population balance modelling for dynamic evolution in discrete systems (Monte Carlo method); Lattice-Boltzmann methods for gas-solid two-phase flows; The mechanism and modeling of turbulent reactive gas-particle flows.

1. Introduction

Multiphase flows are frequently found in industrial process such as energy and power engineering, chemical engineering, environmental engineering and others. Gas solid turbulent combustion process is a complex system with multi-field coupling and time-spatial multiscale structure. Understanding the internal coupling multiphase flow field, temperature field, concentration field and the internal complex physical and chemical process can contribute to the optimization and control of the conventional combustion mode as well as design and scaling-up of new combustion technology.

2. Research findings
(1) Second order moment-Lagrange PDF two phase flow model considering four-way coupling effect

In the framework of Euler- Lagrange model, the flow phase is described by the second order moment model based on Reynolds averaged Navier-Stokes (RANS) methodology, the particle phase is tracked by Lagrange PDF (probability density function) model and the particle collision is solved by direct simulation Monte Carlo (DSMC). In this way, symmetric gas-solid flows can be successfully simulated by the Euler- Lagrange model.

Reference:

Haibo Zhao*,Chaohui Liu, Chuguang Zheng,Yinmi Chen. Ji Zhang Monte-Carlo numerical simualtion of inter-particle collisions in gas-solid flows. Chinese Journal of Computational Mechanics, 2005, 22(3): 299-304

(2) Lattice Boltzmann-Cellular Automata (LB-CA) probabilistic model

This model shows a mesoscopic method for the gas-solid two-phase flow, in which the flow field is described by the Lattice Boltzmann (LB) method and particle field is calculated by Cellular Automata (CA) probabilistic model. In the LB-CA method, the simulation fluid particles as well as solid particles are constraint to motion on the same lattice model and the method has the advantages of demonstrating clear physical picture, simplicity, intrinsic parallelism, and capability to deal with complex and dynamic boundary conditions. Besides, particle collision can be taken into consideration with direct simulation Monte Carlo (DSMC) method to realize four-way coupling. The LB-CA model is successfully applied in the backward-facing step flow and fibrous filtration process of particulate matter.

Simulation of backward facing step flow by Lattice Boltzmann probabilistic: vorticity evolution (left) particle flow average velocity (right)

Reference:

[1] Haoming Wang, Haibo Zhao*, Zhaoli Guo, Chuguang Zheng. Two way Coupling Lattice Boltzmann Model ForGas-Solid Turbulent Flows. Chinese Journal of Computational Physics, 2013, 30(1): 19-26

[2] Haoming Wang, Haibo Zhao*, Zhaoli Guo, Yongxiang He, Chuguang Zheng. Lattice Boltzmann method for simulations of gas-particle flows over a backward-facing step. Journal of Computational Physics, 2013, 239: 57-71

(3) Population Balance-Monte Carlo (PBMC) Method for Particle Dynamics

Besides energy, momentum and mass balances which are usually utilized to describe and model phenomena and mechanisms in chemical engineering/process engineering, a number balance of particles and their important internal properties (e.g., size, surface area, component, electric charge, …) is also required in particulate processes. The number balance (which is formally referred to as the population balance) can be formulated by an extension of the continuous Smoluchowski equation, representing the temporal evolution of distribution function of multiple internal variables of particles under influence of dynamic events including coagulation, breakage, nucleation, surface growth/dissolution (condensation/evaporation) and deposition (settling). Population balance modelling (PBM) quantitatively describes time evolution and spatial diffusion of distribution functions of internal variables of particles in dispersed systems, and can simulate complex interactions between continuous phase and discrete phase through coupling with multiphase flow models. The collision/coagulation rules between differentially weighted simulation particles and corresponding Markov jump models were proposed and high-efficiency and high-precision solutions of population balance-Monte Carlo have been developed.

Comparison of accuracy (left) and precision (right) by differentially weighted Monte Carlo and similar international method in particle population balance

Reference:

[1] Haibo Zhao*, Chuguang Zheng. Population Balance Modeling for dynamic evolution in dispersed systems (in Chinese). Science Press, 2008, Beijing

[2] Haibo Zhao*, F. Einar Kruis, Chuguang Zheng. A differentially weighted Monte Carlo method for two-component coagulation. Journal of Computational Physics, 2010, 229, 6931-6945

[3] Haibo Zhao*, F. Einar Kruis*, and Chuguang Zheng. Reducing Statistical Noise and Extending the Size Spectrum by Applying Weighted Simulation Particles in Monte Carlo Simulation of Coagulation. Aerosol Science and Technology, 2009, 43(8): 781 – 793

[4] Haibo Zhao*, Chuguang Zheng. A new event-driven constant volume method for solution of the time evolution of particle size distribution. Journal of Computational Physics, 2009, 228(5): 1412-1428

[5] Haibo Zhao*, Chuguang Zheng. Correcting the multi-Monte Carlo method for particle coagulation. Powder Technology, 2009, 193: 120–123

(4) Simulation of impinging stream entrained flow gasifiers by two-fluid model

In the framework of Eulerian-Eulerian model, both the particle phase and gas phase are considered as two interpenetrating fluids and they can be described by focusing on a small finite control volume in the suspension. The reduced computational cost due to the description of particle phase is important to the industrial application. The impinging stream gasifiers are successfully simulated by two-fluid model based on the kinetic theory of granular flow, and particular attention is paid to the instability of the impact surface.

Perodic oscillation of gas-solid imping stream in plane slit nozzle

References

1.Hongpeng Xu, Haibo Zhao*, Chuguang Zheng. Simulation and investigation of periodic deflecting oscillation of gas–solid planar opposed jets. Chemical Engineering and Processing: Process Intensification, 2014, 76, 6–15