Reliable prediction of hydraulic performance is challenging for Piano Key Weir (PKW) design because discharge capacity depends on three-dimensional geometry and operating conditions. Surrogate models can accelerate hydraulic-structure design, but progress is limited by scarce large, well-documented datasets that jointly capture geometric variation, operating conditions, and functional performance. This study presents WeirNet, a large 3D CFD benchmark dataset for geometric surrogate modeling of PKWs. WeirNet contains 3,794 parametric, feasibility-constrained rectangular and trapezoidal PKW geometries, each scheduled at 19 discharge conditions using a consistent free-surface OpenFOAM workflow, resulting in 71,387 completed simulations that form the benchmark and with complete discharge coefficient labels. The dataset is released as multiple modalities compact parametric descriptors, watertight surface meshes and high-resolution point clouds together with standardized tasks and in-distribution and out-of-distribution splits. Representative surrogate families are benchmarked for discharge coefficient prediction. Tree-based regressors on parametric descriptors achieve the best overall accuracy, while point- and mesh-based models remain competitive and offer parameterization-agnostic inference. All surrogates evaluate in milliseconds per sample, providing orders-of-magnitude speedups over CFD runtimes. Out-of-distribution results identify geometry shift as the dominant failure mode compared to unseen discharge values, and data-efficiency experiments show diminishing returns beyond roughly 60% of the training data. By publicly releasing the dataset together with simulation setups and evaluation pipelines, WeirNet establishes a reproducible framework for data-driven hydraulic modeling and enables faster exploration of PKW designs during the early stages of hydraulic planning.

翻译：钢琴键堰（PKW）设计中水力性能的可靠预测具有挑战性，因为泄流能力取决于三维几何形状和运行条件。代理模型可以加速水工结构设计，但进展受限于缺乏同时捕捉几何变化、运行条件和功能性能的大型、文档完备的数据集。本研究提出了WeirNet，一个用于PKW几何代理建模的大规模三维计算流体力学基准数据集。WeirNet包含3,794个参数化、可行性约束的矩形和梯形PKW几何体，每个几何体均采用一致的自由表面OpenFOAM工作流程在19种泄流条件下进行模拟，最终完成了71,387次模拟，构成了基准数据集并附有完整的流量系数标签。该数据集以多种模态发布：紧凑的参数化描述符、水密表面网格和高分辨率点云，同时提供标准化任务以及分布内和分布外划分。本研究对代表性的代理模型族进行了流量系数预测的基准测试。基于参数化描述符的树状回归器实现了最佳整体精度，而基于点和网格的模型仍具竞争力，并提供与参数化无关的推理能力。所有代理模型均能在每样本毫秒级时间内完成评估，相比计算流体力学运行时间实现了数量级的加速。分布外测试结果表明，与未见过的泄流值相比，几何形状偏移是主要的失效模式；数据效率实验表明，当训练数据超过约60%时，收益递减。通过公开发布数据集、模拟设置和评估流程，WeirNet为数据驱动的水力建模建立了一个可复现的框架，并能在水利规划的早期阶段实现对PKW设计的更快速探索。