Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Shu Da is active.

Publication


Featured researches published by Shu Da.


Advances in Materials Sciences | 2018

Lattice boltzmann modeling for mass and velocity fields of casting flows

Hu Zhi; Dong Anping; Du Dafan; Sun Dongke; Wang Donghong; Zhu Guoliang; Shu Da; Sun Baode

The Lattice Boltzmann Method (LBM)-D2Q9 model is used to simulate velocity development and mass transfer of flows in casting. To quantify the basic flows in casting, stable flows in planes and pipes are simulated, which confirmed the LBM-D2Q9 model’s validation and numerical stability. Solute diffusion and vortex development are also investigated using LBM-D2Q9 model. The results show that the LBM model is capable to describe the velocity and solution field, which in a good match with the analytical calculations. *Correspondence to: Dong Anping, Shanghai Key Lab of advanced Hightemperature Materials and Precision Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China, Tel: +86 13817882779; E-mail: [email protected] Received: June 28, 2018; Accepted: July 20, 2018; Published: July 23, 2018 Introduction The ongoing demanding of advanced aero engines, which possess high thrust and lightweight, have caused a tremendous application of the near net shape forming technology of complex thin-wall superalloy casts [1]. During the casting, the solidification sequence, temperature and solute concentration distribution are affected by the complexity of geometry shape and thinness of the cast wall. These’re bringing a challenge for cast perfect forming and metallurgical quality improvement. It has been found that counter-gravity casting with additional pressure is more capable for complex thin wall cast near net shape forming than regular gravity casting [2-3]. During the pressured counter-gravity casting, forming and solidification are experiencing forced convection and constrained space condition. The mechanisms of melt flow and crystallization and the relation of microflows between dendrites and porosity suppression and microstructure evolution are complicated and have been a top focused area in the solidification researches [3-5]. Lattice Boltzmann method (LBM) has been proved that is an effective and powerful method to gain a numerical solution of Navier-Stokes equation [6], compared to other traditional numerical solutions of the Navier-Stokes equation, like Lax-Wendroff, MacCormack or SIMPLE method. To reveal the solidification microstructure evolution of superalloy complex thin-wall casting under complex constrained space and forced convective condition, simulations of the mass and heat transfer and distribution in this complex constrained cast is needed to carry out to understand the solidification condition. In the first step, it’s our goal to verify the LBGK model for representing the basic thermo-flow in the casting. Lattice Boltzmann modeling In this work, Lattice Boltzmann Method (LBM) is adapted to simulate fluid flow, solute and heat transfer. The LBM is a discrete approximation of Boltzmann equation, based on gas kinetic theory. The BGK approximation, proposed by Bhatnagar, Gross and Krook who replaced the collision term J(ff1) by a single relaxation time Ωf [7], has been widely accepted and utilized to solve Boltzmann equation. The Lattice BGK (LBGK) evolution equation can be described as: ( ) 1 , ( , ) ( , ) ( , ) ( , ) eq i i i i i i f x e t t t f x t f x t f x t F x t f τ   + ∆ + ∆ − = − +   (1) where, fi(x,t) is the discrete-velocity distribution function, it describes the density of particle with velocity ci at position and time (x,t),ei represents the discrete velocity space {e1,e2,...ei},Δt is the time step, τf is the relaxation time, ( , ) eq i f x t is the discrete equilibrium distribution function, ( , ) i F x t is the force term caused by physical field. The LBM also can be used to simulate the solute transport and heat transfer drive by a different mechanism such as diffusion and convection. Similar to the LBM for fluid flow, the solute distribution function ( , ) i g x t σ can be expressed as follow, using the passive scalar model [8]. , 1 ( ) ( , ) ( , ) ( , ) ( , ) eq i i i i i i g x e t t g x t g x t g x t G x t g σ σ σ σ σ τ   + ∆ + ∆ − = − − +   (2) where σ represents solute, τg is the relaxation time for the solute field, , ( , ) eq i g x t σ is the equilibrium distribution function for the solute field, ( , ) i G x t σ is the solute source term. Zhi H (2018) Lattice boltzmann modeling for mass and velocity fields of casting flows Adv Mater Sci, 2018 doi: 10.15761/AMS.1000140 Volume 3(1): 2-6 Results and Discussion Stable flows in planes and pipes When the melt forming in plane or pipe, stable flows can be achieved when casts are large enough. In present work, we simulated a typical plane flows by means of LBM and verified the results compared with an analytical solution and numerical stability in different meshes. As shown in the Figure 1, alloy melt is forming between two planes with distance h, assumed two planes have infinite width and length and the melt is incompressible viscous fluid. The upper plane is a velocity boundary with velocity U and the bottom plate is fixed. In this circumstance, the governing equation and its analytical solution are: 0 0 u V x ∂ ∇ ⋅ = = + ∂ ( ) 2 2 0 0 d u U or u y y h dy h = = ≤ ≤ Using the LBGK-D2Q9 model, the streamwise velocity distribution of a stable plane flow is simulated as shown in the Figure 2. Reynolds number is set to 100 assuming there is a stable flow. Fluid density ρ is set to unity and upper velocity U is 0.1 and the computation area are meshed by 156×156, 206×206 and 256×256 respectively. The colored velocity distribution suggested that the developed plane flow velocity differs in layers. The dimensionless velocity profile at the position of the middle x-axis is compared with the analytical solution, shown in the Figure 3a. The LBM results in a good agreement with the analytical solution, suggesting LBM is a validated model for simulating basic stable flows. In the Figure 3b, the results suggested that LBM in three different mesh have similar numerical stability. In the Figure 4, the velocity profile u = u(y) evolved from a shapely curve to a diagonal line as the timestep increased, suggesting the flow developed from unstable to stable flow. LBM is capable to simulate the dynamic process fluid flow in plane. The LBM for temperature is calculated using internal energy distribution function model [9]. The internal energy distribution function hi(x,t) is coupled by velocity distribution function fi(x,t), which can be written as: 1 ( ) ( , ) ( , ) ( , ) ( , ) eq i i i i i i h h x e t t h x t h x t h x t H x t τ   + ∆ + ∆ − = − − +   (3) where τh is the relaxation time for temperature field, ( , ) eq i h x t is the equilibrium distribution function, Hi(x,t) is the temperature source term. The two-dimensional D2Q9 model is chosen as the present discrete velocity model. Velocity space is discretized into a square lattice including nine discrete velocities ei, as shown as: where x c t ∆ = ∆ is the lattice speed, Δx is the lattice space, Δt is the time step. Related macroscopic variables such as density ρ, velocity u, concentration Cσ and temperature T, can be calculated from the relevant distribution functions as listed: 1 , , , 2 i i i i i i i i i a f u f e t C g T h σ ρ ρ = = + ∆ = = ∑ ∑ ∑ ∑ (4) The equilibrium distribution functions, which is related to the discrete velocity model, are defined as:


Science China-technological Sciences | 2002

Magnetohydrodynamic study of electromagnetic separation of nonmetallic inclusions from aluminum melt

Shu Da; Sun Baode; Wang Jun; Zhang Xue-ping; Zhou Yaohe

AbstractMagnetohydrodynamic flow around the nonmetallic inclusions in aluminum melt and the force exerted on the inclusions were explored by dimensional analysis and numerical calculations. Dimensional analysis shows that the invariant


Archive | 2014

Precesion casting method for high temperature alloy complex thin-walled castings

Dong Anping; Zhang Jiao; Wang Jun; Yu Zhiwen; Shu Da; Wang Guoxiang; Sun Baode


Journal of Central South University of Technology | 2004

Purification technology of molten aluminum

Sun Baode; Ding Wenjiang; Shu Da; Zhou Yaohe

A = {{JB\rho _f d_p^3 } \over {\mu _f^2 }}


Archive | 2002

Deep-bed apparatus of industrial centrifugal machine for filtering out entrainments from molten aluminium

Zhou Ming; Sun Baode; Shu Da


Archive | 2003

High-energy ultrasonic metal coating method for ceramic surface

Wang Jun; Li Ke; Shu Da

characterizes the force exerted on the inclusions and the flow intensity of the melt. The physical significance of A is represented as a modified particle Reynolds number that reflects the effects of electromagnetic force. The fluid flow around the particle becomes unstable when A>2×103. It is shown that the neglect of the inertial terms has little effect on the force exerted on the inclusions in the range of A≤1×106. However, the analytical solution of the maximum velocity inside the melt does not apply due to the appearance of turbulent flow in the case of A>2×103.


Archive | 2015

Casting and method for evaluating hot crack tendency of high temperature alloy investment casting process

Kang Maodong; Wang Jun; Gao Haiyan; Shu Da; Lai Xinmin; Sun Baode


Archive | 2005

Rotating spray nozzle of de-hydrogen for aluminium melt

Sun Baode; Wu Ruizhi; Shu Da


Archive | 2003

Filtering method of non-metallic inclusion in aluminium melt

Zhou Ming; Sun Baode; Shu Da


Archive | 2003

Zinc liquid corrosion-resistant coating and use method thereof

Wang Jun; Shu Da; Li Ke

Collaboration


Dive into the Shu Da's collaboration.

Top Co-Authors

Avatar

Wang Jun

Shanghai Jiao Tong University

View shared research outputs
Top Co-Authors

Avatar

Sun Baode

Shanghai Jiao Tong University

View shared research outputs
Top Co-Authors

Avatar

Dong Anping

Shanghai Jiao Tong University

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar

Lv Xinyu

Shanghai Jiao Tong University

View shared research outputs
Top Co-Authors

Avatar

Zhou Yaohe

Shanghai Jiao Tong University

View shared research outputs
Top Co-Authors

Avatar

Ding Wenjiang

Shanghai Jiao Tong University

View shared research outputs
Top Co-Authors

Avatar

Zhang Xue-ping

Shanghai Jiao Tong University

View shared research outputs
Researchain Logo
Decentralizing Knowledge