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Sub-space approximations for MDO problems with disparate disciplinary variable dependence
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DOI：10.1007/s00158-016-1496-0.png)
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摘要
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An approach to solving multidisciplinary design optimisation problems using approximations built in sub-spaces of the design variable space is proposed. Each approximation is built in the sub-space significant to the corresponding discipline while the optimisation problem is solved in the full design variable space. Since the approximations are built in a space of reduced dimensionality, the computational budget associated with building them can be reduced without compromising their quality. The method requires the designer to make assumptions on which design variables are significant to each discipline. If such assumptions are deficient, the resulting approximations suffer from errors that are not possible to reduce by additional sampling. Therefore a recovery mechanism is proposed that updates the values of the insignificant variables at the end of each iteration to align with the current best point. The method is implemented within a trust region based optimisation framework and demonstrated on a multidisciplinary optimisation of a thin-walled beam section subject to stiffness and impact requirements.
Keyword:
Multidisciplinary design optimisation
Multi-model optimisation
Sub-space approximations
Mid-range approximations
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