Documents published in Scipedia

• Simulation of reacting flows using artificial neural networks: application to multi-regime combustion

  C. Mehl, D. Aubagnac-Karkar

  eccomas2022.

  
  

  Abstract

  The simulation of reactive flows is a major challenge in several industrial sectors, such as aeronautics or energy production. The coupling between fluid dynamics and chemistry comes however at a cost, as chemical processes involve a wide range of spatial and temporal scales. The resulting equations are stiff and require specific, and expensive, numerical methods. The use of machine learning to estimate the reaction rates has been recently proposed. In particular, Artificial Neural Networks (ANN) have the ability to perform interpolation on high-dimensional data and are thus particularly adapted to chemistry problems. A major issue is then to select an appropriate database on which to train the ANN. It must: (i) be representative of the targeted application; (ii) be sufficiently quick to generate. A promising strategy is to use 0-D stochastics reactors, which mimic reactive and mixing processes in systems while being cheap to compute. This methodology has been successfully applied to non-premixed combustion in the literature. In the present work, the aim is to investigate the ability of the 0D stochastic reactors to be used as a database for a wider range of combustion systems. More specifically, the focus will be on the ability to predict auto-ignition followed by premixed flame propagation. To that purpose, a 2-D turbulent case involving the auto-ignition of a hotspot in a hydrogen/air mixture and the subsequent propagation of a premixed flame is proposed. An ANN model based on stochastic reactors is then built and tested on (i) a 0-D auto-ignition case; (ii) a 1-D laminar premixed flame propagation; (ii) the full 2-D turbulent configuration. Using adequate data transformation at the input and output of the neural network, accurate results are obtained, highlighting the ability of the proposed strategy to deal with a large range of combustion applications.

  Abstract
  The simulation of reactive flows is a major challenge in several industrial sectors, such as aeronautics or energy production. The coupling between fluid dynamics and chemistry [...]

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• Scalability analysis and performance modelling of layer-parallel training of deep residual networks using a non-linear multigrid-in-time algorithm

  C. Datar, H. Köstler

  eccomas2022.

  
  

  Abstract

  Replacing the traditional forward and backward passes in a residual network with a Multigrid-Reduction-in-Time (MGRIT) algorithm paves the way for exploiting parallelism across the layer dimension. In this paper, we evaluate the layer-parallel MGRIT algorithm with respect to convergence, scalability, and performance on regression problems. Specifically, we demonstrate that a few MGRIT iterations solve the systems of equations corresponding to the forward and backward passes in ResNets up to reasonable tolerances. We also demonstrate that the MGRIT algorithm breaks the scalability barrier created by the sequential propagation of data during the forward and backward passes. Moreover, we show that ResNet training using the layer-parallel algorithm significantly reduces the training time compared to the layer-serial algorithm on two non-linear regression tasks. We observe much more efficient training loss curves using layer-parallel ResNets as compared to the layer-serial ResNets on two regression tasks. We hypothesize that the error stemming from approximately solving the forward and backward pass systems using the MGRIT algorithm helps the optimization algorithm escape flat saddle-point-like plateaus or local minima on the optimization landscape. We validate this by illustrating that artificially injecting noise in a typical forward or backward propagation, allows the optimizer to escape a saddle-point-like plateau at network initialization.

  Abstract
  Replacing the traditional forward and backward passes in a residual network with a Multigrid-Reduction-in-Time (MGRIT) algorithm paves the way for exploiting parallelism across [...]

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• Lessons Learnt from Chimera Method Application to a Deploying Krueger Device

  A. Hasabnis, H. Maseland, F. Moens

  eccomas2022.

  
  

  Abstract

  The Chimera method [1] is an established method for simulation of overlapping grids. Meshing parts independently has made this method popular for complex geometries as well as moving bodies like propellers and rotors (e.g. [2]) or control surfaces (e.g. [3]). It is thus a promising method to simulate deflecting high-lift systems. The motion of the Krueger flap – as the most promising leading edge high-lift system device for laminar wing technology – is characterized by a relatively large movement (about 140 deg deflection) at a relatively high deflection speed (up to 200 deg/s) compared to classical leading edge devices. In terms of simulation, the grid properties of the overlapping mesh regions vary throughout the motion from a nearly sealed retracted position to a gapped flow in fully deflected position comparable to a slat device. This expects dynamic effects may get dominant and a valid simulation of this flow is needed for proper design and analysis. In the frame of the UHURA project1 , several partners applied their CFD capabilities based on Chimera in order to validate the method for this specific application in comparison to wind tunnel tests. The presentation outlines the different Chimera approaches ranging from structured/2D to hybrid/3D in steady and unsteady simulations for the different type of setups investigated, namely straight and swept wing with full-span and part-span Krueger flap. It summarizes common challenges and best practice for application of the Chimera approach for such a device. 

  Abstract
  The Chimera method [1] is an established method for simulation of overlapping grids. Meshing parts independently has made this method popular for complex geometries as well [...]

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• Computational modelling of plasma electrolytic oxidation process induced damage in extruded Mg material

  E. Gazenbiller, S. Mansoor, N. Konchakova, M. Serdechnova, C. Blawert, M. Zheludkevich, D. Höche

  eccomas2022.

  
  

  Abstract

  Magnesium (Mg) alloys are an attractive constructive material due to their light weight and high mechanical strength. Plasma electrolyte oxidation (PEO) treatment of Mg alloys creates a thin ceramic coating with protective effects against mechanical wear and corrosion. The coating properties like its porosity and thickness can be adjusted by PEO process parameters and at the same time affects the material behaviour under tensile strength. In this work, dedicated slow-strain rate experiments of differently PEO coated Mg alloy dog-bone shaped specimen were conducted and the coating porosity, thickness and crack spacing were analyzed in order to deduce a predictive Finite Element Method (FEM) damage model. The results indicate that the thicker, more porous coatings lead to material failure at smaller strains in plastic regions. The effect can be implemented via partial differential equation into the FEM model.

  Abstract
  Magnesium (Mg) alloys are an attractive constructive material due to their light weight and high mechanical strength. Plasma electrolyte oxidation (PEO) treatment of Mg alloys [...]

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• Parallel Finite Volume Code for Plasma with Unstructured Adaptive Mesh Refinement

  I. Kissami, S. maazioui, F. Benkhaldoun

  eccomas2022.

  
  

  Abstract

  The present paper describes a parallel unstructured-mesh Plasma simulation code based on Finite Volume method. The code dynamically refines and coarses mesh for accurate resolution of the different features regarding the electron density. Our purpose is to examine the performance of a new Parallel Adaptive Mesh Refinement (PAMR) procedure introduced on the ADAPT platform, which resolves of a relatively complicated system coupling the flow partial differential equations to the Poisson's equation. The implementation deals with the MUMPS parallel multi-frontal direct solver and mesh partitioning methods using METIS to improve the performance of the framework. The standard MPI is used to establish communication between processors. Performance analysis of the PAMR procedure shows the efficiency and the potential of the method for the propagation equations of ionization waves.

  Abstract
  The present paper describes a parallel unstructured-mesh Plasma simulation code based on Finite Volume method. The code dynamically refines and coarses mesh for accurate resolution [...]

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• PREDICTION OF THE BIOMASS PARTICLES THROUGH THE PHYSICS-INFORMED NEURAL NETWORK

  F. darlik, P. Adhav, B. Peters

  eccomas2022.

  
  

  Abstract

  Woody biomass energy is a kind of renewable energy that contributes to the reduction of greenhouse gas emissions, the creation of healthier forests, and the reduction of wildfire danger. Generally speaking, simulations of the motion of biomass particles are a time-consuming process due to a large number of particles and required simulation time. We used a physicsinformed neural network (PINN) model to predict the motion of particles by including their equations of motion to reconstruct the velocity fields and reduce the processing effort. compare to the discrete element methods, the PINNs methods have the advantage of predicting the velocity fields without the knowledge of the simulation's boundary and initial conditions as well as geometry. It has shown that the proposed model has reliable prediction results with a mean percentage error in time less than 1 percent.

  Abstract
  Woody biomass energy is a kind of renewable energy that contributes to the reduction of greenhouse gas emissions, the creation of healthier forests, and the reduction of wildfire [...]

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• Development of a low-level, algebra-based library to provide platform portability on hybrid supercomputers

  X. Álvarez-Farré, À. Alsalti-Baldellou, G. Colomer, A. Gorobets, F. Trias, A. Oliva

  eccomas2022.

  
  

  Abstract

  Continuous enhancement in hardware technologies enables scientific computing to advance incessantly and reach further aims. Since the start of the global race for exascale high-performance computing, massively-parallel devices of various architectures have been incorporated into the newest supercomputers, leading to an increasing hybridization of compute nodes. In this context of accelerated innovation, software portability and efficiency become crucial. Traditionally, scientific computing software development using mesh methods is based on calculations in iterative stencil loops over a discretized geometry--the mesh. Despite being intuitive and versatile, the interdependency between algorithms and their computational implementations in stencil applications usually results in a large number of subroutines and introduces an inevitable complexity when it comes to portability and sustainability. An alternative is to break the interdependency between the algorithm and its implementation, and then to cast the calculations into a minimalist set of kernels. Algebra-based implementations rely on a reduced set of basic linear algebra subroutines, which simplifies the deployment of software in hybrid computing systems. In this work, we tackle the development of a fully-portable, algebraic library that can be coupled beneath other high-level, algebra-oriented framework. Namely, this library provides platform portability in the simplest possible manner (i.e., the user develops applications in a purely sequential style). Internally, algebraic objects are distributed among computing devices using a multilevel decomposition approach. Data exchanges between computing units or between nodes are hidden by a multithreaded overlapping scheme.

  Abstract
  Continuous enhancement in hardware technologies enables scientific computing to advance incessantly and reach further aims. Since the start of the global race for exascale [...]

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• Efficient strategies for solving the variable Poisson equation with large contrasts in the coefficients

  À. Alsalti-Baldellou, X. Álvarez-Farré, A. Gorobets, F. Trias

  eccomas2022.

  
  

  Abstract

  Discrete versions of Poisson's equation with large contrasts in the coefficients result in very ill-conditioned systems. Thus, its iterative solution represents a major challenge, for instance, in porous media and multiphase flow simulations, where considerable permeability and density ratios are usually found. The existing strategies trying to remedy this are highly dependent on whether the coefficient matrix remains constant at each time iteration or not. In this regard, incompressible multiphase flows with high-density ratios are particularly demanding as their resulting Poisson equation varies along with the density field, making the reconstruction of complex preconditioners impractical. This work presents a strategy for solving such versions of the variable Poisson equation.Roughly, we first make it constant through an adequate approximation. Then, we block-diagonalise it through an inexpensive change of basis that takes advantage of mesh reflection symmetries, which are common in multiphase flows. Finally, we solve the resulting set of fully decoupled subsystems with virtually any solver. The numerical experiments conducted on a multiphase flow simulation prove the benefits of such an approach, resulting in up to 6.6x faster convergences.

  Abstract
  Discrete versions of Poisson's equation with large contrasts in the coefficients result in very ill-conditioned systems. Thus, its iterative solution represents a major [...]

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• TN, ROM, ML, PINNs – Four approaches for real-time temperature estimation in electric motors in comparison

  H. Sauerland, A. Miyamoto, A. Ohazulike, H. Xu, R. De Doncker

  eccomas2022.

  
  

  Abstract

  ne of the major trends in electrical machines for automotive applications is towards higher power-densities and more integrated components. With that, accurate thermal management of the machine and capable cooling systems are of great significance to the safety and reliability of the traction system. Thermal simulations are an integral part in the design process of electrical drives. However, recently thermal models are also more frequently used in the context of machine control. The latter demanding for fast, yet accurate, temperature estimations.

  Abstract
  ne of the major trends in electrical machines for automotive applications is towards higher power-densities and more integrated components. With that, accurate thermal management [...]

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• Modelization of a molten salt thermal energy storage for concentrated solar power.

  J. Vera Fernandez, G. Colomer, O. Sanmartí, C. Perez

  eccomas2022.

  
  

  Abstract

  A numerical model for studying a storage tank for concentrated solar power is presented. The model consists of solving the heat equation for the solid part made from ceramic materials, a one-dimensional model for the molten salt circulating inside the solid, and a coupling between them. Then, some results are presented for a reference case with some typical parameters for the storage system.

  Abstract
  A numerical model for studying a storage tank for concentrated solar power is presented. The model consists of solving the heat equation for the solid part made from ceramic [...]

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