Concurrent Patch Optimization of Hybrid Composite Plates Based on Proper Orthogonal Decomposition

Wang, Bo; Ma, Xiangtao; Tian, Kuo; Hao, Peng; Zhou, Yan; Quan, Dongliang

doi:10.2514/1.j058064

Cited by 13 publications

(8 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, VAT composite laminates have evolved from a concept formulated by Olmedo and Gürdal [3] into a proven design technique with sophisticated design frameworks, which incorporate manufacturing, modelling, and analyses, for various types of structural elements. Motivated by the idea of curvilinear fibre-steered composites, curvilinearly stiffened structures have been introduced for the first time by Kapania et al [4] and gained substantial attention owing to their excellent design flexibility [5,6,7]. In particular, curvilinear stiffeners have been demonstrated to be beneficial for cutout reinforcement of thin-walled panels to enhance the strength margins [8].…”

Section: Introductionmentioning

confidence: 99%

Manufacturable insight into modelling and design considerations in fibre-steered composite laminates: State of the art and perspective

Aragh

Farahani

et al. 2021

Computer Methods in Applied Mechanics and Engineering

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Manufacturable insight into modelling and design considerations in fibre-steered composite laminates: State of the art and perspective

Aragh

Farahani

et al. 2021

Computer Methods in Applied Mechanics and Engineering

View full text Add to dashboard Cite

“…For linear systems, most techniques can be categorized as either SVD methods or Krylov methods. The former include techniques such as proper-orthogonal decomposition, likely the most common type of ROM used for aerodynamic systems [20][21][22][23], or balanced truncation methods, for which a novel and efficient approach using frequency-limited truncation tailored to the UVLM was presented recently [24,25].…”

Section: B Model Reduction Methodsmentioning

confidence: 99%

“…In other words, 𝜇 𝜀 [ 𝒑; 𝑍 0 ] is the expected error of an interpolation at 𝒑 ∈ 𝑃 using 𝑍 0 as training data set and given that the true error 𝜀 is known at 𝑃 𝑠 , where 𝑃 𝑠 corresponds to a set of testing points at which the scheme has computed the true system in order to inform its prediction. At these points, 𝑃 𝑠 , it can compute the error 𝜀[ 𝒑 𝑠 ; 𝑍 0 ], 𝒑 𝑠 ∈ 𝑃 𝑠 and use this result to inform the Gaussian process (21). This is an iterative process, and points 𝒑 𝑠 are sampled according to an expected improvement function.…”

Section: Adaptive Multi-purpose Data Bayesian Sampling Schemementioning

confidence: 99%

Adaptive Sampling for Interpolation of Reduced-Order Aeroelastic Systems

2022

View full text Add to dashboard Cite

A new strategy for the interpolation of parametric reduced-order models of dynamic aeroelastic systems is introduced. Its aim is to accelerate the numerical exploration of geometrically-nonlinear aeroelastic systems over large design spaces or multiple flight conditions. The parametric reduced-order models are obtained from high-dimensional models by Krylov subspace projection. They are subsequently interpolated to acquire realizations inexpensively anywhere in the parameter space, where having the state-space as opposed to interpolating an output metric permits the use of linear analysis tools. The interpolation scheme is heavily conditioned by the available training points, thus a novel methodology based on adaptive sampling and a combinatorial use of the available true-systems knowledge is presented, whereby we reuse all known data of the true models as different combinations of training and testing data to build statistical surrogates of the interpolation error across the parameter space and refine the sampling in those regions that need it. This minimizes the number of costly-to-evaluate functions calls and ensures that parameter space regions are sampled according to the underlying system dynamics. The initial implementation of this adaptive sampling strategy is demonstrated on a very flexible wing with a complex stability envelope.

show abstract

“…Discrete material optimization (DMO) method has been widely applied to parameterize the fiber orientations of FRP laminated structures, 38–40 which is briefly described here for the completeness of the article and more details can be consulted in References 13,35. Generally speaking, the DMO framework can be regarded as a multi‐material selection problem, in which different fiber orientations are regarded to be different candidate materials, and the unified constitutive matrix in one element or in one patch equals a weighted average of the constitutive matrix for each candidate orientation.…”

Section: Methodsmentioning

confidence: 99%

Machine learning based topology optimization of fiber orientation for variable stiffness composite structures

Gao

et al. 2021

Numerical Meth Engineering

View full text Add to dashboard Cite

This study proposes a machine learning (ML) based approach for optimizing fiber orientations of variable stiffness carbon fiber reinforced plastic (CFRP) structures, where neural networks are developed to estimate the objective function and analytical sensitivities with respect to design variables as a substitute for finite element analysis (FEA). To reduce the number of training samples and improve the regression accuracy, an active learning strategy is implemented by successively supplying effective samples along with the suboptimal process.After proper training of neural networks, a quasi-global search strategy can be applied by implementing a large number of initial designs as starting points in the optimization. In this article, a mathematical example is first presented to show the superiority of the active learning strategy. Then a benchmark design example of a CFRP plate is scrutinized to compare the proposed ML-based with the conventional FEA-based discrete material optimization (DMO) method.Finally, topology optimization of fiber orientations is performed for design of a CFRP engine hood, in which the structural performance generated from the proposed ML-based approach achieves 12.62% improvement compared with that obtained from the conventional single-initial design method. This article is anticipated to demonstrate a new alternative for design of fiber-reinforced composite structures.

show abstract

Concurrent Patch Optimization of Hybrid Composite Plates Based on Proper Orthogonal Decomposition

Cited by 13 publications

References 67 publications

Manufacturable insight into modelling and design considerations in fibre-steered composite laminates: State of the art and perspective

Manufacturable insight into modelling and design considerations in fibre-steered composite laminates: State of the art and perspective

Adaptive Sampling for Interpolation of Reduced-Order Aeroelastic Systems

Machine learning based topology optimization of fiber orientation for variable stiffness composite structures

Contact Info

Product

Resources

About