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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A mixed-scale dense convolutional neural network for image analysis

Significance Popular neural networks for image-processing problems often contain many different operations, multiple layers of connections, and a large number of trainable parameters, often exceeding several million. They are typically tailored to specific applications, making it difficult to apply a network that is successful in one application to different applications. Here, we introduce a neural network architecture that is less complex than existing networks, is easy to train, and achieves accurate results with relatively few trainable parameters. The network automatically adapts to a specific problem, allowing the same network to be applied to a wide variety of different problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A simple developmental model recapitulates complex insect wing venation patterns

Insect wings are typically supported by thickened struts called veins. These veins form diverse geometric patterns across insects. For many insect species, even the left and right wings from the same individual have veins with unique topological arrangements, and little is known about how these patterns form. We present a large-scale quantitative study of the fingerprint-like “secondary veins.” We compile a dataset of wings from 232 species and 17 families from the order Odonata (dragonflies and damselflies), a group with particularly elaborate vein patterns. We characterize the geometric arrangements of veins and develop a simple model of secondary vein patterning. We show that our model is capable of recapitulating the vein geometries of species from other, distantly related winged insect clades.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Pore-scale investigation on stress-dependent characteristics of granular packs and the impact of pore deformation on fluid distribution

Understanding the effect of changing stress conditions on multiphase flow in porous media is of fundamental importance for many subsurface activities including enhanced oil recovery, water drawdown from aquifers, soil confinement, and geologic carbon storage. Geomechanical properties of complex porous systems are dynamically linked to flow conditions, but their feedback relationship is often oversimplified due to the difficulty of representing pore-scale stress deformation and multiphase flow characteristics in high fidelity. In this work, we performed pore-scale experiments of single- and multiphase flow through bead packs at different confining pressure conditions to elucidate compaction-dependent characteristics of granular packs and their impact on fluid flow. A series of drainage and imbibition cycles were conducted on a water-wet, soda-lime glass bead pack under varying confining stress conditions. Simultaneously, X-ray micro-CT was used to visualize and quantify the degree of deformation and fluid distribution corresponding with each stress condition and injection cycle. Micro-CT images were segmented using a gradient-based method to identify fluids (e.g., oil and water), and solid phase redistribution throughout the different experimental stages. Changes in porosity, tortuosity, and specific surface area were quantified as a function of applied confining pressure. Results demonstrate varying degrees of sensitivity of these properties to confining pressure, which suggests that caution must be taken when considering scalability of these properties for practical modeling purposes. Changes in capillary number with confining pressure are attributed to the increase in pore velocity as a result of pore contraction. Furthermore, this increase in pore velocity was found to have a marginal impact on average phase trapping at different confining pressures.

58 GEOSCIENCES↗
DOE OSTI2022

High–throughput measurement of plant fitness traits with an object detection method using Faster R–CNN

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r 2 = 0.9996) and highly accurate fruit counts (r 2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

A bronchoscopic navigation system using bronchoscope center calibration for accurate registration of electromagnetic tracker and CT volume without markers

Purpose: Various bronchoscopic navigation systems are developed for diagnosis, staging, and treatment of lung and bronchus cancers. To construct electromagnetically navigated bronchoscopy systems, registration of preoperative images and an electromagnetic tracker must be performed. This paper proposes a new marker-free registration method, which uses the centerlines of the bronchial tree and the center of a bronchoscope tip where an electromagnetic sensor is attached, to align preoperative images and electromagnetic tracker systems. Methods: The chest computed tomography (CT) volume (preoperative images) was segmented to extract the bronchial centerlines. An electromagnetic sensor was fixed at the bronchoscope tip surface. A model was designed and printed using a 3D printer to calibrate the relationship between the fixed sensor and the bronchoscope tip center. For each sensor measurement that includes sensor position and orientation information, its corresponding bronchoscope tip center position was calculated. By minimizing the distance between each bronchoscope tip center position and the bronchial centerlines, the spatial alignment of the electromagnetic tracker system and the CT volume was determined. After obtaining the spatial alignment, an electromagnetic navigation bronchoscopy system was established to real-timely track or locate a bronchoscope inside the bronchial tree during bronchoscopic examinations. Results: The electromagnetic navigation bronchoscopy system was validated on a dynamic bronchial phantom that can simulate respiratory motion with a breath rate range of 0–10 min{sup −1}. The fiducial and target registration errors of this navigation system were evaluated. The average fiducial registration error was reduced from 8.7 to 6.6 mm. The average target registration error, which indicates all tracked or navigated bronchoscope position accuracy, was much reduced from 6.8 to 4.5 mm compared to previous registration methods. Conclusions: An electromagnetically navigated bronchoscopy system was constructed with accurate registration of an electromagnetic tracker and the CT volume on the basis of an improved marker-free registration approach that uses the bronchial centerlines and bronchoscope tip center information. The fiducial and target registration errors of our electromagnetic navigation system were about 6.6 and 4.5 mm in dynamic bronchial phantom validation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Poster — Thur Eve — 62: A Retrospective Assessment of the Prevalence and Dosimetric Effect of Lateral Electron Disequilibrium in a Population of Lung Cancer Patients Treated by Stereotactic Body Radiation Therapy

Stereotactic Body Radiation Therapy (SBRT) is a treatment option for early stage non-small cell lung cancer (NSCLC). SBRT uses tightly conformed megavoltage (MV) x-ray beams to ablate the tumour. However, small MV x-ray fields may produce lateral electron disequilibrium (LED) within lung tissue, which can reduce the dose to tumour. The goal of this work is to estimate the prevalence of LED in NSCLC patients treated with SBRT, and determine dose effects for patients prone or averse to LED. Thirty NSCLC patients were randomly selected for analysis. 4-dimensional CT lung images were segmented into the right and left upper and lower lobes (RUL, RLL, LUL, LLL), and the right middle lobe. Dose calculations were performed using volume-modulated arc therapy in the Pinnacle{sup 3} TPS. Most tumours were located in the upper lobes (RUL 53%, LUL 27%) where density was significantly lower (RUL −808±46 HU vs. RLL −743±71 HU; LUL −808 ±56 HU vs. LLL −746±70 HU; p<0.001). In general, the prevalence of LED increased with higher beam energy. Using 6MV photons, patients with a RUL tumour experienced moderate (81 %), and mild (19%) levels of LED. At 18MV, LED became more prominent with severe (50%) and moderate (50%) LED exhibited. Dosimetrically, for patients prone to LED, poorer target coverage (i.e. increased R100 by 20%) and improved lung sparing (i.e. reduced V20 by −46%) was observed. The common location of lung cancers in the upper lobes, coupled with lower lung density, results in the potential occurrence of LED, which may underdose the tumour.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-DE-207B-11: Implementation of Size-Specific 3D Beam Modulation Filters On a Dedicated Breast CT Platform Using Breast Immobilization

Purpose: To implement a 3D beam modulation filter (3D-BMF) in dedicated breast CT (bCT) and develop a method for conforming the patient’s breast to a pre-defined shape, optimizing the effects of the filter. This work expands on previous work reporting the methodology for designing a 3D-BMF that can spare unnecessary dose and improve signal equalization at the detector by preferentially filtering the beam in the thinner anterior and peripheral breast regions. Methods: Effective diameter profiles were measured for 219 segmented bCT images, grouped into volume quintiles, and averaged within each group to represent the range of breast sizes found clinically. These profiles were then used to generate five size-specific computational phantoms and fabricate five size-specific UHMW phantoms. Each computational phantom was utilized for designing a size-specific 3D-BMF using previously reported methods. Glandular dose values and projection images were simulated in MCNP6 with and without the 3DBMF using the system specifications of our prototype bCT scanner “Doheny”. Lastly, thermoplastic was molded around each of the five phantom sizes and used to produce a series of breast immobilizers for use in conforming the patient’s breast during bCT acquisition. Results: After incorporating the 3D-BMF, MC simulations estimated an 80% average reduction in the detector dynamic range requirements across all phantom sizes. The glandular dose was reduced on average 57% after normalizing by the number of quanta reaching the detector under the thickest region of the breast. Conclusion: A series of bCT-derived breast phantoms were used to design size-specific 3D-BMFs and breast immobilizers that can be used on the bCT platform to conform the patient’s breast and therefore optimally exploit the benefits of the 3D-BMF. Current efforts are focused on fabricating several prototype 3D-BMFs and performing phantom scans on Doheny for MC simulation validation and image quality analysis. Research reported in this paper was supported in part by the National Cancer Institute of the National Institutes of Health under award R01CA181081. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institue of Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform

Abstract Many spectral responses in materials science, physics, and chemistry experiments can be characterized as resulting from the superposition of a number of more basic individual spectra. In this context, unmixing is defined as the problem of determining the individual spectra, given measurements of multiple spectra that are spatially resolved across samples, as well as the determination of the corresponding abundance maps indicating the local weighting of each individual spectrum. Matrix factorization is a popular linear unmixing technique that considers that the mixture model between the individual spectra and the spatial maps is linear. Here, we present a tutorial paper targeted at domain scientists to introduce linear unmixing techniques, to facilitate greater understanding of spectroscopic imaging data. We detail a matrix factorization framework that can incorporate different domain information through various parameters of the matrix factorization method. We demonstrate many domain-specific examples to explain the expressivity of the matrix factorization framework and show how the appropriate use of domain-specific constraints such as non-negativity and sum-to-one abundance result in physically meaningful spectral decompositions that are more readily interpretable. Our aim is not only to explain the off-the-shelf available tools, but to add additional constraints when ready-made algorithms are unavailable for the task. All examples use the scalable open source implementation from https://github.com/ramkikannan/nmflibrary that can run from small laptops to supercomputers, creating a user-wide platform for rapid dissemination and adoption across scientific disciplines.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10)

This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, DMREF, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)

This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, GammaPrint-700, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR). To download the dataset: (1) Create a Globus account. (2) Create a Globus Endpoint on your computer. (3) Transfer the dataset from the OLCF DOI-DOWNLOADS Collection to your Collection. Common troubleshooting steps: (a) Confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. (b) Create an exception for Globus in your antivirus software so that it can create an Endpoint. (c) Manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Satellite Image Algorithms

Presented by Conner Hatton at SIAM UQ Conference in Trieste, Italy, on February 27th, 2024. Conference details available online at https://www.siam.org/conferences/cm/conference/uq24

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Computer vision models and advanced TEM imaging for microstructures of irradiated AM316 stainless steels

Advancements were made in automating microscopy-based material characterization, particularly in studying irradiation effects on additively manufactured (AM) materials using machine learning (ML) and computer vision (CV). These automation efforts address the challenges of analyzing complex microstructures, accelerating the detection of irradiation-induced defects. Two CV models were developed at Argonne National Laboratory (ANL) to enhance transmission electron microscopy (TEM) analysis of irradiated AM 316 stainless steel. The first model focused on the detection of irradiation-induced dislocation loops, which contribute to material hardening and embrittlement. These loops, categorized as faulted or perfect, were automatically detected and classified using a Mask R-CNN model trained on TEM images from both in-situ and ex-situ ion irradiation experiments. The model achieved high accuracy, with precision, recall, and F1 scores of 0.839, 0.734, and 0.776, respectively, demonstrating its effectiveness in analyzing dislocation loops in irradiated AM materials. The second CV model was developed to analyze the size and wall thickness of dislocation cells in laser powder bed fusion (LPBF) 316 stainless steel. Using a U-Net++ architecture with EfficientNet as the encoder, the model was trained on TEM images to segment and measure cell size and wall thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Stochastic microstructural analysis of ceramic matrix composites using a high-fidelity multiscale framework

This paper presents a high-fidelity multiscale framework to simulate the mechanical behavior of ceramic matrix composites (CMCs), accounting for the complex micromorphology captured using detailed material characterization. First, high-resolution micrographs are obtained for the specific carbon fiber silicon-carbide-nitride matrix (C/SiNC) CMC to characterize the variability of the architectural features and manufacturing-induced defects at the microscale. An image processing algorithm is then used to precisely estimate the size and distribution of all subscale features and defects from the micrographs. The information is then used to generate a three-dimensional stochastic representative volume element (SRVE) to reconstruct microscale constituents accounting for the variability. As a result, the generated SRVEs are simulated using the high-fidelity generalized method of cells (HFGMC) micromechanics theory to investigate the effects of defects on the elastic properties of C/SiNC CMCs.

Ceramic Matrix Composites↗
DOE OSTI2024

A framework of computer vision-enhanced microfluidic approach for automated assessment of the transient sickling kinetics in sickle red blood cells

The occurrence of vaso-occlusive crisis greatly depends on the competition between the sickling delay time and the transit time of individual sickle cells, i.e., red blood cells from sickle cell disease (SCD) patients, while they are traversing the circulatory system. Many drugs for treating SCD work by inhibiting the polymerization of sickle hemoglobin (HbS), effectively delaying the sickling process in sickle cells (SS RBCs). Most previous studies on screening anti-sickling drugs, such as voxelotor, rely on in vitro testing of sickling characteristics, often conducted under prolonged deoxygenation for up to 1 hour. However, since the microcirculation of RBCs typically takes less than 1 minute, the results of these studies may be less accurate and less relevant for in vitro-in vivo correlation. In our current study, we introduce a computer vision-enhanced microfluidic framework designed to automatically capture the transient sickling kinetics of SS RBCs within a 1-min timeframe. Our study has successfully detected differences in the transient sickling kinetics between vehicle control and voxelotor-treated SS RBCs. This approach has the potential for broader applications in screening anti-sickling therapies.

59 BASIC BIOLOGICAL SCIENCES↗