SEARCH · Engineering Papers

Results for “data base for model validation”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,585 records · Page 88

All sources NASA NTRS DOE OSTI

DOE OSTI2023

L-dwarf Detection from SDSS Images using Improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey (SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L-dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs, including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i – z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

MACHINE LEARNING BASED CHEMICAL EXPLOSIVE MODE ANALYSIS (ML-CEMA)

The software consists of a Machine Learning based Chemical Explosive Mode Analysis (ML-CEMA) tool for advanced computational flame diagnostics. CEMA, originally based on eigen-analysis of the local thermochemical system, is capable of identifying reaction fronts and limit phenomena such as auto-ignition and extinction in practical combustion systems, such as internal combustion engines and gas turbine combustors. However, the original CEMA is computationally expensive for large reaction mechanisms that are typically needed to describe fuel chemistry of practical large-hydrocarbon fuels. This novel ML-CEMA tool employs a ML technique to accelerate the eigen-analysis of the basic CEMA approach by orders of magnitude, thus making it suitable for practical fuels. In ML-CEMA,zero-dimensional (0D) reactors and one-dimensional (1D) premixed flames are first used to generate a large number of data points for neural network based ML training. The trained ML model is then used to perform CEMA prediction. This ML-CEMA tool has been demonstrated in canonical 0D and 1D configurations as well as highly-transient three-dimensional spray flames exhibiting multi-mode turbulent combustion, showing promising results. ML-CEMA, as a standalone tool, can be used for computationally-efficient diagnostics of massive datasets generated from both experiments and simulations. For example, based on spatially resolved measurements of a small set of reactive scalars(such as temperature, hydroxyl radical and formaldehyde), ML-CEMA can effectively identify flame fronts and rare events. ML-CEMA also provides a robust online or offline flame feature detection tool. When used for on-the-fly simulations, ML-CEMA further enables zone-adaptive combustion modeling, in which the predicted eigenvalue is used as a robust mode indicator for judicious assignment of locally-valid combustion models. This ML-CEMA based zone-adaptive model can lead to substantial computational cost savings when used for large-scale simulations of multi-mode combustion systems. Third Party Code Web Page to Download Code Web Page Location of Third Party License

Xu, Chao↗

DOE OSTI2021

Towards robust autonomous impedance spectroscopy analysis: A calibrated hierarchical Bayesian approach for electrochemical impedance spectroscopy (EIS) inversion

Distribution-based analyses, such as the distribution of relaxation times (DRT) and the distribution of diffusion times (DDT), present model-free alternatives to equivalent circuit modeling for analysis of electrochemical impedance spectroscopy (EIS) data. However, reconstructing such distributions from noisy impedance data is an ill-posed problem that must be solved with specialized inversion algorithms, requiring careful control and tuning. Furthermore, most inversion algorithms developed to date can only solve problems of limited complexity. Herein, we present a new hierarchical Bayesian method for EIS inversion, leveraging efficient algorithms for optimization and Hamiltonian Monte Carlo (HMC) sampling to solve models of arbitrary complexity. We overcome the challenge of ad-hoc parameter tuning by encoding intrinsic characteristics of the DRT and DDT into flexible prior distributions and “pre-calibrating” the model to simulated data. This approach is versatile, highly robust to noise, and provides quantitative estimates of both the error structure of the data and the uncertainty in the recovered distributions. The model is validated with simulated data to demonstrate accurate recovery of the DRT and the DDT. The method also shows promise for simultaneous recovery of multiple distributions, raising the intriguing possibility of semi-autonomous EIS analysis and ad-hoc model construction. Finally, the practical utility of the method is illustrated with experimental data. Throughout, we draw comparisons to several recently published EIS inversion methodologies.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Quantifying the hierarchy of structural and mechanical length scales in granular systems

Continuum modeling of granular media is made possible by the existence of a length scale at and above which grain-resolved properties can be meaningfully homogenized. Progress has been made in identifying such length scales relevant to local structural properties such as porosity. However, a systematic analysis of scales above which different mechanical properties can be homogenized has yet to emerge. Here, X-ray tomography and 3D X-ray diffraction data are examined to identify such length scales. The data was obtained in-situ in compressed granular materials with rigid and flexible confinement. The experimental data are supplemented with validated discrete element simulations which examine different system sizes and different boundary conditions. Overall, our study reveals a hierarchy in the length scales of granular solids, with lengths governing structural variables being the shortest, lengths of stress variables being intermediate, and lengths of energy dissipation being the longest. All structural and mechanical length scales obey a power law based on the theory of Geostatistics, implying that the length scales can be found by analyzing samples significantly smaller than the length scales themselves. The length scales are also found to be sensitive to boundary conditions, implying that they are extrinsic features of granular media.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Enhancing the accuracy and generality of the Debye–Grüneisen Model: Optimizing the volume dependence for accurate predictions across varied compositions

In this work, we have introduced an optimized Debye-Grüneisen model that revolutionizes the determination of the Debye temperature and Grüneisen parameters. Unlike conventional methods, our model requires only the 0 K energy volume data for a material as input, eliminating the need to determine the bulk modulus and its pressure derivative, which often pose challenges due to numerical uncertainties. This unique feature sets our model apart from existing approaches and streamlines the process, enabling accurate predictions of thermal expansion behavior across various materials. To demonstrate its effectiveness, we showcase its excellent agreement with measured coefficients of thermal expansion (CTE) for the nickel-cobalt-chromium-aluminum-yttrium (Ni-Co-Cr-Al-Y) bond-coating system. Additionally, we apply our approach by conducting a high-throughput search for potential bond-coating materials among 90,000 compositions within the aluminum-cobalt-chromium-iron-nickel (Al-Co-Cr-Fe-Ni) system. From this extensive search, four compositions are synthesized, and the measured CTE values agree very well with theoretical predictions, hence validating our approach. In conclusion, the current optimized Debye-Grüneisen model combined with Density Functional Theory (DFT)-based thermodynamic database enables reliable and efficient high-throughput calculations of CTE of of a material without expensive phonon calculations.

Bond coating materials↗

DOE OSTI2021

A HPC Theory-Guided Machine Learning Cyberinfrastructure for Communicating Hydrometeorological Data Across Scales

High-resolution predictions of hydrometeorological variables are critical for supporting hydropower generation decisions and flood control at hydroelectric power plants. Traditional climate and hydrologic models rely on the numerical simulation of detailed physical processes. Therefore, running these simulations is time-, labor-, and computation-intensive. Improving the spatial and temporal resolution in these modeling outputs could lead to cubic increases in both the simulation time and computational demands, rendering high-resolution hydrometeorological predictions expensive and impractical. Many past studies apply the super resolution (SR) technique to downscale climate models using deep learners. However, deep learners are deemed “black-boxes,” as their derivation processes from low-resolution outputs to high-resolution outputs are often hidden. Their results are difficult for domain scientists to interpret and validate. Thus, there is a need for an exploratory machine learning approach that can partially integrate domain-specific theory and knowledge into the data-driven mapping process between simulation outputs of different spatial scales. The domain-specific theory and knowledge can be incorporated into the data model through an inductive approach in which process-related environmental variables are used and analyzed as key drivers (i.e., environmental surrogates) to reflect the complex physical processes. Many of these variables, such as land use land cover, soil types, topography, digital elevation, air temperature, and various watershed characteristics, can be directly measured through sensors or remote sensing techniques. Additionally, SR applications that can downscale hydrological and hydrodynamics models to efficiently produce high-resolution (1 m) flood depth grids are still rare. Since the flood depth grid can be used to support critical decisions for flood control operation at hydroelectric power plants, it is crucial to enable an SR-based capability for interpolating high-resolution flood inundation maps.

13 HYDRO ENERGY↗

DOE OSTI2021

Nuclear Materials Process Modeling at the Y-12 National Security Complex

The Y-12 National Security Complex (Y-12) has implemented process modeling for various accountable nuclear materials operations that are performed throughout the plant. Using a discrete, event-based dynamic simulation program, key nuclear material streams are modeled, allowing Y-12 to effectively manage numerous points of interest within the plant’s production operations. Integration of the various material processes into a single, interdependent supply and demand model is one of the ongoing focuses within Y-12’s process modeling effort. The primary purpose of using dynamic simulation modeling is to allow for analysis of the nuclear materials inventories and forecasted supplies based on future demands. Analysis of these inventories includes capacity evaluation, bottleneck mitigation, and assessments of individual pieces of equipment to inform future facility investment decisions and associated project schedules. Modeling of the nuclear materials processes throughout the complex also allows for incorporation of changes relevant to production capabilities such as the upcoming transition of specific operations to the new Uranium Processing Facility. Prior to implementation of process modeling, Y-12 forecasted supply and demand of accountable nuclear materials streams using Microsoft Excel. With deterministic models such as Microsoft Excel, the annual forecasts, generated within data input condition parameters, can only provide a fixed point of data. Fixed data cannot simulate integrated material streams and account for the possibility of occurrences and other changes that dynamic simulations take into consideration. Y-12’s dynamic process modeling allows integrated simulations of multiple accountable nuclear materials processes, including supply and demand forecasting and analysis, and is a coordinated effort involving many steps of verification and validation (V&V), site briefings, testing, reporting, data mining, planning, and documentation that spans various programs throughout the Y-12 complex.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Preliminary clustering properties of the DESI BGS bright targets using DR9 Legacy Imaging Surveys

ABSTRACT We characterize the selection cuts and clustering properties of a magnitude-limited sample of bright galaxies that is part of the Bright Galaxy Survey (BGS) of the Dark Energy Spectroscopic Instrument (DESI) using the ninth data release of the Legacy Imaging Surveys (DR9). We describe changes in the DR9 selection compared to the DR8 one and we also compare the DR9 selection in three distinct regions: BASS/MzLS in the north Galactic Cap (NGC), DECaLS in the NGC, and DECaLS in the south Galactic Cap (SGC). We investigate the systematics associated with the selection and assess its completeness by matching the BGS targets with the Galaxy and Mass Assembly (GAMA) survey. We measure the angular clustering for the overall bright sample (rmag ≤ 19.5) and as function of apparent magnitude and colour. This enables to determine the clustering strength r0 and slope γ by fitting a power-law model that can be used to generate accurate mock catalogues for this tracer. We use a counts-in-cells technique to explore higher order statistics and cross-correlations with external spectroscopic data sets in order to check the evolution of the clustering with redshift and the redshift distribution of the BGS targets using clustering redshifts. While this work validates the properties of the BGS bright targets, the final target selection pipeline and clustering properties of the entire DESI BGS will be fully characterized and validated with the spectroscopic data of Survey Validation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

A dynamic kinetic model captures cell-free metabolism for improved butanol production

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 hours. Here, we present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Estimating internal moisture generation rates in recently constructed, occupied homes in the southeastern United States

Internal moisture generation (IMG), or moisture generated by building occupants via activities such as respiration, cooking, bathing, and cleaning, is a critical input required for design, analysis, and simulation of building enclosure and heating, ventilation, and air conditioning (HVAC) systems. Based on previously published values, ASHRAE Standard 160-2021 provides guidance for estimating IMG rates for moisture control design analysis, which is based on occupied home datasets collected in the 1980s and 1990s. Residential energy use simulation software also utilizes estimates for IMG for energy rating, energy analysis, and code compliance calculations, as specified in ANSI/RESNET/ICC Standard 301. Data quantifying IMG rates in newer homes is useful in determining the continued relevance of current design and simulation guidance, and whether the guidance represents conditions found in new housing stock. ASHRAE Research Project 1844, conducted by the Florida Solar Energy Center (FSEC), a research institute of the University of Central Florida, estimated IMG rates in newer occupied homes built since 2013 in the southeastern US using a moisture balance model approach. Furthermore, the project obtained occupied home data in cooperation with a US Department of Energy Building America research project to characterize indoor air quality (IAQ) in newer US homes, along with presence, functionality, and occupant use of control measures. Full-scale laboratory homes operating with known IMG rates were used to validate a moisture balance model and quantify the accuracy of estimates obtained when the model is applied to data from occupied homes with unknown IMG rates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Semantic Property Graph for Scalable Knowledge Graph Analytics

Graphs are a natural and fundamental representation to describe entities, relationships, activities, and evolution of complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two of the most used data models to encode information in a graph. Both models are similar in terms of using basic graph elements such as nodes and edges but differ in terms of the modeling approach, expressibility, serialization, and target applications. RDF is a flexible data exchange model for expressing information about entities but it tends to a have high memory footprint and inefficient storage, which does not make it a natural choice to perform scalable graph analytics. In contrast, LPG has gained traction as a reliable model to perform scalable graph analytic tasks such as sub-graph matching, network alignment, and real-time knowledge graph query. It provides efficient storage, fast traversal, and flexibility to model various real-world domains. At the same time, the LPG lacks the support of a formal knowledge representation such as an ontology to provide automated knowledge inference. We propose Semantic Property Graph (SPG) as a logical projection of reified RDF into the LPG model. SPG continues to use RDF ontology to define the type hierarchy of the projected graph and validate it against a given ontology. We present a framework to convert reified RDF graphs into SPG using two different computing environments. We also present cloud-based graph migration capabilities using Amazon Web Services.

Purohit, Sumit↗

DOE OSTI2023

Evaluating the Interplay between Trajectory Segmentation and Mode Inference Error

Travel behavior changes are essential to transportation decarbonization. Travel diaries, consisting of sequences of trips between places, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of Global Positioning System tracking surveys. These algorithms have typically been validated using prompted recall of presegmented trips, thus disregarding the continuity of mode inference. Phone operating systems have adopted battery-conserving techniques, but the resulting data collection errors have not been studied extensively. We introduce a framework to evaluate the accuracy of trip length computations and mode inference by analyzing continuous mode-segmented trajectories for groups of trips. We then use the framework to identify the input data quality and the impact of postprocessing. Our primary inputs to this evaluation are MobilityNet, a public dataset containing information from three artificial timelines covering 15 different travel modes, and sample open-source travel diary creation algorithms from the OpenPATH project. Our framework concretely shows that the variance of the distance error drops from (0.217, 0.0848) to (o.011, 0.0407) (Android, iOS) after postprocessing. Similarly, the weighted F-scores for mode inference increase from (0.25, 0.29) to (0.60, 0.74) (iOS, Android) between random forest and geographic information system-based models. We hope that this standardized method will be adapted to evaluate other, potentially proprietary, travel diary algorithms. Finally, the results can be used to understand and improve the state of the art in the travel diary creation field.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Preliminary Plan to Inform Testing of a Heat Exchanger Test Article

This report presents a preliminary plan to guide the qualification testing of advanced heat exchanger (HX) components for nuclear-to-industrial heat transfer applications. The objective is to establish a defensible, physics-based methodology that integrates computational modeling, targeted experimentation, and in-service inspection considerations to demonstrate component performance and reliability under representative reactor conditions. The analysis identifies Sodium-cooled Fast Reactor (SFR) and High-Temperature Gas-cooled Reactor (HTGR) systems as reference configurations in terms of temperature, pressure, and chemical environment. Within these operating envelopes, dominant degradation mechanisms— including creep–fatigue interaction, flow-induced vibration, corrosion, and diffusion-bond deterioration—were evaluated to define test requirements. A comprehensive computationalexperimental framework is proposed to support life prediction and qualification activities. The framework couples high-fidelity structural-mechanics, thermal-hydraulic, and fluid-structure interaction models with accelerated degradation testing to produce a traceable linkage between microstructural evolution, mechanical performance, and remaining useful life (RUL). The approach adheres to established Verification, Validation, and Uncertainty Quantification (VVUQ) standards (ASME V&V 10/20; NUREG-2152) and incorporates a digital-twin architecture for continuous model refinement through data assimilation. The plan further outlines testing methodologies, including pre-test analyses, test-loop design parameters, and sensor placement strategies that maximize information yield while maintaining mechanistic fidelity. Complementary sections describe in-service inspection (ISI), on-line monitoring (OLM), and structural-health-monitoring (SHM) techniques applicable to compact HX geometries typical of advanced reactors. Collectively, these activities establish the technical foundation for demonstrating 40-60-year equivalent service life of advanced heat exchangers in support of the U.S. Department of Energy’s Advanced Reactor and Integrated Energy Systems programs. The forthcoming phase will execute the defined pre-test analyses, initiate hardware fabrication, and implement the integrated testing campaign to validate the proposed qualification methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

A Parametric Reduced-Order Model for Inverter Short-Circuit Response in Protection Studies

This paper presents a reduced-order model (ROM) for grid-following (GFL) inverters that reproduces inverter fault current trajectories, including sub transients, transient, and steady-state phases, across a range of fault types, locations, and pre-fault operating points. . The proposed model is developed by: Constructing the positive- and negative-sequence current with parameterization fitted by large data training and fitting Validating using EMT simulation against EMT full model and demonstrating the ROM's capability to capture fault current magnitude, phase angle, and oscillatory transients. Building a standard EMT simulation platform library component for easy configuration and application.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Predicting System Response at Unmeasured Locations

Traditional techniques to derive dynamic specification for components have a great deal of uncertainty. One of the major sources of uncertainty is that the number of response measurements in the operational system environment is insufficient to determine the component motion. This inadequacy is due to logistical limitations for data recording in field testing and space limitations for accelerometers, strain gages and associated wiring. Available measurements are often some distance from the component and therefore do not represent component motion. Typical straight-line envelopes of these unrepresentative measurements guarantee an increase in the uncertainty. In this paper multiple methods are attempted to expand a sparse set of field test measurements on a system to responses of interest that cannot be measured in the field due to the limitations. Proof of concept is demonstrated on the Modal Analysis Test Vehicle (MATV). The responses of interest, known as “truth responses”, are measured in a system vibration environment along with an optimized sparse set of 30 field responses. Methods to expand the field responses to the truth responses are demonstrated by comparing the acceleration spectral density of the expanded response to the measured response. Two methods utilize a validated finite element model of the MATV. One is developed from purely experiment based frequency response functions of a laboratory pre-test. Finally, these approaches are designed to drastically reduce the uncertainty of the component in-service motion as a basis for developing specifications that are guaranteed to be conservative with a known (instead of unknown) conservatism.

42 ENGINEERING↗

DOE OSTI2020

BISON capability and validation for U3Si2, Cr2O3-doped UO2, FeCrAl, and Cr-coated Zircaloy ATF concepts

Development, implementation, and validation of material and behavior models for accident tolerant fuel (ATF) concepts in the BISON fuel performance code began in 2014 in response to the events that occurred at the Fukushima Daichii nuclear power plant in March 2011. Early on, the focus was on U3Si2 fuel and FeCrAl cladding as part of a high-impact problem through the Nuclear Energy and Advanced Model- ing Simulation program [1]. Then, developments for Cr2O3- doped UO2 fuel, as well as SiC-SiC and Cr-coated Zircaloy cladding began based upon industry interests. Near the end of fiscal year 2018 the Consortium for Advanced Simulation of Light Water Reactors took over further Accident Tolerant Fuel (ATF) work in BISON in support of Nuclear Regulatory Commission (NRC) engagement. Discussions with the NRC clarified their list of priority fuel and cladding concepts, which included Cr2O3-doped UO2 and U3Si2 fuels, as well as Cr-coated Zircaloy and FeCrAl claddings. Further, reports similar in form to NUREG/CR-7024 [2] that was developed for traditional light water reactor (LWR) materials, UO2 fuel with Zircaloy claddings were created for the priority fuel and cladding concepts. In this paper select details from the reports entitled "ATF material model development and validation for priority fuel concepts" [3] and "ATF material model development and validation for priority cladding concepts" [4] are presented. First, a brief overview of the available models and their associated uncertainty are provided for the priority fuel and cladding concepts. Then, for the concepts with experimental data, comparisons between simulation and experiment are shown taking into account forward uncertainty quantification (UQ) techniques. For the concepts without publicly available experimental data, parametric studies incorporating forward UQ are completed to verify claims made about the materials’ behavior.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗