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Lithium metal as an anode has been widely accepted due to its higher negative electrochemical potential and theoretical capacity. Nevertheless, the existing safety and cyclability issues limit lithium metal anodes from practical use in high-energy density batteries. Repeated Li deposition and dissolution processes upon cycling lead to the formation of dendrites at the interface which results in reduced Li availability for electrochemical reactions, disruption in Li transport through the interface and increased safety concerns due to short circuiting. Here, we demonstrate a novel strategy using Ionic Liquid Crystals (ILCs) as the electrolyte cum pseudo-separator to suppress dendrite growth with their anisotropic properties controlling Li-ion mass transport. A thermotropic ILC with two-dimensional Li-ion conducting pathways was synthesized and characterized. Microscopic and spectroscopic analyses elucidate that the ILC formed with a smectic A phase, which can be utilized for wide temperature window operation. The results of electrochemical studies corroborate the efficacy of ILC electrolytes in mitigating dendrite formation even after 850 hours and it is further substantiated by numerical simulation and the mechanism involved in dendritic suppression was deduced.

Dendrite growth affects material systems across applications as diverse as lithium batteries, organic light emitting diodes, turbine blades, and biological sensors. Their unique crystal structure a...

Many materials systems comprise complex structures where multiple materials are integrated to achieve a desired performance. Often in these systems, it is a combination of both the materials and their structure that dictate performance. Here the authors layout an integrated computational–statistical–experimental methodology for hierarchical materials systems that takes a holistic design approach to both the material and structure. The authors used computational modeling of the physical system combined with statistical design of experiments to explore an activated carbon adsorption bed. The large parameter space makes experimental optimization impractical. Instead, a computational–statistical approach is coupled with physical experiments to validate the optimization results.

Fixed bed adsorption is an economical method of removing harmful gases, such as SO2 and CO2, from industrial flue gas. It is possible to reduce the cost and environmental impact of fixed bed adsorption by repurposing waste materials to be used as adsorbents, such as semi-coke derived from oil shale, a possible alternative to fossil fuels. Fixed bed adsorption systems are difficult and time consuming to characterize experimentally, especially on large scales. Computational fluid dynamics (CFD) can expand researchers understaning of how these systems are affected by material selection and operating conditions. This study uses CFD to characterize fixed bed adsorption of SO2 and CO2. The research primarily focuses on SO2 adsorption on semi-coke, with an extension to CO2 adsorption on commercial carbon. The CFD modeling was able to describe the amount of the pollutants each material was able to adsorb over time based on a variety of inputs on a larger scale than experimental research. The model was further able to give more detailed comparisons of materials and operating conditions than the experiments, particularly the SO2 and semi-coke system.

The homogeneous mixture method (HMM) is a popular class of models used in the computational prediction of cavitation. Several cavitation models have been developed for use with HMM to govern the development and destruction of vapor in a fluid system. Two models credited to Kunz and Schnerr–Sauer are studied in this paper. The goal of this work is to provide an assessment of the two cavitation submodels in their ability to predict cavitation in nozzle flow. Validation data were obtained via experiments which employ both passive cavitation detection, (PCD) via acoustic sensing and optical cavitation detection (OCD) via camera imaging. The experiments provide quantitative information on cavitation inception and qualitative information on the vapor in the nozzle. The results show that initial vapor formation is not predicted precisely but within reason. A sensitivity analysis of the models to input parameters shows that the Schnerr–Sauer method does not depend upon the estimation of nuclei size and number density. Small changes in the vapor formation rate but not the total vapor volume can be seen when weighting parameters are modified. In contrast, changes to the input parameters for the Kunz model greatly change the final total vapor volume prediction. The assessment also highlights the influence of vapor convection within the method. Finally, the analysis shows that if the fluid and nozzle walls do not support nuclei larger than 40 μm, the methods would still predict cavitation when indeed there would be none in practice.

Because of its ultrahigh specific capacity, lithium metal holds great promise for revolutionizing current rechargeable battery technologies. Nevertheless, the unavoidable formation of dendritic Li, as well as the resulting safety hazards and poor cycling stability, have significantly hindered its practical applications. A mainstream strategy to solve this problem is introducing porous media, such as solid electrolytes, modified separators, or artificial protection layers, to block Li dendrite penetration. However, the scientific foundation of this strategy has not yet been elucidated. Herein, using experiments and simulation we analyze the role of the porous media in suppressing dendritic Li growth and probe the underlying fundamental mechanisms. It is found that the tortuous pores of the porous media, which drastically reduce the local flux of Li+ moving toward the anode and effectively extend the physical path of dendrite growth, are the key to achieving the nondendritic Li growth. On the basis of the t...

In previous work we developed and implemented a sub-grid model for the efficient simulation of heat transfer in gas-particle flows around immersed horizontal cylinders. In this study we apply verification, validation, and uncertainty quantification methods to the developed model to rigorously examine its capabilities and limitations. Numerical verification with small, unit-cell problems shows excellent transient and steady-state behavior. Validation of a bubbling bed and a turbulent bed showed good agreement with high-resolution simulations. To quantify the error of the constitutive model predictions two methods were used to calculate confidence intervals, showing an error of approximately ± 20%, well within the range of typical Nusselt number approximations. The sub-grid model was applied to a conceptual pilot-scale 1 MWe carbon capture reactor to compare with alternative modeling methods. Results show fair predictions of hydrodynamics, heat transfer, and carbon capture rates with significant savings in computational runtimes.

Biobased fuels resulting from the pyrolysis of lignocellulosic materials suffer from several key issues. Only a portion of the biomass feedstock is converted to pyrolysis oil, and only a portion of compounds in that oil represent desired end products. Bio-oil contains myriad oxygenated and aromatic compounds, many of which form tars and impart high acidity, viscosity, and instability. This necessitates substantial upgrading to generate a stable, valuable product. The inclusion of in situ catalysts during pyrolysis can improve the pyrolysis oil by promoting the cracking of tarry compounds and formation of smaller furans and phenols. This study examines the impact of in situ transition metal catalysts on cellulose pyrolysis, quantifying changes in bio-oil composition and non-condensable gas generation. Cellulose was wet impregnated with six different metal acetates at a concentration of 0.05 M and pyrolyzed at 600 °C, and some samples additionally pyrolyzed at 350 °C. The metals enhanced devolatilization, increasing hydrogen gas production at high and low temperatures and improved bio-oil yields while decreasing the average molecular weight of the oil compounds. Nickel proved to be the most effective at generating hydrogen gas and producing a wider array of light-weight bio-oil compounds. Copper aided dehydrogenation at lower temperatures and began the initial stages of primary pyrolysis by generating levoglucosenone and glucopyranose. These findings shed light on metal-biomass interactions and contribute to the growing body of knowledge of in situ bio-oil upgrading. By understanding how catalysts improve bio-oils we can generate high-density and cleaner-burning liquid fuels to displace the use of fossil fuels.

The formation of dendrites on the anode surface of metal air batteries, such as the Li-air battery, causes significant decreases in performance over the lifetime of the battery and poses safety concerns due to short circuiting. Predictive computational methods are used to investigate novel electrolyte materials which will reduce dendrite growth, in particular liquid crystal materials for Li-air electrolytes. The literature on liquid crystal electrolytes was surveyed for materials data and used to develop a training set of liquid crystal compounds. These compounds were then used as a knowledge base to develop property structure relations for the clearing and melting temperature for liquid crystals, which are a critical design parameter for the design of novel electrolytes. This was accomplished using standard artificial neural networks trained on molecular fingerprints and convolutional neural networks (CNNs) trained on compound images. Transfer learning was also demonstrated to boost predictive performance through pre-training neural networks on larger pre-existing compound datasets. The results show that CNNs achieve comparable accuracy compared to molecular fingerprints, and that both multilayer perceptrons and CNNs can benefit from transfer learning. This study is the first where transfer learning in CNNs aids in the prediction of one experimental property in a small data regime, using data of another experimental property.

Summary: With numerous reports on protecting films for stable lithium (Li) metal electrodes, the key attributes for how to construct these efficient layers have rarely been fully investigated. Here, we report a rationally designed hybrid protective layer (HPL) with each component aligning with one key attribute; i.e., cross-linked poly(dimethylsiloxane) (PDMS) enhances flexibility, polyethylene glycol (PEG) provides homogeneous ion-conducting channels, and glass fiber (GF) affords mechanical robustness. A significant improvement of the electrochemical performance of HPL-modified electrodes can be achieved in Li/HPL@Cu half cells, HPL@Li/HPL@Li symmetric cells, and HPL@Li/LiFePO4 full cells. Even with an industrial standard LiFePO4 cathode (96.8 wt % active material), the assembled cell still exhibits a capacity retention of 90% after 100 cycles at 1 C. More importantly, the functionality of each component has been studied comprehensively via electrochemical and physical experiments and simulations, which will provide useful guidance on how to construct efficient protective layers for next-generation energy storage devices.

Simulating full-scale heated fluidized bed reactors can provide invaluable insight to the design process. Such simulations are typically computationally intractable due to their complex multi-physics and various length-scales. While it may be possible to simulate some large-scale systems, they require significant computing resources and do not lend themselves well to design optimization methods. To overcome these problems coarse-grid simulations can be used with supplementary constitutive sub-grid models to approximate the unresolved physics. This study details the development, implementation, and verification of a sub-grid model for heat transfer in gas-particle flows with immersed horizontal cylinders. Using the two-fluid model for multiphase flow, small periodic unit-cell domains were simulated over a wide range of flow and geometry conditions. The results were filtered and fit using nonlinear regression to build a Nusselt correlation based on the solids fraction, solids velocity, cylinder geometry (diameter and spacing), and the Peclet number. The proposed model is highly nonlinear and includes power-law contributions from each parameter. The model was verified using a nearly orthogonal experiment design where the input parameters were varied randomly to generate combinations not previously considered. The predicted filtered Nusselt numbers agreed well with the observed (simulated) values. Work is on-going to further expand the capabilities of the model, including 3D simulations, vertical cylinders, and uncertainty quantification.

Dendrite formation on the anode surface of a battery is closely related to the safety and capacity of high energy density batteries, thus suppressing dendrite growth will significantly improve the performance of batteries. Many experimental reports reveal that convection near the dendrite nucleation site can change the local mass transport, and ultimately affect dendrite growth. Investigation of the convection effect in batteries will guide the development of strategies to suppress dendrite growth in a convective electrolyte. Most of the existing electro-convection computational models for dendrite growth studies are based on Eulerian frameworks. These methods have difficulty modeling the moving boundaries associated with dendrite growth and are less computationally efficient in simulating convective fluid motion. In this paper we adopt a mesh-free particle based Lagrangian method to address the challenges of previous grid based Eulerian electro-convection models. The developed model is verified by comparison to analytical solutions, including verification of ion migration and the electric potential. Simulation results show that the predicted dendrite growth and electro-convective flow patterns compare well with experimental results during early dendrite growth stages. Parametric studies reveal that low viscosity electrolytes suppress the dendrite growth by increasing the mass transport of ions near the anode/electrolyte interface.

Studies have shown that the lifetime and performance of lithium batteries are greatly influenced by the charge/discharge cycles of the battery. The number of cycles over which a battery operates depends on the rate and depth of charge/discharge. Degradation in battery performance over multiple cycles is directly related to dendrite growth at the electrode-electrolyte interface in the battery. Understanding how cycling effects dendrite growth rates and morphology requires resolution of the chemical-physical processes at the electrode-electrolyte interface. In this study, a numerical model of dendrite growth at this electrode-electrolyte interface over multiple charge/discharge cycles is presented. In this work, dendrite growth over multiple charge/discharge cycles is simulated at the interfacial level where the effects on dendrite growth rate and morphology can be resolved. The simulations are able to predict qualitative dendrite morphologies presented in experimental studies, and the effects of fast charging scenarios on dendrite growth rate and morphology are discussed.

The constant current constant voltage charging protocol has been the standard for battery charging because of its simplicity and ease of implementation. However, with the continued integration of batteries into more complex electronics, i.e. electric vehicles, and medical devices, customizing charging schemes based on the particular need has become more appealing. This is especially true when fast charging is necessary. A major challenge in adopting alternative charging protocols is dendritic growth along the anode surface. The dendrites cause safety and efficiency issues. To mitigate the dendritic growth, a variety of specialized charging protocols, i.e. pulse and boost, have been proposed. Through computational modeling, a direct investigation can be conducted to determine which of the parameters for charging must be balanced in order to rapidly charge the battery and suppress dendrite growth. These parameters include the magnitude of the pulsed charge as well as the frequency. Direct observations of dendritic growth can be used to inform which charging protocols will lead to high performance and longer cycle life.

Cavitation inside fuel injector nozzles has been linked not only to erosion of the solid surface, but also to improved spray atomization. To quantify the effects of the resulting occurrences, the prediction of cavitation through computational modeling is vital. Homogeneous mixture methods (HMM) make use of a variety of cavitation sub-models such as those developed by Kunz, Merkle, and Schnerr-Sauer, to describe the phase change from liquid to vapor and vice-versa in the fluid system. The aforementioned cavitation models all have several free-tuning parameters which have been shown to affect the resulting prediction for vapor volume fraction. The goal of the current work is to provide an assessment of the Kunz and Schnerr-Sauer cavitation models. Validation data have been obtained via experiments which employ both acoustic techniques (passive cavitation detection, or PCD) and optical techniques (optical cavitation detection, or OCD). The experiments provide quantitative information on cavitation inception and qualitative information as to overall vapor fraction as a function of flow rate, and nozzle geometry. It is shown that inception is fairly well captured but the amount of vapor predicted is far too low. A sensitivity analysis on the tuning parameters in the cavitation models leads to some explainable trends, however, several parameter sweeps results in outlier predictions. Recommendations for their usability and suggestions for improvement are presented.

Dendrite formation on the electrode surface of high energy density lithium (Li) batteries causes safety problems and limits their applications. Suppressing dendrite growth could significantly improve Li battery performance. Dendrite growth and morphology is a function of the mixing in the electrolyte near the anode interface. Most research into dendrites in batteries focuses on dendrite formation in isotropic electrolytes (i.e., electrolytes with isotropic diffusion coefficient). In this work, an anisotropic diffusion reaction model is developed to study the anisotropic mixing effect on dendrite growth in Li batteries. The model uses a Lagrangian particle-based method to model dendrite growth in an anisotropic electrolyte solution. The model is verified by comparing the numerical simulation results with analytical solutions, and its accuracy is shown to be better than previous particle-based anisotropic diffusion models. Several parametric studies of dendrite growth in an anisotropic electrolyte are performed and the results demonstrate the effects of anisotropic transport on dendrite growth and morphology, and show the possible advantages of anisotropic electrolytes for dendrite suppression.

4'-Ethynyl-2-fluoro-2'-deoxyadenosine (EFdA) is a nucleoside analog that, unlike approved anti-human immunodeficiency virus type 1 (HIV-1) nucleoside reverse transcriptase inhibitors, has a 3'-OH and exhibits remarkable potency against wild-type and drug-resistant HIVs. EFdA triphosphate (EFdA-TP) is unique among nucleoside reverse transcriptase inhibitors because it inhibits HIV-1 reverse transcriptase (RT) with multiple mechanisms. (a) EFdA-TP can block RT as a translocation-defective RT inhibitor that dramatically slows DNA synthesis, acting as a de facto immediate chain terminator. Although non-translocated EFdA-MP-terminated primers can be unblocked, they can be efficiently converted back to the EFdA-MP-terminated form. (b) EFdA-TP can function as a delayed chain terminator, allowing incorporation of an additional dNTP before blocking DNA synthesis. In such cases, EFdA-MP-terminated primers are protected from excision. (c) EFdA-MP can be efficiently misincorporated by RT, leading to mismatched primers that are extremely hard to extend and are also protected from excision. The context of template sequence defines the relative contribution of each mechanism and affects the affinity of EFdA-MP for potential incorporation sites, explaining in part the lack of antagonism between EFdA and tenofovir. Changes in the type of nucleotide before EFdA-MP incorporation can alter its mechanism of inhibition from delayed chain terminator to immediate chain terminator. The versatility of EFdA in inhibiting HIV replication by multiple mechanisms may explain why resistance to EFdA is more difficult to emerge.

Advanced multiscale modeling and simulation have the potential to dramatically reduce the time and cost to develop new carbon capture technologies. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry, and universities that is developing, demonstrating, and deploying a suite of such tools, including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamics (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk-analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process. The resulting process informs the development of process control systems and more detailed simulations of potential equipment to improve the design and reduce scale-up risk. Quantification and propagation of uncertainty across scales is an essential part of these tools and models.

Within the field of computational fluid dynamics (CFD), uncertainty quantification (UQ) is becoming increasingly important. Reporting simulation results without uncertainties can be misleading and potentially dangerous. In this paper we considered an isothermal, non-reacting bubbling fluidized bed with immersed horizontal tubes as a test problem for implementing a CFD UQ framework. While all CFD model input parameters have some inherent uncertainties associated with them, we focused only on those that have been demonstrated as important in previous studies and are difficult to quantify. These include coefficients of restitution, friction angles, packed bed void fractions, and drag models. Statistical UQ techniques, including sensitivity analysis and Bayesian calibration, were used to analyze the system. The sensitivity analysis results suggested that the friction angles for solid-solid interactions and drag models had significant effects on bubble frequency and phase fraction. From the calibration procedure, a statistical response surface model (emulator) was developed to explore the state-space of the model parameters. The resulting posterior distributions of the model parameters identified low friction angles for solid-solid interactions and the Wen-Yu correlation for the drag model as the optimal model input parameters and values (i.e., values that could have plausibly reproduced the experimental results). The remaining parameters were found to be non-influential. These results are currently being implemented in simulations of a bench-scale carbon capture system.

Dendrite growth is an ongoing challenge on the anode surface in lithium metal batteries. Over multiple charge and discharge cycles dendritic growth leads to a decrease in battery performance and increased safety issues due to short circuiting. The morphology and growth rate of dendrites depend on a number of factors including the rate and depth of charge/discharge. In this work a computational model of dendrite growth at the interface is used to investigate the effects of cycling on dendrite growth rate and morphology. The model resolves the interfacial chemical-physical processes and simulates multiple charge and discharge cycles. Results show that dendrite growth depends on both the charging and discharging cycles, which can lead to heterogeneous growth patterns based on the levels of deposition and dissolution of lithium during the cycles.

Computational fluid dynamics (CFD) simulations are used to investigate a low temperature post-combustion carbon capture reactor. The CFD models are based on a small scale solid sorbent carbon capture reactor design from ADA-ES and Southern Company. The reactor is a fluidized bed design based on a silica-supported amine sorbent. CFD models using both Eulerian–Eulerian and Eulerian–Lagrangian multi-phase modeling methods are developed to investigate the hydrodynamics and adsorption of carbon dioxide in the reactor. Models developed in both FLUENT® and BARRACUDA are presented to explore the strengths and weaknesses of state of the art CFD codes for modeling multi-phase carbon capture reactors. The results of the simulations show that the FLUENT® Eulerian–Lagrangian simulations (DDPM) are unstable for the given reactor design; while the BARRACUDA Eulerian–Lagrangian model is able to simulate the system given appropriate simplifying assumptions. FLUENT® Eulerian–Eulerian simulations also provide a stable solution for the carbon capture reactor given the appropriate simplifying assumptions.

In this paper we investigate transport limitations in the electrodes of lithium-air batteries through computational modeling. We use meso-scale models to consider the effects of dendrites on the current and potential at the anode surface, and to investigate the effects of reaction and transport parameters on the formation of precipitates in the cathode. The formation of dendrites on the anode surface during cycling reduces the transport of ions and can lead to short circuits in the cell. Growth of precipitates in the cathode reduces the specific capacity of the cell due to surface passivation and pore clogging. Both of these degradation mechanisms depend on meso-scale phenomena, such as the pore-scale reactive transport in the cathode. To understand the effects of the meso-scale transport and precipitation on the performance and lifetime of Li-air batteries, meso-scale modeling is needed that is able to resolve the electrodes and their microstructures.

Over their designed lifetime, high-temperature electrochemical devices, such as solid oxide fuel cells (SOFCs), can experience degradation in their electrochemical performance due to environmental conditions, operating conditions, contaminants, and other factors. Understanding the different degradation mechanisms in SOFCs and other electrochemical devices is essential to reducing performance degradation and increasing the lifetimes of these devices. In this paper, SOFC degradation mechanisms are evaluated, and a damage model is presented that describes performance degradation in SOFCs due to damage or degradation in the SOFC electrodes. A degradation classification scheme is presented, dividing the various SOFC electrode degradation mechanisms into categories based on their physical effects on the SOFC. The damage model and classification method are applied both to sulfur poisoning and antimony poisoning, which occur in the SOFC anode. For sulfur poisoning, the model can calculate degradation in SOFC performance based on the operating temperature of the fuel cell and the concentration of gaseous sulfur species in the anode. For antimony poisoning, the effects of nickel consumption from the anode matrix are investigated.

Building energy models provide valuable insight into energy use in commercial and residential buildings based on architecture, materials and thermal loads. They are used in the design of new buildings and retrofitting to increase the efficiency of older buildings. The accuracy of these models is crucial to reducing energy use in the US and building a sustainable energy future. In addition to the architecture and thermal loads, building energy models also must account for the effects of the building's occupants on energy use. Traditionally simple schedule based methods have been used to account for the effects of occupants. However, newer research has shown that these methods often result in large differences between the modeled and actual energy use of buildings. In this paper we discuss building energy models and their accuracy in predicting energy use. In particular we focus on the different types of validation methods which have been used to investigate the accuracy of building energy models and how they account for (or do not) the effects of occupants. We also review newer work on stochastic methods for estimating the effects of occupants on energy use and discuss the improvements necessary to increase the accuracy of building energy models.

The presence of cavitation inside fuel injector nozzles has been linked not only to damage associated with cavity collapse near the walls, but also more intriguingly to improved spray atomization. Previous studies have shown that cavitation is associated with increased spray angle. Our goal is to investigate the underlying mechanics. In this talk we describe our initial efforts to employ both acoustic techniques (passive cavitation detection, or PCD) and optical techniques (optical cavitation detection, or OCD) to characterize nozzle cavitation. Experiments are conducted with acrylic nozzles of various geometry. Unfocused single element transducers are used for PCD, while digital imaging is used for OCD. Cavitation onset thresholds and development are studied as functions of flow rate, nozzle geometry, and upstream fluid preparation. Cavitation characterization results will be compared with an in-house computational code being developed to model cavitation in fuel injectors.

We report key mechanistic differences between the reverse transcriptases (RT) of human immunodeficiency virus type-1 (HIV-1) and of xenotropic murine leukemia virus-related virus (XMRV), a gammaretrovirus that can infect human cells. Steady and pre-steady state kinetics demonstrated that XMRV RT is significantly less efficient in DNA synthesis and in unblocking chain-terminated primers. Surface plasmon resonance experiments showed that the gammaretroviral enzyme has a remarkably higher dissociation rate (k(off)) from DNA, which also results in lower processivity than HIV-1 RT. Transient kinetics of mismatch incorporation revealed that XMRV RT has higher fidelity than HIV-1 RT. We identified RNA aptamers that potently inhibit XMRV, but not HIV-1 RT. XMRV RT is highly susceptible to some nucleoside RT inhibitors, including Translocation Deficient RT inhibitors, but not to non-nucleoside RT inhibitors. We demonstrated that XMRV RT mutants K103R and Q190M, which are equivalent to HIV-1 mutants that are resistant to tenofovir (K65R) and AZT (Q151M), are also resistant to the respective drugs, suggesting that XMRV can acquire resistance to these compounds through the decreased incorporation mechanism reported in HIV-1.

This paper presents a hybrid model for transport in connected macro-pores in porous media. A pore-scale model is used to parameterize the hybrid model. The hybrid model explicitly models the advection and diffusion of species in the connected macro-pores and treats the porous media around the connected macro-pores as a continuum with effective transport properties. The pore-scale model is used to calculate the effective transport properties of the porous continuum. This approach negates the need to calibrate the hybrid model against experimental data, which is common for continuum-scale models of porous media, and allows an arbitrary microstructure to be considered. The paper presents the multi-scale modeling approach along with the details of the hybrid and pore-scale models. Validation of the model is also presented along with several case studies investigating the applicability of the multi-scale modeling approach to different geometries and transport conditions. The case studies show that the multi-scale modeling approach is accurate for various connected macro-pore geometries given that the porosity of the porous medium around the connected macro-pores is sufficiently small. The accuracy of the hybrid model decreases with increasing porosity of the matrix.

This paper describes a distributed electrochemistry model of the solid oxide fuel cell (SOFC) electrodes and electrolyte. The distributed electrochemistry (DEC) model solves the transport, reactions, and electric potential through the thickness of the SOFC electrodes. The DEC model allows the local conditions within the electrodes to be studied and allows for a better understanding of how electrochemical and microstructural parameters affect the SOFC performance. In this paper the governing equations and implementation of the DEC model are presented along with several case studies which are used to investigate the sensitivity of the SOFC performance to the microstructural and electrochemical parameters of the cathode and to explore methods of improving the electrochemical performance of the SOFC cathode.

We present a pore-scale model of a solid oxide fuel cell (SOFC) cathode. Volatile chromium species are known to migrate from the current collector of the SOFC into the cathode where over time they decrease the voltage output of the fuel cell. A pore-scale model is used to investigate the reactive transport of chromium species in the cathode and to study the driving forces of chromium poisoning. A multi-scale modeling approach is proposed which uses a cell level model of the cathode, air channel and current collector to determine the boundary conditions for a pore-scale model of a section of the cathode. The pore-scale model uses a discrete representation of the cathode to explicitly model the surface reactions of oxygen and chromium with the cathode material. The pore-scale model is used to study the reaction mechanisms of chromium by considering the effects of reaction rates, diffusion coefficients, chromium vaporization, and oxygen consumption on chromium's deposition in the cathode. The study shows that chromium poisoning is most significantly affected by the chromium reaction rates in the cathode and that the reaction rates are a function of the local current density in the cathode.

We present a novel smoothed particle hydrodynamics (SPH) method for diffusion equations subject to Neumann and Robin boundary conditions. The Neumann and Robin boundary conditions are common to many physical problems (such as heat/mass transfer), and can prove challenging to implement in numerical methods when the boundary geometry is complex. The new method presented here is based on the approximation of the sharp boundary with a diffuse interface and allows an efficient implementation of the Neumann and Robin boundary conditions in the SPH method. The paper discusses the details of the method and the criteria for the width of the diffuse interface. The method is used to simulate diffusion and reactions in a domain bounded by two concentric circles and reactive flow between two parallel plates and its accuracy is demonstrated through comparison with analytical and finite difference solutions. To further illustrate the capabilities of the model, a reactive flow in a porous medium was simulated and good convergence properties of the model are demonstrated.

The volatility of crude oil prices incentivizes the use of domestic alternative fossil fuel sources such as oil shale. For ex situ oil shale retorting to be economically and environmentally viable, we must convert the copious amounts of semi-coke waste to an environmentally benign, useable by-product. Using acid and acid + base treatments, we increased the surface area of the semi-coke samples from 15 m2/g (pyrolyzed semi-coke) to upwards of 150 m2/g for hydrochloric acid washed semi-coke. This enhancement in porosity and surface area is accomplished without high temperature treatment, which lowers the overall energy required for such a conversion. XRD analysis confirms that chemical treatments removed the majority of dolomite while retaining other carbonate minerals and maintaining carbon contents of approximately 10%, which is greater than many fly ashes that are commonly used as sorbent materials. SO2 gas adsorption isotherm analysis determined that a double HCl treatment of semi-coke produces sorbents for flue gas treatment with higher SO2 capacities than commonly used fly ash adsorbents. Computational fluid dynamics modeling indicates that the sorbent material could be used in a fixed bed reactor to efficiently remove SO2 from the gas stream.

This paper examines the electrochemical and direct internal steam–methane reforming performance of the solid oxide fuel cell when subjected to pressurization. Pressurized operation boosts the Nernst potential and decreases the activation polarization, both of which serve to increase cell voltage and power while lowering the heat load and operating temperature. A model considering the activation polarization in both the fuel and the air electrodes was adopted to address this effect on the electrochemical performance. The pressurized methane conversion kinetics and the increase in equilibrium methane concentration are considered in a new rate expression. The models were then applied in simulations to predict how the distributions of direct internal reforming rate, temperature, and current density are effected within stacks operating at elevated pressure. A generic 10 cm counter-flow stack model was created and used for the simulations of pressurized operation. The predictions showed improved thermal and electrical performance with increased operating pressure. The average and maximum cell temperatures decreased by 3% (20 °C) while the cell voltage increased by 9% as the operating pressure was increased from 1 to 10 atm.

It has been shown that hydrodynamic cavitation within fuel injectors plays a significant role in their performance, with the desirable effect of broadening the resultant fuel spray. Experiments are challenging owing to the relatively small geometries, high pressure, and high Reynold’s number (Re) associated with such flows. Previous studies have observed cavitation in optically transparent nozzles at slower flows. By utilizing acoustic and vibration measurement techniques cavitation activity may be measured in a steel fuel injector at more practical Re flows used in applications. We report here experimental measurements taken using a laser vibrometer and a commercial fuel injector. Previous studies have demonstrated a resonant frequency shift as a function of injection pressure. Among competing hypotheses, our working hypothesis is that this shift is the result of mass unloading of cantilever mode oscillations of the fuel injector. The dynamic void fraction caused by cavitation activity within the fuel injector can then be inferred from the measured frequency shift. We report measurements of mode shapes and frequencies for static and flowing fuel injectors as functions of the flow rate.It has been shown that hydrodynamic cavitation within fuel injectors plays a significant role in their performance, with the desirable effect of broadening the resultant fuel spray. Experiments are challenging owing to the relatively small geometries, high pressure, and high Reynold’s number (Re) associated with such flows. Previous studies have observed cavitation in optically transparent nozzles at slower flows. By utilizing acoustic and vibration measurement techniques cavitation activity may be measured in a steel fuel injector at more practical Re flows used in applications. We report here experimental measurements taken using a laser vibrometer and a commercial fuel injector. Previous studies have demonstrated a resonant frequency shift as a function of injection pressure. Among competing hypotheses, our working hypothesis is that this shift is the result of mass unloading of cantilever mode oscillations of the fuel injector. The dynamic void fraction caused by cavitation activity within the fuel in...

Adequately resolving the hydrodynamics and heat transfer in gas-solid flow simulations typically requires computational grids on the order of 1–10 particle diameters. This requirement is not feasible for most full-scale applications. To overcome these impracticalities, we consider a sub-grid filtering approach where the microscopic heat transfer mechanisms are approximated through coarse grid simulations using constitutive relations. Using the open source CFD code Multiphase Flow with Interphase Exchanges (MFIX), we simulate a periodic unit cell domain with immersed horizontal heat transfer cylinders under varying solid-phase fractions and temperatures. The simulation results are averaged over the domain and are used to fit functional forms describing relations between the flow properties and input conditions. The result is a constitutive function that is added as a source term to the solid-phase energy conservation equation to approximate the effective heat transfer between the cylinders and flow with coarse grid simulations.Copyright © 2014 by ASME

As the effects of climate change continue to rise with increasing carbon dioxide emission rates, it is imperative that we develop an efficient method for carbon capture. This paper outlines the framework used to break down a large, complex carbon capture system into smaller unit problems for model validation, and uncertainty quantification. We use this framework to investigate the uncertainty and sensitivity of the hydrodynamics of a bubbling fluidized bed. Using the open-source computational fluid dynamics code MFIX we simulate a bubbling fluidized bed with an immersed horizontal tube bank. Mesh resolution and statistical steady state studies are conducted to identify the optimal operating conditions. The preliminary results show good agreement with experimental data from literature. Employing statistical sampling and analysis techniques we designed a set of simulations to quantify the sensitivity of the model to model parameters that are difficult to measure, including: coefficients of restitution, friction angles, packed bed void fraction, and drag models. Initial sensitivity analysis results indicate that no parameters may be omitted. Further uncertainty quantification analysis is underway to investigate and quantify the effects of model parameters on the simulations results.Copyright © 2013 by ASME

Solid oxide fuel cells (SOFCs) are high temperature fuel cells, which are being developed for large scale and distributed power systems. SOFCs promise to provide cleaner, more efficient electricity than traditional fossil fuel burning power plants. Research over the last decade has improved the design and materials used in SOFCs to increase their performance and stability for long-term operation; however, there are still challenges for SOFC researchers to overcome before SOFCs can be considered competitive with traditional fossil fuel burning and renewable power systems. In particular degradation due to contaminants in the fuel and oxidant stream is a major challenge facing SOFCs. In this paper we discuss ongoing computational and experimental research into different degradation and design issues in SOFC electrodes. We focus on contaminants in gasified coal which cause electrochemical and structural degradation in the anode, and chromium poisoning which affects the electrochemistry of the cathode. Due to the complex microstructures and multi-physics of SOFCs, multi-scale computational modeling and experimental research is needed to understand the detailed physics behind different degradation mechanisms, the local conditions within the cell which facilitate degradation, and its effects on the overall SOFC performance. We will discuss computational modeling research of SOFCs at the macro-, meso- and nano-scales which is being used to investigate the performance and degradation of SOFCs. We will also discuss the need for a multi-scale modeling framework of SOFCs, and the application of computational and multi-scale modeling to several degradation issues in SOFCs.

Post-combustion solid sorbent carbon capture systems are being studied via computational modeling as part of the U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI). The work focuses on computational modeling of device-scale multi-phase computational fluid dynamics (CFD) simulations for given carbon capture reactor configurations to predict flow properties, outlet compositions, temperature and pressure. The detailed outputs of the device-scale models provide valuable insight into the operation of new carbon capture devices and will help in the design and optimization of carbon capture systems. As a first step in this project we have focused on modeling a 1 kWe solid sorbent carbon capture system using the commercial CFD software ANSYS FLUENT®. Using the multi-phase models available in ANSYS FLUENT®, we are investigating the use of Eulerian-Eulerian and Eulerian-Lagrangian methods for modeling a fluidized bed carbon capture design. The applicability of the dense discrete phase method (DDPM) is being considered along with the more traditional Eulerian-Eulerian multi-phase model. In this paper we will discuss the operation of the 1 kWe solid sorbent system and the setup of the DDPM and Eulerian-Eulerian models used to simulate the system. The results of the hydrodynamics in the system will be discussed and the predictions of the DDPM and Eulerian-Eulerian simulations will be compared. A discussion of the sensitivity of the model to boundary and initial conditions, computational meshing, granular pressure, and drag sub-models will also be presented.Copyright © 2012 by ASME

This report presents a distributed electrochemistry (DEC) model capable of investigating the electrochemistry and local conditions with the SOFC MEA based on the local microstructure and multi-physics. The DEC model can calculate the global current-voltage (I-V) performance of the cell as determined by the spatially varying local conditions through the thickness of the electrodes and electrolyte. The simulation tool is able to investigate the electrochemical performance based on characteristics of the electrode microstructure, such as particle size, pore size, electrolyte and electrode phase volume fractions, and triple-phase-boundary length. It can also investigate performance as affected by fuel and oxidant gas flow distributions and other environmental/experimental conditions such as temperature and fuel gas composition. The long-term objective for the DEC modeling tool is to investigate factors that cause electrode degradation and the decay of SOFC performance which decrease longevity.

A cell-level distributed electrochemistry (DEC) modeling tool has been developed to enable predicting trends in solid oxide fuel cell performance by considering the coupled and spatially varying multi-physics that occur within the tri-layer. The approach calculates the distributed electrochemistry within the electrodes, which includes the charge transfer and electric potential fields, ion transport throughout the tri-layer, and gas distributions within the composite and porous electrodes. The thickness of the electrochemically active regions within the electrodes is calculated along with the distributions of charge transfer. The DEC modeling tool can examine the overall SOFC performance based on electrode microstructural parameters, such as particle size, pore size, porosity, electrolyte- and electrode-phase volume fractions, and triple-phase-boundary length. Recent developments in electrode fabrication methods have lead to increased interest in using graded and nano-structured electrodes to improve the electrochemical performance of SOFCs. This paper demonstrates how the DEC modeling tool can be used to help design novel electrode microstructures by optimizing a graded anode for high electrochemical performance.