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The evaluation of the noise produced by flows has reached a high level of importance in the past years. The physics surrounding flow-induced noise is quite complex and sensitive to various flow conditions like temperature, shape. Empirical models were built in the past for some special geometries but they cannot be used in a general case for a shape optimization for instance. Experimental aeroacoustic facilities represent the main tool for acoustic analyses of flow fields, but are quite expensive because extreme care must be exercised not to introduce acoustic perturbations in the flow (silent facilities). These tools allow a good analysis of the physical phenomena responsible for noise generation in the flow by a comparison of the noise sources and the flow characteristics (pressure, turbulence,...). Nevertheless, the identification and location of noise sources to compare with flow structures requires quite complex methods. The numerical approach complements the experimental one in the sense that the flow characteristics are deeply analyzed where experiments suggest noise production. For the numerical approach, the turbulence modeling is quite important. In the past, some models were appreciated for their good prediction of some aerodynamic parameters as lift and drag for instance. The challenge is now to tune these models for a correct prediction of the noise sources. In the low subsonic range, the flow field is completely decoupled from acoustics, and noise sources can be computed from a purely hydrodynamic simulation before this information is transferred to an acoustical solver which will compute the acoustic field at the listener position. This post processing of the aerodynamic results is not obvious since it can introduce non-physical noise into the solution. This project considers the aspect of noise generation in turbulent jets and especially the noise generated by vortex pairing, as it occurs for instance in jet flows. The axisymmetric version of the flow solver SFELES has been part of this PhD research, and numerical results obtained on the jet are similar to the experimental values. Analyses performed on the numerical results are interesting to go to complete turbulence modeling for aeroacoustics since vortex pairing is one of the basic acoustical processes in vortex dynamics. Currently, a standard static Smagorinski model is used for turbulence modeling. However, this model has well known limitations, and its influence on the noise sources extracted from the flow field is not very clear. For this reason, it is planned to adopt a dynamic procedure in which the subgrid scale model automatically adapts to the flow. We planned also to perform simulations with the variational multiscale approach to better simulate the different interactions between large and unresolved scales. The commercial software ACTRAN distributed by Free Field Technologies is used for the computation of sound propagation inside the acoustic domain.

The aim of the diploma thesis is the simulation of different types of air conditioners using the CFD methods. The main goal of this thesis is to design the most suitable type of air conditioning in the consideration room, due to the air flow rate in the residental area. The conclusion of the work is to devote the design of air conditioning system with the application of selected type of air conditioning based on the results of CFD simulations.

Heart disease is the main cause of death today, and studying causes and treatments are of great interest. Blood flow simulations using computational fluid dynamics shows promise in providing insight into this area. This study builds upon previous work by Larsson et al. and Kronborg et al. who have developed a program for simulating the blood flow through patient specific left ventricles. More specifically we aimed to improve the accuracy of their blood flow simulation by accounting for the protruding structure of the endocardial wall, previously disregarded in the model due to the limitations in spacial accuracy of echocardiography. These structures, consisting of trabeculae carneae and papillary muscles, have been shown to have a significant impact on the blood flow. In a recent study, Sacco et al. proposed a solution were a porous layer could mimic the effects on the blood flow from these structures in a rigid heart model. Our study aimed to apply this modification to the left ventricle of the dynamic model using the Navier-Stokes-Brinkman flow equation and a subdomain defining the porous region. This study has been working towards the end goal of fully implementing the porous layer into the heart simulation. The equations needed have been formulated and simulations have been run on flow in a more simple setting to verify the model. The simulations show promise in being able to recreate the results from Sacco et al. but further development is needed before the porous model can be tested in the dynamic left ventricle model, most notably defining the porous subdomain in the dynamic model. We conclude that the porous domain will affect the flow, possibly breaking up vortices and reducing the wall shear stress. Confirming this requires additional studies, but the implementation of a porous domain would likely result in a more accurate simulation.

<p>Due to strong interactions among various phases and among the phases and fluid motions, multiphase flows (MPFs) are so complex that lots of efforts have to be paid to predict its sequential patterns of phases and motions. The present paper applies the physical constraints inherent in MPFs and enforces them to a physics-informed neural network (PINN) model either explicitly or implicitly, depending on the type of constraints. To predict the unobserved order parameters (OPs) (which locate the phases) in the future steps, the conditional neural processes (CNPs) with long short-term memory (LSTM, combined as CNPLSTM) are applied to quickly infer the dynamics of the phases after encoding only a few observations. After that, the multiphase consistent and conservative boundedness mapping (MCBOM) algorithm is implemented the correction the predicted OPs from CNP-LSTM so that the mass conservation, the summation of the volume fractions of the phases being unity, the consistency of reduction, and the boundedness of the OPs are strictly satisfied. Next, the density of the fluid mixture is computed from the corrected OPs. The observed velocity and density of the fluid mixture then encode in a physics-informed conditional neural processes and long short-term memory (PICNP-LSTM) where the constraint of momentum conservation is included in the loss function. Finally, the unobserved velocity in future steps is predicted from PICNP-LSTM. The proposed physics-informed neural processes (PINPs) model (CNP-LSTM-MCBOM-PICNP-LSTM) for MPFs avoids unphysical behaviors of the OPs, accelerates the convergence, and requires fewer data. The proposed model successfully predicts several canonical MPF problems, i.e., the horizontal shear layer (HSL) and dam break (DB) problems, and its performances are validated.</p>

Electric vehicles (EVs) are increasing in popularity and play an important role in decarbonizing the transport sector. However, a growing EV fleet can cause problems for power grids as the grids are not initially designed for EV charging. The potential of a power grid to accommodate EV loads can be assessed through hosting capacity (HC) analysis. The HC is grid specific and varies, therefore it is necessary to conduct analysis that reflects local conditions and covers uncertainties and correlations over time. This theses aims to investigate the HC for EVs in existing residential power grids, and to gain a better understanding of how it varies based on how the EVs are implemented and charged. The work is in collaboration with a distribution system operator (DSO) and is based on two case studies using real-life data reflecting conditions in Swedish grids. Combinations of different HC assessment methods have been used and the HC is evaluated based on cable loading, transformer loading and voltage deviation. Additionally, the study investigated three distinct charging strategies: charging on arrival, evenly spread charging over whole connection period, and charging at the lowest spot price.  The results show that decisions on acceptable voltage deviation limit can have a large influence on the HC as well as the charging strategy used. A charging strategy based on energy prices resulted in the lowest HC, as numerous EVs charging simultaneously caused high power peaks during low spot price periods. Charging on arrival was the second worst strategy, as the peak power coincided with household demand. The best strategy was to evenly spread out the charging, resulting in fewer violations for 100% EV implementation compared to the other two strategies for 25% EV implementation.  The findings underscore the necessity for coordinated charging controls for EV fleets or diversified power tariffs to balance power on a large scale in order to use the grids efficiently.

Electric vehicles (EVs) are increasing in popularity and play an important role in decarbonizing the transport sector. However, a growing EV fleet can cause problems for power grids as the grids are not initially designed for EV charging. The potential of a power grid to accommodate EV loads can be assessed through hosting capacity (HC) analysis. The HC is grid specific and varies, therefore it is necessary to conduct analysis that reflects local conditions and covers uncertainties and correlations over time. This theses aims to investigate the HC for EVs in existing residential power grids, and to gain a better understanding of how it varies based on how the EVs are implemented and charged. The work is in collaboration with a distribution system operator (DSO) and is based on two case studies using real-life data reflecting conditions in Swedish grids. Combinations of different HC assessment methods have been used and the HC is evaluated based on cable loading, transformer loading and voltage deviation. Additionally, the study investigated three distinct charging strategies: charging on arrival, evenly spread charging over whole connection period, and charging at the lowest spot price.  The results show that decisions on acceptable voltage deviation limit can have a large influence on the HC as well as the charging strategy used. A charging strategy based on energy prices resulted in the lowest HC, as numerous EVs charging simultaneously caused high power peaks during low spot price periods. Charging on arrival was the second worst strategy, as the peak power coincided with household demand. The best strategy was to evenly spread out the charging, resulting in fewer violations for 100% EV implementation compared to the other two strategies for 25% EV implementation.  The findings underscore the necessity for coordinated charging controls for EV fleets or diversified power tariffs to balance power on a large scale in order to use the grids efficiently.

Powder injection molding (PIM) is widely used to manufacture complex-shaped ceramic and metal components in high production volumes. In order to design and fabricate PIM components, it is important to know a number of material properties at different powder- polymer compositions. In this thesis, several predictive models for estimating rheological, thermal and mechanical properties as a function of powder-polymer mixtures were evaluated using experimental data obtained from the literature. Based on this survey, models were selected for predicting rheological, thermal and mechanical properties for aluminum nitride-polymer mixtures at various volume fractions of powder using experimental measurements of unfilled and filled polymers. The material properties were estimated for two aluminum nitride powder-polymer mixtures and used in mold-filling simulations. These results will provide new perspectives and design tools for identifying useful material compositions, component geometry attributes, and process parameters while eliminating expensive and time-consuming trial-and-error practices prevalent in PIM. / Graduation date: 2013

The aim of this thesis is to observe behavior of traffic flow and to verify or refute traditional so called fundamental relationships. It analyze data based on older measurements, personal measurements executed on the I/43 road, and mathematical models.There were three tools used provided by "VUT" in Brno as well as own developed tool based on cellular automat..