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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Statistical analysis of correlations in the x-ray induced Coulomb explosion of iodopyridine

Coulomb explosion imaging is a promising experimental tool to study individual molecules. Here, in this work simulation data for the x-ray induced Coulomb explosion of 2-iodopyridine is analyzed and the involved fragmentation dynamics are described. It is found that particular final ion momenta show correlations that reflect a collision of two atoms during the explosion. Variations of the proton kinetic energies can be associated with variations in the charge build-up time. Covariances of the forces during the explosion can be utilized to simplify the description of the dynamics in reduced dimensionality using only four collective coordinates.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1989

Remote sensing and numerical modeling of suspended sediment in Laguna de Terminos, Campeche, Mexico

It is necessary to understand the complex physical processes at work in coastal lagoons in order to manage them effectively. Improved methods of data collection and analysis must be found to provide synoptic, timely hydrodynamic information because of the sheer size of some lagoons and the difficulty of acquiring in situ data (particularly in the tropics). This paper summarizes research to model salinity and suspended sediment distributions in Laguna de Terminos, Mexico, using (1) a coupled hydrodynamic and dispersion model and (2) analysis of two Landsat Thematic Mapper images collected on November 25, 1984 and April 24, 1987. Atmospherically corrected chromaticity data derived from Thermatic Mapper data were significantly correlated with modeled total suspended sediment concentrations for the two dates. Comparison between numerically modeled and remotely sensed suspended sediment maps at 1.5 x 1.5 km resolution yielded a covariation map useful for identifying areas of discrepancy between the remotely sensed data and model output.

Jensen, John R.↗
NASA NTRS2013

High-degree Gravity Models from GRAIL Primary Mission Data

We have analyzed Ka‒band range rate (KBRR) and Deep Space Network (DSN) data from the Gravity Recovery and Interior Laboratory (GRAIL) primary mission (1 March to 29 May 2012) to derive gravity models of the Moon to degree 420, 540, and 660 in spherical harmonics. For these models, GRGM420A, GRGM540A, and GRGM660PRIM, a Kaula constraint was applied only beyond degree 330. Variance‒component estimation (VCE) was used to adjust the a priori weights and obtain a calibrated error covariance. The global root‒mean‒square error in the gravity anomalies computed from the error covariance to 320×320 is 0.77 mGal, compared to 29.0 mGal with the pre‒GRAIL model derived with the SELENE mission data, SGM150J, only to 140×140. The global correlations with the Lunar Orbiter Laser Altimeter‒derived topography are larger than 0.985 between l = 120 and 330. The free‒air gravity anomalies, especially over the lunar farside, display a dramatic increase in detail compared to the pre‒GRAIL models (SGM150J and LP150Q) and, through degree 320, are free of the orbit‒track‒related artifacts present in the earlier models. For GRAIL, we obtain an a posteriori fit to the S‒band DSN data of 0.13 mm/s. The a posteriori fits to the KBRR data range from 0.08 to 1.5 micrometers/s for GRGM420A and from 0.03 to 0.06 micrometers/s for GRGM660PRIM. Using the GRAIL data, we obtain solutions for the degree 2 Love numbers, k20=0.024615+/-0.0000914, k21=0.023915+/-0.0000132, and k22=0.024852+/-0.0000167, and a preliminary solution for the k30 Love number of k30=0.00734+/-0.0015, where the Love number error sigmas are those obtained with VCE.

Primary 1↗
NASA NTRS2019

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Machine learning (ML) is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using ML algorithms for atmospheric retrieval by implementing a random forest (RF) to perform retrievals in seconds that are consistent with the traditional, computationally expensive nested-sampling retrieval method. We expand upon their approach by presenting a new ML model, plan-net, based on an ensemble of Bayesian neural networks (BNNs) that yields more accurate inferences than the RF for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use BNNs for atmospheric retrieval, we also introduce a new loss function for BNNs that learns correlations between the model outputs. Importantly, we show that designing ML models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply plan-net to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher resolution spectra and a larger number of atmospheric parameters.

Adam D. Cobb↗
NASA NTRS2022

Source Decomposition of Eddy-Covariance CO 2 Flux Measurements for Evaluating a High-Resolution Urban CO 2 Emissions Inventory

We present the comparison of source-partitioned CO2 flux measurements with a high-resolution urban CO 2 emissions inventory (Hestia). Tower-based measurements of CO and 14 C are used to partition net CO 2 flux measurements into fossil and biogenic components. A flux footprint model is used to quantify spatial variation in flux measurements. We compare the daily cycle and spatial structure of Hestia and eddy-covariance derived fossil fuel CO 2 emissions on a seasonal basis. Hestia inventory emissions exceed the eddy-covariance measured emissions by 0.36 µ mol m −2 s −1 (3.2%) in the cold season and 0.62 µ mol m −2 s −1 (9.1%) in the warm season. The daily cycle of fluxes in both products matches closely, with correlations in the hourly mean fluxes of 0.86 (cold season) and 0.93 (warm season). The spatially averaged fluxes also agree in each season and a persistent spatial pattern in the differences during both seasons that may suggest a bias related to residential heating emissions. In addition, in the cold season, the magnitudes of average daytime biological uptake and nighttime respiration at this flux site are approximately 15% and 27% of the mean fossil fuel CO 2 emissions over the same time period, contradicting common assumptions of no significant biological CO 2 exchange in northern cities during winter. This work demonstrates the effectiveness of using trace gas ratios to adapt eddy-covariance flux measurements in urban environments for disaggregating anthropogenic CO 2 emissions and urban ecosystem fluxes at high spatial and temporal resolution.

eddy-covariance flux measurements↗
DOE OSTI2024

Evidence for widespread thermal acclimation of canopy photosynthesis

Plants acclimate to temperature by adjusting their photosynthetic capacity over weeks to months. However, most evidence for photosynthetic acclimation derives from leaf-scale experiments. Here we address the scarcity of evidence for canopy-scale photosynthetic acclimation by examining the correlation between maximum photosynthetic rates (A max,2,000 ) and growth temperature $(\overline{T_{air}})$ across a range of concurrent temperatures and canopy foliage quantity, using data from >200 eddy covariance sites. We detect widespread thermal acclimation of canopy-scale photosynthesis, demonstrated by enhanced A max,2,000 under higher $\overline{T_{air}}$, across flux sites with adequate water availability. A 14-day period is identified as the most relevant timescale for acclimation across all sites, with a range of 12-25 days for different plant functional types. The mean apparent thermal acclimation rate across all ecosystems is 0.41 (-0.38-1.04 for 5th-95th percentile range) µmol m -2 s -1 °C -1 , with croplands showing the largest acclimation rates and grasslands the lowest. Incorporating an optimality-based prediction of leaf photosynthetic capacities into a biochemical photosynthesis model is shown to improve the representation of thermal acclimation. Our results underscore the critical need for enhanced understanding and modelling of canopy-scale photosynthetic capacity to accurately predict plant responses to warmer growing seasons.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Quantum chaos, integrability, and late times in the Krylov basis

Quantum chaotic systems are conjectured to display a spectrum whose fine-grained features (gaps and correlations) are well described by random matrix theory (RMT). We propose and develop a complementary version of this conjecture: quantum chaotic systems display a Lanczos spectrum whose local means and covariances are well described by RMT. To support this proposal, we first demonstrate its validity in examples of chaotic and integrable systems. We then show that for Haar-random initial states in RMTs the mean and covariance of the Lanczos spectrum suffice to produce the full long-time behavior of general survival probabilities including the spectral form factor, as well as the spread complexity. In addition, for initial states with continuous overlap with energy eigenstates, we analytically find the long-time averages of the probabilities of Krylov basis elements in terms of the mean Lanczos spectrum. This analysis suggests a notion of eigenstate complexity, the statistics of which differentiate integrable systems and classes of quantum chaos. Lastly, we clarify the relation between spread complexity and the universality classes of RMT by exploring various values of the Dyson index and Poisson distributed spectra.

Combinatorics↗
DOE OSTI2025

Water Temperature, Prey Concentration and Salmonid Density Influence Daily Growth of Wild Juvenile Salmonids in Tributaries of the Upper Salmon River, Idaho ( USA )

ABSTRACT Theory, experiments and field studies indicate that the somatic growth rate of freshwater consumers is shaped by the individual, additive and multiplicative effects of multiple factors, including consumer size and condition, temperature, prey resources and biotic interactions. While our understanding of how these factors affect wild populations of freshwater consumers is improving, the topic remains poorly studied, especially with respect to mobile species. Here, we report on an 8‐year, seven‐stream ( n = 49 stream‐year combinations) observational study examining the individual and interactive effects of invertebrate prey concentration (F, mg/m 3 ), mean daily water temperature (T, °C) and juvenile Chinook salmon ( Oncorhynchus tshawytscha ) density (D, fish/100 m 2 ) on summer daily growth rates (%/d) of mobile, anadromous, juvenile Chinook salmon (age‐0+, n = 382) and sub‐yearling (age‐0+, n = 61) and yearling (age‐1+) steelhead trout ( O. mykiss , n = 70) rearing in cold (mean daily summer: 12.1°C, range: 4.2°C–16.7°C) mountain tributaries of the Salmon River basin in central Idaho (USA). AIC c model selection indicated that daily juvenile salmonid growth positively correlated with water temperature, prey biomass concentration, local juvenile Chinook density and the interaction between water temperature and food but with species and age‐specific differences. Water temperature was a covariate in all top‐ranked models, with daily growth (%/day) rate increasing (0.05%–0.23%/d) linearly with mean daily summer water temperature. In addition to a direct positive relationship with daily growth rate, there was evidence that prey concentration positively interacted with water temperature to accelerate daily growth (F × T). The positive relationship between juvenile salmonid daily growth rate and juvenile Chinook density is difficult to explain and could result from confounding factors. The individual success observed in these streams may contribute to population‐level benefits for the focal consumers, as prey‐rich, warm summers may result in larger individuals with higher energy reserves at the end of the summer/autumn growing season, contributing to improved overwinter survival. Our results, taken in combination with evidence from models, experiments and observational studies, have climate change implications. Current and predicted increases in water temperature will necessitate higher rates of prey consumption by aquatic ectothermic consumers to offset accelerated metabolic demands. Thus, to improve the resilience of mobile freshwater consumers in a warming climate, we suggest that natural resource managers not only consider physical and chemical habitat conditions but also biotic conditions, including the spatiotemporal quantity and quality of prey resources.

Kiffney, Peter. M. [Fish Ecology, Northwest Fisher↗
NASA NTRS2012

A Practical Methodology for Quantifying Random and Systematic Components of Unexplained Variance in a Wind Tunnel

This paper documents a check standard wind tunnel test conducted in the Langley 0.3-Meter Transonic Cryogenic Tunnel (0.3M TCT) that was designed and analyzed using the Modern Design of Experiments (MDOE). The test designed to partition the unexplained variance of typical wind tunnel data samples into two constituent components, one attributable to ordinary random error, and one attributable to systematic error induced by covariate effects. Covariate effects in wind tunnel testing are discussed, with examples. The impact of systematic (non-random) unexplained variance on the statistical independence of sequential measurements is reviewed. The corresponding correlation among experimental errors is discussed, as is the impact of such correlation on experimental results generally. The specific experiment documented herein was organized as a formal test for the presence of unexplained variance in representative samples of wind tunnel data, in order to quantify the frequency with which such systematic error was detected, and its magnitude relative to ordinary random error. Levels of systematic and random error reported here are representative of those quantified in other facilities, as cited in the references.

Deloach, Richard↗
DOE OSTI2024

Ecosystem Water‐Saving Timescale Varies Spatially With Typical Drydown Length

Abstract Stomatal optimization theory is a commonly used framework for modeling how plants regulate transpiration in response to the environment. Most stomatal optimization models assume that plants instantaneously optimize a reward function such as carbon gain. However, plants are expected to optimize over longer timescales given the rapid environmental variability they encounter. There are currently no observational constraints on these timescales. Here, a new stomatal model is developed and is used to analyze the timescales over which stomatal closure is optimized. The proposed model assumes plants maximize carbon gain subject to the constraint that they cannot draw down soil moisture below a critical value. The reward is integrated over time, after being weighted by a discount factor that represents the timescale ( τ ) that a plant considers when optimizing stomatal conductance to save water. The model is simple enough to be analytically solvable, which allows the value of τ to be inferred from observations of stomatal behavior under known environmental conditions. The model is fitted to eddy covariance data in a range of ecosystems, finding the value of τ that best predicts the dynamics of evapotranspiration at each site. Across 82 sites, the climate metrics with the strongest correlation to τ are measures of the average number of dry days between rainfall events. Values of τ are similar in magnitude to the longest such dry period encountered in an average year. The results here shed light on which climate characteristics shape spatial variations in ecosystem‐level water use strategy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Investigating the impact of environmental factors on West Nile virus human case prediction in Ontario, Canada

West Nile virus is the most common mosquito borne disease in North America and the leading cause of viral encephalitis. West Nile virus is primarily transmitted between birds and mosquitoes while humans are incidental, dead-end hosts. Climate change may increase the risk of human infections as climatic variables have been shown to affect the mosquito life cycle, biting rate, incubation period of the disease in mosquitoes, and bird migration patterns. We develop a zero-inflated Poisson model to investigate how human West Nile virus case counts vary with respect to mosquito abundance and infection rates, bird abundance, and other environmental covariates. We use a Bayesian paradigm to fit our model to data from 2010–2019 in Ontario, Canada. Our results show mosquito infection rate, temperature, precipitation, and crow abundance are positively correlated with human cases while NDVI and robin abundance are negatively correlated with human cases. We find the inclusion of spatial random effects allows for more accurate predictions, particularly in years where cases are higher. Our model is able to accurately predict the magnitude and timing of yearly West Nile virus outbreaks and could be a valuable tool for public health officials to implement prevention strategies to mitigate these outbreaks.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2012

The Atacama Cosmology Telescope: Data Characterization and Map Making

We present a description of the data reduction and mapmaking pipeline used for the 2008 observing season of the Atacama Cosmology Telescope (ACT). The data presented here at 148 GHz represent 12% or the 90 TB collected by ACT from 2007 to 2010. In 2008 we observed for 136 days, producing a total of 142h of data (11 TB for the 148 GHz band only), with a daily average of 10.5 h of observation. From these, 108.5 h were devoted to 850 sq deg stripe (11.2 h by 9 deg.1) centered on a declination of -52 deg.7, while 175 h were devoted to a 280 square deg stripe (4.5 h by 4 deg.8) centered at the celestial equator. We discuss sources of statistical and systematic noise, calibration, telescope pointing and data selection. Out of 1260 survey hours and 1024 detectors per array, 816 h and 593 effective detectors remain after data selection for this frequency band, yielding a 38 % survey efficiency. The total sensitivity in 2008, determined from the noise level between 5 Hz and 20 Hz in the time-ordered data stream (TOD), is 32 muK square root of s in CMB units. Atmospheric brightness fluctuations constitute the main contaminant in the data and dominate the detector and noise covariance at low frequencies in the TOD. The maps were made by solving the lease squares problem using the Preconditioned Conjugate Gradient method, incorporating the details of the detector and noise correlations. Cross-correlation with WMAP sky maps as well as analysis from simulations reveal the our maps are unbiased at l > 300. This paper accompanies the public release of the 148 GHz southern stripe maps from 2008. The techniques described here will be applied to future maps and data releases.

Duenner, Rolando↗
NASA NTRS2013

File Specification for GEOS-5 FP (Forward Processing)

The GEOS-5 FP Atmospheric Data Assimilation System (GEOS-5 ADAS) uses an analysis developed jointly with NOAA's National Centers for Environmental Prediction (NCEP), which allows the Global Modeling and Assimilation Office (GMAO) to take advantage of the developments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). The GEOS-5 AGCM uses the finite-volume dynamics (Lin, 2004) integrated with various physics packages (e.g, Bacmeister et al., 2006), under the Earth System Modeling Framework (ESMF) including the Catchment Land Surface Model (CLSM) (e.g., Koster et al., 2000). The GSI analysis is a three-dimensional variational (3DVar) analysis applied in grid-point space to facilitate the implementation of anisotropic, inhomogeneous covariances (e.g., Wu et al., 2002; Derber et al., 2003). The GSI implementation for GEOS-5 FP incorporates a set of recursive filters that produce approximately Gaussian smoothing kernels and isotropic correlation functions. The GEOS-5 ADAS is documented in Rienecker et al. (2008). More recent updates to the model are presented in Molod et al. (2011). The GEOS-5 system actively assimilates roughly 2 × 10(exp 6) observations for each analysis, including about 7.5 × 10(exp 5) AIRS radiance data. The input stream is roughly twice this volume, but because of the large volume, the data are thinned commensurate with the analysis grid to reduce the computational burden. Data are also rejected from the analysis through quality control procedures designed to detect, for example, the presence of cloud. To minimize the spurious periodic perturbations of the analysis, GEOS-5 FP uses the Incremental Analysis Update (IAU) technique developed by Bloom et al. (1996). More details of this procedure are given in Appendix A. The assimilation is performed at a horizontal resolution of 0.3125-degree longitude by 0.25- degree latitude and at 72 levels, extending to 0.01 hPa. All products are generated at the native resolution of the horizontal grid. The majority of data products are time-averaged, but four instantaneous products are also available. Hourly data intervals are used for two-dimensional products, while 3-hourly intervals are used for three-dimensional products. These may be on the model's native 72-layer vertical grid or at 42 pressure surfaces extending to 0.1 hPa. This document describes the gridded output files produced by the GMAO near real-time operational FP, using the most recent version of the GEOS-5 assimilation system. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary. Documentation about the current access methods for products described in this document can be found on the GMAO products page: http://gmao.gsfc.nasa.gov/products/.

GSI↗
NASA NTRS1999

Measuring Greenland Ice Mass Variation With Gravity Recovery and the Climate Experiment Gravity and GPS

The response of the Greenland ice sheet to climate change could significantly alter sea level. The ice sheet was much thicker at the last glacial maximum. To gain insight into the global change process and the future trend, it is important to evaluate the ice mass variation as a function of time and space. The Gravity Recovery and Climate Experiment (GRACE) mission to fly in 2001 for 5 years will measure gravity changes associated with the current ice variation and the solid earth's response to past variations. Our objective is to assess the separability of different change sources, accuracy and resolution in the mass variation determination by the new gravity data and possible Global Positioning System (GPS) bedrock uplift measurements. We use a reference parameter state that follows a dynamic ice model for current mass variation and a variant of the Tushingham and Peltier ICE-3G deglaciation model for historical deglaciation. The current linear trend is also assumed to have started 5 kyr ago. The Earth model is fixed as preliminary reference Earth model (PREM) with four viscoelastic layers. A discrete Bayesian inverse algorithm is developed employing an isotropic Gaussian a priori covariance function over the ice sheet and time. We use data noise predicted by the University of Texas and JPL for major GRACE error sources. A 2 mm/yr uplift uncertainty is assumed for GPS occupation time of 5 years. We then carry out covariance analysis and inverse simulation using GRACE geoid coefficients up to degree 180 in conjunction with a number of GPS uplift rates. Present-day ice mass variation and historical deglaciation are solved simultaneously over 146 grids of roughly 110 km x 110 km and with 6 time increments of 3 kyr each, along with a common starting epoch of the current trend. For present-day ice thickness change, the covariance analysis using GRACE geoid data alone results in a root mean square (RMS) posterior root variance of 2.6 cm/yr, with fairly large a priori uncertainties in the parameters and a Gaussian correlation length of 350 km. Simulated inverse can successfully recover most features in the reference present-day change. The RMS difference between them over the grids is 2.8 cm/yr. The RMS difference becomes 1.1 cm/yr when both are averaged with a half Gaussian wavelength of 150 km. With a fixed Earth model, GRACE alone can separate the geoid signals due to past and current load fairly well. Shown are the reference geoid signatures of direct and elastic effects of the current trend, the viscoelastic effect of the same trend starting from 5 kyr ago, the Post Glacial Rebound (PGR), and the predicted GRACE geoid error. The difference between the reference and inverse modeled total viscoelastic signatures is also shown. Although past and current ice mass variations are allowed the same spatial scale, their geoid signals have different spatial patterns. GPS data can contribute to the ice mass determination as well. Additional information is contained in the original.

Wu, Xiao-Ping↗
DOE OSTI2021

Correlated $\ n-γ$ angular distributions from the $\ Q$ = 4.4398 MeV 12 C ($\ n, n' γ$) reaction for incident neutron energies from 6.5 MeV to 16.5 MeV

Neutron scattering cross sections and angular distributions represent one of the most glaring sources of uncertainty in calculations of nuclear systems. Even simple nuclei like 12 C show indications of errors in nuclear databases for scattering reactions. Measurements of inelastic neutron scattering have historically measured either the scattered neutrons or the nuclear deexcitation $\ γ$ emission. Only a very small number of experiments attempted correlated measurements of both the neutron and $\ γ$ data simultaneously, even though these $\ n-γ$ correlations could be essential for understanding particle transport in nuclear systems. In this work we describe a measurement of the $\ n, γ$, and correlated $\ n-γ$ angular distributions from the $\ Q$ = 4.4398 MeV 12 C ($\ n, n'γ$) reaction in a single experiment using an EJ-309 liquid scintillator detector array with wide angular coverage, and with a continuous incident neutron energy range from 6.5 to 16.5 MeV. We also provide a thorough covariance description of these results, including normalization of the probability distribution. While the measured n distributions agree well with the relatively large number of available literature measurements, there are comparatively very few measurements of the γ distributions from this reaction. However, our data support the presence of a nonzero α 4 Legendre polynomial component of the γ angular distribution suggested in past measurements, which is currently not incorporated in the ENDF/B-VIII.0 library despite the use of these same literature data for evaluation of the 12 C ($\ n, n'γ$) cross section. The correlated $\ n-γ$ distribution measurements are limited to three measurements at incident neutron energies near 14 MeV. Our results do not generally agree with any of these literature measurements. We observe clear indications of significant changes in the $\ n$ distribution for specific $\ γ$-detection angles and vice versa especially near thresholds for other reaction channels, which shows the potential for significant bias in experiments that, for example, tag on inelastic scattering using a single or small number of $\ γ$ -detection angles and could impact particle transport calculations.

6 ≤ A ≤ 19↗
DOE OSTI2022

Galaxy blending effects in deep imaging cosmic shear probes of cosmology

ABSTRACT Upcoming deep imaging surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time will be confronted with challenges that come with increased depth. One of the leading systematic errors in deep surveys is the blending of objects due to higher surface density in the more crowded images; a considerable fraction of the galaxies which we hope to use for cosmology analyses will overlap each other on the observed sky. In order to investigate these challenges, we emulate blending in a mock catalogue consisting of galaxies at a depth equivalent to 1.3 yr of the full 10-yr Rubin Observatory that includes effects due to weak lensing, ground-based seeing, and the uncertainties due to extraction of catalogues from imaging data. The emulated catalogue indicates that approximately 12 per cent of the observed galaxies are ‘unrecognized’ blends that contain two or more objects but are detected as one. Using the positions and shears of half a billion distant galaxies, we compute shear–shear correlation functions after selecting tomographic samples in terms of both spectroscopic and photometric redshift bins. We examine the sensitivity of the cosmological parameter estimation to unrecognized blending employing both jackknife and analytical Gaussian covariance estimators. An ∼0.025 decrease in the derived structure growth parameter S8 = σ8(Ωm/0.3)0.5 is seen due to unrecognized blending in both tomographies with a slight additional bias for the photo-z-based tomography. This bias is greater than the 2σ statistical error in measuring S8.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1974

On the distribution of source coherency in subsonic jets

Recent experimental and theoretical studies on the sources of far field noise generated by subsonic circular jets have given convincing indications that a coherent wave motion does exist in the potential core of subsonic jets. Cross-covariance measurements using single and cross hot-wire probes in the potential core as well as the turbulent mixing region and microphone measurements in the far field have resulted in cross-correlation coefficients of sufficiently high magnitudes to indicate that significant, if not dominant, noise sources may be located in the potential core. The paper presents the analytical and experimental results of this research.

Seiner, J. M.↗
NASA NTRS1975

Dispersion analysis techniques within the space vehicle dynamics simulation program

The Space Vehicle Dynamics Simulation (SVDS) program was evaluated as a dispersion analysis tool. The Linear Error Analysis (LEA) post processor was examined in detail and simulation techniques relative to conducting a dispersion analysis using the SVDS were considered. The LEA processor is a tool for correlating trajectory dispersion data developed by simulating 3 sigma uncertainties as single error source cases. The processor combines trajectory and performance deviations by a root-sum-square (RSS process) and develops a covariance matrix for the deviations. Results are used in dispersion analyses for the baseline reference and orbiter flight test missions. As a part of this study, LEA results were verified as follows: (A) Hand calculating the RSS data and the elements of the covariance matrix for comparison with the LEA processor computed data. (B) Comparing results with previous error analyses. The LEA comparisons and verification are made at main engine cutoff (MECO).

Snow, L. S.↗