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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evaluation of U-net-based Image Segmentation Model to Digital Mammography

Detecting suspicious lesions in medical imaging is the important first step in computer-aided detection (CAD) systems. However, detecting abnormalities in breast tissue is difficult due to the lesion's varying size, shape, margin, and contrast with the background tissue. We focused on mass segmentation, a method that provides notable morphological features by outlining contours of masses. Accurate segmentation is crucial for correct diagnosis. Recent advancements in deep learning have improved object detection and segmentation, and these techniques are also being applied to medical imaging studies. We focused on U-net, which is a recently developed mass segmentation algorithm based on a fully convolutional network. The U-net architecture consists of (1) a contracting path to increase the resolution of the output and (2) a symmetric expanding path to better locate the region of interest. The performance of a U-net model was tested with 63 digital mammograms from INbreast, a publicly available database. We trained the model with images resized to 40x40 pixels and conducted 10-fold cross-validation to prevent overfitting. The model's performance with respect to breast density and the lesion's BI-RADS rating was also investigated. Dice coefficients (DC) were used as a performance measure to compare the predicted segmentation of the model with the ground truth. Logistic regression and an analysis of variance were performed to determine the significance of the DCs with regards to breast density and lesion behavior and to calculate the 95% confidence interval. The average DC was 0.80. The difference between DCs for BI-RADS 2 and 4c and for BI-RADS 2 and 5 were significant, suggesting that the model has more difficulty in segmenting benign lesions.

Cho, Priscilla↗
DOE OSTI2022

Understanding the Elastic, Plastic, and Damage Features in Fracturing of Self-reinforced Thermoplastic Composites via Non-destructive Digital Imaging Correlation

This work demonstrated the utilization of non-destructive Digital Imaging Correlation (DIC) method to characterize the elastic, plastic, and damage features during the Mode I intra-laminar fracturing process of self-reinforced thermoplastic composites by using a self-reinforced polypropylene (PP) composite as an example. The DIC results clearly showed the development of huge plastic zone (PZ) and non-negligible Fracture Process Zone (FPZ) in front of the notch tip during the fracturing process, and the geometries and sizes of the foregoing zones at the peak load were further quantified. Such an interesting fracturing behavior of self-reinforced thermoplastic composites is way different from brittle materials (e.g., glass, acrylic, etc.), ductile materials (e.g., aluminum, steel, etc.), and even quasi-brittle materials (e.g., concrete, nanoparticle-reinforced composites, tough ceramics, wood, cement, carbon/glass fiber-reinforced polymers, etc.). Thus, understanding the elastic, plastic, and damage features is the first step before better characterizing the material fracture properties of self-reinforced thermoplastic composites through new analytical methods and computational modeling. These efforts are of utmost importance for wide applications of self-reinforced thermoplastic composites in various engineering fields in the future.

Lightweight Composites, Self-reinforced Thermoplas↗
DOE OSTI2018

Toward digitally controlled catalyst architectures: Hierarchical nanoporous gold via 3D printing

Monolithic nanoporous metals, derived from dealloying, have a unique bicontinuous solid/void structure that provides both large surface area and high electrical conductivity, making them ideal candidates for various energy applications. However, many of these applications would greatly benefit from the integration of an engineered hierarchical macroporous network structure that increases and directs mass transport. We report on 3D (three-dimensional)–printed hierarchical nanoporous gold (3DP-hnp-Au) with engineered nonrandom macroarchitectures by combining 3D printing and dealloying. The material exhibits three distinct structural length scales ranging from the digitally controlled macroporous network structure (10 to 1000 μm) to the nanoscale pore/ligament morphology (30 to 500 nm) controlled by dealloying. Supercapacitance, pressure drop, and catalysis measurements reveal that the 3D hierarchical nature of our printed nanoporous metals markedly improves mass transport and reaction rates for both liquids and gases. Our approach can be applied to a variety of alloy systems and has the potential to revolutionize the design of (electro-)chemical plants by changing the scaling relations between volume and catalyst surface area.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development of Emergency Vehicle Preemption Strategies on Smart Corridors in a Digital Twin Environment

Emergency Response Vehicles (ERVs), such as firetrucks, ambulances, etc. operate with the purpose of saving lives and mitigating property damage. As such, ERV travel-time reductions may result in significant benefits to the community. A common strategy to improve travel times is Emergency Vehicle Preemption (EVP). EVP seeks to reduce ERV delays by providing the right-of-way to ERVs as they approach an intersection. This study proposes a new Dynamic Preemption strategy that determines the need for preemption prior to the ERV reaching the vicinity of the intersection, utilizing real-time data streams. This paper evaluates the effectiveness of some existing and proposed preempt control strategies using a digital twin testbed consisting of a series of signalized intersections on an urban arterial in Georgia. The best EVP strategy maximizes the improvement in ERV travel time while minimizing the adverse effect of preemption on the traffic in conflicting directions. Therefore, this study evaluates both the positive impact of EVP on the ERV as well as the adverse impact on the cross-street traffic. The study found that the potential exists for significant improvements in ERV travel time with the proposed Dynamic Preemption strategy, with minimal impact to the conflicting traffic. For the simulation corridor there was a 20% reduction in the ERV travel times with the implementation of the Dynamic Preemption strategy, compared to traditional EVP practices.

Roy, Somdut [Georgia Institute of Technology]↗
DOE OSTI2021

NRIC Digital Engineering Ecosystem

The NRIC Digital Engineering Ecosystem is a software suite designed to eliminate procedural failures in the engineering lifecycle of complex projects. It focuses on improving the flow of scheduling data, safety information, engineering requirements, and CAD files among stakeholders of complex engineering projects. The project comprises software adapters that move engineering data from disparate software tools in and out of a central database. The ecosystem is designed to efficiently incorporate data sources from many different engineering toolsets, making it software agnostic, and adaptable to many different engineering applications.

Harris, BrennanK↗
DOE OSTI2021

TomoTwin: A simple digital twin for synchrotron tomography

A digital twin for synchrotron-based tomography for generating synthetic datasets from ground-truth phantoms. The code outputs greyscale projection images / tomographic volumes with realistic artifacts such as noise and phase-contrast and emulates their dependence on the acquisition parameters defining the beam and detector configuration. Some ground-truth phantoms are included with the code.

TEKAWADE, ANIKET↗
DOE OSTI2017

Digital Image Correlation Engine v.2.0

DICe is a 2D and stereo image correlation software package that determines motion and deformation of objects from sequences of digital images.

Turner, DanielZ.↗
DOE OSTI2024

Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing

Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.

Mandelli, Diego [Idaho National Laboratory (INL), ↗
DOE OSTI2024

TwinMe4AD: WGAN-based Digital Twins for Anomaly Detection

SAND2024-08373O TwinMe4AD is a Python-based software tool designed for anomaly detection using digital twins that closely mimic real, wearable healthcare datasets. The tool is invaluable for scenarios where collecting data is either expensive or impractical, serving as a privacy-preserving solution. Sensitive information is protected by training deep learning models on synthetic data derived from real datasets. One of TwinMe4AD's key features is its anomaly detection capability, which is based on fourth-order moments of parameters. This versatile approach can be applied across a range of datasets, from univariate to multivariate, making it compatible with various types of data. It also generates synthetic twins using Wasserstein Generative Adversarial Networks (WGANs), allowing users to create a small cohort of a population similar to that of a village population. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Poorey, Kunal↗
DOE OSTI2024

Digital Twin for Chemical Science (DTCS) v0.01

Directly visualizing the trajectories of chemistry can unravel novel insights into the behavior of catalysts, gas phase reactions, photo-induced dynamics, and building blocks for quantum information processing. The ability of explicitly identifying, tracking, and tagging the exchange of matter, hence the annihilation and creation of new chemical species, can be best realized through a close coupling of theory and experiment. While the synchrotron-based characterization facilities propelled rapidly in its hardware, providing higher brightness, better resolution, and more precision, the software infrastructure is lagging. We developed DTCS (Digital Twin for Chemical Science) v.01, a central platform that faithfully mimics advanced instrumentations in Scientific User Facilities, by solving a variety of technical challenges in data acquisition, analysis, and model-driven interpretation. Rooted in physics and accelerated by AI, we validated this concept by direct comparison with precise experimental X-ray Photoelectron Spectroscopy (XPS) observations using a ubiquitous metal-water interfacial scenario, i.e., Ag/H2O as our main narrative. The DTCS v.01 input mirrors how the bench chemists work, with the output directly linked to the end station computer, thereby providing a user-friendly, knowledge-driven, and accessible user experience with mechanistic insights standardized in a way that are ready to be published, versioned, and transferred flexibly.

Qian, Jin↗
DOE OSTI2025

Artificial Intelligence–Enabled Digital Twin for U.S. Cities

Over 50 participants—including national laboratory researchers, academic scholars, industry representatives, and stakeholders from the City of Chicago—convened in person and online to assess the readiness and potential of an Artificial Intelligence–Enabled Digital Twin (AIDT) for urban systems, with the Greater Chicago area serving as the benchmark location. The workshop underscored that the Chicago Urban Integrated Field Laboratory provides an unparalleled testbed for developing and validating urban DTs—combining dense, multiscale observations, advanced physics-based and AI modeling capabilities, and strong stakeholder and industry engagement. Discussions highlighted available datasets, AI architectures for high resolution, multipurpose urban DTs, key applications, and near- and long-term priorities for scaling this framework within Chicago and to other U.S. and global cities.

AIDT↗
DOE OSTI2019

Digital Breast Tomosynthesis for Intraoperative Margin Assessment during Breast-Conserving Surgery

Background: Intraoperative margin assessment for breast cancer patients undergoing segmental mastectomy (SM) enables identification of positive margins, with immediate excision of additional tissue to obtain negative margins. Objective: The aim of this study was to determine the ability of digital breast tomosynthesis (DBT) to detect positive margins compared with an institution’s standard extensive processing (SEP). Methods: SM specimens underwent intraoperative SEP with two-dimensional (2D) imaging of the intact and sliced specimen, with review by a breast radiologist and gross assessment by a breast pathologist. Findings guided the surgeon to excise additional tissue. DBT images of intact specimens were prospectively obtained and retrospectively reviewed by a breast radiologist. A positive margin was defined as tumor at ink. Results: Ninety-eight patients underwent 99 SMs. With SEP, 14 (14%) SM specimens had 19 positive margins. SEP did not detect 3 of the 19 positive margins, for a sensitivity of 84%, specificity of 78%, positive predictive value (PPV) of 11%, and negative predictive value (NPV) of 99%. Moreover, DBT did not detect 5 of the 19 positive margins, for a sensitivity of 74% (p > 0.05), specificity of 91% (p < 0.05), PPV of 21.5%, and NPV of 99%. With SEP guidance to excise additional tissue, six cases had final positive margins, with SEP not identifying three of these cases and DBT not identifying two. Pathology from the second surgery of these patients showed either no additional malignancy or only focal ductal carcinoma in situ. Conclusions: DBT is an accurate method for detecting positive margins in breast cancer patients undergoing SM, performing similar to institutional labor-intensive, intraoperative standard processing.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Defects in MX2 Phases (DMX) Dataset: STEM Digital Twins

DFT is used to optimize defects in monolayer MX2 phases. We calculate ~600 optimized defect structures, then use multislice simulations in abTEM to generate STEM digital twins, which can be used to train experimental ML. The reference paper for procedures and demonstration of their utility is https://arxiv.org/abs/2305.02917, and the images here correspond with the discussed Messy dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Defects in MX2 (DMX) 2.0: STEM Digital Twins

DFT is used to optimize defects in monolayer MX2 phases. We calculate ~600 optimized defect structures, then use multislice simulations in abTEM to generate STEM digital twins, which can be used to train experimental ML. The reference paper for procedures and demonstration of their utility is https://arxiv.org/abs/2305.02917, and the images here correspond with the discussed Messy dataset. Version 2 includes an auxiliary dataset matched with experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2022

In-situ thermomechanical measurements during burst testing of Zr claddings using digital image correlation and infrared thermography techniques

Burst testing has long been a relied-on method for assessing fuel cladding materials in light-water reactoraccident scenarios. However, burst testing historically provided minimal in-situ observations beyond a few thermocouple readings, pressure from transducers, and post-test deformation measurements. Acquisition of full cladding thermomechanical data throughout a burst test would be invaluable to fuel system qualification, material model development and fuel performance code validation, such as the BISON fuel performance code. Recently, methodologies for simultaneously applying 2-dimensional digital image correlation and infrared thermography techniques to quantify the relationship between temperature, internal cladding pressure, and deformation during burst testing were developed. This work details results from the application of these techniques to burst testing of Zircaloy-4 cladding segments subjected to loss-of-coolant accident transients in an air environment. Two types of tests were performed with: (a) cladding segments connected to a fixed pressure reservoir as well as (b) closed systems where internal pressure increased during transients. In-situ thermomechanical measurements including strain and thermal gradient data are presented, demonstrating the effectiveness of applying combined surface measurement techniques to burst testing

Bell, Sam↗
DOE OSTI2022

Correcting for digital image correlation speckle inversion at high temperature using color cameras

Digital image correlation (DIC) is a popular, noncontacting technique to measure full-field deformation by using cameras to track the motion of an applied surface pattern. Because it is noncontacting, DIC can be performed for extreme temperature applications (e.g., hot-fire rocket testing of carbon composite rocket nozzles) under harsh conditions during which bonded gauges are damaged. Speckle pattern inversion is a phenomenon that sometimes occurs while performing high-temperature DIC. During speckle pattern inversion, portions of the surface pattern that were initially darker at room temperature (e.g., graphite) may emit more light due to blackbody radiation than the portions that were initially paler, thereby producing images in which the pattern appears inverted at high temperature relative to the initial pattern at room temperature. This phenomenon can prevent the correlation algorithm from being able to resolve the displacements between images. This work compares three methods to mitigate speckle pattern inversion: (A) the subtraction method, a recently-published technique in which two high-temperature images are subtracted to remove unwanted light; (B) the filtering method, a popular technique in which optical bandpass filters screen out unwanted light; and (C) the histogram rescaling method, a proposed new method that pairs a color camera with a blue light source and uses information from the green sensor of the camera to correct against inversion in the blue sensor through postprocessing. The histogram rescaling method is shown to successfully eliminate speckle pattern inversion and has the added advantages that it does not require quasi-static loading to be able to compensate for speckle pattern inversion, nor does it impose thick-glass distortions caused by the optical filter.

Rowley, Lindsey J.↗