arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2601.05042physics.flu-dynphysics.comp-ph
收藏

基于PINN的扩散控制滴状生长解决方案

PINN-Based Solution for a Diffusion Controlled Droplet Growth

Alpha SystematiX;New Jersey Institute of Technology(新泽西理工学院)

Pavel Gol'din, Gennady Y. Gor

AI总结:

本文提出基于PINN的扩散控制滴状生长解决方案,通过耦合扩散方程与界面质量平衡,实现对滴状半径的自适应训练,验证了其在广泛初始条件下的收敛性及计算效率。

摘要:

我们研究了使用物理信息神经网络(PINN)公式来研究具有移动边界的大球滴状生长的扩散控制生长。主导的扩散方程与界面质量平衡耦合,将滴状半径视为时间的额外可训练函数。PINN能够准确重现宽范围初始滴状半径的自相似生长定律和浓度分布,展示了向渐近扩散区域收敛的能力。所提出的方法为解决移动边界扩散问题提供了一个灵活且计算效率高的框架,并可以方便地扩展以包含其他物理效应。

英文摘要:

We study diffusion-controlled growth of a spherical droplet with a moving boundary using a physics-informed neural network (PINN) formulation. The governing diffusion equation is coupled to the interfacial mass balance, with the droplet radius treated as an additional trainable function of time. The PINN accurately reproduces the self-similar growth law and concentration profiles for a wide range of initial droplet radii, demonstrating convergence toward the asymptotic diffusive regime. The proposed approach provides a flexible and computationally efficient framework for solving moving-boundary diffusion problems and can be readily extended to include additional physical effects.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑