Masteruppsatserhttps://hdl.handle.net/2077/673582023-10-03T04:27:17Z2023-10-03T04:27:17ZDevelopment of a DEM-FEM framework for infrastructure simulationsUllrich, Anitahttps://hdl.handle.net/2077/736502022-09-20T15:06:01Z2022-09-20T00:00:00ZDevelopment of a DEM-FEM framework for infrastructure simulations
Ullrich, Anita
This thesis presents a coupling algorithm of the discrete element method (DEM) and finite element method (FEM). The algorithm formulates an explicit coupling of transient simulations of particle systems interacting with elastic bodies. To lay a foundation for the requirements in terms of stability and temporal and spatial resolution, the DEM and FEM methods are introduced. The coupling algorithm is implemented in a Python framework, using the FCC in-house solvers Demify® and LaStFEM. The combined tool is applied to three different main scenarios. As a first case, the solver exchange of forces between the DEM and FEM solver is verified using a fixed elastic beam simulation with uniform load, comparing the deflection under the load of particles to an analytical condition. Second, the dynamic accuracy and stability of the coupling method is proven on a simulation of a steel sheet deflection under the load of particles flowing on the elastic object. The simulations are compared to experimental results and show good agreement with a measured sheet deflection. Finally, the coupled solver is used to simulate the interaction between a timber sleeper and a rock particle ballast bed. The particles are in the third case represented by a polyhedron particle model. The system is studied for variations of both material properties as well as different simulation parameters. The coupled solver is shown to capture dynamic effects in the ballast bed under a dynamic load cycle. The simulation results are compared to experimental results of the pressure distribution in the bed from the open literature and demonstrate good qualitative and quantitative agreement with the experiments. The overall performance of the different parts of the solver is presented and it is shown that the developed tool is capable of simulating large scenarios with very good performance on desktop computers with a single GPU.
2022-09-20T00:00:00ZQuantum Error Correction Using Graph Neural NetworksBergentall, Valdemarhttps://hdl.handle.net/2077/686282021-06-18T01:33:04Z2021-06-17T00:00:00ZQuantum Error Correction Using Graph Neural Networks
Bergentall, Valdemar
A graph neural network (GNN) is constructed and trained with a purpose of using
it as a quantum error correction decoder for depolarized noise on the surface code.
Since associating syndromes on the surface code with graphs instead of grid-like
data seemed promising, a previous decoder based on the Markov Chain Monte Carlo
method was used to generate data to create graphs. In this thesis the emphasis has
been on error probabilities, p = 0.05, 0.1 and surface code sizes d = 5, 7, 9. Two
specific network architectures have been tested using various graph convolutional
layers. While training the networks, evenly distributed datasets were used and the
highest reached test accuracy for p = 0.05 was 97% and for p = 0.1 it was 81.4%.
Utilizing the trained network as a quantum error correction decoder for p = 0.05
the performance did not achieve an error correction rate equal to the reference
algorithm Minimum Weight Perfect Matching. Further research could be done to
create a custom-made graph convolutional layer designed with intent to make the
contribution of edge attributes more pivotal.
2021-06-17T00:00:00ZTopological Band Theory, An OverviewRoderus, Jenshttps://hdl.handle.net/2077/685962021-06-15T01:41:59Z2021-06-14T00:00:00ZTopological Band Theory, An Overview
Roderus, Jens
Topological insulators, superconductors and semi-metals are states of ma er with unique features such as quantized macroscopic observables and robust, gapless edge states. ese states can not be explained
by standard quantum mechanics, but require also the framework of topology to be properly characterized.
Topology is a branch of mathematics having to do with properties that are conserved under continuous
deformations of spaces. is review presents some of the ways in which topology and condensed ma er
physics come together, with a focus on non-interacting models which can be described with a band theory
approach. Furthermore, the focus is on insulating systems but the discussions may sometimes be applied
to superconductors and semi-metals. e eld of topological phases of ma er is not all together new, yet it
lacks elementary introductions to newcomers. is review is meant for those with basic condensed ma er
physics background and aims at providing a self-consistent overview of the central concepts in the eld of
topological ma er.
e structure of the review is as follows: In Chapter 1, a brief historical background is given. Also, a basic
introduction to topology is presented, with focus on how it is used in condensed ma er physics. Following
this, Chapter 2 introduces three important discrete symmetries which are key in characterizing topological
phases of ma er. In particular, the e ect that these symmetries have on a general Bloch Hamiltonian is
shown. In Chapter 3, the e ect of discrete symmetries on certain models is investigated. e well-known
Su-Schrie er-Heeger model is discussed because it is the simplest models known to exhibit a topological
phase and a topological invariant. Chapter 4 broadens the discussion of this topological invariant which is a
winding number. Chapter 5 introduces the geometric phase (Berry phase) which is used to describe another
topological invariant, the Chern number, the subject of Chapter 6. ere the alternative interpretations of
the Chern number are discussed. A erwards, in Chapter 7, the quantum Hall e ect is presented. Following
this, a general classi cation scheme for topological phases of fermionic, non-interacting systems will be
presented in Chapter 8. It will be shown how it can be determined whether a system could possibly host a
topological phase or not based on the symmetries of the Hamiltonian. Chapter 9 focuses on the concepts
pertaining to the physics of the gapless edge states which appear between the interface of a (non-interacting)
topological insulator and a topologically trivially insulator. Among the concepts discussed here is the bulk boundary correspondence and topological protection. Lastly, Chapter 10 contains a brief recap of what has
been established in the review and some conclusionary remarks.
2021-06-14T00:00:00ZOpto-vibrational coupling in molecular solar thermal storage materials: Electronic structure calculations and neural-networkbased analysis Giannis Kostaras Degree projectKostaras, Giannishttps://hdl.handle.net/2077/673622021-01-27T02:41:02Z2021-01-26T00:00:00ZOpto-vibrational coupling in molecular solar thermal storage materials: Electronic structure calculations and neural-networkbased analysis Giannis Kostaras Degree project
Kostaras, Giannis
Molecular solar thermal storage materials are proposed as a clean, renewable energy
solution for a world with ever increasing energy needs. Norbornadiene is an
organic compound suitable for molecular solar thermal storage systems. Computational
methods such as density functional theory offer solutions for improvement of
norbornadiene-based molecular solar thermal storage systems via theoretical spectroscopy.
Machine learning methods, such as artificial neural networks may offer
useful insights to improve theoretical spectroscopy methods.
2021-01-26T00:00:00Z