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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Subtask 1.4 - Techno-Economic Assessment of Regional Carbon Utilization Scenarios and Attendant Monitoring Technology

The U.S. Department of Energy (DOE) Carbon Storage Program seeks to develop technologies to significantly improve the effectiveness of carbon capture and storage (CCS), reduce the cost of implementation, and support the readiness of widespread commercial deployment by 2035. To accomplish this goal, technical and economic barriers must be addressed and the results used to inform regulators and industry on the safety, permanence, and viability of carbon storage. To help overcome these barriers, the Energy & Environmental Research Center (EERC) conducted three activities: 1) investigate the regional techno-economic life cycle of CO2 enhanced oil recovery (EOR) in North Dakota, 2) assist DOE in determining the prospective CO2 storage resource of residual oil zones (ROZs), and 3) develop a seismic technique for monitoring geologic storage of CO2. These three activities address two strategic goals enumerated in the DOE–EERC Cooperative Agreement, namely, 1) to advance foundational science and 2) to inform data-driven policies that enhance U.S. economic growth and job creation, energy security, and environmental quality. Activity 1 In North Dakota, there is synergistic incentive to develop and implement CO2 EOR technologies. Regional industry partners are actively seeking options that can cost-effectively improve the efficiency of power production, while also coproducing CO2 for use in EOR applications. A full-value-chain techno-economic assessment that links lignite resources, power generation, and EOR is needed to determine the implications for a state or region to continue to use these resources or look elsewhere for other energy sources and revenue streams. The integration of North Dakota’s lignite power industry with the state’s oil industry has the potential to revitalize legacy oil fields and produce up to 1 billion barrels of additional oil using CO2 EOR while providing a market for a CO2 commodity captured at coal-fired power plants. Revitalization of North Dakota’s legacy oil fields will bring new industry investment to the state, resulting in the growth of oil-related jobs and revenue through oil sales, severance tax, and associated oilfield development activities (drilling, pipelines, etc.). CO2 demand for EOR and incremental oil production were calculated for 201 conventional oil fields in North Dakota. Infrastructure development in the fields was estimated using 1) a traditional vertical well arrangement of CO2 injectors and oil producers and 2) an advanced case where horizontal wells were used exclusively. Pipeline, compression, and recycling infrastructure requirements were also estimated. When viewed from the CO2 capture and CO2 EOR sectors, the advanced case showed a positive investment scenario when including potential benefits of the 45Q tax credit policy. An economic evaluation of a full implementation of CO2 EOR showed an average $60 billion potential state economic impact estimated over the 37-year construction and production time frame could be realized with an assumed oil price of $65/bbl. Oil price ranges of $35–$90/bbl cause an estimated variation of ±$5 billion over the potential effort. The total net state government revenues were estimated over the 37-year construction and production time frame. With an assumed oil price of $65/bbl, total potential fiscal impact could average $7.4 billion. This result ranges about ±$2.5 billion for a potential EOR implementation effort for oil price ranges of $35–$90/bbl. This broad-brush assessment was focused on “magnitude-of-effect” perspectives of what CO2 capture and use for EOR could mean to the state. Whether CO2 EOR ever becomes widely adopted is not the issue at this stage of evaluation. The question being answered is, How valuable could CO2 capture and CO2 EOR be for the state and local jurisdictions in western North Dakota? The results of the current economic assessment remove any ambiguity to that question. Full implementation of CO2 EOR as modeled in this study provides substantial economic benefits to the state, even at low oil prices (i.e., $35/bbl). Job creation and government revenues could be substantial, depending on the scale of implementation. With a better understanding of what CO2 EOR might mean for the state’s economy, the focus can now shift to the policies and financial mechanisms needed to make private investment in CO2 capture and use as EOR competitive with other energy-related pursuits. Activity 2 ROZs contain remnants of oil not swept away by natural waterflooding. Although nearly all of the identified ROZs are found in West Texas, there is evidence that this category of oil reservoir can be found in other major oil-producing basins of the United States. Because the formation of these oil reservoirs is distinct from other conventional reservoirs, the method to estimate the prospective CO2 storage resource in ROZs may be equally distinct from the method used for conventional fields. As such, the EERC collaborated with DOE’s National Energy Technology Laboratory (NETL) to investigate the feasibility of CO2 storage in ROZs and determine what specific modification (if any) needs to be made to the current DOE volumetric method to estimate prospective CO2 storage resource in oil reservoirs. This activity built on a previous EERC investigation to identify ROZs in the Williston and Powder River Basins and upon collaborations between the EERC and NETL to develop storage resource estimation methods for different types of geologic CO2 storage. Review of the volumetric approach raised questions about the applicability of such an approach to hydrocarbon-bearing zones without the inclusion of potential hydrocarbon production. A production-based approach was presented by the EERC to inform the working group of additional methods of increasing the CO2 storage resource potential. Because the production-based method focuses more heavily on oil recovery than CO2 storage, it was not included in the working group manuscript. Activity 3 The EERC has used sparse and irregular seismic arrays to detect CO2 injected into a reservoir. The variability of seismic noise recorded by the sensors directly impacts the seismic interpretation. However, designing noise attenuation workflows effectively can be a more challenging task using sparse and irregular arrays for data acquisition. Based on the hypothesis that noncoherent noise is stationary during weekly data acquisition, a new machine-learning algorithm was developed to automatically estimate noise conditions using passive records, noise from active records, and ancillary information. The new noise attenuation processing workflow can improve the time-lapse detection of CO2 from sparse and irregular array acquisitions. Results show spatial and temporal noise patterns can be related to the location of the sensors and the weather conditions during the data acquisition and that our attenuation processing workflow can improve the time-lapse detection of CO2 from sparse and irregular array acquisitions. This effort was funded in part through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Department of Commerce.

Peck, Wesly↗
NASA NTRSDate not supplied

Natural Language Processing Methods for Air Traffic Management Text and Speech Data

This presentation discusses two efforts of the NARI AI/ML Intern team during the Fall 2021 OSTEM Internship term. For Letters of Agreement (LoA), we have studied how LoAs are structured and explored the question ‘What is an LoA constraint?’ To do this, our approach is data-driven, iterative, and assisted by machine learning when available. In this presentation, we will walk through our tasks of manually scanning through documents, performing a preliminary entity labelling task, and our unsupervised analysis on LoA procedures sections. After this research phase, we define the smallest constraint unit in an LoA, and start to perform entity extraction. Looking towards constraint extraction, we are also exploring the use of a one-class support vector machine (OneClassSVM) model to identify patterns within the data. The second effort of our team this term is focused on Air Traffic Control System Command Center (ATCSCC) advisory meetings, and the subsequent advisory documents that get published from their content. These advisory documents are important to give readily accessible summaries of daily operations, so that data centers, airline officials, and other stakeholders can easily understand the context of these meetings in real time. In applying machine learning to this scenario, two natural language processing tasks are used. First is developing machine learning models to convert the meeting speech data into text. With this text, use of extractive and abstractive text summarization models are used to automatically generate preliminary versions of the advisory documents.

Natural Language Processing↗
NASA NTRS1972

Data processing at the University of Kansas

Two systems for processing remotely sensed image data have been discussed. The first system, IDECS, is a near real-time hardware system oriented towards processing multi-image data sets quickly and economically. The IDECS has convenient film input and color display output capabilities and implements simple kinds of decision rules. The second system, KANDIDATS, is a software system capable of performing many of the more sophisticated processing methods. Because of its monitor which handles all bookkeeping and its modular design, KANDIDATS easily allows the testing of new automatic processing techniques. After a new technique has been proven on KANDIDATS, it may be simplified and hard-wired in IDECS, thereby keeping the volume processing of remotely sensed data always up to the current state-of-the-art.

Haralick, R. M.↗
DOE OSTI2017

Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank

Motivation: Glycans play a central role in many essential biological processes. Glycan Reader was originally developed to simplify the reading of Protein Data Bank (PDB) files containing glycans through the automatic detection and annotation of sugars and glycosidic linkages between sugar units and to proteins, all based on atomic coordinates and connectivity information. Carbohydrates can have various chemical modifications at different positions, making their chemical space much diverse. Unfortunately, current PDB files do not provide exact annotations for most carbohydrate derivatives and more than 50% of PDB glycan chains have at least one carbohydrate derivative that could not be correctly recognized by the original Glycan Reader. Results: Glycan Reader has been improved and now identifies most sugar types and chemical modifications (including various glycolipids) in the PDB, and both PDB and PDBx/mmCIF formats are supported. CHARMM-GUI Glycan Reader is updated to generate the simulation system and input of various glycoconjugates with most sugar types and chemical modifications. It also offers a new functionality to edit the glycan structures through addition/deletion/modification of glycosylation types, sugar types, chemical modifications, glycosidic linkages, and anomeric states. The simulation system and input files can be used for CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Glycan Fragment Database in GlycanStructure. Org is also updated to provide an intuitive glycan sequence search tool for complex glycan structures with various chemical modifications in the PDB.

Park, Sang-Jun↗
DOE OSTI2022

Serial crystallography with multi-stage merging of thousands of images

KAMO and BLEND provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending BLEND to support additional clustering options such as the use of more accurate cell distance metrics and the use of reflection-intensity correlation coefficients to infer `distances' among sets of reflections. This increases the sensitivity to differences in unit-cell parameters and allows clustering to assemble nearly complete data sets on the basis of intensity or amplitude differences. If the data sets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. When starting from incomplete data sets, one applies KAMO twice, first using unit-cell parameters. In this step, either the simple cell vector distance of the original BLEND or the more sensitive NCDist is used. This step tends to find clusters of sufficient size such that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between reflections with a common hkl to merge clusters in a way that is sensitive to structural differences that may not have perturbed the unit-cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent of one another, it should be possible to greatly improve the efficacy of data-clustering software by using a multi-stage partitioning strategy. Here, one possible approach to multi-stage data clustering is demonstrated. The strategy is to use unit-cell clustering until the merged data are sufficiently complete and then to use intensity-based clustering. Using this strategy, it is demonstrated that it is possible to accurately cluster data sets from crystals that have subtle differences.

36 MATERIALS SCIENCE↗
NASA NTRS1975

An automated atmospheric sampling system operating on 747 airliners

An air sampling system that automatically measures the temporal and spatial distribution of selected particulate and gaseous constituents of the atmosphere has been installed on a number of commercial airliners and is collecting data on commercial air routes covering the world. Measurements of constituents related to aircraft engine emissions and other pollutants are made in the upper troposphere and lower stratosphere (6 to 12 km) in support of the Global Air Sampling Program (GASP). Aircraft operated by different airlines sample air at latitudes from the Arctic to Australia. This system includes specialized instrumentation for measuring carbon monoxide, ozone, water vapor, and particulates, a special air inlet probe for sampling outside air, a computerized automatic control, and a data acquisition system. Air constituents and related flight data are tape recorded in flight for later computer processing on the ground.

Perkins, P.↗
NASA NTRS1976

An automated atmospheric sampling system operating on 747 airliners

An air sampling system that automatically measures the temporal and spatial distribution of particulate and gaseous constituents of the atmosphere is collecting data on commercial air routes covering the world. Measurements are made in the upper troposphere and lower stratosphere (6 to 12 km) of constituents related to aircraft engine emissions and other pollutants. Aircraft operated by different airlines sample air at latitudes from the Arctic to Australia. This unique system includes specialized instrumentation, a special air inlet probe for sampling outside air, a computerized automatic control, and a data acquisition system. Air constituent and related flight data are tape recorded in flight for later computer processing on the ground.

Perkins, P. J.↗
NASA NTRS1984

Transferring data oscilloscope to an IBM using an Apple II+

A set of PASCAL programs permitting the use of a laboratory microcomputer to facilitate and control the transfer of data from a digital oscilloscope (used with photomultipliers in experiments on soot formation in hydrocarbon combustion) to a mainframe computer and the subsequent mainframe processing of these data is presented. Advantages of this approach include the possibility of on-line computations, transmission flexibility, automatic transfer and selection, increased capacity and analysis options (such as smoothing, averaging, Fourier transformation, and high-quality plotting), and more rapid availability of results. The hardware and software are briefly characterized, the programs are discussed, and printouts of the listings are provided.

Miller, D. L.↗
NASA NTRS1989

A rapid prototyping/artificial intelligence approach to space station-era information management and access

Applications of rapid prototyping and Artificial Intelligence techniques to problems associated with Space Station-era information management systems are described. In particular, the work is centered on issues related to: (1) intelligent man-machine interfaces applied to scientific data user support, and (2) the requirement that intelligent information management systems (IIMS) be able to efficiently process metadata updates concerning types of data handled. The advanced IIMS represents functional capabilities driven almost entirely by the needs of potential users. Space Station-era scientific data projected to be generated is likely to be significantly greater than data currently processed and analyzed. Information about scientific data must be presented clearly, concisely, and with support features to allow users at all levels of expertise efficient and cost-effective data access. Additionally, mechanisms for allowing more efficient IIMS metadata update processes must be addressed. The work reported covers the following IIMS design aspects: IIMS data and metadata modeling, including the automatic updating of IIMS-contained metadata, IIMS user-system interface considerations, including significant problems associated with remote access, user profiles, and on-line tutorial capabilities, and development of an IIMS query and browse facility, including the capability to deal with spatial information. A working prototype has been developed and is being enhanced.

Carnahan, Richard S., Jr.↗
DOE OSTI2016

TU-D-209-05: Automatic Calculation of Organ and Effective Dose for CBCT and Interventional Fluoroscopic Procedures

Purpose: To compare PCXMC and EGSnrc calculated organ and effective radiation doses from cone-beam computed tomography (CBCT) and interventional fluoroscopically-guided procedures using automatic exposure-event grouping. Methods: For CBCT, we used PCXMC20Rotation.exe to automatically calculate the doses and compared the results to those calculated using EGSnrc with the Zubal patient phantom. For interventional procedures, we use the dose tracking system (DTS) which we previously developed to produce a log file of all geometry and exposure parameters for every x-ray pulse during a procedure, and the data in the log file is input into PCXMC and EGSnrc for dose calculation. A MATLAB program reads data from the log files and groups similar exposures to reduce calculation time. The definition files are then automatically generated in the format used by PCXMC and EGSnrc. Processing is done at the end of the procedure after all exposures are completed. Results: For the Toshiba Infinix CBCT LCI-Middle-Abdominal protocol, most organ doses calculated with PCXMC20Rotation closely matched those calculated with EGSnrc. The effective doses were 33.77 mSv with PCXMC20Rotation and 32.46 mSv with EGSnrc. For a simulated interventional cardiac procedure, similar close agreement in organ dose was obtained between the two codes; the effective doses were 12.02 mSv with PCXMC and 11.35 mSv with EGSnrc. The calculations can be completed on a PC without manual intervention in less than 15 minutes with PCXMC and in about 10 hours with EGSnrc, depending on the level of data grouping and accuracy desired. Conclusion: Effective dose and most organ doses in CBCT and interventional radiology calculated by PCXMC closely match those calculated by EGSnrc. Data grouping, which can be done automatically, makes the calculation time with PCXMC on a standard PC acceptable. This capability expands the dose information that can be provided by the DTS. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specifc contextual clues and requires no training. This approach generalizes across diferent modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal refectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fdelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufcient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Publisher Correction: Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

Shahbazi, Ali↗
DOE OSTI2013

An online application to measue the dispersion function in AGS

The measurement of the AGS dispersion function is one of the requirements for the development of an accurate online model of the AGS. Currently the zgoubi and the MADX online models of the AGS are employed as online models to set up and view the beam optics of the AGS using the model viewer. In this technical note we describe the method we employ to measure the numerical values of the dispersion function of the AGS at the location of the Beam Position Monitors (BPM’s) and we explain in detail the automatic process to measure experimentally the dispersion function of AGS at the location of the BPM’s. This automatic method of measuring the dispersion of the AGS not only reduces dramatically the time required to collect and analyze the experimental data to calculate the dispersion of the AGS, but also minimizes the possible human error that may occur in the process of the dispersion measurement.

43 PARTICLE ACCELERATORS↗
NASA NTRS1986

A process activity monitor for AOS/VS

With the ever increasing concern for computer security, users of computer systems are becoming more sensitive to unauthorized access. One of the initial security concerns for the Shuttle Management Information System was the problem of users leaving their workstations unattended while still connected to the system. This common habit was a concern for two reasons: it ties up resources unnecessarily and it opens the way for unauthorized access to the system. The Data General MV/10000 does not come equipped with an automatic time-out option on interactive peripherals. The purpose of this memorandum is to describe a system which monitors process activity on the system and disconnects those users who show no activity for some time quantum.

Mckosky, R. A.↗
DOE OSTI2023

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis: Aggregation and Interpretation of Multi-Report Analysis Results

Industry-wide operating experience is a critical source of raw data for reliability and risk model parameter estimations for nuclear power plants. A large portion of operating experience data are failure events stored as reports that contain unstructured data, such as narratives. In current practice, a failure report is usually reviewed and manually coded by analysts. The coding is based on extracting several event characteristics such as system name, component type, sub-part type, failure mode, and failure cause. Event narratives are mostly used to help understand events and extract their characteristics. In this line of research, we aim to maximize the usage of event narratives by leveraging natural language processing (NLP) methods to automatically convert an event narrative to a causal graph. This research has promise to improve physical understanding of failure initiation and propagation and to facilitate use of non-failure data (e.g., near-misses and degradations) to complement the limited data pool of failures. In our previous work, we developed an NLP tool and applied it to analyze a number of licensee event reports submitted by U.S. nuclear power plants to the Nuclear Regulatory Commission. In this paper, we will report our recent research progress in aggregating the results of multiple reports, developing network model(s), and drawing statistical insights.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2008

Reconfigurable Processing for Satellite On-Board Automatic Cloud Cover Assessment (ACCA)

Clouds have a critical role in many studies such as weather- and climate-related investigations. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. In addition, sending cloudy data to ground stations can result in an inefficient utilization of the communication bandwidth. This requires satellite on-board cloud detection capability to mask out cloudy pixels from further processing. Remote sensing satellite missions have always required smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the-art reconfigurable platforms, SRC-6. It will be shown that our work provides higher detection accuracy and over one order of magnitude improvement in performance when compared to previously reported investigations.

El-Araby, Esam↗
NASA NTRS1990

Rule induction techniques

Expert System technology is receiving more and more consideration these days when it comes time for selecting an implementation paradigm. As people become educated as to which domains Expert Systems technology can be appropriately utilized and to what extent this capability can be made useful, they will want to extend this ability to encompass greater responsibilities of their everyday routines. Knowledge Acquisition is one of the most time-consuming phases of an Expert System development process. One must extract the essential knowledge from a source by whatever means, and transform this knowledge into a representation that can be demonstrated. Rule Induction is one technique that can be applied to the knowledge acquisition process for certain example-based problems. Spread Sheets and Data Bases for example, may contain columns of information (criteria) to be used in some sort of decision making policy. These criteria could automatically be converted (represented) into production rules that output an appropriate decision dependent upon a given set of criteria. To generalize this concept, we could state that Rule Induction is the process of generating a Knowledge Base from a given set of examples. Through the use of Rule Induction, I will explain how I took information from a database and generated rules encapsulating the information contained within the database.

Young, Gary B.↗
NASA NTRS1990

Development of multiple source data processing for structural analysis at a regional scale

Various image processing techniques developed for enhancement and extraction of linear features, of interest to the structural geologist, from digital remote sensing, geologic, and gravity data, are presented. These techniques include: (1) automatic detection of linear features and construction of rose diagrams from Landsat MSS data; (2) enhancement of principal structural directions using selective filters on Landsat MSS, Spacelab panchromatic, and HCMM NIR data; (3) directional filtering of Spacelab panchromatic data using Fast Fourier Transform; (4) detection of linear/elongated zones of high thermal gradient from thermal infrared data; and (5) extraction of strong gravimetric gradients from digitized Bouguer anomaly maps. Processing results can be compared to each other through the use of a geocoded database to evaluate the structural importance of each lineament according to its depth: superficial structures in the sedimentary cover, or deeper ones affecting the basement. These image processing techniques were successfully applied to achieve a better understanding of the transition between Provence and the Pyrenees structural blocks, in southeastern France, for an improved structural interpretation of the Mediterranean region.

Carrere, Veronique↗