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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Non-equilibrium approach to prediction of microstructure evolution for metals undergoing severe plastic deformation

Highlights: • New consistent physically-based model of dynamic recrystallization. • The model is based on evolution of fraction of high-angle grain boundaries. • Possibility to predict the evolution of dislocation cells and grain boundaries. • The model is implemented for FEM simulations. • Results demonstrate inhomogeneous distribution of strain within the sample. - Abstract: Available models of dynamic recrystallization have a number of disadvantages that in most cases make them inapplicable for practical predictions of material microstructure evolution. Both the microstructural and the empirically based approaches do not reflect physical processes leading to evolution of material defect structure in the process of plastic deformation. This work presents an attempt to develop a consistent physically-based model of dynamic recrystallization. This model, accounting for physical nature of processes of material defect structure evolution, should provide a possibility to predict evolution of several different experimentally measurable parameters of material microstructure without introduction of big number of fitting parameters. It is suggested that such a model should be based on equation for evolution of fraction of high-angle grain boundaries (HAGBs) in the process of deformation. It is shown, that the new model gives a possibility to predict the evolution of dislocation cells and grain boundaries in copper-based alloys providing good coincidence with experimental observations. Full 3-dimensional numerical simulation of multidirectional forging of copper is performed utilizing the developed dynamic recrystallization model. The same 3D simulations demonstrate new noteworthy effects connected to inhomogeneous distribution of plastic strain within the bulk of the material and material strain hardening.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Characterization of biochar prepared from biogas digestate

Highlights: • Solid digestate (SD) is a potential feedstock for preparing biochars. • The optimal BET surface area of resulting biochar from SD is over 100 m{sup 2}/g. • The higher temperature leads to the increase in true density of resulting biochar, but exhibited a decreased higher heating value. • The SD-based biochar may be used as a biofertilizer due to the mesoporosity and the abundant in nutrient minerals. - Abstract: In the study, the biogas digestate was evaluated as a potential feedstock for preparing biochars at a broad temperature range of 300–900 °C. The physico-chemical and pore properties of the resulting biochars (denoted as SDBC, solid digestate biochar), including calorific value (higher heating value), surface area/pore volume/pore size distribution, true density, scanning electron microscopy – energy dispersive X-ray spectroscopy (SEM-EDS) and X-ray powder diffraction (XRD), were studied. It was found that the higher heating values of the SDBC products were on a decreasing trend as pyrolysis temperature increased, but they indicated an increase in true density. The results are probably associated with the active pyrolysis of the lignocellulosic fragments and the calcination (or shrinkage) processes, thus resulting in the increased contents of aromatic carbon clusters and main mineral constituents remained. Based on the pore properties, pyrolysis temperature at around 800 °C seemed to be the optimal condition for producing SDBC, where its Brunauer-Emmet-Teller (BET) surface area (>100 m{sup 2}/g) largely increased as compared to that of the digestate feedstock (<1 m{sup 2}/g). Furthermore, the main compositions of mineral ash in the resulting biochar could exist as phosphates, carbonates, or oxides of calcium and other alkali/alkaline earth elements. According to the data on EDS and XRD, more pores could be significantly generated under severe pyrolysis (>700 °C) due to the high aromaticity via the thermal decomposition of lignocelluloses and the volatilization of inorganic minerals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Acoustic Energy Release During the Laboratory Seismic Cycle: Insights on Laboratory Earthquake Precursors and Prediction

Abstract Machine learning can predict the timing and magnitude of laboratory earthquakes using statistics of acoustic emissions. The evolution of acoustic energy is critical for lab earthquake prediction; however, the connections between acoustic energy and fault zone processes leading to failure are poorly understood. Here, we document in detail the temporal evolution of acoustic energy during the laboratory seismic cycle. We report on friction experiments for a range of shearing velocities, normal stresses, and granular particle sizes. Acoustic emission data are recorded continuously throughout shear using broadband piezo‐ceramic sensors. The coseismic acoustic energy release scales directly with stress drop and is consistent with concepts of frictional contact mechanics and time‐dependent fault healing. Experiments conducted with larger grains (10.5 μm) show that the temporal evolution of acoustic energy scales directly with fault slip rate. In particular, the acoustic energy is low when the fault is locked and increases to a maximum during coseismic failure. Data from traditional slide‐hold‐slide friction tests confirm that acoustic energy release is closely linked to fault slip rate. Furthermore, variations in the true contact area of fault zone particles play a key role in the generation of acoustic energy. Our data show that acoustic radiation is related primarily to breaking/sliding of frictional contact junctions, which suggests that machine learning‐based laboratory earthquake prediction derives from frictional weakening processes that begin very early in the seismic cycle and well before macroscopic failure.

58 GEOSCIENCES↗
DOE OSTI2019

Electronic band tuning under pressure in MoTe2 topological semimetal

Abstract Topological superconductors (TSC) can host exotic quasiparticles such as Majorana fermions, poised as the fundamental qubits for quantum computers. TSC’s are predicted to form a superconducting gap in the bulk, and gapless surface/edges states which can lead to the emergence of Majorana zero energy modes. A candidate TSC is the layered dichalcogenide MoTe 2 , a type-II Weyl (semi)metal in the non-centrosymmetric orthorhombic (T d ) phase. It becomes superconducting upon cooling below 0.25 K, while under pressure, superconductivity extends well beyond the structural boundary between the orthorhombic and monoclinic (1T′) phases. Here, we show that under pressure, coupled with the electronic band transition across the T d to 1T′ phase boundary, evidence for a new phase, we call T d * is observed and appears as the volume fraction of the T d phase decreases in the coexistence region. T d * is most likely centrosymmetric. In the region of space where T d * appears, Weyl nodes are destroyed. T d * disappears upon entering the monoclinic phase as a function of temperature or on approaching the suppression of the orthorhombic phase under pressure above 1 GPa. Our calculations in the orthorhombic phase under pressure show significant band tilting around the Weyl nodes that most likely changes the spin-orbital texture of the electron and hole pockets near the Fermi surface under pressure that may be linked to the observed suppression of magnetoresistance with pressure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Exceptional points in classical spin dynamics

Abstract Non-conservative physical systems admit a special kind of spectral degeneracy, known as exceptional point (EP), at which eigenvalues and eigenvectors of the corresponding non-Hermitian Hamiltonian coalesce. Dynamical parametric encircling of the EP can lead to non-adiabatic evolution associated with a state flip, a sharp transition between the resonant modes. Physical consequences of the dynamical encircling of EPs in open dissipative systems have been explored in optics and photonics. Building on the recent progress in understanding the parity-time ( $${\mathscr{P}}{\mathscr{T}}$$ P T )-symmetric dynamics in spin systems, we use topological properties of EPs to implement chiral non-reciprocal transmission of a spin through the material with non-uniform magnetization, like helical magnet. We consider an exemplary system, spin-torque-driven single spin described by the time-dependent non-Hermitian Hamiltonian. We show that encircling individual EPs in a parameter space results in non-reciprocal spin dynamics and find the range of optimal protocol parameters for high-efficiency asymmetric spin filter based on this effect. Our findings offer a platform for non-reciprocal spin devices for spintronics and magnonics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Conveyor-belt magneto-optical trapping of molecules

Abstract Laser cooling is used to produce ultracold atoms and molecules for quantum science and precision measurement applications. Molecules are more challenging to cool than atoms due to their vibrational and rotational internal degrees of freedom. Molecular rotations lead to the use of type-II transitions ( F ⩾ F ′ ) for magneto-optical trapping (MOT). When typical red detuned light frequencies are applied to these transitions, sub-Doppler heating is induced, resulting in higher temperatures and larger molecular cloud sizes than realized with the type-I MOTs most often used with atoms. To improve type-II MOTs, Jarvis et al (2018 Phys. Rev. Lett. 120 083201) proposed a blue-detuned MOT to be applied after initial cooling and capture with a red-detuned MOT. This was successfully implemented (Burau et al 2023 Phys. Rev. Lett. 130 193401; Jorapur et al 2024 Phys. Rev. Lett. 132 163403; Li et al 2024 Phys. Rev. Lett. 132 233402), realizing colder and denser molecular samples. Very recently, Hallas et al (2024 arXiv:2404.03636) demonstrated a blue-detuned MOT with a ‘1+2’ configuration that resulted in even stronger compression of the molecular cloud. Here, we describe and characterize theoretically the conveyor-belt mechanism that underlies this observed enhanced compression. We perform numerical simulations of the conveyor-belt mechanism using both stochastic Schrödinger equation and optical Bloch equation approaches. We investigate the conveyor-belt MOT characteristics in relation to laser parameters, g -factors and the structure of the molecule, and find that conveyor-belt trapping should be applicable to a wide range of laser-coolable molecules.

Li, Grace K. (ORCID:0000000277223355)↗
DOE OSTI2021

Avoiding biases in binned fits

Abstract Binned maximum likelihood fits are an attractive option when analysing large datasets, but require care when computing likelihoods of continuous PDFs in bins.For many years the widely used statistical modelling package evaluated probabilities at the bin centre, leading to significant biases for strongly curved probability density functions.We demonstrate the biases with real-world examples, and introduce a PDF class to that removes these biases.The physics and computation performance of this new class are discussed.

Instruments & Instrumentation↗
DOE OSTI2023

Magnetic frame-dragging correction to the electromagnetic solution of a compact neutron star

ABSTRACT Neutron stars are usually modelled as spherical, rotating perfect conductors with a predominant intrinsic dipolar magnetic field anchored to their stellar crust. Due to their compactness, General Relativity corrections must be accounted for in Maxwell’s equations, leading to modified interior and exterior electromagnetic solutions. We present analytical solutions for slowly rotating magnetized neutron stars, taking into account the magnetic frame-dragging correction. For typical compactness values, i.e. Rs ∼ 0.5 [R*], we show that the new terms lead to a per cent order correction in the magnetic field orientation and strength compared to the case, with no magnetic frame-dragging correction. Also, we obtain a self-consistent redistribution of the surface azimuthal current. We verify the validity of the derived solution through two-dimensional particle-in-cell simulations of an isolated neutron star. Defining the azimuthal electric and magnetic field amplitudes during the transient phase as observables, we prove that the magnetic frame-dragging correction reduces the transient wave amplitude, as expected from the analytical solution. We show that simulations are more accurate and stable, when we include all first-order terms. The increased accuracy at lower spatiotemporal resolutions translates into a reduction in simulation runtimes.

Torres, R. (ORCID:0000000291820228)↗
DOE OSTI2019

Milky Way globular clusters in γ-rays: analysing the dynamical formation of millisecond pulsars

ABSTRACT Globular clusters (GCs) are evolved stellar systems containing entire populations of millisecond pulsars (MSPs), which are efficient γ-ray emitters. Observations of this emission can be used as a powerful tool to explore the dynamical processes leading to binary system formation in GCs. In this work, 9 yr of Fermi Large-Area Telescope data were used to investigate the γ-ray emission from all GCs in the Milky Way. Twenty-three clusters were found as γ-ray bright, with two of them never having been reported before. It was also found that magnetic braking probably has a smaller impact on the formation rate of binary systems in metal-rich GCs than previously suggested, while a large value for the two-body encounter rate seems to be a necessary condition. The influence of the encounter rate per formed binary was for the first time explored in conjunction with γ-ray data, giving evidence that if this quantity is very high, binary systems will get destroyed before having time to evolve into MSPs, thus decreasing the total number of MSPs in a GC. No extended emission was found even for clusters whose optical extent is ≈0.5°; all of them are point-like sources spatially in agreement with the optical cores of the GCs, supporting previous X-ray results of heavier objects sinking into the clusters’ cores via dynamical friction. The possibility of extrapolating these results to ultra-compact dwarf galaxies is discussed, as these systems are believed to be the intermediate case between GCs and dwarf galaxies.

de Menezes, Raniere↗
DOE OSTI2023

Understanding palladium–tellurium cluster formation on WTe2: From a kinetically hindered distribution to thermodynamically controlled monodispersity

Abstract A fundamental understanding of the transition metal dichalcogenide (TMDC)–metal interface is critical for their utilization in a broad range of applications. We investigate how the deposition of palladium (Pd), as a model metal, on WTe2(001), leads to the assembly of Pd into clusters and nanoparticles. Using X-ray photoemission spectroscopy, scanning tunneling microscopy imaging, and ab initio simulations, we find that Pd nucleation is driven by the interaction with and the availability of mobile excess tellurium (Te) leading to the formation of Pd-Te clusters at room temperature. Surprisingly, the nucleation of Pd-Te clusters is not affected by intrinsic surface defects, even at elevated temperatures. Upon annealing, the Pd-Te nanoclusters adopt an identical nanostructure and are stable up to ∼523 K. Density functional theory calculations provide a foundation for our understanding of the mobility of Pd and Te atoms, preferential nucleation of Pd-Te clusters, and the origin of their annealing-induced monodispersity. These results highlight the role the excess chalcogenide atoms may play in the metal deposition process. More broadly, the discoveries of synthetic pathways yielding thermally robust monodispersed nanostructures on TMDCs are critical to the manufacturing of novel quantum and microelectronics devices and catalytically active nano-alloy centers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Strangers in a foreign land: ‘Yeastizing’ plant enzymes

Abstract Expressing plant metabolic pathways in microbial platforms is an efficient, cost‐effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour‐intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost‐effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data‐driven machine learning framework designed to extract ‘yeastizing’ rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design‐build‐test cycle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Photoperiod decelerates the advance of spring phenology of six deciduous tree species under climate warming

Abstract Vegetation phenology in spring has substantially advanced under climate warming, consequently shifting the seasonality of ecosystem process and altering biosphere–atmosphere feedbacks. However, whether and to what extent photoperiod (i.e., daylength) affects the phenological advancement is unclear, leading to large uncertainties in projecting future phenological changes. Here we examined the photoperiod effect on spring phenology at a regional scale using in situ observation of six deciduous tree species from the Pan European Phenological Network during 1980–2016. We disentangled the photoperiod effect from the temperature effect (i.e., forcing and chilling) by utilizing the unique topography of the northern Alps of Europe (i.e., varying daylength but uniform temperature distribution across latitudes) and examining phenological changes across latitudes. We found prominent photoperiod‐induced shifts in spring leaf‐out across latitudes (up to 1.7 days per latitudinal degree). Photoperiod regulates spring phenology by delaying early leaf‐out and advancing late leaf‐out caused by temperature variations. Based on these findings, we proposed two phenological models that consider the photoperiod effect through different mechanisms and compared them with a chilling model. We found that photoperiod regulation would slow down the advance in spring leaf‐out under projected climate warming and thus mitigate the increasing frost risk in spring that deciduous forests will face in the future. Our findings identify photoperiod as a critical but understudied factor influencing spring phenology, suggesting that the responses of terrestrial ecosystem processes to climate warming are likely to be overestimated without adequately considering the photoperiod effect.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction

Abstract The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior skill than traditional physics-based NWP. However, among these leading DL models, there is a wide variance in both the training settings and architecture used. Further, the lack of thorough ablation studies makes it hard to discern which components are most critical to success. In this work, we show that it is possible to attain high forecast skill even with relatively off-the-shelf architectures, simple training procedures, and moderate compute budgets. Specifically, we train a minimally modified Swin Transformer V2 (SwinV2) on ERA5 data and find that it attains superior skill in terms of mean-square errors of deterministic forecasts when compared against the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS). Almost all DL–NWP systems share a core set of hyperparameters and design decisions. To aid and expedite future DL–NWP research, we present an in-depth, systematic exploration of different loss functions, model sizes and depths, patch sizes, and multistep training objectives. We also examine the model performance with metrics beyond the typical accuracy (ACC) and RMSE and investigate how the performance scales with model size. Through our open-source code, scoring pipelines, and models, we share our findings on key aspects of the training pipeline. These ablations reduce the necessity for expensive hyperparameter tuning and lower the barrier to entry for future DL–NWP research. Significance Statement This study investigates the potential of using large-scale transformer-based models for weather prediction, showing that it is possible to achieve high forecast accuracy with simpler, off-the-shelf architectures. By training a minimally modified SwinV2 transformer on ERA5 data, we show that the model achieves competitive forecast skill in terms of mean-square error for key variables, outperforming the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS) at all lead times. Our findings suggest that effective training strategies, such as multistep fine-tuning and channel-weighted losses, significantly enhance the model’s performance. However, we also highlight that these improvements come with trade-offs in other areas, such as ensemble spread and high-frequency spatial detail. This work highlights the promise of deep learning in improving weather forecasts, which could lead to better preparedness and response to weather events, ultimately benefiting society by providing more reliable weather predictions.

Willard, Jared D. [Lawrence Berkeley National Labo↗
DOE OSTI2022

MEGASIM: Lifetimes and Resonances of Earth Trojan Asteroids—The Death of Primordial ETAs?

Abstract We present an analysis of lifetimes and resonances of Earth Trojan Asteroids (ETAs) in the MEGASIM data set. Trojan asteroids co-orbit the Sun with a planet, but remain bound to the Lagrange points, L4 (60° leading the planet) or L5 (60° trailing). In the circular three-body approximation, the stability of a Trojan asteroid depends on the ratio of the host planet mass and the central mass. For the inner planets, the range of stability becomes increasingly small, so perturbations from the planets have made primordial Trojans rare. To date, there have been just two ETAs (2010 TK 7 and 2020 XL 5 ), several Mars Trojans, and a Venus Trojan discovered. The estimated lifetimes of the known inner system Trojans are shorter than a million years, suggesting they are interlopers rather than members of a stable and long-lasting population. With the largest ETA n -body simulation to date, we are able to track their survival across a wide initialized parameter space. We find that the remaining fraction of ETAs over time is well fit with a stretched exponential function that, when extrapolated beyond our simulation run time, predicts zero ETAs by 2.33 Gyr. We also show correlations between ETA ejections and the periods of the Milankovitch cycles. Though Earth’s orbital dynamics dominate the instabilities of ETAs, we provide evidence that ETA ejections are linked to resonances found in the variation of the orbital elements of many if not all of the planets.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1976

Observations of fallibility in applications of modern programming methodologies

Errors, inconsistencies, or confusing points are noted in a variety of published algorithms, many of which are being used as examples in formulating or teaching principles of such modern programming methodologies as formal specification, systematic construction, and correctness proving. Common properties of these points of contention are abstracted. These properties are then used to pinpoint possible causes of the errors and to formulate general guidelines which might help to avoid further errors. The common characteristic of mathematical rigor and reasoning in these examples is noted, leading to some discussion about fallibility in mathematics, and its relationship to fallibility in these programming methodologies. The overriding goal is to cast a more realistic perspective on the methodologies, particularly with respect to older methodologies, such as testing, and to provide constructive recommendations for their improvement.

Gerhart, S. L.↗
DOE OSTI2018

Identification of biomass fractions responsible for wear on biomass processing equipment

Abstract: There is a current problem in the DOE supported Integrated Bio refineries where metal parts, including grinder blades and pneumatic ducting, are wearing out too early in the lifetime of the equipment. Repairing and replacing the equipment leads to unexpected equipment costs and the complete shutdown of the affected production lines.. It is believed that the ash content of the biomass is the likely cause of the wear on the metals. To address this ongoing problem, INL staff are analyzing the wear of metals caused by interactions with biomass . A sandblaster was modified to shoot ground biomass at metal coupons to simulate the wear that is occurring in the bio refineries. This system was able to test several variables including which biomass causes erosive and abrasive wear, impingement angle, particle densities of the biomass, and how the quantity of mass impacts the wear of the metals. Coupon mass loss after blasting was used to evaluate the wear caused by different biomass fractions. It was determined that the bark fraction of biomass caused the most wear and the whitewood fraction caused the least wear. Coupon mass loss in logging residues was found to be positively correlated with ash content, indicating that the ash content in the biomass is responsible for wear. It was also found that hand separated forest residues, sodium, aluminum and iron were positively correlated with wear, while silicon showed no correlation with wear.

09 BIOMASS FUELS↗
DOE OSTI2016

Superoxide (Electro)Chemistry on Well-Defined Surfaces in Organic Environments

Efficient chemical transformations in energy conversion and storage systems depend on understanding superoxide anion (O 2 – ) electrochemistry at atomic and molecular levels. Here, in this work, a combination of experimental and theoretical techniques are used for rationalizing, and ultimately understanding, the complexity of superoxide anion (electro)chemistry in organic environments. By exploring the O 2 + e – ↔ O 2 – reaction on well-characterized metal single crystals (Au, Pt, Ir), Pt single crystal modified with a single layer of graphene (Graphene@Pt(111)), and glassy carbon (GC) in 1,2 dimethoxyethane (DME) electrolytes, we demonstrate that (i) the reaction is an outer-sphere process; (ii) the reaction product O 2 – can “attack” any part of the DME molecule, i.e., the C–O bond via nucleophilic reaction and the C–H bond via radical hydrogen abstraction; (iii) the adsorption of carbon-based decomposition products and the extent of formation of a “solid electrolyte interface” (“SEI”) increases in the same order as the reactivity of the substrate, i.e., Pt(hkl)/Ir(hkl) » Au(hkl)/GC > Gaphene@Pt(111); and (iv) the formation of the “SEI” layer leads to irreversible superoxide electrochemistry on Pt(hkl) and Ir(hkl) surfaces. In conclusion, we believe this fundamental insight provides a pathway for the rational design of stable organic solvents that are urgently needed for the development of a new generation of reliable and affordable battery systems.

25 ENERGY STORAGE↗