发表机构
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出边界条件傅里叶神经算子（FNO）替代模型，用于气液界面演化预测，可快速排序喷射工况，虽物理信息扩展模型未显著提升性能，但为喷雾喷射器快速原型开发提供了有效工具。
AI 中文摘要
喷雾雾化可快速形成巨大的气液界面面积，是众多工业过程的核心环节。然而，预测喷雾行为和界面面积仍存在困难：实验无法触及所有喷雾区域，而随着精细结构的发展，计算流体动力学（CFD）的计算成本会变得高得难以承受。数据驱动的替代模型可学习界面演化规律，从而实现快速的设计空间探索、运行工况排序，最终达成喷雾控制。本研究探究了状态表示、神经架构以及物理信息正则化对喷雾界面长时序自回归预测（尤其是守恒性）的影响。我们的主要模型是边界条件傅里叶神经算子（FNO），它可根据气液界面预测符号距离函数（SDF）的演化。该模型在涵盖多种雾化工况的二维尖锐界面流体体积法（Volume-of-Fluid）CFD模拟数据上进行训练。结果表明，SDF-FNO相比直接基于体积分数训练的FNO能更好地保留界面保真度，但性能不及U-Net。目标函数 ablation实验显示，液体存量惩罚项可在局部界面精度略有损失的情况下提升守恒性。我们还引入了一种物理信息扩展模型，结合了开放域目标增量液体平衡惩罚项、保留符号距离几何的窄带程函方程（Eikonal）正则化项以及相位边界惩罚项。尽管该模型训练稳定，但相较于数据驱动基线，它未能显著降低预测误差、界面重叠和存量行为。最后，我们通过该替代模型对固定几何结构运行包络内、单位气体喷射功率产生的界面面积对应的喷射工况进行了排序演示。
英文摘要
Spray atomisation rapidly creates large liquid-gas interfacial areas and is central to many industrial processes. However, predicting spray behaviour and surface area remains difficult: experiments cannot access all spray regions, while CFD becomes prohibitively expensive as finer structures develop. Data driven surrogates can learn interface evolution, enabling rapid design space exploration, operating condition ranking, and ultimately spray control. We investigate how state representation, neural architecture, and physics-informed regularisation affect long horizon autoregressive forecasting of spray interfaces, particularly conservation. Our principal model is a boundary-conditioned Fourier Neural Operator (FNO) that predicts the evolution of the signed distance function (SDF) from the liquid-gas interface. It is trained on 2D sharp interface Volume-of-Fluid CFD simulations spanning several atomisation regimes. The SDF-FNO retains interface fidelity better than an FNO trained directly on volume fraction, but is outperformed by a U-Net. Objective function ablation shows that a liquid inventory penalty improves conservation at a modest cost to local interface accuracy. We also introduce a physics-informed extension combining an open-domain target-increment liquid balance penalty, a narrowband Eikonal regulariser that preserves signed distance geometry, and a phase-boundedness penalty. Although this model trains stably, it leaves forecast error, interface overlap, and inventory behaviour essentially unchanged relative to the data driven baseline. Finally, we demonstrate the surrogate by ranking injection conditions according to interfacial area generated per unit gas injection power across the operating envelope of a fixed geometry.
