The practice of building simulation is split between domains such as energy, multizone airflow, computational fluid dynamics (CFD) airflow, and controls analysis, as well as between the tools which conduct these analyses. Previous work in the integration of these analyses and tools have focused on linking existing tools, written in algorithmic programming languages, together by interfacing them using coupling mechanisms implemented in algorithmic programming languages. This thesis takes a different approach, using the equation-based, object oriented modeling language Modelica to create models in different domains and interfaces between those models within a single framework which has benefits to the modeler/analyst in terms of both representation of physical processes and flexibility in modeling systems composed of many interacting components. Specifically, the simulation of airflows within buildings has historically been compartmentalized into distinct domains such as nodal network (multizone) simulations and CFD. Such airflow simulations are also often treated independently of building energy simulations (via heat transfer) despite their interrelation. Recent work has reported on combining these types of analyses by linking pre-existing simulation software together. Here a prototype CFD package of models is built in Modelica and coupled to models of conductive heat transfer and controls. Comparisons of results of simulations so constituted to analytical solutions and benchmark data available in the literature show good agreement, indicating the technical viability of this approach. Limitations include the absence of turbulence modeling and the lack of modeling features which improve computational efficiency, such as non-uniform grids.

Thermal properties of complex suspension flows are investigated using numerical computations. The objective is to develop an efficient and accurate computational method to investigate heat transport in suspension flows. The method presented here is based on solving the lattice Boltzmann equation for the fluid phase, as it is coupled to the Newtonian dynamics equations to model the movement of particles and the energy equation to find the thermal properties. This is a direct numerical simulation that models the free movement of the solid particles suspended in the flow and its effect on the temperature distribution. Parallel implementations are done using MPI (message passing interface) method. Convective heat transfer in internal suspension flow (low solid volume fraction, φ<10%), heat transfer in hot pressing of fiber suspensions and thermal performance of particle filled thermal interface materials (high solid volume fraction, φ>40%) are investigated. The effects of flow disturbance due to movement of suspended particles, thermo-physical properties of suspensions and the particle micro structures are discussed.

The dynamics of fiber orientation is of great interest for efforts to predict the microstructure and material properties of a suspension flow system. In this research a fiber-level, hybrid simulation method, LBM‒EBF (coupled lattice‒Boltzmann method with the external boundary force method) is undertaken to advance the current understanding of the hydrodynamic interaction induced rotational diffusion mechanism for rigid fibers in semidilute suspension of low Reynolds number flow. The LBM‒EBF simulations correctly predict the orbit constant distribution of fibers in a sheared semidilute suspension flow. It is demonstrated that an anisotropic, weak rotary diffusion model can fit the orbit constant distribution very well, but it can not describe the asymmetry in Stokes flow observed in semidilute suspension. The rotational diffusion process is then characterized with a three dimensional spatial tensor representation of the rotational diffusivity. A scalar measure of the rotational diffusion‒'scalar Folgar‒Tucker constant', C[subscript I], is extracted from this tensor. The study provides substantial numerical evidence that the range of C[subscript I] (0.0038 to 0.0165) obtained by Folgar&Tucker (J. reinf. plast. and comp, v.3, 1984) in a semidilute regime is overly diffusive, and that the correct magnitude is of O(10⁻⁴). The study reveals that the interactions among fibers become more frequent with either the decrease of fiber aspect-ratio, r[subscript p] (keeping nL³ constant, where n is the fiber number density, and L is the fiber length) or with the increase of nL³ (keeping r[subscript p] constant) in the semidilute regime, which in consequence causes an increase in C[subscript I]. The rheological properties of sheared semidilute suspension are also computed with direct LBM‒EBF simulations. The LBM‒EBF investigation is extended to characterize the fiber orientation in a linearly contracting channel similar to a paper machine 'headbox'. It is found that the rotational diffusion is the predominant term over the strain rate in the semidilute regime for a low Reynolds number flow, and it results in a decreasing trend of rotational Peclet number, Pe, along the contraction centerline. Lastly, in order to improve the numerical consistency of the existing LBM‒EBF approach, a modification to the body force term in the LB equation is suggested, which can recover the exact macroscopic hydrodynamics from the mesoscale.

To develop or engineer artificial tissues in tissue engineering, a detailed knowledge of the in vitro culture process including cell and tissue growth inside porous scaffolds, nutrient transport, and the shear stress acting on the cells is of great advantage. It has been shown that obtaining such information by means of experimental techniques is exceedingly difficult and in some ways impossible. Mathematical modelling and computational simulation based on computational fluid dynamics (CFD) has emerged recently to be a promising tool to characterize the culture process. However, due to the complicated structure of porous scaffolds, modelling and simulation of the in vitro cell culture process has been shown to be a challenging task. Furthermore, due to the cell growth during the culture process, the geometry of the scaffold structure is not constant, but changes with time, which makes the task even more challenging. To overcome these challenges, the research presented in this thesis is aimed at developing a CFD-based mathematical model and multi-time scale computational framework for culturing cell-scaffold constructs placed in perfusion bioreactors. To predict the three-dimensional (3D) cell growth in a porous tissue scaffold placed inside a perfusion bioreactor, a model is developed based on the continuity and momentum equations, a convection-diffusion equation and a suitable cell growth equation, which characterize the fluid flow, nutrient transport and cell growth, respectively. To solve these equations in a coupled fashion, an in-house FORTRAN code is developed based on the multiple relaxation time lattice Boltzmann method (MRT LBM), where the D3Q19 MRT LBM and D3Q7 MRT LBM models have been used for the fluid flow and mass transfer simulation, respectively. In the model cell growth equation, the transport of nutrients, i.e. oxygen and glucose, as well as the shear stress induced on the cells are considered for predicting the cell growth rate. In the developed model and computational framework, the influence of the dynamic strand surface on the local flow and nutrient concentration has been addressed by using a two-way coupling between the cell growth and local flow field and nutrient concentration, where a control-volume method within the LBM framework is applied. The simulation results provide quantification of the biomechanical environment, i.e. fluid velocity, shear stress and nutrient concentration inside the bioreactor. The final simulation applied the cell growth model to the culture of a three-zone tissue scaffold where the scaffold strands were initially seeded with cells. The prediction for the 3D cell growth rate indicates that the increase in the cell volume fraction is much higher in the front region of the scaffold due to the higher nutrient supply. The higher cell growth in the front zone reduces the permeability of the porous scaffold and significantly reduces the nutrient supply to the middle and rear regions of the scaffold, which in turn limit the cell growth in those regions. However, implementation of a bi-directional perfusion approach, which reverses the flow direction for second half of the culture period, is shown to significantly improve the nutrient transport inside the scaffold and increase the cell growth in the rear zone of the scaffold. The results in this study also demonstrate that the developed mathematical model and computational framework are capable of realistically simulating the 3D cell growth over extended culture periods. As such, they represent a promising tool for enhancing the growth of tissues in perfusion bioreactors.

Accurate and reliable modeling of cardiovascular hemodynamics has the potential to improve understanding of the localization and progression of heart diseases, which are currently the most common cause of death in Western countries. However, building a detailed, realistic model of human blood flow is a formidable mathematical and computational challenge. The simulation must combine the motion of the fluid, the intricate geometry of the blood vessels, continual changes in flow and pressure driven by the heartbeat, and the behavior of suspended bodies such as red blood cells. Such simulations can provide insight into factors like endothelial shear stress that act as triggers for the complex biomechanical events that can lead to atherosclerotic pathologies. Currently, it is not possible to measure endothelial shear stress in vivo, making these simulations a crucial component to understanding and potentially predicting the progression of cardiovascular disease. In this thesis, an approach for efficiently modeling the fluid movement coupled to the cell dynamics in real-patient geometries while accounting for the additional force from the expansion and contraction of the heart will be presented and examined. First, a novel method to couple a mesoscopic lattice Boltzmann fluid model to the microscopic molecular dynamics model of cell movement is elucidated. A treatment of red blood cells as extended structures, a method to handle highly irregular geometries through topology driven graph partitioning, and an efficient molecular dynamics load balancing scheme are introduced. These result in a large-scale simulation of the cardiovascular system, with a realistic description of the complex human arterial geometry, from centimeters down to the spatial resolution of red-blood cells. The computational methods developed to enable scaling of the application to 294,912 processors are discussed, thus empowering the simulation of a full heartbeat. Second, further extensions to enable the modeling of fluids in vessels with smaller diameters and a method for introducing the deformational forces exerted on the arterial flows from the movement of the heart by borrowing concepts from cosmodynamics are presented. These additional forces have a great impact on the endothelial shear stress. Third, the fluid model is extended to not only recover Navier-Stokes hydrodynamics, but also a wider range of Knudsen numbers, which is especially important in micro- and nano-scale flows. The tradeoffs of many optimizations methods such as the use of deep halo level ghost cells that, alongside hybrid programming models, reduce the impact of such higher-order models and enable efficient modeling of extreme regimes of computational fluid dynamics are discussed. Fourth, the extension of these models to other research questions like clogging in microfluidic devices and determining the severity of co-arctation of the aorta is presented. Through this work, a validation of these methods by taking real patient data and the measured pressure value before the narrowing of the aorta and predicting the pressure drop across the co-arctation is shown. Comparison with the measured pressure drop in vivo highlights the accuracy and potential impact of such patient specific simulations. Finally, a method to enable the simulation of longer trajectories in time by discretizing both spatially and temporally is presented. In this method, a serial coarse iterator is used to initialize data at discrete time steps for a fine model that runs in parallel. This coarse solver is based on a larger time step and typically a coarser discretization in space. Iterative refinement enables the compute-intensive fine iterator to be modeled with temporal parallelization. The algorithm consists of a series of prediction-corrector iterations completing when the results have converged within a certain tolerance. Combined, these developments allow large fluid models to be simulated for longer time durations than previously possible. / Engineering and Applied Sciences

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