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DOE OSTI2025

Magnetic mesh generation and field line reconstruction for scrape-off layer and divertor modeling in stellarators

The design of divertor targets and baffles for optimal heat and particle exhaust from magnetically confined fusion plasmas requires a combination of fast, low-fidelity models (such as EMC3-Lite (Feng 2022 Plasma Phys. Control. Fusion 64 125012)) for scoping studies and high-fidelity ones (such as EMC3-EIRENE (Feng et al 2014 Contrib. Plasma Phys. 54 426–31)) for verification. Both of those approaches benefit from a magnetic flux tube mesh for fast interpolation and mapping of field line segments (Feng et al 2005 Phys. Plasmas 12 052505). A new automated mesh generator for unstructured quadrilateral flux tubes with adaptive refinement is presented and integrated into FLARE (Frerichs 2024 Nucl. Fusion 64 106034). For HSX with an extended first wall, it is found that several layers of flux tubes can span the entire half field period before splitting is required. This is an advantage over the traditional setup of the EMC3-EIRENE mesh where careful construction of several sub- domains is required already for the much tighter present first wall. In particular, there is no longer the need to manually construct a suitable outer boundary for the mesh. The divide and conquer paradigm with unstructured mesh layout offers a powerful alternative for fast head load approximation that is suitable for integration into optimization workflows. Further examples for W7-X and CTH demonstrate the versatile application range.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs

Abstract Flatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Novel platinum group metal-free catalyst ink deposition system for combinatorial polymer electrolyte fuel cell performance evaluation

Here, a new system for rapid and precise fabrication of polymer electrolyte fuel cell (PEFC) membrane-electrode assemblies (MEAs) is introduced to facilitate rapid testing and characterization of platinum group metal-free (PGM-free) catalysts, with minimal waste of materials, using commercial combinatorial hardware and multi-channel potentiostat. The commercial combinatorial system, designed for high-throughput catalyst activity evaluation (i.e., low overpotential and current densities) using gas-diffusion electrodes (GDEs), has been further developed to enable simultaneous evaluation of the high current density performance on air of twenty-five cathodes prepared by coating catalyst-ionomer inks directly onto the membrane using a newly-developed deposition system. A nanopipette or micropipette coupled to a precise peristaltic pump is being used for precise and even catalyst ink deposition onto the membrane in each of twenty-five circular electrodes with areas of <1 cm 2 and in the precise locations required by the commercial combinatorial MEA hardware. The automated ink deposition system is coupled with a custom-designed heated vacuum table mounted on a computer-controlled XY stage to prevent deformation of the membrane and electrode layers during ink deposition onto the membrane. This system was utilized to evaluate the impact of ionomer to catalyst ratio on the hydrogen-air fuel cell performance of the PGM-free catalyst.

25 ENERGY STORAGE↗

DOE OSTI2023

Automatic detection of ship-induced cloud features in satellite imagery

Ships crossing the ocean are known to produce long, curvilinear features called ship tracks visible in satellite imagery via the Twomey effect; however, there has been little exploitation of satellite imagery for broad atmospheric studies or global monitoring of ship emissions due to the difficulty of automated ship track detection. Prior studies are either proof-of-concept, qualitatively assessed, or restricted to a certain time of day. We propose a statistical method for the automated identification of ship tracks and demonstrate using GOES-West ABI data. We first present a human-assisted segmentation method, which we use to generate a ground truth data set of 529 annotated ship tracks in GOES-West ABI products. We then describe a two-stage automated approach comprising a detection stage to generate ship track proposals and a classification stage to reduce false positives. For detection, we present a novel pipeline based around a z-score filtering technique, and for classification, we demonstrate several classifiers from literature. In a final experiment, we quantitatively tune the detection parameters and train the classifier using the ground truth dataset, then test on a sequestered set of images; the detect-then-classify system had an overall Pd of 0.68 and 0.80 for daytime and nighttime data, respectively, and the classifier reduced false positive detections by 67% and 75%.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

DeepGhostBusters: Using Mask R-CNN to Detect and Mask Ghosting and Scattered-Light Artifacts from Optical Survey Images

Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. However, the identification of ghosts and scattered-light artifacts is challenging due to a) the complex morphology of these features and b) the large data volume of current and near-future surveys. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered-light artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms and traditional convolutional neural networks methods. We propose that a multi-step pipeline combining Mask R-CNN segmentation with a classical CNN classifier provides a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

P2P: Point Cloud to Panel Layout Optimization

Building envelope retrofits, despite their benefits on enhancing energy efficiency, progress slowly due to high operating costs. Overclad panelized systems present an attractive solution to make retrofits affordable and easy to install. However, several stages of the retrofit process remain disconnected, suboptimal, and require significant human intervention. This study aims to bridge the gap between digital twin generation and overclad panel installation by automating the design of an optimal panel layout directly from a building envelope point cloud. In this end-to-end approach, the dimensional twin is generated by segmenting the facade point cloud. The facade then undergoes a three-step process to generate an optimized panel layout for integration with automated placement systems.

Philips, Nisha Deborah [ORNL]↗

DOE OSTI2024

Human-in-the-Loop: The Future of Machine Learning in Automated Electron Microscopy

Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Furthermore, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Machine learning-based multidomain processing for texture-based image segmentation and analysis

Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Finally, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique

36 MATERIALS SCIENCE↗

DOE OSTI2026

Persistent Elevated Soot Emissions Induced by Clustered Stochastic Preignition Events

Stochastic Preignition (SPI) is an abnormal combustion phenomenon that can occur in spark-ignition engines particularly under high-load operation. SPI is characterized by uncontrolled initiation of combustion prior to spark discharge, an abnormal combustion process that can lead to severe knock events and significant engine damage. SPI has been associated with fuel properties, lubricant composition, and engine design and operation. Here, in this work, a single-cylinder test engine with a dry-sump oil system was utilized to study the SPI response of E10 and E25 fuels with a range of Reid Vapor Pressure (RVP). An automated test procedure was employed, consisting of ten square-waved load profile segments, with each segment composed of 5 min of low-load operation followed by 25 min of sustained high-load operation. These tests were replicated across multiple days of testing including a lubricant triple flush between tests, and an online Fuel in Oil diagnostic measurement. Exhaust particulate emissions were continuously measured by an AVL microsoot sensor (MSS). Elevated particulate matter emissions were observed to occur concurrently with SPI events as blooms of soot. Particularly after clustered events (i.e., multiple SPI cycles occurring within 10 consecutive engine cycles), high soot emissions were observed to persist over several days of sequential operation despite daily lubricant changes, a complete warm-up procedure, and sustained low-load operation between test segments. This result implies that the particulate emissions trends may be dominated by deposit-based effects, where higher load operation is needed to alter deposition and formation processes. The observed soot blooms were also found to correspond to a reduction in the engine fueling and the fuel engine oil dilution rate despite the engine exhaust remaining at stoichiometric exhaust operation. These observations suggest that post-SPI events, pathways for lubricant migration and consumption into the combustion chamber may occur until these pathways are closed from deposit formation or ring dynamics during extended operation. These observed sooting propensity persisted with all fuels tests, but a linear correlation was observed between the summation of soot and particulate matter index (PMI) value for each fuel as well as SPI events, proving that PMI is a crucial fuel property for reducing SPI.

Splitter, Derek [Oak Ridge National Laboratory (OR↗

DOE OSTI2026

Creating a Training Dataset for Semantic Segmentation of Canal Networks for Irrigation Modernization

Canal infrastructure has provided critical irrigation water to the western United States for over a century. To continue providing vital water resources to the semi-arid West, irrigation systems must undergo maintenance and modernization. Many canal companies are resource-constrained, and because funding opportunities often require detailed knowledge of existing infrastructure, they can struggle to secure financial capital. We address this problem by creating training data for a semantic segmentation deep learning model to map canal networks throughout the western United States. To create a diverse and robust training dataset, we labelled 1-m NAIP imagery with the locations of no canals, wet canals, and dry/vegetated canals. Since creating these datasets is time consuming, we first developed a preprocessing methodology to identify canals within our four study areas. We used NAIP imagery and provided canal centerline data to buffer, standardize, and cluster the imagery, automating the labeling process as much as possible. However, this still required manual cleaning and manual classification of canal type. Challenges arose when canals were interrupted (e.g., road culverts or piped sections) or when nearby features shared similar characteristics (e.g., irrigated fields, trees, and shadows). Combining automated preprocessing with manual refinement produced four detailed canal masks to be used in the semantic segmentation model developed by Richard Tapia.

13 - HYDRO ENERGY↗

DOE OSTI2020

Overcoming small minirhizotron datasets using transfer learning

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massives-cale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. Here, we achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Automated Parameterized CFD Simulations of Phase-Change Material Embedded Heat Exchangers

Thermal storage can be implemented using Phase-Change Materials (PCM), which absorb significant latent heat with a relatively small temperature change. PCM phase-change processes are transient and are driven by thermal diffusion and natural convection – the latter, especially for melting process. Modeling and simulation of PCM heat exchangers is typically computationally intensive due to the relative complex time-dependent underlying physics. This paper presents a study investigating a single straight tube with circular transverse fins in the vertical orientation, using PCM’s with 37oC nominal melting temperature, leveraging automated parametric CFD simulations. The computational domain is reduced to 2D axisymmetric for a segment of the HX comprising a region in between two fins with constant wall temperature as boundary condition. This approach allows for faster evaluations of the transient thermal characteristics for a given design space of interest. Correlations and reduced order models (ROM’s) are developed. Results indicate that the ROM’s reproduce the CFD simulations with high accuracy for designs from the training data set, however, designs from cross-validation and test sets do not always have good agreement. The limited number of designs in the training set is a possible cause for the lack of robustness of the ROM’s and thus additional training designs will be added in future work. The ROM’s solve the simulations on average 500,000 times faster than CFD.

25 ENERGY STORAGE↗

DOE OSTI2026

Prime Time for Model-Predictive Control? Assessing the Technical and Market Readiness of Advanced Controls in Buildings

Despite three decades of extensive research and field testing that have consistently validated the benefits of Model Predictive Control (MPC) in building applications, the technology has seen limited market adoption. This paper evaluates the readiness of MPC for widespread deployment, showcases recent demonstrations and field tests across diverse building types, including residential, small commercial, large commercial, and campus settings. Our results demonstrate that MPC can optimize system operations to achieve load shifting, minimize curtailment of on-site generation, and reduce energy costs by up to 80 %, while maintaining or improving occupant comfort. We also show that MPC can effectively control large assets, such as MW-sized thermal storage systems, and respond to dynamic pricing signals. However, achieving scale remains difficult due to labor-intensive workflows, reliance on a “PhD-in-the-loop” for MPC design and maintenance, susceptibility to fragile data infrastructure, and persistent workforce education and acceptance barriers. To bridge this gap, we outline a transition from bespoke, labor intensive prototypes toward streamlined, segment-targeted deployment strategies that leverage model templates, semantic tools, and generative AI. By automating control configuration and reducing engineering effort, these recommendations provide a pathway for transforming successful research demonstrations into scalable, market ready solutions for MPC-based controls.

Pritoni, Marco↗

DOE OSTI2025

Automated Detection of Qubit Structures on Quantum Chips

In this study, YOLO (You Only Look Once), a well-known object detection and image segmentation model, is used to detect qubits on a quantum chip. The model is trained by exposing it to various images of objects of interest and by tweaking various training parameters to allow the model to learn from a limited and highly specialized dataset of SEM imagery. The model was provided with numerous images of qubits and various prominent features of note located on a qubit. These detections were then integrated into a simple GUI, allowing for real-time feedback alongside SEM usage. This will allow us to create various functions enabling automated functions like chip-wide scanning, auto-focusing, and image correction.

Perjuste, Ruth [Wellesley Coll.]↗

DOE OSTI2023

A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging

Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials

Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.

42 ENGINEERING↗

DOE OSTI2022

Automated detection of photovoltaic cleaning events: A performance comparison of techniques as applied to a broad set of labeled photovoltaic data sets

Extracting accurate soiling loss information from photovoltaic (PV) production data first requires segmenting the time series data per natural or manually occurring cleaning events. Maintenance logs are often incomplete, rain data are often unavailable, and the debate on rain thresholds for cleaning and dew or wind cleanings is still ongoing. The present work aims to overtake these issues by improving automated methods to detect these cleaning events and therefore improve extraction of soiling loss information. Time series power production data from 22 PV inverters were labeled for natural or manually occurring cleaning events. The data sets were carefully selected to include varying degrees of soiling, cleaning events, and noise. Several algorithms, including filtering logic and change point detection, were examined for efficacy at detecting the labeled cleanings. All the methods introduced except for changepoint detection showed significant improvement at detecting the labeled cleaning events per the mean F 1 score. Furthermore, the highest performing cleaning detection algorithm achieved an absolute increase in the mean F 1 score of 43% over the default version of the RdTools stochastic rate and recovery (SRR) algorithm. The highest performing algorithm included irradiance filtering and a cleaning detection threshold, adjusted based on the 40-day centered rolling median of the absolute day-to-day deviations in the daily performance index (PI). Furthermore, these improvements are promising as cleaning detection is an essential step in the automated analysis of PV soiling.

14 SOLAR ENERGY↗

DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗