利用算子网络预测复杂几何结构上的瞬态流动

摘要

针对非定常流动的快速、几何泛化代理模型仍是当前研究难点。本文提出一种时间依赖的几何感知深度算子网络，能够预测参数化与非参数化外形周围中等雷诺数流动的速度场。该模型通过符号距离场主干网络编码几何特征，借助卷积神经网络分支处理流动历史信息，基于841组高精度仿真数据进行训练。在未见过的几何外形上，模型单步预测相对L2误差约为5%，较计算流体力学方法加速达1000倍。我们建立了以物理为中心的滚动预测诊断方法，包括测点相位误差与散度范数，以量化长期预测精度。结果表明模型能准确预测短期瞬态流动，但在精细尺度尾流中会出现误差累积，尖角几何体尤为明显。我们分析了故障模式并提出实用改进方案。代码、数据分割及脚本已开源发布（https://github.com/baskargroup/TimeDependent-DeepONet），以支持复现与基准测试。

English

Fast, geometry-generalizing surrogates for unsteady flow remain challenging. We present a time-dependent, geometry-aware Deep Operator Network that predicts velocity fields for moderate-Re flows around parametric and non-parametric shapes. The model encodes geometry via a signed distance field (SDF) trunk and flow history via a CNN branch, trained on 841 high-fidelity simulations. On held-out shapes, it attains sim 5% relative L2 single-step error and up to 1000X speedups over CFD. We provide physics-centric rollout diagnostics, including phase error at probes and divergence norms, to quantify long-horizon fidelity. These reveal accurate near-term transients but error accumulation in fine-scale wakes, most pronounced for sharp-cornered geometries. We analyze failure modes and outline practical mitigations. Code, splits, and scripts are openly released at: https://github.com/baskargroup/TimeDependent-DeepONet to support reproducibility and benchmarking.

利用算子网络预测复杂几何结构上的瞬态流动

Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks

摘要

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