Search NASA⌕ Search

SEARCH · Search NASA

Results for “Workflows”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Data-Driven, In Situ, Relative Sensor Calibration Based on Waveform Fitting Moment Tensor Inversion

This article presents a deconvolution-based methodology for the relative calibration of acoustic emission sensors. The method uses the observations from multiple acoustic emission arrivals to estimate correction functions that account for instrument response and site effects. The corrections act on the measurements reshaping the wave arrivals, in many cases allowing visible discrimination between compressional and shear phases. The methodology is illustrated with the application on a dataset obtained during the hydraulic fracturing at triaxial stress conditions of a Colton sandstone block. A workflow for the estimation of full moment tensor solutions, including the generation of quality control attributes complements the presentation. The moment tensor solutions display fracture geometries mostly aligned with the main hydraulic fracture’s plane. The style of activation can be associated with an extensive stress regime before breakdown and to a compressional stress regime after breakdown, when the pumps were reversed to withdraw fluid from the block. In conclusion, the source types commonly deviate from the pure double-couple type; however, the deviations are under 30° in the case of opening fractures, and under 20° in the case of closing fractures. Therefore, the acoustic emissions in this experiment are the result of predominantly shearing dislocations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. Here we present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A genetic meta-algorithm-assisted inversion approach: hydrogeological study for the determination of volumetric rock properties and matrix and fluid parameters in unsaturated formations

An evolutionary inversion approach is suggested for the interpretation of nuclear and resistivity logs measured by direct-push tools in shallow unsaturated sediments. The efficiency of formation evaluation is improved by estimating simultaneously (1) the petrophysical properties that vary rapidly along a drill hole with depth and (2) the zone parameters that can be treated as constant, in one inversion procedure. In the workflow, the fractional volumes of water, air, matrix and clay are estimated in adjacent depths by linearized inversion, whereas the clay and matrix properties are updated using a float-encoded genetic meta-algorithm. The proposed inversion method provides an objective estimate of the zone parameters that appear in the tool response equations applied to solve the forward problem, which can significantly increase the reliability of the petrophysical model as opposed to setting these parameters arbitrarily. The global optimization meta-algorithm not only assures the best fit between the measured and calculated data but also gives a reliable solution, practically independent of the initial model, as laboratory data are unnecessary in the inversion procedure. The feasibility test uses engineering geophysical sounding logs observed in an unsaturated loessy-sandy formation in Hungary. The multi-borehole extension of the inversion technique is developed to determine the petrophysical properties and their estimation errors along a profile of drill holes. The genetic meta-algorithmic inversion method is recommended for hydrogeophysical logging applications of various kinds to automatically extract the volumetric ratios of rock and fluid constituents as well as the most important zone parameters in a reliable inversion procedure.

58 GEOSCIENCES↗
DOE OSTI2018

Checklist for a Radiology Information System Go-Live

This article highlights the experience of a single center institution undergoing a change in radiology information system (RIS) software platforms, transitioning to an electronic medical record-RIS driven workflow. Ten planning and execution topics with recommendations are presented in checklist form from the radiology department perspective. The build process of creating a site specific RIS takes many months, beginning with the organization of a steering committee. On Go-Live, several checklist items are offered to help streamline the troubleshooting process and improve communication throughout the radiology department. The groundwork of the group effort in creating the infrastructure of the build process can continue to be useful beyond Go-Live, as RIS features are continually optimized.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm

Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors. We utilized a deep learning-based natural language classification system from IBM Watson, a question-answering supercomputer that gained fame after challenging the best human players on Jeopardy! in 2011. We compared this solution to a series of traditional machine learning-based natural language processing techniques that utilize a term-document frequency matrix. Each classifier was trained with 1240 MRI protocols plus their respective clinical indications and validated with a test set of 280. Ground truth of contrast assignment was obtained from the clinical record. For evaluation of inter-reader agreement, a blinded second reader radiologist analyzed all cases and determined contrast assignment based on only the free-text clinical indication. In the test set, Watson demonstrated overall accuracy of 83.2% when compared to the original protocol. This was similar to the overall accuracy of 80.2% achieved by an ensemble of eight traditional machine learning algorithms based on a term-document matrix. When compared to the second reader’s contrast assignment, Watson achieved 88.6% agreement. When evaluating only the subset of cases where the original protocol and second reader were concordant (n = 251), agreement climbed further to 90.0%. The classifier was relatively robust to spelling and grammatical errors, which were frequent. Implementation of this automated MR contrast determination system as a clinical decision support tool may save considerable time and effort of the radiologist while potentially decreasing error rates, and require no change in order entry or workflow.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Electronic Medical Record Integration for Streamlined DXA Reporting

Dual-energy X-ray absorptiometry (DXA) is the most frequently performed examination to assess bone mineral density in clinical practice. Aside from images and graphical displays, many numerical values are part of DXA reports. These values are typically manually entered into the formal report through the electronic medical record or PACS workstation. The process takes time and is prone to errors. Exporting the DXA numerical data via HL7 engine to the electronic medical record was proposed to improve reporting efficiency and accuracy. The output from the DXA unit computer was reconfigured to export the report content via the HL7 interface engine into the electronic medical record. Radiology interpretive reporting was subsequently done directly in the electronic medical record. In the evaluation of errors, 100 preliminary DXA reports before the change and 100 after the change were examined. These reports were analyzed for errors that included decimal change, number transposition, negative number issue, other incorrect number error, and failure to include prior exam for comparison. In addition, report turnaround times were evaluated before and after the changes were made. Reporting time evaluations included 1-year volume prior to change (3915 reports) and 1 month post-change (206 reports). Of 100 DEXA exams before the change, 15 final reports contained 25 numerical errors. After the change, no numerical errors in the reports were identified. Exam end to final report time decreased from 2159 to 625 min on average. Automating data transmittal from the DXA modality for report generation improves accuracy and turnaround time. This approach did not require any third party software, and healthcare information security concerns were negated since we are using our standard workstations. Secondary to the affordability and applicability to the large percentage of the population using electronic medical record systems, this type of automated workflow is recommended.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Transforming Dermatologic Imaging for the Digital Era: Metadata and Standards

Imaging is increasingly being used in dermatology for documentation, diagnosis, and management of cutaneous disease. The lack of standards for dermatologic imaging is an impediment to clinical uptake. Standardization can occur in image acquisition, terminology, interoperability, and metadata. This paper presents the International Skin Imaging Collaboration position on standardization of metadata for dermatologic imaging. Metadata is essential to ensure that dermatologic images are properly managed and interpreted. There are two standards-based approaches to recording and storing metadata in dermatologic imaging. The first uses standard consumer image file formats, and the second is the file format and metadata model developed for the Digital Imaging and Communication in Medicine (DICOM) standard. DICOM would appear to provide an advantage over using consumer image file formats for metadata as it includes all the patient, study, and technical metadata necessary to use images clinically. Whereas, consumer image file formats only include technical metadata and need to be used in conjunction with another actor—for example, an electronic medical record—to supply the patient and study metadata. The use of DICOM may have some ancillary benefits in dermatologic imaging including leveraging DICOM network and workflow services, interoperability of images and metadata, leveraging existing enterprise imaging infrastructure, greater patient safety, and better compliance to legislative requirements for image retention.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Radiologist Digital Workspace Use and Preference: a Survey-Based Study

Literature regarding the heterogeneity of and preferences for radiology workstation design—and, in particular, the digital workspace of the radiology workstation—is scant. The purpose of this study was to determine the nature of the digital environments across the specialty and the degree of satisfaction users associated with the particular facets of those environments. A survey was sent to the membership of the Association of University Radiologists in February 2015. The survey comprised 10 questions establishing demographics, current typical workstation setup, perceived satisfaction with that setup, and preferences for potential altered setups. A total of 336 radiologists responded, with a cross-section similar to that described in the 2015 ACR annual workforce survey (1). Although there was a rough split in the number of radiologists using one or two non-diagnostic monitors (46 vs. 51%, respectively), the strong majority (75%) of radiologists use two diagnostic monitors. Users of two non-diagnostic monitors were more likely to keep open the case info (87 vs. 68%) and EMR (84 vs 68%). More senior radiologists tended to find the current setup easy more frequent than younger radiologists, and the latter group was more likely to believe additional monitors would be helpful. Although many radiologists are comfortable with their computing workflows, a significant number indicate dissatisfaction and may be interested in being able to specify the amount of monitor space with which they can work. Additional monitors may promote improved quality in addition to any potential productivity gains.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Patient-Centered Radiology with FHIR: an Introduction to the Use of FHIR to Offer Radiology a Clinically Integrated Platform

Fast Healthcare Interoperability Resources (FHIR) is an open interoperability standard that allows external software to quickly search for and access clinical information from the electronic medical record (EMR) in a method that is developer-friendly, using current internet technology standards. In this article, we highlight the new FHIR standard and illustrate how FHIR can be used to offer the field of radiology a more clinically integrated and patient-centered system, opening the EMR to external radiology software in ways unfeasible with traditional standards. We explain how to construct FHIR queries relevant to medical imaging using the Society for Imaging Informatics in Medicine (SIIM) Hackathon application programming interface (API), provide sample queries for use, and suggest solutions to offer a patient-centered, rather than an image-centered, workflow that remains clinically relevant.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System

Open-source development can provide a platform for innovation by seeking feedback from community members as well as providing tools and infrastructure to test new standards. Vendors of proprietary systems may delay adoption of new standards until there are sufficient incentives such as legal mandates or financial incentives to encourage/mandate adoption. Moreover, open-source systems in healthcare have been widely adopted in low- and middle-income countries and can be used to bridge gaps that exist in global health radiology. Since 2011, the authors, along with a community of open-source contributors, have worked on developing an open-source radiology information system (RIS) across two communities—OpenMRS and LibreHealth. The main purpose of the RIS is to implement core radiology workflows, on which others can build and test new radiology standards. This work has resulted in three major releases of the system, with current architectural changes driven by changing technology, development of new standards in health and imaging informatics, and changing user needs. At their core, both these communities are focused on building general-purpose EHR systems, but based on user contributions from the fringes, we have been able to create an innovative system that has been used by hospitals and clinics in four different countries. We provide an overview of the history of the LibreHealth RIS, the architecture of the system, overview of standards integration, describe challenges of developing an open-source product, and future directions. Our goal is to attract more participation and involvement to further develop the LibreHealth RIS into an Enterprise Imaging System that can be used in other clinical imaging including pathology and dermatology.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Optimization of SAMtools sorting using OpenMP tasks

SAMtools is a widely-used genomics application for post-processing high-throughput sequence alignment data. Such sequence alignment data are commonly sorted to make downstream analysis more efficient. However, this sorting process itself can be computationally- and I/O-intensive: high-throughput sequence alignment files in the de facto standard binary alignment/map (BAM) format can be many gigabytes in size, and may need to be decompressed before sorting and compressed afterwards. As a result, BAM-file sorting can be a bottleneck in genomics workflows. Here, this paper describes a case study on the performance analysis and optimization of SAMtools for sorting large BAM files. OpenMP task parallelism and memory optimization techniques resulted in a speedup of 5.9X versus the upstream SAMtools 1.3.1 for an internal (in-memory) sort of 24.6 GiB of compressed BAM data (102.6 GiB uncompressed) with 32 processor cores, while a 1.98X speedup was achieved for an external (out-of-core) sort of a 271.4 GiB BAM file.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2021

Poisson’s ratio of eTPU molded bead foams in compression via in situ synchrotron X-ray microtomography

In situ synchrotron X-ray microtomography was used to characterize the bulk deformation behavior by computing the Poisson's ratio of expanded thermoplastic polyurethane (eTPU) molded bead foams used in footwear midsole during compression. Quantitative data on morphological characteristics were obtained using an iterative image processing workflow. Image correlation on the 4D datasets using DVC was performed to calculate the volumetric and axial strain to estimate the Poisson ratio. Strain maps from DVC showed the influence of variability in ligament thickness distribution on the global mechanical behavior exhibited which dominated the response seen in these bead foams. Lastly, our results showed a strong correlation between Poisson ratio and distribution of ligament thickness in foams.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗
DOE OSTI2017

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.

02 PETROLEUM↗
DOE OSTI2019

Multi-scale Quantitative Risk Analysis of Seabed Minerals: Principles and Application to Seafloor Massive Sulfide Prospects

The potential for mining hydrothermal mineral deposits on the seafloor, such as seafloor massive sulfides, has become technically possible, and some companies (currently not many) are considering their exploration and development. Yet, no present methodology has been designed to quantify the ore potential and assess the risks relative to prospectivity at prospect and regional scales. Multi-scale exploration techniques, similar to those of the play analysis that are used in the oil and gas industry, can help to fulfill this task by identifying the characteristics of geologic environments indicative of ore-forming processes. Such characteristics can represent a combination of, e.g., heat source, pathway, trap and reservoir that all dictate how and where ore components are mobilized from source to deposition. In this study, the understanding of these key elements is developed as a mineral system, which serves as a guide for mapping the risk of the presence or absence of ore-forming processes within the region of interest (the permissive tract). The risk analysis is carried out using geoscience data, and it is paired with quantitative resource estimation analysis to estimate the in-place mineral potential. Resource estimates are simulated stochastically with the help of available data (bathymetric features in this study), conventional grade–tonnage models and Monte Carlo simulation techniques. In this paper, the workflow for a multi-scale quantitative risk analysis, from the definition to the evaluation of a permissive tract and related prospect(s), is described with the help of multi-beam data of a known hydrothermal vent site.

58 GEOSCIENCES↗
DOE OSTI2023

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

Abstract Rare-earth elements (REEs) supply raw materials that constitute many of our modern critical infrastructure, defense, technology, and electrification needs. Despite REE accumulations occurring in conventional bedrock and ion-adsorption deposits sourced from weathering of igneous rocks, unconventional host materials such as coal and related sedimentary strata have been identified as promising sources of REEs to meet growing demand. To maximize the potential of unconventional resources such as REE-coal systems, new approaches are needed overcome challenges from mineral systems with no known deposits and areas with sparse geochemical data. This article presents a systematic knowledge-data resource assessment method for predicting and identifying REE resource potential and occurrence in these unconventional systems. The method utilizes a geologic and geospatial knowledge-data approach informed and guided by REE accumulation mechanisms to systematically assess and identify areas of higher enrichment. An assessment of the Powder River Basin is presented as a test case to demonstrate the method workflow and results. The key output is a potential enrichment score map reported with varying confidence levels based on the amount of supporting evidence. Results from the test case indicate several locations with promising potential for different types of coal-REE deposits, demonstrating the viability of the method for exploration and assessment of unconventional REE resources. The method is flexible by design and, with sufficient applicable knowledge and data, can be adapted for assessing critical mineral systems in other sedimentary systems as well.

58 GEOSCIENCES↗
DOE OSTI2023

Comparative Analysis of Imaging and Measurements of Micrometer-Scale Fracture Aperture Fields Within a Heterogeneous Rock Using PET and X-ray CT

Knowledge of the spatial distribution of fracture apertures is essential for reliable characterization of flow and transport processes in fractured systems and for better understanding of physicochemical matrix–fracture interactions. Here, we propose and test two image-based methods, thereby extending the current experimental capabilities to characterize aperture size distribution in structurally heterogeneous geologic porous media noninvasively. The first approach utilizes an inversion method based on the dataset acquired from positron emission tomography (PET) and the second approach considers an extension of the classic missing attenuation technique that relies on clinical X-ray computed tomography (X-ray CT). Independent sets of imaging experiments are conducted on a fractured basalt core with heterogeneous matrix properties and aperture distributions to compare the two methodologies. A repeat of each experiment is conducted to verify the proposed workflows. The performance of these two imaging techniques is systematically evaluated through the analysis of signal-to-noise ratio, minimum fracture size detectability, and measurement errors. While both approaches provide a reliable estimation of fracture aperture distributions, PET yields a signal-to-noise ratio that is substantially higher than the corresponding X-ray CT measurements. Furthermore, uncertainties of the aperture values for PET are considerably lower ($\bar {\sigma}_{\text {d}} = 15\%$) compared to those obtained from X-ray CT ($\bar {\sigma}_{\text {d}} = 29\%$), allowing for the detection of minimum aperture sizes of 20 $\mu$m with 70% confidence level. Finally, these approaches provide key experimental tools for better understanding dynamic hydromechanical fracture properties in geologic systems.

58 GEOSCIENCES↗