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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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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Small Orbital Stereo Tracking Camera Technology Development

Any exploration vehicle assembled or Spacecraft placed in LEO or GTO must pass through this debris cloud and survive. Large cross section, low thrust vehicles will spend more time spiraling out through the cloud and will suffer more impacts.Better knowledge of small debris will improve survival odds. Current estimated Density of debris at various orbital attitudes with notation of recent collisions and resulting spikes. Orbital Debris Tracking and Characterization has now been added to NASA Office of Chief Technologists Technology Development Roadmap in Technology Area 5 (TA5.7)[Orbital Debris Tracking and Characterization] and is a technical gap in the current National Space Situational Awareness necessary to safeguard orbital assets and crews due to the risk of Orbital Debris damage to ISS Exploration vehicles. The Problem: Traditional orbital trackers looking for small, dim orbital derelicts and debris typically will stare at the stars and let any reflected light off the debris integrate in the imager for seconds, thus creating a streak across the image. The Solution: The Small Tracker will see Stars and other celestial objects rise through its Field of View (FOV) at the rotational rate of its orbit, but the glint off of orbital objects will move through the FOV at different rates and directions. Debris on a head-on collision course (or close) will stay in the FOV at 14 Km per sec. The Small Tracker can track at 60 frames per sec allowing up to 30 fixes before a near-miss pass. A Stereo pair of Small Trackers can provide range data within 5-7 Km for better orbit measurements.

Bryan, Tom↗
NASA NTRS2018

Infrared Band Strengths for Amorphous and Crystalline Methyl Propionate, a Candidate Interstellar Molecule

Mid-infrared spectra of amorphous and crystalline methyl propionate, CH3CH2COOCH3, are presented for the first time from a single laboratory, along with measurements of the refractive index of each solid form. Density estimates are made and IR band strengths and absorption coefficients are calculated. Vapor pressures of crystalline methyl propionate at 140-150 K are reported along with an enthalpy of sublimation. Spectroscopic results are compared to a recent study of this compound, and the phase of methyl propionate in that work is identified. Several applications are described.

Astrochemistry↗
NASA NTRS2019

Architecture and Dynamics of Kepler's Multi-Transiting Planet Systems.III. Comprehensive Investigation Using All Four Years of Kepler Mission Data

We study the orbital architectures of planetary systems orbiting within ∼1 AU of their stars by analyzing the ensemble of Kepler systems having two or more planet candidates. We use data from the entire Kepler mission, and in many cases we apply improved analysis techniques (e.g., replacing histograms by top-hat Kernel Density Estimators that avoid the loss of information resulting from choosing a particular phase for the bin boundaries) to extend and enhance the studies of Lissauer et al. (2011, ApJS 197, 8) and Fabrycky et al. (2014, ApJ 790, 146).These data show ~ 1700 transiting planet candidates in > 600 multiple-planet systems, far more than were available for our previous two studies. The increased numbers and better information about planetary radii and the properties of stellar hosts made possible by Gaia DR2 allow more statistically-robust analyses of the entire ensemble of Kepler multis as well as independent analyses of subsets of the population. We are thus able to contrast the dynamical configurations of small and large planets, short-period and longer-period planets, and planets orbiting various types of host stars. We reinforce our previous findings that most pairs of planets within the same system are neither in nor near low-order mean motion resonances and that there is a substantial excess of planets having period ratios slightly larger than those of first-order mean-motion resonances. However, neglecting three systems whose planets are locked in 3- body resonances and summing over all first-order mean motion resonances, the deficit of planet pairs with period ratios just narrow of resonance is as large as the excess of planets wide of resonance (within statistical uncertainties), suggesting that overall there is no overall excess of planet pairs in the vicinity of resonance. Other aspects of our study, including estimates of the typical relative inclinations of planetary orbits and their variations as functions of orbital period, planet sizes and stellar properties, are in progress, with results expected to be available for presentation by the time of the conference.

Lissauer, Jack↗
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 NTRS2021

Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana

Low-cost sensors (LCSs) for air quality monitoring have enormous potential to improve air quality data coverage in resource-limited parts of the world such as sub-Saharan Africa. LCSs, however, are affected by environment and source conditions. To establish high-quality data, LCSs must be collocated and calibrated with reference grade PM2.5 monitors. From March 2020, a low-cost PurpleAir PM2.5 monitor was collocated with a Met One Beta Attenuation Monitor 1020 in Accra, Ghana. While previous studies have shown that multiple linear regression (MLR) and random forest regression (RF) can improve accuracy and correlation between PurpleAir and reference data, MLR and RF yielded suboptimal improvement in the Accra collocation (R2 = 0.81 and R2 = 0.81, respectively). We present the first application of Gaussian mixture regression (GMR) to air quality data calibration and demonstrate improvement over traditional methods by increasing the collocated PM2.5 correlation and accuracy to R2 = 0.88 and MAE = 2.2 μg/cu. m. Gaussian mixture models (GMMs) are a probability density estimator and clustering method from which nonlinear regressions that tolerate missing inputs can be derived. We find that even when given missing inputs, GMR provides better correlation than MLR and RF performed with complete data. GMR also allows us to estimate calibration certainty. When evaluated, 95% confidence intervals agreed with reference PM2.5 data 96% of the time, suggesting that the model accurately assesses its own confidence. Additionally, clustering within the GMM is consistent with climate characteristics, providing confidence that the calibration approach can learn underlying relationships in data.

Sensors↗
NASA NTRS2022

The Origins of Long-Term Variability in Martian Upper Atmospheric Densities

We quantify and interpret the long-term variability of dayside Martian upper thermosphere and lower exosphere densities within 180–275 km altitudes. Atmospheric CO2, N2, O, and Ar densities are from NASA Mars Atmosphere and Volatile EvolutioN (MAVEN) observations during the time period of 2015–2020 near solar minimum. These neutral measurements, together with contemporaneous solar irradiance measurements at Mars, enable disentanglement of the orbital effect (due to the annual Sun-Mars distance change with solar longitude) and the solar extreme ultraviolet (EUV) effect in atmospheric density variations. The relative importance of these two effects, which is obtained using a statistical method of Dominance Analysis, reveals the competition between the indirect effect of solar infrared (via the upward coupling from the middle atmosphere) and the direct effect of solar EUV (due to local heating). Our results show that, unlike the orbital effect which is relatively constant at low altitudes and then decreases with increasing altitude, the solar EUV effect nearly monotonically increases. These two effects are comparable at high altitudes (about 240/270/205 km for CO2/N2/O). This analysis is extended to include long-term exospheric mass density estimates near 400 km from Mars Global Surveyor and Mars Odyssey data, with a focus on representative solar cycle phases of solar minimum and maximum. It is found that near 400 km, the orbital effect is always a key driver regardless of the solar cycle phase, while the solar EUV effect plays a minor role during solar minimum and is greatly enhanced and slightly exceeds the orbital effect during solar maximum.

Xiaohua Fang↗
NASA NTRSDate not supplied

New Solid Propellant Additives: Evaluation for NASA Mission Use Cases

Researchers and small companies have been investigating and developing new solid rocket motor propellant ingredients with renewed vigor. Lithium-aluminum alloy is an ingredient of particular interest, but most of the advertisement about its benefits are focused on defense tactical rocket applications. This paper develops a framework to evaluate this and other propellants against standard aluminized composite propellant for mass-limited missions like many NASA in-space applications. It shows a way to estimate density’s effect with an exponent less than 1 depending on mission and technology parameters, and an algorithm for directly calculating delta-velocity or payload performance improvement.

Lithium-aluminum↗
NASA NTRS1979

Estimations of electron densities and temperatures in He-3 dominated plasmas

It is very useful to have a method of estimation for electron temperature and electron densities in nuclear pumped plasmas because measurements of such quantities are very difficult. This paper describes a method, based on rate equation analysis of the ionized species in the plasma and the electron energy balance. In addition to the ionized species, certain neutral species must also be calculated. Examples are given for pure helium and a mixture of helium and argon. In the HeAr case, He(+), He2(+), He/2 3S/, Ar(+), Ar2(+), and excited Ar are evaluated.

Depaola, B. D.↗
NASA NTRS1975

A sequential nonparametric pattern classification algorithm based on the Wald SPRT

A sequential nonparametric pattern classification procedure is presented. The method presented is an estimated version of the Wald sequential probability ratio test (SPRT). This method utilizes density function estimates, and the density estimate used is discussed, including a proof of convergence in probability of the estimate to the true density function. The classification procedure proposed makes use of the theory of order statistics, and estimates of the probabilities of misclassification are given. The procedure was tested on discriminating between two classes of Gaussian samples and on discriminating between two kinds of electroencephalogram (EEG) responses.

Poage, J. L.↗
NASA NTRS1995

Estimates of volumetric bone density from projectional measurements improve the discriminatory capability of dual X-ray absorptiometry

To determine whether estimates of volumetric bone density from projectional scans of the lumbar spine have weaker associations with height and weight and stronger associations with prevalent vertebral fractures than standard projectional bone mineral density (BMD) and bone mineral content (BMC), we obtained posteroanterior (PA) dual X-ray absorptiometry (DXA), lateral supine DXA (Hologic QDR 2000), and quantitative computed tomography (QCT, GE 9800 scanner) in 260 postmenopausal women enrolled in two trials of treatment for osteoporosis. In 223 women, all vertebral levels, i.e., L2-L4 in the DXA scan and L1-L3 in the QCT scan, could be evaluated. Fifty-five women were diagnosed as having at least one mild fracture (age 67.9 +/- 6.5 years) and 168 women did not have any fractures (age 62.3 +/- 6.9 years). We derived three estimates of "volumetric bone density" from PA DXA (BMAD, BMAD*, and BMD*) and three from paired PA and lateral DXA (WA BMD, WA BMDHol, and eVBMD). While PA BMC and PA BMD were significantly correlated with height (r = 0.49 and r = 0.28) or weight (r = 0.38 and r = 0.37), QCT and the volumetric bone density estimates from paired PA and lateral scans were not (r = -0.083 to r = 0.050). BMAD, BMAD*, and BMD* correlated with weight but not height. The associations with vertebral fracture were stronger for QCT (odds ratio [QR] = 3.17; 95% confidence interval [CI] = 1.90-5.27), eVBMD (OR = 2.87; CI 1.80-4.57), WA BMDHol (OR = 2.86; CI 1.80-4.55) and WA-BMD (OR = 2.77; CI 1.75-4.39) than for BMAD*/BMD* (OR = 2.03; CI 1.32-3.12), BMAD (OR = 1.68; CI 1.14-2.48), lateral BMD (OR = 1.88; CI 1.28-2.77), standard PA BMD (OR = 1.47; CI 1.02-2.13) or PA BMC (OR = 1.22; CI 0.86-1.74). The areas under the receiver operating characteristic (ROC) curves for QCT and all estimates of volumetric BMD were significantly higher compared with standard PA BMD and PA BMC. We conclude that, like QCT, estimates of volumetric bone density from paired PA and lateral scans are unaffected by height and weight and are more strongly associated with vertebral fracture than standard PA BMD or BMC, or estimates of volumetric density that are solely based on PA DXA scans.

Non-NASA Center↗
NASA NTRS1993

Estimating effective data density in a satellite retrieval or an objective analysis

An attempt is made to formulate consistent objective definitions of the concept of 'effective data density' applicable both in the context of satellite soundings and more generally in objective data analysis. The definitions based upon various forms of Backus-Gilbert 'spread' functions are found to be seriously misleading in satellite soundings where the model resolution function (expressing the sensitivity of retrieval or analysis to changes in the background error) features sidelobes. Instead, estimates derived by smoothing the trace components of the model resolution function are proposed. The new estimates are found to be more reliable and informative in simulated satellite retrieval problems and, for the special case of uniformly spaced perfect observations, agree exactly with their actual density. The new estimates integrate to the 'degrees of freedom for signal', a diagnostic that is invariant to changes of units or coordinates used.

Purser, R. J.↗
NASA NTRS1974

Nonparametric maximum likelihood estimation of probability densities by penalty function methods

When it is known a priori exactly to which finite dimensional manifold the probability density function gives rise to a set of samples, the parametric maximum likelihood estimation procedure leads to poor estimates and is unstable; while the nonparametric maximum likelihood procedure is undefined. A very general theory of maximum penalized likelihood estimation which should avoid many of these difficulties is presented. It is demonstrated that each reproducing kernel Hilbert space leads, in a very natural way, to a maximum penalized likelihood estimator and that a well-known class of reproducing kernel Hilbert spaces gives polynomial splines as the nonparametric maximum penalized likelihood estimates.

Demontricher, G. F.↗
NASA NTRS2012

Combining Ratio Estimation for Low Density Parity Check (LDPC) Coding

The Low Density Parity Check (LDPC) Code decoding algorithm make use of a scaled receive signal derived from maximizing the log-likelihood ratio of the received signal. The scaling factor (often called the combining ratio) in an AWGN channel is a ratio between signal amplitude and noise variance. Accurately estimating this ratio has shown as much as 0.6 dB decoding performance gain. This presentation briefly describes three methods for estimating the combining ratio: a Pilot-Guided estimation method, a Blind estimation method, and a Simulation-Based Look-Up table. The Pilot Guided Estimation method has shown that the maximum likelihood estimates of signal amplitude is the mean inner product of the received sequence and the known sequence, the attached synchronization marker (ASM) , and signal variance is the difference of the mean of the squared received sequence and the square of the signal amplitude. This method has the advantage of simplicity at the expense of latency since several frames worth of ASMs. The Blind estimation method s maximum likelihood estimator is the average of the product of the received signal with the hyperbolic tangent of the product combining ratio and the received signal. The root of this equation can be determined by an iterative binary search between 0 and 1 after normalizing the received sequence. This method has the benefit of requiring one frame of data to estimate the combining ratio which is good for faster changing channels compared to the previous method, however it is computationally expensive. The final method uses a look-up table based on prior simulated results to determine signal amplitude and noise variance. In this method the received mean signal strength is controlled to a constant soft decision value. The magnitude of the deviation is averaged over a predetermined number of samples. This value is referenced in a look up table to determine the combining ratio that prior simulation associated with the average magnitude of the deviation. This method is more complicated than the Pilot-Guided Method due to the gain control circuitry, but does not have the real-time computation complexity of the Blind Estimation method. Each of these methods can be used to provide an accurate estimation of the combining ratio, and the final selection of the estimation method depends on other design constraints.

Mahmoud, Saad↗
NASA NTRS1993

On the use of the noncentral chi-square density function for the distribution of helicopter spectral estimates

A probability density function for the variability of ensemble averaged spectral estimates from helicopter acoustic signals in Gaussian background noise was evaluated. Numerical methods for calculating the density function and for determining confidence limits were explored. Density functions were predicted for both synthesized and experimental data and compared with observed spectral estimate variability.

Garber, Donald P.↗
NASA NTRS1980

Simulation study of geometric shape factor approach to estimating earth emitted flux densities from wide field-of-view radiation measurements

A study was performed on the use of geometric shape factors to estimate earth-emitted flux densities from radiation measurements with wide field-of-view flat-plate radiometers on satellites. Sets of simulated irradiance measurements were computed for unrestricted and restricted field-of-view detectors. In these simulations, the earth radiation field was modeled using data from Nimbus 2 and 3. Geometric shape factors were derived and applied to these data to estimate flux densities on global and zonal scales. For measurements at a satellite altitude of 600 km, estimates of zonal flux density were in error 1.0 to 1.2%, and global flux density errors were less than 0.2%. Estimates with unrestricted field-of-view detectors were about the same for Lambertian and non-Lambertian radiation models, but were affected by satellite altitude. The opposite was found for the restricted field-of-view detectors.

Weaver, W. L.↗