Airflow optimization method for shell dry system

Abstract

The present application relates to the technical field of airflow optimization, in particular to an airflow optimization method for a shell drying system. The method comprises the following steps: obtaining environmental parameters in a shell drying cabin through experimental testing, and performing data analysis on the environmental parameters using standard deviation and coefficient of variation; establishing a CFD simulation model based on Navier-Stokes equation, setting initial CFD simulation model to identify steady-state distribution data of shell surface wind speed, temperature and humidity; determining local airflow insufficient area based on the CFD simulation model, optimizing fan quantity and air supply angle; introducing a brand-new drying environment wind field vector airflow organization and adjustment device, a centralized rotary dehumidification system and an automatic air volume control strategy; and performing experimental verification on the optimized shell drying system again. The airflow optimization method for the shell drying system realizes accurate control of airflow and humidity by combining the wind field vector airflow organization and adjustment device and the centralized rotary dehumidification system with the automatic air volume control strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of airflow optimization, in particular to an airflow optimization method of a shell drying system. BACKGROUND

[0002] The airflow optimization method of the shell drying system aims to improve the uniformity of airflow distribution and maintain the stability of temperature and humidity, control the wind speed difference and humidity fluctuation by introducing a wind field vector airflow organization and adjustment device, a centralized rotary dehumidification system and an automatic air volume control strategy, and realize uniform drying and efficient dehumidification.

[0003] The existing airflow optimization method is usually difficult to effectively reduce the occurrence of local airflow insufficient area, and due to the insufficient flexibility of fan arrangement and the inaccuracy of wind speed adjustment, it may cause the wind speed in some areas of the drying cabin to be too low and the humidity to be too high, thereby reducing the uniform drying efficiency of the shell and the system stability, therefore, the airflow optimization method of the shell drying system is provided. SUMMARY

[0004] The purpose of the present application is to provide an airflow optimization method of a shell drying system to solve the problem of insufficient flexibility of fan arrangement and inaccuracy of wind speed adjustment, which may cause the wind speed in some areas of the drying cabin to be too low and the humidity to be too high, thereby reducing the uniform drying efficiency of the shell and the system stability.

[0005] To achieve the above purpose, the present application provides an airflow optimization method of a shell drying system, comprising the following steps:

[0006] S1, obtaining the environmental parameters in the shell drying cabin through experimental test, and using standard deviation and variation coefficient to analyze the data of the environmental parameters;

[0007] S2, based on the environmental parameters, establishing a CFD simulation model based on Navier-Stokes equation, initializing the CFD simulation model by geometric modeling, mesh division and boundary condition setting to identify the steady-state distribution data of shell surface wind speed, temperature and humidity;

[0008] S3, determining the local airflow insufficient area based on the CFD simulation model, optimizing the number of fans and the air supply angle;

[0009] S4, introducing a brand new drying environment wind field vector airflow organization and adjustment device, a centralized rotary dehumidification system and an automatic air volume control strategy to reduce the wind speed difference and maintain the stability of temperature and humidity;

[0010] S5, retesting the optimized shell drying system and feeding back to the staff.

[0011] As a further improvement of the technical solution, in the S1, the environmental parameters in the shell drying cabin are temperature, humidity and wind speed spatial-time distribution;

[0012] In the S1, the environmental parameters in the shell drying cabin are obtained by experimental test, and the data analysis of the environmental parameters is carried out by using standard deviation and coefficient of variation, and the specific method steps are as follows:

[0013] S1.1, under different fan opening degrees, the temperature (x i ,y i ,z i ,T i ,t), humidity (x i ,y i ,z i ,H i ,t) and wind speed (x i ,y i ,z i ,V i ,t) in the shell drying cabin are obtained by experimental test;

[0014] Wherein, i is the ith data point, i = 1, 2, …, N; N is the total number of data points collected; T i is the ith temperature data; H i is the ith humidity data; V i is the ith wind speed data; x i is the coordinate of the ith data point in the space x axis; y i is the coordinate of the ith data point in the space y axis; z i is the coordinate of the ith data point in the space z axis; t is the time;

[0015] S1.2, based on the temperature (x i ,y i ,z i ,T i ,t), humidity (x i ,y i ,z i ,H i ,t) and wind speed (x i ,y i ,z i ,V i ,t), the temperature spatial-time distribution T(x,y,z,t), the humidity spatial-time distribution H(x,y,z,t) and the wind speed spatial-time distribution V(x,y,z,t) are calculated by using Kriging interpolation;

[0016] S1.3, the mean and standard deviation of temperature, humidity and wind speed are calculated;

[0017] S1.4, calculate the coefficient of variation of temperature, humidity and wind speed;

[0018] S1.5, according to the distribution of wind speed, the wind speed is divided into three wind speed intervals.

[0019] As a further improvement of the technical solution, in S1.3, the mean and standard deviation of temperature, humidity and wind speed are calculated, specifically as follows:

[0020] Temperature mean

[0021]

[0022] Humidity mean

[0023]

[0024] Wind speed mean

[0025]

[0026] Temperature standard deviation SD T :

[0027]

[0028] Humidity standard deviation SD H :

[0029]

[0030] Wind speed standard deviation SD V :

[0031]

[0032] Wherein, is the temperature mean; is the humidity mean; is the wind speed mean; SD T is the temperature standard deviation; SD H is the humidity standard deviation; SD V is the wind speed standard deviation.

[0033] As a further improvement of the technical solution, in S1.4, the coefficient of variation of temperature, humidity and wind speed is calculated, specifically as follows:

[0034] Temperature coefficient of variation CV T :

[0035]

[0036] Humidity coefficient of variation CVH :

[0037]

[0038] Coefficient of variation of wind speed CV V :

[0039]

[0040] wherein, CV T is the coefficient of variation of temperature; CV H is the coefficient of variation of humidity; CV V is the coefficient of variation of wind speed;

[0041] As a further improvement of the technical solution, in S1.5, according to the distribution of wind speed, the wind speed is divided into three wind speed intervals, specifically as follows:

[0042] Interval one: V i <1m / s;

[0043] Interval two: 1≤V i ≤3m / s;

[0044] Interval three: V i >3m / s;

[0045] Calculate the proportion of wind speed in the three intervals:

[0046]

[0047] wherein, PCT V＜1 is the proportion of wind speed data in interval one V i <1m / s; PCT 1≤V≤3 is the proportion of wind speed data in interval two 1≤V i ≤3m / s; PCT V＞3 is the proportion of wind speed data in interval three V i >3m / s.

[0048] As a further improvement of the technical solution, in S2, based on environmental parameters, a CFD simulation model based on Navier-Stokes equation is established, and the steady-state distribution data of wind speed, temperature and humidity on the surface of the identification shell are identified by initializing the CFD simulation model through geometric modeling, mesh division and boundary condition setting. The specific method steps are as follows:

[0049] S2.1, collect the temperature spatial and temporal distribution T(x, y, z, t), humidity spatial and temporal distribution H(x, y, z, t) and wind speed spatial and temporal distribution V(x, y, z, t) in S1, and input them into the CFD simulation model;

[0050] S2.2, based on the physical size and internal structure of the shell drying cabin, a three-dimensional geometric model of the drying cabin is constructed, and the three-dimensional geometric model of the drying cabin is divided into a plurality of small simulation units;

[0051] S2.3, setting boundary conditions for the CFD simulation model;

[0052] S2.4, establishing Navier-Stokes equation, heat transfer equation and humidity diffusion equation;

[0053] S2.5, numerical calculation is carried out by using CFD solver, and steady temperature distribution, steady humidity distribution and steady wind speed distribution are obtained.

[0054] As a further improvement of the technical solution, in S2.3, boundary conditions for the CFD simulation model are set, specifically as follows:

[0055] Temperature boundary condition:

[0056] T(x,y,z,t)=T boundary ;

[0057] Humidity boundary condition:

[0058] H(x,y,z,t)=H boundary ;

[0059] Wind speed boundary condition:

[0060] V(x,y,z,t)=V boundary ;

[0061] Wherein, T boundary is the boundary temperature value; H boundary is the boundary humidity value; V boundary is the boundary wind speed value.

[0062] As a further improvement of the technical solution, in S2.4, Navier-Stokes equation, heat transfer equation and humidity diffusion equation are established;

[0063] Navier-Stokes equation:

[0064]

[0065] Wherein, u is the velocity field vector; p is the pressure; ρ1 is the fluid density; v is the kinematic viscosity of air; f is the volume force term;

[0066] Heat transfer equation:

[0067]

[0068] Wherein, C pρ is the specific heat capacity; T is the temperature in the heat transfer equation; κ is the thermal conductivity; ρ2 is the fluid density.

[0069] Humidity diffusion equation:

[0070]

[0071] Where ρ3 is the air density; D w is the diffusion coefficient of water vapor; H is the general term for humidity in the humidity diffusion equation;

[0072] In step S2.5, a CFD solver is used for numerical calculations to obtain the steady-state temperature distribution, steady-state humidity distribution, and steady-state wind speed distribution.

[0073] Steady-state temperature distribution: Steady-state humidity distribution: Steady-state wind speed distribution:

[0074] As a further improvement to this technical solution, in step S3, the local airflow deficiency area is determined based on the CFD simulation model, and the number of fans and the air delivery angle are optimized. The specific method is as follows:

[0075] S3.1, Set wind speed threshold V threshold Based on steady-state wind speed distribution Identify wind speeds below the wind speed threshold V threshold If a region is characterized by insufficient airflow, then that region is a localized area with insufficient airflow.

[0076] S3.2, Based on the coefficient of variation of wind speed (CV) V and the percentage of wind speed in each range (PCT) V＜1 PCT 1≤V≤ and PCT V＞3 Identify areas with insufficient local airflow:

[0077] In PCT V＜1 The spatial neighborhood of the wind speed data is the area of ​​insufficient local airflow;

[0078] S3.3 In areas with insufficient local airflow, increase the number of fans, adjust the position of existing fans, and optimize the placement angle of fans; increase air volume in areas with low wind speed.

[0079] As a further improvement to this technical solution, in S4, the new dry environment wind field vector airflow organization and adjustment device changes the flow mode of the wind field by guiding the wind speed and adjusting the wind direction. The vector airflow organization is used to adjust the direction and intensity of the wind speed and regulate the multi-directional airflow.

[0080] The centralized rotary dehumidification system adopts rotary dehumidification technology, absorbs and releases moisture through the rotation of the rotary wheel, and is used for maintaining the humidity in a set range;

[0081] The automatic air volume control strategy automatically adjusts the air speed and air volume of the fan according to the real-time temperature and humidity data in the drying cabin.

[0082] Compared with the prior art, the beneficial effects of the present application are:

[0083] 1. In the air flow optimization method of the shell drying system, based on the air field vector air flow organization and adjusting device, the air flow path in the drying cabin can be flexibly controlled by adjusting the direction and intensity of the air flow, and the local air flow insufficient area is reduced.

[0084] 2. In the air flow optimization method of the shell drying system, the humidity and air volume are adjusted in real time through the centralized rotary dehumidification system and the automatic air volume control strategy, so that the humidity of the drying environment is stable and the air flow distribution is optimized. BRIEF DESCRIPTION OF DRAWINGS

[0085] Figure 1 The overall method flowchart of the present application. DETAILED DESCRIPTION

[0086] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0087] Embodiment:

[0088] Please refer to Figure 1 The present embodiment provides an air flow optimization method of a shell drying system, which comprises the following steps:

[0089] S1, the environmental parameters in the shell drying cabin are obtained by experimental test, and the data analysis of the environmental parameters is carried out by using standard deviation and variation coefficient;

[0090] In S1 of the present embodiment, the environmental parameters in the shell drying cabin are temperature, humidity and wind speed spatial time distribution;

[0091] In S1 of the present embodiment, the environmental parameters in the shell drying cabin are obtained by experimental test, and the data analysis of the environmental parameters is carried out by using standard deviation and variation coefficient, and the specific method steps are as follows:

[0092] S1.1, under different fan opening degrees, the temperature (x i ,yi ,z i ,T i ,t), humidity (x i ,y i ,z i H i ,t) and wind speed (x i ,y i ,z i V i ,t);

[0093] Where i represents the i-th data point, i = 1, 2, ..., N; N is the total number of data points collected; T i For the i-th temperature data; H i V represents the i-th humidity data point; i Let x be the i-th wind speed data; i Let be the coordinates of the i-th data point on the x-axis in space; y i Let z be the coordinate of the i-th data point on the y-axis in space; i Let be the coordinate of the i-th data point on the z-axis in space; t is time.

[0094] S1.2, Based on temperature (x) i ,y i ,z i ,T i ,t), humidity (x i ,y i ,z i H i ,t) and wind speed (x i ,y i ,z i V i The spatial-temporal distributions of temperature T(x,y,z,t), humidity H(x,y,z,t), and wind speed V(x,y,z,t) were calculated using Kriging interpolation.

[0095] S1.3 Calculate the mean and standard deviation of temperature, humidity and wind speed;

[0096] S1.4 Calculate the coefficients of variation for temperature, humidity, and wind speed;

[0097] S1.5. Based on the distribution of wind speed, the wind speed is divided into three wind speed ranges.

[0098] In this embodiment, the fan opening is divided into 40%, 60%, 80%, and 100%;

[0099] The experimental tests were conducted using equipment such as a hot-wire anemometer, a HOBO temperature and humidity meter, and an impeller anemometer, as detailed below:

[0100] Test points were evenly arranged in the drying cabin, covering the surface of the shell and the area of the conveying belt, and the reference equipment layout was arranged; 4 test points (M1-M4 and B1-B4) were arranged in the surface layer and back layer drying cabin respectively, focusing on monitoring the conveying belt and the surface of the shell; During the movement of the conveying belt, data under different fan opening degrees were recorded, the sampling frequency was 1 second per time, the duration was 15 minutes, and there were 900 data points.

[0101] In this embodiment S1.3, the mean and standard deviation of temperature, humidity and wind speed were calculated, as follows:

[0102] Temperature mean

[0103]

[0104] Humidity mean

[0105]

[0106] Wind speed mean

[0107]

[0108] Temperature standard deviation SD T :

[0109]

[0110] Humidity standard deviation SD H :

[0111]

[0112] Wind speed standard deviation SD V :

[0113]

[0114] Wherein, is the temperature mean; is the humidity mean; is the wind speed mean; SD T is the temperature standard deviation; SD H is the humidity standard deviation; SD V is the wind speed standard deviation.

[0115] In this embodiment S1.4, the coefficient of variation of temperature, humidity and wind speed was calculated, as follows:

[0116] Temperature coefficient of variation CV T :

[0117]

[0118] Coefficient of variation of humidity CV H :

[0119]

[0120] Coefficient of variation of wind speed CV V :

[0121]

[0122] wherein, CV T is the coefficient of variation of temperature; CV H is the coefficient of variation of humidity; CV V is the coefficient of variation of wind speed;

[0123] In this embodiment S1.5, according to the distribution of wind speed, the wind speed is divided into three wind speed intervals, as follows:

[0124] Interval one: V i <1m / s;

[0125] Interval two: 1≤V i ≤3m / s;

[0126] Interval three: V i >3m / s;

[0127] The proportion of wind speed in the three intervals is calculated:

[0128]

[0129] wherein, PCT V＜1 is the proportion of wind speed data in interval one V i <1m / s; PCT 1≤V≤3 is the proportion of wind speed data in interval two 1≤V i ≤3m / s; PCT V＞3 is the proportion of wind speed data in interval three V i >3m / s.

[0130] S2, based on the environmental parameters, a CFD simulation model based on Navier-Stokes equation is established, and the steady-state distribution data of wind speed, temperature and humidity on the surface of the identification shell are identified by initializing the CFD simulation model through geometric modeling, mesh division and boundary condition setting.

[0131] In this embodiment S2, based on the environmental parameters, a CFD simulation model based on Navier-Stokes equation is established, and the steady-state distribution data of wind speed, temperature and humidity on the surface of the identification shell are identified by initializing the CFD simulation model through geometric modeling, mesh division and boundary condition setting. The specific method steps are as follows:

[0132] S2.1, collect the temperature spatial and temporal distribution T(x, y, z, t), humidity spatial and temporal distribution H(x, y, z, t) and wind speed spatial and temporal distribution V(x, y, z, t) in S1, input into the CFD simulation model;

[0133] S2.2, based on the physical size and internal structure of the shell drying cabin, a three-dimensional geometric model of the drying cabin is constructed, and the three-dimensional geometric model of the drying cabin is divided into a plurality of small simulation units;

[0134] The physical size and internal structure of the shell drying cabin include the fan position, the conveying belt and the shell surface;

[0135] S2.3, set boundary conditions for the CFD simulation model;

[0136] S2.4, establish Navier-Stokes equation, heat transfer equation and humidity diffusion equation;

[0137] S2.5, numerical calculation is carried out by using CFD solver to obtain steady-state temperature distribution, steady-state humidity distribution and steady-state wind speed distribution.

[0138] In S2.3, boundary conditions are set for the CFD simulation model, which are as follows:

[0139] Temperature boundary condition:

[0140] T(x, y, z, t) = T boundaty ;

[0141] Humidity boundary condition:

[0142] H(x, y, z, t) = H boundary ;

[0143] Wind speed boundary condition:

[0144] V(x, y, z, t) = V boundary ;

[0145] Wherein, T boundary is the boundary temperature value; H boundary is the boundary humidity value; V boundary is the boundary wind speed value.

[0146] In this embodiment, at the beginning of the CFD simulation, the initial state of the system is set, including the initial distribution of temperature, humidity and wind speed, and a reasonable initial state is set based on the experimental data in the early stage.

[0147] In this embodiment S2.4, Navier-Stokes equation, heat transfer equation and humidity diffusion equation are established;

[0148] Navier-Stokes equation:

[0149]

[0150] where u is the velocity field vector; p is the pressure; p1 is the fluid density; v is the kinematic viscosity of air; f is the volume force term;

[0151] In this embodiment, the wind speed spatial and temporal distribution V(x, y, z, t) affects the flow of air, and near the air supply port in the drying cabin, the air flow rate is high, and the pressure distribution also changes with the change of the wind speed. The wind speed spatial and temporal distribution V(x, y, z, t) as a boundary condition affects the solution of the Navier-Stokes equation;

[0152] Heat transfer equation:

[0153]

[0154] where C p is the specific heat capacity; T is the temperature in the heat transfer equation; K is the thermal conductivity; p2 is the fluid density;

[0155] In this embodiment, the moisture evaporation absorbs heat, and the humidity spatial and temporal distribution H(x, y, z, t) and the temperature spatial and temporal distribution T(x, y, z, t) affect the heat transfer process;

[0156] Humidity diffusion equation:

[0157]

[0158] where p3 is the air density; D w is the diffusion coefficient of water vapor; H is the humidity in the humidity diffusion equation;

[0159] In this embodiment S2.5, a CFD solver is used for numerical calculation to obtain a steady-state temperature distribution, a steady-state humidity distribution, and a steady-state wind speed distribution:

[0160] Steady-state temperature distribution: Steady-state humidity distribution: Steady-state wind speed distribution:

[0161] S3, determine the local airflow insufficient area based on the CFD simulation model, optimize the number of fans and the air supply angle;

[0162] In this embodiment S3, the local airflow insufficient area is determined based on the CFD simulation model, and the number of fans and the air supply angle are optimized. The specific method is as follows:

[0163] S3.1, set the wind speed threshold V thresholdBased on steady-state wind speed distribution Identify wind speeds below the wind speed threshold V threshold If a region is characterized by insufficient airflow, then that region is a localized area with insufficient airflow.

[0164] S3.2, Based on the coefficient of variation of wind speed (CV) V and the percentage of wind speed in each range (PCT) V＜1 PCT 1≤V≤ and PCT V＞3 Identify areas with insufficient local airflow:

[0165] In PCT V＜1 The spatial neighborhood of the wind speed data is the area of ​​insufficient local airflow;

[0166] S3.3 In areas with insufficient local airflow, increase the number of fans, adjust the position of existing fans, and optimize the placement angle of fans; increase air volume in areas with low wind speed.

[0167] S4. Introduces a brand-new dry environment wind field vector airflow organization and regulation device, centralized rotary dehumidification system and automatic air volume control strategy to reduce wind speed differences and maintain stable temperature and humidity;

[0168] In this embodiment S4, the novel dry environment wind field vector airflow organization and adjustment device changes the flow mode of the wind field by guiding the wind speed and adjusting the wind direction. The vector airflow organization is used to adjust the direction and intensity of the wind speed and regulate the multi-directional airflow.

[0169] Centralized rotary dehumidification systems employ rotary dehumidification technology, using the rotation of a rotary wheel to absorb and release moisture, thereby maintaining humidity within a set range.

[0170] The automated airflow control strategy automatically adjusts the fan speed and airflow based on real-time temperature and humidity data inside the drying chamber.

[0171] S5. Conduct another experiment to verify the optimized shell drying system and provide feedback to the staff.

[0172] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method of airflow optimization for a shell drying system, characterized by: The method comprises the following steps: S1, obtaining the environmental parameters in the mold shell drying cabin through experimental testing, and performing data analysis on the environmental parameters using standard deviation and variation coefficient; S2, based on the environmental parameters, a CFD simulation model based on Navier-Stokes equation is established, and the steady-state distribution data of the wind speed, temperature and humidity on the surface of the mold shell are identified by initializing the CFD simulation model through geometric modeling, mesh division and boundary condition setting; S3, based on the CFD simulation model, the local airflow insufficient area is determined, and the number of fans and the air supply angle are optimized; S4, introduce a new drying environment wind field vector air flow organization and adjustment device, centralized rotary dehumidification system and automatic air volume control strategy, reduce the wind speed difference and maintain the stability of temperature and humidity; S5, the optimized mold shell drying system is verified again through experiment, and the feedback is fed back to the staff; In the S2, based on the environmental parameters, a CFD simulation model based on Navier-Stokes equation is established, and the steady-state distribution data of the wind speed, temperature and humidity on the surface of the mold shell are identified by initializing the CFD simulation model through geometric modeling, mesh division and boundary condition setting, the specific method steps are as follows: S2.1, collecting the temperature spatial and temporal distribution in S1 , the humidity spatial and temporal distribution , and the wind speed spatial and temporal distribution , into the CFD simulation model; S2.2, based on the physical size and internal structure of the mold shell drying cabin, a three-dimensional geometric model of the drying cabin is constructed, and the three-dimensional geometric model of the drying cabin is divided into a plurality of small simulation units; S2.3, set the boundary conditions for the CFD simulation model; S2.4, establish Navier-Stokes equation, heat transfer equation and humidity diffusion equation; S2.5, numerical calculation is carried out by using CFD solver to obtain steady-state temperature distribution, steady-state humidity distribution and steady-state wind speed distribution; In the S3, based on the CFD simulation model, the local airflow insufficient area is determined, and the number of fans and the air supply angle are optimized, the specific method is as follows: S3.1, Set wind speed threshold Based on steady-state wind speed distribution Identify wind speeds below a wind speed threshold If a region is characterized by insufficient airflow, then that region is a localized area with insufficient airflow. S3.2, based on the coefficient of variation of wind speed and the proportion of wind speed in each interval , and determine the local airflow deficiency area: In The position neighborhood of the wind speed data in the space is the local airflow insufficient area. S3.3, in the local airflow insufficient area, increase the number of fans, adjust the position of the existing fans, and optimize the placement angle of the fans; increase the air volume in the area with low wind speed.

2. The method of airflow optimization for a shell drying system of claim 1, wherein: In the S1, the environmental parameters in the mold shell drying cabin are the space-time distribution of temperature, humidity and wind speed; In the S1, the environmental parameters in the mold shell drying cabin are obtained through experimental testing, and data analysis is performed on the environmental parameters using standard deviation and variation coefficient, the specific method steps are as follows: S1.1, under different fan opening degrees, obtain the temperature, humidity and air speed in the shell drying cabin through experimental test ;​​ wherein, is the number of data points, ; ; is the total number of data points collected; is the number of temperature data, ; is the number of humidity data, ; is the number of wind speed data, ; is the number of data points in the spatial x-axis, ; is the number of data points in the spatial y-axis, ; is the number of data points in the spatial z-axis, ; is the time; S1.2, based on temperature , humidity and wind speed , using Kriging interpolation to calculate the temperature spatial-temporal distribution , humidity spatial-temporal distribution and wind speed spatial-temporal distribution ; S1.3, calculate the mean and standard deviation of temperature, humidity and wind speed; S1.4, calculate the variation coefficient of temperature, humidity and wind speed; S1.5, according to the distribution of wind speed, the wind speed is divided into three wind speed intervals.

3. The method of airflow optimization for a shell drying system of claim 2, wherein: In the S1.3, the mean and standard deviation of temperature, humidity and wind speed are calculated, which are as follows: Temperature average : ； Mean value of humidity : ； wind speed average : ； Temperature standard deviation : ； Humidity standard deviation : ； wind speed standard deviation : ； wherein, is the mean value of the temperature; is the mean value of the humidity; is the mean value of the wind speed; is the standard deviation of the temperature; is the standard deviation of the humidity; is the standard deviation of the wind speed.

4. The method of airflow optimization for a shell drying system of claim 3, wherein: In the S1.4, the variation coefficient of temperature, humidity and wind speed is calculated, which is as follows: Temperature variation coefficient : ； Coefficient of variation of humidity : ； Wind speed variation coefficient : ； wherein Tvar is the temperature variation coefficient; Hvar is the humidity variation coefficient; Fvar is the wind speed variation coefficient; 5. The method of airflow optimization for a shell drying system of claim 4, wherein: In the S1.5, according to the distribution of wind speed, the wind speed is divided into three wind speed intervals, which are as follows: Interval one: ; Interval two: ; Interval three: ; Calculate the proportion of wind speed in three intervals: ； ； ； wherein, is the proportion of wind speed data in the interval one ; is the proportion of wind speed data in the interval two ; is the proportion of wind speed data in the interval three .

6. The method of airflow optimization for a shell drying system of claim 1, wherein: In the S2.3, set the boundary conditions for the CFD simulation model, which are as follows: Temperature boundary condition: ； Humidity boundary condition: ； Wind speed boundary condition: ； wherein, is a boundary temperature value; is a boundary humidity value; is a boundary wind speed value.

7. The method of airflow optimization for a shell drying system of claim 6, wherein: In the S2.4, Navier-Stokes equation, heat transfer equation and humidity diffusion equation are established; Navier-Stokes equation: ； wherein is the velocity field vector; is the pressure; is the fluid density; is the kinematic viscosity of air; is the volume force term; Heat transfer equation: ； in, Specific heat capacity; Temperature in the heat transfer equation refers to temperature in general. Thermal conductivity; For fluid density; Moisture diffusion equation: ； wherein, is the air density; is the diffusion coefficient of water vapor; is the humidity generalization in the humidity diffusion equation; In the S2.5, numerical calculation is carried out by using CFD solver to obtain the steady-state temperature distribution, steady-state humidity distribution and steady-state wind speed distribution: Steady state temperature distribution: ; steady state humidity distribution: ; steady state wind speed distribution: .

8. The method of airflow optimization for a shell drying system of claim 1, wherein: In the S4, the new dry environment wind field vector air flow organization and adjusting device is to change the flow mode of the wind field by guiding the wind speed and adjusting the wind direction, the vector air flow organization is used to adjust the direction and intensity of the wind speed, and the multidirectional air flow is regulated; The centralized rotary dehumidification system adopts rotary dehumidification technology, absorbs and releases moisture through the rotation of the rotary wheel, and is used to maintain the humidity in the set range; The automatic air volume control strategy automatically adjusts the wind speed and air volume of the fan according to the real-time temperature and humidity data in the drying cabin.

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