Documents published in Scipedia

• A CFD-based multi-fidelity surrogate model for prediction of flow parameters in a ventilated room

  N. Morozova, F. Trias, V. Vanovskiy, C. Oliet, E. Burnaev

  eccomas2022.

  
  

  Abstract

  In this work, we present a multi-fidelity machine learning surrogate model, which predicts comfort-related flow parameters in a ventilated room with a heated floor. The model uses coarseand fine-grid CFD data obtained using LES turbulence models. The dataset is created by changing the width aspect ratio of the rooms, inlet flow velocity, and temperature of the hot floor. The surrogate model takes the values of temperature and velocity magnitude at four different cavity locations as inputs. These probes are located such that they could be replaced by actual sensor readings in a practical case. The model's output is a set of comfort-related flow parameters. We test two multi-fidelity approaches based on Gaussian process regression (GPR), among them GPR with linear correction (LC GPR), and multi-fidelity GPR (MF GPR) or cokriging. The computational cost and accuracy of these approaches are compared with GPRs based on single-fidelity data. All of the tested multi-fidelity approaches successfully reduce the computational cost of dataset generation compared to high-fidelity GPR while maintaining the required level of accuracy. The co-kriging approach demonstrates the best trade-off between computational cost and accuracy.

  Abstract
  In this work, we present a multi-fidelity machine learning surrogate model, which predicts comfort-related flow parameters in a ventilated room with a heated floor. The model [...]

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• Refinement techniques for spline spaces with cloud-based geomiso TNL software

  P. Karakitsios, P. Kolios, A. Leontaris, G. Karaiskos

  eccomas2022.

  
  

  Abstract

  Geomiso TNL is a new both on-premises and cloud-based software, which delivers isogeometric analysis (IGA) and 3D design with splines. It combines IGA and cloud computing, one of the fastest growing fields in IT industry. The combination of cloud computing and advanced refinement techniques constitutes a real game changer in CAD/CAE fields. Cloud-based IGA represents the future of product engineering, soon to become an industry standard. Automatic mesh refinement has not been widely adopted in industry, because it requires access to the exact geometry. This hybrid program achieves seamless and automatic communication with CAD, thus mesh refinement utilizes the exact geometry, while cloud computing enables users to execute large-scale simulation experiments without the need for dedicated hardware. The recently developed cloud-based platform www.geomiso.cloud is introduced to help engineers and industries make effective use of inelastic static isogeometric analysis and design with advanced spline techniques. It is argued that Geomiso TNL is a new, more efficient, alternative to FEA software packages. This is the first time ever such a cloud-based program has been developed.

  Abstract
  Geomiso TNL is a new both on-premises and cloud-based software, which delivers isogeometric analysis (IGA) and 3D design with splines. It combines IGA and cloud computing, [...]

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• PSYDAC: a high-performance IGA library in Python

  Y. Güçlü, S. Hadjout, A. Ratnani

  eccomas2022.

  
  

  Abstract

  Psydac is a Python 3 library for the solution of partial differential equations, which combines the convenience of a domain specific language with the speed of a high-performance parallel engine. Its main focus is on isogeometric analysis using tensor-product B-spline finite elements; to this end it uses an optimized sparse format called 'stencil matrix', which drastically reduces memory storage compared to the popular CSR/CSC formats. It supports multi-patch mapped geometries, and finite element exterior calculus. It can distribute each domain patch across many MPI processes, with multiple OpenMP threads operating in each block. The users of Psydac define a weak form of the model equations through SymPDE, an extension of Sympy that provides the mathematical expressions and checks their semantic validity. Simple mappings can be defined analytically, and multi-patch NURBS geometries can be imported from file. Once a finite element discretization is chosen, Psydac maps abstract concepts onto concrete objects, the basic building blocks being MPI-distributed vectors and matrices. Python code is automatically generated for the model-specific operations, namely matrix and vector assembly, and user-defined diagnostics. Finally, Psydac accelerates all computationally intensive operations using Pyccel, a transpiler which converts Python code to either C or Fortran. We present the library design, the typical usage workflow, the user interface for a simple 2D example, and the parallel scaling results in a large 3D simulation. In addition we show a few complex applications in fluid dynamics and electromagnetism, where the accuracy of the solver is verified against manufactured and reference solutions.

  Abstract
  Psydac is a Python 3 library for the solution of partial differential equations, which combines the convenience of a domain specific language with the speed of a high-performance [...]

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• Advanced Experiments on Gaussian Process-based Multi-fidelity Methods over Diverse Mathematical Characteristics

  S. Yildiz, H. Pehlivan-Solak, M. Diez, O. Goren, M. Nikbay

  eccomas2022.

  
  

  Abstract

  Advanced applications of multi-fidelity surrogate modelling techniques provide significant improvements in optimization and uncertainty quantification studies in many engineering fields. Multi-fidelity surrogate modelling can efficiently save the design process from the computational time burden caused by the need for numerous computationally expensive simulations. However, no consensus exists about which multi-fidelity surrogate modelling technique usually exhibits superiority over the other methods given for certain conditions. Therefore, the present paper focuses on assessing the performances of the Gaussian Process-based multi-fidelity methods across selected benchmark problems, especially chosen to capture diverse mathematical characteristics, by experimenting with their learning processes concerning different performance criteria. In this study, a comparison of Linear-Autoregressive Gaussian Process and NonlinearAutoregressive Gaussian Process methods is presented by using benchmark problems that mimic the behaviour of real engineering problems such as localized behaviours, multi-modality, noise, discontinuous response, and different discrepancy types. Our results indicate that the considered methodologies were able to capture the behaviour of the actual function sufficiently within the limited amount of budget for 1-D cases. As the problem dimension increases, the required number of training data increases exponentially to construct an acceptable surrogate model. Especially in higher dimensions, i.e. more than 5-D, local error metrics reveal that more training data is needed to attain an efficient surrogate for Gaussian Process based strategies.

  Abstract
  Advanced applications of multi-fidelity surrogate modelling techniques provide significant improvements in optimization and uncertainty quantification studies in many engineering [...]

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• Simulation of wind induced excitation of a membrane structure with ponding water

  N. Kodunthirappully Narayanan, R. Wüchner, J. Degroote

  eccomas2022.

  
  

  Abstract

  This paper proposes a new partitioned coupling approach to simulate the wind induced excitation of a membrane structure with ponding water. This approach uses three different solvers to simulate wind, water and membrane structure. The main assumption here is that the interaction between the wind and water can be neglected due to the small depth and small fetch of the water, relative to the size of the membrane structure. This assumption results in a coupling strategy where the structural solver independently interacts with the wind and water solver. The results from this method is compared with a straightforward approach, where a two-phase solver, modeling the wind and water, is coupled to a structural solver. The obtained results agreed very well with the reference modeling approach, where all the interactions are taken into account. Furthermore, the proposed method was found to be computationally more efficient.

  Abstract
  This paper proposes a new partitioned coupling approach to simulate the wind induced excitation of a membrane structure with ponding water. This approach uses three different [...]

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• Isogeometric analysis of industry applications with Geomiso SEA: a new hybrid software for shell analysis

  P. Karakitsios, V. Tsotoulidi, P. Kolios, G. Mprellas

  eccomas2022.

  
  

  Abstract

  In this paper the new Geomiso SEA software (www.geomiso.com) is proposed for applications on inelastic static isogeometric analysis with shell elements. This hybrid program is applicable to real world structures, while it satisfies the rising need for technical software of dual CAD/CAA nature. It is based on the new isogeometric method, which has attracted a lot of attention for solving boundary value problems. T-spline-based isogeometric shell analysis efficiently handles multi-patch geometries. Geomiso SEA is not just a plug-in, but a complete software solution, used to simulate spline models of structures or machine components, for analyzing their strength and behavior. The utilization of the exact mesh for analysis vanishes geometric errors, while there is no need of repeating the geometry design for refinement purposes. Industry applications on both thick and thin shells are demonstrated with a comparison between Geomiso SEA and FEA programs. This unique solution for seamless integration of the industrial design of shell geometries with its computational realtime testing, appears to be preferable to FEA programs, representing major improvements, such as higher accuracy, and considerably reduced computational cost.

  Abstract
  In this paper the new Geomiso SEA software (www.geomiso.com) is proposed for applications on inelastic static isogeometric analysis with shell elements. This hybrid program [...]

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• Efficient coarse correction for parallel time stepping in plaque growth simulations

  S. Frei, A. Heinlein

  eccomas2022.

  
  

  Abstract

  In order to make the numerical simulation of atherosclerotic plaque growth feasible, a temporal homogenization approach is employed. The resulting macro-scale problem for the plaque growth can be further accelerated by using parallel time integration schemes, such as the parareal algorithm. However, the parallel scalability is dominated by the computational cost of the coarse propagator. Therefore, in this paper, an interpolation-based coarse propagator, which uses growth values from previously computed micro-scale problems, is introduced. For a simple model problem, it is shown that this approach reduces both the computational work for a single parareal iteration as well as the required number of parareal iterations.

  Abstract
  In order to make the numerical simulation of atherosclerotic plaque growth feasible, a temporal homogenization approach is employed. The resulting macro-scale problem for [...]

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• Multi-fidelity active learning for shape optimization problems affected by noise

  J. Wackers, R. Pellegrini, M. Diez, A. Serani, M. Visonneau

  eccomas2022.

  
  

  Abstract

  The efficiency of simulation-driven design optimization based on surrogate models, depends strongly on the suitability of the surrogate model for the simulation data on which it is based. We investigate adaptive surrogate modelling methods that maximize the efficiency and the robustness for any optimization problem. Specific techniques include: adaptive sampling, noise filtering by metamodel tuning, and small initial datasets to give maximum freedom to the adaptation. These methodological advancements are demonstrated for an analytical test problem, as well as the shape optimization of the DTMB 5415 ship model for calm-water resistance.

  Abstract
  The efficiency of simulation-driven design optimization based on surrogate models, depends strongly on the suitability of the surrogate model for the simulation data on which [...]

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• Reduced-order model for large amplitude vibrations of flexible structures coupled with a flow

  T. Flament, J. Deü, A. Placzek, M. Balmaseda, D. Tran

  eccomas2022.

  
  

  Abstract

  This work concerns the numerical modeling of the vibrations of geometrically nonlinear structures coupled with a fluid flow. Firstly, a reduced-order model (ROM) for the geometrically nonlinear structure is proposed. Then, the aforementioned ROM is used to replace a Finite Element solver (FE) in the frame of a fluid-structure partitioned coupling on a two-dimensional example involving vortex induced vibrations.

  Abstract
  This work concerns the numerical modeling of the vibrations of geometrically nonlinear structures coupled with a fluid flow. Firstly, a reduced-order model (ROM) for the geometrically [...]

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• Fluid-structure interaction simulation of a wire-wrapped tube array using overset grids

  H. Dolfen, D. Van Hauwermeiren, A. Bral, J. Degroote

  eccomas2022.

  
  

  Abstract

  Axial flows in tube bundle geometries are omnipresent in nuclear power plants, including fuel assemblies and heat exchangers. The tubes are often long and slender which makes them susceptible to flow-induced vibrations. Current reactor research investigates the use of wires helicoidally wound around each rod, to preserve their mutual distance. This poses new challenges for numerical simulations. The wire-wrapped geometry is complex, among other things due to small gaps, making the meshing process complicated and cumbersome. This research explores the use of overset grids for such wire-wrapped tube bundles. This meshing method allows grids to overlap and the flow solution is interpolated between the different grids, providing more freedom for meshing. In this case the approach consists of making one grid for a single tube with a wire, termed the component mesh, and placing this grid in a so-called background mesh that takes into account other geometrical features (e.g. the bundle duct). In the case of bundles, the same tube mesh can be repeated several times without additional meshing effort, regardless of the bundle size. The approach is verified using 4 cases found in literature, using the same component mesh either one or multiple times for each case, thus reducing the meshing effort to a minimum thanks to the freedom and re-usability overset offers. The first two cases involve a fluid-structure interaction simulation of a single wire-wrapped tube in a cylindrical domain, the first one simulating a steady deformation and the second one a free vibration of the tube. Good agreement with results in literature was found. The latter two cases are Computational Fluid Dynamics simulations of a 7-pin and 19-pin bundle in a hexagonal duct, and excellent agreement with literature results was obtained. The overset approach was proven beneficial for simulating wire-wrapped bundle geometries: with largely reduced effort the same predictive capabilities can be obtained and potentially even extended.

  Abstract
  Axial flows in tube bundle geometries are omnipresent in nuclear power plants, including fuel assemblies and heat exchangers. The tubes are often long and slender which makes [...]

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