用网格细化结构做代理模型，加速喷雾模拟。
Learning Interface Breakup: A Geometry-Conditioned Latent Surrogate for Spray Formation
- 以自适应网格密度为隐变量，压缩流场信息。
- 推理仅需0.045秒，提速超6万倍。
- 适合需要快速迭代的喷嘴设计场景。
喷嘴设计需预测几何如何影响瞬态气液两相破裂，但高保真体积分数（VOF）模拟结合自适应网格细化（AMR）成本过高，难以用于迭代设计探索。标准代理模型也面临挑战，因液气界面与底层自适应离散化随时间和几何变化。本文提出一种几何条件化的潜在代理模型，基于797次两相喷嘴模拟训练，通过编码AMR单元密度场而非完整多通道流态，作为求解器集中计算资源的紧凑代理。由此表示，模型可重建瞬态密度演化和喷嘴几何，轻量级第二阶段恢复其余流动变量。在保留测试模拟中，该方法准确捕捉关键界面动态，推理时间降至每轨迹0.045秒，相比Basilisk CFD提速超过6×10⁴倍。结果表明，AMR细化结构可作为几何条件化瞬态两相流代理建模的紧凑且可学习表征。
原文摘要 · Abstract (English)
Designing spray nozzles requires predicting how geometry shapes transient two-phase breakup, but high-fidelity volume-of-fluid (VOF) simulations with adaptive mesh refinement (AMR) are too expensive for iterative design exploration. Standard surrogate models are also challenged by this setting because both the liquid--gas interface and the underlying adaptive discretization evolve across time and geometries. We introduce a geometry-conditioned latent surrogate trained on 797 two-phase nozzle simulations that addresses this by encoding the AMR cell-density field, rather than the full multi-channel flow state, as a compact proxy for where the solver concentrates resolution. From this representation, the model reconstructs transient density evolution and nozzle geometry, and a lightweight second stage recovers the remaining flow variables. On held-out simulations, the method accurately captures key interface dynamics while reducing inference time to 0.045 seconds per trajectory, corresponding to a speed-up of more than $6\times10^4$ relative to Basilisk CFD. These results suggest that AMR refinement structure can serve as a compact and learnable representation for geometry-conditioned surrogate modeling of transient two-phase flows.
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