Documents published in Scipedia

• 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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• Data-driven geometric modelling methods for digital twinning: manufacturing, geospatial and medical applications

  O. Barrowclough, S. Briseid, T. Dokken, K. Gavriil, G. Muntingh

  eccomas2022.

  
  

  Abstract

  In recent years there has been an explosion of interest in digital twinning in many disciplines, including the manufacturing, geospatial, and medical domains. A core topic of importance in modelling digital twins, is reconstruction of geometric models from raw data. Despite the diversity of requirements in the vast space of digital twin applications, methods for geometric reconstruction can often be transferred between disciplines with only minor modifications. In this paper we present some recent results related to how advances in machine learning over the last decade can be used for data-driven geometric reconstruction in the medical, geospatial and manufacturing domains.

  Abstract
  In recent years there has been an explosion of interest in digital twinning in many disciplines, including the manufacturing, geospatial, and medical domains. A core topic [...]

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• A Parallel Solver for CFD based on the Alternating Anderson-Richardson Method

  L. Chan, S. Marques, N. Hills

  eccomas2022.

  
  

  Abstract

  Recent advancement in the usage and deployment of large supercomputing resources require the need for algorithmic improvements to make use of the increased parallelism architecture. The Alternating Anderson-Richardson (AAR) method has been recently shown to exhibit good performance when solving problems in distributed parallel computers. This research will extend and investigate the performance of the AAR method to solve CFD problems using a modern compressible flow solver. This work will compare its performance and scalability against commonly used linear solvers, such as the Richardson method and the Generalised Minimal RESidual (GMRES), for solving large, sparse linear systems of equations arising from CFD applications. Results using a range of turbomachinery test cases demonstrate that the current AAR implementation offers significant performance improvement over the Richardson method. The speedup of AAR with respect to GMRES is less significant due to the load imbalance across partitions.

  Abstract
  Recent advancement in the usage and deployment of large supercomputing resources require the need for algorithmic improvements to make use of the increased parallelism architecture. [...]

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• Next-generation HPC models for future Rotorcraft applications

  N. Sanguini, T. Benacchio, D. Malacrida, F. Cipolletta, F. Rondina, A. Sciarappa, L. Capone

  eccomas2022.

  
  

  Abstract

  Rotorcraft technologies pose great scientific and industrial challenges for numerical computing. As available computational resources approach the exascale, finer scales and therefore more accurate simulations of engineering test cases become accessible. However, shifting legacy workflows and optimizing parallel efficiency and scalability of existing software on new hardware is often demanding. This paper reports preliminary results in CFD and structural dynamics simulations using the T106A Low Pressure Turbine (LPT) blade geometry on Leonardo S.p.A.'s davinci-1 high-performance computing (HPC) facility. Time to solution and scalability are assessed for commercial packages Ansys Fluent, STAR-CCM+, and ABAQUS, and the open-source scientific computing framework PyFR. In direct numerical simulations of compressible fluid flow, normalized time to solution values obtained using PyFR are found to be up to 8 times smaller than those obtained using Fluent and STAR-CCM+. The findings extend to the incompressible case. All models offer weak and strong scaling in tests performed on up to 48 compute nodes, each with 4 Nvidia A100 GPUs. In linear elasticity simulations with ABAQUS, both the iterative solver and the direct solver provide speedup in preliminary scaling tests, with the iterative solver outperforming the direct solver in terms of time-to-solution and memory usage. The results provide a first indication of the potential of HPC architectures in scaling engineering applications towards certification by simulation, and the first step for the Company towards the use of cutting-edge HPC toolkits in the field of Rotorcraft technologies.

  Abstract
  Rotorcraft technologies pose great scientific and industrial challenges for numerical computing. As available computational resources approach the exascale, finer scales and [...]

  • 19
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• Wind Turbine Control using Machine Learning techniques

  L. Schena

  eccomas2022.

  
  

  Abstract

  This article presents two model-free controllers for wind-turbine torque and pitch control. These controllers are based on reinforcement learning (RL) and Bayesian optimization (BO) and do not rely on any mathematical model of the wind-turbine dynamics, in contrast to classical approaches designed on linearized models. The model-free controllers were benchmarked against a proportional-integral-derivative (PID) regulator in a numerical environment using Blade Element Momentum theory for computing the aerodynamic torque and the blade loads. The results showed that the model-free approaches could increase power harvesting while reducing wind turbine loads.

  Abstract
  This article presents two model-free controllers for wind-turbine torque and pitch control. These controllers are based on reinforcement learning (RL) and Bayesian optimization [...]

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