Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data generation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Parametric Analysis of a Hover Test Vehicle using Advanced Test Generation and Data Analysis

Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.

Gundy-Burlet, Karen↗
DOE OSTI2025

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1994

Error coding simulations in C

When data is transmitted through a noisy channel, errors are produced within the data rendering it indecipherable. Through the use of error control coding techniques, the bit error rate can be reduced to any desired level without sacrificing the transmission data rate. The Astrionics Laboratory at Marshall Space Flight Center has decided to use a modular, end-to-end telemetry data simulator to simulate the transmission of data from flight to ground and various methods of error control. The simulator includes modules for random data generation, data compression, Consultative Committee for Space Data Systems (CCSDS) transfer frame formation, error correction/detection, error generation and error statistics. The simulator utilizes a concatenated coding scheme which includes CCSDS standard (255,223) Reed-Solomon (RS) code over GF(2(exp 8)) with interleave depth of 5 as the outermost code, (7, 1/2) convolutional code as an inner code and CCSDS recommended (n, n-16) cyclic redundancy check (CRC) code as the innermost code, where n is the number of information bits plus 16 parity bits. The received signal-to-noise for a desired bit error rate is greatly reduced through the use of forward error correction techniques. Even greater coding gain is provided through the use of a concatenated coding scheme. Interleaving/deinterleaving is necessary to randomize burst errors which may appear at the input of the RS decoder. The burst correction capability length is increased in proportion to the interleave depth. The modular nature of the simulator allows for inclusion or exclusion of modules as needed. This paper describes the development and operation of the simulator, the verification of a C-language Reed-Solomon code, and the possibility of using Comdisco SPW(tm) as a tool for determining optimal error control schemes.

Noble, Viveca K.↗
NASA NTRS1987

Throughput analysis of the IEEE 802.4 token bus standard under heavy load

It has become clear in the last few years that there is a trend towards integrated digital services. Parallel to the development of public Integrated Services Digital Network (ISDN) is service integration in the local area (e.g., a campus, a building, an aircraft). The types of services to be integrated depend very much on the specific local environment. However, applications tend to generate data traffic belonging to one of two classes. According to IEEE 802.4 terminology, the first major class of traffic is termed synchronous, such as packetized voice and data generated from other applications with real-time constraints, and the second class is called asynchronous which includes most computer data traffic such as file transfer or facsimile. The IEEE 802.4 token bus protocol which was designed to support both synchronous and asynchronous traffic is examined. The protocol is basically a timer-controlled token bus access scheme. By a suitable choice of the design parameters, it can be shown that access delay is bounded for synchronous traffic. As well, the bandwidth allocated to asynchronous traffic can be controlled. A throughput analysis of the protocol under heavy load with constant channel occupation of synchronous traffic and constant token-passing times is presented.

Pang, Joseph↗
NASA NTRS1987

Self-Identifying Emergency Radio Beacons

Rescue teams aided by knowledge of vehicle in distress. Similar to conventional emergency transmitters except contains additional timing and modulating circuits. Additions to standard emergency transmitter enable transmitter to send rescuers identifying signal in addition to conventional distress signal created by sweep generator. Data generator contains identifying code.

Friedman, Morton L.↗
NASA NTRS1990

High rate science data handling on Space Station Freedom

A study by NASA's User Information System Working Group for Space Station Freedom (SSF) has determined that the proposed onboard Data Management System, as initially configured, will be incapable of handling the data-generation rates typical of numerous scientific sensor payloads; many of these generate data at rates in excess of 10 Mbps, and there are at least four cases of rates in excess of 300 Mbps. The SSF Working Group has accordingly suggested an alternative conceptual architecture based on technology expected to achieve space-qualified status by 1995. The architecture encompasses recorders with rapid data-ingest capabilities and massive storage capabilities, optical delay lines allowing the recording of only the phenomena of interest, and data flow-compressing image processors.

Handley, Thomas H., Jr.↗
NASA NTRS1993

Eddy mixing coefficient values derived from simulations with the NASA Ames Mars GCM

Values of eddy mixing coefficients, especially that for vertical mixing, are of particular importance for various photochemical models of the Mars atmosphere, which are either globally or zonally averaged. These models represent atmospheric transport processes in terms of eddy diffusion. While this is not appropriate for the advective transport by the mean meridional circulation (in general, a part of the eddy diffusion tensor actually does represent advection--a 'correction' to the Eulerian-mean circulation), it can be applied to simulate both the small- and larger-scale eddy transports of an atmospheric constituent. An effort is underway to estimate values of the eddy mixing coefficients for the Mars atmosphere using circulation data generated with the NASA Ames Mars GCM. This model simulates the three-dimensional winds in the atmosphere, and these are then used as inputs for a tracer transport model. The latter model has previously been used in interactive dust transport experiments with the Ames GCM. Idealized tracer transport experiments, with a conservative or nearly conservative hypothetical tracer, are performed to generate data from which the eddy diffusion coefficients can be estimated. Carrying out matched pairs of tracer experiments, using two very different initial states, permits all four components of the diffusion tensor to be determined. A large number of GCM experiments have been conducted, spanning a range of seasons and atmospheric dust loading, allowing the eddy mixing coefficients to be estimated for a variety of atmospheric conditions.

Barnes, J. R.↗
NASA NTRS1994

Continued reduction and analysis of data from the Dynamics Explorer Plasma Wave Instrument

The plasma wave instrument on the Dynamics Explorer 1 spacecraft provided measurements of the electric and magnetic components of plasma waves in the Earth's magnetosphere. Four receiver systems processed signals from five antennas. Sixty-seven theses, scientific papers and reports were prepared from the data generated. Data processing activities and techniques used to analyze the data are described and highlights of discoveries made and research undertaken are tabulated.

Gurnett, Donald A.↗
NASA NTRS2020

CyberGAN: Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.

Zhang, Yuening↗
NASA NTRS1989

Optical archiving for scientific data

Write-once-read-many (WORM) optical disk technology is now practical for use as (1) a near online storage media, (2) long-term storage in a passive repository, and (3) under some circumstances, transporting from and to data generators and data users at remote sites. Optical technology provides several significant advantages over magnetic tapes as a data storage media because it is more compact, provides random access, has a longer lifetime, at least 30 years, and is less sensitive to environmental factors such as temperature and humidity. Two major archival projects using optical technology underway are discussed: the Coastal Zone Color Scanner data and the Dynamics Explorer data. Both projects have used the software for optical archive and retrieval (SOAR) to transfer data in a volume format that can be read transparently by VAX/VMS. Through the use of this software technique, (1) data generators and data users do not have to rewrite extant software and (2) the VAX/VMS Files-11 volume format is in wide use, so it can serve as an interim standard and will be accessible in the long-term.

Lowrey, Barbara E.↗
DOE OSTI2024

Data Efficiency Assessment of Generative Adversarial Networks for Critical Heat Flux Synthetic Data Generation

This study investigates the application of generative artificial intelligence techniques, particularly conditional generative adversarial networks (cGAN), in real-world engineering contexts, with a specific focus on synthetic data generation for critical heat flux (CHF). Utilizing a dataset comprising more than 20,000 real experimental CHF measurements, we conduct a series of experiments to examine cGAN’s behavior. These experiments encompass varying sizes of the training dataset, training cGAN on data from diverse experimental sources to generate new data on unseen experimental setups, and assessing the impact of excluding various input features on cGAN’s data generation accuracy. Our findings underscore the pronounced data dependency of cGAN for reliable performance, with decreased efficacy observed with smaller training dataset sizes. Notably, cGAN exhibits varying performance when trained on data from different experiments, with superior predictive capabilities observed for certain experiment sources compared to others. For instance, when cGAN was trained on data from Smolin et al.’s experiments or Zenkevich et al., it exhibited relatively good performance in generating the data from Becker et al., Kirillov et al., and Alekseev et al. experiments. In contrast, when trained with Alekseev et al.’s data and tasked with generating other experimental setups, cGAN showed notably poor performance. In both scenarios, cGAN’s performance was inferior compared to training on samples from all experiments concurrently. A feature importance analysis highlights the significant influence of parameters such as mass flux and heated length on accurate CHF generation, while other parameters like diameter and pressure have less impact. Inlet temperature is identified as a moderating factor by cGAN.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2022

Unique Observational Constraints on the Seasonal and Longitudinal Variability of the Earth’s Planetary Albedo and Cloud Distribution Inferred From EPIC Measurements

Thorough comparison to observations is key to developing a credible climate model forecasting capability. Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar and emitted thermal radiation provide a unique observational perspective that permits a more reliable model/data comparison than is possible with the otherwise available satellite data. The uniqueness is in the DSCOVR satellite’s viewing geometry, which enables continuous viewing of the Earth’s sunlit hemisphere from its Lissajous orbit around the Lagrangian L1 point. The key instrument is the Earth Polychromatic Imaging Camera (EPIC), which views the Earth’s sunlit hemisphere with 1024-by-1024-pixel imagery in 10 narrow spectral bands from 317 to 780 nm, acquiring up to 22 high spatial resolution images per day. The additional feature is that the frequency of EPIC image acquisition is nearly identical to that of the climate GCM data generation scheme where climate data for the entire globe are ‘instantaneously’ calculated at 1-h radiation time-step intervals. Implementation of the SHS (Sunlit Hemisphere Sampling) EPIC-view geometry for the in-line GCM output data sampling establishes a precise self-consistency in the space-time data sampling between EPIC observational and GCM output data generation and sampling. The remaining problem is that the GCM generated data are radiative fluxes, while the EPIC measurements are backscatter-dependent radiances. Radiance to flux conversion is a complex problem with no simple way to convert GCM radiative fluxes into spectral radiances. The more expedient approach is to convert the EPIC spectral radiances into broadband radiances by MODIS/CERES-based regression relationships and then into solar radiative fluxes using the CERES angular distribution models. Averaging over the sunlit hemisphere suppresses the meteorological weather noise, but preserves the intra-seasonal larger scale variability. Longitudinal slicing by the Earth’s rotation permits a self-consistent model/data comparison of the longitudinal model/data differences in the variability of the reflected solar radiation. Ancillary EPIC Composite data provide additional cloud property information for climate model diagnostics. Comparison of EPIC-derived seasonal and longitudinal variability of the Earth’s planetary albedo with the GISS ModelE2 results shows systematic overestimate of cloud reflectivity over the Pacific Ocean with corresponding underestimates over continental land areas.

DSCOVR↗
NASA NTRS1973

Sampling command generator corrects for noise and dropouts in recorded data

Generator measures period between zero crossings of reference signal and accepts as correct timing points only those zero crossings which occur acceptably close to nominal time predicted from last accepted command. Unidirectional crossover points are used exclusively so errors from analog nonsymmetry of crossover detector are avoided.

Anderson, T. O.↗