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  • 1.
    Forsgren, Fritz
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Simulations to determine the drag coefficient of a floating photovoltaic system2021Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A floating photovoltaic (FPV) system is a structure of solar cells placed on water, where the solar cells are mounted on floating modules that have to be anchored. To know the size of the anchoring equipment, the forces on the FPV need to be determined. The main force affecting the FPV is the wind force. The force from the wind is directly correlated with the drag coefficient, hence we need to determine the drag coefficient to understand the system.

    The goal of this thesis is to first find the difference in the drag coefficient between two configurations of FPVs and for a second case with a floater added in front of both setups. To determine the difference in drag coefficient, between the two cases, the wind flow over the FPVs were studied by simulations using computational fluid dynamics (CFD) and calculating the drag coefficient for each case.

    The simulations showed that the difference in drag coefficient in the cases without a floater had the biggest difference between the first FPVs where the difference was a factor of two. For the cases with the floater, the simulations gave a similar result for the two configurations, leading to a smaller difference between the two configurations.

    We conclude that if a system without a floater is built, the configurations of the FPVs are important, while if the floater is added in front of the FPV there is less importance in the configurations of the FPVs. 

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  • 2.
    Hagström, Daniel
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Radio Environment Classification2023Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis has looked into the possibility of classifying radio environment scenarios based on data received in base stations. It was done in order to improve forecasting of electrical output in these base stations. Subsequences of time series data was clustered with the k-means method, using dynamic time warping as the similarity measure and dynamic time warping barycenter averaging to find cluster centers. The subsequences were then classified and the labels were fed to the prediction models. The LSTM architecture was used to predict the electrical output. Two different architectures were used where one trained one model on all data and used the labels from clustering as an additional feature. The other trained multiple models on the different clusters found in clustering. What was found was that the k-means method could separate the subsequences into different radio environment scenarios. The introduction of clustered data decreased the mean square error of the prediction models of both architectures compared to baseline models trained on unclustered data. 

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  • 3.
    Hedström, Lucas
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Classifying the rotation of bacteria using neural networks2019Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Bacteria can quickly spread throughout the human body, making certain diseases hard or impossible to cure. In order to understand how the bacteria can initiate and develop into an infection, microfluidic chambers in a lab environment are used as a template of how bacteria reacts to different types of flows. However, accurately tracking the movement of bacteria is a difficult task, where small objects has to be captured with a high resolution and digitally analysed with computationally heavy methods. Popular imaging methods utilise digital holographic microscopy, where three-dimensional movement is captured in two-dimensional images by numerical reconstruction of the diffraction of light. Since numerical reconstructions become computationally heavy when a good accuracy is required, this master's thesis work focus on evaluating the possibility of using convolutional neural networks to quickly and accurately determine the spatial properties of bacteria. By thorough testing and analysis of state of the art and old networks a new network design is presented, designed to eliminate as many imaging issues as possible. We found that there are certain network design choices that help with reducing the overall error of the system, and with a well chosen training set with sensible augmentations, some networks were able to reach a 60% classification accuracy when determining the vertical rotation of the bacteria. Unfortunately, due to the lack of experimental data where the ground-truth is known, not much experimental testing could be performed. However, a few tests showed that images of high quality could be classified within the expected range of vertical rotation.

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  • 4.
    Kiflemariam, Medet
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Development of a CFD Boundary Condition to Simulate a Perforated Surface2021Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In aircraft with jet propulsion engine intakes at supersonic speed, strong pressure waves referred to as shockwaves occur, which may interact with any present boundary layers along the intake surface. The adverse pressure gradients associated with Shock Wave-Boundary Layer Interaction (SWBLI) may cause boundary layer flow separation, which can result in disturbances of the flow that can be harmful to the device or decrease engine performance. A common way in dealing with the adverse effects of SWBLI is through removal of low-momentum flow in the boundary layer, a process referred to as boundary layer bleed. In the process of bleed, the boundary layer is subjected to a pressure difference promoting flow out of the system, through a porous surface, and into a plenum. The porous surfaces used in the mass flow removal process contain orifices in small scales. Thus, in Computational Fluid Dynamics (CFD), creating a mesh resolving both the orifice scales and the bulk flow is a cumbersome task, and the computational cost becomes substantially increased. To this end, several boundary conditions which effectively model the large-scale effects of bleed have been developed.

    The aim of this study is to implement the Boundary Condition (BC) developed by John W. Slater into M-EDGE, the in-house compressible CFD-solver of SAAB Aeronautics. The bleed boundary condition model is based on a dimensionless surface sonic flow coefficient, which is derived from empirical wind-tunnel measurements of the bleed mass flow. In previous work, the Slater bleed BC has been shown to correlate well with wind-tunnel data. Furthermore, a simple transpiration law formulated by Reynald Bur was implemented in order get familiarized with the M-EDGE Fortran source code. However, this model is expected to yield unsatisfactory results, as reported in previous work in the field. The implemented Slater BC is tested on two different two-dimensional flow cases; flow over a flat plate without SWBLI, and flow including a shock wave generator creating SWBLI. In the flat plate case, simulations were run at Mach numbers 1.27, 1.58, 1.98 and 2.46 over a 6.85cm plate of 19% porosity. In the SWBLI-case, only flow at Mach 2.46 was considered, with a 9.53cm plate of 21% porosity. The Reynolds number range used throughout was 1.39−1.76·10^7/m. Simulations were run at different bleed rates over a structured grid using steady state RANS with the Spalart-Allmaras one-equation turbulence model. The boundary condition performance was assessed by its ability to recreate the sonic flow coefficients on which it is based. Further, the shape of downstream pitot pressure profiles are compared with experimental data.

    Results from the studies indicate that the implementation manages to recreate the data for the sonic flow coefficient with small error margins. The implementation can be used to simulate porous plates of different dimensions and porosities, even though the bleed model is based on empirical mass flow measurements of a 6.85cmplate of 19% porosity. The implementation is able to predict global bleed effects in the flow field, as indicated by comparisons of pitot pressure profiles at various downstream reference planes, despite differences in reference boundary layer intake profiles. Further, the overall flow field was compared visually with other simulation-studies, indicating that the global Mach distributions of the geometries were in accordance with the reference data. However, pitot profiles should be further studied with better matched intake boundary layer profiles. The main limitation of the boundary condition is that it relies on the wind-tunnel data of the surface sonic flow coefficients for specific bleed plate configurations. Furthermore, the implementation has only been verified to work within specific Mach number range of the underlying empirical measurements. In future work, the generality of the model could be increased by extending the data to other configurations and Mach numbers by conducting new experiments or using other published empirical data.

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  • 5.
    Lindroth, Klara
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Streamlining 3D City Modeling for Urban Flow Simulations by Automatic Integration of Multisource Topography2023Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In the workflow of computational fluid dynamics, geometry preparation is commonly the most time-consuming step. For a fast CFD simulation, automatic surface reconstruction to obtain 3D city models for a chosen area is essential. To address this need, a literature study was conducted to map available data suitable for 3D city models. The properties investigated included geographical coverage, resolution, accuracy and licensing. A surface reconstruction using different topographical data was conducted using the 3D finite element mesh generator Gmsh and various GIS analysis tools. The findings of the literature study found no global data enabling a fully automatic solution with sufficient results. However, the open geographic database OpenStreetMap has potential for future work. Today, the method developed in this project is restricted to country-by-country applications and uses a terrain model, LiDAR data and building footprints as input data. The generated 3D city model has a level of detail 1.2, consisting of valid geometries without self-intersection, overlapping or gaps. The method is a semi-automatic workflow with a time consumption of less than one hour, from the extraction of data to a simulation-ready 3D city model. The model shows satisfactory agreement with the reference material but needs improvements regarding the detail of height setting, for more accurate airflow simulations. The method contributes to the field of automatic 3D city model reconstruction. Future work includes improvement regarding level of detail and automation of data attainment.

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    KlaraLindroth
  • 6.
    Lundberg, Simon
    Umeå University, Faculty of Science and Technology, Department of Physics.
    System Simulation of Electric Driveline and Active Suspension using Simcenter Amesim2022Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Computer simulation software’s are arguably some of the most convenient and utilized tools for an engineer as it lets them model real phenomena and observe different operations without having to perform the operation physically, thus saving both time and resources. Naturally these tools varies in design depending on their intended area of application and while a large number of them supports modeling of more than one physical domain, it is often cumbersome to attain a functional interaction between them. In spite of this there do exist simulation software that have been specifically developed for effectively integrating several physical domains known as system simulation software’s. One of these are Siemens Simcenter Amesim, a computer simulation software for modeling multi domain mechatronic systems.

    One company that has recently found an interest in potentially adapting the concept of system simulations into their workflow is BAE Systems Hägglunds, Örnsköldsvik, where a pre-study has previously been conducted in order to define a system requirement specification as well as narrow down the number of promising tools to only a few, with Simcenter Amesim being one of them. The aim of this study is then to evaluate and assess to what degree Simcenter Amesim complies with the requirements specified by the company. The primary source of information in which this analysis will be based upon is through the modeling of two different pilot cases in Simcenter Amesim, an electric driveline as well as the hydraulic component of the active suspension system affiliated with the CV90 vehicle. The electric driveline was developed as a general model featuring a few key functionalities in terms of power setup. This being that two electric motors were to be utilized, one for driving the vehicle forward and the other for steering the vehicle left and right. Powering these two was then an electric generator which by itself was to be powered by an internal combustion engine (ICE). The active suspension system was modeled based on existing schematics and information available through company resources with the ambition of realizing a certain behavior of the system as described by a couple of real tests made.

    Results from simulations made using the electric driveline model indicates that the model succeeds in fulfilling its fundamental functionality. Through plain throttle and steering inputs the corresponding vehicle is able to move about in a simple and predictable fashion with data also showcasing realistic behavior in terms of velocity evolution and power generation. The hydraulic model of the CV90 active suspension system furthermore appears to replicate the behavior of the actual suspension system fairly well based on the real test data available. Analogous with both models however is the fact that they are rather primitive in their current state. The electric driveline model lacks some of the finesses and functionalities that are included in modern driveline systems, mostly coupled to the component steering and feedback system which is more arbitrarily implemented in this model. As for the hydraulic suspension system it would be beneficial to continue develop the model through further evaluation using more real life test data.

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  • 7.
    Söderström, Pontus
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Physics-Informed Neural Networks for Liquid Chromatography2022Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Liquid chromatography is a technique used to separate and purify components of a mixture. The method is frequently used in the biomedicine industry and life science to discover and develop new drugs. Here liquid chromatography can separate the drug candidate from its byproducts. For this, it is essential to achieve high purity to satisfy the requirements for biopharmaceutical drugs. However, the calculations for receiving optimal settings to achieve high purity are often computationally demanding. Thus the biomedicine industry would benefit from more efficient methods for obtaining optimal settings for the specific application. The problem involves solving a system of coupled PDEs which is typically done with numerical methods. Since numerical methods quickly become computationally demanding when increasing the grid size, this thesis focuses on investigating the opportunity to introduce Physics-Informed Neural Networks (PINNs) for solving PDEs in liquid chromatography fast and accurately. The methodology developed two PINNs, one where a network is trained to solve the PDEs for one unique parametrization and another where a PINN is trained to solve the PDEs for any parameterization of the PDEs. We show that PINNs can be trained to become the solution for one parametrization of the related PDEs, with a relative error of 0.60%. Moreover, the results demonstrate that a PINN can predict the solution with an average relative error of 1.53% for any parameterization. Furthermore, we show that this PINN can produce solutions at smaller regions of the solution domain 23 times faster than the numerical solver used for simulating ground truth data. The results give a great insight into how PINNs can be used in liquid chromatography applications and can be seen as a first step in introducing PINNs in liquid chromatography. 

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  • 8.
    Vikström, Axel
    Umeå University, Faculty of Science and Technology, Department of Physics.
    A comparison of different machine learning algorithms applied to hyperspectral data analysis2021Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Hyperspectral image analysis works with image data where each pixel contains hundreds of wavelengths acquired from spectral measurements. It is a growing field of research in the sciences and industries because it can distinguish visually similar objects. While many machine-learning methods work well for analysing regular images, little is known about how they perform on hyperspectral data. Standard methods for quantifying and classifying hyperspectral data include the chemometric methods PLS, PLS-DA and SIMCA. They provide rapid computations along with intuitive modelling and diagnostic tools, but cannot capture more complex data. I benchmarked the chemometric methods against machine learning methods from Microsoft's ML.NET library on six classification and two quantification problems. The ML.NET methods proved to be good complements to the chemometric methods. In particular, the decision tree methods provided accurate classification and quantification while the maximum entropy classification methods balanced between accuracy and computational time the best. While the remaining ML.NET methods performed equally well or better than the chemometric methods, finding their use requires testing on data sets with a wider range of properties. The best ML.NET methods are suitable for analysing more complex hyperspectral images by capturing nonlinearities disregarded by standard image analysis.

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1 - 8 of 8
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