Abstract
An approach is presented for the robust stacking sequence optimisation of composite plate wings with uncertain ply orientations. An aeroelastic model is constructed using the Rayleigh-Ritz technique coupled with modified strip theory aerodynamics. Gaussian process emulators are used in conjunction with Support Vector Machine classifiers to construct a surrogate for the discontinuous and non-smooth aeroelastic instability speed, across the space of lamination parameters. The surrogate model is used to estimate the probability that instability occurs at a given design speed, which is minimised using a genetic algorithm. For each evaluation of the objective function, existing data points are reused and the surrogate is updated when required using an adaptive Design of Experiments based upon a modified Latin Hypercube. Optimised stacking sequences are compared to deterministic optima for maximum instability speed. Three layup strategies are undertaken; (i) a benchmark in which ply orientations are limited to 0°, ±45° and 90°, (ii) in which values of ±30° and ±60° are also permitted, and (iii) in which orientations are fixed to 5° increments. Improvements in reliability of at least 83% are achieved using the benchmark layup strategy, with at least 95% and 97% improvements for the second and third strategies respectively. A factor of twenty reduction in the required number of model runs is achieved by using the adaptive surrogate, though this only corresponds to a factor of four reduction in computation time due to the additional time required to fit the surrogate.
Original language | English |
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Publication status | Published - 2015 |
Externally published | Yes |
Event | 20th International Conference on Composite Materials, ICCM 2015 - Copenhagen, Denmark Duration: 19 Jul 2015 → 24 Jul 2015 |
Conference
Conference | 20th International Conference on Composite Materials, ICCM 2015 |
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Country/Territory | Denmark |
City | Copenhagen |
Period | 19/07/15 → 24/07/15 |
Keywords
- Aeroelastic tailoring
- Composite wings
- Robust optimisation
- Uncertainty quantification