Subgrid models for heat transfer in multiphase flows with immersed geometry

Multiphase flows are ubiquitous across engineering disciplines: water-sediment river flows in civil engineering, oil-water-sand transportation flows in petroleum engineering; and sorbent-flue gas reactor flows in chemical engineering. These multiphase flows can include a combination of momentum, heat, and mass transfer. Studying and understanding the behavior of multiphase, multiphysics flow configurations can be crucial for safe and efficient engineering design. In this work, a framework for the development and validation, verification and uncertainty quantification (VVUQ) of subgrid models for heat transfer in multiphase flows is presented. The framework is developed for a carbon capture reactor; however, the concepts and methods described in this dissertation can be generalized and applied broadly to multiphase/multiphysics problems. When combined with VVUQ methods, these tools can provide accurate results at many length scales, enabling large upscaling problems to be simulated accurately and with calculable errors. The system of interest is a post-combustion solid-sorbent carbon capture reactor featuring a solid-sorbent bed that is fluidized with post-combustion flue gas. As the flue gas passes through the bed, the carbon dioxide is exothermically adsorbed onto the sorbent particle’s surface, and the clean gas is passed onto further processes. To prevent overheating and degradation of the sorbent material, cooling cylinders are immersed in the flow to regulate temperatures. Simulating a full-scale, gas-particle reactor using traditional methods is computationally intractable due to the long time scale and variations in length scales: reactor, O(10 m); cylinders, O(1 cm); and sorbent particles, O(100 um). This research developed an efficient subgrid method for simulating such a system. A constitutive model was derived to predict the effective suspension-cylinder Nusselt number based on the local flow and material properties and the cylinder geometry, analogous to single-phase Nusselt number correlations. This model was implemented in an open source computational fluid dynamics code, MFIX, and has undergone VVUQ. Verification and validation showed great agreement with comparable highly-resolved simulations, achieving speedups of up to 10,000 times faster. Our model is currently being used to simulate a 1 MW, solid-sorbent carbon capture unit and is outperforming previous methods in both speed and physically accuracy.2017-06-21T00:00:00

Dynamic viscoplastic granular flows: A persistent challenge in gas-solid fluidization

Fluidization is a prime example of complex granular flows driven by fluid-solid interactions. The interplay of gravity, particle-particle and fluid-particle forces leads to a rich spectrum of hydrodynamic behavior. A number of complex mathematical formulations exist to describe granular flows. At a macroscopic scale, Eulerian models based on the Kinetic Theory of Granular Flow (KTGF) have been successfully employed to simulate dilute and moderately dense systems, such as circulating fluidized bed reactors. However, their applications to dense flows are challenging, because sustained particle contacts are important. As solid fraction rises, the behavior of granular media responds dramatically to particle properties and changes in concentration. Lacking a coherent transition between formulations of dilute, dense and quasi-static flow behavior, kinetic models are incapable of describing how microstructure emerges and affects the rheology. The behavior of transitional granular flows, such as pulsed fluidized beds, for which the particulate phase transitions between the viscous and plastic regimes, are good reminders of this limitation. In recent years, tremendous effort has been devoted to finding new ways to describe the effects of sustained solids friction and dense flow rheology. This article provides a perspective on this matter from the viewpoint of gas-solid fluidization and discusses advances in describing the dilute-to-dense transition in a continuum framework. Four innovative approaches prevail to extend or supersede the existing kinetic theory: (i) including effective restitution coefficients, (ii) coupling local granular rheological correlations, (iii) introducing rotational granular energy, and (iv) combining non-local laws. While their reliability is still far from that of a Eulerian-Lagrangian approach, they lay a promising foundation for developing a rigorous description of granular media that merges the classical frameworks of continuous fluid and soil mechanics. The progress of continuum formulations does not compete with multi-scale modeling platforms with an applied focus. Ultimately, combining both is a prerequisite to developing new solid stress models that will improve not only the performance of macroscopic models, but also our understanding of granular physics

Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach

In recent years, different non-linear regression techniques using neural networks and genetic programming have been applied for data-driven modelling of fluidized bed gasification processes. However, none of these methods explicitly take into account the uncertainty of the measurements and predictions. In this paper, a Bayesian approach based on Gaussian processes is used to address this issue. This method is used to predict the syngas yield production and the lower heating value (LHV) for municipal solid waste (MSW) gasification in a fluidized bed gasifier. The model parameters are calculated using the maximum a-posteriori (MAP) estimate and compared with the Markov Chain Monte Carlo (MCMC) method. The simulations demonstrate that the Bayesian methodology is a powerful technique for handling the uncertainties in the model and making probabilistic predictions based on experimental data. The method is generic in nature and can be extended to other types of fuels as well

Modeling of Gas-Solid Flows in Industry with Computational Fluid Dynamics Tools: An Assessment of Modeling Methodologies and Computational Challenges

The modeling work and simulation results contained within this thesis come from two different applications that together emphasize multiple of the challenges currently faced by researchers in the field of numerical modeling of gas-solids flows with computational fluid dynamics (CFD) tools, and highlight avenues of potential resolutions to these challenges. In the first body of work, the MFiX CFD suite, developed by the Department of Energy’s National Energy Technology Laboratory (DOE NETL), was utilized to model and simulate several experimental conditions of a fluidized bed (in 2D), and a hopper (in 3D), where solid-solid collision effects play a dominant role. Of concern in these studies are the physical prediction capabilities and associated computational costs of the three different multiphase frameworks available in MFiX: the Discrete Element Model (DEM), the Two Fluid Model (TFM), and the newer hybrid Multiphase Particle in Cell Model (MPIC). Initial selection of an appropriate multiphase modeling framework that satisfies the level of detail and computational cost constraints associated with the problem at hand, is crucial to the successful use of CFD tools in industry. The DEM and TFM frameworks were deemed to be the most accurate in simulating transient pressure profiles in the fluidized bed scenario compared to experimental measurements. TFM framework also proved to be 35% faster. While the MPIC framework was on average 90% faster than the DEM framework, it failed to produce reasonable predictions of physical flow behaviors. An additional motivation behind this research was to test and explore further reductions in computational costs offered by a recently developed interface of MFiX with the linear solver library, PETSc. Using previously identified numerical strategies in PETSc to solve the pressure equations, a more robust solver convergence behavior than the native pressure solver package was achieved across all three frameworks. Most notably, it enabled the use of larger and fewer time steps in the DEM framework, resulting in a 4-20% reduction in overall solve time to simulate 20 seconds of fluidized bed flow. Despite the significant reduction in computational time, simulation accuracy in terms of predicting the average pressure drop was slightly diminished using the PETSc solver in the DEM framework. Simulations of pure granular flow in a hopper revealed that while the TFM framework experienced difficulties converging the solids pressure term, it was still capable of predicting mass discharge rates that were very similar to those of the DEM framework, but at a comparatively lower computational cost. Again, the MPIC framework predictions differed significantly from the DEM results which are considered the benchmark/gold standard for modeling granular multiphase flows. Thus, despite the significant computational advantages of the MPIC framework over the other two, proper caution needs to be exercised when utilizing it to simulate densely packed solid flows. In the second body of work, a collection of CFD models and simulations were developed using the ANSYS Fluent DPM framework to simulate air combustion of three different coal types (Powder River Basin (PRB), Illinois #6, and Sufco 2) from select experiments conducted on a pilot-scale combustor from the University of Utah. The objective of this study was to investigate the sensitivity of ash deposition behaviors to select modeling parameters, with the aim of formulating a particle capture model. Ash formation and deposition is a complex physio-chemical process that negatively affects boiler operation and predicting ash deposit growth rates with CFD modeling techniques is extremely challenging. Many previous attempts by others neglect the importance of adequately resolving the particle size distribution and using an adequate spatial resolution near the heat transfer surface. Combustion modeling methodologies were validated against experimental measurements of flue gas ash concentrations and reactor profiles of temperature, and estimates of velocity. Simulation predictions were deemed to be in satisfactory agreement with experimental measurements. Temperature and velocity profiles were only mildly influenced by the resolutions of both the particle size distribution model and the near-boundary spatial mesh. Simulation predictions for impaction rates on a collector probe boundary were large in comparison to measured values of deposition rates, enforcing the importance of capture efficiency in the effort to accurately predict ash deposit growth rates. Impaction rates also proved to be moderately sensitive to the number of bins used to resolve the particle size distribution, and the degree of this sensitivity was unique to each coal type further emphasizing the challenges in universally modeling combustion and ash deposition across fuel sources. Impaction rates increased significantly when employing a more refined near-boundary mesh which highlights the importance of spatial resolution modeling parameters in successful ash deposition simulation efforts. Additionally, a Weber number criteria capture method was tested across all three coal types and critical Weber numbers were identified in each case which were significantly different between coal types. These values were found to be much smaller than 1 which signifies the importance of considering attraction forces between the particles and the deposition surface. Predicted deposition rates when applying the critical Weber numbers as capture criteria agreed well with measured values, but demonstrated sensitivity to the number of bins in the particle size distribution model. In the PRB and Illinois coal cases, a mere 10% adjustment in the critical Weber cutoff value resulted in a roughly 30% difference in predicted deposition rates, demonstrating that this method of modeling particle capture is not universal and should be used with caution. Results from this work demonstrate that ash deposition processes are still not fully understood, and the reinforces the need for more collaborative efforts between CFD modelers and experimentalists

Modeling and Optimization of a Novel Chilled Ammonia Absorption Process and Amine-Appended Metal-Organic Frameworks for CO2 Capture

Post-combustion capture is one of the leading technologies for CO2 abatement from anthropogenic sources which have contributed significantly to the rise of atmospheric greenhouse gases. Specifically, solvent-based capture post-combustion processes are the industry standard but can suffer drawbacks such as high energy penalties and corrosion. In this work, two possible improvements are investigated which have been recently proposed in the literature. The first is aqueous ammonia as a capture solvent which has been shown to have several advantages including, but not limited to, a lower regeneration energy. The second is a novel solid sorbent, an amine-appended metal-organic framework (MOF). The MOF exhibits several promising attributes, namely, a step-shaped adsorption isotherm which leads to lower working capacities and lower regeneration energies when compared to traditional solid sorbents. The overall goal of this work is to develop rigorous mathematical models which can be used for process design and economic evaluation of these technologies. First, an integrated mass transfer model is developed for the chilled ammonia process (CAP). This model is developed using a simultaneous regression approach that has been recently proposed in the literature with parameter estimation performed using data from a pilot plant source and wetted-wall column. The optimally estimated parameters are shown to have a lower prediction error to validation data than parameters found in literature. The integrated mass transfer model is then used to develop a model for a novel chilled ammonia process. The process includes a NH3 abatement system which utilizes a reverse osmosis membrane to aid in separation and reduce the energy penalty. Simulation of the process shows that the membrane can significantly reduce the energy requirement of the reboiler, condenser, and cooler in the abatement section. Uncertainty of the estimated mass transfer parameters is quantified using a fully Bayesian approach which is demonstrated to show a significant reduction in the prediction uncertainty of key process indicators. Second, isotherm and kinetic models are developed for amine-appended MOFs, dmpn-Mg2(dobpdc) and Mg2(dobpdc)(3-4-3). The step-shaped adsorption isotherms exhibited by these MOFs present a modeling challenge since many of the traditional isotherm models are unable to capture step transitions. Three isotherm models are examined in this work, a weighted dual-site Langmuir model found in literature, a dual-site Sips model developed in this work, and an extended weighted Langmuir model also developed in this work. Parameter estimation is performed using available isotherm data and it is shown that the models are able to predict the CO2 adsorption data well. A kinetic model is then developed using a linear driving force for mass transfer which does an excellent job at predicting time dependent TGA data. An additional goal of this work is development of a chemistry-based model for functionalized solid sorbents that aims to capture the underlying adsorption reaction mechanisms which are not typically considered in solid sorbent modeling. As part of this model, optimal reaction set selection is performed since the reaction pathways for dmpn-Mg2(dobpdc) are still relatively unknown. Parameter estimation is performed, and it is found that the chemistry-based model significantly outperforms the Sips isotherm model with regards to prediction error and other model building criteria. To aid in the evaluation of the commercial feasibility of the MOF, equation-oriented mathematical models for a fixed bed contactor and moving bed contactor are developed. The contactors are then to simulate industrial scale CO2 capture process for coal based and NGCC based flue gas. Using developed cost models, techno-economic analysis and optimization of these processes is then performed and it is found that efficient thermal management can make these MOFs viable alternatives for CO2 capture processes

Dynamically structured fluidization: Oscillating the gas flow and other opportunities to intensify gas-solid fluidized bed operation

Various approaches to structure gas-solid fluidized beds are reviewed, followed by detailed discussion on the use of gas pulsation to induce dynamic structuring. Granular media are dissipative systems, which develop complex spatiotemporal patterns when excited by an oscillating energy source. Here, we discuss how such perturbations initiate surface patterns and how these could propagate into a macroscopically organized flow. We call this dynamically structured fluidization. Vibrated shallow granular layers form ordered surface waves. The hydrodynamics of pulsed gas-fluidized layers are related, but more complex: Under appropriate conditions, surface waves transition into a three-dimensionally ordered bubbling flow. This occurs in much deeper granular beds than under vibration, indicating distinct physics. In this dynamically structured state, bubbles organize into a scalable sub-harmonic, triangular lattice that is highly predictable and responsive to changes in oscillation parameters, allowing for an unprecedented level of control. Structured bubbling is observed only under sufficiently dense conditions; thus, a dynamically structured fluidized bed sits between fixed and fluidized beds, offering opportunities for process intensification, due to less macromixing than traditional fluidization, but a higher level of control through micromixing. This informs new intensified designs for processes that are highly exothermic, involve particle formation, thermally sensitive or high-value materials