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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mean-field limit of a hybrid system for multi-lane car-truck traffic

In the present work we model multi-lane traffic flow in presence of two population of vehicles: cars and trucks. We first develop a finite-dimensional hybrid system which rely on continuous Bando-Follow-the-Leader dynamics coupled with discrete events motivated by the lane-change maneuvers. Then we rigorously prove that the mean-field limit is given by a system of Vlasov-type PDE with source terms generated by the lane-change maneuvers of the human-driven vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Extreme-value statistics in nonlinear optics

We show that, although nonlinear optics may give rise to a vast multitude of statistics, all these statistics converge, in their extreme-value limit, to one of a few universal extreme-value statistics. Specifically, in the class of polynomial nonlinearities, such as those found in the Kerr effect, weak-field harmonic generation, and multiphoton ionization, the statistics of the nonlinear-optical output converges, in the extreme-value limit, to the exponentially tailed, Gumbel distribution. Exponentially growing nonlinear signals, on the other hand, such as those induced by parametric instabilities and stimulated scattering, are shown to reach their extreme-value limits in the class of the Fréchet statistics, giving rise to extreme-value distributions (EVDs) with heavy, manifestly nonexponential tails, thus favoring extreme-event outcomes and rogue-wave buildup.

Zheltikov, Aleksei M. (ORCID:0000000291380576)↗
NASA NTRS2021

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

Dense time-series remote sensing data with detailed spatial information are highly desired for the monitoring of dynamic earth systems. Due to the sensor tradeoff, most remote sensing systems cannot provide images with both high spatial and temporal resolutions. Spatiotemporal image fusion models provide a feasible solution to generate such a type of satellite imagery, yet existing fusion methods are limited in predicting rapid and/or transient phenological changes. Additionally, a systematic approach to assessing and understanding how varying levels of temporal phenological changes affect fusion results is lacking in spatiotemporal fusion research. The objective of this study is to develop an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images. To systematically assess the effects of varying levels of phenological changes, we identify image phenological transition dates and design three temporal phenological change scenarios representing rapid, moderate, and minimal phenological changes. The hybrid deep learning model, alongside three benchmark fusion models, is assessed in different scenarios of phenological changes. Results indicate the hybrid deep learning model yields significantly better results when rapid or moderate phenological changes are present. It holds great potential in generating high-quality time-series datasets of both high spatial and temporal resolutions, which can further benefit terrestrial system dynamic studies. The innovative approach to understanding phenological changes’ effect will help us better comprehend the strengths and weaknesses of current and future fusion models.

spatiotemporal fusion↗
NASA NTRS2020

Exploring the Limits of the Data-Model-Theory Synergy: “Hot” MW Transitions for Rovibrational IR Studies

In order to further improve the accuracy of rovibrational IR line lists generated from the “Best Theory +Reliable High-resolution Experiment” (BTRHE) strategy from 0.01-0.05 cm-1, or 300-1500 MHz, to ~10 MHz, we explore the current limits of the Data-Model-Theory synergy by examining the accuracy and consistency of existing data, then propose that “hot” bands in microwave (MW) spectra is the solution we need for future enhancements. The Ames SO2 J=0-20 rovibrational energy levels computed on the semi-empirically refined Ames-2 potential energy surface (PES) are fit to the Effective Hamiltonian (EH) model regularly used in the experimental infrared (IR) analysis for SO2 isotopologues. In the fitted EH(Ames) model, the rotational constants A/B/C and all 5 quartic centrifugal distortion constants display clear, systematic, and consistent patterns along the vibrational state energy or quanta. Such consistent patterns may facilitate the vibrational assignments for MW hot bands and extract more information from high temperature MW spectra. Some EH(Expt) analyses were carried out with the lowest order Coriolis Coupling term, C1. Their constants should not be directly compared with other EH(Expt) and EH(Ames) results. After excluding them, our  = EH(Ames)- EH(Expt) analyses for 5 isotopologues (626, 636, 646, 628 and 828) indicates some loss of accuracy and consistency starting from vibrational states as low as 22 or 1000 cm-1. Some EH parameters, e.g. K, may have relative deviations as large as 50-100% and totally lose any recognizable patterns. This simply means that current EH(Expt) models do not have the system-wide consistency we need to further refine the EH(Ames) and Ames rovibrational IR line lists. A large part of such defects are probably inherited from the limited precision of experimental line positions, i.e. 1E-3 ~ 1E-4 cm-1, or 3-30 MHz. This is confirmed in a series of truncation tests using the Ames data. Although the EH(Ames) consistency may help identify unreliable rovibrational EH(Expt) parameters, and make reliable predictions for minor isotopologues and unobserved vibrational bands, we believe only the highly accurate “hot” MW transitions can provide real enhancements for EH(Expt) accuracy and consistency. “Hot” MW spectra should play a more significant role in the future synergy of Data, Model, and Theory in the field of rovibrational IR studies.

Xinchuan Huang↗
DOE OSTI2022

High Nickel and No Cobalt - The Pursuit of Next-Generation Layered Oxide Cathodes

The prosperity of the electric vehicle industry is driving the research and development of lithium-ion batteries. As one of the core components in the entire battery system, cathode materials are currently facing major challenges in pushing higher capacity up to their theoretical limits and transitioning away from the unaffordable metals. To meet these requirements, the search for next-generation cathode materials has shifted to high-nickel and cobalt-free cathodes. In this review, we distinctly pointed out the shortcomings of cobalt in stabilizing layered structures, and systematically summarized the recent efforts to eliminate cobalt and achieve higher nickel content in layered cathode materials. Lastly, a reasonable prospect is put forward for further development of layered cathode materials and other promising candidates, which is likely to spur a wave of efforts toward developing high-performance and low-cost Li-ion batteries.

25 ENERGY STORAGE↗
NASA NTRS2011

Sub-Shot Noise Power Source for Microelectronics

Low-current, high-impedance microelectronic devices can be affected by electric current shot noise more than they are affected by Nyquist noise, even at room temperature. An approach to implementing a sub-shot noise current source for powering such devices is based on direct conversion of amplitude-squeezed light to photocurrent. The phenomenon of optical squeezing allows for the optical measurements below the fundamental shot noise limit, which would be impossible in the domain of classical optics. This becomes possible by affecting the statistical properties of photons in an optical mode, which can be considered as a case of information encoding. Once encoded, the information describing the photon (or any other elementary excitations) statistics can be also transmitted. In fact, it is such information transduction from optics to an electronics circuit, via photoelectric effect, that has allowed the observation of the optical squeezing. It is very difficult, if not technically impossible, to directly measure the statistical distribution of optical photons except at extremely low light level. The photoelectric current, on the other hand, can be easily analyzed using RF spectrum analyzers. Once it was observed that the photocurrent noise generated by a tested light source in question is below the shot noise limit (e.g. produced by a coherent light beam), it was concluded that the light source in question possess the property of amplitude squeezing. The main novelty of this technology is to turn this well-known information transduction approach around. Instead of studying the statistical property of an optical mode by measuring the photoelectron statistics, an amplitude-squeezed light source and a high-efficiency linear photodiode are used to generate photocurrent with sub-Poissonian electron statistics. By powering microelectronic devices with this current source, their performance can be improved, especially their noise parameters. Therefore, a room-temperature sub-shot noise current source can be built that will be beneficial for a very broad range of low-power, low-noise electronic instruments and applications, both cryogenic and room-temperature. Taking advantage of recent demonstrations of the squeezed light sources based on optical micro-disks, this sub-shot noise current source can be made compatible with the size/power requirements specific of the electronic devices it will support.

Strekalov, Dmitry V.↗
DOE OSTI2021

Gravitomagnetic vorticity generation in black hole accretion discs: a potential spatial constraint on plasma flow stability

We calculate the vorticity generation rate in the accretion disc near a slowly rotating black hole in the low-velocity, weak-field limit of general relativity. Specifically, we find that the frame-dragging effect due to the black hole’s rotation – manifested through the gravitomagnetic field – can generate vorticity in a moving plasma in the accretion disc. The mechanism remains operational as long as the accretion disc has non-negligible vertical height and is independent of the exact thermodynamical profile of the disc. The enstrophy density generation rate, as a measure of turbulence and dissipation, is presented, which indicates that the frame-dragging effect can disrupt the stability of the disc away from the z = 0 plane.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Efficient Monte Carlo event generation for neutrino-nucleus exclusive cross sections

Modern neutrino-nucleus cross section computations need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for experimental analyses. To address this challenge, we introduce a new Monte Carlo method utilizing normalizing flows to generate surrogate cross sections that closely approximate those of the original model while significantly reducing computational overhead. As a case study, we built a Monte Carlo event generator for the neutrino-nucleus cross section model developed by the Ghent group. This model employs a Hartree-Fock procedure to establish a quantum mechanical framework in which both the bound and scattering nucleon states are solutions to the mean-field nuclear potential. The surrogate cross sections generated by our method demonstrate excellent accuracy with a relative effective sample size of more than 98.4%, providing a computationally efficient alternative to traditional Monte Carlo sampling methods for differential cross sections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Accuracy of the duration estimates of ultrashort laser pulses using a single-shot second-order intensity autocorrelator

The accuracy of estimating the duration of single-cycle laser pulses with centre wavelengths of 910 and 780 nm is analysed using a single-shot second-order intensity autocorrelator. It is shown that estimates of the single-cycle pulse duration with an error of less than 5 % require the use of KDP crystals with a thickness of no more than 10 μm for the second harmonic generation. At the same time, to estimate the duration of a transform-limited pulse containing ten optical cycles, the crystal thickness can be up to 1 mm. In this case, at an optimum angle of convergence of the first-harmonic beams, the duration estimation error is less than 2 %. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Craig Energy Center Feasibility Study

Tri-State Generation and Transmission Association Inc. (“Tri-State”) provides wholesale electric power to its member cooperatives and public power districts that collectively deliver electricity to more than a million consumers in Colorado, Nebraska, New Mexico and Wyoming. The Craig Power Station has been an integral part of Tri-State’s dispatchable generation fleet that ensures an affordable, reliable and resilient energy supply for its members. The ownership of the Craig Station Unit 1&2 is a collaborative between Tri-State, Salt River Project Agricultural Improvement and Power District, Platte River Power Authority, PacifiCorp, and Public Service Company of Colorado and is known as the Yampa Project. Tri-State owns all of Craig Unit 3 and is the operator of all three units. All three of the Craig units have planned retirement dates that will not extend beyond 2028 and Tri-State Generation wanted to evaluate carbon-neutral emitting technologies that could provide approximately 300 megawatts (“MWs) of capacity and energy that will be lost after Unit 3 ceases operation, as well as provide a similar level of economic benefits to the Craig community. This report is to serve as a technology feasibility study constrained to the current land area available at the Craig site and was conducted based on the limitations of the region.

25 ENERGY STORAGE↗
DOE OSTI2025

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗
DOE OSTI2023

Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication

Writing large amounts of data concurrently to stable storage is a typical I/O pattern of many HPC workflows. This pattern introduces high I/O overheads and results in increased storage space utilization especially for workflows that need to capture the evolution of data structures with high frequency as checkpoints. In this context, many applications, such as graph pattern matching, perform sparse updates to large data structures between checkpoints. For these applications, incremental checkpointing techniques that save only the differences from one checkpoint to another can dramatically reduce the checkpoint sizes, I/O bottlenecks, and storage space utilization. However, such techniques are not without challenges: it is non-trivial to transparently determine what data has changed since a previous checkpoint and assemble the differences in a compact fashion that does not result in excessive metadata. State-of-art data reduction techniques (e.g., compression and de-duplication) have significant limitations when applied to modern HPC applications that leverage GPUs: slow at detecting the differences, generate a large amount of metadata to keep track of the differences, and ignore crucial spatiotemporal checkpoint data redundancy. This paper addresses these challenges by proposing a Merkle tree-based incremental checkpointing method to exploit GPUs' high memory bandwidth and massive parallelism. Experimental results at scale show a significant reduction of the I/O overhead and space utilization of checkpointing compared with state-of-the-art incremental checkpointing and compression techniques.

Tan, Nigel↗
DOE OSTI2020

Imaging the Phase Evolution of the Li-N-H Hydrogen Storage System

Complex metal hydrides provide high-density hydrogen storage, which is essential for vehicular applications. However, the utility of these materials has been limited by thermodynamic and kinetic barriers present during the dehydrogenation and rehydrogenation processes as new phases form inside parent phases. Better understanding of the mixed-phase mesostructures and their interfaces may assist in improving cyclability. In this work, the evolution of the phases during hydrogenation of lithium nitride and dehydrogenation of lithium amide with lithium hydride are probed with scanning-transmission X-ray microscopy at the nitrogen K edge. With this technique, intriguing core-shell structures were observed in particles of both partially hydrogenated Li 3 N and partially dehydrogenated LiNH 2 + 2 LiH. The potential contributions of both internal hydrogen mobility and interfacial energies on the generation of these structures are discussed.

08 HYDROGEN↗
NASA NTRS2020

Learning instrument invariant characteristics for generating high-resolution global coral reef maps

Coral reefs are one of the most biologically complex and diverse ecosystems within the shallow marine environment. Unfortunately, these underwater ecosystems are threatened by a number of anthropogenic challenges, including ocean acidification and warming, overfishing, and the continued increase of marine debris in oceans. This requires a comprehensive assessment of the world's coastal environments, including a quantitative analysis on the health and extent of coral reefs and other associated marine species, as a vital Earth Science measurement. However, limitations in observational and technological capabilities inhibit global sustained imaging of the marine environment. Harmonizing multimodal data sets acquired using different remote sensing instruments presents additional challenges, thereby limiting the availability of good quality labeled data for analysis. In this work, we develop a deep learning model for extracting domain invariant features from multimodal remote sensing imagery and creating high-resolution global maps of coral reefs by combining various sources of imagery and limited hand-labeled data available for certain regions. This framework allows us to generate, for the first time, coral reef segmentation maps at 2-meter resolution, which is a significant improvement over the kilometer-scale state-of-the-art maps. Additionally, this framework doubles accuracy and IoU metrics over baselines that do not account for domain invariance.

Domain Adaptation↗
NASA NTRS1996

Interaction of Particles and Turbulence in the Solar Nebula

The most widely accepted theories for the formation of the Solar system claim that small solid particles continue to settle into a thin layer at the midplane of the Solar nebula until it becomes gravitationally unstable and collapses directly into km-sized planetesimals. This scenario has been challenged on at least two grounds: (1) due to turbulence, the particles may not settle into a thin layer, and (2) a thin layer may not be unstable. The Solar nebula contains at least three sources of turbulence: radial shear, vertical shear, and thermal convection. The first of these is small and probably negligible, while the last is poorly understood. However, the second contribution is likely to be substantial. The particle-rich layer rotates at nearly the Keplerian speed, but the surrounding gaseous nebula rotates slower because it is partly supported by pressure. The resulting shear generates a turbulent boundary layer which stirs the particles away from the midplane, and forestalls gravitational instability. Our previous work used a 'zero-equation' (Prandtl) model to predict the intensity of shear-generated turbulence, and enabled us to demonstrate numerically that settling of particles to the midplane is self-limiting. However, we neglected the possibility that mass loading by particles might damp the turbulence. To explore this, we have developed a more sophisticated 'one-equation' model which incorporates local generation, transport, and dissipation of turbulence, as well as explicit damping of turbulence by particles. We also include a background level of global turbulence to represent other sources. Our results indicate that damping flattens the distribution of particles somewhat, but that background turbulence thickens the particle layer.

Dacles-Mariani, Jennifer S.↗
DOE OSTI2022

Entanglement-enhanced matter-wave interferometry in a high-finesse cavity

An ensemble of atoms can operate as a quantum sensor by placing atoms in a superposition of two different states. Upon measurement of the sensor, each atom is individually projected into one of the two states. Creating quantum correlations between the atoms, that is entangling them, could lead to resolutions surpassing the standard quantum limit set by projections of individual atoms. Large amounts of entanglement involving the internal degrees of freedom of laser-cooled atomic ensembles have been generated in collective cavity quantum-electrodynamics systems, in which many atoms simultaneously interact with a single optical cavity mode. Here we report a matter-wave interferometer in a cavity quantum-electrodynamics system of 700 atoms that are entangled in their external degrees of freedom. In our system, each individual atom falls freely under gravity and simultaneously traverses two paths through space while entangled with the other atoms. In this work, we demonstrate both quantum non-demolition measurements and cavity-mediated spin interactions for generating squeezed momentum states with directly observed sensitivity $3.4^{+1.1}_{–0.9}$dB and $2.5^{+0.6}_{–0.6}$dB below the standard quantum limit, respectively. We successfully inject an entangled state into a Mach–Zehnder light-pulse interferometer with directly observed sensitivity $1.7^{+0.5}_{–0.5}$dB below the standard quantum limit. The combination of particle delocalization and entanglement in our approach may influence developments of enhanced inertial sensors, searches for new physics, particles and fields, future advanced gravitational wave detectors and accessing beyond mean-field quantum many-body physics.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scattered measurements. Unlike standard GANs relying solely on data for training, here we encoded into the architecture of GANs the governing physical laws in the form of stochastic differential equations (SDEs) using automatic differentiation. In particular, we applied Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian processes of different correlation lengths based on data realizations collected from simultaneous reads at sparsely placed sensors. We obtained good approximation of the generated stochastic processes to the target ones even if there is a mismatch between the input noise dimensionality and the effective dimensionality of the target stochastic processes. We also studied the overfitting issue for both the discriminator and the generator, and we found that overfitting occurs also in the generator in addition to the discriminator as previously reported. Subsequently, we considered the solution of elliptic SDEs requiring approximations of three stochastic processes, namely the solution, the forcing, and the diffusion coefficient. Here again, we assumed data collected from simultaneous reads at a limited number of sensors for the multiple stochastic processes. Furthermore, three generators were used for the PI-GANs: two of them were feed forward deep neural networks (DNNs), while the other one was the neural network induced by the SDE. For the case where we have one group of data, we employed one feed forward DNN as the discriminator, while for the case of multiple groups of data we employed multiple discriminators in PI-GANs. We solved forward, inverse, and mixed problems without changing the framework of PI-GANs, obtaining both the means and the standard deviations of the stochastic solution and the diffusion coefficient in good agreement with benchmarks. In this work, we have demonstrated the effectiveness of PI-GANs in solving SDEs for about 120 dimensions. In principle, PI-GANs could tackle very high dimensional problems given more sensor data with low-polynomial growth in computational cost.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

Analysis of radiation patterns of interaction tones generated by inlet rods in the JT15D engine

Interaction tones were intentionally generated by circumferential arrays of equally spaced rods that protrude radially from the inlet wall near the face of the 28-blade fan. Arrays of 28 and 41 rods, selected to give specific far field radiation properties, were tested. The expected properties were readily apparent in the measured radiation patterns. A more detailed analysis of the test data showed both the precision and limitations of the applied acoustic theory. Rods protruding 23 percent of the radius predominantly generated only lowest radial order modes, as expected. Measured and predicted radiation patterns were generally in good agreement. The agreement, however, depended on a significant degree of implied refraction due to inlet velocity gradients. Refraction, if present, would impact static-flight noise comparisons.

Heidmann, M. F.↗