Documents published in Scipedia

• Hybrid High-Order Finite Volume/Discontinuous Galerkin Methods for Turbulent Flows

  D. Yuan, P. Tsoutsanis, K. Jenkins

  eccomas2022.

  
  

  Abstract

  In this paper we develop a family of arbitrarily high-order non-oscillatory hybrid Finite Volume/Discontinuous Galerkin schemes for turbulent flows on mixed-element unstructured meshes. The schemes are inherently compact in the sense that the central stencils employed are as compact as possible, and that the directional stencils are reduced in size, simplifying their implementation. Their key ingredient is the switch between a DG method and a FV method based on the CWENOZ scheme when a troubled cell is detected. Therefore, in smooth regions of the computational domain, the high order of accuracy offered by DG is preserved, while in regions with sharp gradients, the robustness of FV is utilized. This paper also presents the time evolution of troubled cells in unsteady test cases and the use of extended bounds for troubled cell detection. We assess the performance of these schemes in terms of accuracy, robustness and computational cost through a series of stringent 2D and 3D test problems. The results obtained demonstrate the accuracy and robustness that the schemes offer and highlight areas of future improvements that are considered.

  Abstract
  In this paper we develop a family of arbitrarily high-order non-oscillatory hybrid Finite Volume/Discontinuous Galerkin schemes for turbulent flows on mixed-element unstructured [...]

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• Deep Neural Network-driven hp-adaptive Finite Element Method in three dimensions

  M. Paszynski, R. Grzeszczuk, W. Dzwinel, D. Pardo

  eccomas2022.

  
  

  Abstract

  Numerical solutions of Partial Differential Equations with Finite Element Method have multiple applications in science and engineering. Several challenging problems require special stabilization methods to deliver accurate results of the numerical simulations. The advection-dominated diffusion problem is an example of such problems. They are employed to model pollution propagation in the atmosphere. Unstable numerical methods generate unphysical oscillations, and they make no physical sense. Obtaining accurate and stable numerical simulations is difficult, and the method of stabilization depends on the parameters of the partial differential equations. They require a deep knowledge of an expert in the field of numerical analysis. We propose a method to construct and train an artificial expert in stabilizing numerical simulations based on partial differential equations. We create a neural network-driven artificial intelligence that makes decisions about the method of stabilizing computer simulations. It will automatically stabilize difficult numerical simulations in a linear computational cost by generating the optimal test functions. These test functions can be utilized for building an unconditionally stable system of linear equations. The optimal test functions proposed by artificial intelligence will not depend on the right-hand side, and thus they may be utilized in a large class of PDE-based simulations with different forcing and boundary conditions. We test our method on the model one-dimensional advection-dominated diffusion problem.

  Abstract
  Numerical solutions of Partial Differential Equations with Finite Element Method have multiple applications in science and engineering. Several challenging problems require [...]

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• A numerical set-up for the simulation of infection probability from SARS- CoV-2 in public transport vehicles

  E. Schillaci, J. Vera, N. Morozova, J. Rigola

  eccomas2022.

  
  

  Abstract

  In this work, a numerical framework aimed at simulating the transport of contaminants and infectious agents within a closed domain is presented. The method employs mature CFD algorithms to calculate air fields with reasonable computational costs. The main objective is to give fast response to stakeholders about air quality indicators in the design phase of HVAC systems. A discussion regarding the size and characteristics of different contaminants is proposed, highlighting the most appropriate methods and coefficients needed to simulate their transport. Next, the methodology employed to evaluate the risk of infection is presented. The numerical set-up, based on the buoyantBoussinesqPimpleFoam solver in OpenFOAM, was tuned by simulating the well-known case of the heated floor cavity, providing accurate results. Hence, the case study of a transport vehicle of generic shape is presented, in order to show possible results in terms of air-age distribution, PM2.5 distribution, and global infection risk matrix.

  Abstract
  In this work, a numerical framework aimed at simulating the transport of contaminants and infectious agents within a closed domain is presented. The method employs mature [...]

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• 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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