Documents published in Scipedia

• Non-newtonian viscous elongation and shear fluid model based on optimal triple tensor decomposition

  M. Rütten, T. Schomberg

  eccomas2022.

  
  

  Abstract

  The modeling of the distinct non-Newtonian fluid properties is an essential prerequisite for the computational simulation of associated flow fields. In particular, some non-Newtonian fluids reveal strong diverse viscosity response behaviours to pure elongational and simple shearing flows. Therefore, it is necessary to be able to distinguish between these flow types even in complex flow configuration. Unfortunately flow types are naturally mixed and this distinction becomes quite difficult. Only in a Lagrangian framework the tracking of the Lagrangian fluid element deformation allows an accurate strain related deformation type assignment. However, most CFD approaches prefer the Eulerian framework accepting the loss of the natural flow path alignment of the moving fluid particles. Consequently shear and elongation rates are barely separable without particular assignment methods. In this work a tensor decomposition method from vortex dynamics is discussed which allows to distinguish between these flow types. In vortex dynamics the problem occurred to separate shearing from purely rotational flows because different hydro- and aerodynamic flow phenomena are caused by shear and vortex related flow types. Thereto, various methods were proposed, among others the optimal triple tensor decomposition method which is able to separate vortical from shearing flows but also, after some modification, elongational from shearing flows. This tensor decomposition is now used to calculate elongation and shearing rates as input variables into non-Newtonian fluid models for the calculation of the local elongational and shear viscosity. The application case is a cross slot channel flow often used as reference. In this numerical simulation study the impact of the elongation rate modeling on the contraction flow topology is shown and discussed. It is shown that the modeling of different viscous elongational and shear-thinning affects the resulting flow significantly  

  Abstract
  The modeling of the distinct non-Newtonian fluid properties is an essential prerequisite for the computational simulation of associated flow fields. In particular, some non-Newtonian [...]

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• Neural network prediction of the flow field in a periodic domain with hyper-neural network parametrization

  O. Bublík, V. Heidler, A. Pecka, J. Vimmr

  eccomas2022.

  
  

  Abstract

  This paper is concerned with fast flow field prediction in a blade cascade for variable blade shapes as well as variable Reynolds number using the machine-learning architecture called convolutional neural network. To generate flow field for a specific Reynolds number, an encoder-decoder convolutional neural network, also called U-Net, is used. The values 500, 1000 and 1500 of the Reynolds number are chosen as the training set. Three U-Nets were trained on CFD results for 100 blade profiles, each U-Net for a different Reynolds number. In order to get a prediction for variable Reynolds number, a so-called hypernetwork in employed. The hypernetwork essentially interpolates between the two trained U-Nets. The architecture of the hypernetwork is fully-connected feedforward neural network with one input neuron corresponding to the Reynolds number, one hidden layer and the output layer corresponds to the weights for the interpolated U-Net. The concept of the hypernetwork-based parametrization is tested on a problem of compressible fluid flow through a blade cascade with three unseen blade profiles and unseen Reynolds number.

  Abstract
  This paper is concerned with fast flow field prediction in a blade cascade for variable blade shapes as well as variable Reynolds number using the machine-learning architecture [...]

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• Symmetry-preserving discretisation methods for magnetohydrodynamics

  J. Hopman, X. Trias, J. Rigola

  eccomas2022.

  
  

  Abstract

  In this work, the symmetry-preserving method [1, 2, 3] is extended to include magnetohydrodynamic effects, using the collocated grid arrangement of Ni et al. [4, 5]. The electromagnetic part is solved explicitly using the induction-less approximation and an electric potential Poisson equation. The proposed solver is implemented in OpenFOAM and tested for accuracy and stability, and compared to the method of Ni et al. [4, 5]. A new benchmark case using a Taylor-Green vortex in a transverse magnetic field is used, for which kinetic energy budget terms are compared to the analytical solutions. Finally, Hunt's case is used to compare flow profiles to the analytical solutions. Influence of the spatial discretisation on accuracy and stability is also examined by solving both cases on meshes with variable degrees of distortion. The symmetry-preserving method showed accuracy on Cartesian meshes and stability even on extremely distorted meshes, whereas the method of Ni et al. [4, 5] showed less accurate conservation of current density and was not able to produce stable solutions on the extremely distorted meshes.

  Abstract
  In this work, the symmetry-preserving method [1, 2, 3] is extended to include magnetohydrodynamic effects, using the collocated grid arrangement of Ni et al. [4, 5]. The electromagnetic [...]

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• Fluidization by gas pore pressure of dense granular flows: numerical simulations versus experiments

  A. Aravena, L. Chupin, T. Dubois, O. Roche

  eccomas2022.

  
  

  Abstract

  A model accounting for fluidisation by pore gas pressure in dense granular flows is presented. A viscoplastic rheology, based on the Drucker-Prager criterium, is used to describe the granular medium which is a mixture of air and glass beads. The pore gas pressure, which satisfies an advection-diffusion equation, reduces the friction between the particles and thus the value of the apparent viscosity. As a consequence, dense fluidised granular flows can travel longer distances. In laboratory experiments, the run-out distance reached by dense granular columns when collapsing is almost doubled when fluidisation is applied. This fundamental result, in the context of pyroclastic density currents, is reproduced by numerical simulations performed with the fluidised model.

  Abstract
  A model accounting for fluidisation by pore gas pressure in dense granular flows is presented. A viscoplastic rheology, based on the Drucker-Prager criterium, is used to describe [...]

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• Computational modelling and characterization of non-Newtonian visco-plastic cementitious building materials

  M. Thiedeitz, J. Timothy, T. Kränkel

  eccomas2022.

  
  

  Abstract

  Cementitious building materials like Ultra High Performance Concrete (UHPC), Self-Compacting Concrete (SCC) or concrete with low clinker content possess complex rheological properties. Due to high packing densities and the use of various additives and chemical admixtures, a huge range of non-Newtonian flow characteristics from shear-thinning to shear-thickening, visco-elastic material behaviour and structural build-up can appear. For these concretes, transient computational modelling using Computational Fluid Dynamics (CFD) requires a meaningful choice of rheological parameters and the associated boundary conditions. The authors present the rheological analysis of five cementitious pastes with low ( = 0.45) to high ( = 0.58) solid volume fraction . Rheological parameters from rheometric flow protocols are compared with empirical stoppage test results for short and steady (slump flow) and transient flow (L-Box) conditions. Following, numerical simulation with the measured rheological parameters as input parameters is compared to the experimental flow results. CFD analysis using OpenFOAM is performed for the flow in empirical stoppage tests. We found that with increasing non-Newtonian behaviour, deviations between real and simulated flow appear due to insufficient transient flow descriptions and unknown secondary effects. The results provide new insight into computational modelling of complex cementitious building materials and serve as basis for further advanced CFD based modelling and characterization of time-dependent non-Newtonian concrete flow.

  Abstract
  Cementitious building materials like Ultra High Performance Concrete (UHPC), Self-Compacting Concrete (SCC) or concrete with low clinker content possess complex rheological [...]

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• Preparing a Fortran legacy code for the upcoming exascale architectures

  J. Legaux, G. Staffelbach

  eccomas2022.

  
  

  Abstract

  Preparing legacy codes for the upcoming exascale systems is a timely topic since the unveiling of the Frontier system in June 2022. In this work we describe the steps taken to prepare the AVBP code for this new step in computing ressources. AVBP [6] is a parallel CFD code that solves the three-dimensional compressible Navier-Stokes equations on unstructured and hybrid grids. AVBP is a cutting-edge software when it comes to distributed memory CPUs, scaling efficiently up to 200.000's of cores on Bluegene or AMD Epyc2 systems. However, other types of architectures such as ARM processors and accelerators are gaining popularity and play a significant role in the exascale era. We first explore the usage of ARM processors, then GPU accelerators through OpenACC[2] directives. This work highlights the difficulties of porting a legacy code to those architectures and solutions implememented so far for performance.

  Abstract
  Preparing legacy codes for the upcoming exascale systems is a timely topic since the unveiling of the Frontier system in June 2022. In this work we describe the steps taken [...]

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• On the algebraic modifications of traditional turbulence models to predict by-pass and separation induced transition

  A. Colombo, A. Crivellini, A. Ghidoni, A. Nigro, G. Noventa

  eccomas2022.

  
  

  Abstract

  Many reliable and robust turbulence models are nowadays available for the ReynoldsAveraged Navier-Stokes (RANS) equations to accurately simulate a wide range of engineering flows. However, turbulence models are not able to correctly predict flow phenomena with low to moderate Reynolds numbers, which are characterized by strong transitions. Laminar to turbulent transition is common in aerospace, turbomachinery, maritime, and automotive. Therefore, numerical models able to accurately predict transitional flows are mandatory to overcome the limits of turbulence models for the efficient design of many industrial applications. A modified version of the k-~ and Spalart-Allmaras turbulence models is proposed in order to predict transition due to the bypass and separation-induced modes. The modifications here proposed are based on the kand the SA-BCM transition models. Both the models are correlation-based algebraic transition models that relies on local flow information and include an intermittency function instead of an intermittency equation. The basic idea behind the models is that, instead of writing a transport equation for intermittency, an intermittency function multiplies the production terms of the turbulent working variables of the formulation of the turbulence models. In particular, the turbulence production is damped until it satisfies some transition onset requirements. The proposed models are implemented in a high-order discontinuous Galerkin (dG) solver and validated on different transitional benchmark cases from the ERCOFTAC T3 suite, with bypass (T3A, T3Aand T3B) and separation-induced (T3L1 and T3L3) transition.

  Abstract
  Many reliable and robust turbulence models are nowadays available for the ReynoldsAveraged Navier-Stokes (RANS) equations to accurately simulate a wide range of engineering [...]

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• Fully integrated mesh generation in fluid-structure interaction

  T. Fries, D. Bošnjak, R. Schussnig

  eccomas2022.

  
  

  Abstract

  In fluid-structure interaction (FSI), the fluid and solid domains are permantently changing, coupled along a time-dependent, moving fluid-structure interface. The update of the fluid domain, i.e., in the numerical context, the mesh update is critical for robust and efficient simulations. Herein, we propose to inherently embed the mesh generation into the simulation.The FSI domain is defined based on structured building blocks that imply all the relevant information needed for the automatic mesh generation: Topology, geometry, and grading information. Transfinite maps play a crucial role for the definition of sub-meshes with any desired order and resolution in each building block. In every time step, a new mesh is generated, taking into account the deforming FSI interface. This generation is fast compared to the overall work load in each time step which is still dominated by the (iterative) solutions of the systems of equations. It is also very robust and removes any mesh entanglement by construction provided that suitable building blocks are selected once initially. Numerical results confirm the success of the proposed FSI strategy with integrated mesh generation.

  Abstract
  In fluid-structure interaction (FSI), the fluid and solid domains are permantently changing, coupled along a time-dependent, moving fluid-structure interface. The update of [...]

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• Reynolds stress correction by machine learning methods with physical constraints

  T. Philibert, A. Ferrero, A. Iollo, F. Larocca

  eccomas2022.

  
  

  Abstract

  For the past three decade, Reynolds Average Navier-Stokes models have been widely used in the industry to simulate complex flows. However, these models suffer from limitations. Indeed there are still large discrepancies in the Reynolds stresses between the RANS model and high-fidelity data provided by DNS or experiments. This paper presents a strategy to correct the Menter SST model using an explicit algebraic model and two different neural networks: an multilayer perceptron (MLP) and a generative adversarial network (GAN). Moreover, in order to preserve the physical properties of the Reynolds stress tensor, we introduce a penalisation term in the loss of the GAN.

  Abstract
  For the past three decade, Reynolds Average Navier-Stokes models have been widely used in the industry to simulate complex flows. However, these models suffer from limitations. [...]

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• Comparison of uncertainty quantification methods for mathematical problems in intermediate dimensions

  J. Peter, Q. Bennehard

  eccomas2022.

  
  

  Abstract

  The efficiency of multidimensional quadrature methods is compared for seven test functions in intermediate dimensions. Following this goal, the numerical evaluations of the mean and variance of the test functions, for two probability density functions, are assessed with respect to (wrt) their known exact values. The retained dimensions (3 to 6) correspond to the number of operational and geometrical uncertain parameters we plan to consider in a near future for realistic sensitivity analysis or robust designs. Most of the numerical quadrature methods rely on a generalized Polynomial Chaos (gPC) defined either by quadrature or by collocation. Two of the gPC collocation techniques, Basis Poursuit Denoise (BPdn) and Least Angle Regression (LAR), search for a sparse gPC while satisfying the collocation equations. Finally, the efficiency of the quadrature methods is discussed in relation with the regularity, the input dimension and the ANOVA decomposition of the test functions.

  Abstract
  The efficiency of multidimensional quadrature methods is compared for seven test functions in intermediate dimensions. Following this goal, the numerical evaluations of the [...]

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