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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-throughput genetics enables identification of nutrient utilization and accessory energy metabolism genes in a model methanogen

Archaea are widespread in the environment and play fundamental roles in diverse ecosystems; however, characterization of their unique biology requires advanced tools. This is particularly challenging when characterizing gene function. Here, we generate randomly barcoded transposon libraries in the model methanogenic archaeon Methanococcus maripaludis and use high-throughput growth methods to conduct fitness assays (RB-TnSeq) across over 100 unique growth conditions. Using our approach, we identified new genes involved in nutrient utilization and response to oxidative stress. We identified novel genes for the usage of diverse nitrogen sources in M. maripaludis including a putative regulator of alanine deamination and molybdate transporters important for nitrogen fixation. Furthermore, leveraging the fitness data, we inferred that M. maripaludis can utilize additional nitrogen sources including $\tiny{L}$-glutamine, $\tiny{D}$-glucuronamide, and adenosine. Under autotrophic growth conditions, we identified a gene encoding a domain of unknown function (DUF166) that is important for fitness and hypothesize that it has an accessory role in carbon dioxide assimilation. Finally, comparing fitness costs of oxygen versus sulfite stress, we identified a previously uncharacterized class of dissimilatory sulfite reductase-like proteins (Dsr-LP; group IIId) that is important during growth in the presence of sulfite. When overexpressed, Dsr-LP conferred sulfite resistance and enabled use of sulfite as the sole sulfur source. The high-throughput approach employed here allowed for generation of a large-scale data set that can be used as a resource to further understand gene function and metabolism in the archaeal domain.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Modeling Spatial Heterogeneity in Surface Turbulent Heat Flux in the U.S. Southern Great Plains

Advances in numerical modeling of cloud dynamics are driving a need for improved land model prediction at convective storm scales. Here, satellite and ground-based vegetation remote sensing data were combined with land model experiments to more accurately characterize land surface spatial heterogeneity in the Community Land Model (CLM4.0). The new subgrid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground-based flux measurements in the U.S. southern Great Plains. Errors in vegetation data sets (inferred from comparison between 250 m satellite and ground-based LAI), while large, had less impact on the simulated characteristics of spatial heterogeneity compared to errors in model representation of surface energy partitioning (between latent and sensible heat flux) and its relationship to LAI. Predicted spatial heterogeneity in surface energy partitioning was enhanced after replacing soil and stomatal resistance parameters with a new set that better predicts the observed relationship to LAI. These modifications increase the number of smaller (mesoscale) dry land patches having higher sensible heat flux. The parameter experiments suggest that vegetation state and processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can influence clouds and convective initiation. Moreover, improvements in vegetation input data and model parameters had partially compensating effects on surface flux heterogeneity, indicating the importance of evaluating input data and parameterizations together to improve prediction at higher spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Preserving nonlinear constraints in variational flow filtering data assimilation

Data assimilation aims to estimate the states of a dynamical system by optimally combining sparse and noisy observations of the physical system with uncertain forecasts produced by a computational model. The states of many dynamical systems of interest obey nonlinear physical constraints, and the corresponding dynamics is confined to a certain sub-manifold of the state space. Standard data assimilation techniques applied to such systems yield posterior states lying outside the manifold, violating the physical constraints. This work focuses on particle flow filters which use stochastic differential equations to evolve state samples from a prior distribution to samples from an observation-informed posterior distribution. The variational Fokker-Planck (VFP)—a generic particle flow filtering framework—is extended to incorporate non-linear, equality state constraints in the analysis. To this end, two algorithmic approaches that modify the VFP stochastic differential equation are discussed: (i) VFPSTAB, to inexactly preserve constraints with the addition of a stabilizing drift term, and (ii) VFPDAE, to exactly preserve constraints by treating the VFP dynamics as a stochastic differential-algebraic equation (SDAE). Additionally, an implicit-explicit time integrator is developed to evolve the VFPDAE dynamics. The strength of the proposed approach for constraint preservation in data assimilation is demonstrated on three test problems: the double pendulum, Korteweg-de-Vries, and the incompressible Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Juno mission as a probe of long-range new physics

Orbits of celestial objects, especially the geocentric and heliocentric ones, have been well explored to constrain new long-range forces beyond the Standard Model (SM), often referred to as fifth forces. In this paper, for the first time, we apply the motion of a spacecraft around Jupiter to probe fifth forces that don’t violate the equivalence principle. The spacecraft is the Juno orbiter, and ten of its early orbits already allow a precise determination of the Jovian gravitational field. We use the shift in the precession angle as a proxy to test non-gravitational interactions between Juno and Jupiter. Requiring that the contribution from the fifth force does not exceed the uncertainty of the precession shift inferred from data, we find that a new parameter space with the mass of the fifth-force mediator around 10 −14 eV is excluded at 95% C.L.

new light particles↗
DOE OSTI2023

Experimental determinations of carbon and hydrogen isotope fractionations and methane clumped isotope compositions associated with ethane pyrolysis from 550 to 600°C

Methane clumped isotope compositions signify the relative natural abundances of rare, doubly substituted isotopic species of methane ( 13 CH 3 D and 12 CH 2 D 2 ) and have emerged as a new isotopic tool to trace the sources, sinks, and lifecycles of methane in the environment. Such measurements can identify equilibration (or reequilibration) temperatures if found to be in isotopic equilibrium or non-equilibrium processes (e.g., kinetically controlled reactions or mixing) if not in isotopic equilibrium. Naturally occurring thermogenic methane—formed by the thermally activated breakdown of larger organic molecules—has been found to have clumped isotope compositions consistent with equilibrium at reasonable gas formation temperatures in some settings and non-equilibrium processes occurring during either formation, migration, storage, or extraction in others. To explore the potential controls on the isotopic composition of thermogenic methane, we conducted isothermal time-series ethane pyrolysis experiments at 550 and 600 °C to measure methane and ethane 13 C/ 12 C and D/H fractionations and methane clumped isotope compositions (resolved 13 CH 3 D and 12 CH 2 D 2 ). We explore the effects of modifying the initial clumped isotope composition of ethane and the addition of water vapor to pyrolysis experiments. We observe that ethane and methane 13 C/ 12 C are controlled by kinetic isotope effects and Rayleigh distillation processes. In contrast, ethane and methane D/H and methane clumped isotope compositions appear to be controlled by a combination of these processes and hydrogen isotope exchange. The hydrogen isotope exchange processes lead to isotopic equilibrium as reaction completion is approached for both D/H (ethane/methane) and methane clumped isotope compositions. Here, we develop a chemical model based on a mass balance approach that accounts for inheritance vs. hydrogen-abstraction formation pathways for singly and doubly substituted isotopologues of ethane and methane that is compared to the experimental data. The model allows the determination of carbon and hydrogen kinetic isotope effects associated with ethane cracking and hydrogen abstraction reactions that, where applicable, we compare to prior theoretical constraints. From the comparison of the model to the experimental data, we infer that the kinetically controlled ethane and methane bulk isotope compositions and methane clumped isotope compositions are controlled by kinetic isotope effects (both primary and secondary) associated with both C–C bond and C–H bond cleavage reactions. Specifically, the methane clumped isotope compositions likely result from a combination of clumped isotope effects associated with ethane breakdown and/or assembly of methane isotopologues (expressed in terms of γ-factor parameters ≠ 1) and combinatorial effects that arise probabilistically. We discuss our experimental results in the context of recent pyrolysis experiments and observations of naturally occurring thermogenic methane. We consider a proposal consistent with observations from nature that the hydrogen isotope exchange reactions that promote equilibration of methane isotopic molecules at or near formation temperature may be facilitated by free radicals generated by pyrolysis reactions. In this framework, isotope exchange effectively ceases when pyrolysis effectively ceases locking in compositions that can be consistent with peak formation temperatures.

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

A deep learning-based direct forecasting of CO 2 plume migration

Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Joint state-parameter estimation for the reduced fracture model via the united filter

Here, in this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured porous media. Fluid flow and transport in fractured porous media are critical in subsurface hydrology, geophysics, and reservoir geomechanics. Reduced fracture models, which represent fractures as lower-dimensional interfaces, enable efficient multi-scale simulations. However, reduced fracture models also face accuracy challenges due to modeling errors and uncertainties in physical parameters such as permeability and fracture geometry. To address these challenges, we propose a United Filter method, which integrates the Ensemble Score Filter (EnSF) for state estimation with the Direct Filter for parameter estimation. EnSF, based on a score-based diffusion model framework, produces ensemble representations of the state distribution without deep learning. Meanwhile, the Direct Filter, a recursive Bayesian inference method, estimates parameters directly from state observations. The United Filter combines these methods iteratively: EnSF estimates are used to refine parameter values, which are then fed back to improve state estimation. Numerical experiments demonstrate that the United Filter method surpasses the state-of-the-art Augmented Ensemble Kalman Filter, delivering more accurate state and parameter estimation for reduced fracture models. This framework also provides a robust and efficient solution for PDE-constrained inverse problems with uncertainties and sparse observations.

Bayesian inference↗
DOE OSTI2020

Characterizing Mars's Magnetotail Topology With Respect to the Upstream Interplanetary Magnetic Fields

The canonical picture of the magnetotail of unmagnetized planets consists of draped interplanetary magnetic fields (IMFs) forming opposite-directed lobes, separated by the current sheet. DiBraccio et al. (2018, https://doi.org/10.1029/2018GL077251) showed that Mars's magnetotail has a twist departing from this picture. Magnetohydrodynamic (MHD) results suggest that the asymmetry in how open field lines connected to the planet populate the tail causes the apparent twist. To validate this interpretation, we compare the tail topology determined from MHD simulations to that inferred from data collected by the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft, in particular, how each topology responds to the upstream IMF orientation. The occurrence rates for open topology from both data and MHD vary with IMF polarities in a similar fashion as the tail twisting. This suggests that Mars's crustal fields have a global effect on the magnetosphere configuration, supporting the picture of a “hybrid” magnetotail that is partly induced/draped and partly intrinsic/planetary in origin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Hansson, Frederik G↗
DOE OSTI2024

CO 2 rock physics modeling for reliable monitoring of geologic carbon storage

Monitoring, verification, and accounting (MVA) are crucial to ensure safe and long-term geologic carbon storage. Seismic monitoring is a key MVA technique that utilizes seismic data to infer elastic properties of CO 2 -saturated rocks. Reliable accounting of CO 2 in subsurface storage reservoirs and potential leakage zones requires an accurate rock physics model. However, the widely used CO 2 rock physics model based on the conventional Biot-Gassmann equation can substantially underestimate the influence of CO 2 saturation on seismic waves, leading to inaccurate accounting. We develop an accurate CO 2 rock physics model by accounting for both effects of the stress dependence of seismic velocities in porous rocks and CO 2 weakening on the rock framework. We validate our CO 2 rock physics model using the Kimberlina-1.2 model (a previously proposed geologic carbon storage site in California) and create time-lapse elastic property models with our new rock physics method. We compare the results with those obtained using the conventional Biot-Gassmann equation. Our innovative approach produces larger changes in elastic properties than the Biot-Gassmann results. Using our CO 2 rock physics model can replicate shear-wave speed reductions observed in the laboratory. Our rock physics model enhances the accuracy of time-lapse elastic-wave modeling and enables reliable CO 2 accounting using seismic monitoring.

58 GEOSCIENCES↗
DOE OSTI2025

Evaluating nonlocal heat transport in directly driven chromium spheres using x-ray spectroscopy

We report on experiments investigating heat transport in laser-generated plasmas using directly driven chromium spheres. The spheres are fielded at the OMEGA laser facility and are driven with laser intensities of 5×10 14 Wcm −2 . Plasma conditions in the corona and scattered light are measured experimentally and compared against predictions from two-dimensional (2D) radiation-hydrodynamic simulations using different heat transport models. Spectroscopic analysis of x-ray self-emission is used as an additional diagnostic. X-ray emission is integrated over a large region of the plasma, probing regions that are not observed by localized optical Thomson scattering. In particular, x-ray emission peaks near the plasma critical density, so emission from optically thin lines provides information on plasma conditions where nonlocal transport is most likely to be significant. Three common heat transport models are considered: local transport with flux limiters f = 0.15 and f = 0.03, and the nonlocal Schurtz–Nicolai–Busquet (SNB) model. Consistent with previous work, both the high-flux (f = 0.15) and SNB models show good agreement with experimentally measured plasma conditions in the corona despite overpredicting laser absorption, whereas the low-flux (f = 0.03) model fails to match any experimental data. Conditions inferred from x-ray self-emission line ratios support this conclusion during the period of laser peak power, although synthetic spectra for all models fail to match the experiment during the transient portions of the pulse. For these reasons, the low-flux model is again rejected.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The COMBS Survey - II. Distinguishing the metal-poor bulge from the halo interlopers

The metal-poor stars in the bulge are important relics of the Milky Way’s formation history, as simulations predict that they are some of the oldest stars in the Galaxy. In order to determine if they are truly ancient stars, we must understand their origins. Currently, it is unclear if the metal-poor stars in the bulge ([Fe/H] < -1 dex) are merely halo interlopers, a unique accreted population, part of the boxy/peanut-shaped bulge, or a classical bulge population. In this work, we use spectra from the VLT/FLAMES spectrograph to obtain metallicity estimates using the Ca-II triplet of 473 bulge stars (187 of which have [Fe/H] < -1 dex), targeted using SkyMapper photometry. We also use Gaia DR2 data to infer the Galactic positions and velocities along with orbital properties for 523 stars. We employ a probabilistic orbit analysis and find that about half of our sample has a >50 percent probability of being bound to the bulge, and half are halo interlopers. We also see that the occurrence rate of halo interlopers increases steadily with decreasing metallicity across the full range of our sample (-3 < [Fe/H] < 0.5). Our examination of the kinematics of the confined compared to the unbound stars indicates the metal-poor bulge comprises at least two populations; those confined to the boxy/peanut bulge and halo stars passing through the inner galaxy. We conclude that an orbital analysis approach, as we have employed, is important to understand the composite nature of the metal-poor stars in the inner region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Stringent σ8 constraints from small-scale galaxy clustering using a hybrid MCMC + emulator framework

ABSTRACT We present a novel simulation-based hybrid emulator approach that maximally derives cosmological and Halo Occupation Distribution (HOD) information from non-linear galaxy clustering, with sufficient precision for DESI Year 1 (Y1) analysis. Our hybrid approach first samples the HOD space on a fixed cosmological simulation grid to constrain the high-likelihood region of cosmology + HOD parameter space, and then constructs the emulator within this constrained region. This approach significantly reduces the parameter volume emulated over, thus achieving much smaller emulator errors with fixed number of training points. We demonstrate that this combined with state-of-the-art simulations result in tight emulator errors comparable to expected DESI Y1 LRG sample variance. We leverage the new abacussummit simulations and apply our hybrid approach to CMASS non-linear galaxy clustering data. We infer constraints on σ8 = 0.762 ± 0.024 and fσ8(zeff = 0.52) = 0.444 ± 0.016, the tightest among contemporary galaxy clustering studies. We also demonstrate that our fσ8 constraint is robust against secondary biases and other HOD model choices, a critical first step towards showcasing the robust cosmology information accessible in non-linear scales. We speculate that the additional statistical power of DESI Y1 should tighten the growth rate constraints by at least another 50–60 ${{\ \rm per\ cent}}$, significantly elucidating any potential tension with Planck. We also address the ‘lensing is low’ tension, which we find to be in the same direction as a potential tension in fσ8. We show that the combined effect of a lower fσ8 and environment-based bias accounts for approximately $50{{\ \rm per\ cent}}$ of the discrepancy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Effect of Ni substitution on the fragile magnetic system La 5 Co 2 Ge 3

La 5⁢ Co 2 ⁢Ge 3 is an itinerant ferromagnet with a Curie temperature T C of ~3.8K and a remarkably small saturated moment of 0.1µ B /Co. Here we present the growth and characterization of single crystals of the La 5 ⁢(Co 1–x⁢ Ni x ) 2 Ge 3 series for 0.00 ≤ x ≤ 0.186. Here we measured powder x-ray diffraction, composition as well as anisotropic temperature-dependent resistivity, temperature and field-dependent magnetization along with heat capacity on these single crystals. We also measured muon-spin rotation/relaxation (μ⁢SR) for some Ni substitutions (x = 0.027,0.036,0.074) to study the evolution of internal field with Ni substitution. Using the measured data we infer a low temperature, transition temperature-composition phase diagram for La 5 ⁢(Co 1–x ⁢Ni x ) 2 Ge 3 . We find that T C is suppressed for low dopings, x ≤ 0.014; whereas for 0.036 ≤ x ≤ 0.186, the samples are antiferromagnetic with a Néel temperature T N that goes through a weak and shallow maximum (T N ~ 3.4K for x~0.07) and then gradually decreases to 2.4 K by x = 0.186. For intermediate Ni substitutions, 0.016 ≤ x ≤ 0.027, two transition temperatures are inferred with T N >T C . Whereas the T–x phase diagram for La 5 ⁢(Co 1–x ⁢Ni x ) 2 Ge 3 and the T–p phase diagram determined for the parent La 5 ⁢Co 2 ⁢Ge 3 under hydrostatic pressure are grossly similar, changing from a low-doping or low-pressure ferromagnetic (FM) ground state to a high-doped or high-pressure antiferromagnetic (AFM) state, perturbation by Ni substitution enabled us to identify an intermediate doping regime where both FM and AFM transitions occur.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Precision beam diagnostics at the NuMI facility using muon monitor observations

The Neutrinos at the Main Injector (NuMI) facility at Fermilab delivers an intense neutrino beam for multiple experiments by producing pions that decay into neutrinos, muons, and other particles. Magnetic horns—the primary pion focusing elements in the NuMI beamline—exhibit predominantly linear optics, enabling a predictable relationship between the proton beam and the resulting pion and muon phase spaces. This study has two primary objectives: first, to evaluate and confirm the linearity of the horn focusing mechanism using analytical models and numerical simulations; and second, to demonstrate that key beam parameters—such as proton beam intensity, beam position on target, and horn current—can be extracted from muon monitor observations within this linear optics framework. Using a machine learning model trained on spill-by-spill muon monitor data, we infer the horn current with a precision of ±0.05%, the beam intensity with ±0.1%, and the beam position on target with ±0.018⁢ mm horizontally and ±0.013⁢ mm vertically. This approach provides a reliable cross-check of beam parameters, helping to reduce systematic uncertainties that are critical for future experiments such as the Deep Underground Neutrino Experiment, which will rely on the neutrino beam produced by the Long-Baseline Neutrino Facility.

Beam control↗
DOE OSTI2026

Validity of a finite temperature expansion for dense nuclear matter

In this work we provide a new, well-controlled expansion of the equation of state of dense matter from zero to finite temperatures (𝑇) while covering a wide range of charge fractions (𝑌 𝑄 ), from pure neutron to isospin symmetric nuclear matter. Our expansion can be used to describe neutron star mergers using the equation of state inferred from neutron star observations. We discuss how knowledge from low-energy nuclear experiments and heavy-ion collisions can be directly incorporated into the expansion. We also suggest new thermodynamic quantities of interest that can be calculated from theoretical models or directly inferred by experimental data that can be used to infer the finite temperature equation of state. With our new method, we can quantify the uncertainty in our finite 𝑇 and 𝑌 𝑄 expansions without making assumptions about the underlying degrees of freedom. We can reproduce results from a microscopic equation of state up to 𝑇 = 100 MeV for baryon chemical potential 𝜇 𝐵 ≳ 1100 MeV [≈(1–2)⁢𝑛 sat ] within 5% error, with even better results for larger 𝜇 𝐵 and/or lower 𝑇. We investigate the sources of numerical and theoretical uncertainty and discuss future directions of study.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Building a genome-based understanding of bacterial pH preferences

The environmental preferences of many microbes remain undetermined. This is the case for bacterial pH preferences, which can be difficult to predict a priori despite the importance of pH as a factor structuring bacterial communities in many systems. We compiled data on bacterial distributions from five datasets spanning pH gradients in soil and freshwater systems (1470 samples), quantified the pH preferences of bacterial taxa across these datasets, and compiled genomic data from representative bacterial taxa. While taxonomic and phylogenetic information were generally poor predictors of bacterial pH preferences, we identified genes consistently associated with pH preference across environments. We then developed and validated a machine learning model to estimate bacterial pH preferences from genomic information alone, a model that could aid in the selection of microbial inoculants, improve species distribution models, or help design effective cultivation strategies. More generally, we demonstrate the value of combining biogeographic and genomic data to infer and predict the environmental preferences of diverse bacterial taxa.

59 BASIC BIOLOGICAL SCIENCES↗