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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Uranium (VI) Adsorbate Structures on Portlandite [Ca(OH)2] Type Surfaces Determined by Computational Modelling and X-ray Absorption Spectroscopy

Portlandite [Ca(OH) 2 ] is a potentially dominant solid phase in the high pH fluids expected within the cementitious engineered barriers of Geological Disposal Facilities (GDF). This study combined X-ray Absorption Spectroscopy with computational modelling in order to provide atomic-scale data which improves our understanding of how a critically important radionuclide (U) will be adsorbed onto this phase under conditions relevant to a GDF environment. Such data are fundamental for predicting radionuclide mass transfer. Surface coordination chemistry and speciation of uranium with portlandite [Ca(OH) 2 ] under alkaline groundwater conditions (ca. pH 12) were determined by both in situ and ex situ grazing incidence extended X-ray absorption fine structure analysis (EXAFS) and by computational modelling at the atomic level. Free energies of sorption of aqueous uranyl hydroxides, [UO 2 (OH) n ] 2–n (n = 0–5) with the (001), (100) and (203) or (101) surfaces of portlandite are predicted from the potential of mean force using classical molecular umbrella sampling simulation methods and the structural interactions are further explored using fully periodic density functional theory computations. Although uranyl is predicted to only weakly adsorb to the (001) and (100) clean surfaces, there should be significantly stronger interactions with the (203/101) surface or at hydroxyl vacancies, both prevalent under groundwater conditions. The uranyl surface complex is typically found to include four equatorially coordinated hydroxyl ligands, forming an inner-sphere sorbate by direct interaction of a uranyl oxygen with surface calcium ions in both the (001) and (203/101) cases. In contrast, on the (100) surface, uranyl is sorbed with its axis more parallel to the surface plane. The EXAFS data are largely consistent with a surface structural layer or film similar to calcium uranate, but also show distinct uranyl characteristics, with the uranyl ion exhibiting the classic dioxygenyl oxygens at 1.8 Å and between four and five equatorial oxygen atoms at distances between 2.28 and 2.35 Å from the central U absorber. These experimental data are wholly consistent with the adsorbate configuration predicted by the computational models. These findings suggest that, under the strongly alkaline conditions of a cementitious backfill engineered barrier, there would be significant uptake of uranyl by portlandite to inhibit the mobility of U(VI) from the near field of a geological disposal facility.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Entropy-based feature selection for capturing impacts in Earth system models with abrupt forcing

This paper presents the development of a new entropy-based feature selection method for identifying and quantifying impacts. Here, impacts are defined as statistically significant differences in spatio-temporal fields when comparing datasets with and without an external forcing in an Earth system model. Temporal feature selection is performed by first computing the cross-fuzzy entropy to quantify similarity of patterns between two datasets and then applying changepoint detection to identify regions of statistically constant entropy. The method is used to capture temperate north surface cooling from a 9-member simulation ensemble of the Mt. Pinatubo volcanic eruption, which injected 10 Tg of SO 2 into the stratosphere. The results estimate a mean difference decrease in near surface air temperature of -0.560 K with a 99% confidence interval between -0.864 K and -0.257 K between April and November of 1992, one year following the eruption. A sensitivity analysis with decreasing SO 2 injection revealed that the impact is statistically significant at 5 Tg but not at 3 Tg. Using identified features, a dependency graph model based on a 9-day lag had significantly fewer nodes than a graph based on monthly means. Furthermore, this demonstrates our method’s ability to perform dimension reduction while still uncovering source-to-impact pathways.

Changepoint detection↗
DOE OSTI2016

High voltage surface potential measurements in ambient conditions: Application to organic thin-film transistor injection and transport characterization

A scanning surface potential measurement technique suited for thin-film devices operating under high voltages is reported. A commercial atomic force microscope has been customized to enable a feedback-controlled and secure surface potential measurement based on phase-shift detection under ambient conditions. Measurements of the local potential profile along the channel of bottom-gate organic thin-film transistors (TFTs) are shown to be useful to disentangle the contributions from the channel and contacts to the device performance. Intrinsic contact current-voltage characteristics have been measured on bottom-gate, top-contact (staggered) TFTs based on the small-molecule semiconductor dinaphtho[2,3-b:2′,3-f]thieno[3,2-b]thiophene (DNTT) and on bottom-gate, bottom-contact (coplanar) TFTs based on the semiconducting polymer polytriarylamine (PTAA). Injection has been found to be linear in the staggered DNTT TFTs and nonlinear in the coplanar PTAA TFTs. In both types of TFT, the injection efficiency has been found to improve with increasing gate bias in the accumulation regime. Contact resistances as low as 130 Ω cm have been measured in the DNTT TFTs. A method that eliminates the influence of bias-stress-induced threshold-voltage shifts when measuring the local charge-carrier mobility in the channel is also introduced, and intrinsic channel mobilities of 1.5 cm{sup 2} V{sup −1} s{sup −1} and 1.1 × 10{sup −3} cm{sup 2} V{sup −1} s{sup −1} have been determined for DNTT and PTAA. In both semiconductors, the mobility has been found to be constant with respect to the gate bias. Despite its simplicity, the Kelvin probe force microscopy method reported here provides robust and accurate surface potential measurements on thin-film devices under operation and thus paves the way towards more extensive studies of particular interest in emerging fields of solid-state electronics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally demanding models and large datasets. We employ an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature and specification of both model and data uncertainties, and it introduced novel approaches to autocorrelation corrections on multiple data streams and emulating the sufficient statistics surface. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard brute-force MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the brute-force approach but reduced computation time by more than 2 orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (brute-force) Bayesian data assimilation techniques. Both models are constrained after assimilation of the observational data with the emulator method, reducing the uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data. Our study furthers efforts toward reducing model uncertainties, showing that the emulator method makes it possible to efficiently calibrate complex models.

58 GEOSCIENCES↗
DOE OSTI2015

Combined frequency modulated atomic force microscopy and scanning tunneling microscopy detection for multi-tip scanning probe microscopy applications

A method which allows scanning tunneling microscopy (STM) tip biasing independent of the sample bias during frequency modulated atomic force microscopy (AFM) operation is presented. The AFM sensor is supplied by an electronic circuit combining both a frequency shift signal and a tunneling current signal by means of an inductive coupling. This solution enables a control of the tip potential independent of the sample potential. Individual tip biasing is specifically important in order to implement multi-tip STM/AFM applications. An extensional quartz sensor (needle sensor) with a conductive tip is applied to record simultaneously topography and conductivity of the sample. The high resonance frequency of the needle sensor (1 MHz) allows scanning of a large area of the surface being investigated in a reasonably short time. A recipe for the amplitude calibration which is based only on the frequency shift signal and does not require the tip being in contact is presented. Additionally, we show spectral measurements of the mechanical vibration noise of the scanning system used in the investigations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Ensemble Spread Behavior in Coupled Climate Models: Insights From the Energy Exascale Earth System Model Version 1 Large Ensemble

AbstractAssessing uncertainty in future climate projections requires understanding both internal climate variability and external forcing. For this reason, single‐model initial condition large ensembles (SMILEs) run with Earth System Models (ESMs) have recently become popular. Here we present a new 20‐member SMILE with the Energy Exascale Earth System Model version 1 (E3SMv1‐LE), which uses a “macro” initialization strategy choosing coupled atmosphere/ocean states based on inter‐basin contrasts in ocean heat content (OHC). The E3SMv1‐LE simulates tropical climate variability well, albeit with a muted warming trend over the twentieth century due to overly strong aerosol forcing. The E3SMv1‐LE's initial climate spread is comparable to other (larger) SMILEs, suggesting that maximizing inter‐basin ocean heat contrasts may be an efficient method of generating ensemble spread. We also compare different ensemble spread across multiple SMILEs, using surface air temperature and OHC. The Community Earth system Model version 1, the only ensemble which utilizes a “micro” initialization approach perturbing only atmospheric initial conditions, yields lower spread in the first ∼30 years. The E3SMv1‐LE exhibits a relatively large spread, with some evidence for anthropogenic forcing influencing spread in the late twentieth century. However, systematic effects of differing “macro” initialization strategies are difficult to detect, possibly resulting from differing model physics or responses to external forcing. Notably, the method of standardizing results affects ensemble spread: control simulations for most models have either large background trends or multi‐centennial variability in OHC. This spurious disequlibrium behavior is a substantial roadblock to understanding both internal climate variability and its response to forcing.

Stevenson, Samantha↗
DOE OSTI2014

New approach to calculating the potential energy of colliding nuclei

The differential method proposed by the present authors earlier for the reduction of volume integrals in calculating the potential energy of a compound nucleus is generalized to the case of two interacting nuclei. The Coulomb interaction energy is obtained for the cases of a sharp and a diffuse boundary of nuclei, while the nuclear interaction energy is found only for nuclei with a sharp boundary, the finiteness of the nuclear-force range being taken into account. The present method of calculations permits reducing the time it takes to compute the potential energy at least by two orders of magnitude.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Effect of Glow Discharge Parameters on the Mean Interparticle Distance in Dusty Plasma Structures in the Cryogenic–Room Temperature Range

The molecular dynamics method is used for studying the effect of forces acting in a dusty plasma and discharge parameters on the mean interparticle distance in structures in a dc glow discharge in the temperature range 50–300 K. We report on the results of the dependence of the mean interparticle distance on the neutral gas temperature in simplified and modified models of the dusty plasma. The simplified model includes the screened Coulomb potential and the ambipolar trap; in the modified model, the thermophoretic force, the ion shading force, and the neutral shading force are accounted for additionally. All these forces are calculated from the experimental values of the discharge parameters (current, pressure, temperature, and type of gas). The modified model of the dusty plasma system makes it possible to explain the experimental form of the dependence of the mean interparticle distance in dusty structures on the discharge gas temperature. We propose a method for estimating the ion concentration in the discharge gas from the mean interparticle distance in dusty plasma structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Geometric multigrid for an implicit-time immersed boundary method

The immersed boundary (IB) method is an approach to fluid-structure interaction that uses Lagrangian variables to describe the deformations and resulting forces of the structure and Eulerian variables to describe the motion and forces of the fluid. Explicit time stepping schemes for the IB method require solvers only for Eulerian equations, for which fast Cartesian grid solution methods are available. Such methods are relatively straightforward to develop and are widely used in practice but often require very small time steps to maintain stability. Implicit-time IB methods permit the stable use of large time steps, but efficient implementations of such methods require significantly more complex solvers that effectively treat both Lagrangian and Eulerian variables simultaneously. Moreover, several different approaches to solving the coupled Lagrangian-Eulerian equations have been proposed, but a complete understanding of this problem is still emerging. This paper presents a geometric multigrid method for an implicit-time discretization of the IB equations. This multigrid scheme uses a generalization of box relaxation that is shown to handle problems in which the physical stiffness of the structure is very large. Numerical examples are provided to illustrate the effectiveness and efficiency of the algorithms described herein. Finally, these tests show that using multigrid as a preconditioner for a Krylov method yields improvements in both robustness and efficiency as compared to using multigrid as a solver. They also demonstrate that with a time step 100–1000 times larger than that permitted by an explicit IB method, the multigrid-preconditioned implicit IB method is approximately 50–200 times more efficient than the explicit method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Unsupervised Learning Based Interaction Force Model for Nonspherical Particles in Incompressible Flows

This project provides a neural network-based interaction force model for gas-solid flows from low to intermediate Reynolds numbers and concentration, which can be linked to MFiX-DEM. We have constructed a database of the interaction force between the irregular-shaped particles using a spherical harmonic method and the fluid phase based on the particle-resolved direct numerical simulation (PR-DNS) with immersed boundary-based gas kinetic scheme. Unsupervised learning method, i.e., variational auto-encoder (VAE) has been applied to extract the primitive shape factors determining the drag force, lifting forces, and torque. The interaction force model has been trained and validated with a simple but effective multi-layer feed-forward neural network: multi-layer perceptron (MLP), which will be concatenated after the encoder of the previously trained VAE for geometry feature extraction for single, irregular particles. We have trained transpose convolutional neural networks with the PR-DNS data to predict the velocity and pressure gradient of the single particle systems and utilized them to calculate drag force of multi-particle systems. This model can provide high computational efficiency because it does not require collecting multiparticle system data from PR-DNS.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Detecting sulphate aerosol geoengineering with different methods

Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of other external forcings. Here, we investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr -1 of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection when no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This then highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Heat-Exchange in One-Phase Liquid-Metal Flows: Databank (Temperature Distribution in Circular Pipes)

In order to develop accurate methods of calculating heat-exchange in forced channel flows, it is necessary to have knowledge about the temperature distribution over the cross section in differ layers of the flow. For ordinary liquids (gas, water, where the Prandtl number Pr ~ 1), such measurements are difficult to perform because of the small thickness of the boundary layer. The problem simplifies for liquid metals, where Pr << 1. This article contains the format of the data as well as an analysis of the primary data from 37 home and foreign works (mercury, sodium-potassium alloy, sodium, lithium, lead-bismuth alloy). The data are presented in electronic form.

42 ENGINEERING↗
DOE OSTI2016

Application of an Evolutionary Algorithm for Parameter Optimization in a Gully Erosion Model

Herein we demonstrate how to use model optimization to determine a set of best-fit parameters for a landform model simulating gully incision and headcut retreat. To achieve this result we employed the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an iterative process in which samples are created based on a distribution of parameter values that evolve over time to better fit an objective function. CMA-ES efficiently finds optimal parameters, even with high-dimensional objective functions that are non-convex, multimodal, and non-separable. We ran model instances in parallel on a high-performance cluster, and from hundreds of model runs we obtained the best parameter choices. This method is far superior to brute-force search algorithms, and has great potential for many applications in earth science modeling. We found that parameters representing boundary conditions tended to converge toward an optimal single value, whereas parameters controlling geomorphic processes are defined by a range of optimal values.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Electromagnetic absorption properties of graphene/Fe nanocomposites

Graphical abstract: - Highlights: • Graphene/Fe nanocomposites were prepared by a facile and green method. • 10 nm Fe nanoparticles were uniformly dispersed over the surface of the graphene sheets. • The nanocomposites exhibited strong electromagnetic wave absorption properties. - Abstract: Graphene (G)/Fe nanocomposites with ferromagnetic properties at room temperature were fabricated by a facile and green method. Transmission electron microscope (TEM) and atomic force microscopy (AFM) amylases reveal that the α-Fe nanoparticles with a diameter of only about 10 nm were uniformly dispersed over the surface of the graphene sheets. Compared with other magnetic materials and the graphene, the nanocomposites exhibited significantly enhanced electromagnetic absorption properties. The maximum reflection loss to electromagnetic wave was up to −31.5 dB at a frequency of 14.2 GHz for G/Fe nanocomposites with a thickness of 2.5 mm. Importantly, the addition of the nanocomposites is only about 20 wt.% in the matrix. The enhanced mechanism is discussed and it is related to high surface areas of G/Fe nanocomposites, interfacial polarizations between graphene and iron, synergetic effect and efficient dispersity of magnetic NPs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structure and Sulfur: Tuning the Viscoelastic and Surface Properties of Natural Keratin Fibers

Natural keratin fibers, such as wool, possess a complex hierarchical structure that governs their mechanical properties and surface energy. However, the extent to which these characteristics are influenced by combined contributions of structural variations (e.g., fiber diameter, intermediate filament (IF) packing) and chemical composition (e.g., disulfide bond density) remains poorly understood. In this study, we investigate wool fibers from five sheep breeds (Merino, Polwarth, Cheviot, Eider, and Devon) to elucidate how these factors influence viscoelasticity and surface interactions. Using a multimodal approach integrating interfacial and bulk characterization methods, including inverse gas chromatography (IGC), atomic force microscopy-infrared spectroscopy (AFM-IR), X-ray photoelectron spectroscopy (XPS), uniaxial tensile testing, and synchrotron small-angle X-ray scattering (SAXS), we show that the nanometer-thick 18-methyleicosanoic acid (18-MEA) layer is consistently present across all wool types and plays a key role in governing hydrophobicity and surface heterogeneity. A controlled isothermal treatment at 200 °C, designed to cleave disulfide bonds, results in a nearly 40% reduction in specific surface area across all fiber types, accompanied by a significant decrease in tensile strength and 80% reduction in elongation at break for Merino and Devon wool, but limited influence on the mechanical properties of Eider fibers. Furthermore, rate-dependent tensile testing within the elastic regime reveals distinct viscoelastic responses among the fiber types, suggesting that the sulfur-rich protein matrix surrounding IFs and its structure contribute actively to stress partitioning. Altogether, when combined with conclusions from SAXS measurements of IF spacing, our work offers compelling insights into the role of the keratin-associated protein (KAP) matrix in shaping wool fiber mechanics. Differences in mechanical behavior among wool types, despite similar IF spacing or sulfur content, highlight the importance of matrix composition and cross-linking density, suggesting that the molecular architecture of the KAP network may be a dominant factor in determining fiber performance.

X-ray scattering↗
DOE OSTI2024

Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM

Abstract Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN‐based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics‐based gravity wave (GW) parameterizations as a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto‐encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO 2 ). However, their performance is significantly improved by applying transfer learning, for example, re‐training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data‐driven parameterizations for various processes, including (but not limited to) GWs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Demonstration of a positron beam-driven hollow channel plasma wakefield accelerator

Plasma wakefield accelerators have been used to accelerate electron and positron particle beams with gradients that are orders of magnitude larger than those achieved in conventional accelerators. In addition to being accelerated by the plasma wakefield, the beam particles also experience strong transverse forces that may disrupt the beam quality. Hollow plasma channels have been proposed as a technique for generating accelerating fields without transverse forces. In this study, we demonstrate a method for creating an extended hollow plasma channel and measure the wakefields created by an ultrarelativistic positron beam as it propagates through the channel. The plasma channel is created by directing a high-intensity laser pulse with a spatially modulated profile into lithium vapour, which results in an annular region of ionization. A peak decelerating field of 230 MeV m -1 is inferred from changes in the beam energy spectrum, in good agreement with theory and particle-in-cell simulations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Teaching a neural network to attach and detach electrons from molecules

Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2–3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.

36 MATERIALS SCIENCE↗