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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Chemo–mechanical properties of carbon fiber reinforced geopolymer interphase

Geopolymers, as a potentially environmentally friendly alternative to Portland cement, are increasingly attracting attention in the construction industry. Various methods have been applied for customizing the properties of geopolymers and improving their commercial viability. One of the promising methods for refining the properties of geopolymers such as their toughness is the use of short fibers. The effectiveness of a high-strength short fiber in the geopolymer matrix is largely dependent on the interfacial bonding between the fiber and its surrounding matrix. While the importance of this interfacial chemistry is highlighted in the literature, the characteristics of this bonding structure have not been fully understood. In this paper, we aim to investigate the bonding mechanism between the carbon fiber and metakaolin-based geopolymer matrix. For the first time, the existence and nature of the chemical bonding at the interfacial region (interphase) between carbon fiber and geopolymer matrix has been revealed. X-ray pair distribution function computed tomography (PDF-CT), field emission-scanning electron microscopy imaging, and nanoindentation techniques are employed to discern the chemo-mechanical properties of the interphase. PDF-CT results show the emergence of a new atom-atom correlation at the interfacial region (around 1.82 angstrom). This correlation is a characteristic of interfacial bonding between the fiber and its surrounding matrix, where the existence of chemical linkages (potentially Al-V-O-C) between fibers and the matrix contributes to the adhesion between the two constituents making up the composite. Due to such chemical bonding, the nanomechanical properties of the interfacial region fall between that of the carbon fiber and geopolymer. The combination of advanced techniques is proved useful for enhancing our understanding of the interfacial chemistry between fibers and the binding matrix. This level of knowledge facilitates the engineering of composite systems through the manipulation of their nanostructure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Geranium Photodiodes for Hard Xray Detection

This report summarizes the investigations engaged at UC Davis under subcontract B632083, as related to the LLNL-LDRD Project “High-Speed X-Ray Imager Arrays.” The first section entails work done within the Electrical and Computer Engineering (ECE) Department. The purpose of the work done so far is to understand photodiode design and which parameters are conducive to the design goals, which are: I: Maximize the detector quantum efficiency. The device must have a quantum efficiency of roughly 5% at 80 keV. II: Minimize temporal response. Collection time is limited to 1ns. All charge must be collected in that time. III: Minimize dark current. In this document, various photodiode parameters and designs are explored, and their impact on these goals are tabulated. In addition to these, various instruments that have been constructed for either testing these parameters or helping teammates with their experiments. The second section entails work done with the Materials Science Engineering (MSE) Department. In accordance with the proposed development of the germanium ROIC backside imagers, a critical step in the micromachining of these devices was the removal of the substrate on which the PIN junctions were epitaxially grown. A common technique for the micromachining of silicon wafers is the use of chemical etching alongside an “etch-stop” layer. In this technique, a chemical etchant will remove material until it reaches the etch-stop layer, at which the etch rate will drop significantly. This high selectivity (ratio of etch rates) allows any unevenness in the initial etching to be effectively smoothed away in the event that part of the wafer surface reaches the etch-stop layer sooner than another. An initial literature review found a paper by Divan et al. in which the authors used ion implantation to dope germanium wafers and achieve selectivity on the order of 100, sufficient for a reliable etch-stop.

42 ENGINEERING↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

CLAS12 remote data-stream processing using ERSAP framework

Implementing a physics data processing application is relatively straightforward with the use of current containerization technologies and container image runtime services, which are prevalent in most high-performance computing (HPC) environments. However, the process is complicated by the challenges associated with data provisioning and migration, impacting the ease of workflow migration and deployment. Transitioning from traditional file-based batch processing to data-stream processing workflows is suggested as a method to streamline these workflows. This transition not only simplifies file provisioning and migration but also significantly reduces the necessity for extensive disk space. Data-stream processing is particularly effective for real-time processing during data acquisition, thereby enhancing data quality assurance. This paper introduces the integration of the JLAB CLAS12 event reconstruction application within the ERSAP data-stream processing framework that facilitates the execution of streaming event reconstruction at a remote data center and enables the return streaming of reconstructed events to JLAB while circumventing the need for temporary data storage throughout the process.

Gyurjyan, Vardan↗
DOE OSTI2022

High-Frequency 3D Photoacoustic Computed Tomography Using an Optical Microring Resonator

3D photoacoustic computed tomography (3D-PACT) has made great advances in volumetric imaging of biological tissues, with high spatial-temporal resolutions and large penetration depth. The development of 3D-PACT requires high-performance acoustic sensors with a small size, large detection bandwidth, and high sensitivity. In this work, we present a new high-frequency 3D-PACT system that uses a microring resonator (MRR) as the acoustic sensor. The MRR sensor has a size of 80 μ m in diameter and was fabricated using the nanoimprint lithography technology. Using the MRR sensor, we have developed a transmission-mode 3D-PACT system that has achieved a detection bandwidth of ~23 MHz, an imaging depth of ~8 mm, a lateral resolution of 114 μ m, and an axial resolution of 57 μ m. We have demonstrated the 3D PACT’s performance on in vitro phantoms, ex vivo mouse brain, and in vivo mouse ear and tadpole. The MRR-based 3D-PACT system can be a promising tool for structural, functional, and molecular imaging of biological tissues at depths.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Self-driving thin film laboratory: autonomous epitaxial atomic-layer synthesis via real-time computer vision analysis of electron diffraction

Emerging materials science platforms with the ability to make autonomous decisions on the fly are fundamentally changing the outlook and protocols for materials optimization and discovery. Because AI-driven self-navigating schemes can effectively reduce the total number of iterations needed to arrive at the "answer" (i.e. the best stochiometric composition for a desired physical property, optimum materials processing parameters, etc.) by significant margins, they have the potential to revolutionize materials and chemical manufacturing processes at large in research laboratory settings as well as in industrial plants. Here, we demonstrate a successful implementation of real-time closed-loop autonomous navigation of a multi-dimensional materials synthesis parameter space for fabricating phase-pure epitaxial films of a metastable phase of a functional oxide in a combinatorial pulsed laser deposition chamber. Sequential epitaxial growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision analysis of reflection high-energy electron diffraction (RHEED) images of the unit-cell level film being deposited. The autonomous scheme regularly resulted in > 30-fold reduction in the number of required experiments compared to a comprehensive mapping of the parameter space. The real-time workflow developed here can be readily extended to a variety of thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous optimization of semiconductor manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hydraulic Conductivity Measurements, Utqiagvik (Barrow), Alaska, 2014

Six individual ice cores were collected from the Barrow Environmental Observatory in Barrow, Alaska, in May of 2013 as part of the Next Generation Ecosystem Experiment (NGEE). Each core was drilled at a different location to varying depths. After drilling, the cores were stored in coolers packed with dry ice and flown to Lawrence Berkeley National Laboratory (LBNL) in Berkeley, CA. 3-dimensional images of the cores were constructed using medical X-ray computed tomography (CT) scanner at 120kV. Hydraulic conductivity samples were extracted from these cores at LBNL Richmond Field Station in Richmond, CA, in February 2014 by cutting 5 to 8 inch segments using a chop saw. Samples were packed individually and stored at -20C freezing temperatures to minimize any changes in structure or loss of ice content prior to analysis. Hydraulic conductivity was determined through falling head tests using a permeameter [ELE International, Model #: K-770B] (Appendix A). Samples were placed in a latex membrane via a membrane stretcher while frozen. Use of a membrane stretcher made the membranes easier to secure and minimized contact with the sample. A clear polycarbonate sleeve, fabricated with a stainless steel ring at the bottom to keep the sleeve from floating, was placed around the sample inside the permeameter to minimize deformation during analysis. The permeameter was filled with water and 1.0 PSI of air was applied for confining pressure during sample defrost. Outflow valves were left open to allow for incremental thawing and samples were left to thaw for approximately 12 hours. After approximately 12 hours of thaw, initial falling head tests were performed. When the flow was significantly too fast or too slow, the analysis was stopped and the burette size was adjusted accordingly (i.e. a larger diameter burette was used for flows that were faster than desired or a smaller diameter burette was used for flows that were slower than desired). Two to four measurements were collected on each sample and collection stopped when the applied head load exceeded 25% change from the original load. Analyses were performed between 2 to 3 times for each sample. The final hydraulic conductivity calculations were computed using methodology of Das et al., 1985.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.

Touzani, Samir↗
DOE OSTI2023

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Eisenmann, Matthias↗
DOE OSTI2023

Model-Based Reconstruction for Multi-Frequency Collimated Beam Ultrasound Systems

Collimated beam ultrasound systems are a technology for imaging inside multi-layered structures such as geothermal wells. These systems work by using a collimated narrow-band ultrasound transmitter that can penetrate through multiple layers of heterogeneous material. A series of measurements can then be made at multiple transmit frequencies. However, commonly used reconstruction algorithms such as Synthetic Aperture Focusing Technique (SAFT) tend to produce poor quality reconstructions for these systems both because they do not model collimated beam systems and they do not jointly reconstruct the multiple frequencies. Here, in this article, we propose a multi-frequency ultrasound model-based iterative reconstruction (UMBIR) algorithm designed for multi-frequency collimated beam ultrasound systems. The combined system targets reflective imaging of heterogeneous, multi-layered structures. For each transmitted frequency band, we introduce a physics-based forward model to accurately account for the propagation of the collimated narrow-band ultrasonic beam through the multi-layered media. We then show how the joint multi-frequency UMBIR reconstruction can be computed by modeling the direct arrival signals, detector noise, and incorporating a spatially varying image prior. Results using both simulated and experimental data indicate that multi-frequency UMBIR reconstruction yields much higher reconstruction quality than either single frequency UMBIR or SAFT.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

An extended trajectory-mechanics approach for calculating two-phase flow paths

A technique originating in quantum dynamics is used to derive a trajectory-based, semi-analytical solution for two-phase flow. The partial differential equation governing the evolution of the aqueous phase is equivalent to a family of ordinary differential equations defined along a path through the porous medium. The trajectories may be found by solving the differential equations directly or by post-processing the output of a numerical solution to the full set of governing equations. The trajectories, which differ from conventional streamlines, are found to bend downward in response to gravitational forces. The curvature is more pronounced as the dip of the porous layer containing the flow increases. Subtle changes in the relative permeability curve can lead to significant variations in the trajectories. The ordinary differential equation for the trajectory provides an expression for the travel time along the path. The expression produces a semi-analytical approximation to the model parameter sensitivities, the partial derivatives of the travel times with respect to changes in the permeability model. The semi-analytical trajectory-based sensitivities generally agree with those computed using a numerical reservoir simulator and a perturbation approach. The sensitivities are useful in tomographic imaging algorithms designed to estimate the spatial variation in permeability within a porous medium using multiphase observations.

58 GEOSCIENCES↗
DOE OSTI2020

Computing for the DUNE Long-Baseline Neutrino Oscillation Experiment

This is a talk given at Computers in High Energy Physics in Adelaide, South Australia, Australia in November 2019. It is partially intended to explain the context of DUNE Computing for computing specialists. The DUNE collaboration consists of over 180 institutions from 33 countries. The experiment is in preparation now with commissioning of the first 10kT fiducial volume Liquid Argon TPC expected over the period 2025-2028 and a long data taking run with 4 modules expected from 2029 and beyond. An active prototyping program is already in place with a short test beam run with a 700T, 15,360 channel prototype of single-phase readout at the neutrino platform at CERN in late 2018 and tests of a similar sized dual-phase detector scheduled for mid-2019. The 2018 test beam run was a valuable live test of our computing model. The detector produced raw data at rates of up to ~2GB/s. These data were stored at full rate on tape at CERN and Fermilab and replicated at sites in the UK and Czech Republic. In total 1.2 PB of raw data from beam and cosmic triggers were produced and reconstructed during the six week test beam run. Baseline predictions for the full DUNE detector data, starting in the late 2020's are 30-60 PB of raw data per year. In contrast to traditional HEP computational problems, DUNE's Liquid Argon TPC data consist of simple but very large (many GB) 2D data objects which share many characteristics with astrophysical images. This presents opportunities to use advances in machine learning and pattern recognition as a frontier user of High Performance Computing facilities capable of massively parallel processing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Understanding Fracture Aperture and Permeability Evolution due to Carbonate Mineralization Utilizing 3D Printing

Caprock formations are a crucial part of subsurface-engineered systems. Composed largely of shale, caprocks act as natural barriers that prevent the upward migration of fluids, thereby ensuring the containment of stored substances in subsurface formations. Fractures in these formations are potential leakage pathways for stored fluids. Mineral precipitation reactions in these fractures, particularly calcite, can significantly restrict the fluid permeability, reducing leakage potential. However, predictive capabilities of mineral precipitation in fractures and associated permeability evolution are limited due to a lack of fundamental understanding of such reactions in natural samples, complicated by mineral heterogeneity and the complexity of the fracture structure. In this study, 3D-printed fracture samples are used to understand the impact of carbonate mineralization on fracture aperture and permeability evolution. Samples were printed using a digital light processing (DLP) 3D printer and commercial liquid resin. Calcite precipitation was first tested on printed 2D films before conducting plug flow column experiments aimed to understand fracture permeability changes due to mineral precipitation. Contact angle measurement and Fourier transform infrared (FTIR) spectroscopy on printed 2D films show evidence of a substantial amount of surface energy for calcite nucleation and precipitation. Surface topography analysis of printed fractured surfaces reveals comparable values, highlighting the high replicability of the printed samples. During the column experiments, the permeability reduces exponentially due to a decrease in fracture aperture. Reductions in fracture aperture estimated from effluent concentration and 3D X-ray computed tomography (CT) show comparable results. Moreover, 3D X-ray CT images suggest the impact of local flow velocities on precipitation. The insights gained from this research contribute to a deeper understanding of the permeability evolution due to carbonate mineralization in caprock formations.

3D printing↗
DOE OSTI2024

Leveraging Afterglow in Scintillation-based x-ray detectors for spacetime-resolved computed tomography for accelerated acquisition and high-speed event capture

Afterglow in x-ray imaging for high-speed radiography is a constraint that limits imaging systems to low-light/fast decay screens which create poor data. Current approaches focus purely on using low-light yield screens with fast decay to avoid multiple exposure pileup due to afterglow. The goal of this work is to develop a statistical estimation approach to leverage afterglow to improve image quality thus allowing for higher quality imaging components to be used. This will allow for bright screens will slow decay to be used, and then a post-processing step applies the statistical estimation to separate each frame with superior signal compared to low-light/fast decay screens.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Automatic method of material identification for computed tomography

A method is provided for isolating and labeling discrete features in a spectral radiographic image recorded as a set of images in different energy channels. The disclosed method involves creating a profile for each of at least some pixels in the spectral radiographic image. The profiles are sequences of pixel values, in which each pixel value is a photon count or a similar radiographic exposure value indicative of the attenuation of a portion of the scanning beam in a respective energy channel. Iterative hierarchical clustering is used to cluster the pixels on the basis of their respective profiles. Labels are assigned to one or more of the resulting clusters. In implementations, each label can be associated with an inferred material composition or with an inference that the material composition is unknown.

Jimenez, Jr., Edward Steven↗
DOE OSTI2023

Design of Monolithic Bi-Layer High-Z PAL-Si Hard X-ray CMOS Image Sensors for Quantum Efficiency Enhancement

This article experimentally investigates the inception of an innovative hard X-ray photon energy attenuation layer (PAL) to advance high-energy X-ray detection (20–50 keV). A bi-layer design with a thin film high-Z PAL on the top and Si image sensor on the bottom has previously demon-strated quantum yield enhancement via computational methods by the principle of photon energy down conversion (PEDC), where high-energy X-ray photon energies are attenuated via inelastic scattering down to ≤10 keV, which is suitable for efficient photoelectric absorption by Si. Quantum yield enhancement has been experimentally confirmed via a preliminary demonstration using PAL-integrated Si-based CMOS image sensors (Si CIS). Furthermore, substituting the high-Z PAL with a lower-Z material—Sn—and alternatively coupling it with a conventional scintillator ma-terial—Lutetium-yttrium oxyorthosilicate (LYSO)—have been compared to demonstrate the most prominent efficacy of monolithic integration of high-Z PAL on Si CIS to detect hard X-rays, paving the way for next-generation high-energy X-ray detection methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

AutoSourceID-Classifier: Star-galaxy classification using a convolutional neural network with spatial information

Aims.Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. Methods.The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results. Results.We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.

Astronomy & Astrophysics↗