A robust and reliability-based aeroelastic tailoring framework for composite aircraft wings

Muhammad F. Othman*, Gustavo H.C. Silva, Pedro H. Cabral, Alex P. Prado, Alberto Pirrera, Jonathan E. Cooper

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)
326 Downloads (Pure)

Abstract

This paper presents a multi-level aeroelastic tailoring framework for the optimisation of composite aircraft wings. The framework is capable of structural sizing and produces detailed composite ply configurations through robust and reliability-based design optimisation, and is demonstrated on a representative regional jet airliner finite element wing box model. The optimisation procedure is divided into two levels. The first level optimises the wing structure for minimum weight subject to multiple constraints including strain, buckling, aeroelastic stability and gust response. These first level solutions are then fed into the second level to be further optimised for robustness or reliability by considering uncertainties in material properties at ply level. Both the principles of robust and reliability-based design optimisation can also be used in combination to ensure a balance between the robustness and reliability of the structural performance. In order to keep computations to an acceptable cost, the second level optimisation employs the Polynomial Chaos Expansion method to approximate the effect of probabilistic uncertainty on structural performance. In comparison to the original benchmark wing, the framework produces an overall weight reduction of 32.1%, despite a 1.5% increase from the first to the second level optimisation that accounts for stochastic design variations.

Original languageEnglish
Pages (from-to)101-113
Number of pages13
JournalComposite Structures
Volume208
Early online date25 Sep 2018
DOIs
Publication statusPublished - 15 Jan 2019

Structured keywords

  • Bristol Composites Institute ACCIS

Keywords

  • Aeroelastic tailoring
  • Polynomial chaos expansion
  • Reliability based design
  • Robust design
  • Uncertainty quantification

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