3,062 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Reliability-based leading edge erosion maintenance strategy selection framework

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    Leading edge erosion has become one of the most prevailing failure modes ofĀ wind turbines. Its effects can evolve from an aerodynamic modification of the properties of the blade to a potential structural failure of the leading edge. The first produces a reduction of energy production and the second can produce aĀ catastrophic failureĀ of the blade. Considering the uncertainties and constraints involved in the design of optimalĀ operation and maintenanceĀ (O&M) strategies for offshore assets and the influence of site-specific parameters on the dynamics of this particular failure mode, the task becomes complex. In this study, a framework to evaluate the influence of different maintenance strategies considering uncertainties in weather,Ā material behaviourĀ and repair success is presented. Monte Carlo Simulation (MCS) is used alongside a computational framework for Leading Edge Erosion (LEE) degradation to evaluate the lifetime cost distribution and probability of failure of the chosen maintenance strategies. The use of the framework is demonstrated in a case study considering a 5-MWĀ offshore wind turbineĀ located in the north ofĀ Germany. The influence of the modification of the maintenance interval or time between repairs and the comparison with maintenance activities executed only during months with milder weather is analysed in terms of cost and reliability. AĀ Pareto frontĀ plot considering the probability of failure and the median of the cost is used to jointly compare strategies considering both aspects to provide a tool for risk-informed maintenance selection. Finally, the potential benefits of condition-based maintenance and autonomous decision-making systems are discussed. The case of study shows the benefits of repairs during summer months and the importance of the relation risk/O&M cost for different maintenance strategies

    Graduate Catalog of Studies, 2023-2024

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    Kinetic energy fluctuation-driven locomotor transitions on potential energy landscapes of beam obstacle traversal and self-righting

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    Despite contending with constraints imposed by the environment, morphology, and physiology, animals move well by physically interactingwith the environment to use and transition between modes such as running, climbing, and self-righting. By contrast, robots struggle to do so in real world. Understanding the principles of how locomotor transitions emerge from constrained physical interaction is necessary for robots to move robustly using similar strategies. Recent studies discovered that discoid cockroaches use and transition between diverse locomotor modes to traverse beams and self-right on ground. For both systems, animals probabilistically transitioned between modes via multiple pathways, while its self-propulsion created kinetic energy fluctuation. Here, we seek mechanistic explanations for these observations by adopting a physics-based approach that integrates biological and robotic studies. We discovered that animal and robot locomotor transitions during beam obstacle traversal and ground self-righting are barrier-crossing transitions on potential energy landscapes. Whereas animals and robot traversed stiff beams by rolling their body betweenbeam, they pushed across flimsy beams, suggesting a concept of terradynamic favorability where modes with easier physical interaction are more likely to occur. Robotic beam traversal revealed that, system state either remains in a favorable mode or transitions to one when energy fluctuation is comparable to the transition barrier. Robotic self-righting transitions occurred similarly and revealed that changing system parameters lowers barriers over which comparable fluctuation can induce transitions. Thetransitionsof animalsin both systems mostly occurred similarly, but sensory feedback may facilitate its beam traversal. Finally, we developed a method to measure animal movement across large spatiotemporal scales in a terrain treadmill.Comment: arXiv admin note: substantial text overlap with arXiv:2006.1271

    High-performance shape memory composites with intrinsic heating capabilities

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    Shape morphing structures have played a significant role within the field of aerospace for more than a century. While the shape morphing aerostructures of the past and present have depended on hinges and motors to achieve morphing, their future is expected to rely on smart materials and structures that have intrinsic shape morphing capabilities. One such smart material, that has previously been developed at Imperial College London, is the carbon fibre reinforced epoxy polymer (CFRP) composite with thermoplastic (TP) interleaves. These interleaved composites exhibit controllable stiffness (CS) and shape memory (SM) capabilities under suitable thermal conditions. While these interleaved composites showed excellent shape morphing capabilities, they had several drawbacks. These composites showed poor flexural modulus and through-thickness shear strength compared to the epoxy-based non-interleaved CFRP. These composites also used an oven to achieve the high temperatures required to exhibit the CS and SM capabilities. This thesis describes studies conducted to mitigate these drawbacks. In the first study described in this thesis, the source of the premature through-thickness shear failure in TP interleaved CFRP composites was discovered to be the low shear strength of the polystyrene (PS) interleaves used in previous works. It was then demonstrated that replacing PS with Poly(styrene-co-acrylonitrile) (SAN) could improve the through-thickness shear strength of the interleaved composites to be almost as high as that of pristine CFRP. Furthermore, the SAN-interleaved CFRP laminates also exhibited excellent CS and SM capabilities. In the next study described in this thesis, it was demonstrated that the flexural modulus of TP interleaved CFRP composites can be substantially improved by two different methods- (i) reducing the thickness of the TP interleaves, and (ii) introducing reinforcements within the TP interleaves. The following study describes how intrinsic heating capability was achieved in TP interleaved CFRP composites, through resistive heating of heater elements such as stainless steel (SS) meshes and woven carbon fabric (WCF) embedded within the layup of the composite. This intrinsic heating strategy was used to supply the temperature necessary for the corresponding composites to exhibit CS and SM capabilities. As a result, these intrinsically heated TP interleaved CFRP composites exhibited successful out-of-oven morphing capabilities. In the final study described in this thesis, composite structures that were initially flat in their as-cured state, but were capable of deployment into planar and curved meshes were designed. Finite element numerical models were used to predict the deployment capabilities of these composite structures. Finally, the deployable composite mesh structures were manufactured and characterised.Open Acces

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM ā€œSchwingungen in rotierenden Maschinenā€. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name ā€œEuropean Conference on Rotordynamicsā€. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    Undergraduate Catalog of Studies, 2022-2023

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    Data-driven deep-learning methods for the accelerated simulation of Eulerian fluid dynamics

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    Deep-learning (DL) methods for the fast inference of the temporal evolution of ļ¬‚uid-dynamics systems, based on the previous recognition of features underlying large sets of ļ¬‚uid-dynamics data, have been studied. Speciļ¬cally, models based on convolution neural networks (CNNs) and graph neural networks (GNNs) were proposed and discussed. A U-Net, a popular fully-convolutional architecture, was trained to infer wave dynamics on liquid surfaces surrounded by walls, given as input the system state at previous time-points. A term for penalising the error of the spatial derivatives was added to the loss function, which resulted in a suppression of spurious oscillations and a more accurate location and length of the predicted wavefronts. This model proved to accurately generalise to complex wall geometries not seen during training. As opposed to the image data-structures processed by CNNs, graphs oļ¬€er higher freedom on how data is organised and processed. This motivated the use of graphs to represent the state of ļ¬‚uid-dynamic systems discretised by unstructured sets of nodes, and GNNs to process such graphs. Graphs have enabled more accurate representations of curvilinear geometries and higher resolution placement exclusively in areas where physics is more challenging to resolve. Two novel GNN architectures were designed for ļ¬‚uid-dynamics inference: the MuS-GNN, a multi-scale GNN, and the REMuS-GNN, a rotation-equivariant multi-scale GNN. Both architectures work by repeatedly passing messages from each node to its nearest nodes in the graph. Additionally, lower-resolutions graphs, with a reduced number of nodes, are deļ¬ned from the original graph, and messages are also passed from ļ¬ner to coarser graphs and vice-versa. The low-resolution graphs allowed for eļ¬ƒciently capturing physics encompassing a range of lengthscales. Advection and ļ¬‚uid ļ¬‚ow, modelled by the incompressible Navier-Stokes equations, were the two types of problems used to assess the proposed GNNs. Whereas a single-scale GNN was suļ¬ƒcient to achieve high generalisation accuracy in advection simulations, ļ¬‚ow simulation highly beneļ¬ted from an increasing number of low-resolution graphs. The generalisation and long-term accuracy of these simulations were further improved by the REMuS-GNN architecture, which processes the system state independently of the orientation of the coordinate system thanks to a rotation-invariant representation and carefully designed components. To the best of the authorā€™s knowledge, the REMuS-GNN architecture was the ļ¬rst rotation-equivariant and multi-scale GNN. The simulations were accelerated between one (in a CPU) and three (in a GPU) orders of magnitude with respect to a CPU-based numerical solver. Additionally, the parallelisation of multi-scale GNNs resulted in a close-to-linear speedup with the number of CPU cores or GPUs.Open Acces
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