外观
论文arXiv 2609.142541 个月前
拥抱流动非定常性：高通量学习平台实现利用涡的仿生推进
原文链接：Embracing Flow Unsteadiness: A High-Throughput Learning Platform Enables Vortex-Exploiting Bioinspired Propulsion（arXiv 2609.14254 · 发布日期：2026-09-13）
Embracing Flow Unsteadiness: A High-Throughput Learning Platform Enables Vortex-Exploiting Bioinspired Propulsion
REEF结合高通量真实流固交互平台与分阶段学习算法，在三类推进器和自由运动机器人上验证非定常涡利用。
核心要点
- 生物游泳者和飞行者利用非定常涡推进，工程车辆通常将其作为扰动抑制。
- REEF集成SHOAL八通道真实流固交互阵列与V-STAR分阶段学习算法。
- 在升力型、阻力型和动量射流推进器上，可达力包络超过参数化搜索两倍。
- PIV显示增益来自协调涡形成、增长和力投影，策略可零样本迁移到自由运动机器人。
验证范围与局限：验证范围包括真实流固交互中的三类推进器，以及到自由运动机器人的零样本迁移。
英文摘要 (Abstract)
Biological swimmers and flyers exploit unsteady vortices for propulsion, whereas engineered vehicles usually suppress them as disturbances. Learning such flow exploitation in machines is difficult because real-fluid interaction data are scarce and unstructured exploration is unstable in high-dimensional, history-dependent flows. Here we present REEF, a co-designed physical-learning framework that integrates SHOAL, an eight-channel high-throughput array for real fluid--structure interaction, with V-STAR, a staged algorithm that converts these interactions into policies through imitation, offline internalization, and online adaptation. Across lift-based, drag-based, and momentum-jet propulsors, REEF expands the attainable force envelope to more than twice that of parameterized search. Particle image velocimetry shows that these gains arise from coordinated vortex formation, growth, and force projection, rather than refinement of a fixed motion-to-force mapping. Force-trained policies transfer zero-shot to free-moving robots whose body motion changes the surrounding flow, suggesting that REEF learns transferable wake-coupling principles for embodied propulsion in unsteady fluids.
作者：Fei Han, Xinyu Cui, Zhipeng Wang, Ning Yang, Hang Xu, Haifeng Zhang, Zhongming Hu, Jun Wang, Junfeng Du, Dixia Fan
来源
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