Airflow optimization method of shell drying system

Abstract

The invention relates to the technical field of airflow optimization, in particular to an airflow optimization method of a shell drying system. The method comprises the following steps that environmental parameters in a shell drying cabin are obtained through experimental testing, and data analysis is conducted on the environmental parameters through standard deviation and variable coefficients; establishing a CFD simulation model based on a Navier-Stokes equation, and setting and initializing the CFD simulation model to identify steady-state distribution data of wind speed, temperature and humidity on the surface of the shell; based on a CFD simulation model, a local airflow insufficient area is determined, and the number of fans and the air supply angle are optimized; a brand-new dry environment wind field vector airflow organization and adjustment device, a centralized rotating wheel dehumidification system and an automatic air volume control strategy are introduced; and the optimized shell drying system is subjected to experimental verification again. According to the airflow optimization method of the shell drying system, the wind field vector airflow organization and adjustment device and the centralized rotating wheel dehumidification system are combined with an automatic air volume control strategy, and accurate control over airflow and humidity is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of airflow optimization, and more specifically, to an airflow optimization method for a shell drying system. Background Art

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

[0003] Existing airflow optimization methods usually have difficulty effectively reducing the occurrence of local airflow shortage areas. Moreover, due to the inflexible fan arrangement and inaccurate wind speed adjustment, the wind speed in some areas of the drying chamber is too low and the humidity is too high, thus reducing the uniform drying efficiency of the shell and the system stability. Therefore, an airflow optimization method for the shell drying system is provided. Summary of the Invention

[0004] The purpose of the present invention is to provide an airflow optimization method for a shell drying system to solve the problem that due to the inflexible fan arrangement and inaccurate wind speed adjustment in the above-mentioned background art, the wind speed in some areas of the drying chamber is too low and the humidity is too high, thus reducing the uniform drying efficiency of the shell and the system stability.

[0005] To achieve the above purpose, the present invention provides an airflow optimization method for a shell drying system, including the following steps:

[0006] S1. Obtain the environmental parameters in the shell drying chamber through experimental tests, and perform data analysis on the environmental parameters using the standard deviation and coefficient of variation.

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

[0008] S3. Determine the local airflow shortage area based on the CFD simulation model, and optimize the number of fans and the air supply angle.

[0009] S4. Introduce a new drying environment wind field vector airflow organization and regulation 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. Conduct experimental verification on the optimized shell drying system again and feedback it to the staff.

[0011] As a further improvement of this technical solution, in S1, the environmental parameters in the shell drying chamber are the spatial-temporal distributions of temperature, humidity, and wind speed;

[0012] In S1, the environmental parameters in the shell drying chamber are obtained through experimental tests, and data analysis of the environmental parameters is performed using the standard deviation and coefficient of variation. The specific method steps are as follows:

[0013] S1.1. At different fan openings, obtain 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 chamber through experimental tests;

[0014] where i is the i-th data point, i = 1, 2,..., N; N is the total number of collected data points; T i is the i-th temperature data; H i is the i-th humidity data; V i is the i-th wind speed data; x i is the coordinate of the i-th data point on the x-axis of the space; y i is the coordinate of the i-th data point on the y-axis of the space; z i is the coordinate of the i-th data point on the z-axis of the space; 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), use Kriging interpolation to calculate the temperature spatial-temporal distribution T(x, y, z, t), humidity spatial-temporal distribution H(x, y, z, t), and wind speed spatial-temporal distribution V(x, y, z, t);

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

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

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

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

[0020] Mean temperature

[0021]

[0022] Mean humidity

[0023]

[0024] Mean wind speed

[0025]

[0026] Temperature standard deviation SD T :

[0027]

[0028] Humidity standard deviation SD H :

[0029]

[0030] Wind speed standard deviation SD V :

[0031]

[0032] Among them, is the mean temperature; is the mean humidity; is the mean wind speed; 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 this technical solution, in S1.4, calculate the coefficient of variation of temperature, humidity, and wind speed, specifically as follows:

[0034] Coefficient of variation of temperature CV T :

[0035]

[0036] Coefficient of variation of humidity 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 this 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 1: V i < 1 m / s;

[0043] Interval 2: 1 ≤ V i ≤ 3 m / s;

[0044] Interval 3: V i > 3 m / 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 1 V i < 1 m / s; PCT 1≤V≤3 is the proportion of wind speed data in Interval 2 1 ≤ V i ≤ 3 m / s; PCT V＞3 is the proportion of wind speed data in Interval 3 V i > 3 m / s.

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

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

[0050] S2.2. Based on the physical dimensions and internal structure of the mold shell drying chamber, construct a three-dimensional geometric model of the drying chamber, and divide the three-dimensional geometric model of the drying chamber into multiple small simulation units;

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

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

[0053] S2.5. Use a CFD solver to perform numerical calculations to obtain the steady-state temperature distribution, steady-state humidity distribution, and steady-state wind speed distribution.

[0054] As a further improvement of this technical solution, in S2.3, the boundary conditions for the CFD simulation model are set 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] Where, 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 this technical solution, in S2.4, the Navier-Stokes equation, heat transfer equation, and humidity diffusion equation are established;

[0063] Navier-Stokes equation:

[0064]

[0065] Where, 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] Where, C pwhere \(c_p\) is the specific heat capacity; \(T\) is the general temperature in the heat transfer equation; \(\kappa\) is the thermal conductivity; \(\rho_2\) is the fluid density;

[0069] Humidity diffusion equation:

[0070]

[0071] where \(\rho_3\) is the air density; \(D\) w is the diffusion coefficient of water vapor; \(H\) is the general humidity in the humidity diffusion equation;

[0072] In S2.5, a CFD solver is used for numerical calculation 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 of this technical solution, in 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:

[0075] S3.1. Set the wind speed threshold \(V\) threshold , and based on the steady-state wind speed distribution identify the area where the wind speed is lower than the wind speed threshold \(V\) threshold , then this area is the local airflow insufficient area;

[0076] S3.2. Based on the wind speed coefficient of variation \(CV\) V and the proportion PCT V＜1 、PCT 1≤V≤ and PCT V＞3 , determine the local airflow insufficient area:

[0077] The position neighborhood in the space where the wind speed data is in PCT V＜1 is the local airflow insufficient area;

[0078] S3.3. In the local airflow insufficient area, increase the number of fans, adjust the positions of the existing fans, and optimize the placement angles of the fans; increase the air volume in the area with a lower wind speed.

[0079] As a further improvement of this technical solution, in S4, the new dry environment wind field vector air flow organization and regulation device changes 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 control the multi-directional air flow;

[0080] The centralized rotary wheel dehumidification system adopts the rotary wheel dehumidification technology, and absorbs and releases moisture through the rotation of the rotary wheel to maintain the humidity within a set range;

[0081] 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 chamber.

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

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

[0084] 2. In the air flow optimization method of the shell drying system, through the centralized rotary wheel dehumidification system and the automatic air volume control strategy, the humidity and air volume are adjusted in real time to ensure the humidity stability of the drying environment and optimize the air flow distribution. Brief Description of the Drawings

[0085] Figure 1 It is the overall method flow chart of the present invention. Detailed Embodiments

[0086] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0087] Embodiment:

[0088] Please refer to Figure 1 As shown, this embodiment provides an air flow optimization method for a shell drying system, including the following steps:

[0089] S1. Obtain the environmental parameters in the shell drying chamber through experimental tests, and perform data analysis on the environmental parameters using the standard deviation and coefficient of variation;

[0090] In S1 of this embodiment, the environmental parameters in the shell drying chamber are the spatial and temporal distributions of temperature, humidity, and wind speed;

[0091] In S1 of this embodiment, the environmental parameters in the shell drying chamber are obtained through experimental tests, and data analysis is performed on the environmental parameters using the standard deviation and coefficient of variation. The specific method steps are as follows:

[0092] S1.1. Under different fan opening degrees, obtain 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 is the i-th data point, i = 1, 2, …, N; N is the total number of collected data points; T i is the i-th temperature data; H i is the i-th humidity data; V i is the i-th wind speed data; x i is the coordinate of the i-th data point on the x-axis of space; y i is the coordinate of the i-th data point on the y-axis of space; z i is the coordinate of the i-th data point on the z-axis of 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 , t), use Kriging interpolation to calculate the temperature spatio-temporal distribution T(x, y, z, t), humidity spatio-temporal distribution H(x, y, z, t) and wind speed spatio-temporal distribution V(x, y, z, t);

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

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

[0097] S1.5. According to the wind speed distribution, divide the wind speed into three wind speed intervals.

[0098] In this embodiment, the fan opening degrees are 40%, 60%, 80% and 100%;

[0099] The experimental tests are completed using equipment such as a thermal anemometer, HOBO temperature and humidity tester, and vane anemometer, as follows:

[0100] Uniformly arrange test points in the drying chamber, covering the surface of the shell mold and the conveyor belt area, referring to the equipment layout diagram; arrange 4 test points (M1 - M4 and B1 - B4) in the drying chambers for the surface layer and the backing layer respectively, with key monitoring of the conveyor belt and the surface of the shell mold; during the movement of the conveyor belt, record the data at different fan opening degrees, with a sampling frequency of 1 time per second and a duration of 15 minutes, for a total of 900 data points.

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

[0102] Mean temperature

[0103]

[0104] Mean humidity

[0105]

[0106] Mean wind speed

[0107]

[0108] Standard deviation of temperature SD T :

[0109]

[0110] Standard deviation of humidity SD H :

[0111]

[0112] Standard deviation of wind speed SD V :

[0113]

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

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

[0116] Coefficient of variation of temperature CV T :

[0117]

[0118] Coefficient of variation of humidity CV H :

[0119]

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

[0121]

[0122] Among them, 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, specifically as follows:

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

[0125] Interval 2: 1 ≤ V i ≤ 3 m / s;

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

[0127] Calculate the proportion of wind speed in the three intervals:

[0128]

[0129] Among them, PCT V＜1 is the proportion of wind speed data in Interval 1 V i < 1 m / s; PCT 1≤V≤3 is the proportion of wind speed data in Interval 2 1 ≤ V i ≤ 3 m / s; PCT V＞3 is the proportion of wind speed data in Interval 3 V i > 3 m / s.

[0130] S2. Based on the environmental parameters, establish a CFD simulation model based on the Navier-Stokes equation, and initialize the CFD simulation model through geometric modeling, mesh generation, and boundary condition setting to identify the steady-state distribution data of wind speed, temperature, and humidity on the surface of the investment shell;

[0131] In this embodiment S2, based on the environmental parameters, establish a CFD simulation model based on the Navier-Stokes equation, and initialize the CFD simulation model through geometric modeling, mesh generation, and boundary condition setting to identify the steady-state distribution data of wind speed, temperature, and humidity on the surface of the investment shell. The specific method steps are as follows:

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

[0133] S2.2. Based on the physical dimensions and internal structure of the shell drying chamber, construct a three-dimensional geometric model of the drying chamber and divide the three-dimensional geometric model of the drying chamber into multiple small simulation units;

[0134] The physical dimensions and internal structure of the shell drying chamber include the fan position, conveyor belt, and shell surface;

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

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

[0137] S2.5. Use the CFD solver for numerical calculation to obtain the steady-state temperature distribution, steady-state humidity distribution, and steady-state wind speed distribution.

[0138] In S2.3, the boundary conditions are set for the CFD simulation model 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] where 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 distributions of temperature, humidity, and wind speed. Based on the previous experimental data, a reasonable initial state is set.

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

[0148] Navier-Stokes equations:

[0149]

[0150] where 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;

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

[0152] Heat transfer equation:

[0153]

[0154] where C p is the specific heat capacity; T is the general temperature in the heat transfer equation; κ is the thermal conductivity; ρ2 is the fluid density;

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

[0156] Humidity diffusion equation:

[0157]

[0158] where ρ3 is the air density; D w is the diffusion coefficient of water vapor; H is the general humidity in the humidity diffusion equation;

[0159] In this embodiment S2.5, a CFD solver is used for numerical calculation to obtain the steady-state temperature distribution, the steady-state humidity distribution, and the 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 deficiency area based on the CFD simulation model, and optimize the number of fans and the air supply angle;

[0162] In this embodiment S3, the local airflow deficiency 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 threshold, based on the steady-state wind speed distribution Identify the area where the wind speed is lower than the wind speed threshold V threshold Then this area is the local air flow deficiency area;

[0164] S3.2. Based on the wind speed coefficient of variation CV V And the proportion PCT of the wind speed in each interval V＜1 、PCT 1≤V≤ And PCT V＞3 , determine the local air flow deficiency area:

[0165] The position neighborhood in the space where the wind speed data of PCT V＜1 is located is the local air flow deficiency area;

[0166] S3.3. In the local air flow deficiency area, increase the number of fans, adjust the positions of the existing fans, and optimize the placement angles of the fans; increase the air volume in the area with lower wind speed.

[0167] S4. Introduce a new drying environment wind field vector air flow organization and regulation device, a centralized rotary wheel dehumidification system, and an automated air volume control strategy to reduce wind speed differences and maintain stable temperature and humidity;

[0168] In step S4 of this embodiment, the new drying environment wind field vector air flow organization and regulation device changes 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 regulate multi-directional airflows;

[0169] The centralized rotary wheel dehumidification system adopts the rotary wheel dehumidification technology, and absorbs and releases moisture by the rotation of the rotary wheel to maintain the humidity within the set range;

[0170] The automated air volume control strategy automatically adjusts the wind speed and air volume of the fans according to the real-time temperature and humidity data in the drying chamber.

[0171] S5. Conduct experimental verification on the optimized shell drying system again and feedback to the staff.

[0172] The above shows and describes 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 by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. Airflow optimization method for a shell mold drying system, characterized in that: It includes the following steps: S1. Obtain the environmental parameters inside the shell drying chamber through experimental tests, and perform data analysis on the environmental parameters using the standard deviation and coefficient of variation; S2. Based on the environmental parameters, establish a CFD simulation model based on the Navier-Stokes equation, and initialize the CFD simulation model through geometric modeling, mesh generation, and boundary condition setting to identify the steady-state distribution data of the wind speed, temperature, and humidity on the surface of the shell; S3. Determine the local airflow insufficient area based on the CFD simulation model, and optimize the number of fans and the air supply angle; S4. Introduce a new drying environment wind field vector air flow organization and regulation 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; S5. Conduct experimental verification on the optimized shell drying system again and feedback it to the staff.

2. The airflow optimization method of the mold shell drying system according to claim 1, characterized in that: In S1, the environmental parameters inside the shell drying chamber are the spatial and temporal distributions of temperature, humidity, and wind speed; In S1, obtain the environmental parameters inside the shell drying chamber through experimental tests, and perform data analysis on the environmental parameters using the standard deviation and coefficient of variation. The specific method steps are as follows: S1.

1. At different fan opening degrees, obtain 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 chamber through experimental tests; where i is the i-th data point, i = 1, 2, …, N; N is the total number of collected data points; T i is the i-th temperature data; H i is the i-th humidity data; V i is the i-th wind speed data; x i is the coordinate of the i-th data point on the x-axis of space; y i is the coordinate of the i-th data point on the y-axis of space; z i is the coordinate of the i-th data point on the z-axis of space; t is the time; 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 , t), use Kriging interpolation to calculate the temperature spatio - temporal distribution T(x, y, z, t), the humidity spatio - temporal distribution H(x, y, z, t) and the wind speed spatio - temporal distribution V(x, y, z, t); S1.

3. Calculate the mean values and standard deviations of temperature, humidity, and wind speed; S1.

4. Calculate the coefficients of variation of temperature, humidity, and wind speed; S1.

5. Divide the wind speed into three wind speed intervals according to the distribution of wind speed.

3. The airflow optimization method of the shell drying system according to claim 2, characterized in that: In S1.3, calculate the mean values and standard deviations of temperature, humidity, and wind speed as follows: Temperature mean value Average humidity Average wind speed Temperature standard deviation SD T : Humidity standard deviation SD H : Standard deviation of wind speed SD V : Among them, is the average temperature; is the average humidity; is the average wind speed; SD T is the standard deviation of temperature; SD H is the standard deviation of humidity; SD V is the standard deviation of wind speed.

4. The airflow optimization method of the mold shell drying system according to claim 3, characterized in that: In S1.4, calculate the coefficients of variation of temperature, humidity, and wind speed as follows: Coefficient of Variation of Temperature CV T : Coefficient of variation of humidity CV H : Coefficient of variation of wind speed CV V : Among them, CV T is the temperature coefficient of variation; CV H is the humidity coefficient of variation; CV V is the wind speed coefficient of variation.

5. The airflow optimization method of the shell drying system according to claim 4, wherein: In S1.5, divide the wind speed into three wind speed intervals according to the distribution of wind speed as follows: Interval 1: V i <1 m / s; Interval 2: 1 ≤ V i ≤ 3 m / s; Interval Three: V i > 3 m / s; Calculate the proportions of wind speed in the three intervals: Among them, PCT V＜1 is the proportion of wind speed data in interval I, V i < 1 m / s; PCT 1≤V≤3 is the proportion of wind speed data in interval II, 1 ≤ V i ≤ 3 m / s; PCT V＞3 is the proportion of wind speed data in interval III, V i > 3 m / s.

6. The airflow optimization method of the mold shell drying system according to claim 5, characterized in that: In S2, based on the environmental parameters, establish a CFD simulation model based on the Navier-Stokes equation, and initialize the CFD simulation model through geometric modeling, mesh generation, and boundary condition setting to identify the steady-state distribution data of the wind speed, temperature, and humidity on the surface of the shell. The specific method steps are as follows: 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; S2.

2. Based on the physical dimensions and internal structure of the shell drying chamber, construct a three-dimensional geometric model of the drying chamber, and divide the three-dimensional geometric model of the drying chamber into multiple small simulation units; S2.

3. Set boundary conditions for the CFD simulation model; S2.

4. Establish the Navier-Stokes equation, heat transfer equation, and humidity diffusion equation; S2.

5. Use a CFD solver to perform numerical calculations to obtain the steady-state temperature distribution, steady-state humidity distribution, and steady-state wind speed distribution.

7. The airflow optimization method of the shell drying system according to claim 6, characterized in that: In S2.3, set boundary conditions for the CFD simulation model as follows: Temperature boundary condition: T(x,y,z,t) = T boundary ; Humidity boundary condition: H(x, y, z, t) = H boundary ; Wind speed boundary condition: V(x, y, z, t) = V boundary ; Among them, T boundary is the boundary temperature value; H boundary is the boundary humidity value; V boundary is the boundary wind speed value.

8. The airflow optimization method of the mold shell drying system according to claim 7, wherein: In S2.4, establish the Navier-Stokes equation, heat transfer equation, and humidity diffusion equation; Navier-Stokes equation: Among them, 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; Heat transfer equation: where C p is the specific heat capacity; T is the general temperature in the heat transfer equation; κ is the thermal conductivity; ρ2 is the fluid density; Humidity diffusion equation: where ρ3 is the air density; D w is the diffusion coefficient of water vapor; H is the general humidity in the humidity diffusion equation; In S2.5, a CFD solver is used for numerical calculation 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:

9. The airflow optimization method of the mold shell drying system according to claim 8, characterized in that: In S3, based on the CFD simulation model, the local airflow deficiency area is determined, and the number of fans and the air supply angle are optimized. The specific method is as follows: S3.

1. Set the wind speed threshold V threshold , based on the steady-state wind speed distribution Identify the area where the wind speed is lower than the wind speed threshold V threshold , then this area is the local air flow shortage area; S3.

2. Based on the wind speed coefficient of variation CV V and the proportion PCT of the wind speed in each interval V＜1 , PCT 1≤V≤3 and PCT V＞3 , determine the local airflow insufficient area: In the PCT V＜1 The position neighborhood in the space where the wind speed data is located is the local airflow deficiency area; S3.

3. In the local airflow deficiency area, increase the number of fans, adjust the positions of the existing fans, and optimize the placement angles of the fans; increase the air volume in the area with a low wind speed.

10. The airflow optimization method of the mold shell drying system according to claim 9, characterized in that: In S4, the new drying environment wind field vector air flow organization and regulation device changes 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 regulate the multi-directional air flow; The centralized rotary wheel dehumidification system adopts the rotary wheel dehumidification technology, and absorbs and releases moisture through the rotation of the rotary wheel to maintain the humidity within the set range; The automatic air volume control strategy automatically adjusts the wind speed and air volume of the fans according to the real-time temperature and humidity data in the drying chamber.

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