发表机构
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出任务保持神经分割方法，同时分割可压缩流中重叠激波与涡核，通过分支适配避免相互干扰，实验验证涡核Dice达0.83，显著优于共享解码器U-Net。
AI 中文摘要
在可压缩流中，激波波前和涡核常常共存并重叠，其中壁面、尾流和剪切层也会产生强梯度。细化网络以检测一种结构可能会降低其对另一种结构的预测性能，而不会在优化分数中揭示这种损失。我们提出了一种用于同时进行激波和涡核分割的任务保持公式。实验使用两种求解器，包括多个迎角和雷诺数下的超声速菱形翼型流动，以及圆柱和椭圆柱绕流。一个共享的原始编码器为独立的激波和涡旋解码器提供输入，这些解码器具有独立的sigmoid输出，允许两个类别重叠。适配仅限于相关分支：零初始化的适配器仅向涡旋解码器提供旋转诊断信息，而修正后的激波监督仅更新激波解码器。受保护输出的所有依赖项保持固定，并在每个评估字段上验证逐位相等性。压缩和守恒跳跃（Rankine-Hugoniot）特征提供弱激波监督；旋转和拓扑提供涡旋候选。解析斜激波射线、Billig弓形激波相关性和等熵涡旋提供了独立于这些标签的参考。在相同的修正目标预算下，受限模型和容量匹配的共享解码器U-Net获得了相当的激波一致性。然而，它们的翼型涡核Dice重叠分数分别为0.83和0.08。软保留惩罚恢复了U-Net丢失的大部分核心一致性。冻结的激波适应模型在0.003弦长内定位了测试的解析斜激波射线。一个额外的分支识别膨胀流动区域，这些区域通过与理想激波-膨胀理论比较，与中心Prandtl-Meyer膨胀扇区分开来。
英文摘要
Shock fronts and vortex cores often coexist and overlap in compressible flows, where walls, wakes and shear layers also produce strong gradients. Refining a network to detect one structure can therefore degrade its prediction of the other without revealing the loss in the optimized score. We present a task-preserving formulation for simultaneous shock and vortex-core segmentation. The experiments use two solvers and include supersonic diamond-airfoil flows at several incidences and Reynolds numbers, together with circular- and elliptical-cylinder flows. A shared primitive encoder feeds separate shock and vortex decoders with independent sigmoid outputs, allowing the two classes to overlap. Adaptation is confined to the relevant branch: zero-initialized adapters supply rotational diagnostics only to the vortex decoder, while corrected shock supervision updates only the shock decoder. All dependencies of the protected output remain fixed, and bitwise equality is verified on every evaluation field. Compression and conservation-jump (Rankine--Hugoniot) signatures provide weak shock supervision; rotation and topology provide vortex candidates. Analytical oblique-shock rays, Billig's bow-shock correlation and an isentropic vortex supply references independent of these labels. Under the same corrected-target budget, the restricted model and a capacity-matched shared-decoder U-Net obtain comparable shock agreement. Their airfoil vortex-core Dice overlap scores, however, are 0.83 and 0.08, respectively. A soft retention penalty recovers most of the U-Net's lost core agreement. The frozen shock-adapted models locate the tested analytical oblique-shock rays within 0.003 chord. An additional branch identifies expanding-flow regions, which are distinguished from centred Prandtl--Meyer expansion fans through comparison with ideal shock--expansion theory.
