AI 中文总结
该研究提出PINN框架，用于模拟典型剪切流中二维壁面反应性溶质弥散，经ADI有限差分基准验证，可准确捕捉不同反应区域的溶质输运特性，为边界耦合反应性输运分析提供新工具。
AI 中文摘要
剪切流中反应性溶质的弥散由平流拉伸、横向扩散和边界交换动力学的相互作用所支配。尽管经典分析方法和基于网格的数值求解器已对这些输运机制进行了广泛表征，但在非对称反应环境中准确求解溶质羽的时空演化仍具有计算挑战性。本研究引入物理信息神经网络（PINN）框架，以模拟受吸收壁约束的典型剪切流（库埃特流、泊肃叶流及库埃特-泊肃叶流）中的二维壁面反应性溶质弥散。通过将控制对流-扩散方程和罗宾边界条件嵌入统一损失函数，无网格PINN重构时空浓度场。网络预测与交替方向隐式（ADI）有限差分基准对比验证，在非反应、对称反应及非对称反应区域均显示出高度一致性。计算针对不可渗透壁面，在佩克莱数Pe=10、对称吸收（β₁,β₂）=(1,1)，以及十倍非对称壁面反应性对比（β₁,β₂）=(0.2,2)和(2,0.2)下开展。利用训练后PINN的可微性，提取壁面解析的输运诊断量，包括表观轴向弥散系数、累积壁面去除动力学及局域吸收通量。结果表明，施加的剪切剖面决定反应性吸收的流向组织，而不等的壁面反应性会诱导横向不对称性，进而改变宏观扩散速率。总体而言，该框架确立了PINN作为可解释的无网格工具，用于分析剪切流中边界耦合的反应性输运。
英文摘要
The dispersion of reactive solutes in shear flows is governed by the interplay between advective stretching, transverse diffusion, and boundary exchange kinetics. While classical analytical methods and grid-based numerical solvers have extensively characterised these transport mechanisms, accurately resolving the spatiotemporal evolution of solute plumes in asymmetric reactive environments remains computationally demanding. In this study, we introduce a physics-informed neural network (PINN) framework to simulate two-dimensional wall-reactive solute dispersion in canonical shear flows (Couette, Poiseuille, and Couette-Poiseuille) bounded by absorbing walls. By embedding the governing convection-diffusion equation and Robin boundary conditions into a unified loss function, the mesh-free PINN reconstructs the spatiotemporal concentration field. The network predictions are validated against an alternating-direction implicit (ADI) finite-difference benchmark, showing close agreement across non-reactive, symmetric, and asymmetric reactive regimes. The computations are carried out at $\mathrm{Pe}=10$ for impermeable walls, symmetric absorption $(β_1,β_2)=(1,1)$, and tenfold asymmetric wall-reactivity contrasts $(β_1,β_2)=(0.2,2)$ and (2,0.2). Leveraging the differentiable nature of the trained PINN, we extract wall-resolved transport diagnostics, including the apparent axial dispersion coefficient, cumulative wall-removal dynamics, and localised uptake fluxes. The results show that the imposed shear profile governs the streamwise organisation of reactive uptake, while unequal wall reactivities induce transverse asymmetry that modifies the macroscopic spreading rate. Overall, this framework establishes PINNs as an interpretable mesh-free tool for analysing boundary-coupled reactive transport in shear flows.
