Abstract
In this research, a Gaussian process (GP) surrogate modelling framework for the forming process of dry carbon-fibre textile was investigated. A particular focus of the work is the development of dimension reduction algorithms, allowing to solve high-dimensional sparse optimisation problems. The concept of active subspace is adopted to find the principal space of the problem. Then, a low-dimensional (i.e., active) subspace can be obtained by selecting the directions with highest explained variance. A kernel-combined GP format is developed. This takes advantage of the active subspace to build a robust, high-dimensional emulator that can be regarded as a special case of multi-fidelity GP. A two-step adaptive sequential design approach is adopted, which further improves the efficiency of data design. Different sequential design strategies are compared. A case study with eight input parameters demonstrates the capability of the proposed approach, where an accurate and robust optimum condition is obtained from only tens of simulations.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have