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DOE OSTI2026

Coalition for Community-Supported Affordable Geothermal Energy Systems (C2SAGES)

The C2SAGES project evaluated the feasibility of a community geothermal system for the planned Windy Ridge affordable housing development in Hinesburg, Vermont. Led by GTI Energy with Vermont Gas Systems, LN Consulting, NREL, and Frontier Energy, the work assessed technical design, energy performance, costs, business models, community engagement, maintenance, workforce development, and permitting. The proposed system was designed to serve 100% of the development’s heating, cooling, and domestic hot water loads. Compared with a baseline using air-source heat pumps and natural gas water heating, the geothermal system was estimated to reduce HVAC and domestic hot water energy use by about 45% to 48%, lower operating and maintenance costs, and reduce 30-year life-cycle costs by 37% for Phase 1 and 10% for Phase 2. Technical testing and modeling indicated that the Windy Ridge site is suitable for a community-scale geothermal system. The project also developed borehole field layouts, piping concepts, pump house designs, controls, maintenance plans, and supporting engineering drawings. The business model analysis found that first cost, ownership structure, and customer affordability remain major deployment challenges. Utility-led maintenance and operation were viewed favorably, but traditional utility cost-recovery models may require subsidy or revised financing structures to be practical for affordable housing. Community engagement highlighted the need for clear public education, transparent financing, reliable long-term maintenance, trained technicians, and the potential to pair geothermal systems with weatherization. Overall, the report concludes that community geothermal is technically feasible and offers meaningful energy, emissions, and life-cycle cost benefits, but broader deployment will depend on workable financing models and workforce readiness.

15 GEOTHERMAL ENERGY↗

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 OSTI2024

Electric Vehicle Charging for Residential and Commercial Energy Codes: Technical Brief

Numerous studies show that sales of electric vehicles (EVs) have grown consistently over recent years in the U.S. The U.S. Energy Information Administration (EIA) estimated 3 million EVs were on the road in 2022, and the Edison Electric Institute (EEI) forecasts a total of 26.4 million EVs on the road by 2030. Based on this forecast, EEI projects the need for an additional 12.9 million EV charge ports by 2030. If EV charging infrastructure fails to keep pace with sales of EVs it could result in consumers stranded without options to power their vehicles. EVs are capable of providing substantial benefits to the consumers. EVs are less expensive to operate than conventional internal combustion engine vehicles, have lower maintenance costs, and have the convenience of fueling (charging) at home or work. Studies conducted in California show that costs associated with installing EV charging infrastructure can be substantially more expensive for retrofit scenarios compared to new construction, making inclusion of EV infrastructure in new construction codes a cost-effective policy option to increase infrastructure to meet growing demands. PNNL tracks adoption of mandatory EV provisions across the U.S. As of December 20, 2024, 12 states (California, Oregon, Washington, Colorado, New Mexico, Illinois, Maryland, Delaware, New Jersey, Rhode Island, Massachusetts and Vermont) and 53 local governments have added EV provisions to their building codes, local ordinances and zoning requirements. Originally published in 2022, this tech brief has been revised to align with recent model energy code committee discussions and published EV infrastructure code language. This technical brief summarizes market trends, costs and benefits, and provides sample code language for EV charging infrastructure for consideration to be included in model codes, such as the International Energy Conservation Code (IECC) and ANSI/ASHRAE/IES Standard 90.1, as well as directly by states and local governments in their building codes. The technical brief summarizes related efforts undertaken by states and local governments, and builds upon language considered during the 2021 and 2024 IECC development cycles.

2021 IECC↗

DOE OSTI2025

An accumulation method for early fault warning and its application to wind turbine systems

Unexpected failures in engineering systems lead to expensive maintenance actions and should be avoided if at all possible. This is particularly true for wind turbine systems for which unexpected failures not only demand costly repairs but also cause long downtime. Motivated by this need, we present an accumulation method for fault early warning and failure anticipation. Here, our research shows that one critical element allowing the ability of early warning is to accumulate the small-magnitude symptoms resulting from gradual changes in an engineering system like wind turbines. Our idea is inspired by the classical cumulative sum method, or CUSUM, but we have to redesign the accumulation mechanism for tackling unique challenges in wind turbine data. The new accumulation method is applied to two real wind turbine datasets, one with gearbox failures and the other with generator failures, and demonstrates superior performance as compared with CUSUM.

17 WIND ENERGY↗

DOE OSTI2024

From Data to Knowledge: A Graph-Based Reliability Approach to Assess System Health

With the goal of maximizing plant reliability and availability, complex systems such as nuclear power plants continuously monitor and record the performance and the health status of many components, assets, and systems. Such data may take the form of online monitoring data, condition reports, and maintenance reports and it carries the potential to provide system engineers with insights into anomalous behaviors or degradation trends as well as the possible causes behind them and to predict their direct consequences. The analysis of such data poses however few challenges. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly tackles these challenges, and it focuses on the integration of all these data elements in order to assist plant system engineers in analyzing component, assets, and systems performances and optimize maintenance activities. This is performed by 1) extracting knowledge from textual data via technical language processing methods, and 2) quantifying system, asset, and component health from numeric condition-based data. We rely on model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Numeric and textual data elements are then associated with an MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Mechanisms Engineering Test Loop (METL) Operations and Testing Report (FY2022)

This report documents the operations, testing, maintenance, and improvements that were performed at the Mechanisms Engineering Test Loop (METL) during FY2022. The METL facility had a very successful fourth year of operations and went through some new experiences such as taking the facility from its frozen state in October 2021 and thawing the facility and also recovering from an oxide plug in one of the cold trap lines. After the facility was fully thawed, the METL staff maintained the facility in a continuous molten state – either flowing sodium or a static flow condition. METL facility continued supporting the Gear Test Assembly (GTA) testing and hosted its first 28” test vessel experiment, called the Thermal Hydraulic Experimental Test Article (THETA) which was inserted into Test Vessel 4. Work to accommodate two additional experiments, a gripper test article (GrTA) and a flow sensor test article (F-STAr) continued as they are expected to make their debut in FY2023. In addition, the steam piping for the B308 scrubber unit was replaced with stainless steel piping and controls and new parts for the scrubber were purchased – such as a new pump, new storage tank, and new blower and motor. These components will ultimately replace the existing scrubber components when those 1970’s era require replacement.

42 ENGINEERING↗

DOE OSTI2026

Firmware Tampering Detection in Heavy-Duty Vehicles through J1939 CAN Analysis

Modern heavy-duty vehicles rely on complex networks of Electronic Control Units (ECUs) that communicate using the J1939 protocol. While this system makes it easier to update and configure vehicle components, it also opens the door to serious cybersecurity risks if not properly secured. This work investigates the potential for firmware tampering through the J1939 communication protocol, which enables ECU configuration and reprogramming over the Controller Area Network (CAN) bus. By monitoring CAN traffic during legitimate maintenance operations and reverse-engineering OEM diagnostic software, we identified common and proprietary J1939 message identifiers, authentication patterns, and vulnerabilities within Unified Diagnostic Services (UDS). These findings demonstrate that inadequate authentication mechanisms can allow malicious actors to alter ECU firmware or disable safety functions, posing severe operational and safety risks. Our analysis contributes to the development of vehicle intrusion detection systems capable of recognizing abnormal reprogramming activity and future firmware fingerprinting methods to verify software integrity across ECUs. This work highlights the importance of standardizing secure firmware authentication across manufacturers to strengthen cyber resilience in heavy-duty vehicle systems.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment of Machine Learning and Artificial Intelligence for Advanced Condition Monitoring Techniques

With the shift away from time-based maintenance and toward condition-based maintenance, and to reduce overall maintenance costs, there has been an upsurge in the usage and development of advanced condition monitoring (ACM) techniques for real-time monitoring of nuclear power plant (NPP) components. ACM is particularly useful in the development of digital twins, which are designed to predict the failure or degradation of plant components. Successful implementation of ACM requires an assessment to inform the development of a risk-informed approach to evaluate the use of ACM to meet Nuclear Regulatory Committee (NRC) regulations for in-service testing (IST) programs. This includes the monitoring and diagnostics of reactor components and systems in current, new, and advanced reactors. A key component in ACM is the usage of machine learning (ML) and artificial intelligence (AI) algorithms that can employ real-time data from instrumentation and sensors to detect and predict reactor component degradations. Such predictive capabilities enable early detection of component degradation so as to help plant personnel plan and execute necessary maintenance. For successful implementation of ML/AI in ACM such that regulatory requirements are met, a risk-informed graded approach is needed to assess the reliability and performance of ML/AI for ACM. The American Society for Mechanical Engineers (ASME) developed their Operations and Maintenance (O&M) Code to provide guidance on safe, reliable O&M of NPPs. The IST section of the O&M Code specifically establishes requirements for IST and examination to gauge operational readiness of components in water-cooled NPPs. This paper presents a state-of-the-art review of how reliability and risk assessment can be integrated with ACM to assess component performance by non-nuclear industries. This is followed by different methodologies and approaches for conducting performance and reliability assessments so as to meet IST requirements for NPP components.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment for Advanced Condition Monitoring Techniques

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Chapter 14 - Reliability of Wind Turbines

The global wind energy industry has grown at a fast pace during the past half-decade. Advancements from design and manufacturing to operation and maintenance have led to reduced capital and maintenance costs, which make wind power an indispensable source for a comprehensive solution to global electricity needs. Once wind turbines are installed, the opportunity to lower wind power costs is mainly through improved operation and maintenance practices. Modern wind turbines are equipped with tens or hundreds of measurement channels and are generating an abundance of data, with lot of efforts being put into data analysis by both the research community and the industry. One type of analysis is through the exploration of reliability engineering methods based on readily available data or maintenance records collected at typical wind power plants. If adopted and conducted appropriately, these analyses can quickly save operation and maintenance costs in a potentially impactful manner. The wind industry has adopted this discipline more broadly in recent years. This chapter discusses wind turbine reliability by highlighting the methodology of reliability engineering life data analysis. It first briefly discusses the fundamentals of wind turbine reliability and the current industry status. Then, the reliability engineering method for life analysis, including data collection, model development, and forecasting, is presented in detail and illustrated through two case studies. The chapter concludes with some remarks on potential opportunities to improve wind turbine reliability. An owner and operator's perspective is taken and mechanical components are used to exemplify the potential benefits of reliability engineering analysis to improve wind turbine reliability and availability.

database↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Hydrogen for Maritime Applications

The maritime industry is investigating a number of fuel options for reducing emissions, including liquefied natural gas (LNG), biofuels, and electrical drive systems powered by batteries and/or hydrogen-fueled fuel cells. Hydrogen-fueled ships offer the potential to significantly reduce, if not eliminate, regulated and unregulated pollutants in maritime applications. Argonne National Laboratory conducted preliminary comparisons of the total cost of ownership (TCO) of several classes of ships to determine how fuel cell technology compares to the current diesel technology, what advancements are needed for hydrogen fuel cell technology to be competitive in the future, and what applications may be appropriate for introducing fuel cells into the maritime industry. These studies included feeder container ships, harbor tugboats, river pushboats, and auto/passenger ferries. For this study, TCO was defined to include the cost of fuel, propulsion system, and fuel storage system, the levelized cost of propulsion/auxiliary engines, and the cost of annual maintenance and consumables. It did not include the cost of the vessel frame or other components, aside from the propulsion system, that the fuel cell and diesel ships have in common. A 10% internal rate of return (IRR) was applied to the initial capital investment and an installation cost factor of 20% was applied to the capital cost. The capital cost of each component (e.g., engine, fuel tank, motor, etc.) was amortized over a period of 20 years, except for the fuel cell system, which was amortized over 6 or 10 years depending on ship class. The initial comparisons for container ships indicate that fuel costs are by far the dominant contributor to the TCO. With the current low cost of low-sulfur marine gasoil (LSMGO) and relatively high cost of hydrogen, it is difficult for hydrogen to compete with LSMGO in container ship applications. The large energy demand for container ships also favors the use of the higher volumetric energy density LSMGO fuel, especially for longer voyages. The space required to store enough hydrogen for the same journey is larger than that needed to store diesel fuels and can reduce the available cargo carrying and revenue generating space available on the ship.

08 HYDROGEN↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

42 - ENGINEERING↗

DOE OSTI2023

Finding of Effect, And Mitigation Documentation for Building 23-620, Mercury, Area 23, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) plans to demolish Building 23-620 in the town of Mercury (Nevada State Historic Preservation Office [SHPO] Resource No. B15283), which is on the Nevada National Security Site (NNSS) in Nye County, Nevada (see Figure 1). The purpose of the undertaking is related to the modernization of Mercury for future mission needs. The NNSA/NFO will implement this undertaking in accordance with the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA. Building 23-620 was built in 1968 as office space for the Los Alamos Scientific Laboratory (LASL), who worked on the design and engineering of nuclear weapons. In the 1990s, it became a maintenance facility for Reynolds Electrical & Engineering Company (REECo), and eventually, it became the Real Estate Services building in 2004. The town of Mercury and the immediate surrounding area have been formally determined eligible for listing in the National Register of Historic Places (National Register, NRHP) as the Mercury Historic District (MHD, SHPO Resource No. D230) under Criteria A and C for its importance in supporting nuclear testing and scientific research from 1951 through 1992. Building 23-620 was identified as a contributing element to the MHD in a 2018 architectural survey of the district (Reno et al.) and recorded on a Nevada Architectural Resource Assessment (ARA) form (Reno et al. 2017). Building 23-620 was also identified in Appendix C of the Mercury PA as a Category I contributing element, indicating that it might be individually eligible for the NRHP. It is a historic property for the purposes of compliance with Section 106 of the National Historic Preservation Act (NHPA) and subject to the stipulations of the Mercury PA. Mitigation Report: The purpose of this letter report is to submit documentation related to the mitigation of the demolition of Building 23-620 (Nevada State Historic Preservation Office [SHPO] Resource No. B15283) in the Mercury Historic District (MHD, SHPO Resource No. D230). This submission is intended to comply with the stipulations in the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Low-cost Retrofit Kit for Integral Reciprocating Compressors (IRCs) to Reduce Emissions and Enhance Efficiency

Methane emissions from natural gas engines within the oil and gas industry pose a significant environmental challenge, contributing approximately 34.1 MMTCO2 eq to the total of 239 MMTCO2 eq of methane emissions in 2021, according to the EPA report. In response to this pressing issue, a collaborative effort involving the University of Oklahoma and key industry partners—WAGO Automation, Mid Continental Rental, Elipsa, and Perscient—has resulted in the development of a retrofit kit designed to reduce emissions from integral reciprocating compressors (IRCs), which are integrated compressors and engines. The retrofit kit developed comprises an Air Management System (AMS), Integrated Sensors, and a Cloud-Connected Control Unit with Graphical User Interface (GUI)/Human-Machine Interface (HMI). This solution enhances operational efficiency, reduces emissions, and expands the operational envelope of IRCs in the natural gas industry. The project successfully completed all tasks, including the installation of a full-size IRC at a designated site in Oklahoma, the development of an optimized AMS, integration of sensors, and implementation of a data acquisition system. Significant achievements include a notable reduction in CH 4 emissions, up to 84% at specific loads, and the successful deployment of the retrofit kit in diverse field conditions. The system's capabilities were enhanced through the creation of a feedback control algorithm for the AMS using a correlation matrix illustrating relationships between engine parameters, and the design of a predictive and preventive maintenance platform. The project concluded with the deployment of the entire retrofit kit to another location, confirming its effectiveness in reducing emissions and enhancing IRC performance. The comprehensive solution offers valuable benefits for IRCs, making them invaluable assets in the natural gas industry.

03 NATURAL GAS↗

DOE OSTI2022

Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states

Maintenance policy optimization usually is faced with challenges that arise from an imperfect knowledge of system degradation models and from the partial observability of system degradation states. Here, this paper proposes a reinforcement learning method to address these two challenges for a class of maintenance problems with Markov degradation processes. The reinforcement learning approach consists of a learning component and a planning component. Using sequentially collected observations, at each step of decision-making the learning component improves the knowledge of system degradation in terms of the probability distributions of the transition rates based on sequential Bayesian inference. Using the updated transition rates, at each step of decision-making the maintenance policy optimization problem is then formulated as a partially observable Markov decision problem, and the planning component computes the optimal maintenance policy that maximizes the expected cumulative reward. The proposed method is illustrated using a numerical example with repair and inspection maintenance actions. The result shows that as more observations are collected, the learning component progressively learns the true system degradation process, and the planning component adjusts the optimal maintenance policy accordingly as well, which leads to increased reward.

42 ENGINEERING↗

DOE OSTI2023

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and “transduce” signals to adjust internal processes. We hypothesized that an ecosystem’s unique stimuli leave a sensor “fingerprint,” able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection’s nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient’s disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.

97 MATHEMATICS AND COMPUTING↗