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2026_Article_RESILINK_ML
Commit 34b4262c by Joaquín Irazábal González
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Update manuscript with new sections and files for surrogate optimization methodology

- Updated the RESILINK surrogate optimization manuscript PDF to reflect recent changes. - Added a new synctex.gz file for improved LaTeX editing and referencing. - Revised the manuscript text to include conclusions in the workflow summary. - Introduced a new section on dataset generation and surrogate modeling, detailing the use of FEM models and damage indicators. - Removed redundant section header for clarity in the design of experiments description.
parent f94d3488
Showing with 12 additions and 11 deletions
Manuscript/Optimal-Design-layout/RESILINK_surrogate_optimization_OptimalDesignLayout.aux
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Manuscript/Optimal-Design-layout/RESILINK_surrogate_optimization_OptimalDesignLayout.tex
......@@ -125,7 +125,7 @@ All these works demonstrate the increasing interest in applying FEM-based and da
The present work addresses this gap through a damage-aware surrogate-assisted optimization methodology in which the objective is not only to maximize distortion or energy dissipation, but also to balance dissipative performance with damage indicators derived from FEM simulations. The proposed approach combines: (i) experimentally calibrated nonlinear FEM models used as numerical ground truth; (ii) supervised surrogate models trained to predict local damage and distortion indicators; (iii) a Differential Evolution (DE) optimizer; and (iv) an adaptive FEM validation and retraining loop.
Figure \ref{fig:MethodologyFlowChart} summarizes the proposed workflow. The different stages of the methodology, together with the surrogate modelling, optimization strategy, validation procedure and corresponding results, are described in the following sections.
Figure \ref{fig:MethodologyFlowChart} summarizes the proposed workflow. The different stages of the methodology, together with the surrogate modelling, optimization strategy, validation procedure and corresponding results and conclusions, are described in the following sections.
\begin{figure*}[htbp]
\centering
......@@ -208,12 +208,12 @@ The model was calibrated and validated against cyclic experimental tests perform
\label{fig:FEM_validation_comparison}
\end{figure*}
\section{Dataset generation and surrogate modelling}\label{sec:surrogates}
Once validated, the FEM model is used to generate the datasets required for surrogate training and optimization. The optimization strategy relies on local damage indicators in the dissipative windows and surrounding frame, together with local distortion measures associated with the activation of the dissipative mechanism. Since these quantities are difficult to measure experimentally, the use of a high-fidelity FEM model provides valuable access to the internal state variables and local fields governing damage evolution and energy dissipation.
The damage indicator adopted in this work is the Triaxial Failure Damage Map (TFDMap) \cite{Rastellini2016}. This stress-triaxiality-based indicator evaluates the proximity of each material point to ductile failure by comparing its stress triaxiality and accumulated equivalent plastic strain with a reference failure envelope \cite{Rice1969,Bao2004,Wierzbicki2005,Bai2008}. In this study, the TFDMap is used as a post-processing damage-screening indicator, not as a constitutive fracture criterion. Its purpose is therefore not to explicitly predict crack initiation, but to compare geometrical configurations and ensure that optimized designs remain within acceptable damage levels.
\section{Dataset generation and surrogate modelling}\label{sec:surrogates}
\subsection{Design of experiments}\label{subsec:doe}
The FEM campaign is planned to cover the admissible parameter domain of each device family while ensuring a homogeneous exploration of the multidimensional space. The design variables are the window thicknesses $t_{w,i}$, whose combinations are generated through a Design of Experiments (DoE) strategy based on Latin Hypercube Sampling (LHS) optimized with the maximin criterion \cite{Joseph2008}. This approach provides a near-random yet space-filling distribution of samples, reducing clustering and improving the representation of the admissible domain.
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