AI 中文总结
本研究开发了一种由物理机制与测量数据驱动的物理信息神经网络框架，用于从稀疏观测重构管道内颗粒流的稳态全场分布，为复杂颗粒流系统的流场重构提供了稳健方法。
AI 中文摘要
颗粒流在自然与工业系统中普遍存在，但其复杂动力学仍难以表征。对于涉及未知入口、出口及壁面边界条件的逆问题，计算流体动力学（CFD）模拟颇具挑战，而从稀疏观测重构完整流场构成一项极具挑战性的逆问题。本研究开发了一种由物理机制与测量数据共同驱动的物理信息神经网络框架，用于重构管道内颗粒流的稳态全场分布。该方法将稀疏测量数据与控制方程及本构关系相融合，采用连续介质模型的CFD解生成的高保真数据集进行训练。该框架包含无量纲损失公式、物理信息初始化、动态全局权重及局部加权颗粒温度数据损失策略。这些处理可实现完整流场演化的精准重构。本研究为复杂颗粒流系统的流场重构建立了一套稳健的方法学框架。
英文摘要
Granular flows are ubiquitous in natural and industrial systems, yet their complex dynamics remain difficult to characterize. For inverse problems involving unknown inlet, outlet, and wall boundary conditions, where CFD simulations are challenging, reconstructing complete flow fields from sparse observations constitutes a challenging inverse problem. In this study, a physics-informed neural network framework driven by both physical mechanisms and measurement data is developed to reconstruct the steady-state full-field distribution of granular flows in a pipe. The proposed approach integrates sparse measurement data with governing equations and constitutive relations and is trained using high-fidelity datasets generated by CFD solutions of a continuum model. The framework incorporates a dimensionless loss formulation, physics-informed initialization, dynamic global weighting, and a locally weighted granular temperature data-loss strategy. These treatments enable accurate reconstruction of the complete flow-field evolution. This work establishes a robust methodological framework for flow-field reconstruction in complex granular flow systems.
