Documents published in Scipedia

• Minimally intrusive nonlinear Model Order Reduction

  R. Rossi, R. Bravo, J. Hernández, C. Roig

  Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET 2021) Conference (2021). San Diego, USA (virtual conference). 26-29 September 2021

  
  

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• From Linear Mappings to Deep Learning for Model Reduction of Numerical Simulations of Industrial Interest:

  R. Bravo, R. Rossi, J. Hernández, C. Roig

  Computational Science and AI in Industry (CSAI 2021) Conference. (2021). Trondheim, Norway (virtual conference). 7-9 June 2021

  
  

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• A Local POD-HROM framework for fast and accurate numerical simulations

  R. Bravo, R. Rossi, J. Hernández

  XI International Conference on Coupled Problems in Science and Engineering (COUPLED 2021), (2021). Chia Laguna, Italy (virtual conference). 13-16 June,2021

  
  

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• Clustering Techniques for Enhanced Reduced Order Model Simulations in Structural Mechanics

  R. Bravo, R. Rossi, J. Hernández

  XVI International Conference on Computational Plasticity (COMPLAS 2021) (2021). Barcelona, Spain. 07-09 September 2021

  
  

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• D3.3 Report of ensemble based parallelism for turbulent flows and release of solvers

  R. Tosi, R. Codina, J. Principe, R. Rossi, C. Soriano

  Open Access Repository of the ExaQUte project: Deliverables (2022). 6

  
  

  Abstract

  In this work we focus on reducing the wall clock time required to compute statistical estimators of highly chaotic incompressible flows on high performance computing systems. Our approach consists of replacing a single long-term simulation by an ensemble of multiple independent realizations, which are run in parallel with different initial conditions. A failure probability convergence criteria must be satisfied by the statistical estimator of interest to assess convergence. Its error analysis leads to the identification of two error contributions: the initialization bias and the statistical error. We propose an approach to systematically detect the burn-in time in order to minimize the initialization bias, accompanied by strategies to reduce simulation cost. The framework is validated on two very high Reynolds number obstacle problems of wind engineering interest in a high performance computing environment.

  Abstract
  In this work we focus on reducing the wall clock time required to compute statistical estimators of highly chaotic incompressible flows on high performance computing systems. [...]

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• D4.5 Framework development and release

  J. Ejarque, R. Tosi, M. Nuñez, S. Böhm, R. Badia, C. Soriano, R. Rossi

  Open Access Repository of the ExaQUte project: Deliverables (2022). 5

  
  

  Abstract

  This deliverable presents the nal release of the ExaQUte framework as result of task 4.6 of the project focused on the framework development and optimization. The rst part of the document presents an overview of the dierent parts of the ExaQUte framework providing the links to the repositories where the code of the dierent components can be found as well as the installation and usage guidelines. These repositories will include the nal version of the ExaQUte API and its implementation for the runtimes provided in the project (PyCOMPSs/COMPSs and Quake). The second part of the document presents a performance analysis of the framework by performing strong and weak scaling experiments. In this case, we have focused on the analysis of the new features introduced during the last part of the project to support and optimize the execution of MPI solvers inside the framework. The support for OpenMP was already reported in Deliverable D4.3 [21]. The results of the experiments demonstrate that the proposed framework allow to reach very good scalability for the analysed Monte Carlo problems.

  Abstract
  This deliverable presents the nal release of the ExaQUte framework as result of task 4.6 of the project focused on the framework development and optimization. The rst part [...]

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• D6.5 Report on stochastic optimisation for wind engineering

  F. Nobile, Q. Ayoul-Guilmard, S. Ganesh, M. Nuñez, A. Kodakkal, C. Soriano, R. Rossi

  Open Access Repository of the ExaQUte project: Deliverables (2022). 4

  
  

  Abstract

  This report presents the latest methods of optimisation under uncertainties investigated in the ExaQUte project, and their applications to problems related to civil and wind engineering. The measure of risk throughout the report is the conditional value at risk. First, the reference method is presented: the derivation of sensitivities of the risk measure; their accurate computation; and lastly, a practical optimisation algorithm with adaptive statistical estimation. Second, this method is directly applied to a nonlinear relaxation oscillator (FitzHugh–Nagumo model) with numerical experiments to demonstrate its performance. Third, the optimisation method is adapted to the shape optimisation of an airfoil and illustrated by a large-scale experiment on a computing cluster. Finally, the benchmark of the shape optimisation of a tall building under a turbulent flow is presented, followed by an adaptation of the optimisation method. All numerical experiments showcase the open-source software stack of the ExaQUte project for large-scale computing in a distributed environment.

  Abstract
  This report presents the latest methods of optimisation under uncertainties investigated in the ExaQUte project, and their applications to problems related to civil and wind [...]

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• D5.5 Report on the application of multi-level Monte Carlo to wind engineering

  Q. Ayoul-Guilmard, F. Nobile, S. Ganesh, M. Nuñez, R. Tosi, C. Soriano, R. Rossi

  Open Access Repository of the ExaQUte project: Deliverables (2022). 3

  
  

  Abstract

  We study the use of multi-level Monte Carlo methods for wind engineering. This report brings together methodological research on uncertainty quantification and work on target applications of the ExaQUte project in wind and civil engineering. First, a multi-level Monte Carlo for the estimation of the conditional value at risk and an adaptive algorithm are presented. Their reliability and performance are shown on the time-average of a non-linear oscillator and on the lift coefficient of an airfoil, with both preset and adaptively refined meshes. Then, we propose an adaptive multi-fidelity Monte Carlo algorithm for turbulent fluid flows where multilevel Monte Carlo methods were found to be inefficient. Its efficiency is studied and demonstrated on the benchmark problem of quantifying the uncertainty on the drag force of a tall building under random turbulent wind conditions. All numerical experiments showcase the open-source software stack of the ExaQUte project for large-scale computing in a distributed environment.

  Abstract
  We study the use of multi-level Monte Carlo methods for wind engineering. This report brings together methodological research on uncertainty quantification and work on target [...]

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• D7.4 Final report on Stochastic Optimization results

  S. Bidier, U. Khristenko, A. Kodakkal, C. Soriano, R. Rossi

  Open Access Repository of the ExaQUte project: Deliverables (2022). 2

  
  

  Abstract

  This deliverable report focuses on the final stochastic optimization results obtained within the EXAscale Quantification of Uncertainties for Technology and Science Simulation (ExaQUte) project. Details on a novel wind inlet generator that is able to incorporate local wind-field data through a deep-learned rapid distortion model and generates the turbulent wind data during run-time is presented in section 2. Section 3 presents the results of the overall stochastic optimization procedure applied to a twisted tapered tower with multiple design parameters within an uncertain synthetic wind field. Thereby, the significance of the developed methods and the obtained results are discussed and their integration in industrial wind-engineering workflows is outlined in section 4.

  Abstract
  This deliverable report focuses on the final stochastic optimization results obtained within the EXAscale Quantification of Uncertainties for Technology and Science Simulation [...]

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• D8.4 Report on dissemination activities

  C. Soriano, R. Rossi

  Open Access Repository of the ExaQUte project: Deliverables (2022). 1

  
  

  Abstract

  In Deliverable D8.1 (M18) we presented a first version of the Dissemination Plan for the ExaQUte project. The present document, prepared during the 2nd (and last) review period of the project, represents the updated version of the Dissemination Plan of ExaQUte, and therefore builds on the aforementioned deliverable. This document, thus, focuses on the new activities that have been undertaken from M18 to M42 regarding the dissemination activities. It should be mentioned that the pandemic situation that started in Mach 2020 (actually, in Spain we were sent to confine to our houses the day after the celebration of the First Review meeting of this project) has had an impact in our life, and in our project, particularly in the dissemination actions. The lockdown made impossible most of the activities related to dissemination (travels, conferences, consortium meetings…). But we reinvented our work and found different ways to do things and fulfil pour obligations. Still, in this deliverable, you will find indeed a difference between the type of action that we carried on up to M18 and our activities after M18.

  Abstract
  In Deliverable D8.1 (M18) we presented a first version of the Dissemination Plan for the ExaQUte project. The present document, prepared during the 2nd (and last) review period [...]

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