推进机械振动下船体结构的神经拓扑优化
Neural topology optimization of ship structures under propulsion machinery vibrations
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中文总结 AI 辅助
本研究提出基于卷积Kolmogorov-Arnold网络的神经拓扑优化方法，以主动输入功率为目标优化船体结构，在多个案例中显著降低振动响应和静态柔度，并大幅提升计算效率。
中文摘要 AI 辅助
船体结构振动会导致噪声、疲劳和设备损坏，而动态柔度拓扑优化在接近共振时可能产生病态设计。本研究将基于卷积Kolmogorov-Arnold网络（KATO）的神经重参数化拓扑优化扩展到以主动输入功率（AIP）为目标的受迫振动设计。应用包括一个100 Hz的发动机支撑甲板面板和一个18 Hz的推进器基座框架。Helmholtz偏微分方程滤波和Heaviside投影控制特征尺寸和制造公差。在两个甲板系列中，所有八个优化布局相对于尺寸优化参考均降低了AIP，并且在有限深度挤出后，还实现了更低的静态柔度。对于无限制、制造感知和应力感知的框架变体，KATO在AIP上与GCMMA相差在0.5 dB以内，同时在匹配体积的二值化重分析后，静态柔度降低了22-36倍。在一个近共振的300 Hz案例中，两种方法都将初始AIP降低了超过32 dB；KATO保持了连通设计，实现了59倍更低的二值化静态柔度，并将1-500 Hz范围内的最大AIP降低了2.7 dB。对于所实现的应力感知公式，KATO比GCMMA快6.4-10.4倍。结果表明，神经AIP驱动的拓扑优化是设计连通、特征尺寸受控且具有改进受迫振动性能的船体结构的有效方法。
英文摘要
Ship structural vibrations contribute to noise, fatigue, and equipment damage, while dynamic-compliance topology optimization can produce pathological designs near resonance. This study extends neural-reparameterized topology optimization using a convolutional Kolmogorov-Arnold network (KATO) to forced-vibration design with active input power (AIP) as the objective. Applications include a 100 Hz engine-supporting deck panel and an 18 Hz thruster foundation frame. Helmholtz PDE filtering and Heaviside projection control feature sizes and manufacturing tolerance. Across both deck families, all eight optimized layouts reduce AIP relative to size-optimized references and, after finite-depth extrusion, also achieve lower static compliance. For unrestricted, manufacturing-aware, and stress-aware frame variants, KATO matches GCMMA in AIP within 0.5 dB while yielding 22-36x lower static compliance after matched-volume binary re-analysis. In a near-resonant 300 Hz case, both methods reduce initial AIP by more than 32 dB; KATO maintains a connected design, achieves 59x lower binary static compliance, and reduces maximum AIP over 1-500 Hz by 2.7 dB. KATO runs 6.4-10.4x faster than GCMMA for the implemented stress-aware formulations. The results demonstrate neural AIP-driven topology optimization as an efficient approach for designing connected, feature-size-controlled ship structures with improved forced-vibration performance.
发表机构
- The University of British Columbia（不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
