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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Modeling Performance in a Real-Time Air Quality Estimation System

Aerosol optical depth (AOD), derived from satellite measurements using Moderate Resolution Imaging Spectrometer (MODIS), offers indirect estimates of particle matter. Research shows a significant positive correlation between satellite-based measurements of AOD and ground-based measurements of particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5). In addition, satellite observations have also shown great promise in improving estimates of PM2.5 air quality surface. Research shows that correlations between AOD and ground PM2.5 are affected by a combination of many factors such as inherent characteristics of satellite observations, terrain, cloud cover, height of the mixing layer, and weather conditions, and thus might vary widely in different regions, different seasons, and even different days in a same location. Analysis of correlating AOD with ground measured PM2.5 on a day-to-day basis suggests the temporal scale, a number of immediate latest days for a given run's day, for their correlations needs to be considered to improve air quality surface estimates, especially when satellite observations are used in a real-time pollution system. The second reason is that correlation coefficients between AOD and ground PM2.5 cannot be predetermined and needs to be calculated for each day's run for a real-time system because the coefficients can vary over space and time. Few studies have been conducted to explore the optimal way to apply AOD data to improve model accuracies of PM2.5 surface estimation in a real-time air quality system. This paper discusses the best temporal scale to calculate the correlation of AOD and ground particle matter data to improve the results of pollution models in real-time system.

Li,Hui↗
NASA NTRS2022

Performance of Flash Lidar with Real-time Image Enhancement Algorithm for Landing Hazard Avoidance

Performance of a 3-D imaging flash lidar employing a novel super-resolution algorithm is characterized by a series of static and dynamic tests. A gantry test of this lidar in which the lidar was placed in an instrumented moving basket above a calibrated hazard field proved an excellent demonstration of hazard avoidance capabilities. Results of the gantry test are reported and potential of flash lidar utilizing super-resolution algorithm for future landing missions are explained.

Hazard Detection↗
DOE OSTI2021

Incorporation of Thermal Hydraulic Models for Thermal Power Dispatch into a PWR Power Plant Simulator

This report describes the development, modeling, and results of a generic pressurized water reactor power plant simulator that incorporates coupled electrical and thermal power dispatch to an industrial process located approximately one kilometer from the nuclear power plant. The simulator is a commercial PWR simulator that has been modified to include thermal power dispatch as described in past milestone reports [ , ]. The commercial PWR simulator is a generic simulator available from GSE SYSTEMS® (Sykesville, MD, USA) that is built using RELAP5-HDTM Real-Time Solution and in-house software developed by GSE Systems. This generic PWR (GPWR) simulator performs real-time simulation of the complete power plant from the reactor neutronics to the electricity generation and distribution. All primary, secondary, and auxiliary systems are modeled including all control logic in order to provide the most accurate representation of actual nuclear power plant (NPP) operation, and the simulator results have been rigorously verified by an actual NPP operating at approximately 1 GWe. This report is a continuation of worked performed in previous years, and supplemental information from previous reports is included in the appendix for reference.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1982

Mission definition study for a VLBI station utilizing the Space Shuttle

The uses of the Space Shuttle transportation system for orbiting VeryLong-Baseline Interferometry (OVLBI) were examined, both with respect to technical feasibility and its scientific possibilities. The study consisted of a critical look at the adaptability of current technology to an orbiting environment, the suitability of current data reduction facilities for the new technique, and a review of the new science that is made possible by using the Space Shuttle as a moving platform for a VLBI terminal in space. The conclusions are positive in all respects: no technological deficiencies exist that would need remedy, the data processing problem can be handled easily by straightforward adaptations of existing systems, and there is a significant new research frontier to be explored, with the Space Shuttle providing the first step. The VLBI technique utilizes the great frequency stability of modern atomic time standards, the power of integrated circuitry to perform real-time signal conditioning, and the ability of magnetic tape recorders to provide essentially error-free data recording, all of which combine to permit the realization of radio interferometry at arbitrarily large baselines.

Burke, B. F.↗
NASA NTRS2016

Novel Photon-Counting Detectors for Free-Space Communication

We present performance data for novel photon-counting detectors for free space optical communication. NASA GSFC is testing the performance of two types of novel photon-counting detectors 1) a 2x8 mercury cadmium telluride (HgCdTe) avalanche array made by DRS Inc., and a 2) a commercial 2880-element silicon avalanche photodiode (APD) array. We present and compare dark count, photon-detection efficiency, wavelength response and communication performance data for these detectors. We successfully measured real-time communication performance using both the 2 detected-photon threshold and AND-gate coincidence methods. Use of these methods allows mitigation of dark count, after-pulsing and background noise effects. The HgCdTe APD array routinely demonstrated photon detection efficiencies of greater than 50% across 5 arrays, with one array reaching a maximum PDE of 70%. We performed high-resolution pixel-surface spot scans and measured the junction diameters of its diodes. We found that decreasing the junction diameter from 31 micrometers to 25 micrometers doubled the e- APD gain from 470 for an array produced in the year 2010 to a gain of 1100 on an array delivered to NASA GSFC recently. The mean single-photon SNR was over 12 and the excess noise factors measurements were 1.2-1.3. The commercial silicon APD array exhibited a fast output with rise times of 300 ps and pulse widths of 600 ps. On-chip individually filtered signals from the entire array were multiplexed onto a single fast output.

Photons↗
DOE OSTI2021

Robust Optical Sensor Technology for Real-Time Monitoring of Solid Oxide Fuel Cells with High Spatial Resolutions

This SOFC core technology development program will develop an integrated fiber optical sensor technology to perform real-time and high-resolution measurements in both planar and tubular fuel cells and fuel cell assembly to monitor operations and structural changes of SOFCs. The new fiber sensing solution can monitor hundreds of points in the 3D fuel cell structures with 5-mm spatial resolutions using one single optical fiber.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗
DOE OSTI2023

Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities

Real-time monitoring of combustion behavior is a crucial step toward actively controlled rotating detonation engine (RDE) operation in laboratory and industrial environments. Various machine learning methods have been developed to advance diagnostic efficiencies from conventional postprocessing efforts to real-time methods. Here this work evaluates and compares conventional techniques alongside convolutional neural network (CNN) architectures trained in previous studies, including image classification, object detection, and time series classification, according to metrics affecting diagnostic feasibility, external applicability, and performance. Real-time, capable diagnostics are deployed and evaluated using an altered experimental setup. Image-based CNNs are applied to externally provided images to approximate dataset restrictions. Image classification using high-speed chemiluminescence images and time series classification using high-speed flame ionization and pressure measurements achieve classification speeds enabling real-time diagnostic capabilities, averaging laboratory-deployed diagnostic feedback rates of 4–5 Hz. Object detection achieves the most refined resolution of 20 μs in postprocessing. Image and time series classification require the additional correlation of sensor data, extending their time-step resolutions to 80 ms. Comparisons show that no single diagnostic approach outperforms its competitors across all metrics. This finding justifies the need for a machine learning portfolio containing a host of networks to address specific needs throughout the RDE research community.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1989

Airborne reconnaissance XIII; Proceedings of the Meeting, San Diego, CA, Aug. 7-9, 1989

The present conference on airborne reconnaissance discusses topics in imagery exploitation, reconsystem modeling and analysis, and reconnaissance optics and electronics configurations. Attention is given to airborne minefield detection, the optimization of an IR linescanner for RPV operations, real-time display of IR linescanner data for RPVs, three-dimensional model-guided site recognition, the AMIDARS high-performance real-time display, and MMW sensor image analysis. Also discussed are reconnaissance concepts for the 3-5 micron spectral window, sensor concept development for hazard detection, a stabilization system for a large aperture camera, three-axis image stabilization with a two-axis mirror, the results of performance tests on the TOW target collimator design, and the replacement of film by electrooptic media in advanced tactical airborne reconnaissance.

Henkel, Paul A.↗
NASA NTRS1974

Processing electrophysiological signals for the monitoring of alertness

Mathematical techniques are described for processing EEG signals associated with varying states of alertness. Fast algorithms for implementing real-time computations of alertness estimates were developed. A realization of the phase-distortionless digital filter is presented which approaches real-time filtering and a transform for EEG signals. This transform provides information for the alertness estimates and can be performed in real time. A statistical test for stationarity in EEG signals is being developed that will provide a method for determining the duration of the EEG signals necessary for estimating the short-time power or energy spectra for nonstationary analysis of EEG signals.

Lai, D. C.↗
DOE OSTI2022

NetGraf: An End-to-End Learning NetworkMonitoring Service (NetGraf) v1

NetGraf is a novel end-to-end learning monitoring system that utilizes current monitoring tools, merges multiple data sources into one dashboard for easy use, and provides machine learning libraries to analyze the data and perform real-time anomaly findings. Using a database backend, NetGraf can learn performance trends and show users if network performance has degraded. We demonstrate how NetGraf can easily be deployed through automation services and linked to multiple monitoring sources to collect data. Via the machine learning innovation and merging various data sources, NetGraf aims to fulfill the need for holistic learning network telemetry monitoring. To the best of our knowledge, this is the first-ever end-to-end learning monitoring service. We demonstrate its use on two network setups to showcase its impact.

Mohammed, Bashir↗
DOE OSTI2026

Real-time simulation of asymmetry generation in fermion-bubble collisions

Motivated by the out-of-equilibrium dynamics during an early-Universe first-order phase transition, we perform real-time simulations of fermion-bubble scattering in 1 + 1 dimensions. This nonequilibrium process can generate a charge-conjugation C asymmetry outside the bubble wall, induced by the complex fermion mass profile. The resulting C asymmetry is the 1 + 1 -dimensional analog of the C P asymmetry in 3 + 1 dimensions, a key ingredient in baryon asymmetry generation at the electroweak scale. Using tensor network methods, we track the real-time evolution of the C asymmetry in the charge density as the fermion interacts with the bubble wall, a regime inaccessible to analytic calculations. We further introduce two observables to quantify the asymmetry in the asymptotic region where reflected particles are well separated from the scattering point: one based on the net charge outside the bubble wall, and the other on the spatial displacement between the reflected particle and antiparticle wave packets. Our study represents a first step toward nonperturbative, real-time computations of C P asymmetry in 3 + 1 dimensions for electroweak baryogenesis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1983

Signal processor architecture for backscatter radars

Real time signal processing for backscatter radars which requires computational throughput and I/O rates is discussed. The operations that are usually performed in real time are highly repetitive simple accumulations of samples or of products of samples. The control logic does not depend on the values of the data and general purpose computers are not required for the initial high speed processing. The implications of these facts on the architectures of preprocessors for backscatter radars are explored and applied to the design of the Radar Signal Compender.

Swartz, W. E.↗
DOE OSTI2026

Deploying MARTe2 and MDSplus for Scalable Real-Time Control Systems: A Proof-of-Concept for the SMART Tokamak

The SMART tokamak is a spherical tokamak with flexible shaping that will need a modular and high-performance real-time control system. This work presents the implementation and validation of the MARTe2 framework at the Princeton Plasma Physics Laboratory (PPPL) as a testbed for supporting SMART (University of Seville, Spain). The system integrates MARTe2, MDSplus for data archiving, and user datagram protocol (UDP)-based communication with both synthetic and physical devices. A deterministic control loop was developed using custom control algorithms, showing the system’s ability to operate under strict real-time constraints. The resulting framework is scalable, maintainable, and adaptable to experimental tokamaks like SMART.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Implementing Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) Cloud-Clearing Algorithm into the NASA GEOS: Focus on the 2017 Atlantic Tropical Cyclone Season

Numerical Weather Prediction (NWP) centers assimilate cloud-free infrared (IR) radiances because the assimilation of all-sky IR radiances is not yet operationally achievable. The cloud-clearing procedure offers a simpler, but effective strategy that produces cloud-affected radiances suitable for assimilation in partially cloudy regions. Several studies conducted by this team have demonstrated that IR Cloud-Cleared Radiances (CCRs), if thinned more aggressively than clear-sky radiances, can improve analysis and forecasts, particularly in meteorologically active areas. However, CCRs are not used by operational centers due partly to the thought that the process of cloud-clearing may affect latency and introduce difficult-to-control external dependencies. This study presents the results of implementing an Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) cloud-clearing procedure into the NASA Goddard Earth Observing System (GEOS) to demonstrate the portability of the procedure. The AIRS and CrIS cloud-clearing algorithms have been deprived of external dependencies, made customizable to any specific model, and the computational efficiency has been improved via parallelization. The revised AIRS and CrIS cloud-clearing algorithms allow a customized choice of channel selection, the use of a user-specified model's fields as first guess, and can perform in real time. Data assimilation experiments with the hybrid 4DEnVar GEOS system were successfully performed for the 2017 tropical cyclones (TC) season with a focus on three major hurricanes (Harvey, Irma, and Maria). This study shows that assimilation of locally-generated CCRs have a positive impact on both global skill and TC representation, compared to the assimilation of AIRS and CrIS clear-sky radiances, and a comparable or slightly improved impact compared to assimilation of CCRs produced by external sources, such as NASA's Distributed Active Archive Centers and NOAA’s Comprehensive Large Array-data Stewardship System. The customization and computational efficiency of the revised procedure would enable its usability in a real-time forecast context.

Niama Boukachaba↗
NASA NTRS1988

Performance modeling and measurement of real-time multiprocessors with time-shared buses

A closed queueing network model is constructed to address workload effects on computer performance for a highly reliable unibus multiprocessor used in real-time control. The queueing model consists of multiserver nodes and a nonpreemptive priority queue. Use of this model requires partitioning the workload into task classes. The time average steady-state solution of the queueing model directly produces useful results that are necessary in performance evaluation. The model is experimentally justified with the Fault-Tolerant Multiprocessor (FTMP) located at the NASA AIRLAB. Extensive experiments are performed on FTMP with a synthetic workload generator (SWG) to directly measure performance parameters, such as processor idle time, system bus contention, and task processing times. These measurements determine values for parameters in the queueing model. Experimental and analytic results are then compared.

Woodbury, Michael H.↗
NASA NTRS1992

The Chimera II Real-Time Operating System for advanced sensor-based control applications

Attention is given to the Chimera II Real-Time Operating System, which has been developed for advanced sensor-based control applications. The Chimera II provides a high-performance real-time kernel and a variety of IPC features. The hardware platform required to run Chimera II consists of commercially available hardware, and allows custom hardware to be easily integrated. The design allows it to be used with almost any type of VMEbus-based processors and devices. It allows radially differing hardware to be programmed using a common system, thus providing a first and necessary step towards the standardization of reconfigurable systems that results in a reduction of development time and cost.

Stewart, David B.↗