A novel conceptual design approach for autonomous underwater helicopter based on multidisciplinary collaborative optimization

Abstract

Autonomous underwater helicopters (AUHs) are complex electromechanical systems consisting of multiple interconnected sub-disciplines, posing a significant challenge for traditional sequential design strategies in managing their interactions. To address this challenge, a multidisciplinary collaborative optimization (MCO) strategy is proposed, integrating structural, hydrodynamic, and energy disciplines to enhance AUH cruising range. Additionally, a novel approximation model is introduced, combining the Logistic-Tent chaos sparrow search algorithm (LTC-SSA) with the backpropagation neural network (BPNN) algorithm. This method aims to replace costly computational fluid dynamics (CFD) and structural mechanics calculations, thus reducing AUH optimization costs. The laminate design of the composite pressure hull follows classic lamination theory (CLT) and the Tsai-Wu failure criterion. Results demonstrate that the multidisciplinary co-design strategy significantly reduces optimization time costs of AUH, from several months to several days. Furthermore, the proposed LTC-SSA-BPNN algorithm accurately evaluates sailing resistance and Tsai-Wu failure index, with maximum errors of 1.64% and 9.32% respectively. Additionally, the composite pressure shell achieves a 30.4% weight reduction and a 4.1% reduction in sailing resistance while maintaining structural stability. Moreover, the maximum cruising range increases by 34.4%. This strategy offers theoretical support for AUH conceptual design and promises performance enhancement in practical engineering applications.

Keywords

• Autonomous underwater helicopter (AUH)
• multidisciplinary collaborative optimization (MCO)
• sparrow search algorithm (SSA)
• logistic-tent chaotic (LTC)
• BP neural network (BPNN)