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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 NTRS2008

Documentation for Three Wake Vortex Model Data Sets from Simulation of Flight 587 Wake Vortex Encounter Accident Case

This document contains a general description for data sets of a wake vortex system in a turbulent environment. The turbulence and thermal stratification of the environment are representative of the conditions on November 12, 2001 near John F. Kennedy International Airport. The simulation assumes no ambient winds. The full three dimensional simulation of the wake vortex system from a Boeing 747 predicts vortex circulation levels at 80% of their initial value at the time of the proposed vortex encounter. The linked vortex oval orientation showed no twisting, and the oval elevations at the widest point were about 20 meters higher than where the vortex pair joined. Fred Proctor of NASA?s Langley Research Center presented the results from this work at the NTSB public hearing that started 29 October 2002. This document contains a description of each data set including: variables, coordinate system, data format, and sample plots. Also included are instructions on how to read the data.

Switzer, George F.↗
DOE OSTI2021

Validation of the EDGES Low-band Antenna Beam Model

The response of the antenna is a source of uncertainty in measurements with the Experiment to Detect the Global Epoch of Reionization Signature (EDGES). We aim to validate the electromagnetic beam model of the low-band (50–100 MHz) dipole antenna with comparisons between models and against data. We find that simulations of a simplified model of the antenna over an infinite perfectly conducting ground plane are, with one exception, robust to changes in the numerical electromagnetic solver code or algorithm. For simulations of the antenna with the actual finite ground plane and realistic soil properties, we find that two out of three numerical solvers agree well. Applying our analysis pipeline to a simulated drift-scan observation from an early EDGES low-band instrument that had a 10 m × 10 m ground plane, we find residual levels after fitting and removing a five-term foreground model from the simulated data binned in local sidereal time (LST) average about 250 mK with ±40 mK variation between numerical solvers. A similar analysis of the primary 30 m × 30 m sawtooth ground plane reduced the LST-averaged residuals to about 90 mK with ±10 mK between the two viable solvers. More broadly we show that larger ground planes generally perform better than smaller ground planes. Simulated data have a power that is within 4% of real observations, a limitation of net accuracy of the sky and beam models. We observe that residual spectral structures after foreground model fits match qualitatively between simulated data and observations, suggesting that the frequency dependence of the beam is reasonably represented by the models. We find that a soil conductivity of 0.02 S m{sup -1} and relative permittivity of 3.5 yield good agreement between simulated spectra and observations. This is consistent with the soil properties reported by Sutinjo et al. for the Murchison Radio-astronomy Observatory, where EDGES is located.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1987

Reynolds stress models of homogeneous turbulence

Existing and new models for the rapid and the return terms in the Reynolds stress equations were tested in two ways. One, by direct comparison of the model with simulation data. The other, by simulating the flows using the models and comparing the predicted Reynolds stresses with the data. It was found that existing linear models can be improved and that nonlinear models are in better agreement with the simulation data for a wide variety of flows.

Shih, T.-H.↗
DOE OSTI2023

Neural networks for parameter estimation in intractable models

The goal is to use deep learning models to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. For instance, inference for max-stable processes is exceptionally challenging even with small datasets, but simulation is straightforward. Data from model simulations are used to train deep neural networks and learn statistical parameters from max-stable models. The proposed neural network-based method provides a competitive alternative to current approaches, as demonstrated by considerable accuracy and computational time improvements. Finally, it serves as a proof of concept for deep learning in statistical parameter estimation and can be extended to other estimation problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

Comparison of measured and predicted flight effects on high-bypass coaxial jet exhaust noise

A recently developed semi-empirical model for predicting the noise generated by conventional-velocity-profile coaxial jets is compared with full-scale flight data and model-scale simulated flight data for high-bypass nozzles. The prediction model has been shown to agree with small-scale static data for primary jet velocities from 215 to 795 m/s for a wide range of area, temperature and velocity ratios between streams. However, there were insufficient model nozzle, simulated flight data available at that time to permit validation of the flight effects prediction. The comparisons presented in this paper demonstrate that the prediction method is also valid in flight.

Stone, J. R.↗
NASA NTRS1999

Backus Effect on a Perpendicular Errors in Harmonic Models of Real vs. Synthetic Data

Measurements of geomagnetic scalar intensity on a thin spherical shell alone are not enough to separate internal from external source fields; moreover, such scalar data are not enough for accurate modeling of the vector field from internal sources because of unmodeled fields and small data errors. Spherical harmonic models of the geomagnetic potential fitted to scalar data alone therefore suffer from well-understood Backus effect and perpendicular errors. Curiously, errors in some models of simulated 'data' are very much less than those in models of real data. We analyze select Magsat vector and scalar measurements separately to illustrate Backus effect and perpendicular errors in models of real scalar data. By using a model to synthesize 'data' at the observation points, and by adding various types of 'noise', we illustrate such errors in models of synthetic 'data'. Perpendicular errors prove quite sensitive to the maximum degree in the spherical harmonic expansion of the potential field model fitted to the scalar data. Small errors in models of synthetic 'data' are found to be an artifact of matched truncation levels. For example, consider scalar synthetic 'data' computed from a degree 14 model. A degree 14 model fitted to such synthetic 'data' yields negligible error, but amplifies 4 nT (rmss) added noise into a 60 nT error (rmss); however, a degree 12 model fitted to the noisy 'data' suffers a 492 nT error (rmms through degree 12). Geomagnetic measurements remain unaware of model truncation, so the small errors indicated by some simulations cannot be realized in practice. Errors in models fitted to scalar data alone approach 1000 nT (rmss) and several thousand nT (maximum).

Voorhies, C. V.↗
NASA NTRS2005

Comparison of Cloud Resolving Model Simulations to Remote Sensing Data

The purpose of this research was to evaluate the ability of a cloud-resolving model (CRM) to simulate the dynamical, radiative, and microphysical properties of deep convective cloud objects identified using CERES (Clouds and the Earth s Radiant Energy System) on board the Tropical Rainfall Measuring Mission (TRMM) satellite platform, for many cases. A deep convective cloud object is a contiguous region that is composed of satellite footprints that fulfill the following selection criteria: 100% cloud fraction, cloud optical depth > 10, and a cloud top height of at least 10 km. Selection criteria have also been formed for different types of boundary-layer clouds, as described in Xu et al. (2005). The purpose of the cloud object approach is to identify specific areas of where the cloud properties simulated by the CRM systematically differ from the observed cloud properties. Where these systematic differences exist, concrete steps can be made to improve the CRM s simulation of an entire class of clouds, rather than by tuning the model to correctly simulate a single case study, as is often done. Additional information regarding detailed approaches and findings are presented.

Randall, David A.↗
NASA NTRSDate not supplied

Comparing the Electrical Modeling and Thermal Analysis Toolbox Simulation Data to Electrified Aircraft Propulsion Test Hardware Data

A model using the Electrical Modeling and Thermal Analysis Toolbox (EMTAT), a National Aeronautics and Space Administration (NASA)-developed Simulink® model block library of electrical components, was developed to mirror the Hybrid Propulsion Emulation Rig (HyPER) hardware, a laboratory focused on Electrified Aircraft Propulsion (EAP) hardware tests. The goal of the model was to demonstrate the utility of the library by comparing the accuracy of the library models to the performance of real hardware, with the primary metrics being the simulation outputs matching physical test hardware data within 5% of full scale.The objective of this paper is to present the background, setup, testing and results of this comparison. It describes some of the adjustments that were necessary to match the system hardware, as well as next steps in verification and validation. The outputs of the model were compared to the results of several tests in HyPER, and in the process captured an additional torque loss that is still being analyzed for the root cause but has been confirmed in the hardware. Across all the test series, only one key model parameter was outside the target 5% full scale matching, and nearly 70% were within 1%.

Mark E Bell↗
NASA NTRS2023

Comparing the Electrical Modeling and Thermal Analysis Toolbox Simulation Data to Electrified Aircraft Propulsion Test Hardware Data

A model using the Electrical Modeling and Thermal Analysis Toolbox (EMTAT), a National Aeronautics and Space Administration (NASA)-developed Simulink® model block library of electrical components, was developed to mirror the Hybrid Propulsion Emulation Rig (HyPER) hardware, a laboratory focused on Electrified Aircraft Propulsion (EAP) hardware tests. The goal of the model was to demonstrate the utility of the library by comparing the accuracy of the library models to the performance of real hardware, with the primary metrics being the simulation outputs matching physical test hardware data within 5 percent of full scale. The objective of this paper is to present the background, setup, testing and results of this comparison. It describes some of the adjustments that were necessary to match the system hardware, as well as next steps in verification and validation. The outputs of the model were compared to the results of several tests in HyPER, and in the process captured an additional torque loss that is still being analyzed for the root cause but has been confirmed in the hardware. Across all the test series, only one key model parameter was outside the target 5 percent full scale matching, and nearly 70 percent were within 1 percent.

Mark E. Bell↗
DOE OSTI2018

The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have had difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to improve and to facilitate the comparison of modeled clouds with observations. Many simulators have (and continue to be developed) for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Klein et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to CloudSat radar observations, ARM radar measurements occur with higher temporal resolution and finer vertical resolution. This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to surface contamination (Mace et al. 2007; Marchand et al. 2008). Therefore, the ARM ground-based cloud observations can provide important observations of clouds that complement measurements from space.

Zhang, Yuying↗
NASA NTRS2013

Challenges in Quantifying Pliocene Terrestrial Warming Revealed by Data-Model Discord

Comparing simulations of key warm periods in Earth history with contemporaneous geological proxy data is a useful approach for evaluating the ability of climate models to simulate warm, high-CO2 climates that are unprecedented in the more recent past. Here we use a global data set of confidence-assessed, proxy-based temperature estimates and biome reconstructions to assess the ability of eight models to simulate warm terrestrial climates of the Pliocene epoch. The Late Pliocene, 3.6-2.6 million years ago, is an accessible geological interval to understand climate processes of a warmer world4. We show that model-predicted surface air temperatures reveal a substantial cold bias in the Northern Hemisphere. Particularly strong data-model mismatches in mean annual temperatures (up to 18 C) exist in northern Russia. Our model sensitivity tests identify insufficient temporal constraints hampering the accurate configuration of model boundary conditions as an important factor impacting on data- model discrepancies. We conclude that to allow a more robust evaluation of the ability of present climate models to predict warm climates, future Pliocene data-model comparison studies should focus on orbitally defined time slices.

Salzmann, Ulrich↗
NASA NTRS1979

Radiometric resolution for monitoring vegetation: How many bits are needed?

The significance of the various number of radiometric quantizing levels required for satellite monitoring of vegetation resources was evaluated by using in situ collected spectral reflectance data, an atmospheric radiative transfer simulation model, and a satellite sensor simulation model. Reflectance data were converted to radiance data; passed through a model atmosphere to an altitude of 706 km; and subsequently quantized at 16, 32, 64, 128, 256, and 512 digital count levels for Thematic Mapper bands TM3(0.63 - 0.69 microns) and TM4(0.76 - 0.90 microns). The simulated digital count data were regressed against the in situ biological data to quantify the relationship between quantizing levels.

Tucker, C. J.↗