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Volume 114
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Particle-resolved direct numerical simulation of reacting particulate two-phase flows
Yiqi Song a b, Xue Li a *, Mao Ye a *, Zhongmin Liu a b
a National Engineering Research Center of Lower-Carbon Catalysis Technology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
b University of Chinese Academy of Sciences, Beijing, 100049, China
10.1016/j.partic.2026.04.021
Volume 114, July 2026, Pages 337-352
Received 2 February 2026, Revised 15 April 2026, Accepted 27 April 2026, Available online 7 May 2026, Version of Record 15 May 2026.
E-mail: lixue@dicp.ac.cn; maoye@dicp.ac.cn
Highlights

• PR-DNS accurately resolves flow fields around particles, revealing key reaction phenomena, such as Stefan flow, phoretic self-propulsion, catalyst deactivation/sintering.

• This review systematically summarizes the characteristics, applicable reaction systems, advantages, and limitations of common reaction models.

• This review outlines PR-DNS application progress in reactive and catalyst particles, clarifying how particle geometry, pore structure, reaction conditions regulate process efficiency.


Abstract

Particulate two-phase flows are characterized by multiscale and nonlinear interactions. The coupling simulation of the reaction and transfer process is a critical problem in many industrial applications. Innovations in modeling techniques, including the treatments of complicated boundary conditions and improved chemical reaction models, have greatly enhanced computational accuracy and feasibility. This review provides a comprehensive overview of the progress made in particle-resolved direct numerical simulation (PR-DNS) for investigating the complex phenomena in reacting particulate two-phase flows, with a focus on insights into reaction models, interphase heat and mass transfer, and hydrodynamic forces. PR-DNS has been applied to a range of reaction processes, such as combustion, gasification, and catalytic reactions, revealing critical phenomena such as Stefan flow effects and particle-scale interactions by resolving detailed flow fields around particles. Despite promising prospects, PR-DNS still presents challenges in terms of computational cost, model refinement, and multiscale integration. Recent advancements in emerging computational methodologies, particularly machine learning frameworks and hybrid multiscale modeling approaches, are expected to offer viable pathways to address these technical limitations.

Graphical abstract
Keywords
Particle-resolved; Particulate two-phase flow; Multiscale; Chemical reaction models
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