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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Envisioning U.S. Climate Predictions and Projections to Meet New Challenges

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways, and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Neutrino Interactions observed with Large Area Picosecond Photodetectors in ANNIE

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton water-based neutrino detector located at Fermilab, approximately 110 m downstream of the Booster Neutrino Beam (BNB). ANNIE utilizes both photomultiplier tubes (PMTs) and advanced photodetectors, specifically Large Area Picosecond Photodetectors (LAPPDs), to detect Cherenkov light emitted by leptons produced in neutrino interactions within ANNIE. LAPPDs are a novel technology designed to detect photons with picosecond-level temporal resolution and sub-millimetre spatial precision. Multiple LAPPDs have been deployed in the ANNIE detector. This is the first use of this technology in a running particle physics experiment and has yielded the first detection of light from neutrino interactions in water with LAPPDs. In this poster, I will showcase the operational performance and functionality of LAPPDs in the ANNIE experiment. Neutrino beam data from the BNB is used to evaluate the timing precision, hit reconstruction performance, and beam response of deployed LAPPDs, demonstrating how this novel picosecond-resolution technology performs in a running neutrino water Cherenkov detector. This work highlights the successful integration of LAPPDs in ANNIE and provides quantitative benchmarks that inform their application in future neutrino experiments requiring high-resolution photon detection.

Aman, Mohammad Adil [Florida State U.] (ORCID:0009↗
DOE OSTI2026

Dynamic Nanoscale Spatial Heterogeneity in a Perovskite-to-Brownmillerite Topotactic Phase Transformation

Phase transitions are omnipresent in modern condensed matter physics and its applications. In solids, first-order phase transformations typically occur by nucleation and growth under nonequilibrium conditions. Under constant external conditions, e.g., constant annealing temperature and pressure, the nucleation and growth dynamics are often thought of as spatially and temporally independent. Here, in situ Bragg X-ray photon correlation spectroscopy (XPCS) reveals nanoscale spatial and dynamical heterogeneity in the perovskite-to-brownmillerite topotactic phase transformation in La 0.7 Sr 0.3 CoO 3 thin films annealed under constant reducing conditions over a time span of multiple hours. Specifically, a time scale associated with domain growth remains stable, with a corresponding domain wall speed of v d = 6 ± 0.5 × 10 –4 nm/s (2 ± 0.2 nm/h), while a slower time scale, associated with temperature-driven depinning of domains, leads to accelerating dynamics with time scales following an aging power law with exponent −2.2 ± 0.5. This experiment demonstrates that Bragg XPCS is a powerful tool to study nanoscale dynamics in structural phase transformations, with the ability to extract quantitative average values related to nanodomain motion in situ. Furthermore, the results are relevant for phase engineering of phase-change devices, as they show that nanoscale dynamics, linked to domain and domain-wall motion, can continuously evolve and speed up with time, even hours after the initiation of the phase transformation, with potential repercussions on electrical performance.

X-ray photon correlation spectroscopy↗
DOE OSTI2025

Detecting gravitational signatures of dark matter with atom gradiometers

We study the purely gravitational signatures of dark matter from the ultralight to the ultraheavy mass range in proposed long-baseline atom gradiometers, focusing on terrestrial designs, such as AION-km and MAGIS-km, as well as space-based concepts, such as MAGIS-space, AEDGE and AEDGE+. Due to its exceptional acceleration sensitivity and depending on astrophysical backgrounds, a detector similar to AEDGE+ could detect a dark matter subcomponent which constitutes 𝒪⁡(10%) of the local dark matter energy density and is populated by compact clumps of mass between 10 6 kg and 10 10 kg (10 −25 ⁢𝑀⊙ ≲ 𝑀 ≲10 −21⁢ 𝑀⊙) in an otherwise unexplored region of dark matter model space. Furthermore, because the gravitational observable depends on the relative gravitational time delay measured by spatially separated atomic clouds, we find that atom gradiometers are parametrically more sensitive than laser interferometers, such as LIGO and LISA, to fast-oscillating spacetime perturbations sourced by energy density and pressure fluctuations of ultralight dark matter. Depending on astrophysical backgrounds, a detector akin to AEDGE+ could probe a DM overdensity of 𝒪⁡(10) times the local dark matter energy density for masses 𝑚 ≲10 −17 eV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Collaborative Research: Properties and Dynamics of the Shallow Crust (Final Report)

Ground motions recorded at one location are often extrapolated to nearby regions within a given radius. Here, we explore the appropriateness of spatial extrapolation using data from seven small aperture seismic network deployments in southern California. Six of these deployments are linear arrays of 4-13 stations, and one is a 2D array of 13 stations at Pinyon Flats Observatory. The spatial footprint array diameters are 3 km or less, and each array was operational for a year or more. From our base catalog (M2.5+ earthquakes; 4038 events; September 2010 - June 2023), automated methods remove temporally overprinted waveforms from nearby events (< 5 km) in quick succession (< 5 min) and data with nonviable waveforms. These 200 samples per second data are filtered at 0.5-25 Hz and must have signal-tonoise ratios (SNRs) of 2.5+. Peak ground acceleration (PGA) and peak ground velocity (PGV) are derived individually from the maximum absolute values of each of the 3-component waveforms (vertical, northsouth, and east-west). Five of the seven arrays traverse the San Jacinto fault, and two do not. Ground motion observations are compared with theoretical estimates from Abrahamson et al., 2014. On average, arrays deployed within and across fault zones consistently record ground motions above theoretical expectations, whereas off-fault arrays record ground motions at or slightly below theoretical expectations. We attribute these differences to site conditions because these trends prevail for the full data suites. For each network and each individual channel, the coefficient of variation indicates that the standard deviations are ~30±6% of the mean. Exploring relative ground motion contributions from all three channels (ternary plots), as expected, most data show that vertical ground motions are attenuated compared to horizontal ones. However, this is not always the case for RA array data, where vertical motions can be ~2-3 times larger than horizontal motions for select events near Cahuilla, CA. These anomalously high vertical motions are focal mechanism-related. These results suggest that ternary plots created using only a small amount of data can be used as a data quality metric and a tool to find anomalous features in three-component data.

58 GEOSCIENCES↗
DOE OSTI2026

Multi-resolution enhancement for full-spectrum neural representations

Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-basedrepresentations increasingly intractable. Implicit neural representations (INRs) offer a promising solutionby encoding signals through coordinate-based neural networks, serving as surrogates of data, withcomputational and storage requirements scaling with network complexity rather than data dimensionality.However, smaller INRs struggle to faithfully represent multiscale structures, high-frequency informationand fine textures that constitute a large proportion of scientific measurements. We propose WIEN-INR, atheoretically guided hierarchical INR framework that distributes modelling across resolution scales andenables improved representation capacity through a novel enhancement network to recover subtle details.This multiscale architecture allows smaller networks to retain the full spatial-frequency content of thesignal as well as preserve training efficiency and lower storage cost. Evaluated on distinct raw experimentalmeasurements across scales and complexities, WIEN-INR represents a practical step towards a broaderadoption of neural representations in scientific workflows, delivering compact, robust and high-fidelityrepresentations.

Ni, Yuan [SLAC National Accelerator Laboratory (SL↗
DOE OSTI2025

A Printed Microscopic Universal Gradient Interface for Super Stretchable Strain‐Insensitive Bioelectronics

Abstract Stretchable electronics capable of conforming to nonplanar and dynamic human body surfaces are central for creating implantable and on‐skin devices for high‐fidelity monitoring of diverse physiological signals. While various strategies have been developed to produce stretchable devices, the signals collected from such devices are often highly sensitive to local strain, resulting in inevitable convolution with surface strain‐induced motion artifacts that are difficult to distinguish from intrinsic physiological signals. Here all‐printed super stretchable strain‐insensitive bioelectronics using a unique universal gradient interface (UGI) are reported to bridge the gap between soft biomaterials and stiff electronic materials. Leveraging a versatile aerosol‐based multi‐materials printing technique that allows precise spatial control over the local stiffnesses with submicron resolution, the UGI enables strain‐insensitive electronic devices with negligible resistivity changes under a 180% uniaxial stretch ratio. Various stretchable devices are directly printed on the UGI for on‐skin health monitoring with high signal quality and near‐perfect immunity to motion artifacts, including semiconductor‐based photodetectors for sensing blood oxygen saturation levels and metal‐based temperature sensors. The concept in this work will significantly simplify the fabrication and accelerate the development of a broad range of wearable and implantable bioelectronics for real‐time health monitoring and personalized therapeutics.

Song, Kaidong [Department of Aerospace and Mechani↗
DOE OSTI2023

Cosmological consequences of first-order general-relativistic viscous fluid dynamics

In this work, we investigate the out-of-equilibrium dynamics of viscous fluids in a spatially flat Friedmann-LemaîtreRobertson-Walker cosmology using the most general causal and stable viscous energy-momentum tensor defined at first order in spacetime derivatives. In this new framework a pressureless viscous fluid having equilibrium energy density ρ can evolve to an asymptotic future solution in which the Hubble parameter approaches a constant while ρ → 0, even in the absence of a cosmological constant (i.e., Λ = 0). Thus, while viscous effects in this model drive an accelerated expansion of the universe, the equilibrium energy density itself vanishes, leaving behind only the acceleration. This behavior emerges as a consequence of causality in first-order theories of relativistic fluid dynamics and it is fully consistent with Einstein’s equations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Embracing fine-root system complexity in terrestrial ecosystem modeling

Projecting the dynamics and functioning of the biosphere requires a holistic consideration of whole-ecosystem processes. However, biases toward leaf, canopy, and soil modeling since the 1970s have constantly left fine-root systems being rudimentarily treated. As accelerated empirical advances in the last two decades establish clearly functional differentiation conferred by the hierarchical structure of fine-root orders and associations with mycorrhizal fungi, a need emerges to embrace this complexity to bridge the data-model gap in still extremely uncertain models. In this work, we propose a three-pool structure comprising transport and absorptive fine roots with mycorrhizal fungi (TAM) to model vertically resolved fine-root systems across organizational and spatial–temporal scales. Emerging from a conceptual shift away from arbitrary homogenization, TAM builds upon theoretical and empirical foundations as an effective and efficient approximation that balances realism and simplicity. A proof-of-concept demonstration of TAM in a big-leaf model both conservatively and radically shows robust impacts of differentiation within fine-root systems on simulating carbon cycling in temperate forests. Theoretical and quantitative support warrants exploiting its rich potentials across ecosystems and models to confront uncertainties and challenges for a predictive understanding of the biosphere. Echoing a broad trend of embracing ecological complexity in integrative ecosystem modeling, TAM may offer a consistent framework where modelers and empiricists can work together toward this grand goal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Modeling the Effects of Microwave Heating on Densification in Chemical Vapor Infiltration

Microwave heating has great potential to accelerate the synthesis of ceramic matrix composites (CMCs) by a process called Chemical Vapor Infiltration (CVI). In CVI, reactive gases ingress a porous preform and undergo chemical transformation to deposit solid ceramic phase within the pores at high temperature thus, densifying the preform. However, the competing effects of chemical kinetics and gas transport are known to result in non-uniform densification as the outer surfaces of the preform experience faster depositions compared to the core. Achieving spatial temperature control plays a key role improving the densification quality. Microwave heating can potentially create temperature inversion such that the core of the preform is hotter than the outer surface and subsequently, lead to improved densification while keeping the manufacturing times and costs low. In the present work, a computational modeling strategy has been developed that accounts for the key physical phenomena responsible for densification of porous preforms using microwave heating. Specifically, a chemical kinetics model has been formulated for Silicon Carbide (SiC) deposition from MTS/H2 precursor. The model is implemented in a pore-resolved reactive transport solver, Quilt. The CVI simulations are performed for several conditions. Initially, parameterized temperature control is imposed to identify optimum conditions for good densification quality at a fraction of processing time. Further, simulations of microwave heating of porous SiC preforms are performed using OpenFOAM. A strategy to achieve and enhance temperature inversion is identified. The resulting temperature profiles are used in the pore-resolved densification simulations to analyze the densification behavior. It is observed that the temperature inversion achieved by microwave heating leads to significant improvements in densification quality and at the same time, keeps the manufacturing time low.

36 MATERIALS SCIENCE↗
DOE OSTI2025

FracML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage

Poster on “FRACML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. The accurate characterization of subsurface fracture networks is essential for the secure operation of carbon capture, utilization, and storage (CCUS) projects. A thorough understanding of the spatial distribution of subsurface faults and fractures is crucial for predicting CO2 plume evolution and minimizing risks such as potential leakage into overlying formations or induced seismicity. In this context, robust fracture network quantification plays a pivotal role in reservoir management, providing the data necessary to fine-tune operational parameters, and ensure the environmental and economic viability of CCUS projects. As part of the U.S. Department of Energy’s SMART (Science-informed Machine Learning for Accelerating Real-time Decisions in Subsurface Applications) initiative, we focused on the development and application of a machine learning-based tool (FRACML) designed to quantify and map fracture networks using real-world (non-synthetic) data from an active CO2 injection site. Our objective is to demonstrate the utility of this tool in improving operational efficiency and safety across CCUS sites.

artifical intelligence / machine learning (AI/ML)↗
DOE OSTI2020

A High Precision Measurement of the Proton Charge Radius at JLab

The elastic electron-proton (e-p) scattering and the spectroscopy of hydrogen atoms are the two traditional methods to determine the proton charge radius (r p ). In 2010, a new method using muonic hydrogen (µH) 1 spectroscopy reported a r p result that was nearly ten times more precise but significantly smaller than the values from the compilation of all previous r p measurements. This discrepancy is often referred to as the "proton charge radius puzzle". In order to investigate the puzzle, the PRad experiment (E12-11-106) was first proposed in 2011 and performed in 2016 in Hall B at the Thomas Jefferson National Accelerator Facility, with both 1.1 and 2.2 GeV electron beams. The experiment measured the e-p elastic scattering cross sections in an unprecedented low values of momentum transfer squared region (Q 2 = 2.1× 10 -4 -0.06 (GeV/c) 2 ), with a sub-percent precision. The PRad experiment utilized a calorimetric method that was magnetic-spectrometer-free. Its detector setup included a large acceptance and high resolution calorimeter (HyCal), and two large-area, high spatial-resolution Gas Electron Multiplier (GEM) detectors. To have a better control over the systematic uncertainties, the absolute e-p elastic scattering cross section was normalized to that of the well-known Møller scattering process, which was measured simultaneously during the experiment. For each beam energy, all data with different Q 2 were collected simultaneously with the same detector setup, therefore sharing the same integrated luminosity. The windowless H 2 gas-flow target utilized in the experiment largely removed a typical background source, the target cell windows. The proton charge radius was determined as r p = 0.831±0.007 stat. ±0.012 p fm, which is smaller than the average r p from previous e-p elastic scattering experiments, but in agreement with the µH spectroscopic results within the experimental uncertainties.

Source record↗
DOE OSTI2020

Runaway electron beam dynamics at low plasma density in DIII-D: energy distribution, current profile, and internal instability

Parameters of the post-disruption runaway electron (RE) beam in the collisionless background plasma achieved after deuterium injection are investigated in DIII-D. The spatially resolved RE energy distribution function is measured for the first time during the RE plateau stage by inverting hard X-ray bremsstrahlung spectra. It has maximum energy up to 20 MeV and a non-monotonous feature at 5{6 MeV observed only in the core of the beam supporting the possibility of kinetic instabilities. The RE current profile is reconstructed for the first time using the spatially resolved RE energy distribution. It is found to be more peaked than the pre-disruption plasma current, with higher internal inductance, suggesting preferential formation of REs in the core plasma or potentially a radially inward motion of the REs. The accessed relatively low-current (180 kA) RE beam is found stable, likely due to its elevated q profile. From this base stable equilibrium, instability is accessed by ramping the solenoid current. Under a large applied accelerating loop voltage an internal kink mode is observed. The kink mode leads to sawtooth-like relaxation of the RE current profile, but drives no RE loss. Under conditions of a large decelerating voltage, chirping Alfvénic instabilities driven by REs during the RE plateau stage are observed for the first time. The low-frequency (0.1{7 MHz) Alfvénic instabilities, supposedly compressional Alfvén eigenmodes, are found to be correlated with a RE loss signal. Finally, this provides novel plasma conditions to study these instabilities and investigate opportunities to utilize them for RE control. Bifurcated electron cyclotron emission (ECE) spectrum is measured during the collisionless RE plateau, with a break point at ≈100 GHz suggesting resonant absorption of the ECE at low frequencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Lightwave terahertz quantum manipulation of nonequilibrium superconductor phases and their collective modes

In this work, we present a gauge-invariant density matrix description of nonequilibrium superconductor (SC) states with spatial and temporal correlations driven by intense terahertz (THz) lightwaves. We derive superconductor Bloch-Maxwell equations of motion that extend Anderson pseudospin models to include the Cooper pair center-of-mass motion and electromagnetic propagation effects. We thus describe quantum control of dynamical phases, collective modes, quasiparticle coherence, and high nonlinearities during cycles of carrier wave oscillations, which relates to our recent experiments. Coherent photogeneration of a nonlinear supercurrent with a dc component, achieved via condensate acceleration by an effective lightwave field, dynamically breaks the equilibrium inversion symmetry. Experimental signatures include high harmonic light emission at equilibrium-symmetry-forbidden frequencies, Rabi-Higgs collective modes and quasiparticle coherence, and nonequilibrium moving condensate states tuned by few-cycle THz fields. We use such lightwaves as an oscillating accelerating force that drives strong nonlinearities and anisotropic quasiparticle populations to control and amplify different classes of collective modes, e.g., damped oscillations, persistent oscillations, and overdamped dynamics via Rabi flopping. Recent phase-coherent nonlinear spectroscopy experiments can be modeled by solving the full nonlinear quantum dynamics including self-consistent light-matter coupling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Distributed Optimization for Nonrigid Nano-Tomography

Resolution level and reconstruction quality in nano-computed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist radiation induced deformations during data acquisition. In such cases, there is no incentive in recovering the sample state at different time steps like in time-resolved reconstruction methods, but instead the goal is to retrieve a single reconstruction at the highest possible spatial resolution and without any imaging artifacts. Here we propose a distributed optimization solver for tomographic imaging of samples at the nanoscale. Our approach solves the tomography problem jointly with projection data alignment, nonrigid sample deformation correction, and regularization. Projection data consistency is regulated by dense optical flow estimated by Farneback's algorithm, leading to sharp sample reconstructions with less artifacts. Synthetic data tests show robustness of the method to Poisson and low-frequency background noise. We accelerated the solver on multi-GPU systems and validated the method on three nano-imaging experimental data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Study of the Transition to Turbulence in a Bed of 67 Spherical Pebbles

Packed beds are commonly found in many engineering systems and have been widely studied for decades. A relatively new packed bed system is the Pebble Bed Reactor, a type of generation-IV nuclear reactor. Unlike many of the packed beds encountered in chemical and process engineering applications, Pebble Bed Reactors are larger and operate at significantly higher Reynolds numbers. As a result of these differences, there is a very limited amount of information on the detailed flow physics that exist in these complex geometries. This work seeks to contribute to a growing database of flow data for Pebble Bed Reactor systems by performing Direct Numerical Simulations of the flow in an experimental bed of 67 pebbles for a range of conditions. Simulations are performed at a Prandtl number of 0.66 and Reynolds numbers from 300–600. These Reynolds numbers are chosen to gain additional knowledge on the spatial development of turbulence in these systems. Analysis of the Turbulent Kinetic Energy, turbulence anisotropy, and Turbulent Heat Flux is performed. Results demonstrate significant development of the TKE across the tested range of Reynolds numbers. Examination of both the TKE and THF reveal that development first occurs near the center of the bed and propagates radially as the flow moves further into the bed. Notable regions of negative production of turbulent kinetic energy are observed in regions where flow accelerates around pebble contact points. Furthermore, these regions are found to coincide with regions of 1-component turbulence.Kindly check and confirm, all authors email id is correctly identified.These are correct

Direct numberical simulation↗
DOE OSTI2021

A field–particle correlation analysis of a perpendicular magnetized collisionless shock

Using the field–particle correlation technique, we examine the particle energization in a three-dimensional (one spatial dimension and two velocity dimensions; 1D-2V) continuum Vlasov–Maxwell simulation of a perpendicular magnetized collisionless shock. The combination of the field–particle correlation technique with the high-fidelity representation of the particle distribution function provided by a direct discretization of the Vlasov equation allows us to ascertain the details of the exchange of energy between the electromagnetic fields and the particles in phase space. We identify the velocity-space signatures of shock-drift acceleration of the ions and adiabatic heating of the electrons arising from the perpendicular collisionless shock by constructing a simplified model with the minimum ingredients necessary to produce the observed energization signatures in the self-consistent Vlasov–Maxwell simulation. We are thus able to completely characterize the energy transfer in the perpendicular collisionless shock considered here and provide predictions for the application of the field–particle correlation technique to spacecraft measurements of collisionless shocks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Prediction on X-ray output of free electron laser based on artificial neural networks

Abstract Knowledge of x-ray free electron lasers’ (XFELs) pulse characteristics delivered to a sample is crucial for ensuring high-quality x-rays for scientific experiments. XFELs’ self-amplified spontaneous emission process causes spatial and spectral variations in x-ray pulses entering a sample, which leads to measurement uncertainties for experiments relying on multiple XFEL pulses. Accurate in-situ measurements of x-ray wavefront and energy spectrum incident upon a sample poses challenges. Here we address this by developing a virtual diagnostics framework using an artificial neural network (ANN) to predict x-ray photon beam properties from electron beam properties. We recorded XFEL electron parameters while adjusting the accelerator’s configurations and measured the resulting x-ray wavefront and energy spectrum shot-to-shot. Training the ANN with this data enables effective prediction of single-shot or average x-ray beam output based on XFEL undulator and electron parameters. This demonstrates the potential of utilizing ANNs for virtual diagnostics linking XFEL electron and photon beam properties.

47 OTHER INSTRUMENTATION↗