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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Rethinking the magnetic properties of lepidocrocite: A density functional theory and cluster expansion study

The iron oxyhydroxide lepidocrocite (g-FeOOH) is an abundant mineral critical to a number of chemical and technological applications. Of particular interest is the ground state and nite temperature magnetic order, and the subsequent impact this has upon crystal properties. The magnetic properties, investigated in this work are governed primarily through superexchange interactions, and have been calculated using density functional theory and cluster expansion methods. Quantication of these exchange terms has facilitated the determination of the ground state magneto-crystalline structure and subsequent calculation of its lattice constants, elastic moduli, cohesive enthalpy, and electronic density of states. Further, using a collinear magnetic conguration model, the magnetic heat capacity versus temperature has been studied and the Neel temperature obtained.

Pope, Daniel J.↗
DOE OSTI2020

An extended trajectory-mechanics approach for calculating two-phase flow paths

A technique originating in quantum dynamics is used to derive a trajectory-based, semi-analytical solution for two-phase flow. The partial differential equation governing the evolution of the aqueous phase is equivalent to a family of ordinary differential equations defined along a path through the porous medium. The trajectories may be found by solving the differential equations directly or by post-processing the output of a numerical solution to the full set of governing equations. The trajectories, which differ from conventional streamlines, are found to bend downward in response to gravitational forces. The curvature is more pronounced as the dip of the porous layer containing the flow increases. Subtle changes in the relative permeability curve can lead to significant variations in the trajectories. The ordinary differential equation for the trajectory provides an expression for the travel time along the path. The expression produces a semi-analytical approximation to the model parameter sensitivities, the partial derivatives of the travel times with respect to changes in the permeability model. The semi-analytical trajectory-based sensitivities generally agree with those computed using a numerical reservoir simulator and a perturbation approach. The sensitivities are useful in tomographic imaging algorithms designed to estimate the spatial variation in permeability within a porous medium using multiphase observations.

58 GEOSCIENCES↗
DOE OSTI2020

Coupling solidification kinetics with phase-behavior computations in hydrodynamic simulations of high-pressure, dynamic-compression processes

In this study, we report a numerical scheme to integrate models for the kinetics of solidification processes together with phase-behavior computations in the context of continuum-scale hydrodynamic simulations. The objective of the phase-behavior computations is to determine the pressure and temperature, given the following three sets of inputs: (1) an appropriate equation of state to describe our system, (2) the phase fraction(s) produced by the kinetic models, (3) and the volume and internal energy obtained by solving the conservation equations that govern the hydrodynamic behavior. The kinetics are assumed to be governed by the Kolmogorov–Johnson–Mehl–Avrami equation, and the nucleation and growth rates that enter into that equation are functions of the pressure and temperature produced by the phase-behavior computations. Our formulation allows for the fluid and solid phases to be at different temperatures (thermal nonequilibrium) and pressures (arising from surface-tension-induced Laplace contributions). The formulation is presented in a fairly general setting that is independent of any particular material, although we demonstrate it in some examples related to high-energy-density science applications where materials are rapidly compressed to pressures exceeding several gigapascals in less than a microsecond. We conclude with a critical evaluation of our approach and provide suggestions for future work to improve the predictive capabilities and generality of the models.

42 ENGINEERING↗
DOE OSTI2021

Filtering, averaging, and scale dependency in homogeneous variable density turbulence

We investigate relationships between statistics obtained from filtering and from ensemble or Reynolds-averaging turbulence flow fields as a function of length scale. Generalized central moments in the filtering approach are expressed as inner products of generalized fluctuating quantities, q ' ( ξ , x ) = q ( ξ ) - q ¯ ( x ) , representing fluctuations of a field q ( ξ ) , at any point ξ, with respect to its filtered value at x. For positive-definite filter kernels, these expressions provide a scale-resolving framework, with statistics and realizability conditions at any length scale. In the small-scale limit, scale-resolving statistics become zero. In the large-scale limit, scale-resolving statistics and realizability conditions are the same as in the Reynolds-averaged description. Using direct numerical simulations (DNS) of homogeneous variable density turbulence, we diagnose Reynolds stresses, T i j , resolved kinetic energy, kr, turbulent mass-flux velocity, a i , and density-specific volume covariance, b, defined in the scale-resolving framework. These variables, and terms in their governing equations, vary smoothly between zero and their Reynolds-averaged definitions at the small and large scale limits, respectively. At intermediate scales, the governing equations exhibit interactions between terms that are not active in the Reynolds-averaged limit. For example, in the Reynolds-averaged limit, b follows a decaying process driven by a destruction term; at intermediate length scales, it is a balance between production, redistribution, destruction, and transport, where b grows as the density spectrum develops and then decays when mixing becomes strong enough. This work supports the notion of a generalized, length-scale adaptive model that converges to DNS at high resolutions and to Reynolds-averaged statistics at coarse resolutions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Binding of noble gas atoms by superhalogens

Because of their closed shells, noble gas (Ng) atoms (Ng = Ne, Ar, Kr, and Xe) seldom take part in chemical reactions, yet finding such mechanisms not only is of scientific interest but also has practical significance. Following a recent work by Mayer et al. [Proc. Natl. Acad. Sci. U. S. A. 116, 8167–8172 (2019)] on the room temperature binding of Ar to a superelectrophilic boron site embedded in a negative ion complex, B 12 (CN) 11 - , we have systematically studied the effect of cluster size and terminal ligands on the interaction of Ng by focusing on B 12 X 11 (Ng) (X = H, CN, and BO) and B 12 X 10 (Ng) 2 (X = CN and BO) whose stabilities are governed by the Wade–Mingos rule and on C 5 BX 5 (Ng) (X = H, F, and CN) and C 4 B 2 (CN) 4 (Ng) 2 whose stabilities are governed by the Huckel’s aromaticity rule. Our conclusion, based on density functional theory, is that both the cluster size and the terminal ligands matter—the interaction between the cluster and the Ng atoms becomes stronger with increasing cluster size and the electron affinity of the terminal ligands. Our studies also led to a counter-intuitive finding—removing multiple terminal ligands can enable electrophilic centers to bind multiple Ng atoms simultaneously without compromising their binding strength.

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

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions

Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the “feature importance” approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.

Chemistry↗
DOE OSTI2025

Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data

Identifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the vacancy surroundings, MD-generated trajectories provided training and comparison datasets, and unsupervised GCN models approximated interatomic dynamics governing vacancy migration in HEAs as ansatzes for KMC. A proof-of-concept model trained on MD data for the Fe, Ni, Cr, Co, and Cu HEA environment was used with two different neighbor interactions to assess the feasibility of training a GCN to predict vacancy defect transition rates in the HEA environment. The resulting setup rapidly generated MD-formatted synthetic trajectories based on dynamics learned from the MD training set, with a time acceleration of roughly two orders of magnitude and a similar diffusion coefficient to MD observations. Additionally, Nudged Elastic Band (NEB) calculations were performed on randomly generated FeNiCrCoCu HEA structures to determine vacancy migration barriers across nearest-neighbor sites. Transition probabilities for each jump, categorized by atomic type, were extracted from these calculations. NEB-based and GCN-based approaches led to similar outcomes.

Reimer, C↗
DOE OSTI2025

Injection locking and coupling dynamics in superconducting nanowire-based cryogenic oscillators

Oscillators designed to function at cryogenic temperatures play a critical role in superconducting electronics and quantum computing by providing stable, low-noise signals with minimal energy loss. Here, in this work, we present a comprehensive numerical study of injection locking and mutual coupling dynamics in superconducting nanowire (ScNW)-based cryogenic oscillators. Using the design space of a standalone ScNW-based oscillator, we investigate two critical mechanisms that govern frequency synchronization and signal coordination in cryogenic computing architectures: (1) injection locking induced by an external AC signal with a frequency near the oscillator's natural frequency, and (2) the mutual coupling dynamics between two ScNW oscillators under varying coupling strengths. We identify key design parameters—such as shunt resistance, nanowire inductance, and coupling strength—that govern the locking range. Additionally, we examine how the amplitude of the injected signal affects the amplitude of the locked oscillation, offering valuable insights for power-aware oscillator synchronization. Furthermore, we analyze mutual synchronization between coupled ScNW oscillators using capacitive and resistive coupling elements. Our results reveal that the phase difference between oscillators can be controlled by tuning the coupling strength, enabling programmable phase-encoded information processing. These findings could enable building ScNW-based oscillatory neural networks, synchronized cryogenic logic blocks, and on-chip cryogenic resonator arrays.

Artificial neural networks↗
DOE OSTI2022

Microscopic dynamics underlying the stress relaxation of arrested soft materials

Arrested soft materials such as gels and glasses exhibit a slow stress relaxation with a broad distribution of relaxation times in response to linear mechanical perturbations. Although this macroscopic stress relaxation is an essential feature in the application of arrested systems as structural materials, consumer products, foods, and biological materials, the microscopic origins of this relaxation remain poorly understood. Here, we elucidate the microscopic dynamics underlying the stress relaxation of such arrested soft materials under both quiescent and mechanically perturbed conditions through X-ray photon correlation spectroscopy. By studying the dynamics of a model associative gel system that undergoes dynamical arrest in the absence of aging effects, we show that the mean stress relaxation time measured from linear rheometry is directly correlated to the quiescent superdiffusive dynamics of the microscopic clusters, which are governed by a buildup of internal stresses during arrest. We also show that perturbing the system via small mechanical deformations can result in large intermittent fluctuations in the form of avalanches, which give rise to a broad non-Gaussian spectrum of relaxation modes at short times that is observed in stress relaxation measurements. These findings suggest that the linear viscoelastic stress relaxation in arrested soft materials may be governed by nonlinear phenomena involving an interplay of internal stress relaxations and perturbation-induced intermittent avalanches.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)—key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO 2 forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

58 GEOSCIENCES↗
DOE OSTI2024

Saccharomycotina yeasts defy long-standing macroecological patterns

The Saccharomycotina yeasts (“yeasts” hereafter) are a fungal clade of scientific, economic, and medical significance. Yeasts are highly ecologically diverse, found across a broad range of environments in every biome and continent on earth; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild. Here, we trained machine learning models on 12,816 terrestrial occurrence records and 96 environmental variables to infer global distribution maps at ~1 km2 resolution for 186 yeast species (~15% of described species from 75% of orders) and to test environmental drivers of yeast biogeography and macroecology. We found that predicted yeast diversity hotspots occur in mixed montane forests in temperate climates. Diversity in vegetation type and topography were some of the greatest predictors of yeast species richness, suggesting that microhabitats and environmental clines are key to yeast diversity. We further found that range limits in yeasts are significantly influenced by carbon niche breadth and range overlap with other yeast species, with carbon specialists and species in high-diversity environments exhibiting reduced geographic ranges. Finally, yeasts contravene many long-standing macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and a positive relationship between latitude and range size (Rapoport’s rule). These results unveil how the environment governs the global diversity and distribution of species in the yeast subphylum. These high-resolution models of yeast species distributions will facilitate the prediction of economically relevant and emerging pathogenic species under current and future climate scenarios.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Enhanced chloroplast FtsZ-ring constriction by the ARC6–ARC3 module in Arabidopsis

Chloroplast division, a process tightly linked to the energy demands of plants, is initiated by the formation of the stromal filamenting temperature-sensitive Z (FtsZ) ring. The Z ring is highly dynamic, and its constriction provides the essential force for chloroplast division. However, the regulatory mechanisms governing Z-ring dynamics and constriction remain poorly understood. Here, we report that the chloroplast inner envelope membrane (IEM) protein ACCUMULATION AND REPLICATION OF CHLOROPLASTS6 (ARC6) interacts with the chloroplast stromal protein ARC3, and this interaction is negatively regulated by the conserved J-like domain of ARC6. ARC3 is found both distributed throughout the stroma and localized to a ring-like structure at the chloroplast division site. We demonstrate that ARC6 recruits ARC3 to the division site to form a ring-like structure, likely through direct interaction. This ARC6–ARC3 interaction enables ARC3 to bind FtsZs. Furthermore, we show that the ARC6–ARC3 complex significantly promotes the dynamics of chloroplast Z rings reconstituted in a heterologous system. Finally, the constriction of these reconstituted Z rings is markedly enhanced by ARC6–ARC3. Our findings reveal a regulatory mechanism that governs Z-ring dynamics and constriction, shedding light on the molecular mechanisms underlying chloroplast division.

Science & Technology - Other Topics↗
DOE OSTI2025

Physical models reveal indirect reader protein interactions that facilitate epigenetic crosstalk

The spatial organization of chromatin is governed by epigenetic factors, including epigenetic marks and the reader proteins that bind them. By dictating the accessibility of genomic loci, epigenetic factors contribute to the physical regulation of gene expression, enabling diverse cellular phenotypes to be encoded by a shared genome in an individual. Epigenetic dysregulation can lead to aberrations in chromatin architecture, contributing to diseases such as neurological disorders and cancers. Despite the known importance of chromatin organization for human health, the physical mechanisms governing chromatin folding remain underspecified. In this work, we develop a physical model of chromatin organization based on contributions from multiple epigenetic factors. Using our model, we evaluate how conditions in the nuclear environment and crosstalk between epigenetic marks affect the compartmentalization of chromatin into dense heterochromatin and loose euchromatin. Our results emphasize the role of reader protein binding in chromatin compartmentalization. We show that reader proteins interact through an indirect mechanism facilitated by the shared chromatin “scaffold” to which they bind. Under a scenario where reader proteins compete for binding sites, we find that indirect interactions affect the program adopted by the chromatin fiber. By isolating indirect modes of epigenetic crosstalk, we demonstrate how the interplay between epigenetic patterning and environmental factors influences chromatin architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Multiscale fatigue crack initiation in hierarchical additively manufactured alloys

Bioinspired hierarchical microstructures offer a route toward engineered fatigue resistance in additively manufactured alloys. However, it remains unclear how discrete structural constituents independently govern damage accumulation, particularly during the critical fatigue initiation regime where short cracks strongly interact with local microstructure. Here, we investigate multiscale fatigue initiation in a dual-phase, nanolamellar AlCoCrFeNi 2.1 high-entropy alloy. By comparing microscale specimens that isolate the nanolamellar structure against macroscale specimens containing the full melt-pool architecture, we identify size-dependent fatigue initiation mechanisms. We find that failure is dictated by nanolamellar interfaces at the microscale, whereas mesoscale melt pool boundaries serve to initiate fatigue at the macroscale. This mechanistic shift is accompanied by a transition from macroscale quasi-brittle failure to microscale plasticity-driven crack extension. Our results provide a physical framework for understanding how structural hierarchy governs the transition from discrete microstructural deformation to continuum fatigue fracture behavior, informing the design of damage-tolerant, additively manufactured alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Verification and validation of detonation-shock-dynamics relations for explosives described by general equation of state and chemical reaction models

Detonation shock dynamics is a powerful method to model the behaviour of High Explosives (HE). However in order to use this method, the underlying relationship between the local radius of curvature and the detonation speed must be known. Previous work has developed methods to calculate this effect using simple, single-step Arrhenius and polytropic gas, models for the chemical reaction and the equation of state, respectively. In recent years, more complex models for both reaction rates and equations of state have been developed which show better agreement with experimental data than these simple models, especially when considering condensed phase explosives.. This work presents the governing equations for solving these problems in a way that is generalised to use arbitrary equations of state as well as reaction models which may have more than a single step and multiple product species. This implementation is verified against exact solutions, demonstrating that the equations were implemented properly. The verified algorithm is then validated against experimental data and high fidelity simulations, showing that it is able to make accurate predictions in a regime where the underlying assumptions of the governing equations are valid. Importantly, this approach has many applications: from creating equivalent detonation shock dynamics models for existing reactive burn calibrations for HE; to developing new functional forms and calibrations of reactive burn models for condensed phase high explosives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

An industrial policy framework for transforming energy and emissions intensive industries towards zero emissions

The target of zero emissions sets a new standard for industry and industrial policy. Industrial policy in the twenty-first century must aim to achieve zero emissions in the energy and emissions intensive industries. Sectors such as steel, cement, and chemicals have so far largely been sheltered from the effects of climate policy. A major shift is needed, from contemporary industrial policy that mainly protects industry to policy strategies that transform the industry. For this purpose, we draw on a wide range of literatures including engineering, economics, policy, governance, and innovation studies to propose a comprehensive industrial policy framework. The policy framework relies on six pillars: directionality, knowledge creation and innovation, creating and reshaping markets, building capacity for governance and change, international coherence, and sensitivity to socio-economic implications of phase-outs. Complementary solutions relying on technological, organizational, and behavioural change must be pursued in parallel and throughout whole value chains. Current policy is limited to supporting mainly some options, e.g. energy efficiency and recycling, with some regions also adopting carbon pricing, although most often exempting the energy and emissions intensive industries. An extended range of options, such as demand management, materials efficiency, and electrification, must also be pursued to reach zero emissions. New policy research and evaluation approaches are needed to support and assess progress as these industries have hitherto largely been overlooked in domestic climate policy as well as international negotiations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Measurement of small island characteristics using high resolution ECE and CER at DIII-D

Abstract The measurements using the high resolution electron cyclotron emission radiometry and the charge exchange and recombination spectroscopy are processed using analytic formulas to allow for the detection of islands as small as 1.9 cm. In contrast to large, saturated magnetic islands which are relatively well understood to be governed by the loss of bootstrap current inside the island, small islands are less well understood due to the difficulty of their accurate measurement in tokamaks. Here, ‘small’ islands are islands comparable in size to the ion banana width, which can be as small as 0.8 cm at DIII-D. The new measurement methods allow for the detection of small island widths when the predicted increase of mode frequency to match the Doppler shifted ion diamagnetic frequency is observed. Therefore, for the first time, the mode frequency increase can be unambiguously associated to the acceleration of the magnetic island propagation. Such association allows for a further development and validation of the much-debated theory of ion polarization currents, which is thought to govern the small island growth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Much of zero emissions commitment occurs before reaching net zero emissions

Abstract We explore the response of the Earth’s coupled climate and carbon system to an idealized sequential addition and removal of CO 2 to the atmosphere, following a symmetric and continuous emissions pathway, in contrast to the discontinuous emissions pathways that have largely informed our understanding of the climate response to net zero and net negative emissions to date. We find, using both an Earth system model and an ensemble of simple climate model realizations, that warming during the emissions reduction and negative emissions phases is defined by a combination of a proportionality of warming to cumulative emissions characterized by the transient climate response to emissions (TCRE), and a deviation from that proportionality that is governed by the zero emissions commitment (ZEC). About half of the ZEC is realized before reaching zero emissions, and the ZEC thus also controls the timing between peak cumulative CO 2 emissions and peak temperature, such that peak temperature may occur before peak cumulative emissions if ZEC is negative, underscoring the importance of ZEC in climate policies aimed to limit peak warming. Thus we argue that ZEC is better defined as the committed warming relative to the expected TCRE proportionality, rather than as the additional committed warming that will occur after reaching net zero CO 2 emissions. Once established, the combined TCRE and ZEC relationship holds almost to complete removal of prior cumulative CO 2 emissions. As cumulative CO 2 emissions approach zero through negative CO 2 emissions, CO 2 concentrations drop below preindustrial values, while residual long-term climate change continues, governed by multicentennial dynamical processes.

54 ENVIRONMENTAL SCIENCES↗