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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Numerical simulations of depressurization-induced gas production from gas hydrates using 3-D heterogeneous models of L-Pad, Prudhoe Bay Unit, North Slope Alaska

Gas production potential is estimated using a three-dimensional reservoir model based on gas hydrate deposits located in the Prudhoe Bay region of the Alaska's North Slope. The model incorporates two hydrate-bearing sand units using detailed reservoir geological and structural information obtained from past and recent drilling programs. Geostatistical porosity models, conditioned to log data from 78 wells drilled in the vicinity of the Prudhoe Bay “L-Pad,” were developed, providing 3D heterogeneity in porosity, porosity-dependent hydrate saturation, and intrinsic permeability. The simulations utilize both vertical and inclined wellbores to induce depressurization of the reservoir at a constant bottom-hole pressure. The results show the superior performance of the inclined well design. Average gas production rates during the first five years were ~6.0 × 10 4 ST m 3 /day (~2.1 MMSCF/day) and ~2.7 × 10 4 ST m 3 /day (~1.0 MMSCF/day) for incline and vertical wells, respectively. After 30 years 5.3 × 10 8 - 5.7 × 10 8 ST m 3 (18.6–20.2 BSCF) and 6.2 × 10 8 - 6.4 × 10 8 ST m 3 (22.0–22.7 BSCF) gas were produced using the vertical and inclined well configurations, respectively. The analysis reveals that 2D reservoir models with homogeneous representations for porosity and hydrate saturation significantly underestimate production potential. The heterogeneity implemented in this work provides complex porous network and preferential pathways for mobile phase flow to a producing well. Consequently, no secondary hydrate formation around a wellbore is predicted in contrast to models that utilize uniform porosity and hydrate saturation distributions.

03 NATURAL GAS↗
DOE OSTI2024

4D-STEM Mapping of Nanocrystal Reaction Dynamics and Heterogeneity in a Graphene Liquid Cell

Chemical reaction kinetics at the nanoscale are intertwined with heterogeneity in structure and composition. However, mapping such heterogeneity in a liquid environment is extremely challenging. Here, in this work, we integrate graphene liquid cell (GLC) transmission electron microscopy and four-dimensional scanning transmission electron microscopy to image the etching dynamics of gold nanorods in the reaction media. Critical to our experiment is the small liquid thickness in a GLC that allows the collection of high-quality electron diffraction patterns at low dose conditions. Machine learning-based data-mining of the diffraction patterns maps the three-dimensional nanocrystal orientation, groups spatial domains of various species in the GLC, and identifies newly generated nanocrystallites during reaction, offering a comprehensive understanding on the reaction mechanism inside a nanoenvironment. This work opens opportunities in probing the interplay of structural properties such as phase and strain with solution-phase reaction dynamics, which is important for applications in catalysis, energy storage, and self-assembly.

four-dimensional scanning transmission electron mi↗
DOE OSTI2022

Interpretable machine learning for knowledge generation in heterogeneous catalysis

Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Here, extracting meaningful physical insights from these black-box models has proved challenging, as the internal logic of these black-box models is not readily interpretable due to their high degree of complexity. Interpretable machine learning methods that merge the predictive capacity of black-box models with the physical interpretability of physics-based models offer an alternative to black-box models. In this Perspective, we discuss the various interpretable machine learning methods available to catalysis researchers, highlight the potential of interpretable machine learning to accelerate hypothesis formation and knowledge generation, and outline critical challenges and opportunities for interpretable machine learning in heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unusual dynamics of tetrahedral liquids caused by the competition between dynamic heterogeneity and structural heterogeneity

Tetrahedral liquids exhibit intriguing thermodynamic and transport properties because of the various ways tetrahedra can be packed and connected. Recently, an unusual temperature dependence of the stretching exponent β in a model tetrahedral liquid ZnCl 2 from T m + 85 K to T m + 35 K has been reported using neutron-spin echo spectroscopy. This discovery stands in sharp contrast to other glass-forming liquids. In this study, we conducted neural network force field driven molecular dynamic simulations of ZnCl 2 . We found a non-monotonic temperature dependence of β from liquid to supercooled liquid temperatures. Further structural decomposition and dynamic analysis suggest that this unusual dynamic behavior is a result of the competition between the decrease in the diversity of tetrahedra motifs (structural heterogeneity) and the increase in glassy dynamic heterogeneity. Furthermore, this result may contribute to new understandings of the structural relaxation of other network liquids.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Solute mixing regulates heterogeneity of mineral precipitation in porous media

Here, synchrotron X-ray microtomography was used to track the spatiotemporal evolution of mineral precipitation and the consequent alteration of the pore structure. Column experiments were conducted by injecting CaCl 2 and NaHCO 3 solutions into granular porous media either as a premixed supersaturated solution (external mixing) or as separate solutions that mixed within the specimen (internal mixing). The two mixing modes produced distinct mineral growth patterns. While internal mixing promoted transverse heterogeneity with precipitation at the mixing zone, external mixing favored relatively homogeneous precipitation along the flow direction. The impact of precipitation on pore water flow and permeability was assessed via 3-D flow simulations, which indicated anisotropic permeability evolution for both mixing modes. Under both mixing modes, precipitation decreased the median pore size and increased the skewness of the pore size distribution. Such similar pore-scale evolution patterns suggest that the clogging of individual pores depends primarily on local supersaturation state and pore geometry.

42 ENGINEERING↗
DOE OSTI2020

Uncovering Uranium Isotopic Heterogeneity of Fuel Pellets from the Fifth Collaborative Materials Exercise of the Nuclear Forensics International Technical Working Group

In 2017, the Nuclear Forensics International Technical Working Group (ITWG) organized their fifth 37 Collaborative Materials Exercise (CMX-5). The exercise samples were two uranium dioxide fuel pellets 38 manufactured from the same starting materials by different processes to have similar bulk isotopic 39 composition, but different spatial uranium isotopic distributions. Sets of identical materials were sent to 40 all participating laboratories, who then utilized their existing nuclear forensic capabilities to 41 independently analyse fuel pellets and identify similarities and differences of the materials’ 42 characteristics. The analytical methods used to probe the fuel pellets included ex situ, such as sectioning 43 or breaking up the pellets and analyzing dissolved pieces using inductively coupled plasma mass 44 spectrometry (ICP-MS), analyzing particles collected from intact or fragmented pellets by secondary ion 45 mass spectrometry (SIMS), as well as in situ methods, such as laser ablation coupled with ICP-MS, 46 autoradiography and nanoSIMS. In this paper we present the results of these independent analyses and 47 compare the capabilities of those nuclear forensic analytical methods to uncover details of the isotopic 48 heterogeneity of uranium fuel pellets.

Nuclear Forensic Analysis of Uranium Fuel Pellets,↗
DOE OSTI2019

Worst crosstalk analysis in heterogeneous and trench assisted heterogeneous multicore fiber for different core counts and layouts

In order to obtain appreciably low worst crosstalk level in high core count multicore fiber, heterogeneous MCF with different core configurations and layouts have been investigated analytically using coupled mode equation. The variations in worst crosstalk behavior with respect to the bending radius, core configurations, core layouts, and outer cladding thickness have been analyzed. Further, the core count dependent variations in worst crosstalk level, core pitch, and peak bending radius in different core layouts have been presented. It has been observed that the selection of core configuration, core layout, and outer cladding thickness have significant impact on the reduction in worst crosstalk level. For example, in 12-core normal-index heterogeneous MCF with SLS structure, the core Configuration 1 can provide the worst crosstalk level reduced by 7.09 dB and 16.92 dB in comparison to the other two core configurations, i.e., Configuration 2, and Configuration 3, respectively. Further, for the same core count with TA Configuration 1, SLS core layout can achieve the reduced worst crosstalk level in comparison with circular ORS and DRS, hexagonal ORS and DRS respectively by 24.62 dB, 25.86 dB, 28.87 dB and 30.64 dB.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Surface Induced Dissociation Coupled with High Resolution Mass Spectrometry Unveils Heterogeneity of a 211 kDa Multicopper Oxidase Protein Complex

Manganese oxidation is an important biogeochemical process that is largely regulated by bacteria through enzymatic reactions. However, the detailed mechanism is poorly understood due to challenges in isolating and characterizing these unknown enzymes. A manganese oxidase, Mnx, from Bacillus sp. PL-12 has been successfully overexpressed in active form as a protein complex with a molecular mass of 211 kDa. We have recently used surface induced dissociation (SID) and ion mobility-mass spectrometry (IM-MS) to release and detect folded subcomplexes for determining subunit connectivity and quaternary structure. The data from the native mass spectrometry experiments led to a plausible structural model of this multicopper oxidase, which has been difficult to study by conventional structural biology methods. It was also revealed that each Mnx subunit binds a variable number of copper ions. Becasue of the heterogeneity of the protein and limited mass resolution, ambiguities in assigning some of the observed peaks remained as a barrier to fully understanding the role of metals and potential unknown ligands in Mnx. In this study, we performed SID in a modified Fourier transform-ion cyclotron resonance (FTICR) mass spectrometer. The high mass accuracy and resolution offered by FTICR unveiled unexpected artificial modifications on the protein that had been previously thought to be iron bound species based on lower resolution spectra. Additionally, isotopically resolved spectra of the released subcomplexes revealed the metal binding stoichiometry at different structural levels. This method holds great potential for in-depth characterization of metalloproteins and protein–ligand complexes. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Self-consistent clustering analysis: an efficient multiscale scheme for inelastic heterogeneous materials

A predictive computational theory is shown for modeling complex, hierarchical materials ranging from metal alloys to polymer nanocomposites. The theory can capture complex mechanisms such as plasticity and failure that span across multiple length scales. This general multiscale material modeling theory relies on sound principles of mathematics and mechanics, and a cutting-edge reduced order modeling method named self-consistent clustering analysis (SCA) [Zeliang Liu, M.A. Bessa, Wing Kam Liu, “Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials,” Comput. Methods Appl. Mech. Engrg. 306 (2016) 319–341]. SCA reduces by several orders of magnitude the computational cost of micromechanical and concurrent multiscale simulations, while retaining the microstructure information. This remarkable increase in efficiency is achieved with a data-driven clustering method. Computationally expensive operations are performed in the so-called offline stage, where degrees of freedom (DOFs) are agglomerated into clusters. The interaction tensor of these clusters is computed. In the online or predictive stage, the Lippmann-Schwinger integral equation is solved cluster-wise using a self-consistent scheme to ensure solution accuracy and avoid path dependence. To construct a concurrent multiscale model, this scheme is applied at each material point in a macroscale structure, replacing a conventional constitutive model with the average response computed from the microscale model using just the SCA online stage. A regularized damage theory is incorporated in the microscale that avoids the mesh and RVE size dependence that commonly plagues microscale damage calculations. The SCA method is illustrated with two cases: a carbon fiber reinforced polymer (CFRP) structure with the concurrent multiscale model and an application to fatigue prediction for additively manufactured metals. For the CFRP problem, a speed up estimated to be about 43,000 is achieved by using the SCA method, as opposed to FE2, enabling the solution of an otherwise computationally intractable problem. The second example uses a crystal plasticity constitutive law and computes the fatigue potency of extrinsic microscale features such as voids. This shows that local stress and strain are capture sufficiently well by SCA. This model has been incorporated in a process-structure-properties prediction framework for process design in additive manufacturing.

Liu, Z.↗
DOE OSTI2019

A two-set order parameters phase-field modeling of crack deflection/penetration in a heterogeneous microstructure

Modeling of crack deflection/penetration or manifested as intergranular/transgranular fracture in polycrystals has long been a challenge in both fracture mechanics and materials science. A phase-field model formulated with two-set order parameters describing the crack field and the microstructure field respectively, is herein established to investigate the competition between crack penetration and deflection at an interface. The advantages of the proposed model are three-fold: (1) the fracture toughness of grain boundaries and/or interfaces, besides single crystal properties (e.g., Young’s modulus, Poisson ratio, and fracture toughness), can be automatically incorporated, (2) cohesive interfaces are treated in one phase-field framework in comparison with the integrated phase-field and cohesive zone model in which cohesive elements have to be manually placed at the interface, and (3) two-set order parameters enable to describe the concurrent crack propagation and microstructure evolution. The proposed model is validated by comparing the simulated critical deflection angles with the results from theoretical analysis of the linear elastic fracture mechanics and the experiments. The model is then applied to a heterogeneous Silicon Nitride polycrystal to determine the effect of interface fracture toughness on the intergranular/transgranular fracture transition and the effective toughness of the materials.

Chen, Hu↗
DOE OSTI2017

Atomic-level heterogeneity and defect dynamics in concentrated solid-solution alloys

Performance enhancement of structural materials in extreme radiation environments has been activelyinvestigated for many decades. Traditional alloys, such as steel, brass and aluminum alloys, normallycontain one or two principal element(s) with a low concentration of other elements. While these existin either a mixture of metallic phases (multiple phases) or in a solid solution (single phase), limited orlocalized chemical disorder is a common characteristic of the main matrix. Fundamentally different fromtraditional alloys, recently developed single-phase concentrated solid-solutio n alloys (CSAs) contain mul-tiple elemental species in equiatomic or high concentrations with different elements randomly arrangedon a crystalline lattice. Due to the lack of ordered elemental arrangement in these CSAs, they exhibit sig-nificant chemical disorder and unique site-to-site lattice distortion. While it is well recognized intraditional alloys that minor additions lead to enhanced radiation resistance, it remains unclear inCSAs how atomic-level heterogeneity affects defect formation, damage accumulation, and microstruc-tural evolution. These knowledge gaps have acted as roadblocks to the development of future-generation energy technology. CSAs with a simple crystal structure, but complex chemical disorder,are unique systems that allow us, through replacing principal alloying elements and modifying concen-trations, to study how compositional complexity influences defect dynamics, and to bridge the knowl-edge gaps through understanding intricate electronic- and atomic-level interactions, mass and energytransfer processes, and radiation resistance performance. Recent advances in defect dynamics and irradi-ation performance of CSAs are reviewed, intrinsic chemical effects on radiation performance arediscussed, and direction for future studies is suggested.

Zhang, Yanwen↗
DOE OSTI2021

Multimodal Analysis of Spatially Heterogeneous Microstructural Refinement and Softening Mechanisms in Three-Pass Friction Stir Processed Al4Si Alloy

Multiple thermally and thermomechanically induced microstructural refinement mechanisms can be activated in metallic alloys when subjected to solid phase processing methods such as friction stir processing (FSP). In this work, we provide detailed descriptions of the relationship between region-specific microstructural refinement mechanisms and the variation in microhardness, through a systematic and multimodal microstructural characterization of an FSP-processed 75% cold-rolled Al-4 at.% Si model binary alloy. Spatially resolved high-energy synchrotron X-ray diffraction, electron backscattered diffraction, and scanning transmission electron microscopy were used to understand the spatially heterogeneous microstructural evolution due to FSP. Results provide insights into how mechanisms such as static recovery, static recrystallization, dynamic recovery and recrystallization, geometric and continuous dynamic recrystallization, and particle-stimulated static or dynamic grain nucleation may occur heterogeneously in the microstructure as a function of the distance from the stir zone in processed alloys, directly influencing the degree of softening. The systematic analysis of microstructures and hardness in the FSP-processed model binary alloy given in this work highlights the rich microstructural domains that can be uniquely harnessed through solid phase processing of metallic alloys.

Al4Si, Friction Stir Processing, Geometric Dynamic↗
DOE OSTI2017

Bayesian and variational Bayesian approaches for flows in heterogeneous random media

In this paper, we study porous media flows in heterogeneous stochastic media. We propose an efficient forward simulation technique that is tailored for variational Bayesian inversion. As a starting point, the proposed forward simulation technique decomposes the solution into the sum of separable functions (with respect to randomness and the space), where each term is calculated based on a variational approach. This is similar to Proper Generalized Decomposition (PGD). Next, we apply a multiscale technique to solve for each term (as in ) and, further, decompose the random function into 1D fields. As a result, our proposed method provides an approximation hierarchy for the solution as we increase the number of terms in the expansion and, also, increase the spatial resolution of each term. We use the hierarchical solution distributions in a variational Bayesian approximation to perform uncertainty quantification in the inverse problem. We conduct a detailed numerical study to explore the performance of the proposed uncertainty quantification technique and show the theoretical posterior concentration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Coaxial Ceramic Direct Ink Writing on Heterogenous and Rough Surfaces: Investigation of Core–Shell Interactions

In this work, coaxial conductor–ceramic direct ink writing enables the printing of sensitive or encapsulated materials onto heterogeneous and rough substrates. While encasing the core fluid within a stiff ceramic shell, continuity may be maintained, even while printing onto conventionally challenging substrates. Here, we report the development of a coaxial ceramic direct ink writing suite and explore coflow interrelationships based on microfluidic principles. A coaxial nozzle is designed to facilitate the coextrusion of an alumina shell, whereas indium–tin-oxide inks constitute the core. In this manner, a core–shell ceramic element may be printed onto rough substrates for future high-temperature applications. Colloidal inks are engineered to provide the required rheological and sintering performance. Moreover, flow simulations in conjunction with microfluidic coflow principles are used to explore the coaxial printing processing space, thus controlling the core–shell architectures. Physical modeling is further used to analyze core deformations and eccentricity. As a result, simulations are validated experimentally, and the analyses are used to deposit coaxial ceramic features onto heterogeneous, high-temperature ceramic substrates.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Method of Distributions for Two‐Phase Flow in Heterogeneous Porous Media

Abstract Multiscale heterogeneity and insufficient characterization data for a specific subsurface formation of interest render predictions of multi‐phase fluid flow in geologic formations highly uncertain. Quantification of the uncertainty propagation from the geomodel to the fluid‐flow response is typically done within a probabilistic framework. This task is computationally demanding due to, for example, the slow convergence of Monte Carlo simulations (MCS), especially when computing the tails of a distribution that are necessary for risk assessment and decision‐making under uncertainty. The frozen streamlines method (FROST) accelerates probabilistic predictions of immiscible two‐phase fluid flow problems; however, FROST relies on MCS to compute the travel‐time distribution, which is then used to perform the transport (phase saturation) computations. To alleviate this computational bottleneck, we replace MCS with a deterministic equation for the cumulative distribution function (CDF) of travel time. The resulting CDF‐FROST approach yields the CDF of the saturation field without resorting to sampling‐based strategies. Our numerical experiments demonstrate the high accuracy of CDF‐FROST in computing the CDFs of both saturation and travel time. For the same accuracy, it is about 5 and 10 times faster than FROST and MCS, respectively.

Yang, Hyung Jun↗
DOE OSTI2015

Revealing spatially heterogeneous relaxation in a model nanocomposite

The detailed nature of spatially heterogeneous dynamics of glycerol-silica nanocomposites is unraveled by combining dielectric spectroscopy with atomistic simulation and statistical mechanical theory. Analysis of the spatial mobility gradient shows no “glassy” layer, but the α-relaxation time near the nanoparticle grows with cooling faster than the α-relaxation time in the bulk and is ∼20 times longer at low temperatures. The interfacial layer thickness increases from ∼1.8 nm at higher temperatures to ∼3.5 nm upon cooling to near bulk T{sub g}. A real space microscopic description of the mobility gradient is constructed by synergistically combining high temperature atomistic simulation with theory. Our analysis suggests that the interfacial slowing down arises mainly due to an increase of the local cage scale barrier for activated hopping induced by enhanced packing and densification near the nanoparticle surface. The theory is employed to predict how local surface densification can be manipulated to control layer dynamics and shear rigidity over a wide temperature range.

77 NANOSCIENCE AND NANOTECHNOLOGY↗