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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Similarity of capacitive radio-frequency discharges in nonlocal regimes

Similarity transformations are essential for correlating discharges at different scales, which are mostly utilized with local field or local energy approximations. In this work, we report the fully kinetic results from particle-in-cell/Monte Carlo collision simulations that unambiguously demonstrate the similarity of radio frequency (rf) discharges in nonlocal regimes where the electron energy relaxation length is much larger than the gap dimension. It is found that at a constant rf voltage amplitude, discharges will be similar if the gas pressure, inverse of gap distance, and rf driving frequency are all changed by the same scaling factor. The scaling relations of fundamental parameters are illustrated for rf discharges in the alpha-mode with secondary electron emission ignored, and the temporal electron kinetics are shown to have invariance in similar discharges. The results explicitly validate the scaling laws in nonlocal kinetic regimes, indicating promising application potentials of the similarity transformations across a wide range of kinetic regimes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Neural Network‐Based Methods for Ocean Surface Wave Measurement Using Submarine Distributed Acoustic Sensing (DAS)

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

Davis, Jacob R. [Univ. of Washington, Seattle, WA ↗
DOE OSTI2023

Adaptive methods of generating complex light arrays

Structured light arrays of various shapes have been a cornerstone in optical science, driven by the complexities of precise and adaptable generation. This study introduces an approach using a spatial light modulator (SLM) as a generator for these arrays. By projecting a holographic mask onto the SLM, it functions simultaneously as an optical convolution device, focusing mechanism, and structured light beam mask. Our approach offers unmatched versatility, allowing for the experimental fabrication of traditional beam arrays like azimuthal Laguerre–Gaussian (LG), Bessel–Gaussian (BG), and Hermite–Gauss (HG) in the far-field. Notably, it has enabled a method of generating Ince–Gauss (IG) and LG radial mode beam arrays using a convolution solution. Our system provides exceptional control over array periodicity and intensity distribution, bypassing the Talbot self-imaging phenomenon seen in traditional setups. We provide an in-depth theoretical discussion, supported by empirical evidence, of our far-field results. This method has vast potential for applications in optical communication, data processing, and multi-particle manipulation. It paves the way for rapid generation of structured light with high spatial frequencies and complex shapes, promising transformative advances in these domains.

Optics↗
DOE OSTI2021

Distributed Acoustic Sensing Based on Coherent Microwave Photonics Interferometry

A microwave photonics method has been developed for measuring distributed acoustic signals. This method uses microwave-modulated low coherence light as a probe to interrogate distributed in-fiber interferometers, which are used to measure acoustic-induced strain. By sweeping the microwave frequency at a constant rate, the acoustic signals are encoded into the complex microwave spectrum. The microwave spectrum is transformed into the joint time–frequency domain and further processed to obtain the distributed acoustic signals. The method is first evaluated using an intrinsic Fabry Perot interferometer (IFPI). Acoustic signals of frequency up to 15.6 kHz were detected. The method was further demonstrated using an array of in-fiber weak reflectors and an external Michelson interferometer. Two piezoceramic cylinders (PCCs) driven at frequencies of 1700 Hz and 3430 Hz were used as acoustic sources. The experiment results show that the sensing system can locate multiple acoustic sources. The system resolves 20 nε when the spatial resolution is 5 cm. The recovered acoustic signals match the excitation signals in frequency, amplitude, and phase, indicating an excellent potential for distributed acoustic sensing (DAS).

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Image registration for accurate electrode deformation analysis in operando microscopy of battery materials

Operando imaging techniques have become increasingly valuable in both battery research and manufacturing. However, the reliability of these methods can be compromised by instabilities in the imaging setup and operando cells, particularly when utilizing high-resolution imaging systems. The acquired imaging data often include features arising from both undesirable system vibrations and drift, as well as the scientifically relevant deformations occurring in the battery sample during cell operation. For meaningful analysis, it is crucial to distinguish and separately evaluate these two factors. To address these challenges, we employ a suite of advanced image-processing techniques. These include fast Fourier transform analysis in the frequency domain, power spectrum-based assessments for image quality, as well as rigid and non-rigid image-registration methods. These techniques allow us to identify and exclude blurred images, correct for displacements caused by motor vibrations and sample holder drift and, thus, prevent unwanted image artifacts from affecting subsequent analyses and interpretations. Additionally, we apply optical flow analysis to track the dynamic deformation of battery electrode materials during electrochemical cycling. This enables us to observe and quantify the evolving mechanical responses of the electrodes, offering deeper insights into battery degradation. Together, these methods ensure more accurate image analysis and enhance our understanding of the chemomechanical interplay in battery performance and longevity.

Sun, Tianxiao↗
DOE OSTI2026

Whistler–Alfvén Turbulence in a Nonneutral Ultrarelativistic Pair Plasma

The large-scale dynamics of most conventional space and astrophysical plasmas are predominantly governed by Alfvén modes, which are low-frequency magnetohydrodynamic modes existing in magnetized media. At scales smaller than the ion gyroscale or frequencies exceeding the ion cyclotron frequency, the Alfvén modes transform into kinetic-Alfvén or whistler modes that significantly contribute to plasma dynamics. However, this scenario reverses in nonneutral pair plasmas, such as those found in the magnetospheres of pulsars and magnetars, around rotating black holes, and in their relativistic jets, as well as in certain laboratory plasmas. In these systems, the large-scale dynamics are governed by hybrid whistler–Alfvén modes, which transform into pure Alfvén modes at smaller scales. We derive the nonlinear equations that describe the dynamics of whistler–Alfvén modes in ultrarelativistic nonneutral magnetically dominated pair plasma and discuss the spectrum of turbulence governed by these equations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Super-resolved time–frequency measurements of coupled phonon dynamics in a 2D quantum material

Abstract Methods to probe and understand the dynamic response of materials following impulsive excitation are important for many fields, from materials and energy sciences to chemical and neuroscience. To design more efficient nano, energy, and quantum devices, new methods are needed to uncover the dominant excitations and reaction pathways. In this work, we implement a newly-developed superlet transform—a super-resolution time-frequency analytical method—to analyze and extract phonon dynamics in a laser-excited two-dimensional (2D) quantum material. This quasi-2D system, 1 T -TaSe 2 , supports both equilibrium and metastable light-induced charge density wave (CDW) phases mediated by strongly coupled phonons. We compare the effectiveness of the superlet transform to standard time-frequency techniques. We find that the superlet transform is superior in both time and frequency resolution, and use it to observe and validate novel physics. In particular, we show fluence-dependent changes in the coupled dynamics of three phonon modes that are similar in frequency, including the CDW amplitude mode, that clearly demonstrate a change in the dominant charge-phonon couplings. More interestingly, the frequencies of the three phonon modes, including the strongly-coupled CDW amplitude mode, remain time- and fluence-independent, which is unusual compared to previously investigated materials. Our study opens a new avenue for capturing the coherent evolution and couplings of strongly-coupled materials and quantum systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study

Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Wavelet Analysis of GPR Data for Belowground Mass Assessment of Sorghum Hybrid for Soil Carbon Sequestration

Among many agricultural practices proposed to cut carbon emissions in the next 30 years is the deposition of carbon in soils as plant matter. Adding rooting traits as part of a sequestration strategy would result in significantly increased carbon sequestration. Integrating these traits into production agriculture requires a belowground phenotyping method compatible with high-throughput breeding (i.e., rapid, inexpensive, reliable, and non-destructive). However, methods that fulfill these criteria currently do not exist. We hypothesized that ground-penetrating radar (GPR) could fill this need as a phenotypic selection tool. In this study, we employed a prototype GPR antenna array to scan and discriminate the root and rhizome mass of the perennial sorghum hybrid PSH09TX15. B-scan level time/discrete frequency analyses using continuous wavelet transform were utilized to extract features of interest that could be correlated to the biomass of the subsurface roots and rhizome. Time frequency analysis yielded strong correlations between radar features and belowground biomass (max R −0.91 for roots and −0.78 rhizomes, respectively) These results demonstrate that continued refinement of GPR data analysis workflows should yield an applicable phenotyping tool for breeding efforts in contexts where selection is otherwise impractical.

Wolfe, Matthew↗
DOE OSTI2021

Extracting dynamical frequencies from invariants of motion in finite-dimensional nonlinear integrable systems

Integrable dynamical systems play an important role in many areas of science, including accelerator and plasma physics. An integrable dynamical system with n degrees of freedom possesses n nontrivial integrals of motion, and can be solved, in principle, by covering the phase space with one or more charts in which the dynamics can be described using action-angle coordinates. To obtain the frequencies of motion, both the transformation to action-angle coordinates and its inverse must be known in explicit form. However, no general algorithm exists for constructing this transformation explicitly from a set of n known (and generally coupled) integrals of motion. In this study we describe how one can determine the dynamical frequencies of the motion as functions of these n integrals in the absence of explicitly known action-angle variables, and we provide several examples.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Suppression of Parasitic Voltage Oscillation in Power Modules by Turn-Off Current Zero Placement

This paper presents a frequency-domain analysis of the turn-off equivalent switching network of a hard-switched semiconductor switching cell. This paper also proposes a novel method to suppress parasitic voltage ringing by placing the turn-off current zero at the parasitic resonant frequency. The turn-off current was decomposed into ramp functions and a constant-valued function to facilitate obtaining the frequency components of the turn-off current using the Laplace transform of standard signals. The proposed frequency-domain modeling of the parasitic voltage ringing and the suppression were validated by detailed circuit simulation using parameters corresponding to prior literature with experimental validation.

Galigekere, Veda Prakash↗
DOE OSTI2023

Frequency-bin photonic quantum information

Discrete frequency modes, or bins, present a blend of opportunities and challenges for photonic quantum information processing. Frequency-bin-encoded photons are readily generated by integrated quantum light sources, naturally high-dimensional, stable in optical fiber, and massively parallelizable in a single spatial mode. Yet quantum operations on frequency-bin states require coherent and controllable multifrequency interference, making them significantly more challenging to manipulate than more traditional spatial degrees of freedom. In this mini-review, we describe recent developments that have transformed these challenges and propelled frequency bins forward. Focusing on sources, manipulation schemes, and detection approaches, we introduce the basics of frequency-bin encoding, summarize the state of the art, and speculate on the field’s next phases. Given the combined progress in integrated photonics, high-fidelity quantum gates, and proof-of-principle demonstrations, frequency-bin quantum information is poised to emerge from the lab and leave its mark on practical quantum information processing—particularly in networking where frequency bins offer unique tools for multiplexing, interconnects, and high-dimensional communications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Making and breaking terahertz waves with fluid plasmas

Vigorous research efforts during the past several decades have successfully closed the “terahertz gap” between microwaves and infrared light, offering new and increasingly efficient ways to produce, detect, and manipulate radiation fields in the terahertz (THz) frequency range. In our laboratory, THz time-domain spectroscopy (THz-TDS) and optical-pump THz-probe (OPTP) experiments have been routinely utilized as ultrafast spectroscopy tools to investigate a variety of emerging quantum materials and metamaterials. The ultrafast THz pulses are produced by femtosecond laser excitation of photoconductive antennas, semiconductor surfaces (InAs), and nonlinear crystals (ZnTe, GaSe, and LiNbO 3 ). Photoconductive antennas and nonlinear crystals also allow for the coherent detection of these pulses in the time domain, with amplitude and phase spectra obtained in the frequency domain via fast Fourier transform. Although such solid-state schemes are highly desirable in many aspects and thus also commonly utilized in many research laboratories and industrial applications, they typically suffer from limited bandwidths due to the absorption and frequency dispersion induced primarily by phonon resonances, limiting THz applications such as spectroscopy of emerging materials, imaging and detection, and biomedical characterization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Quantum JPEG

The joint photographic expert group algorithm compresses a digital image by filtering its high spatial-frequency components. Similarly, we introduce a quantum algorithm that uses the quantum Fourier transform to discard the high spatial-frequency qubits of an image, downsampling it to a lower resolution. This allows one to capture, compress, and send images even with limited quantum resources for storage and communication. We show under which conditions this protocol is advantageous with respect to its classical counterpart.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Time and Frequency Analysis of Load Profile Data

Technology advancements and integration of modern advanced metering systems can monitor, forecast, inform, control, and operate the building's mechanical, electrical, and plumbing (MEP) systems. They offer a higher level of information, which can contribute to making smart buildings more energy efficient and to making them closer to becoming grid-interactive energy efficient buildings (GEB). This paper builds on the ongoing research on variability analysis of a case study building with a 1-minute load profile and examines the Discrete Wavelet Transform (DWT) process in the frequency domain to quantify the signal's energy in each bandwidth, with respect to each end-use category. Moreover, the amount of variability in the total variability is not similar among the end-use categories. This information is needed to understand the behavior of the variability in the frequency domain for future applications, such as generating synthetic load profiles with a similar frequency spectrum as the measured signal.

decomposition↗