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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.

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.

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.

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.

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.

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.

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.

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.