Establishment of location correction system for processing seafood transportation displaced by wind waves and anti-accumulation drying processing method

Abstract

Aiming at insufficient drying seafood on-board caused by hull swings, the present invention involves the establishment of location correction system for processing seafood transportation displaced by wind waves and anti-accumulation drying processing method. This invention employs CFD-DEM method to simulate the state and distribution of the material particles modulated by the deflector angle and wind speed. Finally, the optimized rotation angle of deflector and wind speed are obtained where material particles are equally distributed. Meanwhile, the uniform and fast drying of the marine products in the swinging hull are achieved. This invention shows the great advantages of high efficiency, automation and continuity.

Claims

1. A location correction system for processing seafood transportation vessel displaced by stormy waves, comprising: a programmable logic controller connected with a ship tilt angle sensor, a fan speed sensor, and a rotation angle sensor of a deflector; the programmable logic controller configured to: 1) define and calculate boundary parameters of a system model, the boundary parameters including wind speed of a fan and a deflector rotation angle, wherein the deflector rotation angle is defined as an angle between a deflector and a conveyor belt, a minimum rotation angle of a guide plate is 0° when the vessel is running without an inclination, wherein when a hull of the vessel reaches a maximum sway angle, the deflector achieves a maximum rotation angle ${\theta }_1\left(-\frac{\pi }{4}<\theta <0\right)\mathrm{or}{\theta }_r\left(0<\theta <\frac{\pi }{4}\right),$ wherein a lowest wind speed is F.sub.s when the hull is stable, and a maximum wind speed F.sub.l or F.sub.r is applied when the hull reaches a maximum left or right inclination angle, respectively; 2) design a 3D model of a uniform drying system and conduct a mesh division, wherein a mesh division is conducted by using a tetrahedral mesh with a size of 3 to 8 mm; 3) dynamically simulate an airflow field, wherein a mesh file generated by the mesh division is imported into a computational fluid dynamics (CFD) part of a simulation, a standard k-ε model is selected as a turbulence model which is defined by turbulent viscosity and a hydraulic diameter, the airflow field is analyzed via a coupled CFD-DEM method, and a second-order upwind style is employed as discrete format; wherein the coupled CFD-DEM method comprises airflow field dynamics models as follows: $\begin{array}{cc}\frac{\partial }{\partial t}\left({\alpha }_g\rho \kappa \right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\kappa \right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mathrm{.Math.}}_e}{{\sigma }_k}.Math.\frac{{\partial }_k}{\partial {\chi }_j}\right)+{\alpha }_g{G}_k-C_D{\alpha }_g\rho \epsilon \frac{\partial }{\partial t}\left({\alpha }_g\rho \epsilon \right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\epsilon \right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mu }_e}{{\sigma }_{\epsilon }}.Math.\frac{{\partial }_{\epsilon }}{\partial {\chi }_j}\right)+\frac{\epsilon }{\kappa }{\alpha }_g\left(C_1{G}_k-C_2\rho \epsilon \right)G_k={\mu }_T\left(\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_i}{\partial {\chi }_j}+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}\right)+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}& \left(\mathrm{Formula}\right)\end{array}$ where {right arrow over (μ)}.sub.j, {right arrow over (μ)}.sub.i means velocity component at x, y axis, respectively, C.sub.D is a drag force coefficient of a particle group, ρ is air density, μ represents fluid shear viscosity, υ is kinematic viscosity; μ.sub.T is turbulent viscosity, G.sub.k is turbulent energy, α.sub.g is a volume fraction of gas, viscosity coefficient C.sub.μ=0.09, other constants C.sub.1=1.44, C.sub.2=1.92, σ.sub.k=1.0, σ.sub.ε=1.3; 4) establish a material particle model by using a discrete element model software, wherein the particle model is constructed by a three-dimensional software, which is further imported into the discrete element model software; 5) optimize simulation parameters, wherein a corresponding simulation time step is determined by calculating a motion severity of particles to ensure stability of iterative calculation of the system, wherein a fixed time step is set between 20% and 40% of Rayleigh time step; 6) simulate a movement of materials by the coupled CFD-DEM (computational fluid dynamics and discrete element method) method; wherein a Hertz-Mindlin non-slip model is used as a contact model of a discrete element method part of the simulation, the computational fluid dynamics part of the simulation and the discrete element method part of the simulation are coupled by a Lagrangian model, which is further used to simulate the movement of the materials under the different deflector rotation angles and wind speeds, to optimize processing parameters; and 7) verify accuracy and practicality of the system model, wherein conditions of the deflector rotation angle and wind speed are optimized against materials accumulation by comparing a distribution of material particles in a hull model imposed by inclination of the hull model of 0° to 10°, and meanwhile, a mathematical equation between a given deflector rotation angle (θ), a given wind speed (F) and a given hull swing angle (ω) is also established in Formula {circle around (2)}, and an actual distribution of a material under the optimized conditions is conducted to verify the accuracy of the system model; $\begin{array}{cc}\mathrm{mg}\sin \omega =f+\frac{\partial }{\partial x_i}\left(\rho {s\left(F\cos \theta \right)}^2\right)& \left(\mathrm{Formula}\right)\end{array}$ wherein m is the weight of the material, ω is hull swing angle, f is the friction force on the material, ρ is the fluid density (kg/m.sup.3), F is the wind speed, namely the wind speed, m/s, θ is the deflector rotation angle; 8) optimize an actual deflector rotation angle and an actual wind speed to equally distribute the actual material particles, whereby uniform and fast drying of seafood materials in a swinging hull are achieved.

2. The system according to claim 1, wherein a seafood material of the seafood materials is Silver anchovy with the mass of 0.5±0.1 g, characteristics of ellipsoidal shape with major axis of 2.5±0.5 cm and a minor axis of 0.3±0.05 cm, and wherein in step 4), a plurality of spherical particles are combined to establish the material particle model of the Silver anchovy.

3. The system according to claim 1, wherein a seafood material of the seafood materials is Antarctic krill with the mass of 2±0.5 g, characteristics of major axis of 6±0.5 cm and a minor axis of 0.6±0.1 cm, and wherein in step 4), a plurality of spherical particles are combined to establish the material particle model of the Antarctic krill.

4. The system according to claim 1, wherein a seafood material of the seafood materials is Acetes chinensis with the mass of 0.6±0.1 g, characteristics of major axis of 3±0.1 cm and a minor axis of 0.4±0.1 cm, and wherein in step 4), a plurality of spherical particles are combined to establish the material particle model of the Acetes chinensis.

5. The system according to claim 1, wherein in step 7), the actual distribution (m.sub.a) of the material in different parts of the conveyor belt are calculated, which is imposed by the optimized conditions of the deflector rotation angle and wind speed; furthermore, the accuracy of the system model is verified (Formula {circle around (3)}) by a comparison of the actual distribution (m.sub.a) with simulation distribution (m.sub.s), the drying uniformity is confirmed by measuring the moisture content of the material in different parts of the conveyor belt (Formula {circle around (4)} and {circle around (5)});
|m.sub.a−m.sub.s|/m <10%  Formula{circle around (3)}
|Q.sub.l/r/m−Q.sub.a|/Q.sub.a<10%  Formula{circle around (4)}
Q.sub.a(Q.sub.1+Q.sub.r+Q.sub.m)/3  Formula{circle around (5)} where Q.sub.1, Q.sub.r, Q.sub.m separately represents in the left, middle and right of the hull; Q.sub.a represents the corresponding averages.

6. The system according to claim 1, wherein the seafood materials are transported on the conveyor belt in a rate of 3 to 30 m/min and hot-air dried at 50 to 70° C., wherein the uniform and fast drying for the seafood materials on the conveyor belt is achieved by the airflow field and a rotatable deflector is set above the conveyor belt, wherein an automatic response between the actual deflector rotation angle, the actual wind speed of the fan and the sway amplitude of the swinging hall is achieved through a programmable logic control system, wherein the programmable logic control system is connected with a frequency converter, a ship tilt angle sensor, a rotation angle sensor of deflector and a fan speed sensor.

7. The system according to claim 1, wherein the size of the deflector is 1 m×0.5 m, and the size of the conveyor belt is 2.2 m wide and 16.5 m long.

8. The system according to claim 7, wherein the rotation angle of the deflector is θ.sub.1=20°, θ.sub.r=15°, and the wind speed is F.sub.1=2 m/s and F.sub.r=3 m/s.

9. The system according to claim 7, wherein the rotation angle of the deflector is θ.sub.1=23°, θ.sub.r=17°, and the wind speed is F.sub.1=1.5 m/s and F.sub.r=2.6 m/s.

10. The system according to claim 7, wherein the rotation angle of the deflector is θ.sub.1=25°, θ.sub.r=20°, and the wind speed is F.sub.1=2.5 m/s and F.sub.r=3 m/s.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 describes the flow chart of establishment of location correction system for processing seafood transportation displaced by wind waves and anti-accumulation drying processing method.

(2) FIG. 2 describes the structure diagram of the uniform drying system.

(3) FIG. 3 describes the setting parameters diagram of device geometry imported into discrete element software.

(4) FIG. 4 describes the setting parameters diagram of particle models imported into discrete element software.

(5) FIG. 5 describes the finite element fluid analysis grid and setting parameter diagram.

(6) FIG. 6 describes the air flow diagram (a) and air velocity distribution and vector diagram (b) when the hull runs smoothly.

(7) FIG. 7 describes the air flow diagram (a) and air velocity distribution and vector diagram (b) when the hull swing right.

(8) FIG. 8 describes the air flow diagram (a) and air velocity distribution and vector diagram (b) when the hull swings left.

DETAILED DESCRIPTION OF THE INVENTION

(9) The further description of the present invention is made with examples.

(10) Example 1: Establishment of location correction system for processing Silver anchovy transportation displaced by wind waves and anti-accumulation drying processing method (FIG. 1).

(11) 1) Definition and calculation of the boundary parameters of the model: the angle of rotation of the deflector is defined as the angle between the deflector and the plane of the conveyor belt. When the hull runs smoothly, rotation angle of the deflector reaches the minimum) (θ.sub.s=0°). The rotation angle of the deflector would get the maximum, θ.sub.1 (−π/4<θ<0) or θ.sub.r (0<θ<π/4) when the hull reaches the maximum inclination angle. The lowest wind speed, F.sub.s, is defined as that one when the hull runs smoothly. The wind speed would reach the highest (F.sub.1 or F.sub.r) when the hull is in maximum inclination. The automatic response between deflector rotation angle, wind speed and hull sway can be realized through the PLC control systems. The programmable controller of the control system is connected with the frequency converter, the ship tilt angle sensor, the rotation angle sensor of deflector and the fan speed sensor.

(12) 2) Design and mesh division of the 3D model of uniform drying system: the 3D model of uniform drying system is designed by AutoCAD software (FIG. 2 and FIG. 3), which is further divided in tetrahedral mesh with the grid size of 4 mm using mesh module of Ansys Workbench (FIG. 4).

(13) 3) Simulation of the airflow field: import the mesh file generated by the Mesh module into the computational fluid dynamics (CFD). The standard k-ε model is selected as turbulence model, which is defined by turbulent viscosity and the hydraulic diameter. The flow field is analyzed via COUPLED method, and a second-order upwind style is employed as discrete format.

(14) the used airflow field dynamics models are shown as the follows:

(15) $\frac{\partial }{\partial t}\left({\alpha }_g\rho \kappa \right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\kappa \right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mathrm{.Math.}}_e}{{\sigma }_k}.Math.\frac{{\partial }_k}{\partial {\chi }_j}\right)+{\alpha }_g{G}_k-C_D{\alpha }_g\rho \epsilon \frac{\partial }{\partial t}\left({\alpha }_g\rho \epsilon \right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\epsilon \right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mu }_e}{{\sigma }_{\epsilon }}.Math.\frac{{\partial }_{\epsilon }}{\partial {\chi }_j}\right)+\frac{\epsilon }{\kappa }{\alpha }_g\left(C_1{G}_k-C_2\rho \epsilon \right)G_k={\mu }_T\left(\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_i}{\partial {\chi }_j}+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}\right)+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}$

(16) Where {right arrow over (μ)}.sub.j, {right arrow over (μ)}.sub.i means velocity component at x, y axis, respectively, C.sub.D is the drag force coefficient of particle group, ρ is air density, μ represents fluid shear viscosity, υ is kinematic viscosity; μ.sub.T is turbulent viscosity, G.sub.k is turbulent energy, α.sub.g is the volume fraction of gas, viscosity coefficient C.sub.μ=0.09, other constants C.sub.1=1.44, C.sub.2=1.92, σ.sub.k=1.0, σ.sub.ε=1.3.

(17) 4) Establishment of Silver anchovy model using EDEM: the particle model with the mass of 0.5±0.1 g, characteristics of ellipsoidal shape with major axis of 2.5±0.5 cm and a minor axis of 0.3±0.05 cm, is constructed by three-dimensional software. A plurality of spherical particles in EDEM are combined to establish the model of the Silver anchovy, which is further imported into

(18) 5) Optimization of the simulation parameters: the time step is set as 10.sup.−4s, to ensure the stability of the iterative calculation of the system.

(19) 6) Simulation of movement of materials by the coupled CFD-DEM method: the Hertz-Mindlin non-slip model is used as contact model of the discrete unit. The computational fluid dynamics and the discrete unit are coupled by a Lagrangian model, which is further used to simulate the motion of the material under the different deflector angles and wind speeds, to optimize the processing parameters (FIG. 6-FIG. 8).

(20) 7) Verification of accuracy and practicality of model: The optimized rotation angle of the deflector (θ.sub.1=20°, θ.sub.r=15°) and wind speed (F.sub.1=2 m/s, F.sub.r=3 m/s) against materials accumulation are obtained by comparing the distribution of material particles in the hull model imposed by inclination of the hull model of 5°. Furthermore, the errors between actual and simulated distribution of Silver anchovy at the different place of the hull acted by the obtained optimized conditions is less than 10% (Table 1), which confirm the accuracy of the model. Moreover, the moisture content of materials at the left, middle and right of the hull is 28.3%, 26.7% and 23.9% respectively after 30-minute continuous treatment of 60° C. with the transportation rate of 10 m/min. The uniform drying of materials is verified as evidenced by less than 10% differences in moisture between materials at three locations and the corresponding averages (Table 2), which confirm the practicality of model.

(21) TABLE-US-00001 TABLE 1 The actual (m.sub.a) and simulated (m) distribution of Silver anchovy at the different places of the hull acted by the obtained optimized conditions Percentage θ.sub.l = 20°, F.sub.l = 2 m/s θ.sub.r = 15°, F.sub.r = 3 m/s (%) Left Middle Right Left Middle Right m.sub.a 40.1 32.6 28.3 33.4 30.1 37.3 m.sub.s 39.2 31.7 30.6 32.3 28.2 40.7 error 2.5 9.4 7.1 3.0 6.7 8.1

(22) TABLE-US-00002 TABLE 2 The moisture content of Silver anchovy, Antarctic krill and Acetes chinensis in the left (Q.sub.l), middle (Q.sub.m) and right (Q.sub.r) of the hull and the corresponding averages (Q.sub.a) Species Q.sub.l (error %) Q.sub.m (error %) Q.sub.r (error %) Q.sub.a Silver anchovy 28.3 (7.6%) 26.7 (1.5%) 23.9 (9.1%) 26.3 Antarctic krill 38.8 (4.0%) 35.7 (4.3%) 37.5 (0.5%) 37.3 Acetes chinensis 23.9 (7.2%) 21.3 (4.5%) 20.6 (7.6%) 22.3

(23) Example 2: Establishment of location correction system for processing Antarctic krill transportation displaced by wind waves and anti-accumulation drying processing method.

(24) 1) Definition and calculation of the boundary parameters of the system model: the angle of rotation of the deflector is defined as the angle between the deflector and the plane of the conveyor belt. When the hull runs smoothly, rotation angle of the deflector reaches the minimum) (θ.sub.s=0°). The rotation angle of the deflector would get the maximum,

(25) ${\theta }_1\left(-\frac{\pi }{4}<\theta <0\right)\mathrm{or}{\theta }_r\left(0<\theta <\frac{\pi }{4}\right)$
when the hull reaches the maximum inclination angle. The lowest wind speed, F.sub.s, is defined as that one when the hull runs smoothly. The wind speed would reach the highest (F.sub.1 or F.sub.r) when the hull is in maximum inclination. The automatic response between deflector rotation angle, wind speed and hull sway is realized through the PLC control systems. The programmable controller of the control system is connected with the frequency converter, the ship tilt angle sensor, the rotation angle sensor of deflector and the fan speed sensor.

(26) 2) Design and mesh division of the 3D model of uniform drying system: the 3D model of uniform drying system is designed by AutoCAD software, which is further divided in tetrahedral mesh with the grid size of 8 mm using mesh module of Ansys Workbench.

(27) 3) Simulation of the airflow field: Import the mesh file generated by the Mesh module into the computational fluid dynamics (CFD). The standard k-ε model is selected as turbulence model, which is defined by turbulent viscosity and the hydraulic diameter. The flow field is analyzed via COUPLED method, and a second-order upwind style is employed as discrete format.

(28) the used airflow field dynamics models are shown as the follows:

(29) $\frac{\partial }{\partial t}\left({\alpha }_g\rho \kappa \right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\kappa \right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mathrm{.Math.}}_e}{{\sigma }_k}.Math.\frac{{\partial }_k}{\partial {\chi }_j}\right)+{\alpha }_g{G}_k-C_D{\alpha }_g\rho \epsilon \frac{\partial }{\partial t}\left({\alpha }_g\rho \epsilon \right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\epsilon \right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mu }_e}{{\sigma }_{\epsilon }}.Math.\frac{{\partial }_{\epsilon }}{\partial {\chi }_j}\right)+\frac{\epsilon }{\kappa }{\alpha }_g\left(C_1{G}_k-C_2\rho \epsilon \right)G_k={\mu }_T\left(\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_i}{\partial {\chi }_j}+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}\right)+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}$

(30) Where {right arrow over (μ)}.sub.j, {right arrow over (μ)}.sub.i means velocity component at x, y axis, respectively, C.sub.D is the drag force coefficient of particle group, ρ is air density, μ represents fluid shear viscosity, υ is kinematic viscosity; μ.sub.T is turbulent viscosity, G.sub.k is turbulent energy, α.sub.g is the volume fraction of gas, viscosity coefficient C.sub.μ=0.09, other constants C.sub.1=1.44, C.sub.2=1.92, σ.sub.k=1.0, σ.sub.ε=1.3.

(31) 4) Establishment of Antarctic krill model using EDEM: the particle model with the mass of 2±0.5 g, characteristics of major axis of 6±0.5 cm and a minor axis of 0.6±0.1 cm, is constructed by three-dimensional software. A plurality of spherical particles in EDEM are combined to establish the model of the Antarctic krill.

(32) 5) Optimization of the simulation parameters: the time step is set as 2×10.sup.−4 s to ensure the stability of the iterative calculation of the system.

(33) 6) Simulation of movement of materials by the coupled CFD-DEM method: the Hertz-Mindlin non-slip model is used as contact model of the discrete unit. The computational fluid dynamics and the discrete unit are coupled by a Lagrangian model, which is further used to simulate the motion of the material under the different deflector angles and wind speeds, to optimize the processing parameters.

(34) 7) Drying uniformity verification of Antarctic krill: The optimized rotation angle of the deflector (θ.sub.1=23°, θ.sub.r=17°) and wind speed (F.sub.1=1.5 m/s, F.sub.r2.6 m/s) against materials accumulation are obtained by comparing the distribution of material particles in the hull model imposed by inclination of the hull model of 9°. Furthermore, the moisture content of materials at the left, middle and right of the hull is 38.8%, 35.7% and 37.5% respectively after 28-minute continuous treatment of 70° C. with the transportation rate of 30 m/min. The uniform drying of materials is verified as evidenced by less than 10% differences in moisture between materials at three locations and the corresponding averages (Table 2), which confirm the practicality of model.

(35) Example 3: Establishment of location correction system for processing Acetes chinensis transportation displaced by wind waves and anti-accumulation drying processing method.

(36) 1) Definition and calculation of the boundary parameters of the system model: the angle of rotation of the deflector is defined as the angle between the deflector and the plane of the conveyor belt. When the hull runs smoothly, rotation angle of the deflector reaches the minimum (θ.sub.s=0°). The rotation angle of the deflector would get the maximum,

(37) ${\theta }_1\left(-\frac{\pi }{4}<\theta <0\right)\mathrm{or}{\theta }_r\left(0<\theta <\frac{\pi }{4}\right)$
when the hull reaches the maximum inclination angle. The lowest wind speed, F.sub.s, is defined as that one when the hull runs smoothly. The wind speed would reach the highest (F.sub.1 or F.sub.r) when the hull is in maximum inclination. The automatic response between deflector rotation angle, wind speed and hull sway is realized through the PLC control systems. The programmable controller of the control system is connected with the frequency converter, the ship tilt angle sensor, the rotation angle sensor of deflector and the fan speed sensor.

(38) 2) Design and mesh division of the 3D model of uniform drying system: the 3D model of uniform drying system is designed by AutoCAD software, which is further divided in tetrahedral mesh with the grid size of 3 mm using mesh module of Ansys Workbench.

(39) 3) Simulation of the airflow field: import the mesh file generated by the Mesh module into the computational fluid dynamics (CFD). The standard k-ε model is selected as turbulence model, which is defined by turbulent viscosity and the hydraulic diameter. The flow field is analyzed via COUPLED method, and a second-order upwind style is employed as discrete format.

(40) the used airflow field dynamics models are shown as the follows:

(41) $\frac{\partial }{\partial t}\left({\alpha }_g\rho \kappa \right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\kappa \right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mathrm{.Math.}}_e}{{\sigma }_k}.Math.\frac{{\partial }_k}{\partial {\chi }_j}\right)+{\alpha }_g{G}_k-C_D{\alpha }_g\rho \epsilon \frac{\partial }{\partial t}\left({\alpha }_g\rho \epsilon \right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\epsilon \right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mu }_e}{{\sigma }_{\epsilon }}.Math.\frac{{\partial }_{\epsilon }}{\partial {\chi }_j}\right)+\frac{\epsilon }{\kappa }{\alpha }_g\left(C_1{G}_k-C_2\rho \epsilon \right)G_k={\mu }_T\left(\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_i}{\partial {\chi }_j}+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}\right)+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}$

(42) Where {right arrow over (μ)}.sub.j, {right arrow over (μ)}.sub.i means velocity component at x, y axis, respectively, C.sub.D is the drag force coefficient of particle group, ρ is air density, μ represents fluid shear viscosity, υ is kinematic viscosity; μ.sub.T is turbulent viscosity, G.sub.k is turbulent energy, α.sub.g is the volume fraction of gas, viscosity coefficient C.sub.μ=0.09, other constants C.sub.1=1.44, C.sub.2=1.92, σ.sub.k=1.0, σ.sub.ε=1.3.

(43) 4) Establishment of Acetes chinensis model using EDEM: the particle model with the mass of 0.6±0.1 g, characteristics of major axis of 3±0.1 cm and a minor axis of 0.4±0.1 cm, is constructed by three-dimensional software. A plurality of spherical particles in EDEM are combined to establish the model of the Acetes chinensis.

(44) 5) Optimization of the simulation parameters: the time step is set as 10.sup.−5 s, to ensure the stability of the iterative calculation of the system.

(45) 6) Simulation of movement of materials by the coupled CFD-DEM method: the Hertz-Mindlin non-slip model is used as contact model of the discrete unit. The computational fluid dynamics and the discrete unit are coupled by a Lagrangian model, which is further used to simulate the motion of the material under the different deflector angles and wind speeds, to optimize the processing parameters.

(46) 7) Drying uniformity verification of Acetes chinensis: The optimized rotation angle of the deflector (θ.sub.1=25°, θ.sub.r=20°) and wind speed (F.sub.1=2.5 m/s, F.sub.r=3 m/s) against materials accumulation are obtained by comparing the distribution of material particles in the hull model imposed by inclination of the hull model of 7°. Furthermore, the moisture content of materials at the left, middle and right of the hull is 23.9%, 21.3% and 20.6% respectively after 40-minute treatment of 55° C. with the transportation rate of 3 m/min. The uniform drying of materials is verified as evidenced by less than 10% differences in moisture between materials at three locations and the corresponding averages (Table 2), which confirm the practicality of model.