Investigation On The Statistical Properties Of Gas-Solid Flows Based On Large-Scale Direct Numerical Simulation

Gas-solid flows are typical non-linear and non-equilibrium systems and understanding the meso-scale structures in these systems is a challenging but attractive research topic for which experimental measurements and theoretical analysis are of great difficulty,while numerical simulation has been an indispensable approach.With little consideration to the effect of meso-scale structures,traditional simulation methods such as two-fluid model and discrete particle model can’t provide accurate prediction of the flow behavior.The particle resolved direct numerical simulation(PR-DNS or DNS for short)is able to track the motion of each individual particle and fully resolve the dynamic flow field around the particles.Therefore,it is very important for exploring the formation mechanism of meso-scale structures in gas-solid flows,predicting the behavior at continuum scale and establishing reliable constitutive relations for continuum models.Meso-scale structures in gas-solid flows are the focus of this dissertation,for which a series of DNS has been carried out,and the methods for describing and characterizing the meso-scale structures have been investigated together with the partition of between dilute and dense phases.The effects of meso-scale structures on both statistical properties at micro-scale and hydrodynamic characteristics have been analyzed,and the probability density function of particle fluctuating velocity and a correlation of local-averaged dimensionless drag have been proposed and validated.First,the traditional methods for simulating gas-solid flows and analyzing the state of the art of meso-scale structures therein are reviewed in Chapter 1.The statistical studies on particle phase properties and particle-fluid interactions are summarized,including probability density function of particle fluctuating velocity and drag models.In Chapter 2,the essential numerical procedure for large-scale DNS of gas-solid flows is presented,in which the lattice Boltzmann method,discrete element method and immersed moving boundary are coupled.The simulation settings and the parameters at the simulation cases are explained and validated by analyzing the scale-independence of the domain-averaged hydrodynamic variables on resolution and the auto-correlation function of the flow field.The evolution of the structures and macroscopic hydrodynamic characteristics in the process from homogeneous particle suspension,local instability,aggregates growth to statistically steady state is discussed in detail based on the DNS data.In Chapter 3,the gas-solid flows are partitioned into a gas-rich dilute phase and a solid-rich dense phase for quantifying the effect of meso-scale structures on statistical properties,by quantifying local solid volume fraction and clustering analysis based on Voronoi tessellation.An intrinsic threshold for the partitioning of dilute and dense phases is provided by analyzing the statistical properties of particle in dilute and dense phases.In Chapter 4,the effect of meso-scale structures on statistical properties of the particles are investigated.It is found that both the fluctuating velocity distribution and granular temperature are scale-dependent and anisotropic,and the flows are locally non-equilibrium.A probability density function of the particle fluctuating velocity is proposed by considering the difference of particle kinetic properties between dilute and dense phases,which is in good agreement with the DNS data at different scales.In Chapter 5,the effect of meso-scale structures on drag is investigated.The scale-dependence of local-averaged dimensionless drag is revealed by DNS data.The roles of heterogeneity and granular temperature on the dimensionless drag at different scale are clarified.By fitting the DNS data,a drag correlation with consideration of scale-dependence is obtained.Finally,the main results of this dissertation are summarized.The future of large-scale DNS for exploring the underlying mechanism of gas-solid flows and their constitutive laws through machine learning are prospected.