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NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗
NASA NTRSDate not supplied

GeoAI Advances in Specific Landform Mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation.

machine learning↗
NASA NTRS2012

Sofia Observatory Performance and Characterization

The Stratospheric Observatory for Infrared Astronomy (SOFIA) has recently concluded a set of engineering flights for Observatory performance evaluation. These in-flight opportunities have been viewed as a first comprehensive assessment of the Observatory's performance and will be used to address the development activity that is planned for 2012, as well as to identify additional Observatory upgrades. A series of 8 SOFIA Characterization And Integration (SCAI) flights have been conducted from June to December 2011. The HIPO science instrument in conjunction with the DSI Super Fast Diagnostic Camera (SFDC) have been used to evaluate pointing stability, including the image motion due to rigid-body and flexible-body telescope modes as well as possible aero-optical image motion. We report on recent improvements in pointing stability by using an Active Mass Damper system installed on Telescope Assembly. Measurements and characterization of the shear layer and cavity seeing, as well as image quality evaluation as a function of wavelength have been performed using the HIPO+FLITECAM Science Instrument configuration (FLIPO). A number of additional tests and measurements have targeted basic Observatory capabilities and requirements including, but not limited to, pointing accuracy, chopper evaluation and imager sensitivity. SCAI activities included in-flight partial Science Instrument commissioning prior to the use of the instruments as measuring engines. This paper reports on the data collected during the SCAI flights and presents current SOFIA Observatory performance and characterization.

Temi, Pasquale↗
NASA NTRS1991

A cloud, precipitation and electrification modeling effort for COHMEX

In mid-1987, the Modeling Group of the Institute of Atmospheric Sciences (IAS) began to simulate and analyze cloud runs that were made during the Cooperative Huntsville Meteorological Experiment (COHMEX) Project and later. The cloud model was run nearly every day during the summer 1986 COHMEX Project. The Modeling Group was then funded to analyze the results, make further modeling tests, and help explain the precipitation processes in the Southeastern United States. The main science objectives of COHMEX were: (1) to observe the prestorm environment and understand the physical mechanisms leading to the formation of small convective systems and processes controlling the production of precipitation; (2) to describe the structure of small convective systems producing precipitation including the large and small scale events in the environment surrounding the developing and mature convective system; (3) to understand the interrelationships between electrical activity within the convective system and the process of precipitation; and (4) to develop and test numerical models describing the boundary layer, tropospheric, and cloud scale thermodynamics and dynamics associated with small convective systems. The latter three of these objectives were addressed by the modeling activities of the IAS. A series of cloud modes were used to simulate the clouds that formed during the operational project. The primary models used to date on the project were a two dimensional bulk water model, a two dimensional electrical model, and to a lesser extent, a two dimensional detailed microphysical cloud model. All of the models are based on fully interacting microphysics, dynamics, thermodynamics, and electrical equations. Only the 20 July 1986 case was analyzed in detail, although all of the cases run during the summer were analyzed as to how well they did in predicting the characteristics of the convection for that day.

Orville, Harold D.↗
NASA NTRS1997

Non-standard amino acid recognition by Escherichia coli leucyl-tRNA synthetase

Recombinant E. coli leucyl-tRNA synthetase was screened for amino acid-dependent pyrophosphate exchange activity using noncognate aliphatic amino acids including norvaline, homocysteine, norleucine, methionine, and homoserine. [32P]-labeled reaction products were separated by thin layer chromatography using a novel solvent system and then quantified by phosphorimaging. Norvaline which differs from leucine by only one methyl group stimulated pyrophosphate exchange activity as did both homocysteine and norleucine to a lesser extent. The KM parameters for leucine and norvaline were measured to be 10 micromoles and 1.5 mM, respectively. Experiments are in progress to determine if norvaline is transferred to tRNA(Leu) and/or edited by a pre- or post-transfer mechanism.

Non-NASA Center↗
NASA NTRS1975

Shallow lunar structure determined from the passive seismic experiment

Data relevant to the shallow structure of the moon obtained at the Apollo seismic stations are compared with previously published results of the active seismic experiments. It is concluded that the lunar surface is covered by a layer of low seismic velocity which appears to be equivalent to the lunar regolith defined previously by geological observations. This layer is underlain by a zone of distinctly higher seismic velocity at all of the Apollo landing sites. The regolith thicknesses at the Apollo 11, 12, and 15 sites are estimated from the shear-wave resonance to be 4.4, 3.7, and 4.4 m, respectively. These thicknesses and those determined at the other Apollo sites by the active seismic experiments appear to be correlated with the age determinations and the abundances of extralunar components at the sites.

Nakamura, Y.↗
NASA NTRS2020

An MBSE-based Approach to Architecting a Robotic Sample Capture System Concept for Potential Mars Sample Return

A model-based systems engineering (MBSE) approach was applied to architecting an orbiting sample Capture and Orient Module (COM) system concept for a Capture, Contain, and Return System (CCRS) payload concept for the notional Mars Sample Return (MSR) campaign at the NASA Jet Propulsion Laboratory. An architecture framework was established, covering multiple organizational layers of the system, along with structural, behavioral, data, and requirements perspectives. A workflow process to implement the architecting activities within the COM engineering team was established. The approach helped maintain consistency in terminology, helped ensure alignment of structural, behavioral, data, and requirements elements within each organization layer, and guided the engineering team through an architecting process that helped develop the architecture for a Capture and Orient Module system concept.

Younse, Paulo↗
NASA NTRS1978

Determination of oxygen in silicon and carbide by activation with 27.2 meV alpha particles

The Si sample was polished on one side, and on the other side Ni was applied chemically and soldered with Pb to a water cooled Cu substrate. Optical quartz standard was fixed from the other side. Si carbide samples were soldered to a substrated with In. The prepared samples were irradiated in a cyclotron with a 27.2 MeV alpha particle beam. The layers were removed from the Si and Si carbide samples by grinding and the positron activity of F-18(t sub 1/2 110 min) was measured by using a gamma, gamma coincidence spectrometer with two NaI(TI) crystals. For analysis of Si carbide, the activity decay curve of the samples was recorded to find the contribution of the positron activity of Cu-65(t sub 1/2 12.9 hr) which formed from Ni impurity on irradiation.

Dolgolenko, A. P.↗
NASA NTRS2007

Active-Adaptive Control of Inlet Separation Using Supersonic Microjets

Flow separation in internal and external flows generally results in a significant degradation in aircraft performance. For internal flows, such as inlets and transmission ducts in aircraft propulsion systems, separation is undesirable as it reduces the overall system performance. The aim of this research has been to understand the nature of separation and more importantly, to explore techniques to actively control it. In this research, we extended our investigation of active separation control (under a previous NASA grant) where we explored the use of microjets for the control of boundary layer separation. The geometry used for the initial study was a simple diverging Stratford ramp, equipped with arrays of microjets. These early results clearly show that the activation of microjets eliminated flow separation. Furthermore, the velocity-field measurements, using PIV, also demonstrate that the gain in momentum due to the elimination of separation is at least an order of magnitude larger (two orders of magnitude larger in most cases) than the momentum injected by the microjets and is accomplished with very little mass flow through the microjets. Based on our initial promising results this research was continued under the present grant, using a more flexible model. This model allows for the magnitude and extent of separation as well as the microjet parameters to be independently varied. The results, using this model were even more encouraging and demonstrated that microjet control completely eliminated significant regions of flow separation over a wide range of conditions with almost negligible mass flow. Detailed studies of the flowfield and its response to microjets were further examined using 3-component PIV and unsteady pressure measurements, among others. As the results presented this report will show, microjets were successfully used to control the separation of a much larger extent and magnitude than demonstrated in our earlier experiments. In fact, using the appropriate combination of control parameters (microjet, location, angle and pressure) separation was completely eliminated for the largest separated flowfield we could generate with the present model. Separation control also resulted in a significant reduction in the unsteady pressures in the flow where the unsteady pressure field was found to be directly responsive to the state of the flow above the surface. Hence, our study indicates that the unsteady pressure signature is a strong candidate for a flow state sensor , which can be used to estimate the location, magnitude and other properties of the separated flowfield. Once better understood and properly utilized, this behavior can be of significant practical importance for developing and implementing online control.

Alvi, Farrukh S.↗
NASA NTRS1986

Stability and control of compressible flows over a surface with concave-conves curvature

The active control of spatially unstable disturbances in a laminar, two-dimensional, compressible boundary layer over a curved surface is numerically simulated. The control is effected by localized time-periodic surface heating. We consider two similar surfaces of different heights with concave-convex curvature. In one, the height is sufficiently large so that the favorable pressure gradient is sufficient to stabilize a particular disturbance. In the other case the pressure gradient induced by the curvature is destabilizing. It is shown that by using active control that the disturbance can be stabilized. The results demonstrate that the curvature induced mean pressure gradient significantly enhances the receptivity of the flow localized time-periodic surface heating and that this is a potentially viable mechanism in air.

Maestrello, L.↗
NASA NTRS1988

Tropospheric nitric oxide measurements over the Amazon Basin

Horizontal and vertical distributions of nitric oxide (NO) were measured over the Amazon Basin during NASA's Global Tropospheric Experiment (GTE) Amazon Boundary Layer Experiment mission in July-August 1985. During transit flights between the Virginia coast and Manaus, Brazil, NO mixing ratios were 12-15 pptv at 5 km altitude. Values up to 200 pptv were observed in electrically active clouds. During longitudinal surveys over the Amazon region, NO mixing ratios in the lower planetary boundary layer decreased from 25-60 pptv over the central basin to 10-12 pptv in coastal regions. In the convective cloud layer or free troposphere, NO mixing ratios averaged 13 pptv in regions not influenced by biomass burning. No longitudinal trend above the mixed layer could be detected. A steep negative gradient with increasing altitude was noted within the mixed layer before midday. Mixing ratios decreased from 60 pptv at 0.2 km to about 15 pptv at the top of the mixed layer (1 km). By midday, growth of the mixed layer height and enhanced mixing reduced the gradient, and evidence for mixing of NO into the convective cloud layer was noted.

Torres, A. L.↗
NASA NTRS2017

Active Chevrons for Jet Noise Reduction

Jet noise is often a dominant component of aircraft noise, particularly at takeoff. To meet the stringent noise regulations, the aircraft industry is in a pressing need of advanced noise reduction concepts. In the present study, the potential of piezoelectrically-activated chevrons for jet noise reduction was experimentally investigated. The perturbations near the nozzle exit caused by piezoelectrically-activated chevrons could be used to modify the growth rate of the mixing layer and thereby potentially reduce jet noise. These perturbations are believed to increase the production of small-scale disturbances at the expense of large-scale turbulent structures. These large-scale turbulent structures are responsible for the dominant portion of the jet mixing noise, particularly low-frequency noise. Therefore, by exciting the static chevron geometry through piezoelectric actuators, an additional acoustic benefit could possibly be achieved. To aid in the initial implementation of this concept, several flat-faced faceted nozzles (four, six, and eight facets) were investigated. Among the faceted nozzles, it was found that the eight-faceted nozzle behaves very similarly to the round nozzle. Furthermore, among the faceted nozzles with static chevrons, the four-faceted nozzle with static chevrons was found to be most effective in terms of jet noise reduction. The piezoelectrically-activated chevrons reduced jet noise up to 2 dB compared to the same nozzle geometry without excitation. This benefit was observed over a wide range of excitation frequencies by applying very low voltages to the piezoelectric actuators.

Depuru-Mohan, N. K.↗
NASA NTRS1985

Control plate for shock-boundary layer interaction

Paper describes tests and computations for a relatively unique technique to greatly reduce/eliminate the separation region for shock-boundary layer interactions. A number of studies have shown that the usual effects of such interactions include increased local heating and wall pressures, thickening of the boundary layer and a decrease in the momentum of the flow and, for stronger waves, flow separation. This flow situation is particularly prevalent in supersonic and hypersonic inlets where severe performance degradation can occur due to flow separation. High performance engine design generally requires a uniform entering flow field with little stagnation pressure loss. Previous approaches to the problem involved primarily active devices (e.g., suction or blowing); the present paper considers a passive device. The boundary layer separation control technique considered herein involves the placement of an embedded plate in the outer portion of the boundary layer and parallel to the wall. This control plate is situated such that the incident shock impinges upon and reflects from its surface, thus greatly lessening the pressure gradient in the low momentum near wall region.

Goodman, W. L.↗
NASA NTRS1987

Linear baroclinic instability in the presence of large scale topography

The effect of a planetary-scale, wavenumber 2 topography on baroclinically active disturbances is investigated for a channel domain in a two-layer, quasi-geostrophic context. When the lower-layer zonal velocity is nonzero, the topography influences the disturbances by forcing a stationary wave, and the topography and the forced wave influence the growth rates and the spatial structures of the time-dependent solutions. The case of zero zonal velocity in the lower layer was also investigated, for which no forced wave exists. Asymptotic forms of the equations, valid when the topographic effect (governed by the ratio of the nondimensional topographic height to the rotational Froude number) is small, are used to obtain both the stationary and time-dependent solutions. The latter are also obtained using a numerical approach, in which is determined the eigenvalues and eigenfunctions of a matrix representing the dynamical equations. Agreement is good between the two approaches. Recent laboratory experiments with a baroclinic annulus in which there is a false bottom with wavenumber 2 topography, are used to select governing parameters. The simultaneous presence of a stationary forced wave of wavenumber 2 and a time-dependent baroclinic wave of wavenumber 4, which has wavenumber 2 and 6 sidebands due to the topography, yields a flow field that exhibits some principal features of the laboratory experiments. The position of the forced wave and the location of an excursion in latitude of the storm track show qualitative resemblance to those features observed in the atmosphere.

Reynolds, Nathaniel Dunton↗
NASA NTRS1990

Lidar observations of mesospheric Fe and sporadic Fe layers at Urbana, Illinois

Lidar measurements of mesospheric Fe at Urbana, Illinois were conducted during 4 nights in October, 1989. The average Fe abundances were in the range (1.0-2.0) X 10 to the 10/sq cm. The layer centroid heights range between 89.0 and 90.5 km and the rms widths vary between 3.2 and 4.1 km. Considerable gravity wave and tidal activity are observed in the Fe profiles. The observations are compared with previous measurements of mesospheric Fe and with observations of sporadic Na (Na/S/) layers.

Bills, Richard E.↗