SEARCH · Engineering Papers

Results for “data inference”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,243 records · Page 69

All sources NASA NTRS DOE OSTI

NASA NTRS2013

Monte Carlo Bayesian Inference on a Statistical Model of Sub-gridcolumn Moisture Variability Using High-resolution Cloud Observations : Sensitivity Tests and Results - Part II

Part I presented a Monte Carlo Bayesian method for constraining a complex statistical model of GCM sub-gridcolumn moisture variability using high-resolution MODIS cloud data, thereby permitting large-scale model parameter estimation and cloud data assimilation. This part performs some basic testing of this new approach, verifying that it does indeed significantly reduce mean and standard deviation biases with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud top pressure, and that it also improves the simulated rotational-Ramman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the OMI instrument. Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows finite jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. This paper also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in the cloud observables on cloud vertical structure, beyond cloud top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard (1998) provides some help in this respect, by better honoring inversion structures in the background state.

Monte Carlo Bayesian inference↗

NASA NTRS1992

Laboratory evaluation and application of microwave absorption properties under simulated conditions for planetary atmospheres

Radio absorptivity data for planetary atmospheres obtained from spacecraft radio occultation experiments and earth-based radio astronomical observations can be used to infer abundances of microwave absorbing atmospheric constituents in those atmospheres, as long as reliable information regarding the microwave absorbing properties of potential constituents is available. The use of theoretically-derived microwave absorption properties for such atmospheric constituents, or using laboratory measurements of such properties under environmental conditions which are significantly different than those of the planetary atmosphere being studied, often leads to significant misinterpretation of available opacity data. For example, laboratory measurements performed by Fahd and Steffes have shown that the opacity from gaseous SO2 under simulated Venus conditions can be well described by the Van Vleck-Weisskopf lineshape at wavelengths shortward of 2 cm, but that the opacity of wavelengths greater than 2 cm is best described by a different lineshape that was previously used in theoretical predictions. The recognition of the need to make such laboratory measurements of simulated planetary atmospheres over a range of temperatures and pressures which correspond to the altitudes probed by both radio occultation experiments and radio astronomical observations, and over a range of frequencies which correspond to those used in both radio occultation experiments and radio astronomical observations, has led to the development of a facility at Georgia Tech which is capable of making such measurements. It has been the goal of this investigation to conduct such measurements and to apply the results to a wide range of planetary observations, both spacecraft and earth-based, in order to determine the identity and abundance profiles of constituents in those planetary atmospheres.

Steffes, Paul G.↗

NASA NTRS1976

Modal structure inferred from static far-field noise directivity

Turbofan noise directivity calculated for two directivity models was compared with experimental, blade passing frequency data from two fans at 60 and 90 percent speeds. Experimental data indicated similar directivity patterns which were well represented by a single average data curve. Calculated points using the equal amplitude model showed over-prediction near the fan axis and near the 90 degree position. Calculated points using the equal power model showed a very good match with the average data lending support to theory of equipartition of modal power from a random source such as the interaction of the rotor with inlet flow distortion. The equal modal power model also gave good agreement with individual data points.

Saule, A. V.↗

NASA NTRS1980

Infrared radiative properties of tropical cirrus clouds inferred with aircraft measurements

Longwave emissivities and the vertical profile of cooling rates of tropical cirrus clouds are determined using broadband hemispheric irradiance data. Additionally, a broadband mass absorption coefficient is defined and used to relate emissivity to water content. The data used were collected by the National Center for Atmospheric Research (NCAR) Sabreliner during the GARP Atlantic Tropical Experiment (GATE) in the summer of 1974. Three case studies are analyzed showing that these tropical cirrus clouds approached an emissivity of 1.0 within a vertical distance of 1.0 km. Broadband mass absorption coefficients ranging from 0.076 to 0.096 sq m per g are derived. A comparison of these results with other work suggests that tropical cirrus cloud emissivities may be significantly larger than heretofore believed. Ice water content of the clouds were deduced from data collected by a one-dimensional particle spectrometer. Analyses of the ice water content and the observed particle size distributions are presented.

Griffith, K. T.↗

NASA NTRS1983

Simulating sunflower canopy temperatures to infer root-zone soil water potential

A soil-plant-atmosphere model for sunflower (Helianthus annuus L.), together with clear sky weather data for several days, is used to study the relationship between canopy temperature and root-zone soil water potential. Considering the empirical dependence of stomatal resistance on insolation, air temperature and leaf water potential, a continuity equation for water flux in the soil-plant-atmosphere system is solved for the leaf water potential. The transpirational flux is calculated using Monteith's combination equation, while the canopy temperature is calculated from the energy balance equation. The simulation shows that, at high soil water potentials, canopy temperature is determined primarily by air and dew point temperatures. These results agree with an empirically derived linear regression equation relating canopy-air temperature differential to air vapor pressure deficit. The model predictions of leaf water potential are also in agreement with observations, indicating that measurements of canopy temperature together with a knowledge of air and dew point temperatures can provide a reliable estimate of the root-zone soil water potential.

Choudhury, B. J.↗

NASA NTRS1984

Participation in the Mars data analysis program: Global and regional studies of wind-indicators on the surface of Mars

Global and regional patterns on Mars were inferred from surface aeolian features, such as wind streaks and dune deposits, which were visible in Viking Orbiter images. Precise measurements of the dimensions of topographic obstacles, i.e., craters, hills, ridges, on Mars as well as their associated wind streaks were used to determine the aerodynamic shape of an obstacle affects near surface airflow. A classification of Martian wind streaks was developed on the basis of albedo contrast and the presence or absence of either topographic obstacles or sediment deposits at the point of origin of the wind streaks. It was concluded that local meteorological conditions, such as the stability of the atmospheric boundary layer, play a major role in determining why some Martian craters produce depositional wind streaks while others produce erosional ones.

Veverka, J.↗

NASA NTRS1986

Correlations between Nimbus-7 Scanning Multichannel Microwave Radiometer data and an antecedent precipitation index

Passive microwave brightness temperatures from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) can be used to infer the soil moisture content over agricultural areas such as the southern Great Plains of the United States. A linear regression analysis between three transforms of the five dual polarized SMMR wavelengths of 0.81, 1.36, 1.66, 2.80 and 4.54 cm and an antecedent precipitation index representing the precipitation history showed correlation coefficients greater than 0.90 for pixel aggregates of 25-50 km. The use of surface air temperatures to approximate the temperature of the emitting layer was not required to obtain high correlation coefficients between the transforms and the antecedent precipitation index.

Wilke, G. D.↗

NASA NTRS1989

The nature of cometary dust as determined from infrared observations

The infrared measurements of comets, the compositional information available from interplanetary dust particles (IDPs), and the recent results of flybys to Comet Halley can help in restricting the nature and composition of cometary dust models (c.f., Proceedings of the 20th ESLAB Symposium on Exploration of Halley's Comet, 1986). Researchers tried to incorporate some of these results into a coherent model to account for the observed cometary infrared emission. The presence of 10 and 3.4 micron features in Comet Halley (c.f. Bregman et al. 1987; Wickramasinghe and Allen 1986) indicated the presence of at least two components in the grain material, namely silicates and some form of amorphous carbon. These two components could reside in separate grains or may be parts of composite particles. Both these cases have been considered (see Krishna Swamy el a. 1988a, 1988b). In the absence of refractive index data for cometary analogs, the authors used the optical constants of olivine-rich lunar material 12009.48 (Perry et al. 1972) for the infrared region and that of alpha:C-H film for amorphous carbon (angus et al. 1986). For the visible region, a value of m = 1.38-0.39i was used for the silicates, and values published by Arakawa et al. (1985) were used for the amorphous carbon. These materials should give a representative behavior of the expected results. The model results were compared to observational data. The strength of the 3.4 micron and 10 micron features relative to the adjacent continuum, as well as the slope of the continuum between 2500 and 1250 cm(exp -1) (4 to 8 microns), were used as criteria for comparison. Model calculations with alpha approx. equals -3.5, and also the size distribution function inferred for Comet Halley, with a mass fraction (X) of silicate to amorphous carbon grains of about 40 to 1 can fit the data. A good match is obtained for the infrared spectra of Comets Halley and West from a 40 to 1 mixture of silicate and amorphous carbon grains with a a(exp -3.5) size distribution function. The results are consistent with compositional constraints provided by interplanetary dust particles (IPDs) and Halley flyby data. The variation of grain temperature with heliocentric distance appears to account for the major changes observed in cometary spectra.

Swamy, K. S. Krishna↗

NASA NTRS2014

Local ISM 3D Distribution and Soft X-ray Background Inferences for Nearby Hot Gas

Three-dimensional (3D) interstellar medium (ISM) maps can be used to locate not only interstellar (IS) clouds, but also IS bubbles between the clouds that are blown by stellar winds and supernovae, and are filled by hot gas. To demonstrate this, and to derive a clearer picture of the local ISM, we compare our recent 3D IS dust distribution maps to the ROSAT diffuse Xray background maps after removal of heliospheric emission. In the Galactic plane, there is a good correspondence between the locations and extents of the mapped nearby cavities and the soft (0.25 keV) background emission distribution, showing that most of these nearby cavities contribute to this soft X-ray emission. Assuming a constant dust to gas ratio and homogeneous 106 K hot gas filling the cavities, we modeled in a simple way the 0.25 keV surface brightness along the Galactic plane as seen from the Sun, taking into account the absorption by the mapped clouds. The data-model comparison favors the existence of hot gas in the solar neighborhood, the so-called Local Bubble (LB). The inferred mean pressure in the local cavities is found to be approx.9,400/cu cm K, in agreement with previous studies, providing a validation test for the method. On the other hand, the model overestimates the emission from the huge cavities located in the third quadrant. Using CaII absorption data, we show that the dust to CaII ratio is very small in those regions, implying the presence of a large quantity of lower temperature (non-X-ray emitting) ionized gas and as a consequence a reduction of the volume filled by hot gas, explaining at least part of the discrepancy. In the meridian plane, the two main brightness enhancements coincide well with the LB's most elongated parts and chimneys connecting the LB to the halo, but no particular nearby cavity is found towards the enhancement in the direction of the bright North Polar Spur (NPS) at high latitude. We searched in the 3D maps for the source regions of the higher energy (0.75 keV) enhancements in the fourth and first quadrants. Tunnels and cavities are found to coincide with the main bright areas, however no tunnel nor cavity is found to match the low-latitude b > or approx. 8deg, brightest part of the NPS. In addition, the comparison between the 3D maps and published spectral data favors a NPS central source region location beyond 230 pc, i.e. at larger distance than usually considered. Those examples illustrate the potential use of more detailed 3D distributions of the nearby ISM for the interpretation of the diffuse soft X-ray background.

Xray Emission↗

NASA NTRS1975

Apollo 12, 15, and 16 lunar surface magnetometer experiment data analysis

The polarization of magnetometer signals detected at the Apollo 15 Hadley site by the lunar surface magnetometer has been studied to determine the source of the signal anisotropy which is observed and caused by the polarization. Instrument and data chain malfunction (cross-talk) seems ruled out. The source appears real and apparently connected with the Imbrium basin using reasonable inferences regarding the electromagnetic structure of the Moon. A theory is developed using moons with holes and conducting caps where the Imbrium basin is; results of calculations are consistent, though not unique, in specifying an anomaly in the electrical conductivity underlying Mare Imbrium. Distinct differences are noted from plasma sheet and diamagnetic cavity transfer functions, but the lobes appear, as for all other data, not to be vacuum for study of the moon. A discussion is given of problems connected with transfer of data, software, and theoretical programs from NASA Ames Research Center to the University of Arizona, and a summary is given of the conversion from IBM to CDC formats.

Sonett, C. P.↗

NASA NTRS1985

Inference of high-latitude ionization and conductivity from AE-C measurements of auroral electron fluxes

Electron fluxes in the kilovolt energy range measured by the AE-C satellite have been used to infer the latitudinal distribution of ionization at high latitudes in the altitude range 90 to 200 km. These distributions are compared with simultaneous measurements of electron density by the Chatanika incoherent scatter radar. The study was based on more than 120 electron density profiles obtained on three different nights at magnetic local times between 0200 and 0400. The calculated distributions agree very well with the altitude and latitude distributions measured by the radar. The only obvious difference was the presence in the radar data of additional ionization produced by solar illumination. A comparison was also conducted of the height-integrated electrical conductivities computed from the measured profiles and those inferred from the satellite measurements. For Hall and Pedersen conductances greater than 5 mhos, the agreement is within 25 percent. The data were also used to deduce the altitude profile of the effective recombination coefficient. This profile agrees well with the results of previous studies.

Vondrak, R.↗

NASA NTRS1983

Possible lunar source areas of meteorite ALHA 81005 Geochemical remote sensing information

Antarctic meteorite ALHA 81005 is a regolith breccia apparently sent to earth by an impact event in the lunar highlands. Laboratory studies of this sample provide information that is used to understand the source region on the moon using remote sensing data. The meteorites low thorium content is inconsistent with thorium values measured for the central lunar nearside from orbit with Apollo gamma-ray spectrometers. Similarly, the mineral assemblages inferred from near-IR spectra of small impact craters on the lunar nearside do not exhibit the significant component of olivine and Fe-bearing feldspar that is observed in the meteorite spectra. The existing remote sensing data suggest the most probable source region for ALHA 81005 is the nearside limb or the lunar farside and that the composition of ALHA 81005 represents a surface unit that has not previously been extensively sampled.

Pieters, C. M.↗

NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗

NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗

NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗

NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗

NASA NTRSDate not supplied

International Satellite Cloud Climatology Project (ISCCP) Stage D1 3-Hourly Cloud Product - Revised Algorithm in Hierarchical Data Format (ISCCP_D1)

Since 1983 an international group of institutions has collected and analyzed satellite radiance measurements from up to five geostationary and two polar orbiting satellites to infer the global distribution of cloud properties and their diurnal, seasonal and interannual variations. The primary focus of the first phase of the project (1983-1995) was the elucidation of the role of clouds in the radiation budget (top of the atmosphere and surface). In the second phase of the project (1995 onwards) the analysis also concerns improving understanding of clouds in the global hydrological cycle. [Location=TROPOSPHERE] [Temporal_Coverage: Start_Date=1983-07-01; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=280 Km; Longitude_Resolution=280 Km; Temporal_Resolution=3 Hourly].

CLOUD LIQUID WATER PATH↗

NASA NTRSDate not supplied

International Satellite Cloud Climatology Project (ISCCP) Stage D2 Monthly Cloud Product - Revised Algorithm in Hierarchical Data Format (ISCCP_D2)

CLOUD TOP PRESSURE↗