A hybrid single-loop approach combining the target beta-hypersphere sampling and active learning Kriging for reliability-based design optimization

IF 5 1区 工程技术 Q1 ENGINEERING, AEROSPACE Aerospace Science and Technology Pub Date : 2025-03-11 DOI:10.1016/j.ast.2025.110136
Huanhuan Hu, Pan Wang, Haoqi Chang, Rong Yang, Weizhu Yang, Lei Li
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Abstract

In engineering design, system-level requirements typically provide each subsystem with specific target reliability indexes. This makes reliability-based design optimization (RBDO) under the prescribed target reliability index particularly relevant for practical applications. However, solving complex nonlinear RBDO problems often involves nested double-loop optimization, leading to prohibitive computational costs and potential convergence issues. To address these challenges, this study proposes a minimum performance measure-based hybrid single-loop approach (TSPM-AK-HSLA) that integrates target beta-hypersphere sampling and active learning Kriging. First, a novel sampling strategy combining target beta-hypersphere and local enhancement is introduced to accurately identify the minimum performance target point (MPTP) without requiring gradient calculations or iterative search direction adjustments. Second, an identification criterion for the active constraint is incorporated to determine whether the Kriging model needs updating within the local region around the approximate MPTP, thereby focusing sampling efforts for improved efficiency. Finally, an adaptive strategy is employed to implement the hybrid single-loop approach, accelerating convergence while maintaining robustness for nonlinear problems. Comparative analyses with existing methods, along with two numerical MPTP search examples and two nonlinear RBDO examples demonstrate the superior efficiency and accuracy of the proposed approach. The RBDO application for an engineering clamping mechanism of the aircraft engine guides the design.
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在工程设计中,系统级需求通常会为每个子系统提供特定的目标可靠性指标。因此,在规定的目标可靠性指标下进行基于可靠性的设计优化(RBDO)与实际应用尤为相关。然而,解决复杂的非线性 RBDO 问题往往涉及嵌套双环优化,从而导致过高的计算成本和潜在的收敛问题。为了应对这些挑战,本研究提出了一种基于最小性能指标的混合单环方法(TSPM-AK-HSLA),它集成了目标β-半球采样和主动学习克里金法。首先,引入了一种结合目标β-hypersphere 和局部增强的新型采样策略,无需梯度计算或迭代搜索方向调整即可准确识别最小性能目标点(MPTP)。其次,纳入了主动约束的识别标准,以确定 Kriging 模型是否需要在近似 MPTP 周围的局部区域内进行更新,从而集中采样工作以提高效率。最后,采用自适应策略实施混合单环方法,在加速收敛的同时保持对非线性问题的鲁棒性。通过与现有方法的比较分析,以及两个 MPTP 数值搜索示例和两个非线性 RBDO 示例,证明了所提出方法的卓越效率和准确性。RBDO 应用于飞机发动机的工程夹紧机构,为设计提供了指导。
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来源期刊
Aerospace Science and Technology
Aerospace Science and Technology 工程技术-工程:宇航
CiteScore
10.30
自引率
28.60%
发文量
654
审稿时长
54 days
期刊介绍: Aerospace Science and Technology publishes articles of outstanding scientific quality. Each article is reviewed by two referees. The journal welcomes papers from a wide range of countries. This journal publishes original papers, review articles and short communications related to all fields of aerospace research, fundamental and applied, potential applications of which are clearly related to: • The design and the manufacture of aircraft, helicopters, missiles, launchers and satellites • The control of their environment • The study of various systems they are involved in, as supports or as targets. Authors are invited to submit papers on new advances in the following topics to aerospace applications: • Fluid dynamics • Energetics and propulsion • Materials and structures • Flight mechanics • Navigation, guidance and control • Acoustics • Optics • Electromagnetism and radar • Signal and image processing • Information processing • Data fusion • Decision aid • Human behaviour • Robotics and intelligent systems • Complex system engineering. Etc.
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