https://eprints.ncl.ac.uk/file_store/production/197576/5DDBEC49-AF90-4644-892A-3AE82C3777F6.pdf

A systems approach to reduce urban rail energy consumption

A multi-scale approach for the optimum design of sandwich plates with honeycomb core. Part II: the optimisation strategy

Multiple objective optimisation of composite sandwich structures for rail vehicle floor panels

On low-velocity impact response of foam-core sandwich panels

Numerical study of low-speed impact response of sandwich panel with tube filled honeycomb core

Three population-based optimization techniques — particle swarm optimization, ant colony optimization, and simulated annealing — are applied to the multi-objective design of sandwich materials. The formulation of the algorithms is described, along with metrics for quantifying their performance. For the case study considered of a sandwich beam in bending, it is shown that the ant colony technique out-performs the other two algorithms with respect to identifying optimal geometries, materials, and lay-ups for a stiff sandwich in which low mass and low cost are the design objectives. The particle swarm and simulated annealing algorithms were found to have particular difficulties in handling the optimization of multi-ply oriented fiber-reinforced polymer facing laminates.

A comparison of three population-based optimization techniques for the design of composite sandwich materials

Sandwich materials, consisting of two thin, stiff facings separated by a low density core, can be used to produce structures that are both light and flexurally rigid. However, the optimisation of sandwich materials is not straightforward. This is because there are typically multiple design variables and multiple design objectives. Particle swarm optimisation is a heuristic method that is capable of finding optimal solutions within complex design spaces. The application of particle swarm optimisation to multi-objective sandwich beam problems is described here. The free variables investigated include the facing thickness, and the facing and core materials. Furthermore, for the facings, multiply, oriented laminate constructions are considered. Based on these inputs, sandwich beams are optimised for stiffness, mass and cost. The results show that the particle swarm optimisation algorithm is effective at finding a range of optimal solutions for the given objectives.

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C. W. Hudson

Papers

Multiple objective optimisation of composite sandwich structures for rail vehicle floor panels

A comparison of three population-based optimization techniques for the design of composite sandwich materials

Application of Particle swarm optimisation to Sandwich material design