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Most solid-state electrolytes exhibit significant structural disorder, which requires careful consideration when modeling the defect energetics. Here, we model the native defect chemistry of a disordered solid electrolyte, Li 10 GeP 2 S 12 .
The design of robust and efficient high-temperature thermochemical reactors and determination of operating conditions are critical steps toward enabling high-efficiency long-duration solar energy storage. This work presents a computational model for the thermal reduction of a metal oxide material (Mg-Mn-O) up to 1450 °C and the coupled complex transport phenomena in a novel tubular thermal reactor design that features the capability for a high extent-of-reduction (high energy storage density) and inherent heat recuperation. A one-dimensional model coupling counter-current gas–solid flow, two-phase heat transfer, thermochemical redox reactions, and species transport in a moving-bed reactor is developed. Simplified versions of the model are validated with published results in the literature for packed beds with both inert and reactive particles; the fully coupled model is also validated with experimental measurements of a moving-bed reactor in terms of local temperatures and oxygen release at the exit. Detailed comparisons on the effects of different boundary conditions in the reaction zone (prescribed wall temperature vs. heat flux conditions) and formulations based on a simple uniform flow assumption vs. plug flow using Ergun equation for gas flow are investigated. The results are compared with experimental measurements, and for all cases, the energy flow components in the reactor system and the thermal to chemical conversion efficiency and overall system efficiency are computed. Finally, the predicted high thermal-to-chemical efficiency ~95% and system efficiency ~30% agree with experimental measurements.
Properties of solid–liquid interfaces and surface charge characteristics mediate ionic and molecular transport through porous systems, affecting many processes such as separations. Herein, we report experiments designed to probe the electrochemical properties of solid–liquid interfaces using a model system of a single polyethylene terephthalate (PET) pore in contact with aqueous and propylene carbonate solutions of LiClO 4 . First, the existence and polarity of surface charges were inferred from current–voltage curves recorded when a pore was placed in contact with a LiClO4 concentration gradient. Second, the electro-osmotic transport of uncharged polystyrene particles through the PET pore provided information on the polarity and the magnitude of the pore walls’ zeta potential. Our experiments show that the PET pores become effectively positively charged when in contact with LiClO 4 solutions in propylene carbonate, even though in aqueous LiClO 4 , the same pores are negatively charged. Additionally, the electro-osmotic velocity of the particles revealed a significantly higher magnitude of the positive zeta potential of the pores in propylene carbonate compared to the magnitude of the negative zeta potential in water. The presented methods of probing the properties of solid–liquid interfaces are expected to be applicable to a wide variety of solid and liquid systems.
Pressure solution is an important process in the evolution of sedimentary rocks, which provide storage space for most of our petroleum resources. It directly influences the generation, migration, and storage of petroleum fluids in subsurface sedimentary rocks. Here, in this paper, we develop a pore-scale, mechanochemical model to demonstrate a possible positive feedback between the local porosity and pore surface stress, in which a higher local porosity causes a higher local pore surface stress, thus enhancing pressure solution and consequently further increasing the local porosity. Pore surface stress represents stress on a solid grain adjacent to a pore. Specifically, the pore-scale, mechanochemical model directly simulates the stress distribution over solid and pore surfaces using a finite element model. The dissolution of solids at the solid-pore interfaces under a far-from-equilibrium condition is simulated using a first-order kinetics model that accounts for the local stress distribution. The updated pore geometry, caused by pore surface dissolution, is then used in the stress simulation in the next numerical iteration. Two types of porous media, the Oriskany sandstone and an artificial porous medium with spherical pores, were tested in the mechanochemical simulation. The positive stress-porosity feedback during pressure solution was observed in both samples. In addition, the model quantitatively illustrated the distribution of local mineral dissolution rates on all pore surfaces, as well as its relation to the effective mineral dissolution rate of the entire sample. Based on the comparison between the two porous media, the local mineral dissolution was regulated by pore space distribution, geometry, and coalescence during pressure solution. This work is the first that uses direct, pore-scale numerical simulation to demonstrate the positive stress-porosity feedback during pressure solution, which has the potential to advance the understanding of the mechanical-chemical (MC) coupling in many geological processes that are relevant to subsurface energy systems, such as the recovery of petroleum hydrocarbons and geothermal energy.
Here, in this work, we calculate and study correlations of the Dicke model in the presence of qubit–qubit interaction. Whereas the analysis of correlations among its subsystems is essential for the understanding of corresponding critical phenomena and for performing quantum information tasks, the majority of correlation measures are restricted to bipartitions due to the inherent challenges associated with handling multiple partitions. To circunvent this we employ the calculation of Genuine Multipartite Correlations (GMC) based on the invariance of our model under particle permutation. We then quantify the correlations within each subpart of the system, as well as the percentage contribution of each GMC of order $k$, highlighting the many-body behaviors for different regimes of parameters. Additionally, we show that GMC signal both first- and second-order quantum phase transitions present in the model. Furthermore, as GMC encompasses both classical and quantum correlations, we employ Quantum Fisher Information (QFI) to detect genuine multipartite entanglement. Ultimately, we map the Dicke model with interacting qubits to spin in solids interacting with a quantum field of magnons, thus demonstrating a potential experimental realization of this model.
Rapid charging of Li-ion batteries is limited by lithium plating on graphite anodes, whereby Li+ ions are reduced to Li metal on the graphite particle surface instead of inserting between graphitic layers, which directly contributes to cell capacity loss because of the low reversibility of the Li plating/stripping process. Furthermore, precisely identifying the onset and amount of Li plating is therefore vital in order to remedy these issues. We demonstrate a titration technique with a detection limit of 20 nmol (5 × 10 –4 mAh) of Li that can be used to quantify inactive Li that remains on the graphite electrode after fast charging. The titration is extended to quantify the total amount of solid carbonate species and lithium acetylide (Li 2 C 2 ) within the solid–electrolyte interphase (SEI), and electrochemical modeling is used to determine the Li plating exchange current density (10 A/m 2 ) and stripping efficiency (65%) of plated Li metal on graphite. These techniques provide a highly accurate measure of the onset of Li plating and quantitative insight into graphite SEI evolution during fast charging.
The thermal energy storage (TES) system has the capability to efficiently preserve thermal energy directly derived from the energy source, minimizing any conversion losses. Especially latent heat storage offers distinct advantages, including a substantial increase in energy storage density and minimization of temperature fluctuations within the plants. However, the phase change material (PCM) employed in latent heat storage has low thermal conductivity. Consequently, various studies are being conducted to enhance heat transfer. One approach to enhance heat transfer involves utilizing metal foam to maximize the heat transfer area. However, modeling metal foam with its intricate structure is a challenging task in numerical analysis. For this reason, ongoing research focuses on simplifying the modeling of metal foam. Nevertheless, fully encompassing all the characteristics of actual metal foam proves to be a challenging task for the simplified analytical model. The objective of this paper is to interpret the simple lattice metal foam analysis model from the perspective of behavior induced by buoyancy. When comparing the analysis results of solid PCM and liquid PCM with the same thermal conductivity under changes in porosity and gravity direction, we conducted an analysis to discern the trends in effective thermal conductivity that are overestimated due to convection. In the analysis, a constant heat flux of 10 kW and a constant surface boundary condition of 350 K were applied, and a sensitivity study regarding the mesh was conducted. The results indicate that, from the perspective of gravity in the simple lattice model, the solid analysis yields an effective thermal conductivity 29-47% higher compared to the liquid analysis. Additionally, as porosity increases, there is an observed increase of 24-33% in effective thermal conductivity.
The subsurface mechanical damage (SSD) depth after grinding fused silica glass with a comprehensive set of sub-aperture fixed abrasive grinding tools [cup, wheel, belt, pad, and rotary face mill (with and without ultrasonics)] and process parameters has been statistically measured using the taper wedge technique and evaluated. Consistent with a previously reported grinding model [J. Non-Cryst. Solids 352, 5601–5617 (2006) Crossref , Materials Science and Technology of Optical Fabrication (Wiley & Sons, 2018)], based on the sliding indentation fracture by sliding particles or asperities where the normal load per particle determines the depth of the fracture (and ultimately the overall SSD depth), the dominant factor controlling SSD depth was found to be the abrasive size regardless of the tool type and process conditions. Compared to full aperture grinding methods, the overall SSD depth was higher using the sub-aperture tools, likely due to the higher effective pressure and higher load per particle distribution. Here, in addition to abrasive size, a significant reduction in SSD depth was achieved by: (1) reducing the load distribution on the abrasive particles via increase in contact area and/or decrease in mechanical loading; (2) using a more compliant host tool medium; and (3) in what we believe is a more novel way, using ultrasonics. Combining low abrasive size, larger contact area, and a compliant host, the 6 µm diamond in a resin matrix (Trizact) on a foam pad led to very low SSD depth (~ 4.6 µm), relatively fast grinding rate (186 mm 3 /h), and little or no figure degradation. This grinding tool/process is an attractive choice for final grind, resulting in significantly reduced polish out (i.e., “grey out”) times. With the rotary face mill tool, the use of ultrasonics consistently led to a SSD depth reduction (ranging from 17%–34%). A new fracture mechanics-based model, to the best of our knowledge, where the relevant normal load is parallel to the feed direction, has been developed to explain how ultrasonics leads to lower SSD depth. The key factors, supported by finite element stress analysis and load measurements, are (1) the initiation of fractures at higher z heights during the tool’s ultrasonic vertical oscillations, thus propagating less deep into workpiece; (2) reduction in load (and therefore reduction in fracture propagation distance) due to smaller tool-workpiece feed direction contact area (again caused by higher heights relative to depth of cut); (3) upward movement of the tool during oscillation leads to fracturing toward the surface instead into the depth; and finally (4) at tool’s lowest point of oscillation cycle, there may not be enough time for the fracture to propagate to its full length.
The behavior of porous materials under shock loading is a multi-scale problem bridging orders of magnitude across the macroscale geometry and the microscale pores. Under static loading, this problem is well understood, relating mechanisms of pore closure and crushing to the equivalent macroscale models. The dynamic response of porous solids under shock loading is related to the effects of viscoplasticity and micro-acceleration fields around the void boundaries. The significance of the micro-inertia effects in modeling the dynamic behavior of porous materials remains an open question. In this work, an experimental investigation on closed-cell porous aluminum with small porosity provides the evidence for the first time of micro-inertia’s fundamental role in describing the shock structure in these materials. Materials with different levels of porosity were manufactured using a modified process of additive manufacturing to achieve a mean pore size below 50$\mu$m. Plate impact experiments on porous aluminum samples were conducted at pressures in the range of 2 to 11 GPa. The structure of the steady shock was characterized as a function of porosity and shown to validate behavior revealed by an analytical approach (Czarnota et al. [J. Mech. Phys. Solids 107 (2017)]), highlighting the fundamental role of micro-inertia effects in such cases.
The majority of the ceramic solid electrolytes (LLZO, LATP) demonstrate polycrystalline grain/grain-boundary (G/GB) microstructure. Higher lithium (Li) concentration and lower mechanical stiffness result in current focusing at the GBs. Growth of Li dendrites through local inhomogeneities and subsequent short circuit of the cell is a major concern. Recent studies have revealed that bulk Li metal is a viscoplastic material that has low (~0.3 MPa) and high (~1.0 MPa) yield strength during deformation at smaller and larger rates of strain, respectively. It has been argued that during deposition at smaller current densities, due to its lower yield strength, Li metal should demonstrate plastic flow against stiff ceramic electrolytes, and Li dendrites will be prevented from penetrating through solid electrolytes. In this manuscript, a multiscale modeling framework has been developed for predicting properties of GBs and the bulk of ceramic electrolytes using atomistic calculations for input to mesoscale models. Using the parameters obtained from the atomistic simulations, the mesoscale model reveals that, given enough time, even at low charge rates, lithium dendrites can grow through the GBs of LLZO. The present multiscale model results also provide information regarding the dendrite growth velocity through LLZO.
The control of oxygen vacancy (V O ) formation is critical to advancing multiple metal-oxide-perovskite-based technologies. In this work, we report the construction of a compact linear model for the neutral V O formation energy in ABO 3 perovskites that reproduces, with reasonable fidelity, Hubbard-U-corrected density functional theory calculations based on the state-of-the-art, strongly constrained and appropriately normed exchange-correlation functional. We obtain a mean absolute error of 0.45 eV for perovskites stable at 298 K, an accuracy that holds across a large, electronically diverse set of ABO 3 perovskites. Our model considers perovskites containing alkaline-earth metals (Ca, Sr, and Ba) and lanthanides (La and Ce) on the A-site and 3d transition metals (Ti, V, Cr, Mn, Fe, Co, and Ni) on the B-site in six different crystal systems (cubic, tetragonal, orthorhombic, hexagonal, rhombohedral, and monoclinic) common to perovskites. Physically intuitive metrics easily extracted from existing experimental thermochemical data or via inexpensive quantum mechanical calculations, including crystal bond dissociation energies and (solid phase) reduction potentials, are key components of the model. Beyond validation of the model against known experimental trends in materials used in solid oxide fuel cells, the model yields new candidate perovskites not contained in our training data set, such as (Bi,Y)(Fe,Co)O 3 , which we predict may have favorable thermochemical water-splitting properties. The confluence of sufficient accuracy, efficiency, and interpretability afforded by our model not only facilitates high-throughput computational screening for any application that requires the precise control of V O concentrations but also provides a clear picture of the dominant physics governing V O formation in metal-oxide perovskites.
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Thermal conductivity is an important materials property related to heat transport, which is essential to many applications, ranging from thermoelectrics to nuclear reactor materials. High- quality thermal conductivity data is critical to these materials and their associated technologies, including the nuclear fuel materials like traditional oxide fuels and metallic uranium (U) fuels, e.g. U-Zr and U-Mo alloys. And thermal conductivity modeling is widely used to interpolate or extrapolate experimental data, to obtain high-quality thermal conductivity data over wide temperature and composition range, and to understand the impacts of different factors including defects and microstructures. However, a general thermal conductivity model for metal alloys which can work regrading different phase components is still missing. Also, for U alloys, a thermal conductivity model working with different phases is needed. Therefore, in this work, we developed thermal conductivity models for metal alloys, based on ab-initio calculations, semi-classical physics rules, and limited experimental data. The goals of these models are to help obtain high- quality thermal conductivity data within a little experimental input as possible, have models that are extendable to varying microstructures, including different types of irradiation effects, and provide mechanistic understanding of heat transfer in the modeled alloys. In this work, our model solves several challenges in the development. The DFT-BTE approach is applied to decrease the reliance on experimental data and make the model extendable to composition changes and defects. A practical and efficient way to combine the DFT inputs with physics rules is pointed out in this work too. A staged approach, which starts from simple cases of elemental metals, then extends to solid solutions, different compound phases, and multi-phase mixtures, is presented to work with the metal alloys in different phase components. Our models are demonstrated respectively on aU for elemental metal model, on U-Zr and U-Mo alloys in aU temperature range for multi-phase mixture model, on high-temperature U-Zr, U-Nb, and U-Mo alloys in the body-centered cubic phase for concentrated solid solution model, and on irradiated U-Mo alloys for irradiated metal alloy model. All models show great agreement with experimental data. In these demonstrations, our model shows its advantages compared to previous empirical fitting model. Our model requires fewer experimental data, due to the inputs from DFT. The quantitative insights into the different physical factors are provided in our model, as it incorporates the electron and phonon scattering mechanisms. Our model also can be extended to incorporate the contributions of point defects, grain boundaries, and noble gas bubbles, to integrate their effects on heat transfer. This model can serve as both a foundation for understanding the more complex thermal conductivity of realistic U alloy fuels and a useful tool to guide further modeling of thermal conductivity to aid materials and device design.
Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10 ⁻5 and 10 ⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.
In the event of cladding rupture, which could occur in light water reactor fuel assemblies during a loss-of-coolant accident (LOCA), fuel particles, along with fission gases, can be expelled into the reactor core from the fractured fuel rod. This expulsion of fragmented fuel particles, referred as fuel dispersal, is the subject of investigation to evaluate the safety implications of increasing fuel burnup in light water reactors, with a specific focus on fuel fragmentation, relocation, and dispersal. Particle trajectories and the resulting mass distribution of the settled particles within the reactor pressure vessel can pose a long-term cooling challenge for the reactor core. The fuel dispersal phenomenon is significantly influenced by the ejection characteristics of the fuel fragments, as well as the size and shape of the cladding rupture and fuel rod depressurization history during LOCA transients. In this study, the transport of fuel particles within a scaled 5 × 5 lattice of a pressurized water reactor rod bundle geometry is modeled through a two-fluid Eulerian framework that treats the gas and solid phases as interpenetrating continua. The required boundary conditions are evaluated from the fuel performance code BISON in a postulated large-break LOCA scenario. The modeling framework considers solid fuel particles as granular matter, interacting with the gaseous dry steam phase and fission gases through the governing interfacial momentum and energy exchange between the gas and solid phases. The simulation results provide the volume fraction of the solids settled on the bottom surface of the fuel bundle, quantifying the deposition within the bundle geometry.
Oxygen reduction reaction (ORR) is one of the most important electrochemical reactions. Starting from a common reaction intermediate *–O–OH, the ORR splits into two pathways, either producing hydrogen peroxide (H 2 O 2 ) by breaking the *–O bond or leading to water formation by breaking the O–OH bond. However, it is puzzling why many catalysts, despite the strong thermodynamic preference for the O–OH breaking, exhibit high selectivity for hydrogen peroxide. Moreover, the selectivity is dependent on the potential and pH, which remain not understood. Here, for this work, we develop an advanced first-principles model for effective calculation of the electrochemical reaction kinetics at the solid–water interface, which were not accessible by conventional models. Using this model to study representative catalysts for H 2 O 2 production, we find that breaking the O–OH bond can have a higher energy barrier than breaking *–O, due to the rigidity of the O–OH bond. Importantly, we reveal that the selectivity dependence on potential and pH is rooted into the proton affinity to the former/later O in *–O–OH. For single cobalt atom catalyst, decreasing potential promotes proton adsorption to the former O, thereby increasing the H 2 O 2 selectivity. In contrast, for the carbon catalyst, the proton prefers the latter O, resulting in a lower H 2 O 2 selectivity in acid condition. These findings explain the experiments and highlight the kinetic origins of the selectivity. Our work improves the understanding of ORR by uncovering the proton affinity as a new factor and provides a new model to effectively simulate the atomic-level kinetics of heterogeneous electrochemistry.
Nonlinear 3D magnetohydrodynamic (MHD) modeling of shattered pellet injection (SPI) in JET tokamak plasmas is performed with the JOREK code. The study focuses on the validation of simulation result with respect to experimental observations, addressing in particular figures of merit for the efficiency of the SPI, as a technique to mitigate thermal loads by injecting radiative impurities like neon during the thermal quench (TQ) phase of disruptions. A set of JET pulses with neon/deuterium atomic mixture ratio in the shattered pellet varying from 10% to 100% provides the experimental data to be compared with. Simulations using different models for the ablation of solid fragments and the radiation of neon impurities are considered. Synthetic diagnostics are employed for a direct quantitative comparison with key JET diagnostic systems providing in particular radiation, electron temperature and density, and magnetic measurements. 3D radiation structures from MHD simulations are analyzed to estimate the toroidal asymmetry of radiation, which is difficult to measure in JET due to the presence of only two toroidally displaced multi-channel bolometry systems. JOREK simulations show that, before the TQ, the bulk of the radiation is concentrated in a toroidal region enclosing the radiating fragments that is missed by both multi-channel bolometers, suggesting a possible underestimation of the total radiation by such diagnostic systems in the pre-TQ phase. On the other hand, JOREK predicts that during the TQ the radiation becomes more toroidally symmetric, with positive implications for the heat load to plasma facing components.
Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulating phenomenological constitutive laws that can accurately capture the observed material response. However, even though neural network-based constitutive laws have been shown to generalize proficiently, the generated representations are not easily interpretable due to their high number of trainable parameters. Sparse regression approaches exist that allow for obtaining interpretable expressions, but the user is tasked with creating a library of model forms which by construction limits their expressiveness to the functional forms provided in the libraries. Here, in this work, we propose to train regularized physics-augmented neural network-based constitutive models utilizing a smoothed version of $L^0$-regularization. This aims to maintain the trustworthiness inherited by the physical constraints, but also enables interpretability which has not been possible thus far on any type of machine learning-based constitutive model where model forms were not assumed a priori but were actually discovered. During the training process, the network simultaneously fits the training data and penalizes the number of active parameters, while also ensuring constitutive constraints such as thermodynamic consistency. We show that the method can reliably obtain interpretable and trustworthy constitutive models for compressible and incompressible hyperelasticity, yield functions, and hardening models for elastoplasticity, using synthetic and experimental data. This work aims to set a new paradigm for interpretable machine learning models in the broad area of solid mechanics where low and limited data is available along with prior knowledge of physical constraints that the learned maps need to obey. This paradigm can potentially be extended to a broader spectrum of scientific exploration.