Documents published in Scipedia

• D7.3 (WP7): Events

  J. García-Espinosa

  prodPhD OA (2023). 6

  
  

  Abstract

  This document contains a list of the different events (conferences, seminars, webinars and workshops) which are organised by the project. This document will be updated and re-submitted at each reporting period.

  Abstract
  This document contains a list of the different events (conferences, seminars, webinars and workshops) which are organised by the project. This document will be updated and [...]

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• D2.2 (WP2): Needs and requirements analysis

  E. Sanz, M. Lascurain, A. Serrano, A. Haidar, P. Alonso

  prodPhD OA (2022). 2

  
  

  Abstract

   The prodPhD project aims to address the challenging problem of introducing entrepreneurship training in PhD programmes regardless of discipline. The prodPhD project will create the necessary teaching methodologies and the platform for applying them. The project consists of a consortium of four organizations from across Europe. The main objective of the prodPhD project is to implement innovative social network-based methodologies for teaching and learning entrepreneurship in PhD programmes. The multidisciplinary teaching and learning methodologies will enable entrepreneurship education to be introduced into any PhD programme, providing students with the knowledge, skills, and motivation to engage in entrepreneurial activities. The methodology will be conceived to develop experiential knowledge, involving academics, entrepreneurship experts, and mentors in its development and implementation. Besides, the exchange of experience, competences, and approaches facilitated by social networking will pave the way to crowdsourcing new ideas, improving training methodologies, and stimulating academics’ entrepreneurial skills

  Abstract
   The prodPhD project aims to address the challenging problem of introducing entrepreneurship training in PhD programmes regardless of discipline. The prodPhD project [...]

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• 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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