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DOE OSTI2022

Identifying Modular Construction Worker Tasks Using Computer Vision

Modular construction is increasingly being seen as an attractive method for delivering building projects due to advantages in safety, quality, and lead-time. Despite these benefits, this method still relies heavily on human labor, which causes variability in factory assembly-line performance that can erode performance benefits of modular construction. Continuous improvement methods can alleviate some of these issues, but they also require continuous monitoring of human workers' performance. Due to limitations of manual time study and automated sensor-based monitoring methods, recently computer vision-based methods have gained momentum in identifying the activities of construction workers from the videos of onsite construction. Therefore, this paper explores the use of computer vision-based human activity recognition techniques to identify and classify worker activities in modular construction videos. Computer vision-based tracking method has been used to track the human workers in each frame, and Resnet-50 network has been used to classify the activity of tracked workers. Evaluation of this framework has achieved higher than 90% accuracy and recall in testing.

computer vision↗

DOE OSTI2020

A Benthic Habitat Monitoring Approach for Marine and Hydrokinetic Sites (Final Technical Report)

This final technical report summarizes the work completed as part of the Standardized and Cost-Effective Benthic Habitat Mapping and Monitoring Tools for MHK Environmental Assessments project funded by U.S. Department of Energy (DOE) under contract DE-EE007826 to Integral Consulting Inc. The overall goal of this project was to demonstrate a consistent, repeatable, and semi-automated seafloor and sediment mapping approach for rapidly characterizing benthic physical and biological/habitat conditions to support marine environmental assessments for marine and hydrokinetic energy sites. The approach evaluated combines sediment profile imaging and plan view (SPI/PV) technology with multibeam echosounder surveys as an effective and low-cost benthic habitat mapping protocol. A key innovation was the development of a semi-automated computer vision system that standardizes the extraction of data from the SPI/PV images. Other elements of the project were SPI camera hardware modifications, including the design and fabrication of a prototype “power” SPI camera to improve camera prism penetration in firm substrates, and outreach to agency regulators and other stakeholders on this habitat mapping approach. This technical report consists of five main subsections that summarize: 1) the benthic mapping approach; 2) benthic mapping results from the three areas’ surveys; 3) the development and performance of the image processing algorithms; 4) SPI camera hardware improvements and prototype testing; and 5) the regulatory outreach efforts.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)

This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

TuFF internal WRAP for Rapid Pipeline Repair (TuFF iWRAP)

The goal of “TuFF internal WRAP for Rapid Pipeline Repair” (TuFF iWRAP) program was to develop a novel material system and placement process to fabricate structural pipe within the existing deteriorated pipelines without disruption of gas delivery. The team (University of Delaware – Center for Composite Materials (UD-CCM) and Plitzie Inc.) addressed this challenge by developing a new material feedstock and pipe in pipe (PIP) repair strategy. This allows the potential for significant cost reduction and has minimum operational impact on gas customers. The new robotic based placement design allows discontinuous placement of pipe sections creating a stand-alone structural liner within the legacy pipeline without the need for pipe shutdown. Here, the material is supplied using a tethered material feeding system and is placed and UV cured with the internal Wound Rapid Automated Placement (iWRAP) system. This provides maximum placement efficiency capable of traversing 90 angle bends in 12-inch pipe and overall design customization to meet pipe repair requirements (e.g., variable wall thickness, bridging gaps, etc.). UV-curable fiber reinforced composite material has been optimized to meet structural performance and placement/cure times. Superior strength and fatigue life has been demonstrated by improving fiber-matrix adhesion using new fiber sizing for UV resins. Rapid cure approaches using new liner and resins have been evaluated with industry. The appropriate design of the section joints has been developed and tested. We estimate coating time to be ~100 hours per mile enabling typical pipe repair within 1 week.

03 NATURAL GAS↗

DOE OSTI2024

TEAMER - Field Demonstration of MarineSitu’s Marine Energy Monitoring Tools - CRADA 664 (Abstract)

In order to effectively monitor for marine life around marine energy devices and thus minimize the risk of collision, multiple sensors working in coordination and augmented with around-the-clock automated monitoring algorithms need to be installed in challenging high-energy tidal and wave environments. Such systems are often too expensive for widespread adoption, or lack sufficient sensors or smarts to enable around-the-clock, real-time monitoring without human involvement. MarineSitu has been working to tackle this problem by developing a low-cost, combined sonar and stereo camera sensor array with connected real-time AI-based algorithms for automatically detecting marine life in these marine energy suitable environments. In this TEAMER project with Pacific Northwest National Lab (PNNL), MarineSitu will be testing this novel sensor system for the first time in the high-energy tidal channel environment at PNNL’s Marine and Coastal Research Lab. Throughout this deployment, MarineSitu will be monitoring their system and running analytics on the sensor’s data in real-time. Meanwhile, PNNL Data Scientists and Ocean Engineers, will be evaluating the system’s effectiveness and ease of use both as a tool for plug-and-play environmental monitoring and novel environmental monitoring research. In doing so, the team will improve MarineSitu’s system and software, produce insightful data products, and develop novel visualizations and AI algorithms for combining and analyzing the data produced by systems like MarineSitu’s.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

TEAMER – Field Demonstration of MarineSitu’s Marine Energy Monitoring (Abstract)

16 TIDAL AND WAVE POWER↗

DOE OSTI2020

Characterization and differentiation of aluminum powders used in improvised explosive devices – Part 1: Proof of concept of the utility of particle micromorphometry

Abstract Aluminum (Al) powders are commonly used in improvised explosive devices as metallic fuels, a component of explosive mixtures. These powders can be obtained readily from industrial‐scale and consumer products, and produced using unsophisticated “kitchen chemistry” techniques. This research demonstrates the potential of automated particle micromorphometry for comparisons between known source and questioned Al powders recovered from IEDs, as well as for insight into the method of Al powder manufacture. Al powder samples were obtained from legitimate manufacturers, and 56 samples were produced “in‐house” from Al‐containing spray paints and ball‐milled Al foils. Transmitted light microscope images of Al powder particles were acquired using an automated stage with automated z‐focus; 17 size and shape parameters were measured for all particles. Approximately 37,000–2,500,000 particles/sample were analyzed using an open‐source statistical package with customized code. Dimensionality reduction was required for processing the large datasets: eight of the 17 measured variables were selected based on inspection of the correlation matrix. Data from four subsamples from each of the 56 samples produced using “in‐house” methods were analyzed using ANOVA to assess the within‐ and between‐sample variation. High within‐sample variation was noted; however, ANOVA and post‐hoc Tukey's honestly significant difference (HSD) tests demonstrated that the between‐sample variation was substantially larger than the within‐sample variation. Each sample could be differentiated from all other samples in the test set. Future experiments will focus on ways to reduce the within‐sample variation, and additional statistical and microanalytical methods to classify sources and confidently constrain the method of Al powder manufacture.

Baldaino, JenaMarie↗

DOE OSTI2025

Cryptic cycling by electroactive bacterioplankton in Trout Bog Lake

The potential for extracellular electron transfer (EET) is a prevailing genomic feature of humic lake bacterioplankton. However, there has been little evidence for the substantial ecological contribution predicted by genetics. We hypothesized that anoxygenic phototrophic electrotrophs and accompanying heterotrophic electrogens cycle dissolved organic matter (DOM) between oxidized and reduced states. We predicted that such bacterioplankton would exhibit diel-scale oscillations due to the light dependency of photosynthesis. Using Trout Bog Lake in Wisconsin, USA, as our model ecosystem, we profiled the water column with depth-discrete metagenomic, physiochemical, and electrochemical analyses. We observed variation in oxidation reduction potential (ORP) in response to sunlight, initiating at depths populated by anoxygenic phototrophs with EET genes. We developed an automated buoy to measure electric current flow between many pairs of electrodes simultaneously, observing correlation in electron consumption to sunlight. Our results, combined with published metatranscriptomic analysis, indicate the occurrence of electron cycling between phototrophic oxidation (electrotrophic metabolism) by Chlorobium and anaerobic respiration (electrogenic metabolism) by Geothrix, involving DOM. We also repeatedly observed gradual seasonal increases in hypolimnion ORP throughout summer. These diel and seasonal patterns imply that electroactive DOM mediates the ecology of electroactive bacteria in lakes, controlling humic lake methane emissions.IMPORTANCEWe investigated the physical, chemical, and redox characteristics of a bog lake and electrodes hung therein to test the hypothesis that dissolved organic matter is being cycled between oxidized and reduced states by electroactive bacterioplankton powered by phototrophy. To do so, we performed field-based analyses on multiple timescales using both established and novel instrumentation. We paired these analyses with recently developed bioinformatics pipelines for metagenomics data to investigate genes that enable electroactive metabolism and accompanying metabolisms. Our results are consistent with our hypothesis and yet upend some of our other expectations. Our findings have implications for understanding greenhouse gas emissions from lakes, including electroactivity as an integral part of lake metabolism throughout more of the anoxic parts of lakes and for a longer portion of the summer than expected. Our results also give a sense of what electroactivity occurs at given depths and provide a strong basis for future studies.

carbon emissions↗

DOE OSTI2023

Survey of Time Shift Detection Algorithms for Measured PV Data

In this research, three variations of time shift detection algorithms were tested for their ability to detect time shift issues (including daylight savings time and random time shifts) in measured PV data sets. Two algorithms from the Python PVAnalytics package were assessed, and one algorithm from the Solar-Data-Tools package was assessed. Each algorithm's ability to accurately detect and measure time shifts was assessed.

automated preprocessing↗

DOE OSTI2022

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. In this work, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO 3 using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Acquire and Test IDAES Framework Components

The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Automation and optimization of stopping and range of ions in matter simulation runtime

Prior to every ion implantation experiment a simulation of the ion range and other relevant parameters is performed using Monte-Carlo based codes. Although increasing computing power has improved the speed of these calculations, the demands on Monte-Carlo codes are also increasing, requiring evaluation of the optimal number of simulations while ensuring accuracy within threshold bounds. We evaluate the “Stopping and Range of Ions in Matter” (SRIM) code due to its widespread usage. We show how dividing simulations into multiple parallel simulations with different random seeds can lead to calculation speedup and find lower bounds for the required number of ion traces simulated based on an exemplar system of a Ga focused ion beam and a high energy C beam as used in high linear energy transfer testing. Here our results indicate simulations can yield results within the underlying data accuracy of SRIM at 10X and 100X shorter simulation time than the SRIM default values.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Methods for the Automated Determination of Sustained Maximum Amplitudes in Oscillating Signals

Machine condition monitoring has been proven to reduce machine downtime and increase productivity. The state-of-the-art research uses vibration monitoring for tasks such as maintenance and tool wear prediction. A less explored aspect is how vibration monitoring might be used to monitor equipment sensitive to vibration. In a manufacturing environment, one example of where this might be needed is in monitoring the vibration of optical linear encoders used in high-precision machine tools and coordinate measuring machines. Monitoring the vibration of sensitive equipment presents a unique case for vibration monitoring because an accurate calculation of the maximum sustained vibration is needed, as opposed to extracting trends from the data. To do this, techniques for determining sustained peaks in vibration signals are needed. Here, this work fills this gap by formalizing and testing methods for determining sustained vibration amplitudes. The methods are tested on simulated signals based on experimental data. Results show that processing the signal directly with the novel Expire Timer method produces the smallest amounts of error on average under various test conditions. Additionally, this method can operate in real-time on streaming vibration data.

Industrial Internet of Things↗

DOE OSTI2022

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles

Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. Here, we present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (R g ), pair distance distribution function (PDDF)-derived R g , maximum particle dimension (D max ), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF R g agreement, an important indicator of well-measured data sets. For assistance in D max estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R 2 = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess D max estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Progress Toward Gamma-Ray Imaging for Automated Holdup Measurement in Gloveboxes

Shielded gloveboxes are currently being constructed to facilitate dilution and disposal of many tons of excess plutonium oxide. Measuring holdup in these gloveboxes is expected to be challenging because of the limited available lines of sight through the glovebox shielding. A system of gamma-ray imagers is being developed to provide localization and quantification for holdup. The system will be mounted above the glovebox, where there is minimal shielding. Multiple imagers with overlapping coded-aperture fields of view are employed to enable three-dimensional reconstruction. Compton reconstruction is also available to localize sources outside the coded-aperture field of view. Improved uncertainties with respect to current techniques are expected by virtue of the fixed installation, spectroscopic performance of the detectors, and iterative image reconstruction techniques. Measurement campaigns have been undertaken at an active glovebox at Savannah River Site to test the gamma-ray imaging system in an operational environment, providing a simple test of a single imager that mimics the geometry of the installation proposed for future shielded gloveboxes. Data taken during quiescent periods in the glovebox were used to measure the buildup of material on an outlet filter and record a trend over time. Calibration data was taken with known sources to simplify analysis and provide a reliable assay of the filter. Resulting images make it possible to isolate the filter from other sources and recognize compromised data. This paper will present details of the measurement and analysis methods.

Schmitt, Kyle↗

DOE OSTI2026

An end-to-end workflow for executing a classically bootstrapped variational quantum algorithm on an academic quantum computer

Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.

Clifford↗