SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
An improved method of estimating the signal-to-noise ratio (SNR) of a phase-shift keying (PSK) communication link is founded on a rigorous statistical analysis of the input to, and the output from, the PSK demodulator in the receiver. The improved method is free of unwarranted simplifying assumptions and does not require the use of a propagation receiver.
A summary of methods for making dynamic lateral stability and response calculations and for estimating the aerodynamic stability derivatives required for use in these calculations is presented. The processes of performing calculations of the time histories of lateral motions, of the period and damping of these motions, and of the lateral stability boundaries are presented as a series of simple straightforward steps. Existing methods for estimating the stability derivatives are summarized and, in some cases, simple new empirical formulas are presented. Reference is also made to reports presenting experimental data that should be useful in making estimates of the derivatives. Detailed estimating methods are presented for low-subsonic-speed conditions but only a brief discussion and a list of references are given for transonic- and supersonic-speed conditions.
A summary of methods for making dynamic lateral stability and response calculations and for estimating the aerodynamic stability derivatives required for use in these calculations is presented. The processes of performing calculations of the time histories of lateral motions, of the period and damping of these motions, and of the lateral stability boundaries are presented as a series of simple straightforward steps. Existing methods for estimating the stability derivatives are summarized and, in some cases, simple new empirical formulas are presented. Detailed estimation methods are presented for low-subsonic-speed conditions but only a brief discussion and a list of references are given for transonic and supersonic speed conditions.
Series inductance is a critical energy storage element in isolated power converters. Many have explored methods of estimating the total leakage inductance of transformers as an integrated series inductance without external inductors. However, this poses challenges with medium frequency converters and magnetic ribbon-based cores, e.g., metal amorphous nanocrystalline. The material’s relatively high conductivity allows eddy currents to develop when magnetic flux intersects wide surfaces. These eddy currents result in significant additional losses that increase with increasing power flow. This paper presents a simplified and accurate method of estimating leakage flux while identifying separate leakage flux paths that correlate with the magnetic material surfaces. This enables an understanding the proportion of leakage flux that will contribute to extra losses. The methods presented are applicable to both leakage and fringing (around heterogeneous material interfaces) fluxes. This paper includes detailed FEA studies and 3D flux vector measurements matching the presented analytical models.
Background : Accurately estimating burned area from satellites is key to improving biomass burning emission models, studying fire evolution and assessing environmental impact. Previous studies have found that current methods for estimating burned area of fires from satellite active-fire data do not always provide an accurate estimate. Aims and Methods : In this work, we develop a novel algorithm to estimate hourly accumulated burned area based on the area from boundaries of non-convex polygons containing the accumulated Visible Infrared Imaging Radiometer Suite (VIIRS)active-fire detections. Hourly time series are created by combining VIIRS estimates with fire radiative power (FRP) estimates from GOES-17 data. Conclusions, Key Results and Implication : We evaluate the performance of the algorithm for both accumulated and change in burned area between airborne observations, and specifically examine sensitivity to the choice of the parameter controlling how much the boundary can shrink towards the interior of the area polygon. Results of the hourly accumulation of burned area for multiple fires from 2019 and 2020 generally correlate strongly with airborne infrared (IR) observations collected by the United States Forest Service National Infrared Operations (NIROPS), exhibiting correlation coefficient values usually greater than 0.95 and errors <20%.
In this paper, we present a method for estimating complex impedances using reflectometry and a modified steepest descent inversion algorithm. We simulate spread spectrum time domain reflectometry (SSTDR), which can measure complex impedances on energized systems for an experimental setup with resistive and capacitive loads. A parametric function, which includes both a misfit function and stabilizer function, is created. The misfit function is a least squares estimate of how close the model data matches observed data. The stabilizer function prevents the steepest descent algorithm from becoming unstable and diverging. Steepest descent iteratively identifies the model parameters that minimize the parametric function. We validate the algorithm by correctly identifying the model parameters (capacitance and resistance) associated with simulated SSTDR data, with added 3 dB white Gaussian noise. With the stabilizer function, the steepest descent algorithm estimates of the model parameters are bounded within a specified range. Furthermore, the errors for capacitance (220pF to 820pF) and resistance (50 Ω to 270 Ω) are < 10%, corresponding to a complex impedance magnitude |R +1/jωC| of 53 Ω to 510 Ω.
This paper presents a computationally efficient method for dynamic control derivative estimation technique via control surface oscillation numerical experiments. Unsteady RANS CFD simulations in FUN3D are performed to simulate the control surface oscillations with a prescribed truncated square wave containing sufficient frequencies of interest. The truncated square wave oscillation offers the computational efficiency which reduces the computational cost by almost an order of magnitude compared to a sine wave oscillation. The nonlinear effect of large control surface oscillation amplitudes creates a spillover effect whereby the frequency response at the same input frequencies contains not only the linear aerodynamic response but also nonlinear aerodynamic response. A correction procedure is developed to remove the spillover effect from the linear aerodynamic response. A frequency-domain regression is performed to estimate the dynamic control derivatives after the correction. The results generally agree with the previous results obtained from the sine wave oscillation.
Where do people go when they have nowhere to be? Nonobligate activities are a significant part of our social and cultural lives, but there are no existing large scale data which characterize spatial variability in population allocation for these activities. As large scale population estimates have ever-finer resolutions, gaps in our ability to estimate this population segment have an increasingly large impact on high resolution population estimates. In this paper, we demonstrate an improved method for estimating the spatial allocation of the non-obligate population - people who are not at work, school, or in another residential institution. This method builds upon on anonymized and aggregate data on visits to public places, allocating the non-obligate population proportionally to worker population while accounting for the estimated ratio of visitors to workers in public places.
In this paper, we present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) models. We use PDEs in addition to measurements to train DNNs to approximate unknown parameters and constitutive relationships as well as states. The proposed approach increases the accuracy of DNN approximations of partially known functions when a limited number of measurements is available and allows for training DNNs when no direct measurements of the functions of interest are available. We employ physics informed DNNs to estimate the unknown space-dependent diffusion coefficient in a linear diffusion equation and an unknown constitutive relationship in a non-linear diffusion equation. For the parameter estimation problem, we assume that partial measurements of the coefficient and states are available and demonstrate that under these conditions, the proposed method is more accurate than state-of-the-art methods. For the non-linear diffusion PDE model with a fully unknown constitutive relationship (i.e., no measurements of constitutive relationship are available), the physics informed DNN method can accurately estimate the non-linear constitutive relationship based on state measurements only. Finally, we demonstrate that the proposed method remains accurate in the presence of measurement noise.
We use data from the first-year observations of the DES collaboration to measure the galaxy angular power spectrum (APS), and search for its BAO feature. We test our methodology in a sample of 1800 DES Y1-like mock catalogues. We use the pseudo-C ℓ method to estimate the APS and the mock catalogues to estimate its covariance matrix. We use templates to model the measured spectra and estimate template parameters firstly from the C ℓ ’s of the mocks using two different methods, a maximum likelihood estimator and a Markov Chain Monte Carlo, finding consistent results with a good reduced χ 2 . Robustness tests are performed to estimate the impact of different choices of settings used in our analysis. Finally, we apply our method to a galaxy sample constructed from DES Y1 data specifically for LSS studies. This catalogue comprises galaxies within an effective area of 1318 deg 2 and 0.6 < z < 1.0. We find that the DES Y1 data favour a model with BAO at the 2.6σ C.L. However, the goodness of fit is somewhat poor, with χ 2 /(d.o.f.) = 1.49. We identify a possible cause showing that using a theoretical covariance matrix obtained from C ℓ ’s that are better adjusted to data results in an improved value of χ 2 /(dof) = 1.36 which is similar to the value obtained with the real-space analysis. Lastly, our results correspond to a distance measurement of D A (z eff = 0.81)/r d = 10.65 ± 0.49, consistent with the main DES BAO findings. This is a companion paper to the main DES BAO article showing the details of the harmonic space analysis.
An evaluation of the deconvolution method for estimating ocean surface parameters from ocean altimeter waveforms is presented. It is shown that this method presents a fast, accurate way of determining the ocean surface parameters from noisy altimeter data. Three parameters may be estimated by using this method, including the altimeter-height error, the ocean-surface standard deviation, and the ocean-surface skewness. By means of a Monte Carlo experiment, an 'optimum' deconvolution algorithm and the accuracies with which the above parameters may be estimated using this algorithm are determined. Then the influence of instrument effects, such as errors in calibration and pointing-angle estimation, on the estimated parameters is examined. Finally, the deconvolution algorithm is used to estimate height and ocean-surface parameters from Seasat data.
Isolated power plants with well characterized emissions serve as an ideal test case of methods to estimate emissions using satellite data. In this study we evaluate the Exponentially-Modified Gaussian (EMG) method and the box model method based on mass balance for estimating known NOx emissions from satellite retrievals made by the Ozone Monitoring Instrument (OMI). We consider 29 power plants in the USA which have large NOx plumes that do not overlap with other sources and which have emissions data from the Continuous Emission Monitoring System (CEMS). This enables us to identify constraints required by the methods, such as which wind data to use and how to calculate background values. We found that the lifetimes estimated by the methods are too short to be representative of the chemical lifetime. Instead, we introduce a separate lifetime parameter to account for the discrepancy between estimates using real data and those that theory would predict. In terms of emissions, the EMG method required averages from multiple years to give accurate results, whereas the box model method gave accurate results for individual ozone seasons.
A method for estimating dynamic model parameters from flight data with unknown time skews is described and demonstrated. The method combines data reconstruction, nonlinear optimization, and equation-error parameter estimation in the frequency domain to accurately estimate both dynamic model parameters and the relative time skews in the data. Data from a nonlinear F-16 aircraft simulation with realistic noise, instrumentation errors, and arbitrary time skews were used to demonstrate the approach. The approach was further evaluated using flight data from a subscale jet transport aircraft, where the measured data were known to have relative time skews. Comparison of modeling results obtained from time-skewed and time-synchronized data showed that the method accurately estimates both dynamic model parameters and relative time skew parameters from flight data with unknown time skews.
Density estimation is critical to effectively manage invasive species and elucidate areas of highest concern. For wild pigs (Sus scrofa), the ability to estimate density is complicated because of their variable home range sizes and social structure. Common methods for estimating density (e.g., mark-recapture) may be unsuitable in management applications because additional data needs to be collected before and after management. Removal models offer a suitable alternative to estimate density changes following management and can be applied broadly across areas where management of wild pigs is ongoing. We collected wild pig removal and camera trap data from 25 private properties ranging in size from approximately 0.5 km 2 to 95 km 2 across 3 ecoregions in South Carolina, USA, from 2020–2023. We compared factors affecting consistency and precision of property-level density estimates between removal and spatial mark-resight (SMR) models. In general, excluding 1 large outlier, density estimates from removal models were between 0.60 and 15.85 wild pigs/km 2 (median = 5.34) with a median coefficient of variation (CV) of 0.76 and 95% confidence intervals for the CV between 0.70 and 0.94. Similarly, excluding 1 large outlier, density estimates from SMR were between 0.22 and 30.97 wild pigs/km 2 (median = 5.48) with a median CV of 0.39 and 95% confidence intervals for the CV between 0.38 and 1.20. We found the precision of removal models was affected primarily by the number of wild pigs dispatched in the removal period (3 months) and the ecoregion in which they were removed. None of the covariates, including the number of recaptures (a corresponding measure of sample size), influenced precision of the SMR models, although recaptures did influence the density estimates. At the individual property level, density estimates from our 2 estimators were dissimilar from each other in approximately 80% of instances, although none of the covariates we examined influenced dissimilarity. Our results provide unique insight into how sample size affects density estimates using 2 common methods and into novel SMR models that incorporate both marked and unmarked detections. In addition, the density estimates in this study can be used as a reference for wild pig densities in common land cover types throughout the southeastern United States.
Inverse problems associated with stochastic models constitute a significant portion of scientific and engineering applications. In such cases the unknown quantities are distributions. The applicability of traditional methods is limited because of their demanding assumptions or prohibitive computational consumption; for example, maximum likelihood methods require closed-form density functions, and Markov Chain Monte Carlo needs a large number of simulations. We propose a new method that estimates the unknown distribution by matching the statistical properties between observed and simulated random processes. We leverage the expressive power of neural networks to approximate the unknown distribution and use a discriminative neural network for computing the statistical discrepancies between the observed and simulated random processes. Here we demonstrated numerically that the proposed methods can estimate both the model parameters and learn complicated unknown distributions.
A study of practical limitations on achievement of theoretical leading-edge thrust has been made and an empirical method for estimation of attainable thrust has been developed. The method is based on a theoretical analysis of a set of two-dimensional airfoils to define thrust dependence on airfoil geometric characteristics and arbitrarily defined limiting pressures, an examination of two-dimensional airfoil experimental data to provide an estimate of limiting pressure dependence on local Mach number and Reynolds number, and employment of simple sweep theory to adapt the method to three-dimensional wings. Because the method takes into account the spanwise variation of airfoil section characteristics, an opportunity is afforded for design by iteration to maximize the attainable thrust and the attendant performance benefits. The applicability of the method was demonstrated by comparisons of theoretical and experimental aerodynamic characteristics for a series of wing-body configurations. Generally, good predictions of the attainable thrust and its influence on lift and drag characteristics were obtained over a range of Mach numbers from 0.24 to 2.0.
We present trigonometric parallax and proper motion measurements for two T-type brown dwarfs. We derive our measurements from infrared laser guide star adaptive optics observations spanning five years from the ShaneAO/SHARCS and NIRC2/medium-cam instruments on the Shane and Keck telescopes, respectively. To improve our astrometric precision, we measure and apply a distortion correction to our fields for both instruments. We also transform the Keck and ShaneAO astrometric reference frames onto the ICRS using five-parameter parallax and proper motion solutions for background reference stars from Gaia DR2. Fitting for parallax and proper motion, we measure parallaxes of 73.5 ± 9.2 mas and 70.1 ± 6.7 mas for WISEJ19010703+47181688 (WISE1901) and WISEJ21543294+59421370 (WISE2154), respectively. We utilize Monte Carlo methods to estimate the error in our sparse field methods, taking into account overfitting and differential atmospheric refraction. Comparing to previous measurements in the literature, all of our parallax and proper motion values fall within 2σ of the published measurements, and 4 of 6 measurements are within 1σ. These data are among the first parallax measurements of these T dwarfs and serve as precise measurements for calibrating stellar formation models. These two objects are the first results of an ongoing survey of T dwarfs with Keck/NIRC2 and the Shane Adaptive Optics system at Lick Observatory.
In developmental method of estimating distances between moving platform and nearby stationary objects, positions of objects relative to moving platform deduced from apparent motions of those objects in images generated by video camera affixed to platform. Estimates enhanced by use of extended Kalman filter. Developed for use in controlling military helicopters in low-altitude evasive maneuvers, also potentially applicable in robotics and in autonomous navigation of vehicles in general.