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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Spark plasma sintering (SPS) densified U 3 Si 2 pellets: Microstructure control and enhanced mechanical and oxidation properties

Dense U 3 Si 2 pellets with controlled grain structure and enhanced thermal-mechanical and oxidation properties are synthesized with spark plasma sintering (SPS). Microstructure and phase composition of the SPS densified pellets are characterized systematically using SEM, EDS, and XRD. Thermal-mechanical properties and oxidation behavior of the sintered silicide fuel pellets are analyzed by laser flash, indentation, and dynamic thermogravimetric analysis. Dense U 3 Si 2 pellets are consolidated by combining high energy ball milling and rapid sintering by SPS, and the microstructure structures are controlled from micron-sized (~5.7 μm grain size) for conventional silicide to a nanocrystalline matrix with an average grain size of ~280 nm. A dominant phase of distorted U 3 Si 2 was identified with lattice expansion due to residual thermal stress upon SPS consolidation and rapid cooling processes. Both micron-sized and nano-sized pellets show exceptional thermal transport properties, consistent with monolithic silicides reported in literature. The SPS-densified pellets possess simultaneously high hardness and fracture toughness. The SPS-densified silicide pellets also demonstrate exceptional oxidation performance with extended onset oxidation temperature above 500 °C and reduced oxidation kinetics, particularly for nano-sized pellets. A strong strain effect was proposed in which compressive stress in nano-sized pellets enhances the oxidation resistance of silicide fuels, as evidenced by the degradation of oxidation performance upon strain relaxation by isothermal annealing. The correlation among the sintering process – microstructure control – physical properties and fuel behavior is established. A new concept of strain engineering is proposed further properties optimization, enabling the development of potential oxidation and corrosion-resistant silicides with extended performance, the key technological challenge of U 3 Si 2 as the leading concept of accident tolerant fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Glass Design Using Machine Learning Property Models with Prediction Uncertainties: Nuclear Waste Glass Formulation

The United States Department of Energy is responsible for managing the legacy nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately vitrified as low-activity waste and high-level waste fractions. Waste glass formulation algorithms have been traditionally developed using partial quadratic mixture property-composition models. Recently, machine learning (ML) techniques have been used to predict glass properties and discover new glass materials for nuclear waste vitrification, and these advancements can be utilized to improve waste glass composition design. In this proof-of-principle study, ML algorithms such as Gaussian process regression (GPR) were used to interpolate glass properties (e.g., viscosity, electrical conductivity, chemical durability). After selecting appropriate sets of GPR hyper-parameters for each property, an optimization program was developed to formulate glass compositions to maximize waste loading while simultaneously satisfying property within constraints. The results of the ML-based waste loadings and glass compositions were compared to those obtained using the traditional methods. Comparing to the previous glass design framework, the ML-based optimization methods offer improved glass designs and a streamlined approach to generation of optimally designed data and near real-time updates.

glass formulation, machine learning, constraints, ↗
DOE OSTI2024

Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning

For decades, materials scientists have relied on the process-structure–property paradigm to guide investigations into material behaviors. Traditional studies often examine a limited number of process-structure–property variables, striving to elucidate mechanisms governing material response. However, this approach is time consuming and can limit exploration, as well as the discovery of process-structure–property relations in novel materials. In this paper, we combined combinatorial sputter deposition and multi-modal high-throughput materials characterization with feedforward neural networks to establish high-dimensional process-structure–property relations in Pt-Au alloys, yielding nanocrystalline alloys with high hardness and low resistivity relevant to electrical contact switch applications. We mapped three indicators of process conditions (composition and two atomic deposition characteristics) onto four indicators of material structure (X-ray diffraction, film thickness, density, and surface roughness) and two indicators of material properties (hardness and resistivity), resulting in 784 unique combinations evaluated over a 13-dimensional space. The neural networks predicted Pt-Au alloys with 18–24 at.% Au, when deposited at specific conditions, to have a nanoindentation hardness up to 7.2 GPa. This high hardness value, comparable to some steels, represents a 3-fold improvement in hardness over “hard gold”, a commonly used electrical contact alloy, while maintaining requisite electrical conductivity. The neural network models provide an avenue to identify expected process windows capable of maximizing material performance.

Electrical contact materials↗
DOE OSTI2023

The influence of thermomechanical treatment pathways on texture and mechanical properties in ARB Cu/Nb nanolaminates

Accumulative roll-bonded Cu/Nb nanolaminates (ARB Cu/Nb) possess high strength, thermal stability, and radiation tolerance arising from a high content of heterophase interfaces at fine layer thicknesses. These properties can be tailored by processing parameters used in the ARB Cu/Nb fabrication process, in which layer thickness, thermal history, and strain pathway determine the interface types and resultant properties found in the material. In this work, we subject ARB Cu/Nb to annealing, and then two different rolling pathways – one where rolling direction is held constant (longitudinal rolling, or LR), and one where rolling direction is rotated by 90º and held constant thereafter (cross rolling, or CR). Rolling is performed on ARB Cu/Nb over a targeted range of layer thicknesses from 193 to 25 nm and resultant bulk textures measured by neutron diffraction are correlated with mechanical properties measured by miniaturized tensile tests. The annealing procedure sharpens texture in both phases. We find that Cu mostly develops the same texture in LR and CR. In contrast, Nb develops a distinct texture along the CR pathway that is distinct from the LR texture. The composite texture of Cu/Nb is thus distinct between LR and CR pathways. This difference in texture development between Cu and Nb as a function of strain after change in rolling direction demonstrates the viability for deliberate pairing of Cu LR and Nb CR textures at a desired layer thickness. For mechanical properties, we find that differences in texture do not result in yield or flow stress differences above a layer thickness of 25 nm. Below a layer thickness of 25 nm, despite similar Taylor factors, yield and flow stress and are significantly different. In conclusion, this indicates texture only influences mechanical behavior at low layer thickness, where interface structure dominates mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structural and thermal properties and the origin of the ultralow thermal conductivity in the defect stannite CuIn 2 Se 4

Phase-pure CuIn 2 Se 4 , a ternary metal chalcogenide that forms in a disordered stannite crystal structure, was synthesized to investigate the structure thermal property relationships as well as reveal the origin of the ultralow thermal conductivity this material possesses over a large temperature range. Modeling of the temperature-dependent heat capacity and thermal conductivity revealed distinctive thermal properties and large lattice anharmonicity. Electron localization function calculations highlight the asymmetric bonding inherent to CuIn 2 Se 4 , which together with lattice anharmonicity directly impacts the thermal properties. Our findings reveal the specific atomic arrangement and bonding governing the thermal properties of this ternary metal chalcogenide. Our findings underscore the specific atomic arrangement and bonding governing the thermal properties in this ternary chalcogenide. This study advances the fundamental understanding of stannites, and our findings can be applied to these and other multinary metal chalcogenides of interest for applications where low thermal conductivity is desirable.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Toward a First-Principles Framework for Predicting Collective Properties of Electrolytes

Conspectus Given the universal importance of electrolyte solutions, it is natural to expect that we have a nearly complete understanding of the fundamental properties of these solutions (e.g., the chemical potential) and that we can therefore explain, predict, and control the phenomena occurring in them. In fact, reality falls short of these expectations. But, recent advances in the simulation and modeling of electrolyte solutions indicate that it should soon be possible to make progress toward these goals. In this Account, we will discuss the use of first-principles interaction potentials based in quantum mechanics (QM) to enhance our understanding of electrolyte solutions. Specifically, we will focus on the use of quantum density functional theory (DFT) combined with molecular dynamics simulation (DFT-MD) as the foundation for our approach. The overarching concept is to understand and accurately reproduce the balance between local or short-ranged (SR) structural details and long-range (LR) correlations, allowing the prediction of the thermodynamics of both single ions in solution as well as the collective interactions characterized by activity/osmotic coefficients. In doing so, relevant collective motions and driving forces characterized by chemical potentials can be determined. Here, in this Account, we will make the case that understanding electrolyte solutions requires a faithful QM representation of the SR nature of the ion–ion, ion–water, and water–water interactions. However, the number of molecules that is required for collective behavior makes the direct application of high-level QM methods that contain the best SR physics untenable, making methods that balance accuracy and efficiency a practical goal. Alternatives such as continuum solvent models (CSMs) and empirically based classical molecular dynamics have been extensively employed to resolve this problem but without yet overcoming the fundamental issue of SR accuracy. We will demonstrate that accurately describing the SR interaction is imperative for predicting both intrinsic properties, namely, at infinite dilution, and collective properties of electrolyte solutions. DFT has played an important role in our understanding of condensed phase systems, e.g., bulk liquid water, the air–water interface, ions in bulk, and at the air–water interface. This approach holds huge promise to provide benchmark calculations of electrolyte solution properties that will allow for the development and improvement of more efficient methods, as well as an enhanced understanding of fundamental phenomena. However, the standard protocol using the generalized gradient approximation with van der Waals (vdW) correction requires improvement in order to achieve a high level of quantitative accuracy. Simply simulating with higher level DFT functionals may not be the best route considering the significant computational cost. Alternative methods of incorporating information from higher levels of QM should be explored; e.g., using force matching techniques on small clusters, where high level benchmark calculations are possible, to develop ideal correction terms to the DFT functional is a promising possibility. We argue that DFT with statistical mechanics is becoming an increasingly useful framework enabling the prediction of collective electrolyte properties.

Duignan, Timothy T.↗
DOE OSTI2020

Methodology for the development of empirical models relating 13C NMR spectral features to fuel properties

Effective formulation of new gasoline or diesel fuels for internal combustion engines would benefit from the development of reliable models for predicting key fuel properties based on a set of molecular descriptors obtained from a single measurement. This is particularly relevant in the case of renewable fuels, where the available fuel sample quantity may be limited. In this work, we present a statistically-based methodology for building empirical models to predict multiple properties from one-dimensional 13C nuclear magnetic resonance (NMR) spectra measured on around 200 microliters of a liquid fuel. NMR spectra contain information about the molecular composition of a sample and the carbon types and molecular substructures therein. Our approach uses this information to build sparse, interpretable models, where the predicted properties are linked to specific molecular features. The approach takes into consideration the constrained nature of the features making up the one-dimensional NMR spectrum, which, after standardization, represent a relative fuel composition. We point to the limitations in interpretability that arise when building this type of empirical predictive model and suggest how these limitations may be diminished. Among the many properties important for maximizing engine performance and minimizing emissions, we build models that predict derived cetane number and distillation temperatures as these are of particular interest because of their links to fuel economy, drivability, and engine-out emissions. Results suggest that the properties of interest may be impacted by only a few of the 27 13C NMR regions represented in the data, pointing to new directions for further testing in the development of improved fuels.

predictive models, biofuels., NMR Analysis↗
DOE OSTI2023

Elucidation of Structure and Physical Properties of Pyrolytic Sugar Oligomers Derived from Cellulose Depolymerization/Dehydration Reactions: A Density Functional Theory Study

Fast pyrolysis of lignocellulosic materials is a promising research area to produce renewable fuels and chemicals. Dehydration is known to be among the most important reaction families during cellulose pyrolysis; water is the most important product. Together with water, dehydration reactions also form a range of poorly known oligomer species of varying molecular sizes, often collected as part of bio-oil water-soluble (WS) fraction. In this work, we used electronic structure calculations to evaluate the relative thermodynamic stabilities of several oligomer species resulting from up to three consecutive dehydration events from cellulose depolymerization intermediates. A library of the thermodynamically favored candidate molecular structures was compiled. Results revealed that most of the water molecules are eliminated from the non-reducing end, forming thermodynamically more stable conjugated compounds. This is consistent with results reported by other researchers in literature where dehydration reactions occur preferably at the non-reducing ends of oligomers. The physical-chemical properties of the proposed structures were estimated using quantitative structure-property relationships (QSPRs) and quantitative property-property relationships (QPPRs). The anhydro-sugars derived from cellulose are often blamed for coke formation during bio-oil hydrotreatment. Understanding their chemical structure could help to develop rational strategies to mitigate coke formation. Furthermore, the thermo-physical properties reported (boiling point, melting point, Gibb’s free energy of formation, enthalpy of formation, and solubility parameters among others) are also fundamental to conducting first principle engineering calculations to design and analyze new pyrolysis reactors and bio-oil up-grading units.

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

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO 2

In this work, we construct distinct first-principles-based machine-learning models of CO 2 , reproducing the potential energy surface of the PBE-D3, BLYP-D3, SCAN and SCAN-rvv10 approximations of density functional theory. Here we employ the Deep Potential methodology to develop the models and consequently achieve a significant computational efficiency over ab initio molecular dynamics (AIMD) that allows for larger system sizes and time scales to be explored. Although our models are trained only with liquid phase configurations, they are able to simulate a stable interfacial system and predict vapor-liquid equilibrium properties, in good agreement with results from the literature. Because of the computational efficiency of the models, we are also able to obtain transport properties, such as viscosity and diffusion coefficients. We find that the SCAN-based model presents a temperature shift in the position of the critical point, while the SCAN-rvv10-based model shows improvement but still exhibits a temperature shift that remains approximately constant for all properties investigated in this work. We find that the BLYP-D3-based model generally performs better for liquid phase and vapor-liquid equilibrium properties, but the PBE-D3-based model is better suited for predicting transport properties.

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

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

Materials representation plays a key role in machine learning-based prediction of materials properties and new materials discovery. Currently both graph and three-dimensional (3D) voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD-based 3D convolutional neural networks (CNNs) for predicting the elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2170 $Fm\bar3m$ face-centered-cubic materials show that our ECD-based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Materials-Agnostic Platform for Informatics and Exploration features and ECD descriptors achieved the best fivefold cross-validation performance. More importantly, we showed that our ECD-based CNN models can achieve significantly better extrapolation performance when evaluated over nonredundant data sets, where there are few neighbor-training samples around test samples. As an additional validation, we evaluated the predictive performance of our models on 329 materials of space group $Fm\bar3m$ by comparing to density functional theory calculated values, which shows a better prediction power of our model for bulk modulus than shear modulus. Because of the unified representation power of ECD, it is expected that our ECD-based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Wavelength-Dependent Excitonic Properties of Covalent Organic Frameworks Explored by Theory and Experiments

Many aspects of the correlation between physical structure, light harvesting, and excitonic properties of covalent organic frameworks (COFs) remain unclear despite being key properties determining their photocatalytic function. One area of COF research that could bring clarity is through using both electronic structure theory and time-resolved spectroscopic analysis over a series of isomeric COFs. Here, we show structure-property relationships between four imine COFs built from a combination of ditopic and tritopic monomers using transient absorption spectroscopy together with time-dependent density functional theory. We find that monomer selection only moderately affects the charge transfer (CT) behavior of the COFs. Instead, we infer that imine chemistry profoundly impacts CT by acting as a CT mediator. Moreover, we discover two distinct valence bands arising from varying degrees of locally excited/CT mixing, which is responsible for energy dependent exciton dynamics. Finally, we use theory to hypothesize that interlayer interactions can modify excitonic properties that we correlate with tail states commonly observed, but rarely investigated in COFs. Furthermore, these results reveal that imine chemistry should be recognized as a very important factor to consider in the development of COF photocatalysts and the correlation of their structural environment with light harvesting and CT properties that should ultimately determine their photocatalytic function.

14 SOLAR ENERGY↗
DOE OSTI2025

Efficient Modeling of Structural, Electronic, and Optical Properties of Silver and Gold Metal Nanoclusters and Alloys Using Optimized SCC-DFTB Parameters

Computation of optical properties using conventional time-dependent density functional theory (TD-DFT) is time-consuming and memory-intensive. In this study, we investigate the accuracy and efficiency of the density functional tight binding (DFTB) framework with newly optimized Slater–Koster (SK) parameters for modeling the structural, electronic properties, and absorption spectra of silver and gold nanoclusters and their alloys. Our investigation of the ground state (GS) properties demonstrates that the newly developed GS-SK parameters enable DFTB to closely approximate DFT-calculated bond lengths for octahedron, tetrahedron, icosahedra, and truncated octahedron with sizes Ag n /Au n (n = 19, 20, 38, 55), nanoclusters and Ag 20 /Au 20 nanoalloys, with a maximum deviation of approximately 0.15 Å. Formation energy results indicate that the GS-SK parameters can closely estimate changes in formation energies with alloy composition, and the comparison of electronic structures for Ag 20 , Au 20 , and AgAu alloy nanoclusters using the DFTB approximation reveals good agreement in the projected density of states (DOS) profiles and energy levels. A second set of SK parameters, ES-SK, has been developed to describe excited state (ES) properties, including the absorption spectra of silver octahedron Ag 19 , tetrahedral Ag n (n = 20, 56, 84), truncated octahedron Ag 38 , and icosahedra Ag 55 closed-shell clusters and their gold and alloy counterparts over a broad range of alloy compositions. This parametrization uses TD-DFTB calculations and fine-tunes the d and p eigenvalues by comparing them to reference absorption spectra from first-principles TD-DFT. This enables the generation of absorption spectra that closely match the reference spectra when plasmon excitation is dominant, as demonstrated by studying the plasmonic properties of icosahedral Ag n and Au n (n = 309 and 561) nanoparticles. This includes the rapid loss in plasmon quality when Au partially replaces Ag in alloy clusters. Furthermore, these results provide a foundation for addressing computational bottlenecks in plasmonics and with new prospects for applications in the quantum plasmonics for bimetallic alloys.

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

Pressure Effects on the Electronic and Optical Properties in Low-Dimensional Metal Halide Perovskites

Metal halide perovskites have shown enormous potential in perovskite solar cells and light-emitting diodes and made unprecedented progress in the past decade. Additionally, pressure engineering as an effective technique can systematically modify the electronic structures and physical properties of functional materials. Low-dimensional metal halide perovskites (0D, 1D, and 2D) with a variety of compositions have soft lattices that allow pressure to drastically modulate their structures and properties. High-pressure investigations have obtained a comprehensive understanding of their structure–property relationships. Simultaneously, discoveries of novel pressure-driven properties, such as metallization and partially retained band gap narrowing have contributed significantly to the further development of such materials. In this work, we mainly highlight the effect of pressure on the properties and structures of low-dimensional metal halide perovskites, which is essential for designing new perovskite materials and advancing applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tuning the Properties of Nanocomposites by Trapping Them in Deep Metastable States

We report the interfacial region formed in a polymer matrix around nanoparticles (NPs) controls many macroscopic properties of polymer nanocomposites (PNCs). However, understanding the factors controlling structure and properties of interfacial region remains a challenge. We demonstrated that the initial trapping of polymer chains at surfaces of NPs in solution strongly affects the macroscopic properties of PNCs. The most surprising result is that the differences in properties of PNCs persist even after an extremely long thermal annealing time. We ascribe the observed changes to the formation of interfacial layer that is trapped in a deep metastable state already in solution. Furthermore, presented analysis suggests that PNC equilibration time is defined by the chain desorption time that can be extremely long and, in some cases, even not accessible on a reasonable experimental time scale. These results highlight the importance of polymer solution concentrations on the formation of interfacial layer and the macroscopic properties of PNCs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effect of Surface Oxidation and Crystal Thickness on the Magnetic Properties and Magnetic Domain Structures of Cr 2 Ge 2 Te 6

van der Waals (vdW) magnetic materials, such as Cr 2 Ge 2 Te 6 (CGT), show promise for memory and logic applications. This is due to their broadly tunable magnetic properties and the presence of topological magnetic features such as skyrmionic bubbles. A systematic study of thickness and oxidation effects on magnetic domain structures is important for designing devices and vdW heterostructures for practical applications. Here, we investigate thickness effects on magnetic properties, magnetic domains, and bubbles in oxidation-controlled CGT crystals. We find that CGT exposed to ambient conditions for 5 days forms an oxide layer approximately 5 nm thick. This oxidation leads to a significant increase in the oxidation state of the Cr ions, indicating a change in local magnetic properties. This is supported by real-space magnetic texture imaging through Lorentz transmission electron microscopy. By comparing the thickness-dependent saturation field of oxidized and pristine crystals, we find that oxidation leads to a nonmagnetic surface layer that is thicker than the oxide layer alone. We also find that the stripe domain width and skyrmionic bubble size are strongly affected by the crystal thickness in pristine crystals. Finally, these findings underscore the impact of thickness and surface oxidation on the properties of CGT, such as saturation field and domain/skyrmionic bubble size, and suggest a pathway for manipulating magnetic properties through a controlled oxidation process.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Temperature-Dependent Properties of Molten Li 2 BeF 4 Salt Using Ab Initio Molecular Dynamics

Molten lithium tetrafluoroberyllate (Li 2 BeF 4 ) salt, also known as FLiBe, with a 2:1 mixture of LiF and BeF 2 is being proposed as a coolant and solvent in advanced nuclear reactor designs, such as the molten salt reactor or the fluoride salt cooled high-temperature reactor. We present the results on the structure and properties of FLiBe over a wide range of temperatures, 0–2000 K, from high-throughput ab initio molecular dynamics simulation using a supercell model of 504 atoms. The variations in the local structures of solid and liquid FLiBe with temperature are discussed in terms of a pair distribution function, coordination number, and bond angle distribution. The temperature-dependent electronic structure and optical and mechanical properties of FLiBe are calculated. The optical and mechanical property results are reported for the first time. The results above and below the melting temperature (~732 K) are compared with the experimental data and with data for crystalline FLiBe. The electronic structure and interatomic bonding results are discussed in correlation with the mechanical strength. A novel concept of total bond order density (TBOD), an important quantum mechanical parameter, is used to characterize the internal cohesion and strength in the simulated models. The results show a variation in the rate of change in properties in solid and liquid phases with anomalous behavior across the melting region. The observed trend is the decrease in mechanical strength, band gap, and TBOD in a nonlinear fashion as a function of temperature. The refractive index shows a surprising minimum at 850 K, among the tested temperatures, which lies above the melting point. These findings provide a new platform to understand the interplay between the temperature-dependent structures and properties of FLiBe salt.

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

Synthesis of Butyl-Exchanged Polyoxymethylene Ethers as Renewable Diesel Blendstocks with Improved Fuel Properties

Methyl-terminated polyoxymethylene ethers (MM-POMEs), having the formula CH3O–(CH2O)n–CH3 (n = 3–5), are a high-cetane, low-sooting group of oxygenates that have recently attracted attention as potential diesel blendstocks. Despite these attractive fuel properties, MM-POMEs have shortcomings due to their low energy density and high water solubility. Guided by a computational fuel property assessment for POMEs with longer end-groups, the most promising improvements in the desired compression ignition fuel properties were observed for butyl-terminated POMEs. Here, an acid-catalyzed transacetalization reaction was developed to exchange the methyl end-groups of MM-POMEs (n = 3–6) with butyl end-groups. The reaction utilizes an ion-exchange resin as the acid catalyst at mild reaction conditions of 60 °C and atmospheric pressure. Approximately 100 mL of butyl-exchanged POMEs in the diesel boiling range were produced, enabling laboratory-scale fuel property testing. The butyl-terminated POME mixture possesses the advantaged fuel properties of the parent MM-POMEs (low-soot, high-cetane) while exhibiting improved energy density (lower heating value (LHV) of 30 MJ/kg) and substantially reduced water solubility (7.3 g/L) compared to the parent MM-POME mixture (LHV of 19 MJ/kg , water solubility of 258 g/L).

09 BIOMASS FUELS↗
DOE OSTI2020

Two-Phase Fluid Flow Properties of Rough Fractures With Heterogeneous Wettability: Analysis With Lattice Boltzmann Simulations

Fractures are conduits for fluid flow in low-permeability geological formations. Multiphase flow properties of fractures are important in natural processes and in engineering applications such as the evaluation of the sealing capacity of caprocks and productivity of hydrocarbon-bearing tight rocks. Investigations of flow and transport through fractures typically focus on the effects of fracture geometric and mechanical factors such as aperture, roughness, and compressibility. The wettability of the fracture surfaces and its influence on microscale interfacial phenomena and macroscale effective transport properties are seldom studied. In this study, we investigated the effect of heterogeneous wetting properties on the displacement of water by supercritical CO2 through a series of lattice Boltzmann method simulations. The results show the evolution of the CO2 plume within a fracture is controlled by both the roughness of the aperture field and the wetting distribution. We combined these factors into a capillary pressure map that can be related to the macroscopic flow behavior of the fracture. We observed that heterogeneous wetting distributions promote the residual trapping of water where lower capillary pressures allowed for isolated water pockets in higher capillary pressure zones. Analysis of fracture unsteady relative permeability shows the effect of wetting on permeability evolution and provides support for the viscous-coupling relative permeability model. Finally, analysis of the steady-state relative permeability and saturation demonstrates a strong correlation between permeability and the standard deviation of the capillary pressure field. Thus, characterizing the distribution of wetting properties of fractures is crucial to understanding multiphase fracture flow and transport properties.

58 GEOSCIENCES↗