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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Relationship between microporous structure and light gas transport through glassy polymeric membranes revealed by molecular simulations

Microporous glassy polymers are attractive materials for gas separation membranes, due to their high permeability and tailorable selectivity, provided physical aging can be delayed. The archetypal microporous glassy polymer, PTMSP, can be blended with a hyper-crosslinked isatin–triptycene porous polymer network (PPN) to delay physical aging. However, while PPN is effective at reducing physical aging, it also affects the permeability of light gases through PTMSP. Molecular dynamics simulations were used here to shed fundamental light on the mechanisms responsible for these effects. Atomistic models are developed that satisfactorily reproduce experimental observations such as matrix density and cavity size distributions for neat PTMSP as well as for PTMSP–PPN blends. Analysis of the simulation results suggests that physical aging is delayed because the PPN inclusions slow down PTMSP relaxation while reducing the connectivity between free volume pockets. To understand how PPN inclusions affect light gas permeability, the atomistic models developed are used to probe CO 2 and CH 4 diffusion and sorption. These simulations are conducted for PTMSP matrices exhibiting varying density, towards reproducing experimental permeability data. Interrogating the simulation trajectories, it is discovered that while CH 4 travels preferentially through the free volume cavities, CO 2 preferentially interacts with the available surfaces, especially in the presence of PPN. These differences help interpret experimental observations. The transport of C 2 H 6 and H 2 S through PTMSP matrices was also investigated. These gases, and in particular H 2 S, were found to absorb within PTMSP, yielding very low diffusion coefficients. The ability to predict differences in diffusion pathways for various natural gas components unveils the possibility of engineering membranes to control permeability and selectivity towards large-scale applications.

Bao Le, Tran Thi [Univ. of Oklahoma, Norman, OK (U↗
DOE OSTI2022

Compensating cylindrical Hohlraum mode 4 asymmetry via capsule thickness tailoring and effects on implosions

We report previously, hydrodynamic simulations suggested that precisely tailoring the capsule ablator thickness (shimming) could counterbalance cylindrical Hohlraum Legendre P 4 drive asymmetries at the capsule in laser indirect drive implosions. As a result, the stagnated deuterium–tritium (DT) fuel areal density P 4 asymmetry is reduced, potentially resulting in a nuclear yield increase. Inflight radiographs of various level of shimmed capsules with plastic (CH) ablators showed that shims can indeed control the in-flight capsule shell P 4 asymmetry, with a linear sensitivity to shim amplitude that is close to analytic estimates and simulations. Furthermore, the stagnated DT fuel areal density P 4 asymmetry inferred from downscattered neutron imaging was reduced when the capsule shim was applied, in agreement with simulations matching the inflight shell asymmetry. A nuclear yield improvement via shim was not observed, as predicted, likely due to implosion instabilities and as built capsule shim deviations from an ideal P 4 shape.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Measurements of atoms and metastable species in N 2 and H 2 –N 2 nanosecond pulse plasmas

We report time-resolved, absolute number densities of metastable N 2 (A 3 Σ u + , v = 0, 1) molecules, ground state N 2 and H atoms, and rotational–translational temperature have been measured by tunable diode laser absorption spectroscopy and two-photon absorption laser-induced fluorescence in diffuse N 2 and N 2 –H 2 plasmas during and after a nanosecond pulse discharge burst. Comparison of the measurement results with the kinetic modeling predictions, specifically the significant reduction of the N 2 (A 3 Σ u + ) populations and the rate of N atom generation during the burst, suggests that these two trends are related. The slow N atom decay in the afterglow, on a time scale longer than the discharge burst, demonstrates that the latter trend is not affected by N atom recombination, diffusion to the walls, or convection with the flow. This leads to the conclusion that the energy pooling in collisions of N 2 (A 3 Σ u + ) molecules is a major channel of N 2 dissociation in electric discharges where a significant fraction of the input energy goes to electronic excitation of N 2 . Additional measurements in a 1% H 2 –N 2 mixture demonstrate a further significant reduction of N 2 (A 3 Σ u + , v = 0, 1) populations, due to the rapid quenching by H atoms accumulating in the plasma. Comparison with the modeling predictions suggests that the N 2 (A 3 Σ u + ) molecules may be initially formed in the highly vibrationally excited states. The reduction of the N 2 (A 3 Σ u + ) number density also diminishes the contribution of the energy pooling process into N2 dissociation, thus reducing the N atom number density. The rate of N atom generation during the burst also decreases, due to its strong coupling to N 2 (A 3 Σ u + , v) populations. On the other hand, the rate of H atom generation, produced predominantly by the dissociative quenching of the excited electronic states of N 2 by H 2 , remains about the same during the burst, resulting in a nearly linear rise in the H atom number density. Comparison of the kinetic model predictions with the experimental results suggests that the yield of H atoms during the quenching of the excited electronic state of N 2 by molecular H 2 is significantly less than 100%. The present results quantify the yield of N and H atoms in high-pressure H 2 –N 2 plasmas, which have significant potential for ammonia generation using plasma-assisted catalysis

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The curled wake model: equivalence of shed vorticity models

In this work, we explore the spanwise velocities in the wake of a yawed wind turbine. In the curled wake model, these motions are induced by a collection of vortices shed from the rotor plane. The direction of the vorticity generated by yaw is aligned with the main flow (streamwise) direction. The streamwise vorticity induces velocities in the spanwise directions. These are the motions responsible for creating the curled wake mechanism. In this work, we explore a more accurate formulation for this mechanism, using a vortex cylinder. Under certain assumptions, the new and original curled wake models yield the same mathematical formulation. Also, both models predict an elliptic distribution of vortex strength, where the main difference is the location of the vortices.

17 WIND ENERGY↗
DOE OSTI2026

Development of an ab initio learned model of electron deposition range in deuterium-tritium plasmas through time-dependent density functional theory calculations and machine learning

Accurate hydrodynamic modeling for laser-direct-drive (LDD) inertial-confinement-fusion (ICF) relies on precise calculations of the electron thermal conduction in all target materials. The nonlocal stopping range of electrons in ICF plasmas directly influences thermal conduction; yet, no first principles model exists for the electron mean free path in the conduction-zone regime. This work utilized time-dependent stochastic density-functional theory (TD-sDFT) to calculate the electron stopping power in deuterium-tritium (DT) plasmas at (ρ, T) conditions relevant to the conduction zone and the compressed shell in ICF. Using a combination of our TD-sDFT data and already established analytical models, we developed and trained an artificial neural network to create a global model for the nonlocal electron deposition range, λ E . We compared our machine-learning (ML) based model for λ E to the currently-used modified-Lee-More model in LDD radiation-hydrodynamic codes, such as lilac, and saw an overall decrease in the deposition range. To understand the effects of λ E on LDD ICF implosion dynamics, we implemented the ML-based model into lilac; specifically, we looked at designs consistent with a current experiment on the OMEGA laser and for a newly designed LDD-ICF target for the future OMEGA-Next facility. In both cases, we saw an overall drop in predicted ablation pressure, peak areal density, and neutron yield due to the reduced thermal conduction (smaller λ E ) in DT plasmas. Comparisons with the experiment on OMEGA are also made.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Temperature and conductivity in shock compressed bridgmanite MgSiO 3 up to 2 TPa

The melting behavior and transport properties of MgSiO 3 at multi-megabar pressures remain poorly constrained despite their importance for high-pressure silicate physics. Here we report the first direct measurements of temperature and optical reflectivity in shock-compressed bridgmanite (MgSiO 3 ) using laser-driven decaying shock compression combined with velocimetry and optical pyrometry. Temperature and reflectivity data spanning approximately 4000–60 000 K were used to constrain the MgSiO 3 melting curve and to infer its electrical conductivity. We find that the MgSiO 3 melting curve becomes shallower than that of iron above 400 GPa, yielding lower melting temperatures in planetary mantles than predicted by several previous theoretical estimates. Across the solid-liquid transition, the inferred electrical conductivity increases significantly, reaching ∼2000 Ω cm −1 . These results provide experimental benchmarks for theoretical models of silicate melting and transport under extreme pressure-temperature conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Uncertainty-quantified phenomenological optical potentials for single-nucleon scattering

Optical-model potentials (OMPs) continue to play a key role in nuclear reaction calculations. However, the uncertainty of phenomenological OMPs in widespread use—inherent to any parametric model trained on data—has not been fully characterized, and its impact on downstream users of OMPs remains unclear. Here we assign well-calibrated uncertainties for two representative global OMPs, those of Koning-Delaroche and Chapel Hill '89, using Markov-chain Monte Carlo for parameter inference. By comparing the canonical versions of these OMPs against the experimental data originally used to constrain them, we show how a lack of outlier rejection and a systematic underestimation of experimental uncertainties contributes to bias of, and overconfidence in, best-fit parameter values. Our updated, uncertainty-quantified versions of these OMPs address these issues and yield complete covariance information for potential parameters. Scattering predictions generated from our ensembles show improved performance both against the original training corpora of experimental data and against a new “test” corpus comprising many of the experimental single-nucleon scattering data collected over the last twenty years. Finally, we apply our uncertainty-quantified OMPs to two case studies of application-relevant cross sections. We conclude that, for many common applications of OMPs, including OMP uncertainty should become standard practice. Furthermore, to facilitate their immediate use, digital versions of our updated OMPs and related tools for forward uncertainty propagation are included as Supplemental Material.

150 ≤ A ≤ 189↗
DOE OSTI2024

Evidence for the Higgs Boson Decay to a $Z$ Boson and a Photon at the LHC

The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb –1 for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2 ± 0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

The seventh blind test of crystal structure prediction: structure generation methods

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.

Chemistry↗
DOE OSTI2020

The Effects of Atmospheric Models on the Estimation of Infrasonic Source Functions at the Source Physics Experiment

We invert infrasound signals for an equivalent seismoacoustic source function using different atmospheric models to produce the necessary Green’s functions. The infrasound signals were produced by a series of underground chemical explosions as part of the Source Physics Experiment (SPE). In a previous study, we inverted the infrasound data using so-called predictive atmospheric models, which were based on historic, regional-scaled, publicly available weather observations interpolated onto a 3D grid. For the work presented here, we invert the same infrasound data, but using atmospheric models based on weather data collected in a time window that includes the approximate time of the explosion experiments, which we term postdictive models. We build two versions of the postdictive models for each SPE event: one that is based solely on the regional scaled observations, and one that is based on both regional scaled observations combined with on-site observations obtained by a weather sonde released at the time of the SPE. We then invert the observed data set three times, once for each atmospheric model type. We find that the estimated seismoacoustic source functions are relatively similar in waveform shape regardless of which atmospheric model that we used to construct the Green’s functions. However, we find that the amplitude of the estimated source functions is systematically dependent on the atmospheric model type: using the predictive atmospheric models to invert the data generally yields estimated source functions that are larger in amplitude than those estimated using the postdictive models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Morphological-informed Thermal Property Prediction in the Engineering Domain

Using first-principle, atomic scale methods to predict thermal carrier (electron and phonon) behavior at nanoscale yields highly resolved thermal properties. Density functional theory simulations can model myriad effects on the transport of phonons and electrons (e.g., phonon and electron coupling, electron correlation, defect presence, carrier scattering), and yield thermal properties for small geometric domains such as thermal and electrical conductivity, or heat capacity. While these thermal properties are accurate in atomistic and nanoscopic systems, they must be scaled up to the microstructural domain to be a useful predictor for experimental basis, as the microscale is where myriad changes and physical phenomenon occur within a material (e.g., grain boundaries, precipitate aggregations, interfaces, defect clusters). My talk will discuss the necessity of thermal property predictions for nuclear fuels applications and more. Additionally I will discuss the necessity in developing multi-physics, multi-scale methods for future applications in predictions for not only nuclear fuels, but other materials applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Reformulating a Gurson-based dynamic damage model and demonstrating improved predictive power and numerical robustness

The original Tepla (TEnsile PLAsticity) ductile damage model, based on the Gurson yield surface, has long been used to model damage evolution and material failure under dynamic loading. Unfortunately, Tepla suffered from mesh sensitivity, numerical instability, and limited predictive capability. Here, we theoretically reformulate Tepla to address these issues. We especially focus on the prediction of porosity, which is the key state variable used for modeling ductile damage, as compared to more easily measured surface velocities, which are at best an indirect measure of damage. Key model changes include separating the viscosity during volumetric void growth from underlying shear strength behavior and switching to an iterative bisection solver. The new Tepla is then calibrated on incipient spall experiments on half-hard copper and tantalum, which demonstrate its ability to simultaneously fit the model to recovered porosity distributions and measured surface velocities, a stringent test. Improved numerical behavior, such as greatly reduced mesh sensitivity, is also shown in those simulations. Finally, the new Tepla model is applied to several high-explosive loaded, sweeping wave experiments, showing the ability of the model to predict behavior on tests with significantly different loading conditions and histories than the calibration data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A mechanism-based quantitative multi-scale framework for investigating irradiation hardening of tungsten at low temperature

We present the development and validation of a mechanism-based multi-scale modeling framework to quantitatively link the irradiation defect evolution kinetics at the microscopic scale to the macroscopic yield strength and flow stress evolution of tungsten irradiated at low temperature (0.08 < T/T m < 0.18, where T m is the melting point and is equal to 3673 K). The mechanism-based strength model, proposed as the superposition of thermal softening and modified dispersed barrier hardening, is developed to understand the underlying strengthening mechanisms. The thermal softening exponent as defined in the Johnson-Cook model is obtained by fitting unirradiated yield strength at different temperatures. A set of irradiation-induced defect kinetics equations with the thermodynamics parameters derived from the atomic calculations reported in the literature is used to determine the densities and sizes of defect clusters at the meso-scale, and the predicted irradiation damage characteristics are in reasonable quantitative agreement with experimental data from literature. The effects of irradiation condition (temperature and irradiation dose) and test temperature on yield strength are quantitatively predicted and compared with experimental measurements from the literature. Here, the predicted irradiation hardening and strain hardening are compared with experimental data as model validation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Investigation of the yielding transition in concentrated colloidal systems via rheo-XPCS

We probe the microstructural yielding dynamics of a concentrated colloidal system by performing creep/recovery tests with simultaneous collection of coherent scattering data via X-ray Photon Correlation Spectroscopy (XPCS). This combination of rheology and scattering allows for time-resolved observations of the microstructural dynamics as yielding occurs, which can be linked back to the applied rheological deformation to form structure–property relations. Under sufficiently small applied creep stresses, examination of the correlation in the flow direction reveals that the scattering response recorrelates with its predeformed state, indicating nearly complete microstructural recovery, and the dynamics of the system under these conditions slows considerably. Conversely, larger creep stresses increase the speed of the dynamics under both applied creep and recovery. The data show a strong connection between the microstructural dynamics and the acquisition of unrecoverable strain. By comparing this relationship to that predicted from homogeneous, affine shearing, we find that the yielding transition in concentrated colloidal systems is highly heterogeneous on the microstructural level.

42 ENGINEERING↗
DOE OSTI2021

Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.

42 ENGINEERING↗
DOE OSTI2021

Comparing the Expense and Accuracy of Methods to Simulate Atomic Vibrations in Rubrene

Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal–organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy–efficiency tradeoffs have not been examined in depth. In this work, rubrene is used as a model system to predict atomic vibrational properties using six different simulation methods: density functional theory, density functional tight binding, density functional tight binding with a Chebyshev polynomial-based correction, a trained machine learning model, a pretrained machine learning model called ANI-1, and a classical forcefield model. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight-binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction.

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

Catalytic Strategy for Conversion of Triacetic Acid Lactone to Potassium Sorbate

Here this study shows a new route to produce potassium sorbate (KS) from triacetic acid lactone (TAL), which is a chemical platform that can be biologically synthesized from natural sources. Sorbic acid and its potassium salt (KS) are widely used as preservatives in foods and pharmaceuticals. Three steps are used to produce KS from TAL: 1) hydrogenation of TAL into 4-hydroxy- 6-methyltetrahydro-2-pyrone (HMP), 2) dehydration of HMP to parasorbic acid (PSA), and 3) ring-opening and hydrolysis of PSA to KS. TAL can be fully hydrogenated over Ni/SiO2 to give near quantitative yields of HMP. A three-step reaction kinetics model was developed for dehydration of HMP into PSA. This model was used to show that the highest PSA yield occurs at low temperatures. An experimental PSA yield of 84.2% with respect to TAL was obtained which agreed with the prediction of the reaction kinetics model. KOH was used as a coreactant for the ring-opening hydrolysis of PSA to produce >99.9% yield of KS from PSA. Tetrahydrofuran (THF) was used to purify the TAL derived-KS (TAL-KS). The TAL-KS had a KS purity of 95.5%. The overall yield of TAL-KS with respect to TAL was calculated to be 77.3%. TAL-KS produced in this study had similar antimicrobial activities as commercial KS.

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

Data for Catalytic Strategy for Conversion of Triacetic Acid Lactone to Potassium Sorbate

This study shows a new route to produce potassium sorbate (KS) from triacetic acid lactone (TAL), which is a chemical platform that can be biologically synthesized from natural sources. Sorbic acid and its potassium salt (KS) are widely used as preservatives in foods and pharmaceuticals. Three steps are used to produce KS from TAL: 1) hydrogenation of TAL into 4-hydroxy-6-methyltetrahydro-2-pyrone (HMP), 2) dehydration of HMP to parasorbic acid (PSA), and 3) ring-opening and hydrolysis of PSA to KS. TAL can be fully hydrogenated over Ni/SiO2 to give near quantitative yields of HMP. A three-step reaction kinetics model was developed for dehydration of HMP into PSA. This model was used to show that the highest PSA yield occurs at low temperatures. An experimental PSA yield of 84.2% with respect to TAL was obtained which agreed with the prediction of the reaction kinetics model. KOH was used as a coreactant for the ring-opening hydrolysis of PSA to produce >99.9% yield of KS from PSA. Tetrahydrofuran (THF) was used to purify the TAL derived-KS (TAL-KS). The TAL-KS had a KS purity of 95.5%. The overall yield of TAL-KS with respect to TAL was calculated to be 77.3%. TAL-KS produced in this study had similar antimicrobial activities as commercial KS.

Catalysis↗