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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The U.S. DOE ARM User Facility Establishes a New Site for Studies of Land–Aerosol–Cloud Interactions in the Southeastern United States

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility has established a new site in the Bankhead National Forest (BNF) in northern Alabama that will gather data on how clouds, the land surface, and aerosols interact at a hierarchy of scales important to understanding and simulating the Earth system. Starting its operations in October 2024, the BNF site provides a multi-year opportunity for scientists to unravel complex land-atmosphere interactions. A suite of ground-based sensors, elevated tower-based instrumentation, and aerial facilities will enable scientists to investigate those interactions from within the canopy to the clouds. The Southeastern United States was recommended by the DOE ARM and its collaborators in the broader community as an important region to address their common scientific questions given the region’s abundant surface-forced convective clouds and mesoscale convective systems that pose ongoing challenges in Earth system models. The region is also home to significant terrain complexity and land-use heterogeneity that will unleash new understanding of anthropogenic and biogenic aerosol processes, boundary-layer aerosol-cloud interactions, and the interactions between the terrestrial ecosystem and coupled aerosol-cloud-radiation processes.

Aerosols↗
DOE OSTI2025

Let’s Stick Together: Interplay of Moisture, Particle Size, and Anatomical Fraction in the Flowability of Corn Stover Crumbles

Handling of agricultural waste biomass feedstocks (such as corn stover) is a persistent challenge in the production of biofuels and chemicals at integrated biorefineries. Inconsistent flowability and high feedstock variability creates equipment downtime and processing challenges. This work investigates the influence of anatomical fractionation and moisture content on the bulk solids handling behavior of corn stover crumbles. A combination of rotational shear testing, tribological measurements, and surface and sorption characterization techniques (inverse gas chromatography (IGC), dynamic vapor sorption (DVS), and electron microscopy) were used to evaluate the contributions of particle composition, size, and surface properties to flow behavior. Shear and frictional testing revealed that moisture content has the greatest effect on flowability, significantly increasing unconfined yield strength (ƒ c ) and reducing the flow function coefficient (F F C), regardless of anatomical fraction. While differences in surface energy were observed between fractions, particularly in polar contributions (γ AB ), these did not correspond to meaningful differences in flow behavior. Coarser 6 mm particles exhibited poorer flow performance than 4 mm crumbles, likely due to broader particle size distributions and increased particle interlocking. Taken together, these results suggest that anatomical fractionation provides minimal benefit from a bulk handling perspective. Moisture content and particle size heterogeneity dominate handling behavior, reinforcing the utility of unfractionated corn stover in processing environments. These insights can inform the design of more efficient and reliable feedstock processing systems for agricultural biomass in integrated biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2025

A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems

The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.

Taufer, Michela [University of Tennessee, Knoxvill↗
DOE OSTI2024

Numerical Simulations in Support of a Long-Term Test of Gas Production From Hydrate Accumulations on the Alaska North Slope: Water Production and Associated Design and Management Issues

Here, we investigated numerical simulation strategies for a long-term test of depressurization-induced gas production from the B1 Sand of Unit B at the Hydrate-01 Stratigraphic Test Well. The main objective of this study was to estimate fluid production rates (with emphasis on water production) under a variety of conditions and production scenarios and contribute new insights to the design and management of the field test. In the first part of the study, we investigated the system response to a three-step depressurization process using two limiting sets of flow properties─the expected maximum and minimum intrinsic and effective permeabilities─for the very heterogeneous reservoir. In the second part, we investigated the effect of the production interval length and placement within the formation relative to the boundaries of the hydrate-bearing unit. The best performing well configuration was used in the third part of the study, which used the most representative subsurface flow properties to investigate the effect of the depressurization strategy on the production performance. The best overall performance (largest gas production with modest water production and a strong response at the observation wells) was obtained with a 10 m-long well situated 3 m below the top of the formation and a three-step depressurization scheme at 15-day intervals to a terminal bottomhole pressure of 2.8 MPa. The overall production performance was enhanced by a faster rate of depressurization. Estimated water production rates in all cases were limited and easily manageable. None of the tested well configurations or depressurization strategies significantly reduced water production without also severely reducing gas production. In all the investigated cases, 95% of the long-term fraction of produced water was replenished by inflows from the boundaries and could not be reduced. These substantial water inflows are an unavoidable feature of HU-B and cannot be easily mitigated by a hydraulic control.

02 PETROLEUM↗
DOE OSTI2025

A review of thermo-hydro-mechanical modeling of coupled processes in fractured rock: From continuum to discontinuum perspective

Coupled thermo-hydro-mechanical (THM) processes in fractured rock are playing a crucial role in geoscience and geoengineering applications. Diverse and conceptually distinct approaches have emerged over the past decades in both continuum and discontinuum perspectives leading to significant progress in their comprehending and modeling. This review paper offers an integrated perspective on existing modeling methodologies providing guidance for model selection based on the initial and boundary conditions. By comparing various models, one can better assess the uncertainties in predictions, particularly those related to the conceptual models. The review explores how these methodologies have significantly enhanced the fundamental understanding of how fractures respond to fluid injection and production, and improved predictive capabilities pertaining to coupled processes within fractured systems. It emphasizes the importance of utilizing advanced computational technologies and thoroughly considering fundamental theories and principles established through past experimental evidence and practical experience. The selection and calibration of model parameters should be based on typical ranges and applied to the specific conditions of applications. The challenges arising from inherent heterogeneity and uncertainties, nonlinear THM coupled processes, scale dependence, and computational limitations in representing field scale fractures are discussed. Realizing potential advances on computational capacity calls for methodical conceptualization, mathematical modeling, selection of numerical solution strategies, implementation, and calibration to foster simulation outcomes that intricately reflect the nuanced complexities of geological phenomena. Future research efforts should focus on innovative approaches to tackle the hurdles and advance the state-of-the-art in this critical field of study.

Coupling scheme↗
DOE OSTI2025

Summer Aerosol and Trace Gas Observations in Houston, Texas Using an Adaptable Mobile Facility

An aerosol container featuring a shared inlet system was deployed to Houston, Texas in July 2022, enabling direct, high-time-resolution in situ measurements of aerosols and trace gases. The internal rack system and floorplan was designed for adaptable modularity to elucidate aerosol physicochemical processes at fine scales. The design allowed for the deployment of a core instrument suite and additional customized research grade instruments. A heterogeneous mixture of aerosols was observed during three regimes: (1) intermittent black carbon (BC) and diurnal variations in aerosol chemical composition, (2) observed particle growth associated with SO 2 , (3) transported supermicron dust. The high variability of observed particles and gases in high time resolution indicated a complex urban area with multiple local and regional sources and processes. Particle growth rates of 7–16 nm/hr were observed for submicron particles during periods when SO 2 was >0.5 ppbv. Two periods of multi-day long-range transport events of dust from the African Sahara were observed in the supermicron and submicron particle modes with total mass concentrations up to 30 μg m −3 . Aerosol scattering angstrom exponents and extinction coefficients (B ext ) increased with humidity as a function of particle composition. The measurements demonstrate collaborative capabilities that can be used to increase observations of aerosol processing, microphysical and optical properties, internal mixing state, and supermicron aerosol that are not parameterized or missing in global Earth energy system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Benchmarking Operators in Deep Neural Networks for Improving Performance Portability of SYCL

SYCL is a portable programming model for heterogeneous computing, so it is important to obtain reasonable performance portability of SYCL. Towards the goal of better understanding and improving performance portability of SYCL for machine learning workloads, we have been developing benchmarks for basic operators in deep neural networks (DNNs). These operators could be offloaded to heterogeneous computing devices such as graphics processing units (GPUs) to speed up computation. In this paper, we introduce the benchmarks, evaluate the performance of the operators on GPU-based systems, and describe the causes of the performance gap between the SYCL and Compute Unified Device Architecture (CUDA) kernels. We find that the causes are related to the utilization of the texture cache for read-only data, optimization of the memory accesses with strength reduction, use of local memory, and register usage per thread. We hope that the efforts of developing benchmarks for studying performance portability will stimulate discussion and interactions within the community.

Jin, Zheming [ORNL] (ORCID:000000027197780X)↗
DOE OSTI2024

Recent Advances in Immobilizing and Benchmarking Molecular Catalysts for Artificial Photosynthesis

Transition metal complexes have been widely used as catalysts or chromophores in artificial photosynthesis. Traditionally, they are employed in homogeneous settings. Despite their functional versatility and structural tunability, broad industrial applications of these catalysts are impeded by the limitations of homogeneous catalysis such as poor catalyst recyclability, solvent constraints (mostly organic solvents), and catalyst durability. Over the past few decades, researchers have developed various methods for molecular catalyst heterogenization to overcome these limitations. Here, in this review, we summarize recent developments in heterogenization strategies, with a focus on describing methods employed in the heterogenization process and their effects on catalytic performances. Alongside the in-depth discussion of heterogenization strategies, this review aims to provide a concise overview of the key metrics associated with heterogenized systems. We hope this review will aid researchers who are new to this research field in gaining a better understanding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluating Operators in Deep Neural Networks for Improving Performance Portability of SYCL

SYCL is a portable programming model for heterogeneous computing, so it is important to obtain reasonable performance portability of SYCL. Towards the goal of better understanding and improving performance portability of SYCL for machine learning workloads, we have been developing benchmarks for basic operators in deep neural networks (DNNs). These operators could be offloaded to heterogeneous computing devices such as graphics processing units (GPUs) to speed up computation. In this work, we introduce the benchmarks, evaluate the performance of the operators on GPU-based systems, and describe the causes of the performance gap between the SYCL and Compute Unified Device Architecture (CUDA) kernels. We find that the causes are related to the utilization of the texture cache for read-only data, optimization of the memory accesses with strength reduction, shared local memory accesses, and register usage per thread. We hope that the efforts of developing benchmarks for studying performance portability will stimulate discussion and interactions within the community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Opportunities and challenges in thermochemical conversion of municipal solid waste: A comprehensive review

Recent advancements in thermochemical conversion processes have elucidated new pathways for converting municipal solid waste into valuable resources. This review explores the primary thermochemical conversion methods, including combustion, gasification, pyrolysis, torrefaction, hydrothermal carbonization, and hydrothermal liquefaction, emphasizing their potential roles in waste management and energy recovery. Key challenges including feedstock variability, ash behavior, and scale-up limitations are discussed alongside opportunities for hybrid systems and circular economy integration. A comparative analysis of research publications indicates a significant focus on thermochemical pathways within the broader context of municipal solid waste research, underscoring the growing interest in these technologies. Recent advancements in each thermochemical process, alongside their operational, technical, and economic challenges, are discussed. Comparative data reveal that torrefaction enhances the hydrophobicity and grindability of municipal solid waste components, though its energy densification benefits are more modest than those observed in biomass. Hydrothermal carbonization and liquefaction are highlighted for their ability to process high-moisture and heterogeneous waste streams. The review also synthesizes recent findings on reactor configurations, emissions control, and synergistic effects in co-processing municipal solid waste fractions. The findings underscore the importance of developing standardized protocols for municipal solid waste characterization and the need for innovative hybrid systems to improve efficiency.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗
DOE OSTI2026

Solubilization of PET in binary mixtures of HFIP and DCM

The dissolution of polyethylene terephthalate (PET) is a critical step for a solvent-based process, yet it typically requires highly corrosive or toxic solvents. Here, we investigate the solubilization and conformational behavior of PET in binary mixtures of hexafluoro-2-propanol (HFIP) and dichloromethane (DCM) as a strategy to reduce HFIP usage while maintaining effective dissolution. Small-angle neutron scattering (SANS) measurements reveal that PET remains molecularly dissolved in HFIP/DCM mixtures up to 50 vol% DCM. Analysis of PET chain conformations shows a transition from Gaussian behavior at low HFIP fractions to more swollen chains at intermediate compositions, accompanied by a counter-intuitive minimum in the radius of gyration at 50% HFIP. Complementary SANS measurements of the binary solvents demonstrate that compositional heterogeneity is maximized at this same solvent composition, suggesting a direct coupling between solvent microstructure and polymer dimensions. Molecular dynamics simulations corroborate the experimental findings, revealing solvent domain formation, preferential solvation of PET by HFIP, and a “caging” effect arising from solvent heterogeneity that leads to polymer coil compaction. Together, these results provide molecular-level insight into polymer behavior in mixed solvent systems and establish HFIP/DCM mixtures as a promising, more sustainable solvent platform for the PET post-process.

Arifuzzaman, Arif [ORNL]↗
DOE OSTI2025

Discharge Rate‐Driven Li 2 O 2 Growth Exhibits Unconventional Morphology Trends in Solid‐State Li‐O 2 Batteries

Solid-state lithium oxygen batteries (LOBs) are known for their enhanced safety, higher electrochemical stability, and improved energy density compared to liquid-state LOBs. However, the investigation of solid-state LOBs is limited with little understanding of their discharge and charge processes. In this work, a polymer-based solid-state LOB is used to investigate the effect of discharge rate on lithium peroxide (Li 2 O 2 ) formation, the oxygen evolution reaction (OER), and cycle performance. Notably, we observe a counterintuitive trend: Li 2 O 2 particle size increases with increasing discharge current density, in contrast to liquid systems. This behavior arises from inherent space charge layers that restrict Li⁺ transport under high current, and spatially heterogeneous active sites at the solid electrolyte–cathode interface, directly evidenced by small angle X-ray scattering (SAXS), which govern nucleation accessibility and promote site-selective Li 2 O 2 growth. Furthermore, higher current densities improve ORR and OER efficiency but accelerate anode degradation, while lower currents promote side reactions. These opposing effects result in a trade-off that defines an optimal discharge rate (0.1 mA cm -2 ) for maximizing cycle life. This study provides a new mechanistic perspective on discharge-driven processes in solid-state LOBs and offers practical guidelines for performance optimization in future high-energy battery systems.

Discharge current density↗
DOE OSTI2026

Ecological Insights from Transferable Plant Biomass Mapping across the Arctic using High-resolution Structure-from-Motion and LiDAR Data

Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of Unoccupied Aerial Systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based Structure-from-Motion (SfM) or Light Detection and Ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and MODIS, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a Random Forest (RF) model (overall RMSE: 0.336 kg/m2), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within 2 years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings demonstrate the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the potential of our approach to be broadly applied to generate high-quality AGB data for ecological monitoring and model benchmarking across the Arctic.

Yang, Daryl [ORNL] (ORCID:0000000317057823)↗
DOE OSTI2026

Theoretical Insights into Reaction-Induced Transformation and Tuning of Catalytic Behavior in Heterogenous Catalysis

Reaction-induced transformations in heterogenous catalysis represent diverse phenomena that challenge traditional views of static catalyst surfaces. From surface adsorbate dynamics, atomic rearrangements, to composition and phase transitions, these processes reveal the profound differences between idealized model systems under ultrahigh vacuum and the complex, evolving interfaces that govern real catalytic behaviors under reaction conditions. Here, this perspective reviews recent theoretical efforts to provide atomic-level mechanistic insights into significant reaction-induced transformations and their impact on catalytic activity and selectivity. It underscores the need for an integrated framework that combines predictive simulations with operando characterization to uncover active sites and mechanisms under realistic operating conditions. Achieving this requires accelerating existing simulations to fully capture diverse reaction-induced surface dynamics, enabling scalable and accurate modeling of catalysts as condition-dependent, dynamically evolving systems. Such approaches are critical to bridge the gap between theory and practice, offering a pathway to more impactful and predictive catalyst design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

EchemAMR (electro-chemical microsctructure scale models with adaptive meshing) [SWR-23-111]

A 3D microstructure resolving electrochemical transport and interfacial chemistry solver. Electrode microstructure plays an important role in determining the performance of an electrochemical system, e.g. lithium ion battery. EchemAMR is a microstructure scale model that solves the governing equations for ion transport, electrical current continuity, interfacial chemistry and structural mechanics. Complex microstructure geometries from imaging can be directly imported into EchemAMR. A volume fraction based description of the geometry on Cartesian grid with an immersed interface formulation enables simplified meshing and large-scale simulations with millions of degrees of freedom. EchemAMR has been tested against systems with analytic solutions for numerical convergence and highly resolved lithium ion battery microstructures. EchemAMR demonstrates excellent mass conversation and efficient scaling on heterogenous High-Performance Computing (HPC) with central and graphics processing units.

Sitaraman, Hariswaran↗
DOE OSTI2024

Scalable Thin Light-Emitting Diode (LED) Light Sheet Platform

The goal of the Scalable Thin Light-Emitting Diode (LED) Light Sheet Platform project is to derisk the manufacturing scalability for a high chip count heterogeneous smart lighting platform, with the potential to enable significant energy savings through dynamic directional light control and custom. Specifically, the project derisks the scalability of a disruptive new computer controlled microassembly fabrication process SRI International is developing, to address fundamental cost barriers to mass production of high chip count systems. This is key for enabling mass adoption and thus maximum societal energy savings impact. The LED light sheet technology is a smart illumination platform that can be applied to lighting, signage, and display. It has features and a form factor similar to bendable or conformal OLED light sheets but uses more efficient LEDs with a remote phosphor layer that is very close to the LED to deliver a luminous efficacy that exceeds 125 lumen/Watt and enable > 50% Lighting Application Efficiency (LAE) energy savings. During Budget Period 1 (BP1), all of the BP1 milestones were successfully completed: (M2.0.1) Design demonstrator details that will meet the final project goals, (M6.1.1) Show lighting output model can predict illuminance, spatial, spectral distribution for specific light-sheet designs and use case, (M3.2.1) Automated loading supports 200 chip arrays, and (M3.3.1) Finish first interconnect process run for 200 chip array. The BP1 Go/No-Go Decision Point (G/NG 1) 200 chip demonstrator sample was in the process of final assembly and test at the end of BP1 on December 31, 2023. The objective was successfully achieved on January 16, 2024, when a wired 200-chip sample underwent confirmation testing that demonstrated a 93.75% first pass electrical yield that successfully exceeded the 75% first-pass electrical yield BP1 G/NG requirement. In addition, all 93.75% of the sample devices lit up, further demonstrating the ability to assemble and transfer small LED chips without damaging their functionality.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗