Design-space assessment and dimensionality reduction: An off-line method for shape reparameterization in simulation-based optimization

A method based on the Karhunen–Loève expansion (KLE) is formulated for the assessment of arbitrary design spaces in shape optimization, assessing the shape modification variability and providing the definition of a reduced-dimensionality global model of the shape modification vector. The method is based on the concept of geometric variance and does not require design-performance analyses. Specifically, the KLE is applied to the continuous shape modification vector, requiring the solution of a Fredholm integral equation of the second kind. Once the equation is discretized, the problem reduces to the principal component analysis (PCA) of discrete geometrical data. The objective of the present work is to demonstrate how this method can be used to (a) assess different design spaces and shape parameterization methods before optimization is performed and without the need of running simulations for the performance prediction, and (b) reduce the dimensionality of the design space, providing a shape reparameterization using KLE/PCA eigenvalues and eigenmodes. A demonstration for the hull-form optimization of the DTMB 5415 model in calm water is shown, where three design spaces are investigated, namely provided by free-form deformation, radial basis functions, and global modification functions.