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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Adaptive Multirate Mass Transfer (aMMT) Model: A New Approach to Upscale Regional‐Scale Transport Under Transient Flow Conditions

Abstract The long‐term evaluation of regional‐scale groundwater quality needs efficient upscaling methods for transient flow. Upscaling techniques, such as the Multirate Mass Transfer (MRMT) method with constant upscaling parameters, have been used for transport with steady‐state flow, yet the upscaling parameters (i.e., rate coefficients) may be time dependent. This study proposed and validated an adaptive MRMT (aMMT) method by allowing the mass transfer coefficients in MRMT to change with the flow field. Advective‐dispersive contaminant transport simulated in a 3‐D heterogeneous medium was used as a reference solution. Equivalent transport under homogeneous flow conditions was evaluated by applying the MRMT and aMMT models for upscaling. The relationship between mass transfer coefficients and flow rates was fitted under steady‐state flow driven by various hydraulic gradients. A power law relationship was obtained, which was then used to update the mass transfer coefficients in each stress period under transient flow conditions in the aMMT method. Results indicated that for advection‐dominated transport, both the MRMT and aMMT methods can upscale the anomalous transport dynamics affected by subgrid heterogeneity under transient flow conditions. Whereas for diffusion‐dominated systems, the MRMT model failed to capture the tails of tracer breakthrough curves after the boundary condition changed, but the results from the aMMT model were significantly improved. However, if the overall flow direction changed, both MRMT and aMMT failed to represent the breakthrough curve tail generated by the heterogeneous system. The results point toward a promising path for upscaling transport in complex aquifers with transient flow.

Guo, Zhilin↗
DOE OSTI2020

Structural distortion and incommensurate noncollinear magnetism in EuAg 4 As 2

Layered pnictide materials have provided a fruitful platform to study various emergent phenomena, including superconductivity, magnetism, charge density waves, etc. In this work, we report the observation of structural distortion and noncollinear magnetism in layered pnictide EuAg 4 As 2 via transport, magnetization, single crystal x-ray, and neutron diffraction data.EuAg 4 As 2 single crystal shows a structural distortion at 120 K, where two sets of superlattice peaks with the propagation vectors of q 1 =±(0, 0.25, 0.5) and q 2 =±(0.25, 0, 1) emerge. Between 9–15 K, the hexagonal Eu 2+ sublattice enters an unpinned incommensurate magnetic state, with magnetic Bragg reflections pictured as circular sectors. Furthermore, below 9 K, it orders in an incommensurate noncollinear antiferromagnetic state with a well-defined propagation wavevector of (0, 0.1, 0.12) and a very rare magnetic structure, which is helical along the c axis and cycloidal along the b axis with a moment of 6.4 μB/Eu 2+ . Furthermore, rich magnetic phases under magnetic fields, large magnetoresistance, and strong coupling between charge carriers and magnetism in EuAg 4 As 2 are revealed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Combined Influence of Rotation and Scrape-Off Layer Drifts on Recycling Asymmetries in Tokamak Plasmas

Coupled 2D fluid-kinetic simulations of a DIII-D high confinement tokamak plasma show that plasma rotation coupled with drift effects near the plasma edge play a significant role in the creation of the observed poloidal distribution of neutrals. It is observed that including either drift or rotation effects enhances particle flux at the inner target in the case of ion 𝐵×∇𝐵 drift toward the 𝑋-point. However, the particle flux asymmetry is significantly higher with the combination of drifts and rotation than either effect alone. The heightened particle flux asymmetry allows for improved simulation of the strong in-out asymmetry of the Lyman-𝛼 brightness profiles measured in the experiment. Enhancement of radial transport of parallel momentum changes the upstream scrape-off layer flow pattern, increasing the fraction of deuterium flux that reaches the inboard divertor entrance while lowering that which arrives at the outboard. In conclusion, this Letter indicates that by combining drifts, rotation, and viscous coupling, existing boundary plasma models can achieve a satisfactory agreement with experimentally measured neutral asymmetries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

O’Hare Airport Short-Term Ground Transportation Modal Demand Forecast Using Gaussian Processes

Here, the principal objective of this study is to analyze the spatial and temporal variation of ground transportation airport demand and provide demand forecast to inform planning capability and explore alternatives for investments to accommodate airport growth. Because of its good adaptability and strong generalization ability for dealing with high-dimensional input, small-sample, and nonlinear spatial data, Gaussian process (GP) regression is used to provide forecast estimates using data from transportation network company (TNC) trips and urban rail passengers at Chicago's O'Hare International Airport. TNC airport trips differ significantly, with three times more distance, more than twice the travel time, and half of the share requests compared with nonairport trips. This highlights the need for separate demand models. Hourly analysis of the rail service indicates that this is likely heavily used by airport workers, whereas TNC services focus on travelers because of variations in the peak demand hours. Heteroscedastic GP regression is implemented because of differences in trip variance between night and day hours. Estimates are given for weekdays and weekend trips, and the 95% confidence intervals are calculated. The introduction of flight schedule information into the models shows marginal improvements in their performance. However, fitting a GP regression becomes computationally expensive with increased sample size and the introduction of spatial components. Transportation planners and policymakers can use the results and methods implemented in this study to optimize transportation assets and provide long-range simulations of the current and future conditions in the area.

42 ENGINEERING↗
DOE OSTI2020

Sequential ensemble transform for Bayesian inverse problems

In this work, we present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores the posterior distribution by solving a sequence of discrete optimal transport problems to produce a series of transport plans which map prior samples to posterior samples. We prove that the sequence of Dirac mixture distributions produced by the SET method converges weakly to the true posterior as the sample size approaches infinity. Furthermore, our numerical results indicate that, when compared to standard Sequential Monte Carlo (SMC) methods, the SET approach is more robust to the choice of Markov mutation kernels and requires less computational efforts to reach a similar accuracy when used to explore complex posterior distributions. Finally, we describe adaptive schemes that allow to completely automate the use of the SET method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Dynamic Species Reduction for Multi-Cycle CFD Simulations (Final Technical Report)

This project primarily sought to address some of the computational cost concerns of detailed simulations by developing improved methods of handling species transport, and chemical kinetics evaluation in a commercial 3D Computational Fluid Dynamics (CFD) environment. Secondary goals were to apply these techniques to fuels and conditions of interest to better understand the key species and reactions required to adequately capture cycle to cycle coupling. Improved modeling will lead to better understanding of these combustion modes and their dependence on fuel composition, which can then enable more clean and efficient engines, ultimately benefiting the consumer as well as the general public. Two approaches were used to address the computational cost. A “Dynamic Species Reduction” (DSR) procedure was developed to remove chemical species from the simulation during periods when chemical reactions were not expected to be important, particularly during gas exchange when temperatures are low and little fuel remains. This modelling procedure automatically detects relevant species to retain in the simulation domain based on their local concentration, removes species below a specified concentration threshold, and then adjusts the remaining species mass to conserve not only the number of H, C, and O atoms in each cell, but also the relative proportions between species and the heating value of the mixture in every cell. The second procedure was a “Product Directed Remapping” (PDR) updates the algorithm used to group individual computational cells for chemical kinetics evaluation to account for non-uniform temperature distributions and the CO to CO 2 ratio in the cells. The model techniques developed in this work successfully demonstrated computational performance improvements for a range of conditions relevant for Highly Dilute SI and HCCI engine operation. Runtime reductions of 10% were observed for small mechanisms, with further reductions of up to 36% observed for larger mechanisms. Improvements were primarily related to reducing the number of chemical species tracked during the gas exchange process using the Dynamic Species Reduction method. With smaller benefits observed from changes to the kinetics binning and evaluation strategy in the post combustion region using the PDR method. The results of this work show the potential for improved computational runtime for complicated simulations. They can and should be extended to additional conditions and new bio-derived and renewable fuels as they are developed and new kinetic mechanisms become available.

10 SYNTHETIC FUELS↗
DOE OSTI2020

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Monte Carlo Thermal Radiative Transfer Solver with Nonlinear Elimination

Here in this paper, we present a new Monte Carlo method for solving the thermal radiative transfer (TRT) equations via the method of nonlinear elimination (NLEM). This method is inspired by the previous application of NLEM to thermal radiation diffusion. Our approach, called diffusion accelerated Implicit Monte Carlo (DAIMC), is a hybrid technique which combines a Monte Carlo method for solving a purely-absorbing transport equation and a diffusion solution that accounts for effective scattering, or absorption–reemission. The method aims to improve the implicitness of the traditional implicit Monte Carlo (IMC) method. We derive DAIMC generally for 3D Cartesian geometries, but in this paper, we present results and analysis in 1D slab geometry. These preliminary results indicate that DAIMC implementations may provide more accurate and robust TRT solutions than IMC in certain test problems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Visualizing Energy Transfer at Buried Interfaces in Layered Materials Using Picosecond X-Rays

Understanding the fundamentals of nanoscale heat propagation is crucial for next-generation electronics. For instance, weak van der Waals bonds of layered materials are known to limit their thermal boundary conductance (TBC), presenting a heat dissipation bottleneck. In this paper we present a new non-destructive method to probe heat transport in nanoscale crystalline materials using time-resolved x-ray measurements of photo-induced thermal strain. This technique directly monitors time-dependent temperature changes in the crystal and the subsequent relaxation across buried interfaces by measuring changes in the c-axis lattice spacing after optical excitation. We investigate films of five different layered transition metal dichalcogenides MoX 2 [X= S, Se, Te] and WX 2 [X= S, Se] as well as graphite and a W-doped alloy of MoTe 2 , on c-plane sapphire substrates, finding TBC values in the range 10-30 MWm -2 K -1 at room temperature. In conjunction with molecular dynamics simulations, we show that the high thermal resistances are a consequence of weak interfacial van der Waals bonding and low phonon irradiance. This work paves the way for an improved understanding of thermal bottlenecks in emerging three-dimensional heterogeneously integrated technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unique Role of GeO 2 as a Noninvasive Promoter of Nano‐Sized Zeolite Crystals

Abstract The synthesis of zeolites with nano‐sized dimensions is often limited to a narrow design space that conventionally relies upon the design of organics to direct hierarchical materials. Here, it is demonstrated that the addition of an inorganic modifier, germanium oxide (GeO 2 ), to a zeolite growth mixture directs the formation of crystals with ultrasmall dimensions. This effect is observed for zeolites ZSM‐11 and ZSM‐5 over a range of synthesis conditions wherein the role of GeO 2 in zeolite crystallization deviates from its typical function as a heteroatom. Notably, the final products contain trace amounts of Ge, which indicates the inorganic modifier does not compete for sites in the zeolite framework based on its formation of a discrete phase that enables GeO 2 recovery. Catalytic tests using the methanol‐to‐hydrocarbons reaction reveal significant enhancement in the performance of zeolite catalysts prepared with GeO 2 compared to reported examples of nano‐sized zeolites. These findings highlight a potentially generalizable and commercially viable synthesis method to reduce mass‐transport limitations in zeolites for diverse applications.

Chemistry↗
DOE OSTI2021

Reconstructing porous media using generative flow networks

One area of intense scientific interest for the study of sandstones, carbonates, and shale at the pore scale is the use of limited image and petrophysical data to generate multiple realizations of a rock’s pore structure. Such images aid efforts to quantify uncertainty in petrophysical properties, including porosity–permeability transforms. We develop and evaluate a deep learning-based method to synthesize porous media volumes using a so-called generative flow model trained on x-ray microscope images of rock texture and pore structure. These models are optimized on a log-likelihood objective and they synthesize large and realistic three-dimensional images. Further, we demonstrate the rapid generation of sandstone image volumes that display realism as gauged by quantitative comparison of topological features using Minkowski functionals of porosity, specific surface area, and the Euler–Poincaré characteristic (i.e., pore connectivity). We also evaluate the single-phase permeability using Navier–Stokes and lattice Boltzmann methods and show that transport properties of the generated samples match measured trends.

58 GEOSCIENCES↗
DOE OSTI2025

Substitutional doping of 2D transition metal dichalcogenides for device applications: Current status, challenges and prospects

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as a class of materials with exceptional electronic, optical, and mechanical properties, making them highly tunable for diverse applications in nanoelectronics, optoelectronics, and catalysis. Here, this review focuses on substitutional doping of TMDs, a key strategy to tailor their properties and enhance device performance, with a focus on its applications over the past five years (2019–2024). We delve into both theoretical and experimental doping approaches, including established methods like chemical vapor transport (CVT) and chemical vapor deposition (CVD) alongside liquid phase exfoliation (LPE) and post-synthesis treatments. Advanced growth techniques are also explored. Challenges like dopant uniformity, concentration control, and stability are addressed. The influence of various dopants on the electronic band structure, carrier concentration, and defect engineering is analyzed in detail. We further explore recent advancements in utilizing doped TMDs for field-effect transistors (FETs), photodetectors, sensors, photovoltaics, optoelectronic devices, energy storage and conversion, and even quantum computers. By examining both the potential and limitations of substitutional doping, this review aims to propel future research and technological advancements in this exciting field.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A priori analysis of a power-law sub-filter scale mixing model for transported PDF modeling of high Karlovitz turbulent premixed flames

Accurate modeling of mixing in large-eddy simulation (LES) / transported probability density function (PDF) modeling of turbulent combustion remains an outstanding issue. The issue is particularly salient in turbulent premixed combustion under extreme conditions such as high-Karlovitz number Ka. Here, the present study addresses this issue by conducting an a priori analysis of a power-law scaling based mixing timescale model for the transported PDF model. A recently produced DNS dataset of a high-Ka turbulent jet flame is used for the analysis. A power-law scaling is observed for a scaling factor used to model the sub-filter scale mixing timescale in this high-Ka turbulent premixed DNS flame when the LES filter size is much greater than the characteristic thermal thickness of a laminar premixed flame. The sensitivity of the observed power-law scaling to the different viewpoints (local or global) and to the different scalars for the data analysis is examined and the dependence of the model parameters on the dimensionless numbers Ka and Re (the Reynolds number) is investigated. Different model formulations for the mixing timescale are then constructed and assessed in the DNS flame. The proposed model is found to be able to reproduce the mixing timescale informed by the high-Ka DNS flame significantly better than a previous model.

42 ENGINEERING↗
DOE OSTI2023

Contrasting changes in ozone during 2019–2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions

Ozone pollution is one of the most severe air quality issues in China that poses a serious threat to human health and ecosystems. During 2019–2021, the maximum daily 8-h average ozone concentrations in eastern China (110–122.5°E, 26–42°N) and the rest of China (ROC) show different decreasing patterns, with ozone concentrations in eastern China decreasing by 14.9 μg/m 3 , which is much larger than 4.8 μg/m 3 in ROC. Here, based on two independent methods, the atmospheric chemical transport model (GEOS-Chem) simulations and the machine learning (ML) model (LightGBM) predictions, the reasons for the differences in ozone changes between eastern China and ROC during the warm season (April to September) are investigated. According to the GEOS-Chem (LightGBM) results, changes in the meteorological conditions contributed to an ozone decrease by 7.3 (6.8) μg/m 3 in eastern China due to decreased chemical production and an ozone decrease by 6.8 (7.0) μg/m 3 in ROC attributed to the weakened horizontal and vertical advection. With the influence of meteorological factors excluded, the observations show that changes in anthropogenic emissions resulted in an ozone decrease by 7.6 (8.1) μg/m 3 in eastern China and an ozone increase by 2.0 (2.2) μg/m 3 in ROC, which is primarily induced by the changes in NO x emissions. The surface measurements and satellite retrievals also indicate that the reduction in NO x emissions in ROC is less efficient than that in the more developed eastern China, leading to contrasting changes in ozone concentrations between eastern China and ROC during 2019–2021. In conclusion, our results highlight the critical need to reduce ozone precursor emissions in the rest regions of China apart from eastern China.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Design of High-Voltage Stable Hybrid Electrolyte with an Ultrahigh Li Transference Number

Considering the high energy consumption during processing, and the low compliance and adhesion of ceramic electrolytes, the integration of polymer into ceramic electrolytes provides a way to mitigate the interfacial issues. However, the severe ion concentration gradient, low ionic conductivity, and instability toward Li metal and high-voltage cathodes become the major concerns in applying hybrid electrolytes. In this work, we report a single-ionconducting hybrid electrolyte (SIE-LLZO) with 64 wt % Li 7 La 3 Zr 2 O 12 (LLZO) particles embedded in a fluoroboron-centered Li-conductive polymer framework (LiBFSIE). The SIE-LLZO electrolyte exhibited a high Li transference number of 0.94 and electrochemical stability up to 5.6 V vs Li/Li + . Promising averaged Coulombic efficiencies of 99.97% and 99.91% were achieved in cells with LiNi 0.8 Co 0.15 Al 0.05 O 2 and LiNi 0.6 Mn 0.2 Co 0.2 O2 cathodes for 400 and 200 cycles, respectively. Finally, the Li-conducting pathway in the hybrid electrolyte was further investigated by a 6 Li-to- 7 Li isotope replacement method, indicating that Li transport mainly relies on the LLZO and interface between LiBFSIE and LLZO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Observing differential spin currents by resonant inelastic X-ray scattering

Controlling spin currents, that is, the flow of spin angular momentum, in small magnetic devices, is the principal objective of spin electronics, a main contender for future energy-efficient information technologies. A pure spin current has never been measured directly because the associated electric stray fields and/or shifts in the non-equilibrium spin-dependent distribution functions are too small for conventional experimental detection methods optimized for charge transport. Here we report that resonant inelastic X-ray scattering (RIXS) can bridge this gap by measuring the spin current carried by magnons—the quanta of the spin wave excitations of the magnetic order—in the presence of temperature gradients across a magnetic insulator. This is possible due to the sensitivity of the momentum- and energy-resolved RIXS intensity to minute changes in the magnon distribution under non-equilibrium conditions. Furthermore, we use the Boltzmann equation in the relaxation time approximation to extract transport parameters, such as the magnon lifetime at finite momentum, essential for the realization of magnon spintronics.

36 MATERIALS SCIENCE↗