Parametric Model Order Reduction for Structural Optimization of Fiber Composite Structures: Training dataset

<p dir="ltr">The training dataset and results associated with the journal article titled "Parametric Model Order Reduction for Structural Optimization of Fiber Composite Structures" is provided here. This work addresses the inherent computational demand of metaheuristic optimization techniques by developing a novel optimization framework that uses parametric model order reduction (PMOR). The improvement in efficiency is demonstrated using the buckling optimization of variable angled tow (VAT) composite panels, as navigating the complex design space of VAT composite structures with global optimization techniques requires a substantial number of function evaluations. A new affine summation relationship conducive to PMOR was derived for its finite element stiffness matrix using the concept of lamination parameters and material invariant matrices, and principal component analysis (PCA) was used to extract the reduced basis vectors. Particle swarm optimization was conducted using a full-order model (FOM) and a reduced-order model. PMOR-based optimization exhibited a 0.55% relative error in the optimal objective value compared to the FOM analysis and exhibited a similar convergence history. It was observed that the optimization time was reduced by 93% by the novel affine decomposition alone, but PMOR achieved a significant reduction of 99.8% in memory requirements compared to the FOM. As the affine-decomposition-based assembly of the FOM is not feasible for large problems, PMOR functions as an enabling tool for leveraging the improvement offered by it. [2024 Nov: Paper is currently in press, AIAA Journal]</p>