Millionfold accelerated AI solver for 3D multi-physical simulations of ultrapermeable membranes

IF 10.4 1区 工程技术 Q1 ENGINEERING, CHEMICAL
Yanjin Liu, Jiu Luo, Mingming Huang, Hong Liu, Zhiwei Wang, Yi Heng
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引用次数: 0

Abstract

Solving three-dimensional (3D) multi-physics forward and inverse problems is indispensable for fundamental understanding and optimal design of membrane-based desalination systems. Unfortunately, it is computationally expensive when applying traditional numerical methods. Herein, a modified Fourier neural operator (FNO)-based method is proposed to efficiently solve complex 3D multi-physics problems. The intelligent solver solves the 3D forward problems in seconds, which is approximately 105-106 times faster than traditional finite-element based method with a comparable solution quality. The average prediction accuracy is more than 96%. Moreover, the proposed FNO-based method is mesh-independent and has zero-shot super-resolution ability. It can be used to provide a fast solution for the optimal design of membrane module to mitigate concentration polarization and membrane fouling for next-generation ultrapermeable membrane system.

Abstract Image

超透膜三维多物理模拟的百万倍加速AI求解器
求解三维多物理场正反问题是对膜基脱盐系统进行基本认识和优化设计的必要条件。不幸的是,当应用传统的数值方法时,计算成本很高。为此,提出了一种基于改进傅立叶神经算子(FNO)的三维多物理场复杂问题求解方法。智能求解器在数秒内求解三维正演问题,在求解质量相当的情况下,比传统的基于有限元的方法快约105-106倍。平均预测精度在96%以上。此外,所提出的基于fno的方法具有网格无关性和零镜头超分辨能力。该方法可为下一代超透膜系统中膜组件的优化设计提供快速解决方案,以减轻浓度极化和膜污染。
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来源期刊
npj Clean Water
npj Clean Water Environmental Science-Water Science and Technology
CiteScore
15.30
自引率
2.60%
发文量
61
审稿时长
5 weeks
期刊介绍: npj Clean Water publishes high-quality papers that report cutting-edge science, technology, applications, policies, and societal issues contributing to a more sustainable supply of clean water. The journal's publications may also support and accelerate the achievement of Sustainable Development Goal 6, which focuses on clean water and sanitation.
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