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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How Atomic Bonding Plays the Hardness Behavior in the Al–Co–Cr–Cu–Fe–Ni High Entropy Family

A systematic study on a face-centered cubic-based compositionally complex alloy system Al–Co–Cr–Cu–Fe–Ni in its single-phase state is carried out, where a mother senary compound Al 8 Co 17 Cr 17 Cu 8 Fe 17 Ni 33 and five of its suballoys, obtained by removing one element at a time, are investigated and exhaustively analyzed determining the contribution of each alloying element in the solid solution. The senary and the quinaries are compared using experimental techniques including X-ray absorption spectroscopy, X-ray diffraction, transmission electron microscopy, and first principles hybrid Monte Carlo/molecular dynamics simulations. Chemical short-range order and bond length distances have been determined both at the experimental and computational level. Electronic structure and local atomic distortions up to 5.2 Å have been correlated to the microhardness values. A linear regression model connecting hardness with local lattice distortions is presented.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Shock compression of vanadium at extremes: Theory and experiment

Here, the equation of state (EOS) and shock compression of bulk vanadium were investigated using canonical ab initio molecular dynamic simulations, with experimental validation to 865 GPa from shock data collected at Sandia's Z Pulsed Power Facility. In simulations the phase space was sampled along isotherms ranging from 3000 K to 50 000 K, for densities between ρ = 3 and 15 g / cm 3 , with a focus on the liquid regime and the body-centered-cubic phase in the vicinity of the melting limit. The principal Hugoniot predicted from first principles is overall consistent with shock data, while it showed that current multiphase SESAME-type EOS for vanadium needed revision in the liquid regime. A more accurate SESAME EOS was developed using constraints from experiments and simulations. This work emphasizes the need to use a combined theoretical and experimental approach to develop high-fidelity EOS models for extreme conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

High-pressure hydrogen decompression in sulfur crosslinked elastomers

High-pressure storage and cyclic (de)pressurization of hydrogen gas is known to result in degradation and failure of gas canisters, hoses, linings, and O-rings as the relatively small hydrogen molecule can readily permeate most materials. Hence, identifying material compositions that are less susceptible to hydrogen-induced damage is of significant importance to the hydrogen energy infrastructure. Here, we use classical atomistic molecular dynamics simulations to study hydrogen exposed ethylene-propylene-diene monomer (EPDM) rubber, an elastomer typically used in O-rings. We make chemical modifications to the model by adjusting the crosslink density and report on gas solubility, diffusivity, and molecular restructuring in response to rapid decompression. Our simulations indicate that increases in crosslink density can reduce volumetric expansion during decompression and result in smaller free volume pore sizes. However, these favorable properties for sealing materials come with a tradeoff. At pressure, crosslinks introduce extra free volume, providing potential sites for gas localization, the precursor to cavitation-induced failure.

08 HYDROGEN↗
DOE OSTI2024

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment

Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. Here, we investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Binding kinetics of harmonically confined random walkers

Diffusion-mediated binding of molecules under the influence of discrete spatially confining potentials is a commonly encountered scenario in systems subjected to explicit fields or implicit fields arising from tethering restraints. Here, in this work, we derive analytical expressions for the mean binding time of two random walkers geometrically confined by means of two harmonic potentials in one- and two-dimensional systems, which show excellent agreement with Brownian dynamics simulations. As a demonstration of its utility, we use this theory to maximize the communication speed in existing DNA walkers, obtaining quantitative agreement with previously reported experimental findings. The analytical expressions derived in this paper are broadly applicable to diverse systems, providing ways to characterize communication processes and optimize the rate of signal propagation for sensing and computing applications at the nanoscale.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A three-dimensional model of terrain-induced updrafts for movement ecology studies

Abstract Background Spatially explicit simulation models of animal movements through the atmosphere necessarily require a representation of the spatial and temporal variation of atmospheric conditions. In particular, for movements of soaring birds that rely extensively on vertical updrafts to avoid flapping flight, accurate and reliable estimation of the vertical component of wind is critical. The interaction between wind and complex terrain shapes both the horizontal and vertical wind fields, highlighting the need to model the coupling between local terrain features and atmospheric conditions at scales relevant to animal movement. Methods In this work, we propose a new empirical model for estimating the orographic updraft field. The model is developed using computational fluid dynamics simulations of canonical atmospheric conditions over moderately complex terrain. To isolate buoyancy and thermal effects, and focus on terrain-induced effects, we use only simulations of a neutrally stratified atmosphere to develop the model. The model, which we name Engineering Vertical Velocity Estimator (EVVE), is simple to implement and is a function of the underlying terrain elevation map, the desired height above ground level (AGL), and wind conditions at a reference height (80 m). We validate the model with data from the Alaiz mountain (Spain) field campaign. Results Compared to observations, the proposed improved model estimates the updrafts at 120 m AGL with a mean error of 0.11 m/s ( $$\sigma =0.28$$ σ = 0.28 m/s), compared to 0.85 m/s ( $$\sigma =0.58$$ σ = 0.58 m/s) for its baseline. For typical land-based wind turbine hub heights of 80 m AGL, the proposed model has a mean error of 0.04 m/s ( $$\sigma =0.25$$ σ = 0.25 m/s), compared to baseline 0.54 m/s ( $$\sigma =0.45$$ σ = 0.45 m/s) estimations. We illustrate an application of the model in movement ecology by comparing simulated tracks and presence maps of golden eagles ( Aquila chrysaetos ) moving across two distinct landscapes. The tracks and presence maps are obtained using a simple heuristic-based movement model, with the updraft field given by the proposed model and a wind vector-based estimation approach that is currently in wide use in movement ecology studies of raptors and other soaring birds. Conclusions We highlight that movement model results can be sensitive to the underlying orographic updraft model, especially in studies of fine-scale movements in regions of complex topography. We suggest adopting the proposed model rather than the wind vector estimation method for studies of soaring bird movements.

17 WIND ENERGY↗
DOE OSTI2022

Revealing hidden defects through stored energy measurements of radiation damage

With full knowledge of a material’s atomistic structure, it is possible to predict any macroscopic property of interest. In practice, this is hindered by limitations of the chosen characterization techniques. For example, electron microscopy is unable to detect the smallest and most numerous defects in irradiated materials. Instead of spatial characterization, we propose to detect and quantify defects through their excess energy. Differential scanning calorimetry of irradiated Ti measures defect densities five times greater than those determined using transmission electron microscopy. Our experiments also reveal two energetically distinct processes where the established annealing model predicts one. Molecular dynamics simulations discover the defects responsible and inform a new mechanism for the recovery of irradiation-induced defects. The combination of annealing experiments and simulations can reveal defects hidden to other characterization techniques and has the potential to uncover new mechanisms behind the evolution of defects in materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

The denatured state of HIV ‐1 protease under native conditions

Abstract The denatured state of several proteins has been shown to display transient structures that are relevant for folding, stability, and aggregation. To detect them by nuclear magnetic resonance (NMR) spectroscopy, the denatured state must be stabilized by chemical agents or changes in temperature. This makes the environment different from that experienced in biologically relevant processes. Using high‐resolution heteronuclear NMR spectroscopy, we have characterized several denatured states of a monomeric variant of HIV‐1 protease, which is natively structured in water, induced by different concentrations of urea, guanidinium chloride, and acetic acid. We have extrapolated the chemical shifts and the relaxation parameters to the denaturant‐free denatured state at native conditions, showing that they converge to the same values. Subsequently, we characterized the conformational properties of this biologically relevant denatured state under native conditions by advanced molecular dynamics simulations and validated the results by comparison to experimental data. We show that the denatured state of HIV‐1 protease under native conditions displays rich patterns of transient native and non‐native structures, which could be of relevance to its guidance through a complex folding process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Origin of increased helium density inside bubbles in Ni (1–$x$) Fe $x$ alloys

Due to virtually no solubility, He atoms implanted or created inside materials tend to form bubbles, which are known to damage material properties through embrittlement. Higher He density in nano-sized bub- bles was observed both experimentally and computationally in Ni (1–$x$) Fe $x$ -alloy samples compared to Ni. The bubbles in the Ni (1–$x$) Fe $x$ -alloys were observed to be faceted, whereas in elemental Ni they were more spherical. Molecular dynamics simulations showed that stacking fault structures formed around bubbles at maximum He density. Higher Fe concentrations stabilize stacking fault structures, suppress evolution of dislocation network around bubbles and suppress complete dislocation emission, leading to higher He density.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Enabling robust offline active learning for machine learning potentials using simple physics-based priors

Abstract Machine learning surrogate models for quantum mechanical simulations have enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a substantial amount of data to make reliable predictions of the potential energy landscape or careful active learning (AL) and uncertainty estimates. When starting with small datasets, convergence of AL approaches is a major outstanding challenge which has limited most demonstrations to online AL. In this work we demonstrate a Δ-machine learning (ML) approach that enables stable convergence in offline AL strategies by avoiding unphysical configurations with initial datasets as little as a single data point. We demonstrate our framework’s capabilities on a structural relaxation, transition state calculation, and molecular dynamics simulation, with the number of first principle calculations being cut down anywhere from 70%–90%. The approach is incorporated and developed alongside AMP torch , an open-source ML potential package, along with interactive Google Colab notebook examples.

Shuaibi, Muhammed↗
DOE OSTI2021

Energetics of polymeric carbon monoxide

The transformation of carbon monoxide (CO) from a molecular liquid to a polymeric solid under isothermal compression at room temperature is investigated using first principles theory. We report structural and thermodynamic properties from ambient density up to 2.45 g/cc obtained using density functional theory molecular dynamics simulations, including hybrid exchange corrections. The theoretical results are compared with newly obtained polymeric CO samples, synthesized in a large volume press. The explosive performance of polymeric CO is predicted and discussed. Under most favorable assumptions, it is found to be comparable to trinitrotoluene.

25 ENERGY STORAGE↗
DOE OSTI2022

Role of Local Structure in the Enhanced Dynamics of Deformed Glasses

External stress can accelerate molecular mobility of amorphous solids by several orders of magnitude. The changes in mobility are commonly interpreted through the Eyring model, which invokes an empirical activation volume. Here, we analyze constant-stress molecular dynamics simulations and propose a structure-dependent Eyring model, connecting activation volume to a machine-learned field, softness. We show that stress has a heterogeneous effect on the mobility that depends on local structure through softness. The barrier impeding relaxation reduces more for well-packed particles, which explains the narrower distribution of relaxation time observed under stress.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Functional lipid pairs as building blocks of phase-separated membranes

Biological membranes exhibit a great deal of compositional and phase heterogeneity due to hundreds of chemically distinct components. As a result, phase separation processes in cell membranes are extremely difficult to study, especially at the molecular level. It is currently believed that the lateral membrane heterogeneity and the formation of domains, or rafts, are driven by lipid–lipid and lipid–protein interactions. Nevertheless, the underlying mechanisms regulating membrane heterogeneity remain poorly understood. In the present work, we combine inelastic X-ray scattering with molecular dynamics simulations to provide direct evidence for the existence of strongly coupled transient lipid pairs. These lipid pairs manifest themselves experimentally through optical vibrational (a.k.a. phononic) modes observed in binary (1,2-dipalmitoyl- sn -glycero-3-phosphocholine [DPPC]–cholesterol) and ternary (DPPC–1,2-dioleoyl- sn -glycero-3-phosphocholine/1-palmitoyl-2-oleoyl-glycero-3-phosphocholine [DOPC/POPC]–cholesterol) systems. The existence of a phononic gap in these vibrational modes is a direct result of the finite size of patches formed by these lipid pairs. The observation of lipid pairs provides a spatial (subnanometer) and temporal (subnanosecond) window into the lipid–lipid interactions in complex mixtures of saturated/unsaturated lipids and cholesterol. Our findings represent a step toward understanding the lateral organization and dynamics of membrane domains using a well-validated probe with a high spatial and temporal resolution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Superlubric-pinned transition of a two-dimensional solid dusty plasma under a periodic triangular substrate

Here, the superlubric-pinned transition in the depinning dynamics of a two-dimensional (2D) solid dusty plasma modulated by 2D triangular periodic substrates is investigated using Langevin dynamical simulations. When the lattice structure of the 2D solid dusty plasma perfectly matches the triangular substrate, two distinctive pinned and moving ordered states are observed as the external uniform driving force gradually increases from zero. When there is a mismatch between the lattice structure and the triangular substrate, however, on shallow substrates, it is discovered that all of the particles can slide freely on the substrate even when the applied driving force is tiny. This is a typical example of superlubricity, which is caused by the competition between the substrate-particle and particle-particle interactions. If the substrate depth increases further, as the driving force increases from zero, there are three dynamical states consisting of the pinned state, the disordered plastic flow state, and the moving ordered state. In an underdense system, where there are fewer particles than potential well minima, it is found that the occurrence of the three different dynamical states is controlled by the depth of the substrate, which is quantitatively characterized using the average mobility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Development of a machine learning model for polyethylene pyrolysis using a detailed reaction mechanism

Waste plastics have recently received significant attention as the issue of waste generation continues to increase. Thermal conversion processes, such as pyrolysis and gasification, are attractive potential technologies for utilizing waste plastics and reducing overall waste generation. Efficient utilization of plastics requires a detailed understanding of the conversion process such as pyrolysis and gasification. However, a mechanistic understanding of these processes lead to large and complex kinetic schemes that are not suited for large-scale and long-time simulation methods. Currently, most modeling approaches for pyrolysis and gasification rely on globally lumped, simplified kinetic schemes that provide results that are classified by their product type and not individual species, which limit the level of fidelity achieved via modeling. A machine learning (ML) model has been developed for the primary reactions of high-density polyethylene (HDPE) in an attempt to increase computational efficiency while still maintaining a high level of detail and accuracy. The ML model is trained on a detailed reaction mechanism containing 42 total species and 737 chemical reactions. A DeepONet branch and trunk architecture was adopted to train the model using time-steps relevant to computational fluid dynamics simulations. The ML used physics-informed loss functions to ensure mass conservation. The surrogate model has been deployed in simple MFiX CFD simulations, single particle and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗
DOE OSTI2024

Thermodynamic driving forces in contact electrification between polymeric materials

Abstract Contact electrification, or contact charging, refers to the process of static charge accumulation after rubbing, or even simple touching, of two materials. Despite its relevance in static electricity, various natural phenomena, and numerous technologies, contact charging remains poorly understood. For insulating materials, even the species of charge carrier may be unknown, and the direction of charge-transfer lacks firm molecular-level explanation. Here, we use all-atom molecular dynamics simulations to investigate whether thermodynamics can explain contact charging between insulating polymers. Based on prior work suggesting that water-ions, such as hydronium and hydroxide ions, are potential charge carriers, we predict preferred directions of charge-transfer between polymer surfaces according to the free energy of water-ions within water droplets on such surfaces. Broad agreement between our predictions and experimental triboelectric series indicate that thermodynamically driven ion-transfer likely influences contact charging of polymers. Furthermore, simulation analyses reveal how specific interactions of water and water-ions proximate to the polymer-water interface explain observed trends. This study establishes relevance of thermodynamic driving forces in contact charging of insulators with new evidence informed by molecular-level interactions. These insights have direct implications for future mechanistic studies and applications of contact charging involving polymeric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Influence of strong Coulomb coupling on diffusion in atmospheric pressure plasmas

Ion diffusion in atmospheric pressure plasmas is examined and particular attention is paid to the fact that ion–ion interactions can be influenced by strong Coulomb coupling. Three regimes are identified. At low ionization fractions (x i ≲ 10 —6 ), standard weakly correlated ion-neutral interactions set the diffusion rate. At moderate ionization fractions (10 —6 ≲ x i ≲ 10 —2 ) there is a transition from ion-neutral to ion–ion collisions setting the diffusion rate. In this regime, the effect of strong Coulomb coupling in ion–ion collisions is accounted for by applying the mean force kinetic theory. Since both ion-neutral and ion–ion interactions contribute a comparable amount to the total diffusion rate, models (such as particle-in-cell or fluid) must account for both contributions. At high ionization fractions (x i ≳ 10 —2 ), strongly correlated ion–ion collisions dominate and the plasma is heated substantially by a disorder-induced heating (DIH) process associated with strong correlations. The temperature increase due to DIH strongly influences the ion diffusion rate. This effect becomes even more important, and occurs at lower ionization fractions, as the pressure increases above atmospheric pressure. In addition to ion diffusion, DIH affects the neutral gas temperature, therefore influencing the neutral diffusion rate. Model predictions are tested using molecular dynamics simulations, which included a Monte Carlo collision routine to simulate the effect of ion-neutral collisions at the lowest ionization fractions. The model and simulations show good agreement over a broad range of ionization fractions. Importantly, the results provide a model for ion diffusion, on a wide range of ionization fractions and pressures, solely considering the elastic contribution to the diffusion coefficient—as an illustration of how strong Coulomb coupling influences diffusion processes in general.

fast neutral gas heating↗