A multi-fidelity model management framework for multi-objective aerospace design optimisation

Abstract

This paper presents a multi-fidelity meta-modelling and model management framework designed to efficiently incorporate increased levels of simulation fidelity from multiple, competing sources into early-stage multidisciplinary design optimisation scenarios. Phase specific/invariant low-fidelity physics-based subsystem models are adaptively corrected via iterative sampling of high(er)-fidelity simulators. The correction process is decomposed into several distinct parametric/non-parametric stages, each leveraging alternate aspects of the available model responses. Globally approximating surrogates are constructed at each degree of fidelity (low, mid, and high) via an automated hyper-parameter selection and training procedure. The resulting hierarchy drives the optimisation process, with local refinement managed according to a confidence-based multi-response adaptive sampling procedure, with bias given to global parameter sensitivities. An application of this approach is demonstrated via the aerodynamic response prediction of a parametrized re-entry vehicle, subjected to a static/dynamic parameter optimisation for three separate single-objective problems. It is found that the proposed data correction process facilitates increased efficiency in attaining a desired approximation accuracy relative to a single-fidelity equivalent model. When applied within the proposed multi-fidelity management framework, clear convergence to the objective optimum is observed for each examined design optimisation scenario, outperforming an equivalent single-fidelity approach in terms of computational efficiency and solution variability.