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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Moisture Sources of Precipitation in the Great Lakes Region: Climatology and Recent Changes

Abstract Given the critical role of precipitation on hydroclimate, we quantified the contributions of moisture source regions to precipitation in the Great Lakes Region (GLR) using multiple reanalysis data sets. Results show that the Great Plains (GPs) and the GLR itself are the primary sources of moisture. The moisture sources for the double peaks in the GLR precipitation that occur in June and September are identified, which is caused by a shift in the peak timing of moisture contribution from the GLR and GPs. In particular, moisture from the GPs contributes more to the heavy precipitation, while moisture from the GLR contributes more to the light precipitation. We also found a statistically significant ( p < 0.05) increasing trend in the moisture contribution from the mid‐Pacific, caused by an intensified zonal moisture transport from the mid‐Pacific through changes in atmospheric circulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Assessing Clouds in GFDL's AM4.0 With Different Microphysical Parameterizations Using the Satellite Simulator Package COSP

We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two‐moment Morrison‐Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4‐MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud‐to‐precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under‐prediction of total and low‐level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over‐prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low‐level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4‐MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high‐level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Synchronization of the Recent Decline of East African Long Rains and Northwestern Eurasian Warming

Abstract The East African March–April–May (MAM, “long rains”) precipitation decline in recent decades remains a puzzle marked by various proposed large‐scale drivers. Here, the interannual variability of the long rains and their recent drying trend are examined using global model simulations and observations. Comparison of a control simulation and re‐initialized simulations in which land‐surface feedback is suppressed shows that much of the long rains deficit experienced between 1980 and 2014 is synchronized with the warming of the Northwestern Eurasian landmass. In agreement with the modeling results, multiple observational data sets reveal a strong negative correlation between MAM mean East African rainfall amount and the surface temperature over Northwestern Eurasia. Idealized simulations further indicate that warming in Northwestern Eurasia weakens the regional Hadley Cell and diverts the monsoonal transport of moisture away from Eastern Africa toward Europe and southern Africa, highlighting the role of remote land surface warming on the observed precipitation decline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Relaxation of stationary states on a quantum computer yields a unique spectroscopic fingerprint of the computer’s noise

Abstract Quantum computing has the potential to revolutionize computing, but its significant sensitivity to noise requires sophisticated error correction and mitigation. Traditionally, noise on the quantum device is characterized directly through qubit and gate measurements, but this approach has drawbacks in that it does not adequately capture the effect of noise on realistic multi-qubit applications. In this paper, we simulate the relaxation of stationary quantum states on a quantum computer to obtain a unique spectroscopic fingerprint of the computer’s noise. In contrast to traditional approaches, we obtain the frequency profile of the noise as it is experienced by the simulated stationary quantum states. Data from multiple superconducting-qubit IBM processors show that noise generates a bath within the simulation that exhibits both colored noise and non-Markovian behavior. Our results provide a direction for noise mitigation but also suggest how to use noise for quantum simulations of open systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Modeling ionization quenching in organic scintillators

Ionization quenching models were assessed by evaluating light yield data from multiple organic scintillators and recoil ions over a fission spectrum neutron energy range, important for basic science and applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ultrafast disruptive probing: Simultaneously keeping track of tens of reaction pathways

Ultrafast science depends on different implementations of the well-known pump–probe method. Here, we provide a formal description of ultrafast disruptive probing, a method in which the probe pulse disrupts a transient species that may be a metastable ion or a transient state of matter. Disruptive probing has the advantage of allowing for simultaneous tracking of the yield of tens of different processes. Our presentation includes a numerical model and experimental data on multiple products resulting from the strong-field ionization of two different molecules, partially deuterated methanol and norbornene. The correlated enhancement and depletion signals between all the different fragmentation channels offer comprehensive information on photochemical reaction pathways. In combination with ion imaging and/or coincidence momentum imaging or as complementary to atom-specific probing or ultrafast diffraction methods, disruptive probing is a particularly powerful tool for the study of strong-field laser–matter interactions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Unsupervised learning of ferroic variants from atomically resolved STEM images

An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and is shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in this article is available at https://github.com/saimani5/ferroelectric_domains_rVAE.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

An analytic and complete equation of state for condensed phase materials

Analytic equations of state (EOS) are intended to reproduce theoretical and experimental data in a single phase portion of the thermodynamic space. We devise a complete and thermodynamically consistent model with four distinct features: (1) a reference isotherm that remains thermodynamically stable, (2) a flexible specific heat model based on a fourth-order rational polynomial, (3) a Grüneisen parameter that depends on specific volume and temperature, and (4) pressure and internal energy functions that can be inverted analytically in temperature. The model aims to improve the accuracy of existing equations of state while remaining computationally efficient. To demonstrate its features, we include calibrations for single-crystal pentaerythritol tetranitrate (PETN), liquid nitromethane (NM), and hexagonal close-packed beryllium (Be) metal. The parameter optimization uses the specific heat capacity, Grüneisen parameter, and static compression curves obtained from density functional theory for the crystalline solids and molecular dynamics simulations for liquid NM. We also present a velocity autocorrelation function that yields accurate phonon densities of states for the EOS calibration from the molecular dynamics trajectories. Each of the three calibrations is constrained to enforce the ambient state from experimental measurements and validated against experimental Hugoniot data from multiple sources. We also include one-dimensional hydrodynamic simulations of the isentropic compression experiments for beryllium conducted at the Z facility.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

X-ray scattering signatures of early-time accelerations in iodine dissociation

Ultrafast time-resolved x-ray scattering (TRXS) from a photoexcited molecular ensemble measures a distribution $S(Q, τ)$ of the x-ray momentum transfer Q and pump-probe delay τ in which all modes of motion induced by the excitation overlap. Frequency-resolved x-ray scattering (FRXS) based on $\tilde {S}\left(Q,\omega \right)$ separates each oscillation and dissociation channel in the TRXS data, enabling measurements of vibrational frequencies and phases, and dissociation velocities and time shifts. In this work, we extend FRXS analysis to study early-time accelerations as well. We show how these appear as diffuse scattering patterns with characteristic phase evolution in FXRS and we develop a set of transformations that isolate individual channels to measure the early-time accelerated motion. This procedure is used to analyze diatomic iodine x-ray scattering data with multiple dissociations, and the ability of this technique to characterize early-time accelerations of one dissociation channel even in the presence of another dissociation is demonstrated.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

An analysis of parameter compression and Full-Modeling techniques with Velocileptors for DESI 2024 and beyond

In anticipation of forthcoming data releases of current and future spectroscopic surveys, we present the validation tests and analysis of systematic effects within velocileptors modeling pipeline when fitting mock data from the AbacusSummit N-body simulations. We compare the constraints obtained from parameter compression methods to the direct fitting (Full-Modeling) approaches of modeling the galaxy power spectra, and show that the ShapeFit extension to the traditional template method is consistent with the Full-Modeling method within the standard ΛCDM parameter space. We show the dependence on scale cuts when fitting the different redshift bins using the ShapeFit and Full-Modeling methods. We test the ability to jointly fit data from multiple redshift bins as well as joint analysis of the pre-reconstruction power spectrum with the post-reconstruction BAO correlation function signal. We further demonstrate the behavior of the model when opening up the parameter space beyond ΛCDM and also when combining likelihoods with external datasets, namely the Planck CMB priors. Finally, we describe different parametrization options for the galaxy bias, counterterm, and stochastic parameters, and employ the halo model in order to physically motivate suitable priors that are necessary to ensure the stability of the perturbation theory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

New heat flux model for non-axisymmetric divertor infrared structures

A convective heat flux model for perturbed plasmas, based on guiding center ion drift in vacuum elds (A. Wingen, et al., Phys. Plasmas 21 (2014) 012509), has been updated. The old model only considered ion heat flux, while here also electron heat flux is included. The updated model predicts divertor heat flux distributions in non-axisymmetric (3D) plasmas with applied Resonant Magnetic Perturbation (RMP) fields, and includes electric scalar potentials. It is found that a radial electric field in the near Scrape-o Layer (SOL) can considerably shift the footprints toroidally, leading to a smearing out eff ect of the incident heat flux, while a simple model for sheath potential has little impact on footprints. Various approaches to model electron heat flux are studied. A convective electron model, based on collisionless free streaming, is found to yield the best agreement with measurements, while a conductive model requires a at temperature gradient inside lobes to yield acceptable peak heat ux values. A heuristic heat flux layer approach, based on a fixed layer width also requires a limited heat flux inside the last closed flux surface (LCFS); by selecting various locations of the LCFS, the results of the conductive or convective model can be recovered respectively. The sum of ion and electron heat fluxes, both obtained by the convective model, is compared to experimental data for multiple time slices in DIII-D. Strike point splitting is observed with peak heat fluxes and layer widths that compare well to infrared camera (IR) measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. Here, the explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973) tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Response of salt intrusion in a tidal estuary to regional climatic forcing

Abstract Salinity distribution in a large tidal estuary is subject to estuarine adjustment under the influences of multiple physical drivers such as freshwater pulses and sea level rise, and is crucial to upstream water quality, aquaculture, and ecosystem functions of the estuary. To better understand the estuarine salinity response to climate change, the unstructured-grid Finite Volume Community Ocean Model was implemented to simulate the salt intrusion in the Delaware Bay Estuary. The model was first validated by multiple observational data sets and subsequently applied in an idealized setting to examine the response of salt front to freshwater pulses in high flow conditions, followed by a long-term drought condition supported by a multi-decadal streamflow drought analysis in the estuary. The model results showed that after the freshwater pulses the salt front location moved further upstream with sea level rise. Under the simulated long-term drought condition, the adjustment timescale of salt intrusion varies nonlinearly with sea level rise. With a significant increase in sea level rise, the adjustment timescale starts to decrease. This shift suggests a transition into a different regime where the estuary becomes more stratified, as indicated by an increasing bulk Simpson number with rising sea levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A robust statistical analysis of the role of hydropower on the system electricity price and price volatility

Abstract Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage and large reservoir storage) for providing energy in times of high demand on the grid (S. Rehman, L M Al-Hadhrami, and M M Alam), (2015 Renewable and Sustainable Energy Reviews , 44 , 586–98). This study examines the impact of hydropower on system electricity price and price volatility in the region served by the New England Independent System Operator (ISONE) from 2014-2020 (ISONE, ISO New England Web Services API v1.1 .” https://webservices.iso-ne.com/docs/v1.1/ , 2021. Accessed: 2021-01-10). We perform a robust holistic analysis of the mean and quantile effects, as well as the marginal contributing effects of hydropower in the presence of solar and wind resources. First, the price data is adjusted for deterministic temporal trends, correcting for seasonal, weekend, and diurnal effects that may obscure actual representative trends in the data. Using multiple linear regression and quantile regression, we observe that hydropower contributes to a reduction in the system electricity price and price volatility. While hydropower has a weak impact on decreasing price and volatility at the mean, it has greater impact at extreme quantiles (>70th percentile). At these higher percentiles, we find that hydropower provides a stabilizing effect on price volatility in the presence of volatile resources such as wind. We conclude with a discussion of the observed relationship between hydropower and system electricity price and volatility.

13 HYDRO ENERGY↗
DOE OSTI2025

Harnessing Machine Learning for Agnostic Biodetection

The United States’ current list-based approach to biodefense is limited because it considers only known biological agents. Alternatively, developing and adopting a system based on agent-agnostic signatures would enable detection and characterization of both known and novel agents, thereby engendering greater adaptability in the face of an evolving threat landscape. Machine learning (ML) could aid in such a transition, as it can recognize and encode highly complex patterns from multiple input data modalities and has already demonstrated success in many healthcare and defense applications. Functionalizing ML for environmental biodetection requires understanding current technical capabilities. In this article, we provide a systematic review of existing ML platforms and discuss anticipated development efforts needed to achieve effective ML-enabled, agnostic biodetection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

LATTE: open-source, high-performance traveltime computation, tomography and source location in acoustic and elastic media

Traveltime-based tomography and source location are fundamental approaches for imaging subsurface structures and understanding the spatiotemporal distribution of seismicity from local to global scales. We present an open-source, high-performance framework integrating eikonal equation solvers and adjoint-state theory for traveltime computation, velocity tomography, source location and joint tomography-location in 2-D/3-D acoustic and elastic media. We introduce novel regularization schemes based on total generalized p-variation, structural similarity and multitask machine learning to enhance the fidelity and interpretability of inverted models and source locations. Key features of our implementation also include the ability to leverage both absolute-difference and double-difference traveltime misfits for high-fidelity velocity tomography and source parameter estimation; support for traveltime computation and inversion in diverse 2-D/3-D scenarios with arbitrary source and receiver distributions; and a perturbation-based optimal step-size estimation method to reduce computational costs. In addition, our implementation employs shared-memory and distributed-memory parallelization to provide an efficient solution for traveltime computation, tomography, and source location. In conclusion, we validate the efficacy and accuracy of our approach through multiple synthetic data examples.

58 GEOSCIENCES↗
DOE OSTI2020

Core-collapse supernova neutrino emission and detection informed by state-of-the-art three-dimensional numerical models

ABSTRACT Based on our recent three-dimensional core-collapse supernova (CCSN) simulations including both exploding and non-exploding models, we study the detailed neutrino signals in representative terrestrial neutrino observatories, namely Super-Kamiokande (Hyper-Kamiokande), DUNE, JUNO, and IceCube. We find that the physical origin of difference in the neutrino signals between 1D and 3D is mainly proto-neutron-star convection. We study the temporal and angular variations of the neutrino signals and discuss the detectability of the time variations driven by the spiral standing accretion shock instability (spiral SASI) when it emerges for non-exploding models. In addition, we determine that there can be a large angular asymmetry in the event rate (${\gtrsim} 50 {{\ \rm per\ cent}}$), but the time-integrated signal has a relatively modest asymmetry (${\lesssim} 20 {{\ \rm per\ cent}}$). Both features are associated with the lepton-number emission self-sustained asymmetry and the spiral SASI. Moreover, our analysis suggests that there is an interesting correlation between the total neutrino energy (TONE) and the cumulative number of neutrino events in each detector, a correlation that can facilitate data analyses of real observations. We demonstrate the retrieval of neutrino energy spectra for all flavours of neutrino by applying a novel spectrum reconstruction technique to the data from multiple detectors. We find that this new method is capable of estimating the TONE within the error of ∼20 per cent if the distance to the CCSN is ≲6 kpc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A catalogue of cataclysmic variables from 20 yr of the Sloan Digital Sky Survey with new classifications, periods, trends, and oddities

ABSTRACT We present a catalogue of 507 cataclysmic variables (CVs) observed in SDSS I to IV including 70 new classifications collated from multiple archival data sets. This represents the largest sample of CVs with high-quality and homogeneous optical spectroscopy. We have used this sample to derive unbiased space densities and period distributions for the major sub-types of CVs. We also report on some peculiar CVs, period bouncers and also CVs exhibiting large changes in accretion rates. We report 70 new CVs, 59 new periods, 178 unpublished spectra, and 262 new or updated classifications. From the SDSS spectroscopy, we also identified 18 systems incorrectly identified as CVs in the literature. We discuss the observed properties of 13 peculiar CVS, and we identify a small set of eight CVs that defy the standard classification scheme. We use this sample to investigate the distribution of different CV sub-types, and we estimate their individual space densities, as well as that of the entire CV population. The SDSS I to IV sample includes 14 period bounce CVs or candidates. We discuss the variability of CVs across the Hertzsprung–Russell diagram, highlighting selection biases of variability-based CV detection. Finally, we searched for, and found eight tertiary companions to the SDSS CVs. We anticipate that this catalogue and the extensive material included in the Supplementary Data will be useful for a range of observational population studies of CVs.

79 ASTRONOMY AND ASTROPHYSICS↗