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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Photoinduced correlations in stochastic dynamics of a solid-state ionic conductor

Photoexcitation by ultrashort laser pulses plays a crucial role in controlling reaction pathways, creating nonequilibrium material properties, and probing complex molecular dynamics. The photoresponse following a laser pulse is generally nonidentical between exposures due to spatiotemporal fluctuations or the stochastic nature of dynamical pathways. However, most ultrafast pump-probe experiments struggle to distinguish intrinsic sample fluctuations from extrinsic apparatus noise, often missing deviations from the averaged response. Leveraging the stability and high photon flux of time-resolved X-ray micro-diffraction at a synchrotron, we characterized stochastic photoinduced dynamics in a solid-state ionic conductor. By analyzing temporal evolutions of the lattice parameter of a single grain, we found that shot-to-shot fluctuations are not independent. Instead, correlations exist between nonequilibrium lattice trajectories following adjacent shots, with a characteristic correlation length of approximately 1500 shots, corresponding to an energy barrier of 0.4 ± 0.1 eV, close to the activation energy of lithium-ion diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Command of three-dimensional solitary waves via photopatterning

Multidimensional solitons are prevalent in numerous research fields. In orientationally ordered soft matter system, three-dimensional director solitons exemplify the localized distortion of molecular orientation. However, their precise manipulation remains challenging due to unpredictable and uncontrolled generation. Here, we utilize preimposed programmable photopatterning in nematics to control the kinetics of director solitons. This enables both unidirectional and bidirectional generation at specific locations and times, confinement within micron-scaled patterns of diverse shapes, and directed propagation along predefined trajectories. A focused dynamical model provides insight into the origins of these solitons and aligns closely with experimental observations, underscoring the pivotal role of anchoring conditions in soliton manipulation. Our findings pave the way for diverse fundamental research avenues and promising applications, including microcargo transportation and optical information processing.

Science & Technology - Other Topics↗
DOE OSTI2023

The transport dynamics of tens of micrometer-sized water droplets in RF atmospheric pressure glow discharges

Abstract Charging of particles having diameters of tens of microns has been extensively studied at atmospheric pressure in the context of, for example, electrostatic precipitators where the focus was on unipolar charging. The ambipolar charging of particles in atmospheric pressure plasmas, and of droplets in particular, has received less attention. The plasma activation of droplets is of interest for water purification, fertilizer production and materials synthesis, all of which depend on the transport of the droplets through the plasma, which in turn depends on their charging. In this paper, we report on the transport dynamics of water droplets, tens of microns in diameter, carried by the gas flow through an atmospheric pressure radiofrequency glow discharge sustained in helium. The droplets pass through the plasma with minimal evaporation and without reaching the Rayleigh limit. The droplet trajectory in the presence and absence of the plasma provides insights on the forces acting on the droplet. The measurements were analyzed using results from a three-dimensional fluid model and a two-dimensional plasma hydrodynamics model. We found that the transport dynamics as the droplet enters and leaves the plasma are due to differential charging of the droplet in the plasma gradients of the bounding sheaths to the plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Quantum Computing and Visualization Research Challenges and Opportunities

Here, quantum computing (QC) has experienced rapid growth in recent years with the advent of robust programming environments, readily accessible software simulators and cloud-based QC hardware platforms, and growing interest in learning how to design useful methods that leverage this emerging technology for practical applications. From the perspective of the field of visualization, this article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.

Data visualization↗
DOE OSTI2022

Evaluation of Fish-Related Properties of Kaplan Turbines at the Design Phase: Simulation-based outcomes vs. Experimental Data

The development of turbine technology faces growing demands to maximize the survival of migratory fish passing through turbine flows. In this context, two strategies have emerged for quantifying hazardous hydraulic conditions: computer-based evaluations at design stage and recordings with autonomous sensors deployed in prototypes. The former is a desktop evaluation with many modelling assumptions (idealization) and the latter is a field technique that introduces various unknown and uncontrollable factors (uncertainty.) The present work introduces and implements a third method based on test rig measurements and the corresponding computer-based predictions of conditions that negatively affect fish survivability. The experimental work was conducted in a five-bladed Kaplan turbine model in which miniaturized autonomous sensors (SF Mini, developed at the Pacific Northwest National Laboratory, U.S.) measured fish-relevant hydraulic features. The modelling work involved flow simulations according to industry practices and the representation of fish trajectories through the simulated flow conditions. We compared both the experimental measurements and CFD outcomes and discussed the challenges and advantages of the modelling strategies, as well as the benefits for turbine engineers in need of incorporating effective design concepts to mitigate fish mortality through turbines.

Romero-Gomez, Pedro↗
DOE OSTI2020

Trajectory of a flying plasma mirror traversing a target with density gradient

It has been proposed that laser-induced relativistic plasma mirror can accelerate if the plasma has a properly tailored density profile. Such accelerating plasma mirrors can serve as analog black holes to investigate Hawking evaporation and the associated information loss paradox. Here we reexamine the underlying dynamics of mirror motion in a graded-density plasma to provide an explicit trajectory as a function of the plasma density and its gradient. Specifically, a decreasing plasma density profile (down-ramp) along the direction of laser propagation would in general accelerate the mirror. In particular, a constant-plus-exponential density profile would generate the Davies–Fulling trajectory with a well-defined analog Hawking temperature, which is sensitive to the plasma density gradient but not to the density itself. Furthermore, we show that without invoking nano-fabricated thin-films, a much lower density gas target at, for example, ~1 × 10 17 cm -3 , would be able to induce an analog Hawking temperature, k B T H ~3.1 × 10 -2 eV, in the far-infrared region. We hope that this would help to better realize the experiment proposed by Chen and Mourou.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Spectral broadening from turbulence in multiscale lower hybrid current drive simulations

Abstract The scattering of lower hybrid (LH) waves due to scrape-off layer filaments is investigated. It is revealed that scattering can account for the LH spectral gap without any ad hoc modification to the wave-spectrum. This is shown using a multiscale simulation approach which allows, for the first time, the inclusion of full-wave scattering physics in ray-tracing/Fokker–Planck calculations. In this approach, full-wave scattering probabilities are calculated for a wave interacting with a statistical ensemble of filaments. These probabilities are coupled to ray-tracing equations using radiative transfer theory. This allows the modeling of scattering along the entire ray-trajectory, which can be important in the multi-pass regime. Simulations are conducted for LH current drive (LHCD) in Alcator C-Mod, resulting in excellent agreement with experimental current and hard x-ray profiles. A region in filament parameter space is identified in which the impact of scattering on LHCD is saturated. Such a state coincides with experimental LHCD measurements, suggesting saturation indeed occurs in C-Mod, and therefore the exact statistical properties of the filaments are not important.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The ab initio amorphous materials database: Empowering machine learning to decode diffusivity

Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven ex- ploration and design of amorphous materials is hampered by the absence of a com- prehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from sys- tematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductiv- ity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in uni- versal machine learning potentials, impacting design beyond that of non-crystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep probabilistic direction prediction in 3D with applications to directional dark matter detectors

Abstract We present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on the sphere S 2 , giving a simple way to quantify aleatoric uncertainty. This approach generalizes the cosine distance loss which is a special case of our loss function when the uncertainty is assumed to be uniform across samples. We develop approximations required to make the likelihood function and gradient calculations stable. The method is applied to the task of predicting the 3D directions of electrons, the most complex signal in a class of experimental particle physics detectors designed to demonstrate the particle nature of dark matter and study solar neutrinos. Using simulated Monte Carlo data, the initial direction of recoiling electrons is inferred from their tortuous trajectories, as captured by the 3D detectors. For 40 keV electrons in a 70% He 30% CO 2 gas mixture at STP, the new approach achieves a mean cosine distance of 0.104 (26 ∘ ) compared to 0.556 (64 ∘ ) achieved by a non-machine learning algorithm. We show that the model is well-calibrated and accuracy can be increased further by removing samples with high predicted uncertainty. This advancement in probabilistic 3D directional learning could increase the sensitivity of directional dark matter detectors.

Computer Science↗
DOE OSTI2023

Neutral Atomic Beam Techniques for Diagnosis of Edge and Scrape-Off Layer Plasmas (2017-2019) [and subsequently titled] Neutral Atomic Beam Techniques for Diagnosis of Interior and Scrape-Off Layer Plasmas (2019-2022) (Final Report)

This is the Final Technical Report for the DOE Measurement Innovation grant DE-SC0017998 titled Neutral Atomic Beam Techniques for Diagnosis of Edge and Scrape-Off Layer Plasmas (2017-2019) and subsequently titled Neutral Atomic Beam Techniques for Diagnosis of Interior and Scrape-Off Layer Plasmas (2019-2022). The grant was funded by the Office of Fusion Energy Sciences (FES) for the period 1 September 2017 – 31 May 2022. Work performed through the grant has resulted in diagnostic innovations and new measurement capabilities that could improve understanding of magnetic equilibrium, edge localized modes in tokamaks, and bootstrap and Pfirsch-Schlüter currents in stellarators. We have performed simulations of a novel non-hydrogen beam-based diagnostic that would determine a local value (in the plasma) of the poloidal flux function by measuring the toroidal angle of the beam particles' velocity at a detector external to the plasma. The approach is similar to that of a Heavy Ion Beam Probe (HIBP), but would be able to operate with a smaller, more economical detection systems and, in doing so, may enable substantial extension of scenarios in which beam-based diagnostics are deployed. Key results include: Identification of the neutral and singly charged atoms that are most appropriate for use in the beam based diagnostic and the ionization cross-sections that are important for studying the plasma edge of a large device; Development of a promising geometry for operation in which the beam is injected horizontally near the torus midplane; Implementation of simulations using banana orbit trajectories of secondary beam ions to access plasma regions that are commonly in-accessible to an HIBP diagnostic, including sample volumes near the X-point of a diverted tokamak; Assessment of the ability of the diagnostic concept (originally developed under the assumption of, and reliant on, symmetry) to work in stellarators by performing simulations with both quasi-symmetric fields and non-quasi-symmetric fields; Measurement of noise levels with a prototype detector on a stellarator and exploration of techniques to minimize effects of noise on a diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of magnetic fields on HPGe tracking detectors

Here, we present a study of magnetic fields effects on the position resolution and energy response of hyper-pure germanium detectors. Our results provide realistic estimates of the potential impact on the resolving power of tracking-arrays from (fringe) magnetic fields present when operating together with large spectrometers. By solving the equations of motion for the electron and holes in the presence of both electric and magnetic fields, we analyzed the drift trajectories of the charge carriers to determine the deviations in the positions at the end point of the trajectories, as well as changes in drift lengths affecting the energy resolution and peak shift due to trapping. Our results show that the major effect is in the deviation of the transverse (to the electric field direction) position and suggest that, if no corrective action is taken in the pulse-shape and tracking data analysis procedures, a field strength ≳ 0.1 T will start to impact the intrinsic position resolution of 2 mm (RMS). At fields above ~1 T, the degradation of the energy response becomes observable.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Expanding the momentum field of view in angle-resolved photoemission systems with hemispherical analyzers

In photoelectron spectroscopy, the measured electron momentum range is intrinsically related to the excitation photon energy. Low photon energies <10 eV are commonly encountered in laser-based photoemission and lead to a momentum range that is smaller than the Brillouin zones of most materials. This can become a limiting factor when studying condensed matter with laser-based photoemission. An additional restriction is introduced by widely used hemispherical analyzers that record only electrons photoemitted in a solid angle set by the aperture size at the analyzer entrance. Here, we present an upgrade to increase the effective solid angle that is measured with a hemispherical analyzer. We achieve this by accelerating the photoelectrons toward the analyzer with an electric field that is generated by a bias voltage on the sample. Our experimental geometry is comparable to a parallel plate capacitor, and therefore, we approximate the electric field to be uniform along the photoelectron trajectory. With this assumption, we developed an analytic, parameter-free model that relates the measured angles to the electron momenta in the solid and verify its validity by comparing with experimental results on the charge density wave material TbTe3. By providing a larger field of view in momentum space, our approach using a bias potential considerably expands the flexibility of laser-based photoemission setups.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithiumion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery’s current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery’s degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. Here, in this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning.

battery aging reconstruction↗
DOE OSTI2026

Reshaping the Quantum Arrow of Time

While the microscopic laws of physics are often symmetric under time reversal, most natural processes that we observe are not. The emergent asymmetry between typical and time-reversed processes is referred to as the arrow of time. In quantum physics, an arrow of time emerges when a sequence of measurements is performed on a system. We introduce quantum control tools that can yield dynamics more consistent with time flowing backward than forward. The control tools are based on the explicit construction of a Hamiltonian that can replicate the stochastic trajectories of a monitored quantum system. Such a Hamiltonian can reverse the effect of monitoring and, via a feedback process, generate trajectories consistent with a reversed arrow of time. It can also be used to simulate the backward-in-time dynamics of an open quantum system. Finally, we design a feedback-driven continuous measurement engine powered by the energy pumped into the system by the monitoring process. We show that the engine can operate under experimentally realizable conditions with feedback delay and finite-efficiency measurements.

Entropy production↗
DOE OSTI2023

Low-Energy Ion Scattering Intensities from Supported Nanoparticles: The Spherical Cap Model

Supported nanoparticles are of great importance to many technologies like fuel processing and chemical synthesis using catalysts and electrocatalysts, energy storage and generation using fuel cells and batteries, electrochemistry, magnetic information storage, and more. Low-energy ion scattering spectroscopy (LEIS) with noble gas ions like He+ is a powerful tool for the characterization of nanoparticles dispersed across flat support surfaces due to its ability to probe the elemental composition in the topmost atomic layer of a surface, providing quantitative information regarding the size and number density of nanoparticles. In this work, we present a derivation of the LEIS intensities expected from nanoparticles and the support material as a function of the average particle size, their number per unit area, and their contact angle with the support when modeled as spherical caps of the nanoparticle material dispersed over the surface of a flat support. The model assumes that the ion intensities are determined only by the physical blocking of linear ion trajectories and independent of the tilt angle of the local surface relative to the incident and scattered ion directions, an assumption we support by quantitative modeling of published data which tested tilt-angle effects. The model is a generalization to arbitrary contact angles of the hemispherical cap model, which assumes 90° contact angle and has been widely used to model spectroscopic signals in LEIS (and also in Auger and photoelectron spectroscopies) during nanoparticle growth. Here, this new model quantitatively reveals how LEIS signals are sensitive not only to the diameter and number density of the nanoparticle but also to their contact angle (or height/diameter ratio). With the use of additional data (e.g., from microscopy or adsorption microcalorimetry), the model presented here will enable more accurate determination of the average size, shape, and number density of supported nanoparticles based on LEIS intensity measurements.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Automated experimental design of safe rampdowns via probabilistic machine learning

Abstract Typically the rampdown phase of a shot consists of a decrease in current and injected power and optionally a change in shape, but there is considerable flexibility in the rate, sequencing, and duration of these changes. On the next generation of tokamaks it is essential that this is done safely as the device could be damaged by the stored thermal and electromagnetic energy present in the plasma. This works presents a procedure for automatically choosing experimental rampdown designs to rapidly converge to an effective rampdown trajectory. This procedure uses probabilistic machine learning methods paired with acquisition functions taken from Bayesian optimization. In a set of 2022 experiments at DIII-D, the rampdown designs produced by our method maintained plasma control down to substantially lower current and energy levels than are typically observed. The actions predicted by the model significantly improved as the model was able to explore over the course of the experimental campaign.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗