Documents published in Scipedia

• A modified Constitutive Relation Error (mCRE) framework to learn nonlinear constitutive models from strain measurements with thermodynamics-consistent Neural Networks

  A. Benady, L. Chamoin, E. Baranger

  admos2023.

  
  

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• A Coupled HDG-FV Method for Incompressible Flows Simulations

  A. Felipe, R. Sevilla, O. Hassan

  admos2023.

  
  

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• A Convergence Proof for Adaptive Parametric PDEs with Unbounded Coefficients

  N. Farchmin, M. Eigel

  admos2023.

  
  

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• A new Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs

  B. Haasdonk, H. Kleikamp, M. Ohlberger, F. Schindler, T. Wenzel

  admos2023.

  
  

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• Adaptive Flow Modelling for Coupled Thin Film and Bulk Fluid Flow

  P. Suchde

  admos2023.

  
  

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• Goal-oriented placement of depolluting panels in urban areas - application to a Paris district

  J. Waeytens, T. Hamada, R. Chakir, D. Lejri, F. Dugay

  admos2023.

  
  

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• A Mesh Adaptation algorithm for highly deforming domains in the Particle Finite Element Method

  T. Leyssens, J. Remacle

  admos2023.

  
  

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• On the Discretization of the Continuous Adjoint to the Euler Equations in Aerodynamic Shape Optimization

  M. Kontou, X. Trompoukis, V. Asoutis, K. Giannakoglou

  admos2023.

  
  

  Abstract

  In aerodynamic shape optimization, gradient-based algorithms usually rely on the adjoint method to compute gradients. Working with continuous adjoint offers a clear insight into the adjoint equations and their boundary conditions, but discretization schemes significantly affect the accuracy of gradients. On the other hand, discrete ad joint computes sensitivities consistent with the discretized flow equations, with a higher memory footprint though. This work bridges the gap between the two adjoint variants by proposing consistent discretization schemes (inspired by discrete adjoint) for the con tinuous adjoint PDEs and their boundary conditions, with a clear physical meaning. The capabilities of the new Think-Discrete-Do-Continuous adjoint are demonstrated, for in viscid flows of compressible fluids, in shape optimization in external aerodynamics.

  Abstract
  In aerodynamic shape optimization, gradient-based algorithms usually rely on the adjoint method to compute gradients. Working with continuous adjoint offers a clear insight [...]

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• Generation of Massive Databases for Deep Learning Inversion: A Goal-Oriented hp-Adaptive Strategy

  J. Álvarez-Aramberri, V. Darrigrand, F. Caro, D. Pardo

  admos2023.

  
  

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• A Multiscale Method with Continuous Matter Addition in DED Additive Manufacturing Processes

  M. Picos, Q. Quintela, J. Rodríguez, P. Barral

  admos2023.

  
  

  Abstract

  Additive manufacturing (AM) is a production method with great potential for creating complex geometries and reducing material and energy waste. Numerical simulations are crucial to minimize fabrication failures and optimize designs. Nevertheless, the high computational cost of simulating the multi-scale behaviour of AM processes is a challenge. To address this, an Arlequin-based method is proposed, which uses two distinct meshes to capture the high thermal gradients near the melt pool: a coarse mesh for the entire domain and a fine mesh that moves with the heating source. Additionally, a change of variable simplifies calculations on each time step by transforming the moving fine mesh into a fixed mesh. The proposed methodology has the potential to reduce computational costs and improve the efficiency of AM simulations

  Abstract
  Additive manufacturing (AM) is a production method with great potential for creating complex geometries and reducing material and energy waste. Numerical simulations are crucial [...]

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  •  read