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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Substorm Driven Chorus Waves: Decay Timescales and Implications for Pulsating Aurora

Abstract Energetic electron precipitation (EEP) associated with pulsating aurora can transfer greater than 30 keV electrons from the outer radiation belt region into the upper atmosphere and can deplete atmospheric ozone via collisions that produce NOx and HOx molecules. Our knowledge of exactly how EEP occurs is incomplete. Previous studies have shown that pitch angle scattering between electrons and lower‐band chorus waves can cause pulsating aurora associated with EEP and that substorms play an important role. In this work, we quantify the timescale of chorus wave decay following substorms and compare that to previously determined timescales. We find that the chorus decay e‐folding time varies based on magnetic local time (MLT), magnetic latitude, and wave frequency. The shortest timescales occur for lower‐band chorus in the 21 to 9 MLT region and compares, within uncertainty, to the energetic pulsating aurora timescale of Troyer et al. (2022, https://doi.org/10.3389/fspas.2022.1032552 ) for energetic pulsating aurora. We are able to further support this connection by modeling our findings in a quasi‐linear diffusion simulation. These results provide observations of how chorus waves behave after substorms and add additional statistical evidence linking energetic pulsating aurora to substorm driven lower‐band chorus waves.

Troyer, Riley N.↗
DOE OSTI2021

Machine learning assisted modeling of mixing timescale for LES/PDF of high-Karlovitz turbulent premixed combustion

Accurate modeling of mixing in the transported probability density function (PDF) method remains a great challenge, especially for turbulent premixed combustion under extreme conditions such as high Karlovitz number Ka. Recently, a power-law based mixing timescale model was developed for the large-eddy simulations (LES)/PDF modeling of high-Ka number turbulent premixed flames. It is found in this work that the power-law mixing timescale model is highly sensitive to the model parameters. It is thus critically needed to develop accurate calibration of these model parameters. The empirical specification of the model parameters developed in Zhang et. al. is found to be inadequate for accurate modeling of the mixing timescale. Here, machine learning is introduced as an attractive alternative in this work for the specification of the model parameters. A high-Ka number DNS jet flame is used as the training and validation of the machine learning models. The choices of the input parameters are discussed and compared for the machine learning models. The effect of differential molecular diffusion on mixing is examined by including the effect of the Lewis number in the training of the machine learning models. The performance of different machine learning algorithms is compared for the specification of the mixing model parameters. Overall, excellent performance of the machine learning models is observed for assisting the mixing modeling. The feasibility, interpretability, applicability, generality, and portability of using machine learning are discussed in general to provide a perspective on applying data-driven machine learning for turbulent combustion modeling studies.

42 ENGINEERING↗
DOE OSTI2024

Heterogeneity in Permeability and Particulate Organic Carbon Content Controls the Redox Condition of Riverbed Sediments at Different Timescales

Abstract The hydrological and biogeochemical properties of the hyporheic zone in stream and riverine ecosystems have been extensively studied over the past two decades. Although it is widely acknowledged that sediment heterogeneity can influence biogeochemical reactions, little effort has been made to understand the role of heterogeneity on the spatiotemporal variability of riverbed redox conditions under changing flow dynamics at different timescales. Here we integrate a mechanistic model and field data to demonstrate that heterogeneity in permeability plays a vital role in modulating sediment redox conditions at both seasonal (annual) and event (daily‐to‐weekly) timescales, whereas heterogeneity in particulate organic carbon (POC) content only has a comparable influence on redox conditions at the seasonal timescale. These findings underscore the importance of accurately characterizing sediment heterogeneity, in terms of permeability and POC content, in quantifying biogeochemical dynamics in the riverbed and hyporheic zones of riverine ecosystems.

Geology↗
DOE OSTI2022

Global gyrokinetic simulations of ASDEX Upgrade up to the transport timescale with GENE–Tango

An accurate description of turbulence up to the transport timescale is essential for predicting core plasma profiles and enabling reliable calculations for designing advanced scenarios and future devices. Here, we exploit the gap separation between turbulence and transport timescales and couple the global gyrokinetic code GENE to the transport-solver Tango, including kinetic electrons, collisions, realistic geometries, toroidal rotation and electromagnetic effects for the first time. This approach overcomes gyrokinetic codes' limitations and enables high-fidelity profile calculations in experimentally relevant plasma conditions, significantly reducing the computational cost. We present numerical results of GENE–Tango for two ASDEX Upgrade discharges, one of which exhibits a pronounced peaking of the ion temperature profile not reproduced by TGLF–ASTRA. We show that GENE–Tango can correctly capture the ion temperature peaking observed in the experiment. By retaining different physical effects in the GENE simulations, e.g., collisions, toroidal rotation and electromagnetic effects, we show that the ion temperature profile's peaking can be linked to electromagnetic effects of submarginal (stable) KBM modes. Furthermore, based on these results, the expected GENE–Tango speedup for the ITER standard scenario is larger than two orders of magnitude compared to a single gyrokinetic simulation up to the transport timescale, possibly making first-principles ITER simulations feasible on current computing resources.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Reinforcement Learning-Guided Long-Timescale Simulation of Hydrogen Transport in Metals

Diffusion in alloys is an important class of atomic processes. However, atomistic simulations of diffusion in chemically complex solids are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interest. In this work, long-timescale simulation methods are developed using reinforcement learning (RL) that extends simulation capability to match the duration of experimental interest. Two special limits, RL transition kinetics simulator (TKS) and RL low-energy states sampler (LSS), are implemented and explained in detail, while the meaning of general RL are also discussed. As a testbed, hydrogen diffusivity is computed using RL TKS in pure metals and a medium entropy alloy, CrCoNi, and compared with experiments. The algorithm can produce counter-intuitive hydrogen-vacancy cooperative motion. We also demonstrate that RL LSS can accelerate the sampling of low-energy configurations compared to the Metropolis–Hastings algorithm, using hydrogen migration to copper (111) surface as an example.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Inner Belt Electron Decay Timescales: A Comparison of Van Allen Probes and DREAM3D Losses Following the June 2015 Storm

Abstract NASA's Van Allen Probes observed significant, long‐lived fluxes of inner belt electrons up to ∼1 MeV after geomagnetic storms in March and June 2015. Reanalysis of Magnetic Electron Ion Spectrometer (MagEIS) data with improved background correction showed a clearer picture of the relativistic electron population that persisted through 2016 and into 2017 above the Fennell et al. (2015, https://doi.org/10.1002/2014gl062874 ) limit. The intensity and duration of these enhancements allow estimation of decay timescales for comparison with simulated decay rates and theoretical lifetimes. We compare decay timescales from these data and DREAM3D simulations based on them using geomagnetic activity‐dependent pitch angle diffusion coefficients derived from plasmapause‐indexed wave data (Malaspina et al., 2016, https://doi.org/10.1002/2016gl069982 , 2018, https://doi.org/10.1029/2018gl078564 ) and phase space densities derived from MagEIS observations. Simulated decay rates match observed decay rates more closely than the theoretical lifetime due to significantly nonequilibrium pitch angle distributions in simulation and data. We conclude that nonequilibrium effects, rather than a missing diffusion or loss process, account for observed short decay rates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of deep time-series models

Stably stratified turbulence (SST), a model that is representative of the turbulence found in the oceans and atmosphere, is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying scenarios. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations—used to numerically simulate approximate solutions for such turbulent flows. Rather than attempting to model each term in URANS separately, it is attractive to explore the capability of machine learning (ML) to model groups of terms, i.e. to directly model the force balances. We develop deep time-series ML for closure modeling of the URANS equations applied to SST. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: long short-term memory and neural ordinary differential equation. Both models perform accurately and are numerically stable in a posteriori (online) tests. Furthermore, we explore the data requirements of the time-series ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of high-dimensional complex dynamical system like SST flows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems

The neural ordinary differential equation (ODE) framework has shown considerable promise in recent years in developing highly accelerated surrogate models for complex physical systems characterized by partial differential equations (PDEs). For PDE-based systems, state-of-the-art neural ODE strategies leverage a two-step procedure to achieve this acceleration: a nonlinear dimensionality reduction step provided by an autoencoder, and a time integration step provided by a neural-network based model for the resultant latent space dynamics (the neural ODE). This work explores the applicability of such autoencoder-based neural ODE strategies for PDEs in which advection terms play a critical role. More specifically, alongside predictive demonstrations, physical insight into the sources of model acceleration (i.e., how the neural ODE achieves its acceleration) is the scope of the current study. Such investigations are performed by quantifying the effects of both autoencoder and neural ODE components on latent system time-scales using eigenvalue analysis of dynamical system Jacobians. To this end, the sensitivity of various critical training parameters – de-coupled versus end-to-end training, latent space dimensionality, and the role of training trajectory length, for example – to both model accuracy and the discovered latent system timescales is quantified. Furthermore, this work specifically uncovers the key role played by the training trajectory length (the number of rollout steps in the loss function during training) on the latent system timescales: larger trajectory lengths correlate with an increase in limiting neural ODE time-scales, and optimal neural ODEs are found to recover the largest time-scales of the full-order (ground-truth) system. Demonstrations are performed across fundamentally different unsteady fluid dynamics configurations influenced by advection: (1) the Kuramoto–Sivashinsky equations (2) Hydrogen-Air channel detonations (the compressible reacting Navier–Stokes equations with detailed chemistry), and (3) 2D Atmospheric flow.

Advection-dominated dynamical systems↗
DOE OSTI2022

Coupled Interfacial and Bulk Kinetics Govern the Timescales of Multiphase Ozonolysis Reactions

Chemical transformations in aerosols impact the lifetime of particle phase species, the fate of atmospheric pollutants, and both climate- and health-relevant aerosol properties. Timescales for multiphase reactions of ozone in atmospheric aqueous phases are governed by coupled kinetic processes between the gas phase, the particle interface, and its bulk, which respond dynamically to reactive consumption of O 3 . However, models of atmospheric aerosol reactivity often do not account for the coupled nature of multiphase processes. Herein, to examine these dynamics, we use new and prior experimental observations of aqueous droplet reaction kinetics, including three systems with a range of surface affinities and ozonolysis rate coefficients ( trans -aconitic acid (C 6 H 6 O 6 ), maleic acid (C 4 H 4 O 4 ), and sodium nitrite (NaNO 2 )). Using literature rate coefficients and thermodynamic properties, we constrain a simple two-compartment stochastic kinetic model which resolves the interface from the particle bulk and represents O 3 partitioning, diffusion, and reaction as a coupled kinetic system. Our kinetic model accurately predicts decay kinetics across all three systems, demonstrating that both the thermodynamic properties of O 3 and the coupled kinetic and diffusion processes are key to making accurate predictions. An enhanced concentration of adsorbed O 3 , compared to gas and bulk phases is rapidly maintained and remains constant even as O 3 is consumed by reaction. Multiphase systems dynamically seek to achieve equilibrium in response to reactive O 3 loss, but this is hampered at solute concentrations relevant to aqueous aerosol by the rate of O 3 arrival in the bulk by diffusion. As a result, bulk-phase O 3 becomes depleted from its Henry's law solubility. This bulk-phase O 3 depletion limits reaction timescales for relatively slow-reacting organic solutes with low interfacial affinity (i.e., trans-aconitic and maleic acids, with k rxn ≈ 10 3 -10 4 M -1 s -1 ), which is in contrast to fast-reacting solutes with higher surface affinity (i.e., nitrite, with k rxn ≈ 10 5 M -1 s -1 ) where surface reactions strongly impact the observed decay kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Species, Pathways, and Timescales for NH 3 Formation by Low-Temperature Atmospheric Pressure Plasma Catalysis

Species, pathways, and timescales for NH3 production by plasma catalysis over transition-metal wools are determined by measuring plasma-derived species densities [N, H, and N 2 (v)], quantitatively correlating consumption of these species with NH 3 formation, and measuring consumption of plasma-derived species at different residence times. These findings are enabled by a capillary flow through Ar/N 2 /H 2 plasma jet reactor setup that allows for the measurement of gas-phase species densities by molecular beam mass spectrometry. Surface-mediated reactions involving N radicals are responsible for NH 3 formation over Fe, Ni, and Ag surfaces. N reacts to form NH 3 with ~100% selectivity over Ni and Ag when H/N > 3 and % H 2 ≥ 0.5. The selectivity to ammonia drops as H and H 2 densities decrease for each catalyst. A comparison between amounts of NH 3 formed and N consumed with and without catalysts present shows that surface reactions enable higher and more selective conversion of N to NH 3 than gas-phase reactions alone. The conversion of N to NH 3 is negligible in the absence of H, demonstrating that H is required to produce NH 3 at these operating conditions. The consumption of N occurs on the same timescale as NH 3 formation, further confirming that reactions involving N contribute to NH 3 formation. Though vibrationally excited N 2 [N 2 (v)] is produced in quantities exceeding N by 100-fold, consumption of N 2 (v) on the catalytic surface does not contribute to NH 3 formation. Furthermore, these findings show that for low-temperature atmospheric pressure plasma catalysis, surface- mediated reactions among radical N and H species drive NH 3 formation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Timescale Dependence of the Precipitation Response to CO 2 ‐Induced Warming in Millennial‐Length Climate Simulations

Abstract Previous work has shown that estimates of climate sensitivity vary over time in response to abrupt CO 2 forcing in climate model simulations. The energy fluxes that drive warming in response to increasing CO 2 also influence precipitation, which prompts the question: Does the precipitation response therefore also vary over time? We investigate by examining the response of precipitation to warming forced by greenhouse gases—the hydrological sensitivity—in a set of millennial‐length climate simulations with multiple climate models, Long Run Model Intercomparison Project (LongRunMIP). We compare hydrological sensitivity calculated from three different timescales of the simulations: years 1–20, 21–150, and 151–1000. We show that the hydrological sensitivity lacks a consistent dependence on timescale, in contrast to climate sensitivity. Decomposition of the surface energy budget reveals that the relative muting of the multi‐model mean hydrological sensitivity is driven by surface downwelling shortwave flux.

Him (Kinen) Kao, Wing↗
DOE OSTI2025

Increased water availability at various timescales has different effects on stomatal closure point in isohydric piñon pine and anisohydric juniper

Abstract Stomatal Closure Point (SCP) has commonly been used to describe drought response strategies in plants, with isohydric species maintaining relatively high, constant SCP compared to anisohydric species that can lower SCP with increasing drought severity. However, there is evidence that, within these groups, SCP may respond dynamically to environmental conditions. Here, we explored how increasing water availability affects SCP in classically isohydric piñon pine and anisohydric one-seed or Utah juniper at various spatial- (i.e., from branch, to tree, to ecosystem) and temporal- (i.e., hours to decades) scales. Our results show that short-term increases in water availability decreased SCP in isohydric piñon pine, making it more anisohydric, while short-term rehydration had no effect on SCP in anisohydric juniper. Increasing mean annual precipitation, on the other hand, increased SCP in both species. Our findings are consistent with documented differences in the use of ABA to control stomata in iso- and aniso-hydric species on short timescales, and with structural acclimation in both species at long timescales. These results illustrate that the local environment plays a large role in determining SCP.

Kraklow, Vachel↗
DOE OSTI2022

Rapid early gas accretion for the inner Galactic disc: A case for short accretion timescale

Recent observations of the Milky Way and galaxies at high redshifts suggest that galaxy discs were already in place soon after the Big Bang. While the gas infall history of the Milky Way in the inner disc has long been assumed to be characterised by a short accretion timescale, this has not been directly constrained using observations. Using data for the inner regions of the Milky Way recently produced by APOGEE and Gaia and of unprecedented quantity and quality, we aim to derive strong constraints on the infall history of the inner (< 6 kpc) Galaxy (with a focus on stars between 4 and 6 kpc, which we show is an appropriate proxy for the entire inner disc). We implemented gas infall into a chemical evolution model of the Galaxy disc, and used a Schmidt–Kennicutt law to connect the infall to the star formation. We explore a number of models, and two different formulations of the infall law. In one formulation, the infall is non-parametric, and in the other the infall has an explicitly exponential form. We fit the model parameters to the time–[Si/Fe] distribution of solar vicinity stars, and the metallicity and [Si/Fe] distribution function of stars with a galactocentric radius of between 4 and 6 kpc from APOGEE. Our results point to a fast, early gas accretion, and an upper limit on the accretion timescale of around 2 Gyr in the inner disc of the Milky Way. This suggests that at least half the baryons were in place within 2–3 Gyr of the Big Bang, and that half the stars of the inner disc formed within the first 5 Gyr, during the thick disc formation phase. This implies that the stellar mass of the inner disc is dominated by the thick disc, supporting our previous work, and that the gas accretion onto the inner disc was rapid and early.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Multi-timescale operations of nuclear-renewable hybrid energy systems for reserve and thermal product provision

In this paper, an optimal operation strategy of a nuclear-renewable hybrid energy system (N-R HES), in conjunction with a district heating network, is developed within a comprehensive multi-timescale electricity market framework. The grid-connected N-R HES is simulated to explore the capabilities and benefits of N-R HES of providing energy products, different reserve products, and thermal products. An N-R HES optimization and control strategy is formulated to exploit the benefits from the hybrid energy system in terms of both energy and ancillary services. A case study is performed on the customized NREL-118 bus test system with high renewable penetrations, based on a multi-timescale (i.e., three-cycle) production cost model. Both day-ahead and real-time market clearing prices are determined from the market model simulation. In conclusion, the results show that the N-R HES can contribute to the reserve requirements and also meet the thermal load, thereby increasing the economic efficiency of N-R HES (with increased revenue ranging from 1.55% to 35.25% at certain cases) compared to the baseline case where reserve and thermal power exports are not optimized.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Timescales of excited state relaxation in α–RuCl 3 observed by time-resolved two-photon photoemission spectroscopy

The nonequilibrium properties of strongly correlated materials present a target in the search for new phases of matter. It is important to observe the types of excitations that exist in these materials and their associated relaxation dynamics. We have studied the photoexcitations in a spin-orbit assisted Mott insulator α–RuCl 3 using time-resolved two-photon photoemission spectroscopy and transient reflection spectroscopy. We find that photoexcited carriers (doublons) in the upper Hubbard band rapidly relax to Mott-Hubbard excitons on a timescale of less than 200 fs. Subsequently, further relaxation of these lower-energy quasiparticles occurs with an energy-dependent time constant of that ranges from 370 to 600 fs due to exciton cooling. The population of Mott-Hubbard excitons persists for timescales up to several microseconds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Dynamic wavelength control of laser pulse profiles at picosecond to nanosecond timescales

We report on a novel combined laser pulse shaping and dynamic wavelength encoding capability based on a simple architecture implementing direct space to time mapping. There are several potential applications that can be enabled by the ability to control the instantaneous intensity or wavelength of an optical waveform on a picosecond-to-nanosecond timescale. To our knowledge, no known methods can access this temporal regime with a practical architecture. Here, we demonstrate an extension of the Space–Time Induced Linearly Encoded Transcription for Temporal Optimization (STILETTO) technique that can generate optical waveforms with a programmable instantaneous wavelength vs. time. We experimentally demonstrate the technique by generating self-gated spectrograms and show that it can encode dynamic wavelength vs time profiles at timescales not achievable by any other known method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Abbreviated Technical Report: Experimentally Interrogating Detonation Chemistry on Sub-Nanosecond to Nanosecond Timescales

Direct experimental measurement of chemical reactions during high explosive detonation remains challenging. Theory and modeling have long preceded experiment in the fundamental physical and chemical kinetic properties of detonation, and experimentation at the relevant timescales are needed to both validate models and provide fundamental understanding of detonation. In this LDRD-ER project, two approaches, x-ray diffraction and core-level x-ray Raman, were developed and used to further experimental capabilities to address this gap. We further developed dynamic x-ray diffraction to directly detect nanodiamond formation during detonation, providing experimental data towards resolving longstanding controversy in the scientific literature, and although the full kinetics have not yet been fully mapped, diamond diffraction appears on the same timescales as detonation soot formation. In the second research thrust, we have developed core-level x-ray Raman for use with high explosives. This technique provides information analogous to x-ray absorption spectroscopy and electron energy loss spectroscopy, but uses inelastic scattering of hard x-rays that can interrogate chemistry around light elements much deeper into the material. The low cross section and requisite high solid angle collection have hindered its use for ultra-fast spectroscopy. We developed and tested a high-q spectrometer which will substantially increase cross section and signal-to-noise, showing this method will also not dramatically alter, compared to x-ray absorption, the most discriminating spectral features of C, N, and O from various high explosives and expected detonation products. We have also used x-ray Raman combined with OCEAN electronic structure calculations to explore dynamic photodegradation mechanisms in PETN and CL-20 explosives. This provides a pathway towards implementing capability to dynamically explore chemistry at an x-ray free electron laser.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-Period Optimization of Multi-Timescale Energy Systems: Application to Solid-Oxide Electrolysis Cells

A presentation detailing the recent progress in the optimization of Solid-Oxide Cells under Chemical Degradation. The existing methods typically involve a quasi-steady state assumption to make this large-scale problem tractable. Here, we introduce an extension to this method that allows for adaptive coupling of degradation with the fast-timescale process based on a variety of error thresholds. This reduces the error accumulated during long-term optimization of the SOC under degradation and allows for decision making at multiple timescales. The results presented include long-term operating profiles of the SOC under degradation with both steady-state and fluctuating day to day operation.

Giridhar, Nishant↗