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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 NTRS2015

Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading: Appendices - Volume 2, Part 1

The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (approximately 9 inches from the source) dominated by direct wave propagation, mid-field environment (approximately 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This document contains appendices to the Volume I report.

Gentz, Steven J.↗
NASA NTRS2015

Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading

The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (~9 inches from the source) dominated by direct wave propagation, mid-field environment (approximately 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This document contains appendices to the Volume I report.

Gentz, Steven J.↗
NASA NTRS2015

Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading: Appendices

The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (approx. 9 inches from the source) dominated by direct wave propagation, mid-field environment (approx. 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This document contains appendices to the Volume I report.

Gentz, Steven J.↗
NASA NTRS2018

A Case Study Comparing Citizen Science Aurora Data with Global Auroral Boundaries Derived from Satellite Imagery and Empirical Models

Aurorasaurus is a citizen science project that offers a new, global data source consisting of ground-based reports of the aurora. For this case study, aurora data collected during the 17-18 March 2015 geomagnetic storm are examined to identify their conjunctions with Defense Meteorological Satellite Program (DMSP) satellite passes over the high latitude auroral regions. This unique set of aurora data can provide ground-truth validation of existing auroral precipitation models. Particularly, the solar wind driven, Oval Variation, Assessment, Tracking, Intensity, and Online Nowcasting (OVATION) Prime 2013 (OP-13) model and a Kp-dependent model of Zhang-Paxton (Z-P) are utilized for our boundary validation efforts. These two similar models are compared for the first time. Global equatorward auroral boundaries are derived from the OP 13 model and the DMSP Special Sensor Ultraviolet Spectrographic Imager (SSUSI) far ultraviolet (FUV) data using the Z-P model at a fixed flux level of 0.2 erg cm(exp -2)s(exp -1). These boundaries are then compared with citizen science reports as well as with each other. Even though there are some large differences between the global boundaries for a few cases, the average difference is about 1.5 deg in geomagnetic latitude, with OP-13 being equatorward of Z-P model. When these boundaries are compared with each other as a function of local time, no clear overall trend as a function of local time was observed. It is also found that the ground based reports are more consistent with the predictions of the OP-13 model.

Kosar, Burcu C.↗
NASA NTRS2018

Semi-Empirical Modeling and Prediction of Direct Combustor Noise

Due to turbofan design trends, advances in fan-noise-mitigation techniques, and expected aircraft configuration changes, the impact on overall airport community noise from certain aircraft-propulsion noise sources, such as fan and jet noise, will be reduced at all certification points in the future. The situation with regard to turbofan-core noise is less clear however. Combustor noise in particular—which presently is an important core-noise component, but not dominant from an overall noise perspective—could become more prominent in upcoming high-power-density core designs. Its detailed source structure in the combustor, including the indirect combustor-noise mechanism, and the effects of the propagation path through the engine and exhaust nozzle need to be better understood. This report reviews the current status of modeling and prediction of direct combustor noise. This work is carried out under the NASA Advanced Air Vehicles Program, Advanced Air Transport Technology Project, Aircraft Noise Reduction Subproject.

Turbomachinery Noise↗
NASA NTRS1971

Empirical solar continuum models

Empirical solar photosphere continuum models, calculating temperature distribution for absolute intensities at center and limb darkening ratios

Remo, J. L.↗
NASA NTRS2004

Interpretive Tools for Analysis of Coronal Images

Contents include list of publications resulting from grant: Magneto-Hydrodynamic Model of the Solar Corona and Interplanetary Medium. Coronal magnetic field topology and source of fast solar wind. Geophysical Importance of Global Magnetic Field Geometry and Density Distribution in Solar Wind Modeling. Empirical Model of the Corona-Solar Wind with Multiple Current Sheets. Model of the Solar Wind and its Comparison with ULYSSES. Physical properties of a coronal hole from a coronal diagnostic spectrometer, Mauna Loa Coronagraph, and LASCO observations during the Whole Sun Month. Semi-Empirical 2-D MHD Model of the Solar corona and Solar Wind: Energy Flow in the Corona. Source Region of High and Low Speed Wind during the Spartan. Three-dimensional coronal density structure. Cone Model for Halo CME's: Application to Space Weather Forecasting.

Ofman, Leon↗
NASA NTRS2023

An Evaluation of A Physics-Based Firn Model and A Semi-Empirical Firn Model Across the Greenland Ice Sheet (1980–2020)

The Greenland Ice Sheet's (GrIS) firn layer buffers the ice sheet's contribution to sea level rise by storing meltwater in its pore space. However, available pore space and meltwater retention capability is lost due to ablation of the firn layer and refreezing of meltwater as near-surface ice slabs in the firn. Understanding how firn properties respond to climate is important for constraining the GrIS's future contribution to sea level rise in a warming climate. Observations of firn density provide detailed information about firn properties, but they are spatially and temporally limited. Here we use two firn models, the physics-based SNOWPACK model and the Community Firn Model configured with a semi-empirical densification equation (CFM-GSFC), to quantify firn properties across the GrIS from 1980 through 2020. We use an identical forcing (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) atmospheric reanalysis) for SNOWPACK and the CFM-GSFC in order to isolate firn model differences. To evaluate the models, we compare simulated firn properties, including firn air content (FAC), to measurements from the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) dataset of snow and firn density. Both models perform well (mean absolute percentage errors of 14 % in SNOWPACK and 16 % in the CFM-GSFC), though their performance is hindered by the spatial resolution of the atmospheric forcing. In the ice-sheet-wide simulations, the 1980–1995 average spatially integrated FAC (i.e., air volume in the firn) for the upper 100 m is 34 645 km 3 from SNOWPACK and 28 581 km 3 from the CFM-GSFC. The discrepancy in the magnitude of the modeled FAC stems from differences in densification with depth and variations in the sensitivity of the models to atmospheric forcing. In more recent years (2005–2020), both models simulate substantial depletion of pore space. During this period, the spatially integrated FAC across the entire GrIS decreases by 3.2 % (−66.6 km 3 yr −1 ) in SNOWPACK and 1.5 % (−17.4 km 3 yr −1 ) in the CFM-GSFC. These differing magnitudes demonstrate how model differences propagate throughout the FAC record. Over the full modeled record (1980–2020), SNOWPACK simulates a loss of pore space equivalent to 3 mm of sea level rise buffering, while the CFM-GSFC simulates a loss of 1 mm. The greatest depletion in FAC is along the margins and especially along the western margin where observations and models show the formation of near-surface, low-permeability ice slabs that may inhibit meltwater storage.

Megan Thompson-Munson↗
NASA NTRS2020

An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model

An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment‐CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment‐CN model are divided into three subtypes, based on the bias between the model‐simulated and MODIS‐observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single‐PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (∼10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment‐CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements.

J. Kolassa↗
NASA NTRS1988

High altitude atmospheric modeling

Five empirical models were compared with 13 data sets, including both atmospheric drag-based data and mass spectrometer data. The most recently published model, MSIS-86, was found to be the best model overall with an accuracy around 15 percent. The excellent overall agreement of the mass spectrometer-based MSIS models with the drag data, including both the older data from orbital decay and the newer accelerometer data, suggests that the absolute calibration of the (ensemble of) mass spectrometers and the assumed drag coefficient in the atomic oxygen regime are consistent to 5 percent. This study illustrates a number of reasons for the current accuracy limit such as calibration accuracy and unmodeled trends. Nevertheless, the largest variations in total density in the thermosphere are accounted for, to a very high degree, by existing models. The greatest potential for improvements is in areas where we still have insufficient data (like the lower thermosphere or exosphere), where there are disagreements in technique (such as the exosphere) which can be resolved, or wherever generally more accurate measurements become available.

Hedin, Alan E.↗
NASA NTRS2021

Nonlinear Unsteady Aerodynamic Modeling Using Empirical Orthogonal Functions

Empirical orthogonal function modeling is explained and applied to identify compact discrete-time nonlinear unsteady aerodynamic models from data generated by an unsteady three-dimensional compressible Navier-Stokes flow solver for an airfoil undergoing various pitching motions. Model structures, model parameter estimates, and model parameter uncertainty estimates for nondimensional lift, drag, and pitching moment coefficient models were determined autonomously and directly from the data. Prediction tests using data that were not used in the modeling process showed that the identified models exhibited excellent prediction capability, which is a strong indicator of an accurate model.

empirical↗
NASA NTRS1995

Study of tropospheric correction for intercontinental GPS common-view time transfer

Current practice is to incorporate general empirical models of the troposphere, which depend only on the station height and the elevation of the satellite, in GPS time receivers used for common-view time transfer. Comparisons of these models with a semi-empirical model based on weather measurements show differences of several nanoseconds. This paper reports on a study of tropospheric correction during GPS common-view time transfer over a short baseline of about 700 km, and three long baselines of 6400 km, 9000 km and 9600 km. It is shown that the use of a general empirical model of the troposphere within a region where the climate is similar does not affect time transfer by more than a few hundreds of picoseconds. For the long distance links, differences between the use of a general empirical model and the use of a semi-empirical model reach several nanoseconds.

Lewandowski, Wlodzimierz W.↗
NASA NTRS1994

Empirical convection models for northward IMF

It is clear that polar cap convection during times of northward Interplanetary Magnetic Field (IMF) is more structured and of lower mean speed than at times of southward IMF. This, coupled with the fact that the polar cap is smaller, means that empirical models are more difficult to construct with certainty. It is also clear that sunward flow deep in the polar cap is often observed, but its connection with the rest of the flow pattern is controversial. At present, empirical models are of three types: 'statistical' models wherein data from different days but with similar IMF conditions are averaged together; 'pattern recognition' models, which are built up by examining individually hundreds of passes to derive a 'typical' pattern which embodies features frequently observed; and 'assimilative' models, which use data of different types and from as many locations as possible, but all taken at the same time, in order to derive a snapshot (or series of snapshots) of the entire pattern. Each type of model has its own difficulties. Statistical models, by their very nature, smooth out flow features (e.g. the convection reversal, and the locus of sunward flow deep in the polar cap) which are not found at precisely the same invariant latitudes and magnetic local times on different days. Pattern recognition models are better at reproducing small-scale features, but the large-scale pattern can be a matter of interpretation. Assimilative models (such as AMIE) hold out the best hope for creating instantaneous, global convection patterns; however, the analysis technique tends to be most irregular (and least reliable) in the regions which are not well covered by in situ data. It appears that, at least at times, a four cell model with sunward flow at the highest and lowest latitudes, and antisunward flow in between, is consistent with the observations. At other times, the observations may be consistent with a two-cell convection pattern, but which includes significant meanders within the polar cap.

Moses, Julie J.↗
NASA NTRS1993

Recent solar extreme ultraviolet irradiance observations and modeling: A review

For more than 90 years, solar extreme ultraviolet (EUV) irradiance modeling has progressed from empirical blackbody radiation formulations, through fudge factors, to typically measured irradiances and reference spectra was well as time-dependent empirical models representing continua and line emissions. A summary of recent EUV measurements by five rockets and three satellites during the 1980s is presented along with the major modeling efforts. The most significant reference spectra are reviewed and threee independently derived empirical models are described. These include Hinteregger's 1981 SERF1, Nusinov's 1984 two-component, and Tobiska's 1990/1991/SERF2/EUV91 flux models. They each provide daily full-disk broad spectrum flux values from 2 to 105 nm at 1 AU. All the models depend to one degree or another on the long time series of the Atmosphere Explorer E (AE-E) EUV database. Each model uses ground- and/or space-based proxies to create emissions from solar atmospheric regions. Future challenges in EUV modeling are summarized including the basic requirements of models, the task of incorporating new observations and theory into the models, the task of comparing models with solar-terrestrial data sets, and long-term goals and modeling objectives. By the late 1990s, empirical models will potentially be improved through the use of proposed solar EUV irradiance measurements and images at selected wavelengths that will greatly enhance modeling and predictive capabilities.

Tobiska, W. Kent↗
NASA NTRS2017

Modeling for Battery Prognostics

For any battery-powered vehicles (be it unmanned aerial vehicles, small passenger aircraft, or assets in exoplanetary operations) to operate at maximum efficiency and reliability, it is critical to monitor battery health as well performance and to predict end of discharge (EOD) and end of useful life (EOL). To fulfil these needs, it is important to capture the battery's inherent characteristics as well as operational knowledge in the form of models that can be used by monitoring, diagnostic, and prognostic algorithms. Several battery modeling methodologies have been developed in last few years as the understanding of underlying electrochemical mechanics has been advancing. The models can generally be classified as empirical models, electrochemical engineering models, multi-physics models, and molecular/atomist. Empirical models are based on fitting certain functions to past experimental data, without making use of any physicochemical principles. Electrical circuit equivalent models are an example of such empirical models. Electrochemical engineering models are typically continuum models that include electrochemical kinetics and transport phenomena. Each model has its advantages and disadvantages. The former type of model has the advantage of being computationally efficient, but has limited accuracy and robustness, due to the approximations used in developed model, and as a result of such approximations, cannot represent aging well. The latter type of model has the advantage of being very accurate, but is often computationally inefficient, having to solve complex sets of partial differential equations, and thus not suited well for online prognostic applications. In addition both multi-physics and atomist models are computationally expensive hence are even less suited to online application An electrochemistry-based model of Li-ion batteries has been developed, that captures crucial electrochemical processes, captures effects of aging, is computationally efficient, and is of suitable accuracy for reliable EOD prediction in a variety of operational profiles. The model can be considered an electrochemical engineering model, but unlike most such models found in the literature, certain approximations are done that allow to retain computational efficiency for online implementation of the model. Although the focus here is on Li-ion batteries, the model is quite general and can be applied to different chemistries through a change of model parameter values. Progress on model development, providing model validation results and EOD prediction results is being presented.

Prognostics↗