Composite material structure optimization design and aeroelastic analysis on forward swept wing

Abstract

The static aeroelastic torsion divergence problem is the main obstacle to bring forward swept wing into massive applications. The aeroelastic tailoring technique-based radial basis function neural networks (RBFNNs) and genetic algorithm (GA) optimization in MATLAB considering the material orientation, thickness, and lay-up are elucidated in the present work. RBFNNs are used to build a surrogate model between the composite parameters and structure displacement, which is proved robust and accurate. Then an optimal structure is obtained by GA global search based on RBFNNs model with the weight constrain. The displacements of the forward swept wing caused by an approximate aerodynamic load are decreased 32.5% through finite element method (FEM) static structural analysis. The modal analysis illustrates that the first mode frequency increases by 33.0% and the second mode increases by 37.9%. A computational aeroelasticity approach is developed by in-house Hybrid Unstructured Reynolds-Averaged Navier-Stokes solver associating an open source FEM code – Calculix. The results of coupling calculations show effectiveness of aeroelastic tailoring optimization of composite forward swept wing without weight penalty. The results obtained demonstrate that for the forward swept wing, the most violent situation appears around Mach Number 1.0 where the aeroelastic tailoring optimization could decrease the torsion angle by nearly 70.0%. The torsion of forward swept wing will increase at subsonic and decrease at supersonic with the increase of velocity.

Design and optimization of self-deployable damage tolerant composite structures: A review

“…The results obtained by Nasab et al show that the decomposition strategy allowed the solving of a complex problem at a reasonable computation cost. Rongrong et al (2018) [195] applied a hybrid approach to the design and optimization of a composite forward swept wing. The authors generated a surrogate model of the aeroelastic torsion divergence problem using radial basis function neural networks (RBFNNs), which was then solved with a GA.…”

Estimation of the divergence characteristics of a jet transport aircraft wing using numerical method.

“…This research group applied an aeroelastic tailoring technique-based radial basis function neural networks (RBFNNs) and genetic algorithm (GA) optimization in MATLAB on the bases of the material orientation, thickness, and lay-up as a design decision parameter through finite element method (FEM) structural analysis. According to the report, the torsional instability of forward swept wing increased at subsonic speeds and decreased at supersonic with the increase of flight velocity [20]. Using the finite element method, the effect of in-plane stresses and lamination parameter on the flutter or divergence instabilities for constant stiffness and variable stiffness of edge-supported and cantilever composite trapezoidal panels were investigated [21].…”

Experimental and Numerical Studies on Static Aeroelastic Behaviours of a Forward‐Swept Wing Model

“…e CFD/CSD coupling analysis is essential when the flows are in transonic region because of the shock wave and flow separation [23,24]. e static divergence of wing structure must be predicted accurately by wind tunnel test, because the divergence speed directly reflects the general stiffness of the wing structure and must be considered in the certification process (CS-25 and FAR-25) [1].…”