Computational Fluid Dynamics Simulations to Inform Cancer Therapeutics.

Improved understanding of structure and function relationships in the human lungs in individuals and subpopulations is fundamentally important to the future of pulmonary medicine. Image-based measures of the lungs can provide sensitive indicators of localized features, however to provide a better prediction of lung response to disease, treatment, and environment, it is desirable to integrate quantifiable regional features from imaging with associated value-added high-level modeling. With this objective in mind, recent advances in computational fluid dynamics (CFD) of the bronchial airways-from a single bifurcation symmetric model to a multiscale image-based subject-specific lung model-will be reviewed. The interaction of CFD models with local parenchymal tissue expansion-assessed by image registration-allows new understanding of the interplay between environment, hot spots where inhaled aerosols could accumulate, and inflammation. To bridge ventilation function with image-derived central airway structure in CFD, an airway geometrical modeling method that spans from the model 'entrance' to the terminal bronchioles will be introduced. Finally, the effects of turbulent flows and CFD turbulence models on aerosol transport and deposition will be discussed.

Drug transport modeling in solid tumors: A computational exploration of spatial heterogeneity of biophysical properties

Computational fluid dynamics

This work focuses on the development of a multiscale computational fluid dynamics (CFD) simulation framework with application to plasma-enhanced chemical vapor deposition of thin film solar cells. A macroscopic, CFD model is proposed which is capable of accurately reproducing plasma chemistry and transport phenomena within a 2D axisymmetric reactor geometry. Additionally, the complex interactions that take place on the surface of a-Si:H thin films are coupled with the CFD simulation using a novel kinetic Monte Carlo scheme which describes the thin film growth, leading to a multiscale CFD model. Due to the significant computational challenges imposed by this multiscale CFD model, a parallel computation strategy is presented which allows for reduced processing time via the discretization of both the gas-phase mesh and microscopic thin film growth processes. Finally, the multiscale CFD model has been applied to the PECVD process at industrially relevant operating conditions revealing non-uniformities greater than 20% in the growth rate of amorphous silicon films across the radius of the wafer.

A three dimensional computational fluid dynamics (CFD) model of the Fall Cerebri and a composite cylinder representing gray matter and white matter of the human brain is developed to predict the transport of interstitial infusion of chemotherapeutic drugs. Brain tissues are treated as porous media and characterized by the porosity and the resistance coefficient. White matter, which is anisotropic in nature due to the presence of axon fibers, has different properties in longitudinal and transverse directions. Anisotropy has been defined as the ratio of resistance coefficients in longitudinal and transverse directions. The transport of the drug in white and gray matter is governed by convection and/or diffusion. Temporal and spatial mass concentration of the drug is determined in each case. It was observed that bulk flow or convection enhanced delivery (CED) was more effective for the increase of mass concentration and penetration of the drug molecules into the brain. Also, in white matter penetration of the drug molecules in the fiber direction was greater than penetration in the transverse direction. Obtaining an analytical solution will be incorporated in the next phase of the research.

Multi-scale modeling and control of fluidized beds for the production of solar grade silicon

This work focuses on the development of a computational fluid dynamic (CFD) model of a batch atomic layer deposition (ALD) process and an associated run-to-run control scheme. Specifically, a cylindrical furnace reactor is analyzed for a SiO 2 thin-film ALD using BTBAS and ozone as precursors. First, a high-fidelity 2D axisymmetric multiscale CFD model for an industrial-scale furnace ALD system is developed in ANSYS Fluent to characterize the gas-phase development and the surface deposition, which is based on the previously constructed database using the kinetic Monte-Carlo (kMC) algorithm. After the validation of the multiscale CFD model, it is utilized to investigate a wide range of operating conditions, from which a regression model is developed to describe the input-output relationship between the inlet feed flow rate and the half-cycle time. Next, a run-to-run (r2r) control scheme is formulated, which uses the post-batch feedback information to adjust the operating conditions using the regression relationship and an exponentially weighted moving average (EWMA) algorithm. Finally, the multiscale CFD model and the r2r controller are integrated to generate a closed-loop system via a message-passing interface (MPI) and a data synchronization scheme to evaluate the performance of the r2r controller.

CFD simulations of gas–liquid–solid flow in fluidized bed reactors — A review

Mixed ionic–electronic conducting (MIEC) oxygen-permeable membranes have emerged as a frontier in oxygen separation technology due to their high efficiency, low energy consumption, and broad application potential. In recent years, computational fluid dynamics (CFD) has become a pivotal tool in advancing MIEC membrane technology, offering precise insights into the intricate mechanisms of oxygen permeation, heat transfer, and mass transfer through numerical simulations of coupled multiphysics phenomena. In this review, we comprehensively explore the application of CFD in MIEC membrane research, heat and mass transfer analysis, reactor design optimization, and the enhancement of membrane module performance. Additionally, we delve into how CFD, through multiscale modeling and parameter optimization, improves separation efficiency and facilitates practical engineering applications. We also highlight the challenges in current CFD research, such as high computational costs, parameter uncertainties, and model complexities, while discussing the potential of emerging technologies, such as machine learning, to enhance CFD modeling capabilities. This study underscores CFD’s critical role in bridging the fundamental research and industrial applications of MIEC membranes, providing theoretical guidance and practical insights for innovation in clean energy and sustainable technologies.

The ventilations systems play a key role in underground infrastructures for health and safety of occupants during normal operation as well as during accidents. Their performances are affected by selection of the optimal design, operation and control that is investigated by predicting air flow. The calculation of ventilation flows and their interaction with fires can be done with different modelling approaches that differ in the accuracy and in the required resources. The 3D computational fluid dynamics (CFD) tools approximate the flow behaviour with a great accuracy but they require high computational resources. The one dimensional (1D) models allow a compact description of the system with a low computational time but they are unsuitable to simulate thermal fluid-dynamic scenarios characterized by turbulence and gradients. Innovative tools are necessary in order to make the analysis and optimization of these systems possible and accurate in a reasonable time. This can be achieved both with appropriate numerical approaches to the full domain as the model order reduction techniques and with the domain decompositions methods as the multiscale physical decomposition technique. The reduced order mode techniques as the proper orthogonal decomposition (POD) is based on the snapshots method provides an optimal linear basis for the reconstruction of multidimensional data. This technique has been applied to non-dimensional equations in order to produce a reduced model not depending on the geometry, source terms, boundary conditions and initial conditions. This type of modelling is adapted to the optimization strategies of the design and operation allowing to explore several configuration in reduced times, and for the real time simulation in the control algorithms. The physical decomposition achieved through multiscale approaches uses the accuracy of the CFD code in the near field e.g. the region close to the fire source, and takes advantage of the low computational cost of the 1-D model in the region where gradients in the transversal direction are negligible. In last years, the multiscale approach has been proposed for the analysis of tunnel ventilation. Among the several CFD codes used in this field, the Fire Dynamic Simulator (FDS) is suitable for the multiscale modelling. This is an open source CFD package developed by NIST and VTT and presents the HVAC routine in which the conservation equations of mass, energy and momentum are implemented. Currently, the HVAC module does not allow one to consider heat and mass transfer, which significanltly limits the applications. For these reasons a multiscale simulator has been created through the fully integration of a 1D continuity, momentum, energy and mass transport equation in FDS modifying its source codes. The multiscale simulator thus obtained, is based on a direct coupling by means of a Dirichlet-Neumann strategy. At each 1-D-CFD interface, the exchange flow information occurs prescribing thermo-fluid dynamic boundary conditions. The 1-D mass transport equation computes the diffusion of the exhaust gas from the CFD domain and the relative concentration that is particularly interesting in the case of back layering of smoke. The global convergence of the boundary conditions at each 1-D-CFD interface has been analyzed by monitoring the evolution of thermo-fluid dynamic variables (temperature, velocity, pressure and concentration. The multiscale simulator is suitable for parametric and sensitivity studies of the design and the operation ventilation and fire safety systems. This new tool will be available for all the scientific community. In this thesis, Chapter 1 provides a general introduction to the role of the system ventilation in underground infrastructures and to the innovative modelling strategies proposed for these systems. Chapter 2 offers a description of the 1D network modelling, its fluid-dynamic application to the Frejus tunnel and its thermal application to ground heat exchangers. In Chapter 3, the proper orthogonal decomposition method is presented and its application to the optimal control of the sanitary ventilation for the Padornelo Tunnel is discussed. To demonstrate the applicability of POD method in other fields, boreholes thermal energy storage systems have been considered in same chapter. In particular, a multi-objective optimization strategy is applied to investigate the optimal design of these system and an optimization algorithm for the operation is proposed. Chapter 4 describes the multiscale approach and the relative simulator. The new open tool is used for modeling the ventilation system of the Monte Cuneo road tunnel in case of fire. Results show that in the case of the current configuration of the ventilation system, depending on the atmospheric conditions at portals, smoke might not be fully confined. Significant improvements in terms of safety conditions can be achieved through increase of in smoke extraction, which requires the installation of large dumpers and of deflectors on the jet fans. The developed tool shows to be particularly effective in such analysis, also concerning the evaluation of local conditions for people evacuation and fire-brigades operation.

The primary goal of sinonasal surgery is to improve a patient's quality of life, which is generally achieved by enhancing drug delivery (eg, saline rinses, nasal steroids) and nasal airflow. Both drug delivery and nasal airflow are dependent on the anatomic structure of the sinonasal cavity and the relationship between this anatomy and airflow and drug delivery can be studied using computational fluid dynamics (CFD). CFD generally uses computed tomography scans and computational algorithms to predict airflow or drug delivery and can help us understand surgical outcomes and optimize drug delivery for patients. This study employs CFD to simulate nasal airflow dynamics and optimize drug delivery in the nasal cavity to highlight the utility of CFD for studying sinonasal disease. Utilizing COMSOL Multiphysics software, we developed detailed models to analyze changes in airflow characteristics before and after functional endoscopic sinus surgery, focusing on pressure distribution, velocity profiles, streamline patterns, and heat transfer. This research examines the impact of varying levels of nasal airway obstruction on airflow and heat transfer. In addition, we explore the characteristics of nasal drug delivery by simulating diverse spray parameters, including particle size, spray angle, and velocity. Our comprehensive approach allows for the visualization of drug particle trajectories and deposition patterns, providing crucial insights for enhancing surgical outcomes and improving targeted drug administration. By integrating patient-specific nasal cavity models and considering factors such as airway outlet pressure, this study offers valuable data on pressure cross-sections, flow rate variations, and particle behavior within the nasal passages. The findings of this research can be useful for both surgical planning and the development of more effective nasal drug delivery methods, potentially leading to enhanced clinical outcomes in respiratory treatment.

A multi-scale three-dimensional computational fluid dynamics (CFD) model was developed to predict airflow, heat and mass transfer in a typical full loaded cool storage. In order to reduce the computational costs, the porous media parameters of the bed of the apples inside the vented containers were extracted using a series of wind tunnel CFD simulations and then applied in the cool storage model. The model was validated against experiments by means of velocity, product temperature, and product weight loss measurements in cool storage. The errors of about 23.2 and 9.1% were achieved for velocity magnitude prediction in the cool storage and the product weight loss after 54 days of cooling in the loaded cool storage, respectively. The model over predicted the cooling rate of the products temperature; however, it showed a good trend of cooling rate. About 11°C difference was observed between the hottest and the coldest product temperatures at half cooling time by experiments that were in good agreement with the simulation results with about 10°C. This difference changes versus time of cooling and reached to about 4°C at the end of the cooling time. The product’s temperature heterogeneity was predicted 1.9°C between the 7 and 9 hours of cooling and reduced to 0.6°C at the end of the cooling. The multi-scale model was capable of predicting air velocity, product temperature, and weight loss with reasonable accuracy and was reliable enough for numerical studies on larger domain with high reduction in computational costs.

The computational fluid dynamics (CFD) simulators emerged as an excellent tool to support engineering problem-solving in the past decades. There are limits to the application of a CFD simulator, for example, the validation need of the model, and the computation demand of the simulation. With multiple core computers, the computation time can be lowered, and GPU computing can also be a way to decrease the computation demand. But in some cases, for example in catalyst beds, the number of individual particles is too high to calculate with a CFD simulator. In this study, we show a way to segregate the geometry of the device into smaller parts (decomposition of the geometry) and calculate only the parts of the simulation at a time, instead of whole. In this way, the computation need can be significantly lowered, without losing the crucial information, which can be stored between the geometrical steps. In this article, a framework was developed for the segregation of the geometry. The operation of the framework is shown using different case studies.

Numerical models that simulate the behaviors of the coronary arteries have been greatly improved by the addition of fluid-structure interaction (FSI) methods. Although computationally demanding, FSI models account for the movement of the arterial wall and more adequately describe the biomechanical conditions at and within the arterial wall. This offers greater physiological relevance over Computational Fluid Dynamics (CFD) models, which assume the walls do not move or deform. Numerical simulations of patient-specific cases have been greatly bolstered by the use of imaging modalities such as Computed Tomography Angiography (CTA), Magnetic Resonance Imaging (MRI), Optical Coherence Tomography (OCT), and Intravascular Ultrasound (IVUS) to reconstruct accurate 2D and 3D representations of artery geometries. The goal of this study was to conduct a comprehensive review on CFD and FSI models on coronary arteries, and evaluate their translational potential. This paper reviewed recent work on patient-specific numerical simulations of coronary arteries that describe the biomechanical conditions associated with atherosclerosis using CFD and FSI models. Imaging modality for geometry collection and clinical applications were also discussed. Numerical models using CFD and FSI approaches are commonly used to study biomechanics within the vasculature. At high temporal and spatial resolution (compared to most cardiac imaging modalities), these numerical models can generate large amount of biomechanics data. Physiologically relevant FSI models can more accurately describe atherosclerosis pathogenesis, and help to translate biomechanical assessment to clinical evaluation.

Spoilage of agrifood produce is a major issue in the industry. Cooling is an effective technique for extending the shelf life of fresh agrifood produce to minimize spoilage. Due to the practical inability of directly solving the wide spatial and temporal scales in large industrial agrifood cooling systems, the porous medium approach is mostly used. However, improvements of current porous medium models and modeling across much wider scales are needed to better understand the multiscale cooling process and system problems. Recently, as a result of increased computational capacity, multiscale computational fluid dynamics (CFD) modeling approaches have been developed to tackle some of these challenges. The associated problems and applications of CFD in the design and process optimization of cooling processes and systems at different scales are considered. CFD solution and scale bridging techniques relevant for handling multiscale cooling processes and systems problems are discussed. Innovative applications of various CFD modeling techniques at different scales in cooling processes and systems are reviewed. CFD modeling techniques can be used to handle multiscale cooling process and system problems. Lattice Boltzmann method (LBM) is a potentially viable discrete modeling technique for complimentary usages alongside current continuum techniques in future multiscale CFD modeling. The multiscale CFD modeling paradigm can overcome the computational resource limitations associated with the direct modeling approach and enhance model extension across wider spatial and temporal scales. Information from multiscale CFD could be used to improve the accuracy of current porous medium models, and thus the design of more efficient cooling systems.