method can be uniformly applied to photovoltaic plants across a range of scales and configurations to assess performance, quickly detect underperformance, and determine the source and location of failures. The results inform and improve operations and maintenance at PV plants, ultimately aiding in improved affordability, reliability, availability, and resiliency of solar electricity.

14 SOLAR ENERGY↗

DOE OSTI2021

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants: 2nd Annual Report

For economic reasons, the nuclear industry is witnessing premature closure of nuclear power plants, despite excellent safety records. Operations and Maintenance (O&M) activities are some of the largest costs in operating legacy light-water plants. By reducing O&M costs, nuclear energy can become more economically competitive with other energy sources. This can be achieved by leveraging machine-learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose potential faults within the system. Improved accuracy of the models can lead to a reduction in unnecessary maintenance, thus reducing costs associated with parts, labor, and unnecessary planned, forced, or extended outages. To address these challenges, the goal of this project is to perform research and development in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and data science based analytic capabilities, to advance online monitoring and predictive maintenance in nuclear plants and improve plant performance (efficiency gain and economic competitiveness). This report summarizes the fiscal year 2020 research progress encompassing (1) different wireless vibration sensor and data indicators used to assess the health of a plant asset; (2) development of diagnostic models for fault detection; and (3) development of prognostic models for estimating the health of the system up to 7 days ahead.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1975

Cluster man/system design requirements and verification

Discussion of the procedures employed for determining the man/system requirements that guided Skylab design, and review of the techniques used for implementing the man/system design verification. The foremost lesson learned from the design need anticipation and design verification experience is the necessity to allow for human capabilities of in-flight maintenance and repair. It is now known that the entire program was salvaged by a series of unplanned maintenance and repair events which were implemented in spite of poor design provisions for maintenance.

Orbital workers↗

DOE OSTI2020

Competing Interests and Creating Value - Balancing Durable and Enforceable Institutional Controls with Beneficial Reuse - 20320

US DOE LM has various real property interests at the 100 sites within its current portfolio, with an anticipated total of 127 sites within the next 5 years. Each site has its own narrative with respect to the real property assets. Since the regulatory time frame for long-term surveillance and maintenance activities at some sites can be in perpetuity, durable and effective instruments and documentation are necessary to support LM's unique mission as a federal land manager and steward. More than half of the sites currently in LM's inventory do not allow unrestricted use, and institutional controls (ICs) are required to limit human and environmental exposures to residual contamination. LM's Asset Management program balances protection of human health and the environment with a goal of optimizing its land portfolio through mitigation of risk, limiting liability, and creating value, whether tangible or intangible, in these types of assets. The IC layering strategy utilizes multiple types, restrictions, and legal authorities to protect human health and the environment. This layering strategy influences the transition of these properties into LM's portfolio to assess potential beneficial reuses. The LM's beneficial reuse program aims to repurpose former contaminated sites to restore the environment, protect the public health, revitalize communities, and spur economic growth. LM strives to create value in a perceived valueless asset and reevaluates and revisits the potential opportunities for these sites as long-term surveillance and maintenance activities are performed and objectives are achieved. Beneficial reuse also promotes protectiveness by ensuring activities are compatible with long-term maintenance and protection of public health and the environment as well as ensuring activities are environmentally sound by retaining good stewardship of natural resources. LM has recently revised both the Institutional Control Program Guidance and its Beneficial Reuse Management Plan to integrate the issues of land use with ICs, creating value in these impaired assets. This paper presents the following topics: (1) a general overview of LM's IC and beneficial reuse programs; (2) the unique aspects of each of these programs as a result of the multiple regulatory requirements and programs that affect LM sites; (3) case studies of LM sites to show examples of how LM created value while complying with the ICs and layering strategies; and (4) the most common challenges to beneficial reuses at LM sites and potential options for value creation in impaired assets. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

The importance of delayed neutron precursors in gamma dose calculations for activated primary heat exchanger components in the Molten Salt Breeder Reactor

The Molten Salt Reactor (MSR) Multiphysics Applications technical area in the Nuclear Energy Advanced Modeling and Simulation program has supported the development of 3D Monte Carlo models of the Molten Salt Breeder Reactor (MSBR) over the last couple of years. This MSBR model was previously run with the Shift Monte Carlo code to perform radiation shielding calculations in the reactor cell area. The MSBR is a 2250 MWth (1000 MWe) liquid-fueled molten salt reactor design developed at Oak Ridge National Laboratory in the 1970s. Determining the source terms from activated primary heat exchanger (HX) components is important because delayed and prompt neutron fluxes incident on these components affect the dose rate in the primary HX maintenance areas. This information can be used in the development of remote handling procedures required during shutdown for maintenance. A methodology has been developed and is proposed in this paper to quantify the activated source term from the primary HX components as a result of the movement of the delayed neutron precursors in flowing primary fuel salt through the primary HXs in the MSBR. The goal of this research is to evaluate the gamma dose rates in the maintenance hatches above the primary HX using the activated HX source terms. The study showed that the gamma dose rates are approximately two orders of magnitudes higher when accounting for the neutron activation from the movement of delayed neutron precursors through the HXs than when flowing fuel is not considered. Thus, the movement of delayed neutron precursors must be taken into account for accurately predicting the neutron activation of primary loop components.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗

DOE OSTI2021

Continuous NF-κB pathway inhibition promotes expansion of human phenotypical hematopoietic stem/progenitor cells through metabolism regulation

Highlights: • Small molecular inhibition of NF-κB pathway efficiently expands human HSPCs. • NF-κB inhibition with TPCA-1 improves the self-renewal potential and biological function maintenance of expanded CD34{sup +} cells. • NF-κB pathway inhibition reprograms metabolism and enhances glycolysis. • Continuous NF-κB inhibition is required in the culture process to facilitate primitive HSC maintenance. Hematopoietic stem/progenitor cells (HSPCs) ex vivo expansion is critical in facilitating their widespread clinical application. NF-κB pathway is implicated in the energy homeostasis and metabolic adaptation. To explore the effect of NF-κB pathway on the ex vivo HSPC expansion and metabolism, the 50 nM–1 μM inhibitor of NF-κB pathway TPCA-1 was used to expand cord blood derived CD34{sup +} cells in serum-free culture. The expansion folds, function, mitochondrial profile and metabolism of HSPCs were determined. After 10 days of culture with 100 nM TPCA-1, the expansion of total cells CD34{sup +}CD38{sup −} cells and CD34{sup +}CD38{sup −}CD45RA{sup −}CD90{sup +}CD49f{sup +} cells were significantly increased compared to the cytokine priming alone. Notably, TPCA-1 treatment generated ~ 2-fold greater percentage of CD34{sup +}EPCR{sup +} and CD34{sup +}CD38{sup −}CD45RA{sup −}CD90{sup +}CD49f{sup +} cells compared to cytokine only conditions. Moreover, TPCA-1 expanded CD34{sup +} cells displayed enhanced serial colonies forming potential and secondary expansion capability. NF-κB inhibition increased the expression of self-renewal related genes, while downregulated the expression of mitochondrial biogenesis regulator (Pgc1α) and mitochondrial chaperones and proteases (ClpP, Hsp10, Hsp60). Mitochondrial mass and membrane potential were markedly decreased with TPCA-1 treatment, leading to the reduced mitochondrial reactive oxygen species (ROS) level in HSPCs. NF-κB inhibition displayed augmented glycolysis rate with compromising mitochondrial metabolism. This study demonstrated that NF-κB pathway inhibition improved glycolysis and limited ROS production that promoted the ex vivo expansion and maintenance of functional HSPCs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Accelerating Simulation for High-Fidelity PV Inverter System Reliability Assessment with High-Performance Computing

The overall cost of photovoltaic (PV) systems has shown a downward trend during the last decade; however, PV inverter failures account for the highest cost of operation and maintenance. To address this, reliability tools with powerful computation and better accuracy are required for the lifetime prediction and degradation evaluation of PV inverters. This paper proposes an event-driven parallel computing-based simulator. The proposed simulator applies high-performance computing techniques and other accessory optimization techniques-including cluster merging, adaptive model updates, and steady-state identification-to make reliability assessments for PV inverters under given input mission profiles and operating conditions with high efficiency and high fidelity. The main idea of the simulator and its workflow are introduced. Then, a demo PV inverter system simulator is implemented, and the speedup of the total simulations of the switching model reaches 123.03 times.

high-performance computing↗

DOE OSTI2020

Evaluating the Incident Energy of Arcs in Photovoltaic DC Systems: Comparison Between Calculated and Experimental Data

Solar Photovoltaic (PV) systems have permeated the energy generation world at a very high rate, some of the safety codes and standards are still lagging in accurately assessing the hazards and risks associated with PV array arcing energies. Safety professionals and maintenance workers using NFPA 70E have utilized the Doan, Stokes & Oppenlander or Enrique models, meant to determine arc energies in DC power systems using the maximum power method. These methods may lead to an overestimate of energy available in PV systems during a fault. Since PV modules/arrays are non-linear, current limited DC devices, some of these calculation methods may not accurately predict fault energy. This paper will validate current arc energy models for PV systems by comparing experimental and calculated data. Additionally, this data will help modify the current NFPA 70E models related to smaller solar arrays. Understanding where the real safety threshold for DC arc flash in PV systems exists will help maintenance and safety professionals better prepare for a variety of work related activities. This paper will analyze real arc data taken for PV systems <1000VDC and <60amps and compare this to the calculated incident energy models, to include 70E. Thus, using these comparisons, it may be possible to reduce the safety hazard severity and thus relax the PPE requirements for installation and maintenance crews.

14 SOLAR ENERGY↗

DOE OSTI2021

CD8 Lymphocyte Depletion Enhances the Latency Reversal Activity of the SMAC Mimetic AZD5582 in ART-Suppressed Simian Immunodeficiency Virus-Infected Rhesus Macaques

ABSTRACT Inducing latency reversal to reveal infected cells to the host immune system represents a potential strategy to cure human immunodeficiency virus (HIV) infection. In separate studies, we have shown previously that CD8 + T cells may contribute to the maintenance of viral latency and have identified a novel SMAC (second mitochondrial activator of caspases) mimetic/IAP (inhibitor of apoptosis protein) inhibitor (AZD5582) capable of reversing HIV/simian immunodeficiency virus (SIV) latency in vivo by activating the noncanonical (nc) NF-κB pathway. Here, we use AZD5582 in combination with antibody (Ab)-mediated depletion of CD8α + cells to further evaluate the role of CD8 + T cells in the maintenance of viral latency. Six rhesus macaques (RMs) were infected with SIV mac239 and treated with antiretroviral therapy (ART) starting at week 8 postinfection. After 84 to 85 weeks of ART, all animals received a single dose of the anti-CD8α depleting Ab MT807R1 (50 mg/kg of body weight, administered subcutaneously [s.c.]), followed by five weekly doses of AZD5582 (0.1 mg/kg, administered intravenously [i.v.]). Following combined treatment with CD8α depletion and AZD5582, 100% of RMs experienced on-ART viremia levels above 60 copies per ml of plasma. In comparator groups of SIV-infected, ART-suppressed RMs treated with AZD5582 only or CD8α depletion only, on-ART viremia was experienced by 56% and 57% of the animals, respectively. Furthermore, the increased frequency of viremic episodes during the treatment period was greater in the group treated with CD8α depletion plus AZD5582 than in the other groups. Mathematical modeling of virus reactivation suggested that in addition to viral dynamics during acute infection, CD8α depletion influenced the response to AZD5582. This work suggests that the latency reversal induced by activation of the ncNF-κB signaling pathway with AZD5582 can be enhanced by CD8α + cell depletion. IMPORTANCE A favored approach to curing HIV infection aims at inducing viral expression using latency-reversing agents (LRAs) to allow the elimination of infected cells. Here, we tested a combination of two recently identified LRAs, the SMAC mimetic/IAP inhibitor AZD5582, which activates the noncanonical NF-κB pathway, and the antibody (Ab) MT807R1, which depletes CD8α + cells, in SIV-infected rhesus macaques (RMs) on ART. Latency reversal, as defined by increased on-ART viremia, was observed in all six SIV-infected, ART-treated RMs that received this combined treatment. Furthermore, comparison of viral reactivation between these animals and groups of SIV-infected, ART-treated RMs treated with AZD5582 only or CD8α + cell depletion only showed more frequent increases in viremic episodes when the two treatments were combined. This study provides additional evidence that CD8 + T cells may contribute to the maintenance of HIV/SIV latency on ART and potentially inhibit latency reversal during HIV cure approaches.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

maranasgroup/uncFBA

In the repository are example scripts that perform uncertainty injection and propagation to flux balance analysis with outputs for a small sample size (for demonstration purpose only). For proper analysis, user should download the scripts and run for a large sample size (e.g., 10,000 samples). If you use the scripts, please cite the following Metabolic Engineering article: “Quantifying the propagation of parametric uncertainty on flux balance analysis” (https://doi.org/10.1016/j.ymben.2021.10.012) There are two subdirectories: /uncFBA/uncBiom: injection of normally distributed noise to biomass precursor coeffcients and ATP maintenance (growth-associated ATP maintenance (GAM) and non-growth associated ATP maintenance (NGAM)) /uncFBA/uncRHS: departure from steady-state by adding noise drawn from normal distribution to the RHS terms of mass balance constraints

Dinh, Hoang↗

DOE OSTI2022

Scalable Predictive And Risk Technologies

The research involves developing scalable technologies for risk-informed predictive analytics to achieve condition-based monitoring and maintenance strategies to reduce overall maintenance costs. The research utilizes data (real-time data, periodic data, and institutional knowledge) related to a particular plant asset from a specific nuclear plant site to develop technologies to scale risk-informed predictive analytic algorithms across different plant assets at the plant site and across the nuclear fleet. The developed algorithms and codes are used to optimize the maintenance strategy and estimate/forecast generation costs based on the state of health of the plant asset. Developed codes specifically include 1. Parameter estimation using plant operation data 2. Federated and Transfer learning model 3. Feature group based Multi-kernel SVM 4. Three state markov model

Manjunatha, KoushikAraseethota↗

DOE OSTI2024

Considerations for Introducing Artificial Intelligence into Nuclear Power Plants

Advanced computational tools and techniques such as artificial intelligence and machine learning (AI/ML) can transform the nuclear power industry. This is necessary given that the economic viability of the existing fleet is in jeopardy and its labor-centric approach to operations and maintenance. Currently, AI/ML research is being undertaken for reactor system design and analysis including fault and accident prognosis, nuclear risk analysis such as plant safety and security evaluation, and plant operations and maintenance including predictive maintenance. Applications include both existing and advanced reactor technologies with the aim of improving operational and business efficiencies. Most every aspect of the organization can benefit, from instrumentation and control, to work planning, to human-machine interactions and business management. AI/ML in nuclear can simplify complex problems and produce more effective decision-making. Nonetheless, careful consideration must be given to the implementation of an AI/ML initiative. The aims of this research are to 1) review barriers to AI/ML adoption within the nuclear power industry, and 2) suggest potential solutions. These barriers are organized along five distinct categories (Figure 1) that are interconnected. The first are historical barriers that track the industry’s development over the decades including worldwide nuclear events that shaped public perceptions. The resulting federal scrutiny and intense safety culture that emerged are discussed. Technical barriers to AI/ML adoption are considerable, and include data privacy concerns, data governance, and the current lack of AI/ML expert knowledge at the plants. The main business case barrier remains cost, but an absence of an industry-wide vision and wide-scale adoption also produces reluctance. Stakeholder readiness is reviewed with special attention given to regulatory readiness. The 5-year strategic plan for AI readiness recently published by the U.S. Nuclear Regulatory Commission is highlighted. Last, adoption barriers at the user level are addressed including the importance of user experience and explainable AI. The AI adoption barriers described here are inter-related and ideally should be addressed in a holistic fashion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗