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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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At least 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD

Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗
DOE OSTI2016

Automated System of Diagnostic Monitoring at Bureya HPP Hydraulic Engineering Installations: a New Level of Safety

This article presents the design, hardware, and software solutions developed and placed in service for the automated system of diagnostic monitoring (ASDM) for hydraulic engineering installations at the Bureya HPP, and assuring a reliable process for monitoring hydraulic engineering installations. Project implementation represents a timely solution of problems addressed by the hydraulic engineering installation diagnostics section.

42 ENGINEERING↗
DOE OSTI2018

Hydraulic Drive of a Shipping Lock Gate with Automated Frequency-Throttle Control System

This project developed and built a hydraulic actuator prototype with an automated control system for moving the lifting and lowering gates of a shipping lock. In the course of the design engineering, a hydraulic actuator was experimentally developed. Trial operation of a hydraulic actuator was conducted on Lock No. 26 of the Balakovo RGSiS. This design of a hydraulic actuator is recommended to be used in upgrading and designing locks of hydraulic engineering constructions.

42 ENGINEERING↗
DOE OSTI2021

Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM)

Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along with appropriate training encoder-decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An Automated, High-Throughput Method for Interpreting the Tandem Mass Spectra of Glycosaminoglycans

The biological interactions between glycosaminoglycans (GAGs) and other biomolecules are heavily influenced by structural features of the glycan. The structure of GAGs can be assigned using tandem mass spectrometry (MS{sup 2}), but analysis of these data, to date, requires manually interpretation, a slow process that presents a bottleneck to the broader deployment of this approach to solving biologically relevant problems. Automated interpretation remains a challenge, as GAG biosynthesis is not template-driven, and therefore, one cannot predict structures from genomic data, as is done with proteins. The lack of a structure database, a consequence of the non-template biosynthesis, requires a de novo approach to interpretation of the mass spectral data. We propose a model for rapid, high-throughput GAG analysis by using an approach in which candidate structures are scored for the likelihood that they would produce the features observed in the mass spectrum. To make this approach tractable, a genetic algorithm is used to greatly reduce the search-space of isomeric structures that are considered. The time required for analysis is significantly reduced compared to an approach in which every possible isomer is considered and scored. The model is coded in a software package using the MATLAB environment. This approach was tested on tandem mass spectrometry data for long-chain, moderately sulfated chondroitin sulfate oligomers that were derived from the proteoglycan bikunin. The bikunin data was previously interpreted manually. Our approach examines glycosidic fragments to localize SO{sub 3} modifications to specific residues and yields the same structures reported in literature, only much more quickly. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Systematic Ion Source Parameter Assessment by Automated Determination of the Distribution of Ion Acceptance (DIA) Using APLI

The determination of the spatially resolved ion signal with atmospheric pressure laser ionization (APLI), which was introduced as distribution of ion acceptance (DIA), serves as a valuable tool for the understanding of complex and highly dynamical conditions in modern atmospheric pressure (AP) ion sources. DIA provides information about fluid dynamics, ion transport, and ion transformation processes in such sources and is an ideal basis for the validation of numerical models of the dynamics in the ion source enclosure. We present a fully automated setup for DIA measurements, which enabled us to acquire a comprehensive dataset of over 700 individual DIA measurements in a commercial AP ion source (Bruker Multi Purpose Ion Source, MPIS). Ion source parameters as voltages, gas heater temperatures and gas flows, were varied, and the effect of those parameters on the DIA of a chemically inert analyte, pyrene, was systematically investigated. It is shown that the response of the DIA is nonlinear and that gas dynamics largely dominates the ion transport in the ion source. Particularly, the position of the heated nebulizer, which is used to introduce one of two gas flows and the analyte into the ion source chamber, had a profound effect on the DIA. This suggests that the gas dynamics in the source switches between different states. The now available comprehensive DIA dataset reveals such critical effects and will guide further numerical modeling efforts to understand the details of the dynamics of ions in the source chamber. .

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Automated Model Updating Effect on the Linear and Nonlinear Dynamic Responses of Historical Masonry Structures

Finite element models supply the simulation of the problems to obtain the structural behaviors under different loading conditions. The updating processes based on changing the uncertain parameters such as material properties, boundary conditions, meshing size etc. are made to create actual finite element models in order to represent the real conditions of the problems. This procedure can be made using two methods, namely manual model updating and global/local automatic model updating. The global/local automatic model updating procedures are very popular owing to the applicability to all kind of engineering structures, minimizing the differences nearly to zero, availability of damage localization and structural health monitoring. In this paper a historical masonry armory building selected as an application and its finite element analyses, experimental measurements and automated model updating processes are carried out. The finite element model of the structures was developed with ANSYS software and first three natural frequencies are attained. The experimental dynamic characteristics such as natural frequencies, damping ratios and mode shapes are obtained using ambient vibration method. The differences between experimental and numerical frequencies are minimized up to the range of 0–1%, by using local updating procedure. The linear and nonlinear seismic analyses of the building are conducted on initial and updated models. At the end of the analyses, maximum displacements, principal stresses and strains are given with detail. In addition, crack distributions diagrams are attained to validate the stresses accumulation points. It can be seen that there is an increasing trend in the displacement, stress and strain values after finite element model updating both linear and nonlinear analysis. In addition, linear and nonlinear analysis results are compared with each other to evaluate the nonlinear analysis effect. It is concluded that the displacement and strain values are increased after nonlinear analysis. But, there is not any agreement in stress values.

42 ENGINEERING↗
DOE OSTI2023

Automated extraction of interfacial dislocations and disconnections from atomistic data

Here we introduce Interfacial Line Defect Analysis (ILDA), a method for identifying and extracting interfacial dislocations and disconnections with little-to-no input from the user on the nature of the interface. By simply providing the orientations and coherency strains for the crystals in a coherent reference state of the interface, ILDA provides exact Burgers vectors. Alternatively, these orientations and strains can be estimated using local atomic deformation gradients, making ILDA fully automated and providing estimated Burgers vectors. ILDA also determines the step height associated with each defect line segment, in case the associated defect is a disconnection. The heart of the method is the identification of atoms residing at co-incidence sites between the two crystals and the construction of a surface mesh connecting these sites that is used to compose Burgers circuits and insert defect line segments. We demonstrate the performance of ILDA in two test cases: a twist grain boundary and a phase boundary.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Automated detection of part quality during two-photon lithography via deep learning

Two-photon lithography (TPL) is an additive manufacturing technique for fabricating three-dimensional objects with nanoscale features. A main challenge of TPL is the routine and labor-intensive task of finding suitable light dosage parameters, i.e. writing speed and laser intensity that induce photo-polymerization within a wide variety of candidate photo-curing polymers. Another challenge is the monitoring required during fabrication. In this work, we apply machine learning (ML) models to accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. We curate TPL videos of different parts fabricated under a range of light dosage parameters using different resins and train spatial-temporal ML models on this data. Our results show that ML models can detect TPL part quality with a 95.1% accuracy in milliseconds. We also evaluate classification failures and identify two operating modes: parameter optimization and part quality detection. Last but not least, we publicly release this labelled dataset so that it may serve as a useful benchmark to the community. Our approach to process optimization and part quality detection addresses important aspects of TPL industrialization, is applicable beyond TPL and should benefit other additive manufacturing techniques with similar barriers to operating at industrial scale.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Automated contact angle estimation for three-dimensional X-ray microtomography data

Multiphase flow in capillary regimes is a fundamental process in a number of geoscience applications. The ability to accurately define wetting characteristics of porous media can have a large impact on numerical models. In this paper, a newly developed automated three-dimensional contact angle algorithm is described and applied to high-resolution X-ray microtomography data from multiphase bead pack experiments with varying wettability characteristics. The algorithm calculates the contact angle by finding the angle between planes fit to each solid/fluid and fluid/fluid interface in the region surrounding each solid/fluid/fluid contact point. Results show that the algorithm is able to reliably compute contact angles using the experimental data. The in situ contact angles are typically larger than flat surface laboratory measurements using the same material. Furthermore, wetting characteristics in mixed-wet systems also change significantly after displacement cycles.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

We report high quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, the use of these data is frequently challenged by its sheer volume and complexity, precluding manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey and review of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including, for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations and trends in the literature as a whole, and discuss opportunities for innovation. These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access. We also find that there are persistent challenges: limited standardization and rigor of performance assessments; limited sharing of code, which would improve replicability; and a limited consideration of the ethics and privacy of data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

An automated synthesizer for electrochemical 18 F-fluorination of organic compounds

Electrochemical 18 F-fluorination of organic compounds provides a means to synthesize Positron-Emission-Tomography (PET) tracers difficult to obtain otherwise. The first automated synthesizer that enables radiolabeling through carrier-added electrochemical 18 F-fluorination is described. The system provides capabilities for all necessary operations such as drying of cyclotron derived [ 18 F]fluoride, electrochemical incorporation of the radioisotope into a precursor molecule, subsequent reactions such as protecting group removals, HPLC-purification and formulation of the final tracer. Demonstrated is the aliphatic electrochemical 18 F-fluorination of methyl 2-(phenylthio)acetate.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Automated concentration of [ 18 F]fluoride into microliter volumes

Concentration of [ 18 F]fluoride has been mentioned in literature, however, reports have lacked details about system designs, operation, and performance. Here, we describe in detail a compact, fast, fully-automated concentration system based on a micro-sized strong anion exchange cartridge. The concentration of radionuclides enables scaled-up microfluidic synthesis. Our system can also be used to provide highly concentrated [ 18 F]fluoride with minimal water content. We demonstrate how the concentrator can produce varying concentrations of [ 18 F]fluoride for the macroscale synthesis of N-boc-5-[ 18 F]fluoroindole without an azeotropic drying process, while enabling high starting radioactivity. By appropriate choice of solid-phase resin, flow conditions, and eluent solution, we believe this approach can be extended beyond [ 18 F]fluoride to other radionuclides.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Dynamic analysis of fully constrained Cable-Driven Parallel Robots for automated prefabricated component installation

This paper presents a dynamic analysis and validation framework to assess a fully constrained six-anchor Cable-Driven Parallel Robot (CDPR) for automated installation of prefabricated facade components. Compared with conventional eight-anchor systems, the six-anchor configuration simplifies setup and reduces cost, but it also reduces control authority, shrinks the wrench-feasible workspace, and tightens orientation limits. Consequently, it is unclear a priori whether dynamically feasible trajectories exist to move the end effector from pickup to the facade. A constrained trajectory optimization is formulated to enforce the system dynamics, cable-tension bounds, and pose/velocity limits, and the framework is evaluated in simulation at three levels: (i) an idealized reference model, (ii) a lab-scale prototype model incorporating measured anchor misalignments and identified damping, and (iii) a full-scale three-story building model with load decomposition for structural feasibility checks. Across these scenarios, the analysis shows that optimal, constraint-satisfying trajectories exist that move the end effector from pickup to installation while maintaining a near-plumb, level orientation at the final pose. Collectively, this multi-scale dynamic analysis and validation framework supports the deployment readiness of the six-anchor CDPR and provides a prototype-based sensitivity case study of how measured anchor placement deviations affect feasibility.

CDPR↗
DOE OSTI2026

Extending SLUSCHI for Automated Diffusion Calculations

We present an extension of the SLUSCHI package (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) to enable automated diffusion calculations from first-principles molecular dynamics. While the original SLUSCHI workflow was designed for melting temperature estimation via solid-liquid coexistence, we adapt its input and output handling to isolate the volume search stage and generate one production trajectory suitable for diffusion analysis. Post-processing tools parse VASP outputs, compute mean-square displacements (MSD), and extract tracer diffusivities using the Einstein relation with robust error estimates through block averaging. Diagnostic plots, including MSD curves, running slopes, and velocity autocorrelations, are produced automatically to help identify diffusive regimes. The method has been validated through representative case studies: self-diffusion in Al-Cu liquid alloys, sublattice melting in Li7La3Zr2O12 and Er2O3, interstitial oxygen transport in bcc and fcc Fe, and oxygen diffusivity in Fe-O liquids with variable Si and Al contents. Viscosity and diffusivity are linked through the Stokes-Einstein relation, with composition dependence assessed via simple linear mixing. This capability broadens SLUSCHI from melting-point predictions to transport property evaluation, enabling high-throughput, fully first-principles datasets of diffusion coefficients and viscosities across metals and oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Automated processing of environmental transmission electron microscopy images for quantification of thin film dewetting and carbon nanotube nucleation dynamics

We report scalable production of carbon nanotubes (CNTs) requires catalysts and reaction conditions that provide high nucleation efficiency. In situ characterization methods such as environmental transmission electron microscopy (ETEM) can reveal fundamental mechanisms of synthesis, but to date have primarily provided qualitative observations on small sample sizes. Here, quantitative analysis is performed using high-resolution, high-rate video capture of ETEM experimentation coupled with automated image processing, involving computer vision algorithms and convolutional neural networks. By this approach, we detect distinct nanoparticle formation from an alumina-supported iron thin film and subsequent CNT nucleation from the nanoparticles. The statistical summary of particles in each video shows that, compared to a H 2 -only atmosphere, pretreatment of the catalyst with carbon added to the H 2 atmosphere results in a smaller average particle diameter, a 2-fold increase in particle density (to 5300 particles/μm 2 ), a 3-fold increase in CNT nucleation efficiency (to 92%), and more than a 5-fold increase in CNT density (to 4800/μm 2 ). Addition of carbon during exposure to H 2 is also more effective than NH 3 at dewetting the catalyst film and increasing the CNT nucleation efficiency, in spite of NH 3 being a stronger reducing agent for iron. Insights from this study are applicable to improving CNT yield and productivity in both batch-style and continuous processes.

25 ENERGY STORAGE↗

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