471 research outputs found

    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 “Schwingungen in rotierenden Maschinen”. 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 “European Conference on Rotordynamics”. 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

    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 fluid-dynamics systems, based on the previous recognition of features underlying large sets of fluid-dynamics data, have been studied. Specifically, 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 offer higher freedom on how data is organised and processed. This motivated the use of graphs to represent the state of fluid-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 fluid-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 defined from the original graph, and messages are also passed from finer to coarser graphs and vice-versa. The low-resolution graphs allowed for efficiently capturing physics encompassing a range of lengthscales. Advection and fluid flow, 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 sufficient to achieve high generalisation accuracy in advection simulations, flow simulation highly benefited 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 author’s knowledge, the REMuS-GNN architecture was the first 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

    Finite element interpolated neural networks for solving forward and inverse problems

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    We propose a general framework for solving forward and inverse problems constrained by partial differential equations, where we interpolate neural networks onto finite element spaces to represent the (partial) unknowns. The framework overcomes the challenges related to the imposition of boundary conditions, the choice of collocation points in physics-informed neural networks, and the integration of variational physics-informed neural networks. A numerical experiment set confirms the framework's capability of handling various forward and inverse problems. In particular, the trained neural network generalises well for smooth problems, beating finite element solutions by some orders of magnitude. We finally propose an effective one-loop solver with an initial data fitting step (to obtain a cheap initialisation) to solve inverse problems

    Adaptive Discontinuous Galerkin Methods for Variational Inequalities with Applications to Phase Field Models

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    Solutions of variational inequalities often have limited regularity. In particular, the nonsmooth parts are local, while other parts of the solution have higher regularity. To overcome this limitation, we apply hp-adaptivity, which uses a combination of locally finer meshes and varying polynomial degrees to separate the different features of the the solution. For this, we employ Discontinuous Galerkin (DG) methods and show some novel error estimates for the obstacle problem which emphasize the use in hp-adaptive algorithms. Besides this analysis, we present how to efficiently compute numerical solutions using error estimators, fast algebraic solvers which can also be employed in a parallel setup, and discuss implementation details. Finally, some numerical examples and applications to phase field models are presented

    The design of a Python library for the automatic definition and simulation of transient ionization fronts

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    In recent years, the interest in nonthermal plasma dynamics has grown significantly, within both industry and research. This has been driven by the development of several novel cold plasma technologies across a wide range of different fields, for example, for plasma medicine, chemical processing, pollution control, and surface treatment. The optimization of these technologies relies heavily upon the understanding of gas discharge plasmas: their generation, electrical characteristics, and interaction with their surroundings. Moreover, the manifestation of nonthermal plasmas in the form of streamers is of high relevance and critical importance to high voltage insulation technology, and has further significance to geophysical research concerning atmospheric discharges. The present work describes the development of the StrAFE (Streamers on Adaptive Finite Elements) package, a dedicated Python library built atop the popular open-source FEniCS finite element software, designed with the objective to simplify and to automate the solution of ionization front models. The library features support for mesh adaptivity, distributed memory parallelism, and an intuitive programming interface, while providing an exceptionally high level of user configurability. This article presents the software implementation, describes its features, and presents several code verification studies performed within simple and complex domains. It is concluded that the numerical results gained from this open-source framework are comparable to other well-established software in terms of accuracy. Therefore, it further demonstrates the great potential for open-source software to make significant contributions to technologies involving nonthermal plasmas, ionization fronts, and gas discharges

    Solution-Acceleration Strategies for High-Order Unstructured Methods

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    The design of next-generation aircraft relies on computational fluid dynamics (CFD) to minimize testing requirements at reduced cost and risk. However, current industry reliance on Reynolds-averaged Navier-Stokes (RANS)-based CFD is limited in predicting transitional and turbulent flows. Large-eddy simulation (LES) offers accuracy where RANS methods fail, but can have prohibitive computational cost. To address this, we propose a high-order CFD framework to advance flux reconstruction (FR) methods toward industrial-scale simulations. FR is a family of high-order, unstructured schemes that provide accuracy at reduced cost per degree-of-freedom (DOF) compared to low-order methods, with proven potential for LES. We develop practical strategies to reduce the computational cost of FR methods for explicit and implicit formulations. Due to the low cost per time step, explicit time stepping is typically used in FR methods. However, stability constraints prohibitively limit time-step sizes in numerically stiff problems. Hence, implicit time stepping is preferred in these cases, but it requires solving large, nonlinear systems and can be computationally expensive. This thesis introduces optimal Runge-Kutta methods to alleviate stability limits and reduce wall-clock times by approximately half in moderately low stiffness problems. For increased stiffness, we hybridize implicit FR methods using a trace variable, which allows a reduction of the implicit system via static condensation, decreasing implicit time stepping costs, especially at higher orders. Hybridization with both discontinuous (HFR) and continuous function spaces (EFR) is suitable for advection and advection-diffusion type problems within the FR method and enables significant speedup gains over standard FR. We incorporate polynomial adaptation to the hybridized framework, varying the solution polynomial’s degree locally within each element, which results in an overall reduction in DOF and significant speedup gains in a two-dimensional problem against standard polynomial-adaptive formulations. Finally, we combine implicit-explicit (IMEX) time stepping with hybridization to tackle geometry-induced numerical stiffness. The resulting method reduces computational cost at least fifteen times over explicit methods in a multi-element airfoil problem at Reynolds 1.7 million. Our proposed framework enables substantial reductions in both moderate and high stiffness problems, thus advancing high-order methods toward large industrial-scale problems

    Ultra-high temperature concentrated solar thermal energy

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    Given the extremely high surface temperature of the Sun (~5778 K), solar radiation has the theoretical potential, in accordance with the second law of thermodynamics, to heat a receiver on Earth up to ultra-high temperatures (specified in this thesis as >1300 K). However, there is a gap between theory and practice, as contemporary solar thermal energy systems are still limited to temperatures below 900 K due to material and mechanical limitations. Running solar thermal energy at ultra-high temperatures promises greater energy conversion efficiencies for power plants by upgrading their basic cycles to include more advanced power cycles. Furthermore, the provision of solar thermal energy at ultra-high temperatures can unlock a wide range of energy-intensive industrial applications, including hydrogen and cement production, which can contribute to decarbonising sectors which are difficult to electrify. This thesis proposes a novel concept of an ultra-high temperature solar cavity receiver based on an optically exposed liquid metal heat transfer fluid, which flows down a corrugated back plate. The concept is investigated using a quasi-steady-state analytical energy model, in addition to a radiation-coupled Computational Fluid Dynamics (CFD) solution. The developed analysis methods are tailored to the proposed class of receivers, nonetheless, they can be generalised for broad solar receiver analysis or for analysing similar problems involving volumetric radiation absorption in other thermal applications. The concept is shown implementable at its absorptive cavity configuration with an overall (optical and thermal) receiver efficiency exceeding 70%. The proposed concept is a step towards narrowing the technological mismatch, in terms of temperature and scale, between state-of-the-art thermal energy storage and concentrated solar thermal at ultra-high temperatures. A characterisation of prospective ultra-high temperature receivers is presented, which involved a review of state-of-the-art solar thermal technologies with the purpose of identifying the existing challenges to operating at ultra-high temperatures. Based on this characterisation, the proposed receiver is designed to address the literature concerns. The proposed receiver concept involved novel engineering features, including the use of refractory containment materials and a transparent ceramic window to seal the aperture. Therefore, the conceptual investigation attempted to address possible concerns that might be introduced by the new features. Finally, the proposed receiver is demonstrated in a concentrated solar power plant application to emphasise, using quantitative terms, the benefits of operating the receiver at ultra-high temperatures for large-scale applications

    Algebraic, Block and Multiplicative Preconditioners based on Fast Tridiagonal Solves on GPUs

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    This thesis contributes to the field of sparse linear algebra, graph applications, and preconditioners for Krylov iterative solvers of sparse linear equation systems, by providing a (block) tridiagonal solver library, a generalized sparse matrix-vector implementation, a linear forest extraction, and a multiplicative preconditioner based on tridiagonal solves. The tridiagonal library, which supports (scaled) partial pivoting, outperforms cuSPARSE's tridiagonal solver by factor five while completely utilizing the available GPU memory bandwidth. For the performance optimized solving of multiple right-hand sides, the explicit factorization of the tridiagonal matrix can be computed. The extraction of a weighted linear forest (union of disjoint paths) from a general graph is used to build algebraic (block) tridiagonal preconditioners and deploys the generalized sparse-matrix vector implementation of this thesis for preconditioner construction. During linear forest extraction, a new parallel bidirectional scan pattern, which can operate on double-linked list structures, identifies the path ID and the position of a vertex. The algebraic preconditioner construction is also used to build more advanced preconditioners, which contain multiple tridiagonal factors, based on generalized ILU factorizations. Additionally, other preconditioners based on tridiagonal factors are presented and evaluated in comparison to ILU and ILU incomplete sparse approximate inverse preconditioners (ILU-ISAI) for the solution of large sparse linear equation systems from the Sparse Matrix Collection. For all presented problems of this thesis, an efficient parallel algorithm and its CUDA implementation for single GPU systems is provided

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum
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