发表机构
Leibniz University Hannover; Tsinghua University; Bauhaus-Universität Weimar（莱布尼茨汉诺威大学; 清华大学; 魏玛包豪斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对浸式边界柔性游泳体运动，开发神经算子代理模型预测其流场，平面与体积型模型分别达到对应精度，验证了该代理模型的可行性并明确了后续改进方向。
AI 中文摘要
高保真浸式边界模拟可解析变形游泳体与其周围流体的耦合运动，但由此产生的计算成本限制了其在工程设计、参数研究和控制中的重复评估。我们开发了神经算子代理模型，用于预测平面和体积型鳗鱼游泳体产生的流体动力学场的时间演化。这些代理模型基于自适应流固耦合模拟输出的规则网格场进行训练，并以游泳体几何形状和雷诺数为条件。平面模型可联合预测两个速度分量、标量涡量和压力，在5个保留的高雷诺数轨迹上，其全域全局相对L²误差为3.51%。体积型公式采用3个目标特定模型，共享多通道输入：一个模型预测三维速度，一个预测涡量，一个预测压力，它们在5个保留的范围内轨迹上的全域全局相对L²误差分别为3.44%、5.58%和19.2%。这些结果共同证明了针对运动边界游泳体流动的场解析神经代理模型的可行性，同时指出压力精度和物理一致性是进一步发展的优先方向。
英文摘要
High-fidelity immersed-boundary simulation resolves the coupled motion of a deforming swimmer and its surrounding flow, but the resulting cost limits repeated evaluations for engineering design, parameter studies, and control. We develop neural-operator surrogates for temporal prediction of the hydrodynamic fields generated by planar and volumetric eel swimmers. The surrogates are trained on regular-grid fields exported from adaptive fluid--structure simulations and are conditioned on swimmer geometry and Reynolds number. The planar model jointly predicts two velocity components, scalar vorticity, and pressure. On five held-out high-Reynolds-number trajectories, its full-domain global relative L^2 error is 3.51 %. The volumetric formulation uses three target-specific models with a common multichannel input: one model predicts three-dimensional velocity, one predicts vorticity, and one predicts pressure. Their full-domain global relative L^2 errors on five held-out within-range trajectories are 3.44 %, 5.58 %, and 19.2 %. Together, the results demonstrate the feasibility of field-resolved neural surrogates for moving-boundary swimmer flows while identifying pressure accuracy and physical consistency as priorities for further development.
