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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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NASA NTRS1974

A STOL airworthiness investigation using a simulation of an augmentor wing transport. Volume 2: Simulation data and analysis

A simulator study of STOL airworthiness was conducted using a model of an augmentor wing transport. The approach, flare and landing, go-around, and takeoff phases of flight were investigated. The simulation and the data obtained are described. These data include performance measures, pilot commentary, and pilot ratings. A pilot/vehicle analysis of glide slope tracking and of the flare maneuver is included.

Heffley, R. K.↗

NASA NTRSDate not supplied

Evaluating the Feasibility of Data Simulation for Radiation Research: Estimating RBE Values for HZE Radiation

As the drive for deep space exploration intensifies, a comprehensive understanding of the health effects of radiation exposure becomes paramount to the future of human space flight. However, epidemiological data for radiation exposure, particularly to high-energy (HZE) ions, is limited, partially due to the financial and logistical costs of radiation studies. As an alternative, this study aims to assess the viability of data simulation strategies to accurately model potential study parameters prior to utilizing laboratory conditions. This study estimates a relative biological effectiveness (RBE) factor based on the solid tumor data for outbred mice provided by Edmundson et al. 2020. Excess relative risk (ERR) models for HZE-Fe ions and gamma radiation were estimated using Poisson regression with Weibull models to represent the background solid tumor hazard without radiation. RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope. The parameters from these models were then applied to simulate iterations of 300 datasets across HZE-Fe doses of 0.05, 0.2, 0.4, and 0.75 Gy; gamma radiation doses of 0.75, 2, and 3 Gy, as well as an unirradiated control group. Sample size per dose varied from 100-500 mice across simulations (800-4000 mice total per dataset). 1500 datasets were generated total (300 each for sample sizes 800, 1600, 3200, and 4000). For each dataset, ERR per radiation type and RBE for HZE-Fe were calculated. The RBE from the Edmondson data was calculated to be 5.55. The RBEs from the simulated data converged around this value across the increasing sample sizes. For n = 4000, the mean RBE across the 300 datasets was 5.71 (95% CI: 5.47-5.95). These results suggest that RBEs calculated from simulated data are credible. Based on this exploration, data simulation is a viable method of testing radiation studies. It provides a method of testing study assumptions and refining research questions prior to embarking on costly laboratory experiments.

Sneha Mehta↗

DOE OSTI2020

ContourNet: Salient Local Contour Identification for Blob Detection in Fusion Plasma Simulation Data

We present ContourNet, a deep learning approach to identify salient local isocontours as blobs in large-scale 5D gyrokinetic tokamak simulation data. Blobs-regions of high turbulence that run along the edge wall down toward the diverter and can damage the tokamak-are non-well-defined features but have been empirically localized by isocontours in 2D normalized fluctuating density fields. The key of our study is to train ContourNet to follow the empirical rules to detect blobs over the time-varying simulation data. The architecture of ContourNet is a convolutional neural segmentation network: the inputs are the density field and a rasterized isocontour; the output is a set of isocontour encircling blobs. At the training stage, we feed the network with manually identified isocontours and propagated labels. At the inference stage, we extract isocontours from the segmented blob regions. Results show that our approach can achieve both high accuracy and performance, which enables scientists to understand the blob dynamics influencing the confinement of the plasma.

Blob detection↗

NASA NTRS1997

Development of a Simulated Data Set for the SeaWiFS Mission

A realistic simulated data set is essential for mission readiness preparations and can potentially assist in all phases of ground support for a future mission. Such a data set was created for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), a global ocean color mission due for launch in 1997. This data set incorporates a representation of virtually every known aspect of the flight mission. Thus, it provides a high fidelity data set for testing most phases of the ground system, Including data processing, data transfers, calibration and validation, quality control, and mission operations. The data set is constructed for a seven-day period, March 25-31, 1994. Specific features of the data set: it includes Global Area Coverage (GAC), recorded Local Area Coverage (LAC), and real-time High Resolution Picture Transmission (HRFIT) data for the seven-day period; it includes a realistic orbit which is propagated using a Brouwer-Lyddane model with drag; the data correspond to a command schedule based on the orbit for this seven-day period; it includes total (at-satellite) radiances for ocean, land, clouds, and ice; it utilizes a high-resolution land/sea mask; it includes actual SeaWiFS spectral responses; it includes the actual sensor saturation responses; it is formatted according to current onboard data structures; and it includes corresponding telemetry (instrument and spacecraft) data. The methods are described and some examples of the output are given.

Gregg, Watson W.↗

NASA NTRS1984

Evaluation of corn/soybeans separability using Thematic Mapper and Thematic Mapper Simulator data

Multitemporal Thematic Mapper, Thematic Mapper Simulator, and detailed ground truth data were collected for a 9- by 11-km sample segment in Webster County, IA, in the summer of 1982. Three dates were acquired each with Thematic Mapper Simulator (June 7, June 23, and July 31) and Thematic Mapper (August 2, September 3, and October 21). The Thematic Mapper Simulator data were converted to equivalent TM count values using TM and TMS calibration data and model based estimates of atmospheric effects. The July 31, TMS image was compared to the August 2, TM image to verify the conversion process. A quantitative measure of proportion estimation variance (Fisher information) was used to evaluate the corn/soybeans separability for each TM band as a function of time during the growing season. The additional bands in the middle infrared allowed corn and soybeans to be separated much earlier than was possible with the visible and near-infrared bands alone. Using the TM and TMS data, temporal profiles of the TM principal components were developed. The greenness and brightness exhibited behavior similar to MSS greenness and brightness for corn and soybeans.

Pitts, D. E.↗

NASA NTRS2002

Software for Processing Flight and Simulated Data of the ATIC Experiment

ATIC (Advanced Thin Ionization Calorimeter) is a balloon borne experiment designed to measure the cosmic ray composition for elements from hydrogen to iron and their energy spectra from approx.50 GeV to near 100 TeV. It consists of a Si-matrix detector to determine the charge of a CR particle, a scintillator hodoscope for tracking, carbon interaction targets and a fully active BGO calorimeter. ATIC had its first flight from McMurdo, Antarctica from 28/12/2000 to 13/01/2001. The ATIC flight collected approximately 25 million events. A C++-class library for building different programs for processing flight and simulated data of the ATIC balloon experiment is described. This library is compatible with the ROOT-system and includes classes and methods for solving a number of problems as the following: Reading data files in different formats (raw-data format, ROOT-format, ASCII-format, different formats for simulated data); Transferring all these formats to the only inner format of the library; Reconstruction of trajectories of primary particles with BGO calorimeter only. The Monte-Carlo simulations with GEANT code were used to obtain the basic tables for computing error corridors and chi(sup 2)-values for the trajectories. Obtaining error corridors for searching for signal of primary particle in the Si-matrix; Searching for hit of primary particle in the Si-matrix with using of error corridor and other criteria (chi(sup 2)-values, agreement between signals in Si-matrix and in the upper layer of scintillator and others); Determination of charge of primary particle; Determination of energy deposit in BGO calorimeter.

Panov, A. D.↗

NASA NTRS2011

The VIIRS Ocean Data Simulator Enhancements and Results

The VIIRS Ocean Science Team (VOST) has been developing an Ocean Data Simulator to create realistic VIIRS SDR datasets based on MODIS water-leaving radiances. The simulator is helping to assess instrument performance and scientific processing algorithms. Several changes were made in the last two years to complete the simulator and broaden its usefulness. The simulator is now fully functional and includes all sensor characteristics measured during prelaunch testing, including electronic and optical crosstalk influences, polarization sensitivity, and relative spectral response. Also included is the simulation of cloud and land radiances to make more realistic data sets and to understand their important influence on nearby ocean color data. The atmospheric tables used in the processing, including aerosol and Rayleigh reflectance coefficients, have been modeled using VIIRS relative spectral responses. The capabilities of the simulator were expanded to work in an unaggregated sample mode and to produce scans with additional samples beyond the standard scan. These features improve the capability to realistically add artifacts which act upon individual instrument samples prior to aggregation and which may originate from beyond the actual scan boundaries. The simulator was expanded to simulate all 16 M-bands and the EDR processing was improved to use these bands to make an SST product. The simulator is being used to generate global VIIRS data from and in parallel with the MODIS Aqua data stream. Studies have been conducted using the simulator to investigate the impact of instrument artifacts. This paper discusses the simulator improvements and results from the artifact impact studies.

Robinson, Wayne D.↗

NASA NTRS1984

Landsat-4 thematic mapper and thematic mapper simulator data for a porphyry copper deposit

Aircraft thematic mapper (TM) data were analyzed to evaluate the potential utility of the Landsat-4 thematic mapper for geologic mapping and detection of hydrothermal alteration zones in the Silver Bell porphyry copper deposit in southern Arizona. The data allow a comparison between aircraft TV simulator data and the Landsat-4 TM satellite data which possess similar spectral bands. A color rationcomposite of 30-m pixels was resampled, in order to clearly define a number of hydroxyl bearing minerals, (kaolinite, sericite, white mica), pyrite and iron oxide/hydroxide minerals. The iron oxide minerals have diagnostic absorption bands in the 0.45 and 0.85 micron regions of the spectrum, and the hydrous minerals are characterized by an absorption in the 2.2 micron region. The position of the spectral bands allow the TM to identify regions of hydrothermal alteration without resorting to a data processing algorithm. The comparison of the aircraft and Landsat-4 TM data showed considerable agreement, and confirmed the utility of TM data for identifying hydrothermal alteration zones. Samples of some color TM images are provided.

Abrams, M. J.↗

NASA NTRS1992

Modeling the dissipation-rate equation with the aid of direct simulation data

The epsilon-budget was computed from the direct simulation data (DNS) of Kim (1990) for developed channel flow at Re(tau) = 395. The relative magnitude of the terms in the epsilon-equation is shown with the aid of scaling arguments, and the parameter governing this magnitude is established. The modeling of the terms in the equation is then addressed in the context of eddy-viscosity k-epsilon models. Some existing models for the sum of all source and sink terms in the epsilon-equation are tested against DNS data, and an improved model is proposed on the basis of these data.

Rodi, W.↗

DOE OSTI2024

Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data

Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Fast and Invertible Simplicial Approximation of Magnetic‐Following Interpolation for Visualizing Fusion Plasma Simulation Data

We introduce a fast and invertible approximation for fusion plasma simulation data represented as 2D planar meshes with connectivities approximating magnetic field lines along the toroidal dimension in deformed 3D toroidal spaces. Scientific variables (e.g., density and temperature) in these fusion data are interpolated following a complex magnetic-field-line-following scheme in the toroidal space represented by a cylindrical coordinate system. This deformation in the 3D space poses challenges for root-finding and interpolation. To this end, we propose a novel paradigm for visualizing and analyzing such data based on a newly developed algorithm for constructing a 3D simplicial mesh within the deformed 3D space. Our algorithm generates a tetrahedral mesh that connects the 2D meshes using tetrahedra while adhering to the constraints on node connectivities imposed by the magnetic field-line scheme. Specifically, we first divide the space into smaller partitions to reduce complexity based on the input geometries and constraints on connectivities. Then, we independently search for a feasible tetrahedralization of each partition, considering nonconvexity. We demonstrate our method with two X-Point Gyrokinetic Code (XGC) simulation datasets on the International Thermonuclear Experimental Reactor (ITER) and Wendelstein 7-X (W7-X), and use an ocean simulation dataset to substantiate broader applicability of our method. An open source implementation of our algorithm is available at https://github.com/rcrcarissa/DeformedSpaceTet.

Ren, Congrong [The Ohio State Univ., Columbus, OH ↗

DOE OSTI2022

2-D Surface Temperature Simulation Data and Matlab Scripts

SAND2023-07249O The 2-D Surface Temperature Simulation Data and Matlab software can estimate 2D temperature fields from electrical measurements along the edge of the 2D domain. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Sjoberg, Carl Magnus↗

NASA NTRS1978

Aircraft simulation data management - A prototype system

Piloted flight simulations are used throughout the aircraft development process to evaluate design concepts, handling qualities and operational procedures. Simulation project managers are often inundated with data but without a convenient and efficient way to make the correlations and analyses necessary to evaluate system performance. A computer-based Simulation Management System (SIMS) is under development. SIMS will permit simulation project engineers to quickly acquire, access, display, edit, analyze, and document the information necessary to more efficiently manage the research program. SIMS features interactive, associative access to simulation data. This paper describes SIMDEM, a prototype system designed to demonstrate these concepts and procedures in order to obtain feedback from simulator users to guide system design.

Crane, D. F.↗

NASA NTRS1981

A statistical analysis of simulated data from potential stratospheric satellite missions

A simplified one-dimensional model is used to analyze simulated data from typical proposed satellite measurements of trace species in the stratosphere. Uncertainties are introduced in simulated species concentration measurements and applied to the basic physical and chemical model data. The results of the calculations are then used to predict uncertainties in the species concentrations computed by the model. Candidate satellite missions can be evaluated for net information acquired by applying the error analysis. An abbreviated depiction of the computational procedure is given, and sample histograms based on an accumulation of 200 calculations are shown for H2O2 and ClNO3, demonstrating that emergent species distributions are roughly Gaussian in the logarithm of number density. Calculated vertical concentration profiles for these two species are presented.

Nealy, J. E.↗

NASA NTRS1986

Analysis of forest structure using thematic mapper simulator data

The potential of Thematic Mapper Simulator (TMS) data for sensing forest structure information has been explored by principal components and feature selection techniques. In a survey of forest structural properties conducted for 123 field sites of the Sequoia National Park, the canopy closure could be well estimated (r = 0.62 to 0.69) by a variety of channel bands and band ratios, without reference to the forest type. Estimation of the basal area was less successful (r = 0.51 or less) on the average, but could be improved for certain forest types when data were stratified by floristic composition. To achieve such a stratification, individual sites were ordinated by a detrended correspondence analysis based on the canopy of dominant species. The analysis of forest structure in the Sequoia data suggests that total basal area can be best predicted in stands of lower density, and in younger even-aged managed stands.

Peterson, D. L.↗

NASA NTRS1997

SimGraph: A Flight Simulation Data Visualization Workstation

Today's modern flight simulation research produces vast amounts of time sensitive data, making a qualitative analysis of the data difficult while it remains in a numerical representation. Therefore, a method of merging related data together and presenting it to the user in a more comprehensible format is necessary. Simulation Graphics (SimGraph) is an object-oriented data visualization software package that presents simulation data in animated graphical displays for easy interpretation. Data produced from a flight simulation is presented by SimGraph in several different formats, including: 3-Dimensional Views, Cockpit Control Views, Heads-Up Displays, Strip Charts, and Status Indicators. SimGraph can accommodate the addition of new graphical displays to allow the software to be customized to each user s particular environment. A new display can be developed and added to SimGraph without having to design a new application, allowing the graphics programmer to focus on the development of the graphical display. The SimGraph framework can be reused for a wide variety of visualization tasks. Although it was created for the flight simulation facilities at NASA Langley Research Center, SimGraph can be reconfigured to almost any data visualization environment. This paper describes the capabilities and operations of SimGraph.

Kaplan, Joseph A.↗

DOE OSTI2022

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the uvgriz multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first-year observation of Mephisto W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with purity = 95.4% and completeness = 96.9% for the RR Lyrae RFC and purity = 91.4% and completeness = 90.2% for the quasar RFC. In conclusion, we have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

(galaxies:) quasars: general↗

NASA NTRSDate not supplied

GMAO Simulated Data

Updates on GMAO simulated observations for PACE OCI.

Patricia Castellanos↗