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<p>During the conceptual design phase of aircraft development, wing structural optimization is frequently omitted or excessively simplified due to the intensive manual labor involved in geometry modeling, meshing, and finite-element analysis. These challenges are exacerbated for innovative configurations like tilt-duct aircraft, which must resolve conflicting requirements from vertical takeoff and landing (VTOL) and cruise modes. This paper presents an automated framework for conceptual-stage wing structural design and optimization, tailored for tilt-duct aircraft. The primary innovation is the incorporation of Bayesian optimization (BO) driven by a novel dynamic expected improvement (DEI) strategy. DEI adaptively manages the exploration–exploitation trade-off, enabling efficient navigation of a mixed-variable design space encompassing discrete variables (e.g., rib and spar counts) and continuous variables (e.g., rib and spar positions and thicknesses). This approach facilitates global search and rapid convergence in computationally expensive black-box evaluations. The framework integrates parametric geometry generation, high-fidelity meshing, and multi-mode finite-element analysis. Applied to a 1000 kg-class laboratory-developed tilt-duct aircraft wing, the DEI-based BO achieved a 22.61% mass reduction while complying with strength and stiffness constraints under both VTOL and cruise conditions. Furthermore, to demonstrate the framework’s versatility and generalizability, we applied it to a second, 110 kg-class aircraft case study, which resulted in a significant 16.06% mass reduction. This framework substantially shortens wing structural layout optimization, improves reliability via automation, and supports extensibility to multidisciplinary contexts, advancing eVTOL aircraft structural optimization for urban air mobility.</p>

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Bayesian optimization framework for mixed-variable wing structural design and optimization of tilt-duct aircraft

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Shangru Xu,
  • Jifa Zhang,
  • Yao Zheng,
  • Yaolong Liu

摘要

During the conceptual design phase of aircraft development, wing structural optimization is frequently omitted or excessively simplified due to the intensive manual labor involved in geometry modeling, meshing, and finite-element analysis. These challenges are exacerbated for innovative configurations like tilt-duct aircraft, which must resolve conflicting requirements from vertical takeoff and landing (VTOL) and cruise modes. This paper presents an automated framework for conceptual-stage wing structural design and optimization, tailored for tilt-duct aircraft. The primary innovation is the incorporation of Bayesian optimization (BO) driven by a novel dynamic expected improvement (DEI) strategy. DEI adaptively manages the exploration–exploitation trade-off, enabling efficient navigation of a mixed-variable design space encompassing discrete variables (e.g., rib and spar counts) and continuous variables (e.g., rib and spar positions and thicknesses). This approach facilitates global search and rapid convergence in computationally expensive black-box evaluations. The framework integrates parametric geometry generation, high-fidelity meshing, and multi-mode finite-element analysis. Applied to a 1000 kg-class laboratory-developed tilt-duct aircraft wing, the DEI-based BO achieved a 22.61% mass reduction while complying with strength and stiffness constraints under both VTOL and cruise conditions. Furthermore, to demonstrate the framework’s versatility and generalizability, we applied it to a second, 110 kg-class aircraft case study, which resulted in a significant 16.06% mass reduction. This framework substantially shortens wing structural layout optimization, improves reliability via automation, and supports extensibility to multidisciplinary contexts, advancing eVTOL aircraft structural optimization for urban air mobility.