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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Adsorption of a single Pt atom on graphene: spin crossing between physisorbed triplet and chemisorbed singlet states

Diffusion Monte Carlo (DMC) calculations have been performed to study the adsorption of a single Pt atom on pristine graphene. In this work, we obtain the adsorption energy curves of a single Pt atom adsorbed at three different adsorption sites (bridge, on-top, hollow) as functions of the vertical distance from a graphene surface for both spin singlet and triplet states. The bridge-site adsorption in a singlet spin state is found to be energetically most stable, which is consistent with previous theoretical predictions. As the Pt atom moves away from a graphene surface, spin triplet states are favored over spin singlet states for all three adsorption sites, reflecting that the ground state of an isolated Pt atom is in a spin triplet state. Furthermore, our DMC calculations reveal local-minimum features in the triplet region which is understood to be due to van der Waals interaction between the Pt atom and graphene. This provides a comprehensive understanding for a spin crossing from a physisorbed triplet state to a chemisorbed singlet state in the adsorption process of a single Pt atom on graphene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Operando video microscopy of Li plating and re-intercalation on graphite anodes during fast charging

Despite the demand for fast-charging lithium (Li)-ion batteries, high-energy-density batteries with thick graphite anodes are limited by Li plating when charging at >4C rates. In this work, plan-view operando video microscopy is applied on >3 mA h cm –2 calendared graphite electrodes to study the dynamic evolution of local state-of-charge (SoC) and Li plating during fast charging. This technique allows for visualization of the spatial heterogeneity in SoC across the electrode, nucleation and growth of Li filaments, Li re-intercalation into graphite, “dead Li” formation, and SoC equilibration. The operando microscopy analysis is complemented by ex situ imaging of through-plane gradients in SoC to gain a three-dimensional visualization of spatial heterogeneity. We demonstrate that (1) Li plating preferentially nucleates on the graphite particles that lithiate fastest during fast charging; (2) the onset of Li plating correlates with the local minimum of the graphite electrode potential; (3) galvanic corrosion currents are responsible for Li re-intercalation, dead Li formation, and SoC re-equilibration after fast charging; and (4) electrochemical signatures during OCV rest or discharge are associated with Li re-intercalation into graphite. Furthermore, this work provides insight into the Li–graphite interactions at the composite electrode level and can be used to inform strategies to diagnose and mitigate Li plating during fast charging.

25 ENERGY STORAGE↗
DOE OSTI2020

Machine learning with bond information for local structure optimizations in surface science

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. Furthermore, we introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Amplification of primordial perturbations from the rise or fall of the inflaton

The next generation of cosmic microwave background, gravitational wave, and large scale structure, experiments will provide an unprecedented opportunity to probe the primordial power spectrum on small scales. An exciting possibility for what lurks on small scales is a sharp rise in the primordial power spectrum: this can lead to the formation of primordial black holes, providing a dark matter candidate or the black holes observed by the LIGO-Virgo collaboration. In this study we develop a mechanism for the amplification of the small-scale primordial power spectrum, in the context of single-field inflation with a step-like feature in the inflaton potential. Specifically, we consider both the upward and the downward step in the potential. We also discuss the possibility of the strong coupling between perturbations because the rapid changes of the potential derivatives with the time-dependent field value, caused by the step-like feature, could make the coupling stronger. As a result, we find that the perturbations can remain weakly coupled yet sufficiently enhanced if the step realizes the rapid changes of the potential derivatives in some fraction of an e-fold, Script O(P R 1/2) ≲ ΔN < 1, where P R is the power spectrum of the curvature perturbation at that time. We also discuss the PBH formation rate from the inflaton trapping at the local minimum, which can occur in the potential with an upward step.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Overview of the MAST Upgrade physics programme: testing novel concepts at low aspect ratio to inform future devices

The research programme performed on the Mega Amp Spherical Tokamak (MAST) Upgrade experiment has made significant advances in developing the physics understanding of low aspect ratio tokamaks in support of the operation of ITER and design of fusion powerplants. High performance plasma scenarios have been developed to facilitate a broad programme of experiments, in which confinement is constrained by the presence of m/n = 2/1 modes that cause substantial losses of fast ions. The onset of these modes coincides with the q = 2 surface residing in a local minimum in the toroidal current density profile. The maximum electron temperature at the pedestal top, T e,ped is limited with gas fuelling to ∼350 eV to maintain regular ELMs; higher T e,ped results in a transition to a non-stationary ELM-free regime. The operational space of spherical tokamaks has been expanded into small and ELM-free regimes. Strong shaping of the last closed flux surface can induce a transition from large to small ELMs, and ELM suppression with resonant magnetic perturbations has been observed for the first time in a low aspect ratio tokamak. Negative triangularity shaping has induced a transition from ELMy H-mode to a high-performance L-mode regime for the first time in a low aspect ratio tokamak. In studies of fast ion confinement, losses of fast particles due to Global Alfvén Eigenmodes have been identified. Interactions between fast ions generated by off-axis neutral beam injection and thermal neutrals can result in significant losses of fast ions. Experiments with on- and off-axis neutral beam injection exhibit a flux pumping mechanism, where the central safety factor is held to ∼1 in the absence of sawteeth. In studies of pedestal physics, it has been found that elevated main chamber neutral pressures result in an increase in the electron density and reduction in the temperature at the pedestal top. Advances in understanding plasma exhaust include the integration of a high-performance plasma core with detached outer divertors in the X-point target configuration. A newly commissioned lower divertor cryopump reduces the lower divertor neutral pressure by up to 50%, with minimal effect on the main chamber or upper divertor. New measurements and SOLPS-ITER simulations emphasise the importance of plasma–neutral interactions on divertor detachment in the conditions accessible in experiments. Real-time control of the ionisation front location in both divertor chambers independently has been demonstrated in double null experiments, enabled by the tightly baffled divertor chambers.

MAST Upgrade↗
DOE OSTI2023

Recipes for when physics fails: recovering robust learning of physics informed neural networks

Abstract Physics-informed neural networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function. This paper shows that a PINN can be sensitive to errors in training data and overfit itself in dynamically propagating these errors over the domain of the solution of the PDE. It also shows how physical regularizations based on continuity criteria and conservation laws fail to address this issue and rather introduce problems of their own causing the deep network to converge to a physics-obeying local minimum instead of the global minimum. We introduce Gaussian process (GP) based smoothing that recovers the performance of a PINN and promises a robust architecture against noise/errors in measurements. Additionally, we illustrate an inexpensive method of quantifying the evolution of uncertainty based on the variance estimation of GPs on boundary data. Robust PINN performance is also shown to be achievable by choice of sparse sets of inducing points based on sparsely induced GPs. We demonstrate the performance of our proposed methods and compare the results from existing benchmark models in literature for time-dependent Schrödinger and Burgers’ equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Persistence of charge density wave fluctuations in the absence of long-range order in a hole-doped kagome metal

The kagome metals 𝐴⁢V 3 ⁢Sb 5 (𝐴=K, Rb, Cs) exhibit a complex interplay between charge density wave (CDW) order and superconductivity. Here, in this study, we use ultrafast coherent phonon spectroscopy to probe the evolution of CDW order in hole-doped CsV 3 ⁢Sb 5−𝑥 ⁢Sn 𝑥 across a broad range of compositions (0 ≤ 𝑥 ≤ 0.68). While thermodynamic and diffraction measurements show long-range CDW order vanishes above 𝑥 ≈ 0.05, we observe persistent signatures of CDW fluctuations up to the highest doping levels, with correlation times on the order of several picoseconds. These results indicate the presence of a robust fluctuating charge order that survives well beyond the established CDW phase boundary. Furthermore, these fluctuations are enhanced near a doping-tuned quantum phase transition at 𝑥* ≈ 0.15, which coincides with a local minimum in the superconducting 𝑇 c double-dome. Additional measurements on Ti- and K-substituted samples confirm that this behavior is intrinsic to hole doping and not tied to disorder. Overall, our findings suggest that CDW fluctuations play a central role in the electronic phase diagram of 𝐴⁢V 3 ⁢Sb 5 and may mediate or compete with superconductivity.

Kongruengkit, Terawit [Univ. of California, Santa ↗
DOE OSTI2021

Anomalous nematic state to stripe phase transition driven by in-plane magnetic fields

Anomalous nematic states, recently discovered in ultraclean two-dimensional electron gas, emerge from quantum Hall stripe phases upon further cooling. These states are hallmarked by a local minimum (maximum) in the hard (easy) longitudinal resistance and by an incipient plateau in the Hall resistance in nearly half-filled Landau levels. In this work, we demonstrate that a modest in-plane magnetic field, applied either along $\langle$110$\rangle$ or $\langle1\bar{1}0\rangle$ crystal axis of GaAs, destroys anomalous nematic states and restores quantum Hall stripe phases aligned along their native $\langle$110$\rangle$ direction. These findings confirm that anomalous nematic states are distinct from other ground states and will assist future theories to identify their origin.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Optimizing temperature distributions for training neural quantum states using parallel tempering

Parametrized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work, we focus on the role played by the temperature distribution of the parallel tempering replicas. Using an adaptive method that adjusts the temperatures in order to equate the exchange probability between neighboring replicas, we show that this temperature optimization can significantly increase the success rate of the variational algorithm with negligible computational cost by eliminating bottlenecks in the replicas' random walk. Furthermore, we demonstrate this using two different neural networks, a restricted Boltzmann machine and a feedforward network, which we use to study a toy problem based on a permutation invariant Hamiltonian with a pernicious local minimum and the 𝐽 1 −𝐽 2 model on a rectangular lattice.

Neural network simulations↗
DOE OSTI2025

Thermally induced structural competitiveness and metastability of body-centered-cubic iron under nonequilibrium conditions

The structure and stability of iron near melting at multi-megabar pressures are of significant interest in high pressure physics and earth and planetary sciences. While the body-centered cubic (BCC) phase is generally recognized as unstable at lower temperatures, its stability relative to the hexagonal close-packed (HCP) phase at high temperatures (approximately 0.5 eV) in the Earth’s inner core (IC) remains a topic of ongoing theoretical and experimental debate. Motivated by a series of studies attempting to understand the state of the IC and resulting in contradictory findings, we aim to explore the physical factors that may contribute to this confusion in a broader context. Our ab initio calculations show a significant drop in energy, the emergence of a plateau and a local minimum in the potential energy surface, and stabilization of all phonon modes of BCC iron at elevated electron temperatures (>1–1.5 eV). These effects increase the competition among the BCC, HCP, and the face-centered cubic (FCC) phases and lead to the metastability of the BCC structure. Furthermore, at pressure-temperature conditions relevant to the IC, we can expect substantial coexistence of FCC and HCP iron due to their similar free energies and low transition barrier (∼0.2 eV/atom), and the thermodynamic stability of BCC iron is enhanced by its substantial lattice vibration entropy. In conclusion, these findings provide a clear theoretical framework for understanding iron phase relations and solidification processes, both experimentally and in the IC.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Early arrival waveform inversion using data uncertainties and matching filters with application to near-surface seismic refraction data

We develop an early arrival waveform inversion (EAWI) technique for high-resolution near-surface velocity estimation by iteratively updating the P-wave velocity model to minimize the difference between the observed and calculated seismic refraction data. Traditional EAWI uses a least-squares penalty function and an acoustic forward-modeling engine. Conventional least-squares error is sensitive to data with low signal-to-noise ratio (S/N) and iterations of EAWI stop at a local-minimum data misfit or at the preassigned maximum number of iterations. These stopping criteria can result in overfitting the data. In addition, fitting the elastic field data with an acoustic modeling engine can introduce artifacts in velocity estimation, especially in land data with significant elastic effects. To overcome these challenges, we develop a robust EAWI (REAWI) method by (1) incorporating the data uncertainties into the penalty function and (2) mitigating the elastic effects using a matching filter workflow. The data uncertainties are estimated from waveform reciprocal errors. When full-waveform reciprocity is not available, trace interpolation is applied. The proposed method prevents closely fitting data with low S/N, avoids overall overfitting by stopping the iterations when a normalized chi-square ([Formula: see text]) waveform misfit of one is achieved, and is less affected by elastic effects. Numerical examples and application to near-surface refraction data at a groundwater contamination site suggest that the final REAWI models are more accurate than the corresponding EAWI models, at the same level of misfit. This is the first known application of a matching filter workflow to real land data. The final REAWI models satisfy an appropriate misfit between the real data and predicted elastic P-wave data, making this approach in this respect equivalent to elastic waveform inversion. We also develop a method to analyze model constraint by examining the energy of the wavefield Fréchet derivative thereby avoiding the influence of the data residuals in traditional Fréchet kernels.

Geochemistry & Geophysics↗
DOE OSTI2022

Droughts Reduce Growth Rates and Increase Vulnerability to Increasingly Frequent and Severe Drying Events in an Aquatic Ectotherm

Many aquatic organisms are experiencing increasingly severe and frequent droughts and drying events. Simultaneously, drought effects are carrying over to nondrought years as ecosystems remain in incomplete states of recovery. Aquatic organisms are thus faced with fewer sequential years under degraded environmental conditions to prepare for increasingly severe droughts and potential drying events. We assessed the effect of droughts and sex on the growth, mass, and mass-dependent estivation potential of long-lived aquatic salamanders (Greater Sirens, Siren lacertina) that estivate during drying events brought on by severe droughts. We calculated growth rates of S. lacertina based on mark–recapture data spanning 11 yr of a severe drought local minimum (of past 50 yr) in the southeastern United States. Sirens showed a distinct seasonal gain in body length and mass from March through September and little growth for the rest of the year. Gains during the growth season were strongly reduced by drought conditions. Although male and female sirens were predicted to reach a similar maximum body size, females grew much slower. Recruitment into drying event ‘‘size refugia’’ is constrained by drying event severity (determines minimum size required), frequency (determines available time between events to grow), and environmental conditions between drying events (determines the rate of growth). Thus, increases in drying event severity and frequency will require faster growth to a larger body size for successful recruitment into a size class that is resistant to drying events. The slower growth of females and reduction of growth during suboptimal years (mild to moderate droughts) suggest that the life history strategy of Greater Sirens for persisting through drying events potentially increases their demographic susceptibility to the predicted effects of climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Quantum-inspired tempering for ground state approximation using artificial neural networks

A large body of work has demonstrated that parameterized artificial neural networks (ANNs) can efficiently describe ground states of numerous interesting quantum many-body Hamiltonians. However, the standard variational algorithms used to update or train the ANN parameters can get trapped in local minima, especially for frustrated systems and even if the representation is sufficiently expressive. We propose a parallel tempering method that facilitates escape from such local minima. This methods involves training multiple ANNs independently, with each simulation governed by a Hamiltonian with a different "driver" strength, in analogy to quantum parallel tempering, and it incorporates an update step into the training that allows for the exchange of neighboring ANN configurations. We study instances from two classes of Hamiltonians to demonstrate the utility of our approach using Restricted Boltzmann Machines as our parameterized ANN. The first instance is based on a permutation-invariant Hamiltonian whose landscape stymies the standard training algorithm by drawing it increasingly to a false local minimum. The second instance is four hydrogen atoms arranged in a rectangle, which is an instance of the second quantized electronic structure Hamiltonian discretized using Gaussian basis functions. We study this problem in a minimal basis set, which exhibits false minima that can trap the standard variational algorithm despite the problem’s small size. We show that augmenting the training with quantum parallel tempering becomes useful to finding good approximations to the ground states of these problem instances.

Albash, Tameem↗
DOE OSTI2022

Technoeconomics of Particle-based CSP Featuring Falling Particle Receivers with and without Active Heliostat Control

This report documents the results and conclusions of a recent project to understand the technoeconomics of utility-scale, particle-based concentrating solar power (CSP) facilities leveraging unique operational strategies. This project included two primary objectives. The first project objective was to build confidence in the modeling approaches applied to falling particle receivers (FPRs) including the effect s of wind. The second project objective was to create the necessary modeling capability to adequately predict and maximize the annual performance of utility-scale, particle-based CSP plants under anticipated conditions with and without active heliostat control. Results of an extensive model validation study provided the strongest evidence to date for the modeling strategies typically applied to FPRs, albeit at smaller receiver scales. This modeling strategy was then applied in a parametric study of candidate utility-scale FPRs, including both free-falling and multistage FPR concepts, to develop reduced order models for predicting the receiver thermal efficiency under anticipated environmental and operating conditions. Multistage FPRs were found to significantly improve receiver performance at utility-scales. These reduced order models were then leveraged in a sophisticated technoeconomic analysis to optimize utility-scale , particle-based CSP plants considering the potential of active heliostat control. In summary, active heliostat control did not show significant performance benefits to future utility-scale CSP systems though some benefit may still be realized in FPR designs with wide acceptance angles and/or with lower concentration ratios. Using the latest FPR technologies available, the levelized-cost of electricity was quantified for particle-based CSP facilities with nominal powers ranging from 5 MW e up to 100 MW e with many viable designs having costs < 0.06 $/kWh and local minimums occurring between ~25–35 MW e .

14 SOLAR ENERGY↗
DOE OSTI2023

LongBondEliminator: A Molecular Simulation Tool to Remove Ring Penetrations in Biomolecular Simulation Systems

We develop a workflow, implemented as a plugin to the molecular visualization program VMD, that can fix ring penetrations with minimal user input. LongBondEliminator, detects ring piercing artifacts by the long, strained bonds that are the local minimum energy conformation during minimization for some assembled simulation system. The LongBondEliminator tool then automatically treats regions near these long bonds using multiple biases applied through NAMD. By combining biases implemented through the collective variables module, density-based forces, and alchemical techniques in NAMD, LongBondEliminator will iteratively alleviate long bonds found within molecular simulation systems. Through three concrete examples with increasing complexity, a lignin polymer, an viral capsid assembly, and a large, highly glycosylated protein aggrecan, we demonstrate the utility for this method in eliminating ring penetrations from classical MD simulation systems. The tool is available via gitlab as a VMD plugin, and has been developed to be generically useful across a variety of biomolecular simulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Energetic Electrons Accelerated and Trapped in a Magnetic Bottle above a Solar Flare Arcade

Where and how flares efficiently accelerate charged particles remains an unresolved question. Recent studies revealed that a “magnetic bottle” structure, which forms near the bottom of a large-scale reconnection current sheet above the flare arcade, is an excellent candidate for confining and accelerating charged particles. However, further understanding its role requires linking the various observational signatures to the underlying coupled plasma and particle processes. Here we present the first study combining multiwavelength observations with data-informed macroscopic magnetohydrodynamics and particle modeling in a realistic eruptive flare geometry. The presence of an above-the-loop-top magnetic bottle structure is strongly supported by the observations, which feature not only a local minimum of magnetic field strength but also abruptly slowing plasma downflows. It also coincides with a compact above-the-loop-top hard X-ray source and an extended microwave source that bestrides the flare arcade. Spatially resolved spectral analysis suggests that nonthermal electrons are highly concentrated in this region. Our model returns synthetic emission signatures that are well matched to the observations. The results suggest that the energetic electrons are strongly trapped in the magnetic bottle region due to turbulence, with only a small fraction managing to escape. The electrons are primarily accelerated by plasma compression and facilitated by a fast-mode termination shock via the Fermi mechanism. Our results provide concrete support for the magnetic bottle as the primary electron acceleration site in eruptive solar flares. They also offer new insights into understanding the previously reported small population of flare-accelerated electrons entering interplanetary space.

79 ASTRONOMY AND ASTROPHYSICS↗