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, the system configured to: 1) define and calculate boundary parameters of a system model, wherein ranges of wind speed of a a fan and rotation angle of a deflector are defined, the deflector rotation angle is defined as the angle between a deflector and a conveyor belt, a minimum rotation angle of a guide plate is 0° when the vessel is running smoothly, wherein when the 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.1 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 condlact a mesh division, wherein the 3D model of uniform drying system is designed by AutoCAD software, mesh division is conducted by using mesh module of Ansys Workbench, wherein a tetrahedral mesh with the size of 3 to 8 mm is set; 3) dynamically simulate an airflow field, wherein a mesh file generated by the Mesh module is imported into a computational fluid dynamics (CFD) a standard k-ε model is selected as a turbulence model which is defined by turbulent viscosity and the hydraulic diameter, the airflow field is analyzed via the COUPLED method, and a second-order upwind style is employed as discrete format; wherein the airflow field dynamics models are shown as the follows: $\begin{array}{cc}\frac{\partial }{\partial t}\left({\alpha }_g\rho \kappa \right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\kappa \right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mathrm{.Math.}}_e}{{\sigma }_k}.Math.\frac{{\partial }_k}{\partial {\chi }_j}\right)+{\alpha }_g{G}_k-C_D{\alpha }_g\rho \mathrm{.Math.}\frac{\partial }{\partial t}\left({\alpha }_g\rho \mathrm{.Math.}\right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\mathrm{.Math.}\right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mu }_e}{{\sigma }_{\mathrm{.Math.}}}.Math.\frac{{\partial }_{\mathrm{.Math.}}}{\partial {\chi }_j}\right)+\frac{\mathrm{.Math.}}{\kappa }{\alpha }_g\left(C_1{G}_k-C_2\rho \mathrm{.Math.}\right)G_k={\mu }_T\left(\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_i}{\partial {\chi }_j}+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}\right)+\frac{\partial {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j}{\partial {\chi }_j}& \left(\mathrm{Formula}\right)\end{array}$ wherein {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; 4) establish a material particle model by using a discrete element model software EDEM, wherein the particle model is constructed by a three-dimensional software, which is further imported into the discrete element model software EDEM, 5) optimize simulation parameters, wherein a corresponding simulation time step is determined by calculating a motion severity of particles to ensure the stability of iterative calculation of the system, wherein a fixed time step is set between 20% and 40% of the Rayleigh time step; 6) simulate a movement of materials by a 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 unit, the computational fluid dynamics and the discrete unit are coupled by a Lagrangian model, which is further used to simulate the movement of the materials under the different deflector angles and wind speeds, to optimize the processing parameters; and 7verify accuracy and practicality of model, wherein a rotation angle of the deflector and wind speed against materials accumulation are optimized by comparing a distribution of the material particles in a hull model imposed by inclination of the hull model of 0° to 10°, and meanwhile the mathematical equation between rotation angle (θ) of the deflector, the wind speed (F) and the hull swing angle (ω) is also established in (Formula {circle around (2)}), and the actual distribution of the material under the optimized conditions is conducted to verify the accuracy of the 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 rotation angle of the deflector 8) optimize the rotation angle of the deflector and the wind speed to equally distribute the material particles, whereby the uniform and fast drying of the seafood materials in the swinging hull ar achieved.

2. The system according to claim 1, wherein the seafood material 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 in the EDEM are combined to establish the model of the Silver anchovy.

3. The system according to claim 1, wherein the seafood material 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 in EDEM are combined to establish the model of the Antarctic krill.

4. The system according to claim 1, wherein the seafood material 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, wherein in step 4), a plurality of spherical particles in EDEM are combined to establish the model of the Acetes chinensis.

5. The system according to claim 1, wherein in step 7), the actual distribution (m.sub.a) of the materials in different parts of the conveyor belt are calculated, which is imposed by the optimized deflector rotation angle and wind speed conditions: furthermore, the accuracy of the model is verified (Formula {circle around (3)}) by the 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 materials in different parts of the conveyor belt (Formula {circle around (4)}and {circle around (5)}).
|m.sub.a−m.sub.s|/m.sub.a<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)} wherein Q.sub.l, 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 rotation angle of the deflector, the speed of the fan and the sway amplitude of the hall is achieved through a programmable logic control system, wherein the programmable logic control system is connecred with a frequency converter, a ship tilt angle sensor, a rotatation 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

[0026] 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.

[0027] FIG. 2 describes the structure diagram of the uniform drying system.

[0028] FIG. 3 describes the setting parameters diagram of device geometry imported into discrete element software.

[0029] FIG. 4 describes the setting parameters diagram of particle models imported into discrete element software.

[0030] FIG. 5 describes the finite element fluid analysis grid and setting parameter diagram.

[0031] FIG. 6 describes the air flow diagram (a) and air velocity distribution and vector diagram (b) when the hull runs smoothly.

[0032] FIG. 7 describes the air flow diagram (a) and air velocity distribution and vector diagram (b) when the hull swing right.

[0033] 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

[0034] The further description of the present invention is made with examples.

[0035] Example 1: Establishment of location correction system for processing Silver anchovy transportation displaced by wind waves and anti-accumulation drying processing method (FIG. 1).

[0036] 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.

[0037] 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).

[0038] 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.

[0039] the used airflow field dynamics models are shown as the follows:

[00003]$\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 \mathrm{.Math.}$$\frac{\partial }{\partial t}\left({\alpha }_g\rho \mathrm{.Math.}\right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\mathrm{.Math.}\right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mu }_e}{{\sigma }_{\mathrm{.Math.}}}.Math.\frac{{\partial }_{\mathrm{.Math.}}}{\partial {\chi }_j}\right)+\frac{\mathrm{.Math.}}{\kappa }{\alpha }_g\left(C_1{G}_k-C_2\rho \mathrm{.Math.}\right)$$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}$

[0040] 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.

[0041] 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 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.

[0042] 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).

[0043] 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.

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

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

[0044] Example 2: Establishment of location correction system for processing Antarctic krill transportation displaced by wind waves and anti-accumulation drying processing method.

[0045] 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,

[00004]${\theta }_1\left(-\frac{\pi }{4}<\theta <0\right)\mathrm{or}{\theta }_r\left(0<\theta <\frac{\pi }{4}\right)$

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.

[0046] 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.

[0047] 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.

[0048] the used airflow field dynamics models are shown as the follows:

[00005]$\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 \mathrm{.Math.}$$\frac{\partial }{\partial t}\left({\alpha }_g\rho \mathrm{.Math.}\right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\mathrm{.Math.}\right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mu }_e}{{\sigma }_{\mathrm{.Math.}}}.Math.\frac{{\partial }_{\mathrm{.Math.}}}{\partial {\chi }_j}\right)+\frac{\mathrm{.Math.}}{\kappa }{\alpha }_g\left(C_1{G}_k-C_2\rho \mathrm{.Math.}\right)$$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}$

[0049] 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.249 =1.3.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] Example 3: Establishment of location correction system for processing Acetes chinensis transportation displaced by wind waves and anti-accumulation drying processing method.

[0055] 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,

[00006]${\theta }_1\left(-\frac{\pi }{4}<\theta <0\right)\mathrm{or}{\theta }_r\left(0<\theta <\frac{\pi }{4}\right)$

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.

[0056] 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.

[0057] 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.

[0058] the used airflow field dynamics models are shown as the follows:

[00007]$\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 \mathrm{.Math.}$$\frac{\partial }{\partial t}\left({\alpha }_g\rho \mathrm{.Math.}\right)+\frac{\partial }{\partial {\chi }_j}\left({\alpha }_g\rho {\stackrel{.fwdarw.}{\mathrm{.Math.}}}_j\mathrm{.Math.}\right)=\frac{\partial }{\partial {\chi }_j}\left(\frac{{\alpha }_g{\mu }_e}{{\sigma }_{\mathrm{.Math.}}}.Math.\frac{{\partial }_{\mathrm{.Math.}}}{\partial {\chi }_j}\right)+\frac{\mathrm{.Math.}}{\kappa }{\alpha }_g\left(C_1{G}_k-C_2\rho \mathrm{.Math.}\right)$$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}$

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.