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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗
NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗
DOE OSTI2025

From Existing and New Nuclear and Astrophysical Constraints to Stringent Limits on the Equation of State of Neutron-Rich Dense Matter

Through continuous progress in nuclear theory and experiment and an increasing number of neutron-star (NS) observations, a multitude of information about the equation of state (EOS) for matter at extreme densities is available. To constrain the EOS across its entire density range, this information needs to be combined consistently. However, the impact and model dependency of individual observations vary. Given their growing number, assessing the various methods is crucial to compare the respective effects on the EOS and discover potential biases. For this purpose, we present a broad compendium of different constraints and apply them individually to a large set of EOS candidates within a Bayesian framework. Specifically, we explore different ways of how chiral effective field theory and perturbative quantum chromodynamics can be used to place a likelihood on EOS candidates. We also investigate the impact of nuclear experimental constraints, as well as different radio and x-ray observations of NS masses and radii. This is augmented by reanalyses of the existing data from binary neutron star coalescences, in particular of GW170817, with improved models for the tidal waveform and kilonova light curves, which we also utilize to construct a tight upper limit of 2.39 M ⊙ on the TOV mass based on GW170817’s remnant. Our diverse set of constraints is eventually combined to obtain stringent limits on NS properties. We organize the combination in a way to distinguish between constraints where the systematic uncertainties are deemed small and those that rely on less conservative assumptions. For the former, we find the radius of the canonical 1.4 M ⊙ neutron star to be R 1.4 = 12.2 6 − 0.91 + 0.80 km and the TOV mass at M TOV = 2.2 5 − 0.22 + 0.42 M ⊙ (95% credibility). Including all the presented constraints yields R 1.4 = 12.2 0 − 0.48 + 0.50 km and M TOV = 2.3 0 − 0.20 + 0.07 M ⊙ . When comparing these limits to individual data points, we find that the quoted radius of HESS J1731-347 displays noticeable tension with other constraints. Constraining microphysical properties of the EOS proves more challenging. For instance, the symmetry energy slope is restricted to L sym = 48 − 25 + 21 MeV , where this constraint is mainly dominated by our reanalysis of the PREX-II and CREX experiment. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2021

Tests of General Relativity with Binary Black Holes from the second LIGO–Virgo Gravitational-Wave Transient Catalog

Gravitational waves enable tests of general relativity in the highly dynamical and strong-field regime. Using events detected by LIGO-Virgo up to 1 October 2019, we evaluate the consistency of the data with predictions from the theory. We first establish that residuals from the best-fit waveform are consistent with detector noise, and that the low- and high-frequency parts of the signals are in agreement. We then consider parametrized modifications to the waveform by varying post-Newtonian and phenomenological coefficients, improving past constraints by factors of ∼2; we also find consistency with Kerr black holes when we specifically target signatures of the spin-induced quadrupole moment. Looking for gravitational-wave dispersion, we tighten constraints on Lorentz-violating coefficients by a factor of ∼2.6 and bound the mass of the graviton to m(g)≤1.76 x 10^(-23) eV/sq. c with 90% credibility. We also analyze the properties of the merger remnants by measuring ringdown frequencies and damping times, constraining fractional deviations away from the Kerr frequency to δ{\hat {f}}(220)=0.03(+0.38,-0.35) for the fundamental quadrupolar mode, and δ{\hat {f}}(221)=0.04(+0.27,-0.32) for the first overtone; additionally, we find no evidence for postmerger echoes. Finally, we determine that our data are consistent with tensorial polarizations through a template-independent method. When possible, we assess the validity of general relativity based on collections of events analyzed jointly. We find no evidence for new physics beyond general relativity, for black hole mimickers, or for any unaccounted systematics.

R. Abbott↗
DOE OSTI2019

A progressive deep transfer learning approach to cycle-skipping mitigation in FWI, In SEG Technical Program Expanded Abstracts 2019, pp. 2348-2352. Society of Exploration Geophysicists, 2019.

To effectively mitigate the cycle-skipping phenomenon in full waveform inversion (FWI), we developed an inception-based deep learning neural network to reconstruct the absent low frequency data by exploiting the subsurface low wavenumber information buried in the acquired high frequency data. Two unique features of our deep learning network for the low frequency data reconstruction are 1) Dual Data Feed; 2) Progressive Transfer Learning. With the Dual Data Feed feature, not only the high frequency data, but also the corresponding Beat Tone data are fed into the neural network, significantly reducing the network complexity and the network training cost. The Progressive Transfer Learning technique enables us to initiate the deep learning network with only one training dataset. Unlike other deep learning approaches in this category, our training dataset is not fixed. Instead, the training dataset and the network accuracy are progressively enhanced in an iterative manner within the Progressive Transfer Learning workflow. The numerical experiments validated that the low frequency data reconstructed by this progressive transfer learning network are sufficiently accurate for a successful FWI inversion without suffering from the cycle-skipping without any priori geological information.

58 GEOSCIENCES↗
DOE OSTI2026

Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion

Partial differential equation (PDE)-governed inverse problems are fundamental across various scientific and engineering applications; yet they face significant challenges due to nonlinearity, ill-posedness, and sensitivity to noise. Here, we introduce a computational framework, regularization by denoising using diffusion models for partial differential equations (RED-DiffEq), by integrating physics-driven inversion and data-driven learning. RED-DiffEq leverages pretrained diffusion models as a regularization mechanism for PDE-governed inverse problems. We apply RED-DiffEq to solve the full waveform inversion problem in geophysics, a challenging seismic imaging technique that seeks to reconstruct high-resolution subsurface velocity models from seismic measurement data. Our method shows enhanced accuracy and robustness compared to benchmark methods. Additionally, it exhibits strong generalization and domain decomposition capacity, enabling the inversion of more complex velocity models with larger domains than those used in training the diffusion model. Our framework can also be directly applied to diverse PDE-governed inverse problems.

Shan, Siming [Yale University, New Haven, CT (Unit↗
DOE OSTI2025

Single-shot electro-optic sampling with arbitrary terahertz polarization

With the recent development of diversity electro-optic sampling (DEOS), significant progress has been made in the range of applicability of single-shot EOS measurements, allowing broadband THz waveforms to be captured in a single shot over large temporal windows. In addition to the decrease in acquisition time compared to standard multishot data acquisition, this technique allows measurements on systems far from equilibrium with large shot-to-shot noise or with irreversible or poorly repeatable dynamics. Although DEOS has been demonstrated and verified for linearly polarized THz waveforms, we investigate the effects resulting from the presence of a secondary polarization component. This imposes new challenges for accurate waveform reconstruction, and opens the opportunity to measure out complex polarization states such as arbitrary elliptically polarized THz field. We demonstrate a single-shot diversity-electro-optic-sampling-based approach to capture both x- and y-THz fields simultaneously with a single (110)-cut EO crystal for THz polarimetry and ellipsometry over a wide range of frequencies.

Lenz, Maximilian [University of California, Los An↗
NASA NTRS1974

Evaluation of communication systems using both time and frequency domain techniques

The present work investigates the measurement of the mean-square error of linear systems and the power in a random waveform using digital simulation. A postprocessor is described which yields all necessary information for determining input signal power, output power, average system gain, average system time delay, and system mean-square error. It is shown that a minimum of 10,000 data points must be processed if 4% accuracy with 98% confidence is required for the determination of the power in a random waveform.

Tranter, W. H.↗
DOE OSTI2010

Search for spinning black hole binaries in mock LISA data using a genetic algorithm

Coalescing massive black hole binaries are the strongest and probably the most important gravitational wave sources in the LISA band. The spin and orbital precessions bring complexity in the waveform and make the likelihood surface richer in structure as compared to the nonspinning case. We introduce an extended multimodal genetic algorithm which utilizes the properties of the signal and the detector response function to analyze the data from the third round of mock LISA data challenge (MLDC3.2). The performance of this method is comparable, if not better, to already existing algorithms. We have found all five sources present in MLDC3.2 and recovered the coalescence time, chirp mass, mass ratio, and sky location with reasonable accuracy. As for the orbital angular momentum and two spins of the black holes, we have found a large number of widely separated modes in the parameter space with similar maximum likelihood values.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

The Effects of Atmospheric Models on the Estimation of Infrasonic Source Functions at the Source Physics Experiment

We invert infrasound signals for an equivalent seismoacoustic source function using different atmospheric models to produce the necessary Green’s functions. The infrasound signals were produced by a series of underground chemical explosions as part of the Source Physics Experiment (SPE). In a previous study, we inverted the infrasound data using so-called predictive atmospheric models, which were based on historic, regional-scaled, publicly available weather observations interpolated onto a 3D grid. For the work presented here, we invert the same infrasound data, but using atmospheric models based on weather data collected in a time window that includes the approximate time of the explosion experiments, which we term postdictive models. We build two versions of the postdictive models for each SPE event: one that is based solely on the regional scaled observations, and one that is based on both regional scaled observations combined with on-site observations obtained by a weather sonde released at the time of the SPE. We then invert the observed data set three times, once for each atmospheric model type. We find that the estimated seismoacoustic source functions are relatively similar in waveform shape regardless of which atmospheric model that we used to construct the Green’s functions. However, we find that the amplitude of the estimated source functions is systematically dependent on the atmospheric model type: using the predictive atmospheric models to invert the data generally yields estimated source functions that are larger in amplitude than those estimated using the postdictive models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Electromagnetic Reciprocity.

A reciprocity theorem is an explicit mathematical relationship between two different wavefields that can exist within the same space - time configuration. Reciprocity theorems provi de the theoretical underpinning for mod ern full waveform inversion solutions, and also suggest practical strategies for speed ing up large - scale numerical modeling of geophysical datasets . In the present work, several previously - developed electromagnetic r eciprocity theorems are generalized to accommodate a broader range of medi um, source , and receiver types. Reciprocity relations enabling the interchange of various types of point sources and point receivers within a three - dimensional electromagnetic model are derived. Two numerical modeling algorithms in current use are successfully tested for adherence to reciprocity. Finally, the reciprocity theorem forms the point of departure for a lengthy derivation of electromagnetic Frechet derivatives. These mathe matical objects quantify the sensitivity of geophysical electromagnetic data to variatio ns in medium parameters, and thus constitute indispensable tools for solution of the full waveform inverse problem. ACKNOWLEDGEMENTS Sandia National Labor atories is a multi - program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the US Department of Energy's National Nuclear Security Administration under contract DE - AC04 - 94AL85000. Signif icant portions of the work reported herein were conducted under a Cooperative Research and Development Agreement (CRADA) between Sandia National Laboratories (SNL) and CARBO Ceramics Incorporated. The author acknowledges Mr. Chad Cannan and Mr. Terry Pa lisch of CARBO Ceramics, and Ms. Amy Halloran, manager of SNL's Geophysics and Atmospheric Sciences Department, for their interest in and encouragement of this work. Special thanks are due to Dr . Lewis C. Bartel ( recently retired from Sandia National Labo ratories and now a geophysical consultant ) and Dr. Chester J. Weiss (recently rejoined with Sandia National Laboratories) for many stimulating (and reciprocal!) discussions regar ding the topic at hand.

Aldridge, David F.↗
NASA NTRS2021

Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data

Airborne laser scanning (ALS) has been widely used to map gap probability and leaf area index (LAI) distribution at plot and landscape scales. As an indirect measurement, most ALS methods to estimate LAI combine waveform or point density information with supporting field measurements such as the leaf angle distribution, gap probability, or direct LAI measures. The development of a more independent estimation approach would facilitate more widespread use of existing ALS data to investigate patterns of forest structure and build realistic 3-D vegetation scenes to simulate remote sensing imagery and energy balance. Here, we develop a data processing workflow (named PVlad) using ALS point cloud apparent reflectance to estimate LAI and voxel-based leaf area density (LAD), aiming to reduce the need for associated field measurements such as the gap probability. The adaptation of the path volume (PV) concept derived from apparent reflectance integrates information from multi-directional ALS pulses, and quantifies the percentage exploration of each voxel for classification and occlusion correction, such that rigorous volumetric sampling approaches can be developed to derive LAI and LAD. The PVlad workflow was applied to discrete-return lidar data (Riegl VQ480i) acquired by NASA Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) Airborne Imager during leaf-on (summer) and leaf-off (spring) conditions at the Smithsonian Environmental Research Center (SERC). The estimates of LAI and LAD captured structural differences between mature, logged, and intermediate-aged stands over eight deciduous forest plots. The derived LAI values were compared to field litter collection measurements, and the derived LAD vertical distribution was compared to the output of the VoxLAD model using terrestrial laser scan (TLS) field survey data. Using voxel sizes ranging from 0.5 m to 5 m, overall LAI estimation showed linear fitting coefficient bias and for 1 and 2 m voxel sizes, and vertical LAD distribution showed strong correlation with and for 0.5 and 1m voxel sizes. For every forest stand, upper-canopy LAD had a low variance for voxel sizes of ≤ . Application of PVlad to the G-LiHT and other similar ALS data archives enables the development of fine-resolution LAI map products, including voxelization of LAD for ecosystem science and radiative transfer simulations of remote sensing imagery or surface energy balance.

Tiangang Yin↗
DOE OSTI2022

Lossy checkpoint compression in full waveform inversion: a case study with ZFPv0.5.5 and the overthrust model

This paper proposes a new method that combines checkpointing methods with error-controlled lossy compression for large-scale high-performance full-waveform inversion (FWI), an inverse problem commonly used in geophysical exploration. This combination can significantly reduce data movement, allowing a reduction in run time as well as peak memory. In the exascale computing era, frequent data transfer (e.g., memory bandwidth, PCIe bandwidth for GPUs, or network) is the performance bottleneck rather than the peak FLOPS of the processing unit. Like many other adjoint-based optimization problems, FWI is costly in terms of the number of floating-point operations, large memory footprint during backpropagation, and data transfer overheads. Past work for adjoint methods has developed checkpointing methods that reduce the peak memory requirements during backpropagation at the cost of additional floating-point computations. Combining this traditional checkpointing with error-controlled lossy compression, we explore the three-way tradeoff between memory, precision, and time to solution. We investigate how approximation errors introduced by lossy compression of the forward solution impact the objective function gradient and final inverted solution. Empirical results from these numerical experiments indicate that high lossy-compression rates (compression factors ranging up to 100) have a relatively minor impact on convergence rates and the quality of the final solution.

58 GEOSCIENCES↗
DOE OSTI2017

Multistep inversion workflow for 3D long-offset damped elastic waves in the Fourier domain

We present a new workflow for imaging damped three-dimensional elastic wavefields in the Fourier domain. The workflow employs a multiscale imaging approach, in which offset lengths are laddered, where frequency content and damping of the data are changed cyclically. Thus, the inversion process is launched using short-offset and low-frequency data to recover the long spatial wavelength of the image at a shallow depth. Increasing frequency and offset length leads to the recovery of the fine-scale features of the model at greater depths. For the fixed offset, we employ (in the imaging process) a few discrete frequencies with a set of Laplace damping parameters. The forward problem is solved with a finite-difference frequency-domain method based on a massively parallel iterative solver. The inversion code is based upon the solution of a least squares optimisation problem and is solved using a nonlinear gradient method. It is fully parallelised for distributed memory computational platforms. Our full-waveform inversion workflow is applied to the 3D Marmousi-2 and SEG/EAGE Salt models with long-offset data. The maximum inverted frequencies are 6 Hz for the Marmousi model and 2 Hz for the SEG/EAGE Salt model. The detailed structures are imaged successfully up to the depth approximately equal to one-third of the maximum offset length at a resolution consistent with the inverted frequencies.

3D full waveform inversion↗
NASA NTRS2011

High-Performance Wireless Telemetry

Prior technology for machinery data acquisition used slip rings, FM radio communication, or non-real-time digital communication. Slip rings are often noisy, require much space that may not be available, and require access to the shaft, which may not be possible. FM radio is not accurate or stable, and is limited in the number of channels, often with channel crosstalk, and intermittent as the shaft rotates. Non-real-time digital communication is very popular, but complex, with long development time, and objections from users who need continuous waveforms from many channels. This innovation extends the amount of information conveyed from a rotating machine to a data acquisition system while keeping the development time short and keeping the rotating electronics simple, compact, stable, and rugged. The data are all real time. The product of the number of channels, times the bit resolution, times the update rate, gives a data rate higher than available by older methods. The telemetry system consists of a data-receiving rack that supplies magnetically coupled power to a rotating instrument amplifier ring in the machine being monitored. The ring digitizes the data and magnetically couples the data back to the rack, where it is made available. The transformer is generally a ring positioned around the axis of rotation with one side of the transformer free to rotate and the other side held stationary. The windings are laid in the ring; this gives the data immunity to any rotation that may occur. A medium-frequency sine-wave power source in a rack supplies power through a cable to a rotating ring transformer that passes the power on to a rotating set of electronics. The electronics power a set of up to 40 sensors and provides instrument amplifiers for the sensors. The outputs from the amplifiers are filtered and multiplexed into a serial ADC. The output from the ADC is connected to another rotating ring transformer that conveys the serial data from the rotating section to the stationary section. From there, a cable conveys the serial data to the remote rack, where it is reconditioned to logic level specifications, de-serialized, and converted back to analog. In the rotating electronics are code generators to indicate the beginning of files for data synchronization.

Griebeler, Elmer↗
DOE OSTI2020

Seismic signal augmentation to improve generalization of deep neural networks

Deep learning has emerged as an effective approach for seismic data processing in general, and for earthquake monitoring in particular. The ability of deep learning models to generalize beyond the training and validation data is important for comprehensive earthquake monitoring; this ability furthermore depends on the availability of a sufficiently large and complete training dataset. However, this requirement can prove challenging to meet due to significant effort and time for data collection and labeling. Data augmentation provides an efficient and effective approach for increasing the dimension of training samples and improving generalization to unseen samples. In this paper, we present augmentation methods appropriate for seismic waveforms and demonstrate their ability to reduce bias and increase performance. Furthermore, these augmentation methods can be applied to a wide range of deep learning applications designed for seismic data.

58 GEOSCIENCES↗
DOE OSTI2015

NANODUST DETECTION BETWEEN 1 AND 5 AU USING CASSINI WAVE MEASUREMENTS

The solar system contains solids of all sizes, ranging from kilometer-sized bodies to nano-sized particles. Nanograins have been detected in situ in the Earth's atmosphere, near cometary and giant planet environments, and more recently in the solar wind at 1 AU. The latter nanograins are thought to be formed in the inner solar system dust cloud, mainly through the collisional break-up of larger grains, and are then picked up and accelerated by the magnetized solar wind because of their large charge-to-mass ratio. In the present paper, we analyze the low frequency bursty noise identified in the Cassini radio and plasma wave data during the spacecraft cruise phase inside Jupiter's orbit. The magnitude, spectral shape, and waveform of this broadband noise are consistent with the signatures of the nano particles that traveled at solar wind speed and impinged on the spacecraft surface. Nanoparticles were observed whenever the radio instrument was turned on and able to detect them at different heliocentric distances between Earth and Jupiter, suggesting their ubiquitous presence in the heliosphere. We analyzed the radial dependence of the nanodust flux with heliospheric distance and found that it is consistent with the dynamics of nanodust originating from the inner heliosphere and picked up by the solar wind. The contribution of the nanodust produced in the asteroid belt appears to be negligible compared to the trapping region in the inner heliosphere. In contrast, further out, nanodust is mainly produced by the volcanism of active moons such as Io and Enceladus.

79 ASTRONOMY AND ASTROPHYSICS↗