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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO 2 ) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO 2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO 2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat threedimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

25 ENERGY STORAGE↗
DOE OSTI2024

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

A determination of the sea level muon spectrum using accelerator data and some implications

The differential sea-level muon spectrum is calculated from first principle for a pure beam of cosmic-ray protons cascading in the atmosphere by using a representation of the invariant cross section that describes the observed data from 6 GeV to 1500 GeV. This spectrum is compared with the observed muon spectrum to deduce information on the interaction characteristics of nuclei-nuclei collisions.

Badhwar, G. D.↗
NASA NTRS1990

Acquisition and analysis of accelerometer data

Acceleration data reduction must be undertaken with a complete understanding of the physical process, the means by which the data are acquired, and finally, the calculations necessary to put the data into a meaningful format. Discussed here are the acceleration sensor requirements dictated by the measurements desired. Sensor noise, dynamic range, and linearity will be determined from the physical parameters of the experiment. The digitizer requirements are discussed. Here the system from sensor to digital storage medium will be integrated, and rules of thumb for experiment duration, filter response, and number of bits are explained. Data reduction techniques after storage are also discussed. Time domain operations including decimating, digital filtering, and averaging are covered, as well as frequency domain methods, including windowing and the difference between power and amplitude spectra, and simple noise determination via coherence analysis. Finally, an example experiment using the Teledyne Geotech Model 44000 Seismometer to measure from 1 Hz to 10(exp -6) Hz is discussed. The sensor, data acquisition system, and example spectra are presented.

Verges, Keith R.↗
NASA NTRS2001

International Space Station Increment-2 Quick Look Report

The objective of this quick look report is to disseminate the International Space Station (ISS) Increment-2 reduced gravity environment preliminary analysis in a timely manner to the microgravity scientific community. This report is a quick look at the processed acceleration data collected by the Microgravity Acceleration Measurement System (MAMS) during the period of May 3 to June 8, 2001. The report is by no means an exhaustive examination of all the relevant activities, which occurred during the time span mentioned above for two reasons. First, the time span being considered in this report is rather short since the MAMS was not active throughout the time span being considered to allow a detailed characterization. Second, as the name of the report implied, it is a quick look at the acceleration data. Consequently, a more comprehensive report, the ISS Increment-2 report, will be published following the conclusion of the Increment-2 tour of duty. NASA sponsors the MAMS and the Space Acceleration Microgravity System (SAMS) to support microgravity science experiments, which require microgravity acceleration measurements. On April 19, 2001, both the MAMS and the SAMS units were launched on STS-100 from the Kennedy Space Center for installation on the ISS. The MAMS unit was flown to the station in support of science experiments requiring quasisteady acceleration data measurements, while the SAMS unit was flown to support experiments requiring vibratory acceleration data measurement. Both acceleration systems are also used in support of the vehicle microgravity requirements verification. The ISS reduced gravity environment analysis presented in this report uses mostly the MAMS acceleration data measurements (the Increment-2 report will cover both systems). The MAMS has two sensors. The MAMS Orbital Acceleration Research Experiment Sensor Subsystem, which is a low frequency range sensor (up to 1 Hz), is used to characterize the quasi-steady environment for payloads and vehicle. The MAMS High Resolution Acceleration Package is used to characterize the ISS vibratory environment up to 100 Hz. This quick look report presents some selected quasi-steady and vibratory activities recorded by the MAMS during the ongoing ISS Increment-2 tour of duty.

Jules, Kenol↗
NASA NTRS1997

Summary Report of Mission Acceleration Measurements for STS-79. Launched 16 Sep. 1996

The Space Acceleration Measurement System (SAMS) collected acceleration data in support of the Mechanics of Granular Materials experiment during the STS-79 Mir docking mission, September 1996. STS-79 was the first opportunity to record SAMS data on an Orbiter while it was docked to Mir. Crew exercise activities in the Atlantis middeck and the Mir base module are apparent in the data. The acceleration signals related to the Enhanced Orbiter Refrigerator Freezer had different characteristics when comparing the data recorded on Atlantis on STS-79 with the data recorded on Mir during STS-74. This is probably due, at least in part, to different transmission paths and SAMS sensor head mounting mechanisms. Data collected on Atlantis during the STS-79 docking indicate that accelerations due to vehicle and solar array structural modes from Mir transfer to Atlantis and that the structural modes of the Atlantis-Mir complex are different from those of either vehicle independently. A 0.18 Hz component of the SAMS data, present while the two vehicles were docked, was probably caused by the Mir solar arrays. Compared to Atlantis structural modes of about 3.9 and 4.9 Hz, the Atlantis-Mir complex has structural components of about 4.5 and 5.1 Hz. After docking, apparent structural modes appeared in the data at about 0.8 and 1.8 Hz. The appearance, disappearance, and change in the structural modes during the docking and undocking phases of the joint Atlantis-Mir operations indicates that the structural modes of the two spacecraft have an effect on the microgravity environment of each other. The transfer of structural and equipment related accelerations between vehicles is something that should be considered in the International Space Station era.

Rogers, Melissa J. B.↗
NASA NTRS2005

STS-107 Microgravity Environment Summary Report

This summary report presents the results of the processed acceleration data measured aboard the Columbia orbiter during the STS-107 microgravity mission from January 16 to February 1, 2003. Two accelerometer systems were used to measure the acceleration levels due to vehicle and science operations activities that took place during the 16-day mission. Due to lack of precise timeline information regarding some payload's operations, not all of the activities were analyzed for this report. However, a general characterization of the microgravity environment of the Columbia Space Shuttle during the 16-day mission is presented followed by a more specific characterization of the environment for some designated payloads during their operations. Some specific quasi-steady and vibratory microgravity environment characterization analyses were performed for the following payloads: Structure of Flame Balls at Low Lewis-number-2, Laminar Soot Processes-2, Mechanics of Granular Materials-3 and Water Mist Fire-Suppression Experiment. The Physical Science Division of the National Aeronautics and Space Administration sponsors the Orbital Acceleration Research Experiment and the Space Acceleration Measurement System for Free Flyer to support microgravity science experiments, which require microgravity acceleration measurements. On January 16, 2003, both the Orbital Acceleration Research Experiment and the Space Acceleration Measurement System for Free Flyer accelerometer systems were launched on the Columbia Space Transportation System-107 from the Kennedy Space Center. The Orbital Acceleration Research Experiment supported science experiments requiring quasi-steady acceleration measurements, while the Space Acceleration Measurement System for Free Flyer unit supported experiments requiring vibratory acceleration measurement. The Columbia reduced gravity environment analysis presented in this report uses acceleration data collected by these two sets of accelerometer systems: The Orbital Acceleration Research Experiment is a low frequency sensor, which measures acceleration up to 1 Hz, but the 1 Hz acceleration data is trimmean filtered to yield much lower frequency acceleration data up to 0.01 Hz. This filtered data can be mapped to other locations for characterizing the quasi-steady environment for payloads and the vehicle. The Space Acceleration Measurement System for Free Flyer measures vibratory acceleration in the range of 0.01 to 200 Hz at multiple measurement locations. The vibratory acceleration data measured by this system is used to assess the local vibratory environment for payloads as well as to measure the disturbance causes by the vehicle systems, crew exercise devices and payloads operation disturbances. This summary report presents analysis of selected quasi-steady and vibratory activities measured by these two accelerometers during the Columbia 16-day microgravity mission from January 16 to February 1, 2003.

Jules, Kenol↗
NASA NTRS1997

Comparison Tools for Assessing the Microgravity Environment of Missions, Carriers and Conditions

The Principal Component Spectral Analysis and the Quasi-steady Three-dimensional Histogram techniques provide the means to describe the microgravity acceleration environment of an entire mission on a single plot. This allows a straight forward comparison of the microgravity environment between missions, carriers, and conditions. As shown in this report, the PCSA and QTH techniques bring both the range and median of the microgravity environment onto a single page for an entire mission or another time period or condition of interest. These single pages may then be used to compare similar analyses of other missions, time periods or conditions. The PCSA plot is based on the frequency distribution of the vibrational energy and is normally used for an acceleration data set containing frequencies above the lowest natural frequencies of the vehicle. The QTH plot is based on the direction and magnitude of the acceleration and is normally used for acceleration data sets with frequency content less than 0.1 Hz. Various operating conditions are made evident by using PCSA and QTH plots. Equipment operating either full or part time with sufficient magnitude to be considered a disturbance is very evident as well as equipment contributing to the background acceleration environment. A source's magnitude and/or frequency variability is also evident by the source's appearance on a PCSA plot. The PCSA and QTH techniques are valuable tools for extracting useful information from acceleration data taken over large spans of time. This report shows that these techniques provide a tool for comparison between different sets of microgravity acceleration data, for example different missions, different activities within a mission, and/or different attitudes within a mission. These techniques, as well as others, may be employed in order to derive useful information from acceleration data.

DeLombard, Richard↗
NASA NTRS1996

Combining Acceleration and Displacement Dependent Modal Frequency Responses Using an MSC/NASTRAN DMAP Alter

Solving for dynamic responses of free-free launch vehicle/spacecraft systems acted upon by buffeting winds is commonly performed throughout the aerospace industry. Due to the unpredictable nature of this wind loading event, these problems are typically solved using frequency response random analysis techniques. To generate dynamic responses for spacecraft with statically-indeterminate interfaces, spacecraft contractors prefer to develop models which have response transformation matrices developed for mode acceleration data recovery. This method transforms spacecraft boundary accelerations and displacements into internal responses. Unfortunately, standard MSC/NASTRAN modal frequency response solution sequences cannot be used to combine acceleration- and displacement-dependent responses required for spacecraft mode acceleration data recovery. External user-written computer codes can be used with MSC/NASTRAN output to perform such combinations, but these methods can be labor and computer resource intensive. Taking advantage of the analytical and computer resource efficiencies inherent within MS C/NASTRAN, a DMAP Alter has been developed to combine acceleration- and displacement-dependent modal frequency responses for performing spacecraft mode acceleration data recovery. The Alter has been used successfully to efficiently solve a common aerospace buffeting wind analysis.

Barnett, Alan R.↗
NASA NTRS2003

International Space Station Increment-4/5 Microgravity Environment Summary Report

This summary report presents the results of some of the processed acceleration data measured aboard the International Space Station during the period of December 2001 to December 2002. Unlike the past two ISS Increment reports, which were increment specific, this summary report covers two increments: Increments 4 and 5, hereafter referred to as Increment-4/5. Two accelerometer systems were used to measure the acceleration levels for the activities that took place during Increment-4/5. Due to time constraint and lack of precise timeline information regarding some payload operations and station activities, not a11 of the activities were analyzed for this report. The National Aeronautics and Space Administration sponsors the Microgravity Acceleration Measurement System and the Space Acceleration Microgravity System to support microgravity science experiments which require microgravity acceleration measurements. On April 19, 2001, both the Microgravity Acceleration Measurement System and the Space Acceleration Measurement System units were launched on STS-100 from the Kennedy Space Center for installation on the International Space Station. The Microgravity Acceleration Measurement System supports science experiments requiring quasi-steady acceleration measurements, while the Space Acceleration Measurement System unit supports experiments requiring vibratory acceleration measurement. The International Space Station Increment-4/5 reduced gravity environment analysis presented in this report uses acceleration data collected by both sets of accelerometer systems: The Microgravity Acceleration Measurement System, which consists of two sensors: the low-frequency Orbital Acceleration Research Experiment Sensor Subsystem and the higher frequency High Resolution Accelerometer Package. The low frequency sensor measures up to 1 Hz, but is routinely trimmean filtered to yield much lower frequency acceleration data up to 0.01 Hz. This filtered data can be mapped to arbitrary locations for characterizing the quasi-steady environment for payloads and the vehicle. The high frequency sensor is used to characterize the vibratory environment up to 100 Hz at a single measurement location. The Space Acceleration Measurement System, which deploys high frequency sensors, measures vibratory acceleration data in the range of 0.01 to 400 Hz at multiple measurement locations. This summary report presents analysis of some selected quasi-steady and vibratory activities measured by these accelerometers during Increment- 4/5 from December 2001 to December 2002.

Jules, Kenol↗
NASA NTRS1985

On the retention of high-energy protons and nuclei with charges Z or equal to 2 in large solar flares after the process of their acceleration

Data which suggest that the protons with energies of up to several GeV should be retained on the Sun after the process of their acceleration are presented. The protons are on the average retained for 15 min, irrespectively of the solar flare heliolatitude and of the accelerated particle energy ranging from 100 MeV to several GeV. It is suggested that the particles are retained in a magnetic trap formed in a solar active region. No Z or = 2 nuclei of solar origin during large solar flares. The absence of the 500 MeV/nucleon nuclei with Z or = 2 may be due to their retention in the magnetic trap which also retains the high-energy protons. During the trapping time the approx. 500 MeV/nucleon nuclei with Z or = 2 may escape due to nuclear interactions and ionization loss.

Volodichev, N. N.↗
NASA NTRS2003

Accelerating Large Data Analysis By Exploiting Regularities

We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

Moran, Patrick J.↗