Synthesis and characterization of sintered H–Y zeolite-derived waste forms for dehalogenated electrorefiner salt
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Flow visualization, Particle Image Velocimetry (PIV) and dissolved oxygen charging experiments are carried out in a small-scale cylindrical air-water bubble column at different superficial gas velocities. Void fraction profile, total gas holdup, liquid phase velocity field and volumetric mass transfer coefficient are obtained. Details of each experiment are given, with a new procedure for obtaining a 2D void fraction profile from flow visualization by image processing and time averaging which could provide useful information for CFD model validation. The experimental results are analyzed and compared with simulations where good agreement is found for both local and global flow patterns. The averaged governing equations and constitutive relations used in the simulation are discussed briefly, with emphasis on the proper comparison between simulations and experiments based on the derivation of the two-fluid model. Finally, by conducting three sets of experiments related to phase distribution, velocity field and mass transfer in the same apparatus, a validated CFD model for multiphase mass transfer is established and can be used in the future design of a multi-phase reactor.
Hydrodynamics, oxygen transfer, and oxygen uptake in bubble-column and airlift bioreactors were studied using multiphase Euler-Euler computational fluid dynamics (CFD) simulations. Interphase mass transfer of oxygen was modeled with a liquid mass-transfer coefficient and sustained driving force provided by a phenomenological model for microbial oxygen uptake rate (OUR). Laboratory-scale reactor simulations of a bubble-column (0.15 m diameter and 0.75 m initial liquid height) showed reasonable agreement with gas holdup and mass transfer experiments reported in the literature. Commercial-scale bubble-column and airlift reactor simulations (5 m diameter and 25 m initial liquid height) were simulated using this validated model. Similar trends for the variation of overall gas holdup and oxygen concentrations with superficial velocity were observed for both of the commercial-scale reactors. However, the simulation results indicate differences in hydrodynamics, such as better recirculation in the airlift reactors that enables favorable distribution of oxygen in the reactor.
The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space, allowing a characterization of differential operators while providing structure which may be used to impose biases related to symmetry, isotropy, and conservation form. Here, we demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration.
We study in detail the pressure stabilizing effects of the non-iterated fixed-stress splitting in poromechanical problems which are nearly undrained and incompressible. When applied in conjunction with a spatial discretization which does not satisfy the discrete inf–sup condition, namely a mixed piecewise linear–piecewise constant spatial discretization, the explicit fixed-stress scheme can have a pressure stabilizing effect in transient problems. This effect disappears, however, upon time step refinement or the attainment of steady state. The interpretation of the scheme as an Augmented Lagrangian method similar to Uzawa iteration for incompressible flow helps explain these results. Moreover, due to the slowly evolving solution within undrained seal regions, we show that the explicit fixed-stress scheme requires very large time steps to reveal its pressure stabilizing effect in examples of geologic CO 2 sequestration. We note that large time steps can result in large errors in drained regions, such as the aquifer or reservoir regions of these examples, and can prevent convergence of nonlinear solvers in the case of multiphase flows, which can make the explicit scheme an unreliable source of pressure stabilization. We conclude by demonstrating that pressure jump stabilization is as effective in the explicit fixed-stress setting as in the fully implicit setting for undrained problems, while maintaining the stability and convergence of the fixed-stress split for drained problems.
CO 2 -enhanced coalbed methane recovery, also known as CO 2 -ECBM, is a potential win-win approach for enhanced methane production while simultaneously sequestering injected anthropogenic CO 2 to decrease CO 2 emissions into the atmosphere. Here, CO 2 -ECBM is simulated using a coupled thermal–hydrological–mechanical (THM) numerical model that considers multiphase (gas and water) flow and solubility, multicomponent (CO 2 and CH 4 ) diffusion and adsorption, heat transfer and coal deformation. The coupled model is based on the TOUGH-FLAC simulator, which is applied here for the first time to model CO 2 -ECBM. The capacity of the simulator for modeling methane production is verified by a code-to-code comparison with the general-purpose finite-element solver COMSOL. Then, the TOUGH-FLAC simulator is applied in an isothermal simulation to study the variations in permeability evolution during a CO 2 -ECBM operation while considering four different stress-dependent permeability models that have been implemented into the simulator. Finally, the TOUGH-FLAC simulator is applied in non-isothermal simulations to model THM responses during a CO 2 -ECBM operation.Our simulations show that the permeability evolution, mechanical stress, and deformation are all affected by changes in pressure, temperature and adsorption swelling, with adsorption swelling having the largest effect. The calculated stress changes do not induce any mechanical failure in the coal seam, except near the injection well in one case of a very unfavorable stress field.
Bicontinuous microemulsions (BμEs), consisting of water and oil nanodomains separated by surfactant monolayers of near-zero curvature, are potentially valuable systems for purification and delivery of biomolecules, for hosting multiphasic biochemical reactions, and as templating media for preparing nanomaterials. We formed Winsor-III systems by mixing aqueous protein and sodium dodecyl sulfate (SDS) solutions with dodecane and 1-pentanol (cosurfactant) to efficiently extract proteins into the middle (BμE) phase. Bovine serum albumin (BSA) and cytochrome c partitioned to the BμE phase at 64% and 81% efficiency, respectively, producing highly concentrated protein solutions (32 and 44 g L –1 , respectively), through release of water and oil from the BμEs. Circular dichroism spectroscopic analysis demonstrated that BSA underwent minor secondary structural changes upon incorporation into BμEs, while the secondary structure of cytochrome c and pepsin underwent major changes. Small-angle x-ray scattering (SAXS) results show that proteins promoted an increase of the interfacial fluidity and surface area per volume for the BμE surfactant monolayers, and that each protein uniquely altered self-assembly in the Winsor-III systems. Cytochrome c partitioned via electrostatic attractions between SDS and the protein’s positively-charged groups, residing near the surfactant head groups of BμE monolayers, where it decreased surfactant packing efficiency. BSA partitioned through formation of SDS-BSA complexes via hydrophobic and electrostatic attractive interactions. As the BSA-SDS ratio increased, complexes’ partitioning favored BμEs over the oil excess phase due to the increased hydrophilicity of the complexes. In conclusion, this study demonstrates the potential utility of BμEs to purify proteins and prepare nanostructured fluids possessing high protein concentration.
The exothermic reactions in the ZrF 4 –Mg-Si and ZrF 4 -Al-Si systems are investigated by a fast temperature recording (thermocouple) technique, differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). A quenching method is used to arrest the combustion process and conduct a layer-by-layer analysis of the products by x-ray diffraction (XRD) and electron microscopy. Two seemingly similar reactive systems exhibited considerably different combustion characteristics, composition, and morphology. Based on these investigations, we propose and discuss phase formation mechanisms at the early stages for each system. Three different pathways involving the reaction of ZrF 4 with other reagents and the Mg 2 Si intermediate are identified to occur in the ZrF 4 –Mg-Si system. Contrary to the complex mechanism in the ZrF 4 –Mg-Si system, the early stage of the combustion process for the ZrF 4 -Al-Si system involves the interaction of ZrF 4 with Al-Si eutectic melt. The exothermic reaction between reduced solid Zr and Si melt is the primary heat-generating step for both systems in spite of substantial differences in the early stages of the reactions. The silicon content in the reactive mixtures governs the phase composition of products. The ZrSi 2 phase, with a high growth rate, forms first on the Zr particle surfaces and then grows by a reactive diffusion mechanism. The ZrSi 2+ Zr reaction produces silicon-lean phases (e.g., ZrSi) when the silicon supply is limited. The combustion temperature also has a considerable influence on the phase compositions of the products. High combustion temperature in the ZrF 4 +2Mg+Si mixture enables the formation of multiphase products (α-ZrSi and β-ZrSi), whereas the relatively lower temperatures in the 3ZrF 4 +4Al+3Si mixture yields a single-phase α-ZrSi. As a result, lower combustion temperatures also make the ZrF 4 -Al-Si system more advantageous for the preparation of zirconium silicides.
Our objective in the present study is to provide basic insights into the coupling between external-gas and solid biomass vegetation processes that control the dynamics of flame spread in wildland fire problems. Here we focus on a modeling approach that resolves processes occurring at vegetation and flame scales, i.e., the formation of flammable vapors due to the thermal degradation of the solid biomass, the subsequent combustion in ambient air, the thermal feedback to the biomass through radiative and convective heat transfer, and the possible transition from flaming combustion (taking place outside of the solid biomass) to smoldering combustion (taking place inside the solid biomass). The capability uses a multiphase combustion framework and treats external-gas processes through a Large Eddy Simulation solver and solid biomass processes through a discrete particle model. The discrete particle model adopts a one-dimensional porous medium formulation, includes descriptions of drying, thermal pyrolysis, oxidative pyrolysis, and char oxidation, as well as a description of the external-gas-to-solid-biomass diffusion of oxygen mass; the discrete particle model thereby provides a treatment of in-depth oxidative processes and allows the simulation of smoldering combustion. The modeling capability is applied to the simulation of fire spread across a surrogate biomass vegetation bed corresponding to a discrete array of cylindrical-shaped, vertically-oriented, pine wood sticks, characterized by a monomodal size distribution, in horizontal flat terrain and under wind-aided conditions. The numerical results demonstrate that the model can simulate successful flaming-to-smoldering transition followed by complete biomass consumption.
Progress in numerical methods in computing thermodynamic equilibria is presented that is particularly well suited to large multicomponent multiphase systems. This method exploits fundamental principles of equilibrium thermodynamics that results in simplifying the numerical approach. The chemical potentials of all species and phases are defined by the chemical potentials of the component elements and the objective is to systematically partition the Gibbs energy of the system in a manner that minimizes the residuals of the mass balance equations. Several numerical advantages are obtained through this simplification that improve the rate of convergence while simultaneously promoting numerical stability.
A mesoscale model is developed by integrating the rate theory and phase-field models and is used to study the fission-induced recrystallization in U-7Mo alloy. The rate theory model is used to predict the dislocation density and the recrystallization nuclei density due to irradiation. The predicted fission rate and temperature dependences of the dislocation density are in good agreement with experimental measurements. This information is used as input for the multiphase phase-field model to investigate the fission-induced recrystallization kinetics. The simulated recrystallization volume fraction and bubble induced swelling agree well with experimental data. The effects of the fission rate, initial grain size, and grain morphology on the recrystallization kinetics are discussed based on an analysis of recrystallization growth rate using the modified Avrami equation. Here, we conclude that the initial microstructure of the U-Mo fuels, especially the grain size, can be used to effectively control the rate of fission-induced recrystallization and therefore swelling.
In this study, we compute changes in the lattice parameters and elastic stiffness coefficients C ij of body-centered tetragonal (bct) Fe due to Al, B, C, Cu, Mn, Si, and N solutes. Solute strain misfit tensors determine changes in the lattice parameters as well as strain contributions to the changes in the C ij . We also compute chemical contributions to the changes in the C ij , and show that the sum of the strain and chemical contributions agree with more computationally expensive direct calculations that simultaneously incorporate both contributions. Octahedral interstitial solutes, with C being the most important addition in steels, must be present to stabilize the bct phase over the body-centered cubic phase. We therefore compute the effects of interactions between interstitial C solutes and substitutional solutes on the bct lattice parameters and C ij for all possible solute configurations in the dilute limit, and thermally average the results to obtain effective changes in properties due to each solute. Finally, the computed data can be used to estimate solute-induced changes in mechanical properties such as strength and ductility, and can be directly incorporated into mesoscale simulations of multiphase steels to model solute effects on the bct martensite phase.
X-ray computed tomography (CT) is an established non-destructive tool for 3D imaging of multiphasic composites. Numerous applications of X-ray CT in medical diagnosis and materials characterization have been reported, many involving field-specific innovations in the imaging technology itself. Yet, quantitative summarization to link image features to properties of interest has been rare. Here we address this issue by employing state-of-the-art technics in scalar field topology for the summarization of X-ray CT images of an example biphasic system. By varying processing-parameters we create different microstructures, evolve them through accelerated thermal aging, CT-image them pre- and post-aged, and demonstrate the ability of our image summarization method to systematically track process- and age-related changes, which can often be very subtle. A novel aspect of the algorithm involves recognition over multiple resolution levels, which provides deeper insight into the pattern relationship between grain-like features and their neighbors. The method is general, adaptable to diverse image reconstruction methods and materials systems, and particularly useful in applications where practical constraints on the sample-size limits the reliable use of more complex models, e.g., convolutional neural networks.
In this work, we present a phase-field model that captures the evolution of ionic concentrations and phase fractions during solid-state metathesis (SSM) reactions where diffusion limits the rate of transformation. The evolution of the mole fraction of each ion is obtained via governing equations that describe the reduction of a free energy, which includes an energy landscape with local minima located at compositions corresponding to stable products. We utilized two Lagrange multipliers to impose constraints of electroneutrality as well as on the sum of mole fractions, which were then eliminated to derive set of two partial differential equations that describe the dynamics of the mole fraction evolution. From these governing equations, the expressions for effective mobilities for the cations and the anions were obtained. We first study the effect of mobilities of ions on the reaction kinetics, using a simple model considering the ions with an identical absolute value of charge numbers. The simulation results show that the overall characteristic mobility, defined as the sum of the two effective ionic mobilities, provides an excellent measure of the rate at which reaction progresses and that the ratio of the effective mobilities of the anions and the cations signifies the manner by which the reaction progresses. We then generalize the model to consider ions with different charge numbers and tuned the mobility of ions based on their diffusion coefficients reported in the literature and experimental data from a thin-film experiment for the synthesis of FeS 2 to demonstrate the capability of the model to predict the phase evolution during SSM reactions. In particular, the simulation predicts nonplanar phase evolution, which is recently observed in thin-film reactions for the synthesis of FeS 2 via transmission electron microscopy. The approach can serve as a basis for models for phase transformations in other multiphase ionic mixtures, such as in all-solid-state batteries and in ionic liquids.
Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.
Performance assessment (PA) of geologic radioactive waste repositories requires three-dimensional simulation of highly nonlinear, thermo-hydro-mechanical-chemical (THMC), multiphase flow and transport processes across many kilometers and over tens to hundreds of thousands of years. Integrating the effects of a near-field geomechanical process (i.e. buffer swelling) into coupled THC simulations through reduced-order modeling, rather than through fully coupled geomechanics, can reduce the dimensionality of the problem and improve computational efficiency. In this study, PFLOTRAN simulations model a single waste package in a shale host rock repository, where re-saturation of a bentonite buffer causes the buffer to swell and exert stress on a highly fractured disturbed rock zone (DRZ). Three types of stress-dependent permeability functions (exponential, modified cubic, and Two-part Hooke’s law models) are implemented to describe mechanical characteristics of the system. Overall, our modeling study suggests that compressing fractures reduces DRZ permeability, which could influence the rate of radionuclide transport and exchange with corrosive species in host rock groundwater that could accelerate waste package degradation. Less permeable shale host rock delays buffer swelling, consequently retarding DRZ permeability reduction as well as chemical transport within the barrier system.
In this report, we describe methods to enhance mechanical properties of a renewable thermoplastic rubber by incorporating functional graphene platelets into an equal-mass mixture of lignin, a feedstock from plant biomass, and a nitrile-butadiene rubber having 41 mol % acrylonitrile content (NBR41) that form a nanocomposite. Not all lignins yield mechanically strong material when they are combined with NBR41, and thus, limit its use for such rubbery products. Here, we evaluate different graphene oxides (GO) and reduced graphene oxides (rGO) dry-powders with different surface areas, functionality, and wettability in the synthesis of performance-enhanced nanocomposites of soft lignin/NBR41 matrix via a solvent-free, high shear reactive process. High surface area GO (HSGO) platelets with strong hydrophilicity exhibit good dispersion in the multiphase lignin/NBR41 composites with lignin dispersion varying from 200 to 2000 nm, resulting in superior reinforcement over other graphene derivatives. The addition of 1–4 wt % HSGO increased the tensile stress of the lignin/NBR41 thermoplastic rubber matrix by 60–160% (15–24 MPa) and the modulus by 200–700% (60–140 MPa). Scanning electron microscopy and small angle X-ray scattering results show that the well-dispersed HSGO platelets effectively disrupt the lignin-rich aggregates in NBR41 matrix, resulting in both strength and stiffness enhancements in the formed nanocomposites. This report on performance enhancement of lignin-based renewable polymers via the production of nanocomposite would increase potential for ‘finding the value of lignin’—a grand challenge associated with modern biorefineries.
The oxidation behavior of a Ni-10(wt%)Cr alloy under high-temperature O 2 conditions is investigated using transmission electron microscopy and first-principles calculations. Results reveal that chromium segregation plays a central role in driving the evolution of complex oxide phase structures during oxidation. At low Cr concentrations, Cr preferentially segregates to NiO grain boundaries or internal pores, substituting for Ni atoms and forming Ni(Cr)O solid solutions. As Cr content increases, enhanced diffusion promotes Cr penetration into the NiO lattice, leading to the formation of multiphase oxide structures. First-principles modeling corroborates these findings: at low Cr concentrations, Cr atoms favor surface and grain-boundary segregation, while higher concentrations lead to Cr aggregation within the NiO bulk. Furthermore, the integrated experimental-theoretical approach provides atomistic insights into Cr-mediated mass transport mechanisms during alloy oxidation and offers valuable guidance for controlling oxide growth kinetics and phase stability in Ni-Cr alloys, with implications for improving oxidation resistance in high-temperature structural applications.