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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Tracking 2020 Decreases in Carbon Dioxide Due to the COVID19 Pandemic in NASA’s GEOS Modeling system: Implications for Space-Based Carbon Monitoring

The COVID19 pandemic led to abrupt, worldwide changes in human activity and related emissions of air pollutants and greenhouse gases that are unprecedented in modern times. NASA satellites have demonstrated their ability to observe some of these impacts, particularly in relation to short-lived gases like nitrogen dioxide (NO2), which is emitted primarily from transportation. Bottom-up analyses of carbon dioxide (CO2) emissions suggest that the growth of atmospheric CO2 has also slowed, but differences are much more subtle than for NO2 because of the lifetime of atmospheric CO2 and sectoral differences in emission reductions. Estimates of CO2 from NASA’s Orbiting Carbon Observatory 2 (OCO-2) provide a unique view of COVID19 impacts, but observed changes in the column-average CO2 can be difficult to interpret because of gaps in spatial coverage. Assimilating these data into the Goddard Earth Observing System (GEOS), an integrated Earth system model with an advanced Constituent Data Assimilation System (CoDAS), helped to reveal changes in CO2 that are consistent with separate, bottom-up analyses of emissions reductions. Both indicate that from February-April of 2020, the growth in CO2 over Europe, North America, and Asia was roughly 0.3 ppm less than during the previous four years. Anomalies derived from gap-filled GEOS OCO-2 CoDAS products contribute to a joint effort by the world’s space agencies to track COVID19 impacts on the Earth. However, attribution of these changes is complicated by interannual variability in atmospheric circulation and the influence of climate on ocean and land carbon sinks. We discuss these results from the perspective of space-based carbon monitoring, which has received considerable support over the past decade from NASA. Our results demonstrate the accomplishments of current sensors and data assimilation systems, but also highlight challenges in providing high quality, low latency information to the public. In particular, understanding and attributing CO2 changes during 2020 requires year-specific information about land and ocean fluxes, which is often delayed for months or even years. We discuss current limitations and potential solutions to address these lags, which would support more reliable and timely space-based carbon monitoring.

COVID-19↗
NASA NTRS1992

Design and evaluation of an advanced air-ground data-link system for air traffic control

The design and evaluation of the ground-based portion of an air-ground data-link system for air traffic control (ATC) are described. The system was developed to support the 4D Aircraft/ATC Integration Study, a joint simulation experiment conducted at NASA's Ames and Langley Research Centers. The experiment focused on airborne and ground-based procedures for handling aircraft equipped with a 4D-Flight Management System (FMS) and the system requirements needed to ensure conflict-free traffic flow. The Center/TRACON Automation System (CTAS) at Ames was used for the ATC part of the experiment, and the 4D-FMS-equipped aircraft was simulated by the Transport Systems Research Vehicle (TSRV) simulator at Langley. The data-link system supported not only conventional ATC communications, but also the communications needed to accommodate the 4D-FMS capabilities of advanced aircraft. Of great significance was the synergism gained from integrating the data link with CTAS. Information transmitted via the data link was used to improve the monitoring and analysis capability of CTAS without increasing controller input workload. Conversely, CTAS was used to anticipate and create prototype messages, thus reducing the workload associated with the manual creation of data-link messages.

Denbraven, Wim↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗
NASA NTRS2009

Global Land Data Assimilation System (GLDAS) Products, Services and Application from NASA Hydrology Data and Information Services Center (HDISC)

The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data holdings include a set of 1.0 degree resolution data products from the four models, covering 1979 to the present; and a 0.25 degree data product from the Noah model, covering 2000 to the present. The products are in Gridded Binary (GRIB) format and can be accessed through a number of interfaces. Users can search the products through keywords and perform on-the-fly spatial and parameter subsetting and format conversion of selected data. More advanced visualization, access and analysis capabilities will be available in the future. The long term GLDAS data are used to develop climatology of water cycle components and to explore the teleconnections of droughts and pluvial.

Fang, Hongliang↗
NASA NTRSDate not supplied

NASA's Research & Development in Airspace Management for Drones, Air Taxis, and Beyond

Today's National Airspace System is extremely safe. Multiple people and organizations are involved in the handling of flights at all phases. We are, however, at the dawn of a new era in aviation where new entrants are emerging from multiple domains. These new entrants will need to be integrated into the airspace safely and given the flexibility to scale. With that integration and flexibility comes the need and responsibility to develop concepts and systems that will work with the regulatory and operational needs of the Federal Aviation Administration while accounting for the needs of industry. NASA is at the leading edge of research, development, and testing across multiple domains to enable the realization of new entrants such as drones, air cargo and taxis, and high altitude vehicles. Addressing the potential for public good coming from these new capabilities is also a NASA focus area. While there has been considerable effort in the engineering aspects of this research, another thrust that is of importance is that of data integration and visualization. With respect to visualization, NASA Ames Research Center has been leading the advancement in data visualization for these new domains and has spent considerable efforts in the exploration of incorporating Augmented and Mixed Reality as a situation awareness platform. This presentation will outline the new developments in airspace integration and tie in NASA's efforts in visualizing the associated operational data.

airspace management↗
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
NASA NTRS1989

Performance of the all-digital data-transition tracking loop in the advanced receiver

The performance of the all-digital data-transition tracking loop (DTTL) with coherent or noncoherent sampling is described. The effects of few samples per symbol and of noncommensurate sampling rates and symbol rates are addressed and analyzed. Their impacts on the loop phase-error variance and the mean time to lose lock (MTLL) are quantified through computer simulations. The analysis and preliminary simulations indicate that with three to four samples per symbol, the DTTL can track with negligible jitter because of the presence of earth Doppler rate. Furthermore, the MTLL is also expected to be large engough to maintain lock over a Deep Space Network track.

Cheng, U.↗
NASA NTRS1993

Test-Signal Generator For SNR Calibration

Assembly of commercial and custom-made electronic equipment designed to generate noisy intermediate-frequency or baseband received phase-modulation data-communication signals with accurately known signal-to-noise ratios. Signals used to perform signal-to-noise-ratio calibrations and other tests of responses of data-communication receivers to noisy incoming signals. Underlying principle applicable to generation of test signals for other advanced data-communication receivers.

Gutierrez-Luaces, Benito O.↗
NASA NTRS2002

Slow Crack Growth of Brittle Materials With Exponential Crack-Velocity Formulation: Constant Stress and Cyclic Stress Experiments - Part 3

The previously determined life prediction analysis based on an exponential crack-velocity formulation was examined using a variety of experimental data on advanced structural ceramics tested under constant stress and cyclic stress loading at ambient and elevated temperatures. The data fit to the relation between the time to failure and applied stress (or maximum applied stress in cyclic loading) was very reasonable for most of the materials studied. It was also found that life prediction for cyclic stress loading from data of constant stress loading in the exponential formulation was in good agreement with the experimental data, resulting in a similar degree of accuracy as compared with the power-law formulation. The major limitation in the exponential crack-velocity formulation, however, was that the inert strength of a material must be known a priori to evaluate the important slow-crack-growth (SCG) parameter n, a significant drawback as compared with the conventional power-law crack-velocity formulation.

Choi, Sung R.↗
DOE OSTI2021

Evaluating Future US Hydropower Opportunities

Uncertainties, and a lack of confidence regarding issues, such as water availability/storage capacities and operational constraints, can be major obstacles in assessing the range of future opportunities for existing dams, which are certainly substantial. The authors discuss specific challenges facing current infrastructure and water resource data analysis that can inhibit large-scale evaluation of potential hydro development at existing dams. Recent advances in data and tools that may address these challenges are introduced.

13 HYDRO ENERGY↗
NASA NTRS1987

NAECON 87; Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, OH, May 18-22, 1987. Volumes 1, 2, 3, & 4

The present conference discusses topics in VLSI components and their packaging, signal processing, uses of cartographic data, data transmission, advanced avionics architectures, fiber-optics, information control and display, image processing, airborne radar and fire control, navigation, air data, Kalman filtering, power generation and control, spacecraft power structures, aircraft flying qualities, flight management, fault-tolerant computer architectures, actuation technologies, self-repairing flight control system technology, multivariable control, stability and control methods, and AFTI/F-16 flight test reports. Also discussed are the ADA/JOVIAL language and its applications, software acquisition and testing, advanced software concepts, software management, computer graphics and visual systems softwear, ADA in embedded avionics, 16- and 32-bit architectures, voice interaction applications, human/machine systems analysis, human factors and AI, mental workloads and displays, pilot acceleration protection research, communications system technology, space communications, reliability and maintainability, managerial techniques, engineering management, EM compatibility and nuclear hardening, expert systems, AI language/knowledge representation, expert system implementation, machine vision/optical processing, and advanced AI concepts and architectures.

Avionics↗
NASA NTRS1990

A simulation-based test bed for data-association algorithms

A modular All-Source System-evaluation Test bed (ASSET) designed and developed for aiding in the testing and evaluation of advanced data-acquisition algorithms is described, with focus placed on the problem of integrating surveillance information in supporting command decision making. The evaluation procedure employed is based on a measure of the distance between a baseline representation and the representation produced by the association algorithm at some time instant. Choices for the baseline representation are presented, and scores are defined between these baselines and an algorithm representation. The test-bed architecture implementing the evaluation procedure is described, and sample outputs from performing algorithm evaluations in the test bed are outlined.

Brown, Donald E.↗
NASA NTRS1995

The Telestation: A product for distributed data gathering and operations

Advances in computer, navigation and wireless telecommunication technologies are enabling better electronic devices at reduced costs. Individually, these devices have been applied to increase the performance of many common systems, such as desktop computers, surveying equipment, and modems. However, we believe the convergence of these three technologies will create a product that simultaneously lowers life cycle costs while increasing operational effectiveness. Space Systems/Loral (SS/L) is developing portions of this product, known as the Telestation, for both terrestrial and orbital applications. The Telestation consists of an advanced microprocessor for command and data handling; a GPS receiver for position, time, and attitude information; and a Globalstar Transceiver for two-way digital communications. The Telestation provides the user with real-time command and control of globally distributed hardware elements. This capability can be applied terrestrially for gathering information (e.g., science, environmental, etc.) in remote or inhospitable locations, or where logistical support is inadequate. An in-orbit version can be used for spacecraft or payload operations, allowing principal investigators instant access to their payloads during all phases of a mission. This paper describes some cost effectiveness metrics of the Telestation, its development status, and its utility in both terrestrial and orbital applications.

Cleave, Robert R.↗
NASA NTRS2010

High-Performance CCSDS AOS Protocol Implementation in FPGA

The Consultative Committee for Space Data Systems (CCSDS) Advanced Orbiting Systems (AOS) space data link protocol provides a framing layer between channel coding such as LDPC (low-density parity-check) and higher-layer link multiplexing protocols such as CCSDS Encapsulation Service, which is described in the following article. Recent advancement in RF modem technology has allowed multi-megabit transmission over space links. With this increase in data rate, the CCSDS AOS protocol implementation needs to be optimized to both reduce energy consumption and operate at a high rate.

Clare, Loren P.↗
DOE OSTI2023

Online real-time learning of dynamical systems from noisy streaming data

Abstract Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: (a) it allows for online real-time monitoring of a dynamical system; (b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; (c) it is computationally fast and less intensive than the popular extended dynamic mode decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the chaotic attractor of the Henon map, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

97 MATHEMATICS AND COMPUTING↗