A Framework for simulation-driven engineering design

The modern engineering design process often
employs computer simulations to evaluate candidate designs.
This setup transforms the design process into an optimization
problem which involves a computationally-expensive black-box
function, namely, which lacks an analytic expression and where
each function evaluation requires large computational
resources. An additional challenge in such settings is that
simulation runs may consistently fail for some specific
candidate designs, but with the reason for failure being
unknown. To effectively handle such challenging problems this
paper proposes an engineering optimization framework which
incorporates a classifier whose goal is to predict if a candidate
design is likely to result in a failed simulation run. This
prediction is then used to dynamically divert the optimization
search away from such designs, without sending them to the
simulation. Numerical experiments based on an airfoil
optimization problem show the effectiveness of the proposed
approach. (Abstract)
Keywords— engineering design optimization, computer
simulations, metamodels, classifiers (key words)