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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations

Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.

Kelling, Jeffrey [Helmholtz-Zentrum Dresden Rossen↗
NASA NTRS1976

Preliminary design review package for the solar heating and cooling central data processing system

The Central Data Processing System (CDPS) is designed to transform the raw data collected at remote sites into performance evaluation information for assessing the performance of solar heating and cooling systems. Software requirements for the CDPS are described. The programming standards to be used in development, documentation, and maintenance of the software are discussed along with the CDPS operations approach in support of daily data collection and processing.

Source record↗
NASA NTRS1979

The epsilon Geminorum occultation - Evidence for waves or turbulence

Intercomparison of occultation data from various observatories shows poor correlation of the raw data but apparent correlations in the derived temperature profiles, as discussed in a paper by French and Elliott. We point out that the data are at least as consistent with Martian scintillation as they are with a semiglobal wave model.

Hubbard, W. B.↗
NASA NTRS2001

New tracking implementation in the Deep Space Network

As part of the Network Simplification Project, the tracking system of the Deep Space Network is being upgraded. This upgrade replaces the discrete logic sequential ranging system with a system that is based on commercial Digital Signal Processor boards. The new implementation allows both sequential and pseudo-noise types of ranging. The other major change is a modernization of the data formatting. Previously, there were several types of interfaces, delivering both intermediate data and processed data (called 'observables'). All of these interfaces were bit-packed blocks, which do not allow for easy expansion, and many of these interfaces required knowledge of the specific hardware implementations. The new interface supports four classes of data: raw (direct from the measuring equipment), derived (the observable data), interferometric (multiple antenna measurements), and filtered (data whose values depend on multiple measurements). All of the measurements are reported at the sky frequency or phase level, so that no knowledge of the actual hardware is required. The data is formatted into Standard Formatted Data Units, as defined by the Consultative Committee for Space Data Systems, so that expansion and cross-center usage is greatly enhanced.

ranging↗
NASA NTRS2007

Portable Unit for Metabolic Analysis

The Portable Unit for Metabolic Analysis (PUMA) is an instrument that measures several quantities indicative of human metabolic function. Specifically, this instrument makes time-resolved measurements of temperature, pressure, flow, and the partial pressures of oxygen and carbon dioxide in breath during both inhalation and exhalation. Portable instruments for measuring these quantities have been commercially available, but the response times of those instruments are too long to enable temporal resolution of phenomena on the time scales of human respiration cycles. In contrast, the response time of the PUMA is significantly shorter than characteristic times of human respiration phenomena, making it possible to analyze varying metabolic parameters, not only on sequential breath cycles but also at successive phases of inhalation and exhalation within the same breath cycle. In operation, the PUMA is positioned to sample breath near the subject s mouth. Commercial off-the-shelf sensors are used for three of the measurements: a miniature pressure transducer for pressure, a thermistor for temperature, and an ultrasonic sensor for flow. Sensors developed at Glenn Research Center are used for measuring the partial pressures of oxygen and carbon dioxide: The carbon dioxide sensor exploits the relatively strong absorption of infrared light by carbon dioxide. Light from an infrared source passes through the stream of inhaled or exhaled gas and is focused on an infrared- sensitive photodetector. The oxygen sensor exploits the effect of oxygen in quenching the fluorescence of ruthenium-doped organic molecules in a dye on the tip of an optical fiber. A blue laser diode is used to excite the fluorescence, and the optical fiber carries the fluorescent light to a photodiode, the temporal variation of the output of which bears a known relationship with the rate of quenching of fluorescence and, hence, with the partial pressure of oxygen. The outputs of the sensors are digitized, preprocessed by a small onboard computer, and then sent wirelessly to a desktop computer, where the collected data are analyzed and displayed. In addition to the raw data on temperature, pressure, flow, and mole fractions of oxygen and carbon dioxide, the display can include volumetric oxygen consumption, volumetric carbon dioxide production, respiratory equivalent ratio, and volumetric flow rate of exhaled gas.

Dietrich, Daniel L.↗
NASA NTRS2011

Ground-Based Network and Supersite Observations to Complement and Enrich EOS Research

Since 1997 NASA has been successfully launching a series of satellites - the Earth Observing System (EOS) - to intensively study, and gain a better understanding of, the Earth as an integrated system. Space-borne remote sensing observations, however, are often plagued by contamination of surface signatures. Thus, ground-based in-situ and remote-sensing measurements, where signals come directly from atmospheric constituents, the sun, and/or the Earth-atmosphere interactions, provide additional information content for comparisons that confirm quantitatively the usefulness of the integrated surface, aircraft, and satellite datasets. Through numerous participations, particularly but not limited to the EOS remote-sensing/retrieval and validation projects over the years, NASA/GSFC has developed and continuously refined ground-based networks and mobile observatories that proved to be vital in providing high temporal measurements, which complement and enrich the satellite observations. These are: the AERO NET (AErosol RObotic NETwork) a federation of ground-based globally distributed network of spectral sun-sky photometers; the MPLNET (Micro-Pulse Lidar NETwork, a similarly organized network of micro-pulse lidar systems measuring aerosol and cloud vertical structure continuously; and the SMART-COMMIT (Surface-sensing Measurements for Atmospheric Radiative Transfer - Chemical, Optical & Microphysical Measurements of In-situ Troposphere, mobile observatories, a suite of spectral radiometers and in-situ probes acquiring supersite measurements. Most MPLNET sites are collocated with those of AERONET, and both networks always support the deployment of SMART-COMMIT worldwide. These data products follow the data structure of EOS conventions: Level-0, instrument archived raw data; Level-1 (or 1.5), real-time data with no (or limited) quality assurance; Level-2, not real high temporal and spectral resolutions. In this talk, we will present NASA/GSFC groundbased facilities, serving as network or supersite observations, which have been playing key roles in major international research projects over diverse aerosol regimes to complement and enrich the EOS scientific research.

Tsay, Si-Chee↗
NASA NTRSDate not supplied

Application of Lidar-Radar Combined Data Products during the IMPACTS 2020 Field Campaign

The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a three-year field campaign investigating the formation and lifecycle of banded, precipitation structures within intense wintertime cyclones impacting the highly-populated northeast United States. Unlike previous campaigns in the region, IMPACTS mission scientists have access to and coordinate the NASA ER-2 and P-3 aircraft to collect observations from both a remote sensing and in-situ perspective, respectively. For IMPACTS, the ER-2 aircraft was equipped with lidar (CPL), radar (CRS, HIWRAP, EXRAD), microwave radiometer (CoSMIR, AMPR) instruments, each with their own unique capabilities and limitations. Our on-going work effort aims to develop IMPACTS lidar and radar merged data products, which can be used to supplement the raw data sources, tease out additional aspects of the mechanisms underpinning wintertime cyclones, and enhance our understanding of microphysical properties in sensor overlap regions. Preliminary merged data products combine CPL lidar with any one of the NASA High Altitude Radar (HAR) radar products (CRS, HIWRAP, EXRAD) and represent these data as both normalized values and translated to reflectivity signal (dB). Continuing work will further refine our merged data product algorithm to provide additional information on hydrometeor species identification, ice crystal habit, and ice-water content, which will help IMPACTS achieve its mission goal of improving microphysical properties retrievals from both airborne and spaceborne platforms.

Stephen D. Nicholls↗
NASA NTRSDate not supplied

Managing the Digital Thread for Structural Applications With Fit for Purpose Materials

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Consequently, at NASA Glenn Research Center a robust information management system that manages the digital thread across the full material life (i.e., capture, analysis, maintenance, and dissemination of data) cycle directed at the design of ‘fit-for-purpose materials’ is under development. To this end the Application Table has been incorporated within NASA Glenn Research Center’s ICME Information Management framework within the ANSYS Granta MI tool. The Application Table provides a place where material and structural application information/requirements can be linked to marry the “design-the-material” (structural engineering) and the “design-with-material” (material science) paradigms and thereby enable application-driven design and optimization of materials and structures. In additional several associated toolsets, specifically: AIMAOS (Automated Information Management Across Organizations and Scales), Py MILab, and JARIMIS (Just A Rather Intelligent Material Interrogation System) are also under development to assist in the judicious automation of this process. AIMOAS offers users an interactive graphical user interface for connecting material information management systems with both commercial and in-house simulation tools at various length scales to enable such automation in the handoff across scales and maintenance of material digital twins and the digital thread. Py MILab, is an automatic framework for the capture, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. Finally, JARIMIS is an expert system that integrates various materials informatics tools (e.g., MicroNet, Surrogate ML models, ANSYS Granta MI, etc.) to enable inverse design of materials and facilitate the application of machine learning (ML) and data science with human in the loop decision making to rapidly discover and optimize new materials.

Digital Transformation↗
NASA NTRS1984

Revised Radiometric Calibration Technique for LANDSAT-4 Thematic Mapper Data by the Canada Centre for Remote Sensing

A technique for the radiometric correction of LANDSAT-4 Thematic Mapper data was proposed by the Canada Center for Remote Sensing. Subsequent detailed observations of raw image data, raw radiometric calibration data and background measurements extracted from the raw data stream on High Density Tape highlighted major shortcomings in the proposed method which if left uncorrected, can cause severe radiometric striping in the output product. Results are presented which correlate measurements of the DC background with variations in both image data background and calibration samples. The effect on both raw data and on data corrected using the earlier proposed technique is explained, and the correction required for these factors as a function of individual scan line number for each detector is described. It is shown how the revised technique can be incorporated into an operational environment.

Murphy, J.↗
NASA NTRS1969

Towards an optimal inversion method for remote atmospheric sensing

The inference of atmospheric structure from satellite radiometric observations requires an inversion algorithm. A variety of techniques was spawned to meet these demands. One class, the nonlinear inversion methods, copes with the problem of data noise. Unlike linear techniques which require a priori data smoothing, the nonlinear method can be applied directly to raw data. The algorithm discriminates the noise input by resolving the inferences into two types of solution, associating the real roots with atmospheric structure while ascribing the imaginary roots to noise.

King, J. I. F.↗
NASA NTRS1972

Testing relativistic gravity theories using radio tracking data from planetary orbiting spacecraft.

We present a thorough analysis of a computational method for determining the numerical values of the relativity and other related dynamical parameters using two-way Doppler and ranging data from planetary orbiting spacecraft. The computational method consists of two parts. From Doppler data we first determine the earth-planet components of the position of the orbiting spacecraft relative to the center of gravity of the planet to high accuracy; adding the observed spacecraft range yields a range value to the center of the planet. These constructed earth-planet range data, referred to as normal points, are then treated as raw data in a regression analysis combined with planetary radar delay and meridian circle measurements to solve for the significant solar system dynamical parameters. The major errors sources in the planetary orbiter process are enumerated and their individual effects on the overall accuracy of the normal point accuracies are presented. The accuracies of the parameter estimates as a function of time, data sampling, and a priori assumptions are illustrated.

Jordan, J. F.↗
NASA NTRS1982

GPS on-orbit battery performance

Three batteries designed for the GPS system were evaluated. The batteries were wired together, and during the eclipse period were discharged through parallel diodes into a boost converter which then boosted the battery voltage up to the totally regulated bus voltage. Each battery, with a system life of approximately five years, had its own charger. During testing the battery was maintained or used on trickle charge. Data are presented for the battery capacity during the reconditioning. Raw data for each eclipse period and calculations for the average discharge voltage for each battery are given. Thermal cycling for one of the batteries and reconditioning cycles and regimes for all of the batteries are discussed. A reconditioning discharge curve is also given. The problem of temperature cycling in one of the batteries was resolved by redesigning the battery radiator system.

Kasten, J. L.↗
NASA NTRS1986

Data reduction, management, and analysis software for CID

In an overview of the Data Reduction System, three major steps are examined. First, the raw data tapes were selected from the onboard recorders. These tapes should provide the best quality data for the data reduction software system. These tapes contained 352 channels of data, plus the monitor channels recorded in 8 bit Pulsed Coded Modulation (PCM) words. The next step consists of transcribing the PCM tapes from 8 bit serial digital data to 8 bit parallel digital data. This puts the data in the correct format for processing. The transcription process was accomplished here at LaRC in the Central Data Transportation Facility (CDTF). The last step in this 3 step process is to process the data through the reduction system developed for the Impact Dynamic Research Facility in the early part of 1980. Processing system criteria, system interface routines, and engineering units program that reads digitized data from tapes, and file management programs are discussed.

Davis, C. W.↗
NASA NTRS1995

Profiles of Gamma-Ray Bursts and Their Component Pulses

One physically informative regularity of their otherwise heterogeneous ensemble, is that many Gamma-Ray Bursts consist of well defined pulses. To objectively quantify the temporal structure of BATSE bursts, we have developed an automatic modeling procedure that separates overlapping pulses and determines the energy-dependence of the pulse-shape parameters. No binning of photon arrival times is needed, so when applied to time-tagged events (TTE) the procedure captures variability information down to the shortest time scales present in the raw data. Maximizing the Bayesian likelihood function Pr(data/model) yields estimates of the model parameters, including the number of pulses present, and allows intercomparison of models of different forms. As with any nonlinear optimization, good initial guesses are crucial to avoid convergence to undesirable local minima. We find excellent initial pulse decompositions by wavelet-denoising a cumulative distribution of the raw photon arrival data; differentiation then gives a time profile mostly free of the systematic effects of degraded resolution (as in ordinary Fourier smoothing) and binning. We present statistical information on pulse rise-time, decay-time, peakedness, and amplitudes, plus their energy dependences - both within a single burst and for a large ensemble of bursts.

Scargle, Jeff D.↗
NASA NTRS2014

Influence of Mixed Mode I-Mode II Loading on Fatigue Delamination Growth Characteristics of a Graphite Epoxy Tape Laminate

Mixed mode I-mode II interlaminar tests were conducted on IM7/8552 tape laminates using the mixed-mode bending test. Three mixed mode ratios, G(sub II)/G(sub T) = 0.2, 0.5, and 0.8, were considered. Tests were performed at all three mixed-mode ratios under quasi-static and cyclic loading conditions, where the former static tests were used to determine initial loading levels for the latter fatigue tests. Fatigue tests at each mixed-mode ratio were performed at four loading levels, Gmax, equal to 0.5G(sub c), 0.4G(sub c), 0.3G(sub c), and 0.2G(sub c), where G(sub c) is the interlaminar fracture toughness of the corresponding mixed-mode ratio at which a test was performed. All fatigue tests were performed using constant-amplitude load control and delamination growth was automatically documented using compliance solutions obtained from the corresponding quasi-static tests. Static fracture toughness data yielded a mixed-mode delamination criterion that exhibited monotonic increase in Gc with mixed-mode ratio, G(sub II)/G(sub T). Fatigue delamination onset parameters varied monotonically with G(sub II)/G(sub T), which was expected based on the fracture toughness data. Analysis of non-normalized data yielded a monotonic change in Paris law exponent with mode ratio. This was not the case when normalized data were analyzed. Fatigue data normalized by the static R-curve were most affected in specimens tested at G(sub II)/G(sub T)=0.2 (this process has little influence on the other data). In this case, the normalized data yielded a higher delamination growth rate compared to the raw data for a given loading level. Overall, fiber bridging appeared to be the dominant mechanism, affecting delamination growth rates in specimens tested at different load levels and differing mixed-mode ratios.

Ratcliffe, James G.↗
DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗
DOE OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗