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NASA NTRSDate not supplied

Computational Prediction of Broadband Noise from a Representative Unmanned Aerial System Rotor

This work details the application of a lattice-Boltzmann method–very-large-eddy simulation (LBM-VLES) employed by the software suite, PowerFLOW. This LBM-VLES simulation predicted the aeroacoustic noise emanating from a representative, small unmanned aircraft system rotor, namely, the DJI-9450 in a hover condition. Predicted total aerodynamic loading as well as 2D aerodynamic loading along discrete spanwise sections of a rotor blade were compared to lower fidelity predictions and experimental results acquired in the Structural Acoustic Loads and Transmission anechoic chamber facility at the NASA Langley Research Center. The total acoustic spectra were decomposed into tonal and broadband components, which showed that broadband noise was a dominant contributor above 1 kHz for this rotor. These data were then compared to experimentally acquired data, showing good agreement up to approximately 11 kHz. Above 11 kHz, however, a grid sensitivity study showed dependency of the highest resolvable frequency on the spatial resolution of the computational domain, explaining the roll off in predicted data. Individual broadband noise sources were further investigated by calculating one-third octave sound pressure levels of the unsteady pressure fluctuations acting on the rotor, providing evidence that blade self-noise was the prominent noise source. Using these results, blade wake interaction noise was seen to be negligible for this particular rotor, which was further validated by calculating blade vortex miss distances and comparing to theory.

Christopher S. Thurman↗

NASA NTRS2020

Microscale Characterization of Damage Accumulation in CMCs

The developing roles of damage mechanisms in the failure response of SiC/SiC minicomposites was investigated by the characterization of microscale damage accumulation with respect to microstructure. A multi-modal approach combining spatially resolved acoustic emission (AE) with tensile testing in-SEM (scanning electron microscope) was used to simultaneously examine surface (observed in-SEM) and bulk damage (monitored via AE). Strong agreement was shown between the evolving crack density estimated by AE and in-SEM measurements. The following were observed: (i) in-plane matrix content and distribution impacted crack growth; (ii) spatially-distributed matrix cracks generated varying stress-dependent AE; and (iii) certain individual cracks became more probable failure locations due to unique combinations of damage mechanisms that drove their growth. This approach enabled characterizing potential failure determinants and suggests that early damage behavior is related to certain microstructural features (e.g. surface flaws), while subsequent damage behavior is coupled to interactions of local mechanisms evolving with stress.

ceramic matrix composites↗

NASA NTRS1986

Time-dependent numerical modeling of dust halo formation at comets

The evolution of gas and dust distributions following a spatially and temporally localized comet outburst was calculated using a hybrid kinetic-hydrodynamic method. In the inner coma the time-dependent continuity, momentum, and energy equations of the dusty gas flow were solved simultaneously using 12 dust sizes. Beyond 300 km a three-dimensional kinetic model was used to calculate the trajectory of each individual dust grain. It was found that following the onset of the comet outburst a gas-dust blast wave propagates outward in the inner coma. About 15 minutes after the increased gas and dust production was initiated at the nucleus, a new equilibrium was reached in the inner coma. The most important feature of this new steady state was the significant increase of the dust terminal velocities. These higher terminal velocity values resulted in larger apex distances for dust particles emitted during the outburst. The dust particles spend a relatively long time near their apex points; therefore, the outburst generates long-lasting distinct dust envelopes in front of the regular dust coma.

Gombosi, T. I.↗

NASA NTRS2001

Spectral Dimensionality and Scale of Urban Radiance

Characterization of urban radiance and reflectance is important for understanding the effects of solar energy flux on the urban environment as well as for satellite mapping of urban settlement patterns. Spectral mixture analyses of Landsat and Ikonos imagery suggest that the urban radiance field can very often be described with combinations of three or four spectral endmembers. Dimensionality estimates of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) radiance measurements of urban areas reveal the existence of 30 to 60 spectral dimensions. The extent to which broadband imagery collected by operational satellites can represent the higher dimensional mixing space is a function of both the spatial and spectral resolution of the sensor. AVIRIS imagery offers the spatial and spectral resolution necessary to investigate the scale dependence of the spectral dimensionality. Dimensionality estimates derived from Minimum Noise Fraction (MNF) eigenvalue distributions show a distinct scale dependence for AVIRIS radiance measurements of Milpitas, California. Apparent dimensionality diminishes from almost 40 to less than 10 spectral dimensions between scales of 8000 m and 300 m. The 10 to 30 m scale of most features in urban mosaics results in substantial spectral mixing at the 20 m scale of high altitude AVIRIS pixels. Much of the variance at pixel scales is therefore likely to result from actual differences in surface reflectance at pixel scales. Spatial smoothing and spectral subsampling of AVIRIS spectra both result in substantial loss of information and reduction of apparent dimensionality, but the primary spectral endmembers in all cases are analogous to those found in global analyses of Landsat and Ikonos imagery of other urban areas.

Small, Christopher↗

NASA NTRS2020

Temperature-Dependent Changes in the Normal Albedo of the Lunar Surface at 1064 nm

Over the extreme temperature variations experienced in a single lunar day (∆T≈300 K), particular minerals common to the lunar surface show spectral changes at near-infrared wavelengths in laboratory settings (Singer and Roush, 1985; Roush and Singer, 1986, 1987). Variations in temperature can cause variations in the size and shape of crystallographic sites, which control the position, shape, and depth of crystal field absorptions. At an observation wavelength of 1064 nm, the Lunar Orbiter Laser Altimeter (LOLA) should be highly sensitive to temperature-dependent changes of orthopyroxene. Here we analyze temperature-dependent spectral changes of the lunar surface as measured from orbit by LOLA. We couple LOLA measurements of normal albedo with measurements of surface temperature from the Diviner Lunar Radiometer Experiment, analyzing the maria and highlands terranes between ±50° in 1°x1° spatial bins. We provide the first evidence of temperature-dependent spectral changes on the lunar surface from orbital observations, finding that the majority of the lunar surface between 50°N and 50°S demonstrates a small, yet measurable, negative change in normal albedo at 1064 nm with temperature (-∆R/∆T). The measurable effect is on the order of a few percent change in reflectance per ~80 K, indicating temperature changes do not have a large effect on measurements of albedo at the sensitivity of the LOLA instrument. Stronger -∆R/∆T values tend to be associated with the maria, and regions with elevated orthopyroxene levels. Our results suggest that single-wavelength lasers may be powerful tools for understanding the distribution of particular minerals on the lunar surface.

Ariel N Deutsch↗

NASA NTRS2023

Future Atmospheric Rivers and Impacts on Precipitation: Overview of the ARTMIP Tier2 High-Resolution Global Warming Experiment

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth’s hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common dataset consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection.

Atmospheric Rivers↗

NASA NTRSDate not supplied

Investigating the Numerical Stability of Using an Impedance Boundary Condition to Model Broadband Noise Scattering With Acoustic Liners

Reducing aircraft noise is a major objective in the field of computational aeroacoustics. When designing next generation quiet aircraft, it is important to be able to accurately and efficiently predict the acoustic scattering by an aircraft body from a given noise source. Acoustic liners are an effective tool for achieving aircraft noise reduction and are characterized by a frequency-dependent impedance value. Converted into the time-domain using Fourier transforms, an impedance boundary condition can be used to simulate the acoustic wave scattering by geometric bodies treated with acoustic liners. A Broadband Impedance Model will be discussed in which the liner impedance is specified along a wide range of frequencies. The liner impedance boundary condition will be derived and coupled with a time-domain boundary integral equation to model acoustic scattering by a flat plate consisting of both un-lined and lined surfaces. The scattering solution will be obtained iteratively using both spatial and temporal basis functions and the stability will be demonstrated through eigenvalue analysis. Stability will be assessed for its dependence on time step, spatial discretization, as well as temporal basis function order. Both second- and third-order Lagrange temporal basis functions are considered.

acoustics↗

NASA NTRSDate not supplied

Investigating the Numerical Stability of Using an Impedance Boundary Condition to Model Broadband Noise Scattering With Acoustic Liners

acoustics↗

NASA NTRS1991

The measurement of atmospheric water vapor - Radiometer comparison and spatial variations

Two water vapor radiometer (WVR) experiments were conducted to evaluate whether such instruments are both suitable and necessary to correct for propagation effects that are induced by precipitable water vapor (PWV) on signals from GPS and VLBI. WVRs are suitable for these corrections if they provide wet path delays to better than 0.5 cm. They are needed if spatial variations of PWV result in complicated, direction-dependent propagation effects that are too complex to be parametrized in the GPS or VLBI geodetic solution. The suitability of radiometers was first addressed by comparing six airport WVRs for two weeks. While two WVRs showed an average wet path delay bias of only 0.1 cm, others were biased by 1-3 cm relative to each other and relative to radiosondes. The second experiment addressed the question whether radiometers are needed for the detection of inhomogeneities in the wet delay. Three JPL D-series radiometers were operated at three sites 50 km apart. The WVRs simultaneously sampled PWV at different azimuths and elevations in search of spatial variations of PWV. On one day of this second experiment evidence was found for spatial variations of the wet path delay as high as 20 percent of the total wet path delay.

Rocken, C.↗

NASA NTRS1991

Visual sensitivity to spatially sampled modulation in human observers

Thresholds were measured for detecting spatial luminance modulation in regular lattices of visually discrete dots. Thresholds for modulation of a lattice are generally higher than the corresponding threshold for modulation of a continuous field, and the size of the threshold elevation, which depends on the spacing of the lattice elements, can be as large as a one log unit. The largest threshold elevations are seen when the sample spacing is 12 min arc or greater. Theories based on response compression cannot explain the further observation that the threshold elevations due to spatial sampling are also dependent on modulation frequency: the greatest elevations occur with higher modulation frequencies. The idea that this is due to masking of the modulation frequency by the spatial frequencies in the sampling lattice is considered.

Mulligan, Jeffrey B.↗

NASA NTRS1976

Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

Sadowski, F. E.↗

NASA NTRS1980

The spatial-temporal ambiguity in auroral modeling

The paper examines the time-dependent models of the aurora which show that various ionospheric parameters respond to the onset of auroral ionization with different time histories. A pass of the Atmosphere Explorer C satellite over Poker Flat, Alaska, and ground based photometric and photographic observations have been used to resolve the time-space ambiguity of a specific auroral event. The density of the O(+), NO(+), O2(+), and N2(+) ions, the electron density, and the electron temperature observed at 280 km altitude in a 50 km wide segment of an auroral arc are predicted by the model if particle precipitation into the region commenced about 11 min prior to the overpass.

Rees, M. H.↗

NASA NTRSDate not supplied

Optimizing spectroheliogram deconvolution methods : MaGIXS - A case study

Over the past five years, new methods to reconstruct spectrally pure maps of the Sun from spectroheliogram data have emerged. In particular, with the rebirth of long-abandoned slitless imaging spectroscopy, one may obtain both spatial and spectral information over a large field of view simultaneously. Yet, depending on the size of the extended source combined with the extent of spectral dispersion, there will be locations in the focal plane where spectral lines from different spatial locations overlap and must be deconvolved. An unfolding method has been successfully developed and demonstrated on the recent rocket flight MaGIXS, which observed several strong emission lines (9 to 30$\AA$) from different portions of two active regions. In the work we are going to present, we conduct a systematic investigation of the parameters that controls and optimizes the inversion method to unfold overlappogram data. We also demonstrate a derived method of the inversion that does not require previous assumptions on the thermal and ionization equilibrium and abundance state of the plasma.

Spectroheliogram↗

NASA NTRS2006

Estimating Temperature Retrieval Accuracy Associated With Thermal Band Spatial Resolution Requirements for Center Pivot Irrigation Monitoring and Management

This study explores the use of synthetic thermal center pivot irrigation scenes to estimate temperature retrieval accuracy for thermal remote sensed data, such as data acquired from current and proposed Landsat-like thermal systems. Center pivot irrigation is a common practice in the western United States and in other parts of the world where water resources are scarce. Wide-area ET (evapotranspiration) estimates and reliable water management decisions depend on accurate temperature information retrieval from remotely sensed data. Spatial resolution, sensor noise, and the temperature step between a field and its surrounding area impose limits on the ability to retrieve temperature information. Spatial resolution is an interrelationship between GSD (ground sample distance) and a measure of image sharpness, such as edge response or edge slope. Edge response and edge slope are intuitive, and direct measures of spatial resolution are easier to visualize and estimate than the more common Modulation Transfer Function or Point Spread Function. For these reasons, recent data specifications, such as those for the LDCM (Landsat Data Continuity Mission), have used GSD and edge response to specify spatial resolution. For this study, we have defined a 400-800 m diameter center pivot irrigation area with a large 25 K temperature step associated with a 300 K well-watered field surrounded by an infinite 325 K dry area. In this context, we defined the benchmark problem as an easily modeled, highly common stressing case. By parametrically varying GSD (30-240 m) and edge slope, we determined the number of pixels and field area fraction that meet a given temperature accuracy estimate for 400-m, 600-m, and 800-m diameter field sizes. Results of this project will help assess the utility of proposed specifications for the LDCM and other future thermal remote sensing missions and for water resource management.

Ryan, Robert E.↗

NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗

NASA NTRS1985

Utilizing remote sensing of Thematic Mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

The purpose of the project is to refine and validate a probabilistic spatial computer model through the analyses of thematic mapper imagery. The model is designed to determine how the interface between marshland and water changes as marshland is converted to water in a disintegrating marsh. Coastal marshland in Louisiana is disintegrating at the rate of approximately 40 sq mi a year, and an evaluation of the potential impact of this loss on the landings of estuarine-dependent fisheries is needed by fisheries managers. Understanding how marshland-water interface changes as coastal marshland is lost is essential to the process of evaluating fisheries effects, because several studies suggest that the production of estuarine-dependent fish and shellfish may be more closely related to the interface between marshland and water than to acreage of marshland. The need to address this practical problem has provided an opportunity to apply some scientifically interesting new techniques to the analyses of satellite imagery. Progress with the development of these techniques is the subject of this report.

Browder, Joan A.↗

NASA NTRS1993

Theory of Metastable State Relaxation for Non-Critical Binary Systems with Non-Conserved Order Parameter

A new mathematical ansatz for a solution of the time-dependent Ginzburg-Landau non-linear partial differential equation is developed for non-critical systems such as non-critical binary solutions (solute + solvent) described by the non-conserved scalar order parameter. It is demonstrated that in such systems metastability initiates heterogeneous solute redistribution which results in formation of the non-equilibrium singly-periodic spatial solute structure. It is found how the time-dependent period of this structure evolves in time. In addition, the critical radius r(sub c) for solute embryo of the new solute rich phase together with the metastable state lifetime t(sub c) are determined analytically and analyzed.

Izmailov, Alexander↗

NASA NTRS2015

Simulation and Analysis of Technology and Operational Procedures to Reduce the Combined Effects of Emissions and Contrails

The development and evaluation of concepts and technology to support future air traffic management systems require a hierarchy of models ranging from real-time simulations to extensive field evaluations. Air traffic simulation models such as Airspace Concept Evaluation System, Center Tracon Automation System, Future Air traffic management Concept Evaluation Tool and others are used to design air traffic systems balancing the conflicting objectives of maximizing safety, meeting future demands for airports and airspace and increase efficiency of traffic flows in the presence of uncertain weather. The impact of aviation emissions and contrails on climate imposes another constraint on the design of aircraft and aviation operations. The understanding of the complex interaction between physical climate system, the carbon and other greenhouse gas emissions and aviation activity can be improved by the development of integrated assessment models that include emission and climate models together with air traffic simulations. The modeling of aircraft emissions and their interaction with each other to change the concentration levels of different gasses in the atmosphere and the resulting impact of the radiative forcing on the equilibrium of the Earth's atmosphere is complex and requires the use of coupled atmosphere-ocean general circulation models together with three-dimensional models of carbon cycle and chemistry of other non-CO2 greenhouse gases. These models are computationally intensive and unsuitable for studies involving the generation of multiple scenarios. Simple emission and climate models, based on the input-output relations of linear systems, capture the fundamental emission to climate impact behavior by careful selection of key variables and their dynamics. The impact of various greenhouse gases depends on the total concentration, effect per unit change in atmospheric concentration and the spatial distribution of the gas. All these quantities are influenced by the lifetime of the gas. The impact of a greenhouse gas depends on the interval of assessment, which may vary from a few decades to a few centuries. Climate metrics are aimed at providing a common scale to compare different greenhouse gases. If the metrics are to be used as a tool in developing and evaluating aviation operations, they should be transparent and easy to apply. Global Warming Potential and Aggregate Global Temperature Potential are some of the commonly used metrics. This paper integrates a national-level air traffic simulation and optimization capability with simple climate models and carbon cycle models, and climate metrics to assess the impact of aviation on climate. The capability brings together metrics, which are useful in aviation operations together with metrics used in climate studies. The capability can be used to make trade-offs between extra fuel cost and reduction in climate impact. There is considerable uncertainty in our understanding of the radiative forcing associated with emissions and contrails. The parameters in the simulation can be used to evaluate the effect of various uncertainties in emission models and contrails. It can also be used to evaluate the impact of different decision horizons. Alternatively, the optimization results from the simulation can be used as inputs to other tools that monetize global climate impacts like the FAA's Aviation Environmental Portfolio Management Tool for Impacts.

impact of aviation on the environment↗