Abstract:

Internal uncertainties are inevitable in rotor systems, and may significantly affect their dynamic characteristics and render conventional deterministic optimization approaches inapplicable. Meanwhile, existing uncertainty optimization methods often entail prohibitive computational costs. In view of the above issues, in this paper, an efficient interval structural analysis method is proposed based on a surrogate model, and an uncertain multi-objective optimization framework is established. The framework improves the computational efficiency by providing a surrogate-based method for interval evaluation, which avoids the need for the nested optimization loop and reduces the reliance on the direct invocations of the computationally expensive high-fidelity model. The numerical examples illustrate the potential of the proposed method to balance the computational accuracy with efficiency. The subsequent application to the dynamic optimization of an uncertain rotor system demonstrates its engineering applicability and computational economy, offering a computationally efficient approach for robust design of such systems.

Key words: rotor dynamics, uncertainty, surrogate model, multi-objective optimization