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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Estimation of a remote sensing system point-spread function from measured imagery

A general approach to identifying the point spread function (PSF) of a remotely sensed scene is demonstrated in terms of a step function for an abrupt change in the gray level along the row or column of the image data. The estimate of the PSF is made in terms of a finite sum of basis functions, employing a sequence of rectangular pulses covering the spatial extent of the PSF. The approximation, if narrow impulses are employed, provides accurate fidelity to the PSF. The method becomes practical when the geometrical structure of the scene elements producing the measured response is known. The field boundary is obtained through consideration of the differing intensities on each side of the boundary, which is a step discontinuity. The mathematical procedure for the technique is provided, together with a sample problem from Landsat-4 Thematic Mapper data. Atmospheric blurring and electronic effects on the overall PSF and the cubic convolution resampling effects are noted.

Mcgillem, C. D.↗
DOE OSTI2014

A connectivity-based modeling approach for representing hysteresis in macroscopic two-phase flow properties

During CO 2 injection and storage in deep reservoirs, the injected CO 2 enters into an initially brine saturated porous medium, and after the injection stops, natural groundwater flow eventually displaces the injected mobile-phase CO 2 , leaving behind residual non-wetting fluid. Accurate modeling of two-phase flow processes are needed for predicting fate and transport of injected CO 2 , evaluating environmental risks and designing more effective storage schemes. The entrapped non-wetting fluid saturation is typically a function of the spatially varying maximum saturation at the end of injection. At the pore-scale, distribution of void sizes and connectivity of void space play a major role for the macroscopic hysteresis behavior and capillary entrapment of wetting and non-wetting fluids. This paper presents development of an approach based on the connectivity of void space for modeling hysteretic capillary pressure-saturation-relative permeability relationships. The new approach uses void-size distribution and a measure of void space connectivity to compute the hysteretic constitutive functions and to predict entrapped fluid phase saturations. Two functions, the drainage connectivity function and the wetting connectivity function, are introduced to characterize connectivity of fluids in void space during drainage and wetting processes. These functions can be estimated through pore-scale simulations in computer-generated porous media or from traditional experimental measurements of primary drainage and main wetting curves. The hysteresis model for saturation-capillary pressure is tested successfully by comparing the model-predicted residual saturation and scanning curves with actual data sets obtained from column experiments found in the literature. A numerical two-phase model simulator with the new hysteresis functions is tested against laboratory experiments conducted in a quasi-two-dimensional flow cell (91.4cm×5.6cm×61cm), packed with homogeneous and heterogeneous sands. Initial results show that the model can predict spatial and temporal distribution of injected fluid during the experiments reasonably well. However, further analyses are needed for comprehensively testing the ability of the model to predict transient two-phase flow processes and capillary entrapment in geological reservoirs during geological carbon sequestration.

58 GEOSCIENCES↗
DOE OSTI2026

The DESI DR1 peculiar velocity survey: Growth rate measurements from the galaxy power spectrum

The large-scale structure of the Universe and its evolution encapsulate a wealth of cosmological information. A powerful means of unlocking this knowledge lies in measuring the auto-power spectrum and/or the cross-power spectrum of the galaxy density and momentum fields, followed by the estimation of cosmological parameters based on these spectrum measurements. In this study, we generalize the cross-power spectrum model to accommodate scenarios in which the density and momentum fields are derived from distinct galaxy surveys. The growth rate of the large-scale structures of the Universe, commonly represented as fσ 8 , was extracted by jointly fitting the monopole and quadrupole moments of the auto-density power spectrum, the monopole of the auto-momentum power spectrum, and the dipole of the cross-power spectrum. Our estimators, theoretical models, and parameter-fitting framework were tested using mocks, confirming their robustness and accuracy in retrieving the fiducial growth rate from simulation. These techniques were then applied to analyse the power spectrum of the DESI Bright Galaxy Survey and Peculiar Velocity Survey. The fit result of the growth rate is fσ8 = 0.440$^{+0.080}_{−0.096}$ at effective redshift zeff = 0.07. By synthesizing the fitting outcomes from correlation functions, maximum likelihood estimation, and the power spectrum, a consensus value is yielded of fσ 8 (z eff = 0.07) = 0.450$^{+0.055}_{−0.055}$, and correspondingly we obtain γ = 0.580$^{+0.110}_{−0.110}$, Ω m = 0.301$^{+0.011}_{−0.011}$, and σ 8 = 0.834$^{+0.032}_{−0.032}$. The measured fσ8 and γ are consistent with the prediction of the Λ cold dark matter model and general relativity.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

Elasticity of some mantle crystal structures. I - Pleonaste and hercynite spinel.

The elasticity of high-pressure mantle phases can be characterized by using data for chemically similar crystal compounds. The single-crystal elastic constants are determined as a function of pressure and temperature for pleonaste spinel and at room conditions for hercynite spinel. The bulk modulus increases from 1.95 Mb for pleonaste spinel to 2.10 Mb for hercynite spinel. Low or negative values of the pressure derivatives of shear constants are characteristic of the spinel structure and imply a low kinetic barrier to phase transformations and diffusion. Compressional and shear velocities of the spinel phase of olivine are estimated as a function of mean atomic weight by using the pleonaste and hercynite data.

Wang, H.↗
DOE OSTI2024

Model-free stabilization via Extremum Seeking using a cost neural estimator

In this paper, a fully model-free architecture for vertical stabilization of thermonuclear plasmas in tokamak experimental reactors is presented. For the first time, an Extremum Seeking control algorithm is combined with neural networks to estimate the Lyapunov function to be minimized, resulting in a fully data-driven control architecture. The performance of different neural networks are compared. Specifically, Multilayer Perceptrons and Extreme Learning Machines are considered. The proposed architecture is tested in simulation to show that it can counteract relevant plasma disturbances, resulting in a significant improvement in terms of the achievable operative space compared to the Extremum Seeking algorithm, which still relies on model-based cost estimator.

42 ENGINEERING↗
DOE OSTI2026

Particle Filter Based Inference Testing

The primary intent of PAR-FIT (Particle Filter based Inference Testing) is to provide hard inductive evidence that a machine learning model is capable and proven for an individual test input. By examining training data used to form the underlying model functional correlation, an estimate of the reliability that a model will make the correct prediction can be made. The Sequential Probability Ratio Test is used to derive a qualitative evaluation for reliability based on hypothesis testing. The PAR-FIT framework achieves this by implementing a particle filter and the sequential probability ratio test algorithms on the machine learning model training data to determine relevancy of new individual test samples to the training dataset. The kernel function evaluates the local proximity and density of training data used to derive a prediction outcome. Particles are used to probabilistically determine which training data to evaluate for proximity. For test samples that are within a close proximity to and surrounded by multiple training data points, the evaluated reliability of the prediction is high. For test samples that are anomalies not represented by the training dataset, in low density data clusters, or are far from existing data points, the evaluated reliability is low as insufficient training evidence exists to suggest the model is capable of making the correct prediction. Sequential Probability Ratio Test is further used to determine when a hypothesis on whether a signal can be rejected or accepted for use. The ratio test collects sequence information from the particle filter to test whether the signal is anomalous or normal via hypothesis testing of the underlying distributions.

Chen, Edward [Idaho National Laboratory (INL), Ida↗
DOE OSTI2020

Probability Density Function Control for Stochastic Nonlinear Systems using Monte Carlo Simulation

This paper presents a fast implementation framework of output probability density function (PDF) control for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. The statistical properties of the system outputs can be adjusted by shaping the dynamic output probability density function to track the reference stochastic distribution. However, the dynamic probability density function evolution is very difficult to obtain analytically even if the system model and the stochastic distributions of the noises are known. Motivated by Monte Carlo simulation, the dynamic probability density function can be estimated by sampling data which forms the contribution of this paper. In particular, the sampling points are generated following the stochastic distribution of the noise for each instant. These points go through the system would generate the histogram for system outputs, then the dynamic model can be established based on the dynamic histogram which reflects the randomness and the nonlinear dynamics of the investigated system. Based on the established model, the output probability density function tracking can be achieved and the simulation results and discussions show the effectiveness and benefits of the presented framework.

Zhang, Qichun↗
DOE OSTI2025

The DESI DR1 Peculiar Velocity Survey: Growth Rate Measurements from the Galaxy Power Spectrum

The large-scale structure of the Universe and its evolution encapsulate a wealth of cosmological information. A powerful means of unlocking this knowledge lies in measuring the auto-power spectrum and/or the cross-power spectrum of the galaxy density and momentum fields, followed by the estimation of cosmological parameters based on these spectrum measurements. In this study, we generalize the cross-power spectrum model to accommodate scenarios where the density and momentum fields are derived from distinct galaxy surveys. The growth rate of the large-scale structures of the Universe, commonly represented as $fσ_8$, is extracted by jointly fitting the monopole and quadrupole moments of the auto-density power spectrum, the monopole of the auto-momentum power spectrum, and the dipole of the cross-power spectrum. Our estimators, theoretical models and parameter-fitting framework have been tested using mocks, confirming their robustness and accuracy in retrieving the fiducial growth rate from simulation. These techniques are then applied to analyze the power spectrum of the DESI Bright Galaxy Survey and Peculiar Velocity Survey, and the fit result of the growth rate is $fσ_8=0.440^{+0.080}_{-0.096}$ at effective redshift $z_{\rm eff}=0.07$. By synthesizing the fitting outcomes from correlation functions, maximum likelihood estimation and power spectrum, yields a consensus value of $fσ_8(z_{\rm eff}=0.07) = 0.450 ^{+0.055}_{-0.055}$, and correspondingly we obtain $γ=0.580^{+0.110}_{-0.110}$, $Ω_\mathrm{m}=0.301^{+0.011}_{-0.011}$ and $σ_8=0.834^{+0.032}_{-0.032}$. The measured $fσ_8$ and $γ$ are consistent with the prediction of the $Λ$ Cold Dark Matter Model and General Relativity.

Qin, F. [Marseille, CPPM] (ORCID:0000000179507864)↗
DOE OSTI2020

Capacitor Voltage Ripple Estimation and Optimal Sizing of Modular Multi-Level Converters for Variable-Speed Drives

This article deals with the sizing of submodule capacitors for modular multilevel converters (MMCs) used for variable speed drives for medium voltage and high voltage electric machines. Variable speed drives are required to run over a wide frequency range from zero to a few hundred Hz or even over 1 kHz. Under low frequency operation (<30 Hz), a large current ripple flowing through the MMC submodule capacitors will cause significant capacitor voltage fluctuation. This article proposes an optimal submodule capacitor selection method based on the common-mode voltage injection. The proposed sizing model includes two functions: 1) accurately estimating the capacitor voltage ripple based on the drive system parameters; and 2) identifying the minimum capacitance of the submodule capacitors based on the allowed maximum capacitor voltage fluctuations. Simulation and experimental results show that the error between the actual and estimated values of the voltage fluctuation is within 3% using the proposed model.

42 ENGINEERING↗
DOE OSTI2013

Atypical water lattices and their possible relevance to the amorphous ices: A density functional study

Of the fifteen known crystalline forms of ice, eleven consist of a single topologically connected hydrogen bond network with four H-bonds at every O. The other four, Ices VI–VIII and XV, consist of two topologically connected networks, each with four H-bonds at every O. The networks interpenetrate but do not share H-bonds. This article presents two new periodic water lattice families whose topological connectivity is “atypical”: they consist of many two-dimensional layers that share no H-bonds. Layers are held together only by dispersion forces. Within each layer there are still four H-bonds at each O. Called “Hexagonal Bilayer Water” (HBW) and “Pleated Sheet Water” (PSW), they have computed densities of about 1.1 g/mL and 1.3 g/mL respectively, and nearest neighbor O-coordination is 4.5 to 5.5 and 6 to 8 respectively. Using density functional theory (BLYP-D/TZVP), various proton ordered forms of HBW and PSW are optimized and categorized. There are simple pathways connecting Ice-Ih to HBW and HBW to PSW. Their computed properties suggest similarities to the high density and very high density amorphous ices (HDA and VHDA) respectively. It is unknown whether HDA, VHDA, and Low Density Amorphous Ice (LDA) are fully disordered glasses down to the molecular level, or whether there is some short-range local order. Based on estimated radial distribution functions (RDFs), one proton ordered form of HBW matches HDA best. The idea is explored that HDA could contain islands with this underlying structure, and likewise, that VHDA could contain regions of PSW. A “microlattice model version 1” (MLM1) is presented as a device to compare key experimental data on the amorphous ices with these atypical structures and with a microlattice form of Ice-XI for LDA. Resemblances are found with the amorphs’ RDFs, densities, Raman spectra, and transition behaviors. There is not enough information in the static models to assign either a microlattice structure or a partial microlattice structure to any amorphous ice phase.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2023

Understanding Volume Estimation Uncertainty of Lakes and Wetlands Using Satellites and Citizen Science

We studied variations in the volume of water stored in small lakes and wetlands using satellite remote sensing and lake water height data contributed by citizen scientists. A total of 94 water bodies across the globe were studied using satellite data in the optical and microwave wavelengths from Landsat 8, Sentinel-1, and Sentinel-2. The uncertainty in volume estimation as a function of geography and geophysical factors, such as cloud cover, precipitation, and water surface temperature, was studied. The key finding that emerged from this global study is that uncertainty is highest in regions with a distinct precipitation season, such as in the monsoon dominated South Asia or the Pacific Northwestern region of the USA. This uncertainty is further compounded when small lakes and wetlands are seasonal with alternating land use as a water body and agricultural land, such as the wetlands of Northeastern Bangladesh. On an average, 45% of studied lakes could be estimated of their volume change with a statistical significant uncertainty that is less than the expected volume in South Asia. In North America, this statistically significant uncertainty in volume estimation was found to be around 50% in lakes eastward of the 108th meridian with lowest uncertainty found in lakes along the East coast of the USA. The article provides a baseline for understanding the current state of the art in estimating volumetric change of lakes and wetlands using citizen science in anticipation of the recently launched Surface Water and Ocean Topography Mission.

Shahzaib Khan↗
DOE OSTI2015

Relaxation approximations to second-order traffic flow models by high-resolution schemes

A relaxation-type approximation of second-order non-equilibrium traffic models, written in conservation or balance law form, is considered. Using the relaxation approximation, the nonlinear equations are transformed to a semi-linear diagonilizable problem with linear characteristic variables and stiff source terms with the attractive feature that neither Riemann solvers nor characteristic decompositions are in need. In particular, it is only necessary to provide the flux and source term functions and an estimate of the characteristic speeds. To discretize the resulting relaxation system, high-resolution reconstructions in space are considered. Emphasis is given on a fifth-order WENO scheme and its performance. The computations reported demonstrate the simplicity and versatility of relaxation schemes as numerical solvers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1995

Revised predictions of long-period ocean tidal effects on Earth's rotation rate

The rotational response of Earth to long-period tidal forces, embodied in a 'zonal response function,' can be expected to vary with frequency because of variable contributions by the oceans, mantle, and core. The zonal response function has been estimated from 9 years of International Radio Interferometric Surveying (IRIS) universal time (UT1) data and compared with theoretical predictions, using a spherical harmonic tide model to compute the oceans' dynamic response, at semiannual, monthly, fortnightly, and 9-day lunisolar tidal frequencies. Different amounts of mantle anelasticity have been considered for both the oceanic and soild earth responses; predictions have been made assuming axial core-mantle coupling which is either complete or absent. Additionally, an extensive recalibration of the ocean model's frictional parameters was performed using constraints derived in part from Space92 polar motion data; zonal response function predictions have also been made employing this recalibrated ocean tide model. Our results indicate that any amount of core coupling can be ruled out at a fortnightly period and probably at a 9-day period, but not at a monthly period. Our results also suggest that the mantle responds purely elastically at a 9-day period but may behave increasingly anelastically at longer periods. A simple dispersive rule is postulated for periods ranging up to the 14-month Chandler wobble period.

Dickman, S. R.↗
NASA NTRSDate not supplied

Perceptual Attributes of eVTOL Noise Noticeability and Blend with Reference to the Ambient

In most prior laboratory research, studies of human response to aircraft and rotorcraft noise have emphasized annoyance ratings, usually as a function of level. Estimates of detection and noticeability have also been addressed, e.g. in surveys of community response. This presentation explores an alternative subjective criterion, based on an average threshold where trained subjects estimate the level at which a new auditory object (an eVTOL event) “blends” into the existing ambient, as an experimentally defined threshold of acceptance. Contrasting an annoyance rating, subjects rate the level at which sound is no more significant than any other sound source in ambient, with a prescribed attitude focusing on the acceptance of an inevitable new transportation source with positive attributes for the community. The approach utilizes manipulations of ambient type as an experimental variable, using spatially veridical recordings in the laboratory. In one example, the method of limits can be used for determining blend as a function of eVTOL level; other manipulations include the spatial trajectory of the eVTOL relative to the listener, and/or manipulations of its tonal spectra. It is likely that a “blend” threshold lies somewhere between a detection and annoyance threshold as a function of level.

urban air mobility↗
DOE OSTI2014

Rigorous Two-Step Activation for Fusion Systems with PyNE - Paper 127

In fusion systems, high energy neutrons activate system components. After shutdown, the biological dose rate from photons emitted from these components must be estimated as a function of position and time for maintenance planning. High resolution results can be obtained using the mesh-based Rigorous Two-Step (R2S) method. A neutron transport simulation provides mesh-based neutron fluxes. Using these fluxes and an appropriate irradiation scenario, a nuclear inventory analysis code is used to determine the photon source density within each mesh volume element at any decay time after shutdown. The photon source density mesh is used as a source for photon transport. Photon fluxes are tallied at positions of interest and flux-to-dose rate conversion factors are used to estimate the biological dose rate. Previous work by the authors involved the creation of R2S-ACT, an open source R2S workflow, using the Direct Accelerated Geometry Monte Carlo (DAGMC) version of MCNP5 (DAG-MCNP5) for both transport steps and the ALARA nuclear inventory analysis code. Various use cases may require different combinations of transport and activation codes, and nearly all of the capabilities of R2S-ACT have broader utility. For these reasons, the individual components of R2S-ACT were reimplemented within PyNE. PyNE is a collaborative, trans-institutional, open source project consisting of Python, Cython, and C++ code pertinent to nuclear simulations and analysis. This work involved additions to the PyNE mcnp, mesh, material, alara, dagmc, and sampling modules. A lightweight r2s module was then added to connect the code from these modules. The r2s and sampling modules, together with DAG-MCNP5 and ALARA form a complete R2S workflow for obtaining shutdown dose rate results. Mesh operations, geometry discretization, physics code input/output parsing, and mesh-based source sampling capabilities have been added to PyNE generically, eliminating the need to duplicate this effort across multiple applications. The PyNE r2s module uses these capabilities to couple MCNP and ALARA to form an R2S workflow, which has been validated with the FNG shutdown dose rate benchmark. Future work will involve benchmarking the workflow using tetrahedral mesh and also non-analog sampling options. added so that they can be used interchangeably. This will allow for comparisons between results obtained by different physics codes with minimal human effort. Work will also be done to create and implement an importance sampling method for biasing neutron transport towards regions of phase space of high importance to photon dose rate tallies. This will improve the efficiency of obtaining shutdown dose rate results in large, highly-attenuating geometries. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Estimation of volume-weighted average grain size in Fe-based nanocrystalline soft magnetic materials by autocorrelation function

Highlights: • Normalized ACF of TEM images estimates the volume-weighted average grain size. • Grain size estimation by normalized ACF shows good accuracy and time efficiency. • Normalized ACF can reveal grain size difference in different areas of a TEM sample. • Normalized ACF helps modelling of grain size distributions in Fe-B nanostructures. - Abstract: The coercivity of Fe-based nanocrystalline soft magnetic materials depends critically on the grain size of bcc-Fe grains. Accurately estimating the volume-weighted mean grain size is extremely important to develop new Fe-based nanocrystalline soft magnetic materials with low coercivity. This article shows that autocorrelation analysis of dark-field transmission electron microscope (TEM) images provides a very good estimation of bcc-Fe grain size in nanocrystalline soft magnetic materials. This method can be used to efficiently estimate the volume-weighted average grain size from multiple TEM images with much less inherent bias than manual measurement techniques. The estimated grain sizes show good consistency to the grain sizes estimated by X-ray diffraction (XRD) and manual measurement. In contrast to XRD, this method can also reveal the differences in grain size from different micron-sized regions of a sample.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Using ultrasonic attenuation in cortical bone to infer distributions on pore size

Here, in this work we infer the underlying distribution on pore radius in human cortical bone samples using ultrasonic attenuation data. We first discuss how to formulate polydisperse attenuation models using a probabilistic approach and the Waterman Truell model for scattering attenuation. We then compare the Independent Scattering Approximation and the higher-order Waterman Truell models’ forward predictions for total attenuation in polydisperse samples. Following this, we formulate an inverse problem under the Prohorov Metric Framework coupled with variational regularization to stabilize this inverse problem. We then use experimental attenuation data taken from human cadaver samples and solve inverse problems resulting in nonparametric estimates of the probability density function on pore radius. We compare these estimates to the “true” microstructure of the bone samples determined via microCT imaging. We find that our methodology allows us to reliably estimate the underlying microstructure of the bone from attenuation data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Making Steppingstones out of Stumbling Blocks: A Bayesian Model Evidence Estimator with Application to Groundwater Transport Model Selection

Bayesian model evidence (BME) is a measure of the average fit of a model to observation data given all the parameter values that the model can assume. By accounting for the trade-off between goodness-of-fit and model complexity, BME is used for model selection and model averaging purposes. For strict Bayesian computation, the theoretically unbiased Monte Carlo based numerical estimators are preferred over semi-analytical solutions. This study examines five BME numerical estimators and asks how accurate estimation of the BME is important for penalizing model complexity. The limiting cases for numerical BME estimators are the prior sampling arithmetic mean estimator (AM) and the posterior sampling harmonic mean (HM) estimator, which are straightforward to implement, yet they result in underestimation and overestimation, respectively. We also consider the path sampling methods of thermodynamic integration (TI) and steppingstone sampling (SS) that sample multiple intermediate distributions that link the prior and the posterior. Although TI and SS are theoretically unbiased estimators, they could have a bias in practice arising from numerical implementation. For example, sampling errors of some intermediate distributions can introduce bias. We propose a variant of SS, namely the multiple one-steppingstone sampling (MOSS) that is less sensitive to sampling errors. We evaluate these five estimators using a groundwater transport model selection problem. SS and MOSS give the least biased BME estimation at an efficient computational cost. If the estimated BME has a bias that covariates with the true BME, this would not be a problem because we are interested in BME ratios and not their absolute values. On the contrary, the results show that BME estimation bias can be a function of model complexity. Thus, biased BME estimation results in inaccurate penalization of more complex models, which changes the model ranking. This was less observed with SS and MOSS as with the three other methods.

54 ENVIRONMENTAL SCIENCES↗