11,314 research outputs found

    Theory and simulation of moiré graphene multilayers

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    Graphene has been hailed as a material which is going to revolutionise myriad technologies due to its extraordinary stability, mechanical strength yet flexibility, and remarkable transport properties. Furthermore, it was recently discovered that if two graphene layers are stacked and twisted relative to one another, referred to as twisted bilayer graphene (tBLG), correlated insulating states and superconductivity are observed, even though graphene does not intrinsically exhibit these properties. These phases only emerge at twist angles close to the "magic angle" of 1.1 degrees, and by tuning the temperature and doping level, the system can undergo electronic phase transitions between these states. I studied electron interactions and electronic screening in tBLG and other moiré graphene multilayers. In the absence of external and internal electronic screening, I found the on-site Hubbard parameter of the flat bands of tBLG scales linearly with twist angle. Upon considering internal screening, this linear scaling breaks down, where the Hubbard interaction energy decreases more rapidly towards the magic angle owing to increased screening. Moreover, external screening, from proximity to metallic gates which dope tBLG, was found to substantially affect these Hubbard interactions, owing to the moiré length scale of the magic-angle being comparable to the distance to these metallic gates. For a sufficiently small separation to these gates, I predicted that the correlated insulating states should be screened-out and the superconducting phase should be stabilised. Long-ranged Hartree interactions were found to induced doping-dependent band-flattening in tBLG that I predicted to increase the magic-angle range of tBLG. For moiré graphene multilayers, the role of these Hartree interactions were found to sensitively depend on the stacking sequence of the structure: systems with alternating twist angles have similar interaction-driven band flattening, but systems where there are also adjacent layers that are aligned have no such interaction-driven band flattening.Open Acces

    Elasto-plastic deformations within a material point framework on modern GPU architectures

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    Plastic strain localization is an important process on Earth. It strongly influ- ences the mechanical behaviour of natural processes, such as fault mechanics, earthquakes or orogeny. At a smaller scale, a landslide is a fantastic example of elasto-plastic deformations. Such behaviour spans from pre-failure mech- anisms to post-failure propagation of the unstable material. To fully resolve the landslide mechanics, the selected numerical methods should be able to efficiently address a wide range of deformation magnitudes. Accurate and performant numerical modelling requires important compu- tational resources. Mesh-free numerical methods such as the material point method (MPM) or the smoothed-particle hydrodynamics (SPH) are particu- larly computationally expensive, when compared with mesh-based methods, such as the finite element method (FEM) or the finite difference method (FDM). Still, mesh-free methods are particularly well-suited to numerical problems involving large elasto-plastic deformations. But, the computational efficiency of these methods should be first improved in order to tackle complex three-dimensional problems, i.e., landslides. As such, this research work attempts to alleviate the computational cost of the material point method by using the most recent graphics processing unit (GPU) architectures available. GPUs are many-core processors originally designed to refresh screen pixels (e.g., for computer games) independently. This allows GPUs to delivers a massive parallelism when compared to central processing units (CPUs). To do so, this research work first investigates code prototyping in a high- level language, e.g., MATLAB. This allows to implement vectorized algorithms and benchmark numerical results of two-dimensional analysis with analytical solutions and/or experimental results in an affordable amount of time. After- wards, low-level language such as CUDA C is used to efficiently implement a GPU-based solver, i.e., ep2-3De v1.0, can resolve three-dimensional prob- lems in a decent amount of time. This part takes advantages of the massive parallelism of modern GPU architectures. In addition, a first attempt of GPU parallel computing, i.e., multi-GPU codes, is performed to increase even more the performance and to address the on-chip memory limitation. Finally, this GPU-based solver is used to investigate three-dimensional granular collapses and is compared with experimental evidences obtained in the laboratory. This research work demonstrates that the material point method is well suited to resolve small to large elasto-plastic deformations. Moreover, the computational efficiency of the method can be dramatically increased using modern GPU architectures. These allow fast, performant and accurate three- dimensional modelling of landslides, provided that the on-chip memory limi- tation is alleviated with an appropriate parallel strategy

    A high-performance open-source framework for multiphysics simulation and adjoint-based shape and topology optimization

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    The first part of this thesis presents the advances made in the Open-Source software SU2, towards transforming it into a high-performance framework for design and optimization of multiphysics problems. Through this work, and in collaboration with other authors, a tenfold performance improvement was achieved for some problems. More importantly, problems that had previously been impossible to solve in SU2, can now be used in numerical optimization with shape or topology variables. Furthermore, it is now exponentially simpler to study new multiphysics applications, and to develop new numerical schemes taking advantage of modern high-performance-computing systems. In the second part of this thesis, these capabilities allowed the application of topology optimiza- tion to medium scale fluid-structure interaction problems, using high-fidelity models (nonlinear elasticity and Reynolds-averaged Navier-Stokes equations), which had not been done before in the literature. This showed that topology optimization can be used to target aerodynamic objectives, by tailoring the interaction between fluid and structure. However, it also made ev- ident the limitations of density-based methods for this type of problem, in particular, reliably converging to discrete solutions. This was overcome with new strategies to both guarantee and accelerate (i.e. reduce the overall computational cost) the convergence to discrete solutions in fluid-structure interaction problems.Open Acces

    Modern Acquisition of Personalised Head-Related Transfer Functions: An Overview

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    Head-related transfer functions (HRTFs) describe the spatial filtering of acoustic signals by a listener’s anatomy. With the increase of computational power, HRTFs are nowadays more and more used for the spatialised headphone playback of 3D sounds, thus enabling personalised binaural audio playback. HRTFs are traditionally measured acoustically and various measurement systems have been set up worldwide. Despite the trend to develop more user-friendly systems and as an alternative to the most expensive and rather elaborate measurements, HRTFs can also be numerically calculated, provided an accurate representation of the 3D geometry of head and ears exists. While under optimal conditions, it is possible to generate said 3D geometries even from 2D photos of a listener, the geometry acquisition is still a subject of research. In this chapter, we review the requirements and state-of-the-art methods for obtaining personalised HRTFs, focusing on the recent advances in numerical HRTF calculation

    CRUSOE: A Toolset for Cyber Situational Awareness and Decision Support in Incident Handling

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    The growing size and complexity of today’s computer network make it hard to achieve and maintain so-called cyber situational awareness, i.e., the ability to perceive and comprehend the cyber environment and be able to project the situation in the near future. Namely, the personnel of cybersecurity incident response teams or security operation centers should be aware of the security situation in the network to effectively prevent or mitigate cyber attacks and avoid mistakes in the process. In this paper, we present a toolset for achieving cyber situational awareness in a large and heterogeneous environment. Our goal is to support cybersecurity teams in iterating through the OODA loop (Observe, Orient, Decide, Act). We designed tools to help the operator make informed decisions in incident handling and response for each phase of the cycle. The Observe phase builds on common tools for active and passive network monitoring and vulnerability assessment. In the Orient phase, the data on the network are structured and presented in a comprehensible and visually appealing manner. The Decide phase opens opportunities for decision-support systems, in our case, a recommender system that suggests the most resilient configuration of the critical infrastructure. Finally, the Act phase is supported by a service that orchestrates network security tools and allows for prompt mitigation actions. Finally, we present lessons learned from the deployment of the toolset in the campus network and the results of a user evaluation study
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