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Volume 116
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Multi-scale computations of reactive multiphase flows: Towards digital twins for energy transition
Mengyu Wang a, Shuai Wang a b, Junjie Lin a b, Kun Luo a b *, Jianren Fan a b
a State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China
b Shanghai Institute for Advanced Study of Zhejiang University, Shanghai 200120, China
10.1016/j.partic.2026.06.029
Volume 116, September 2026, Pages 311-328
Received 8 April 2026, Revised 19 May 2026, Accepted 18 June 2026, Available online 2 July 2026, Version of Record 13 July 2026.
E-mail: zjulk@zju.edu.cn
Highlights

• Advances in multi-scale modeling of reactive multiphase flows are reviewed.

• Physical-based models and AI-driven methods like ROM are highlighted.

• Integrating multi-scale computing with AI paves the way for digital twins.

• Future perspectives on intelligent, self-evolving digital twins are provided.


Abstract

The global energy transition toward carbon neutrality demands transformative innovations in modeling and optimizing complex energy conversion systems. Reactive multiphase flow in industrial processes, particularly circulating fluidized bed (CFB) boilers, is central to clean and efficient energy conversion and constitutes the key physical basis for boiler-state prediction and digital-twin development. However, their multi-scale and multi-physics nature, involving gas-solid flow dynamics, heat and mass transfer, and chemical reactions, presents formidable challenges for real-time prediction and control. This review summarizes recent advances in multi-scale computational modeling for reactive multiphase flows, focusing on how particle-resolved direct numerical simulation (PR-DNS) and computational fluid dynamics-discrete element method (CFD-DEM) provide high-fidelity physical understanding and training data, while reduced-order model (ROM) enables fast boiler-state prediction and digital-twin implementation. The integration of these approaches forms the computational foundation for constructing digital twins of complex energy systems. Finally, challenges and future research directions are discussed, focusing on bridging scales, enhancing real-time prediction capability, and achieving intelligent, self-evolving digital twins to support the global energy transition.

Graphical abstract
Keywords
Multi-scale modeling; Reactive multiphase flow; Digital twin; Reduced-order modeling
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