Comparison of reliability based structural optimization methodologies in the design of aircraft structures

Hernández, Santiago; Díaz, J.; Baldomir, Aitor; Cid, M.; López, Carlos

doi:10.1201/b16387-716

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“…This discipline requires high computational effort since it combines DO and RA and has been applied to several multidisciplinary areas as automotive (Sinha (2007), Youn et al (2004) or Cid Montoya et al (2015)), naval (Leheta and Mansour (1997)) or civil engineering (Karadeniz et al (2009) or Kusano et al (2015)). Talking about RBDO applied to aeronautical stiffened panels, Hernandez et al (2013) draws a comparison of three RBDO methods in a metallic panel under buckling constraints, Qu and Haftka (2003) performs RBDO using Monte Carlo Simulations (MCS) and a Design Response Surface (DRS) in order to compute the reliability constraints while Díaz et al (2016) compares stochastic expansions with moment-based methods in the RA and genetic with gradient-based algorithms in the DO phase.…”

Section: Introductionmentioning

confidence: 99%

Reliability-based design optimization of composite stiffened panels in post-buckling regime

López

Bacarreza

Baldomir

et al. 2016

Struct Multidisc Optim

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This paper focuses on Deterministic and Reliability Based Design Optimization (DO and RBDO) of composite stiffened panels considering post-buckling regime and progressive failure analysis. The ultimate load that a post-buckled panel can hold is to be maximised by changing the stacking sequence of both skin and stringers composite layups. The RBDO problem looks for a design that collapses beyond the shortening of failure obtained in the DO phase with a target reliability while considering uncertainty in the elastic properties of the composite material. The RBDO algorithm proposed is decoupled and hence separates the Reliability Analysis (RA) from the deterministic optimization. The main code to drive both the DO and RBDO approaches is written in MATLAB and employs Genetic Algorithms (GA) to solve the DO loops because discrete design variables and highly nonlinear response functions are expected. The code is linked with Abaqus to perform parallel explicit nonlinear finite element analyses in order to obtain the structural responses at each generation. The RA is solved through an inverse Most Probable failure Point (MPP) search algorithm that benefits from a Poly-

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