Numerical calculation method of mechanical characteristics of tea grinding based on CFD-DEM thermal-mechanical coupling
Through the CFD-DEM coupled simulation model, the problem of green tea powder particle contact parameter calibration and grinding process simulation is solved, and the accurate simulation and quality improvement of the tea powder grinding process is achieved, providing a theoretical basis for equipment optimization.
Patent Information
- Application Number
- CN202410774713.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-17
- Publication Date
- 2024-12-31
- Estimated Expiration
- 2044-06-17
AI Technical Summary
The prior art lacks effective methods to accurately obtain the contact parameters of green tea powder particles and the construction of simulation model of the grinding process, especially when temperature changes are taken into account, the caloric factors affecting the quality of tea powder are not fully utilized.
Using the numerical calculation method of tea grinding mechanical properties based on CFD-DEM thermal coupling, the particle model was established through electron microscope analysis, combined with physical experiments and discrete element simulation, the interaction parameters of tea powder particles were calibrated, and the CFD-DEM coupling simulation model was built. , simulates particle movement and heat transfer during grinding.
It realizes accurate calibration of tea powder particle contact parameters and reliable simulation of grinding process, reduces physical experiment needs, improves research efficiency, provides a theoretical basis for equipment research and development and parameter optimization, and ensures the quality of green tea powder.
Smart Images
Figure CN118709509B_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to discrete element simulation technology and computer fluid dynamics technology, and specifically relates to a numerical calculation method for tea grinding mechanical characteristics based on CFD-DEM thermal coupling. Background Art
[0002] Ultrafine green tea powder is a product of deep processing of tea. It is a green and healthy food that enhances emotional cognition, reduces stress and anxiety, has anti-inflammatory and antibacterial effects, reduces fat and blood, and fights cancer. It has high nutritional value. At present, green tea is the main industry of China's tea, and the market demand is large, with great development space.
[0003] Ball mill grinding is one of the key processes in the processing of ultrafine green tea powder. Therefore, the production of high-quality green tea powder depends on appropriate ball mill equipment and grinding process, and accurate particle contact parameters are the key to realizing the mechanized processing of tea powder particles, and are also the need for the development of corresponding equipment and parameter optimization; the change of the motion characteristics of particles during the grinding process of green tea powder particles is also crucial to the optimization of process parameters and ball mill structure. Therefore, the model construction, parameter calibration and simulation model research of tea powder particles can provide a theoretical basis for the subsequent improvement of green tea powder grinding quality. At present, there are still challenges in obtaining accurate particle contact parameters and building simulation models for the grinding process. The physical parameters of particles such as particle density, volume density and angle of repose (angle of repose, the angle between the slope of the accumulation body and the horizontal plane) are usually determined experimentally. However, due to the anisotropy of green tea powder particles, interaction parameters such as rolling friction coefficient are difficult to obtain experimentally. In order to overcome these experimental difficulties, numerical simulation can be used to simulate the physical experiment of tea powder accumulation and derive the parameters of particles. For example, the rolling friction coefficient and static friction coefficient can be calibrated by discrete element method (DEM). In addition, there are temperature changes during the grinding process. However, heat is an important factor affecting the quality of tea powder. Therefore, the changes in heat inside the ball mill must be taken into account when building a simulation model of the green tea powder grinding process. Computational Fluid Dynamics (CFD) can simulate the flow and heat transfer process of particles.
[0004] However, there is still a lack of research on the simulation model of green tea powder grinding process, which involves the establishment of tea powder particle model, the calibration of particle contact parameters, and the construction of coupling model. Therefore, the present invention proposes a numerical calculation method for the mechanical properties of tea grinding based on CFD-DEM thermal coupling. Summary of the invention
[0005] In order to solve the shortcomings of the prior art and achieve the purpose of calibrating the contact parameters of tea powder particles and simulating the grinding process, the present invention adopts the following technical solutions:
[0006] A numerical calculation method for tea grinding mechanical properties based on CFD-DEM thermal coupling includes the following steps:
[0007] Step 1: Collect green tea powder particle samples at different grinding times, and take electron microscope images of the samples at different grinding times;
[0008] Step 2: Establish a tea powder particle model by measuring the aspect ratio (Ar) of the tea powder particles in the electron microscope image;
[0009] Step 3: Physical experiments and discrete element method simulations of tea powder particle accumulation were performed at different grinding times to measure the angle of repose of the powder cone pile. Based on the variation law of the angle of repose in the physical experiment, the interaction parameters of the tea powder particles in the simulation were calibrated.
[0010] Step 4: Collect the grinding temperature, and combine the tea powder particle model with the calibrated tea powder particle interaction parameters. Through the coupling interface, build a computer fluid dynamics and discrete element method CFD-DEM coupling simulation model for the simulation of the tea powder grinding process.
[0011] Furthermore, in step 2, a tea powder particle shape model is established by measuring the aspect ratio of the tea powder particles in the electron microscope image, and a statistical analysis is performed on the particle contour shape to simplify the particle shape model. Based on the simplified tea powder particle shape model, a typical contour model of the tea powder particles is constructed using EDEM software, and a soft ball model is used to fill the particle contour as much as possible.
[0012] Furthermore, the construction of the typical contour model of the tea powder particles comprises the following steps:
[0013] First, the electron microscope image of tea powder particles is pre-processed by threshold, which can facilitate the extraction of the outline of green tea powder particles; however, the particles after threshold processing still have noise, making it difficult to accurately identify the outline of the particles. Therefore, the image needs to be filled with holes to reduce the noise in the image information; the particle outline size is measured, and after statistical analysis, a group of main distribution ranges of the aspect ratio of the tea powder particle outline are obtained;
[0014] Then, a plurality of tea powder particles are selected within each aspect ratio range and the average value of their outline dimensions is measured as the typical representative particle outline within the range.
[0015] Furthermore, the physical experiment in step 3 is to measure the angle of repose of green tea powder particles after accumulation at different grinding times by injection method, a funnel is placed on a bracket, the bottom of the funnel is kept at a certain distance from the horizontal plane, tea powder particles are slowly added from the top of the funnel, so that the tea powder particles fall due to their own gravity, and the tea powder particles leaking from the bottom of the funnel form a conical accumulation on the horizontal plane. The inclination angle of the cone of the conical pile is the angle of repose of the physical experiment.
[0016] Furthermore, the simulation in step 3 adopts the hollow cylinder method to simulate the free fall motion of tea powder particles in the funnel, fills the tea powder particles into the hollow cylinder placed on the base, and then lifts the cylinder at a specific speed to allow the tea powder particles to flow under the action of their own gravity to form a conical pile on the base, and obtains the simulated angle of repose by slicing and angle measurement of the conical pile based on the discrete element method.
[0017] Furthermore, in the step 3, the angle of repose of different simulation test sets is obtained by changing the interaction parameters of the tea powder particles in the discrete element method simulation, and the response surface diagram of the angle of repose is drawn based on the angle of repose obtained from different simulation test sets. At the same time, the relationship function between the interaction parameters of the tea powder particles and the angle of repose is calculated. Taking the angle of repose of the physical experiment as the target and the range of the interaction parameters as the constraint, a single-objective multivariate optimization model is established, and based on the single-objective multivariate optimization model, the interaction parameters of the tea powder particles are calculated.
[0018] Furthermore, the interaction parameters include rolling friction coefficient A and static friction coefficient B. The relationship function between the interaction parameters of tea powder particles and the repose angle θ is as follows:
[0019] θ=k1-k2A+k3B+k4AB+k5A 2 +k6B 2
[0020] Among them, k1 to k6 are polynomial coefficients;
[0021] Single-objective diversified optimization model:
[0022] θ(A,B)=θ i
[0023] constraint:
[0024] Among them, θ i It represents the angle of repose in physical experiments;
[0025] Based on the single-objective multivariate optimization model, the solution set of the rolling friction coefficient A and the static friction coefficient B of the interaction parameters of the tea powder particles was calculated.
[0026] Furthermore, in order to verify the applicability of the obtained static friction coefficient and rolling friction coefficient, and to verify the accuracy of the obtained parameter set, the solution set was brought into the simulation of tea powder particle samples at other grinding times, and the applicability of the solution set was verified by comparing the repose angle obtained from the simulation with the repose angle from the physical experiment.
[0027] Furthermore, in step 4, a CFD-DEM coupling simulation model is built based on the DPM coupling interface, and parameters of the discrete element simulation software and the computational fluid dynamics software are configured respectively. The interaction parameters of the tea powder particles are configured in the discrete element simulation software, and the tea powder particle shape and grinder information are imported to establish models for different interactions, and temperature updates are added to the tea powder particles; time, gravity, and material properties are configured in the computational fluid dynamics software; and the mechanical properties of the particles are analyzed and calculated: particle collision frequency, inter-particle interaction force, inter-particle heat transfer, and particle motion behavior.
[0028] Furthermore, the multiphase flow model and the discrete phase are enabled in the computational fluid dynamics software to establish a coupling interface.
[0029] The advantages and beneficial effects of the present invention are:
[0030] The DEM simulation of the present invention can greatly reduce the need for physical experiments, thereby reducing costs and improving research efficiency; DEM simulation can quickly obtain simulation results under different conditions by modifying model parameters, which is convenient for optimizing the design of particle systems; DEM simulation can provide detailed information on the interaction between particles, all parameters and conditions can be accurately set and repeated, and the simulation results are consistent and reliable; the calibration method of contact parameters of tea powder particles based on discrete element method can provide a theoretical basis for the development of corresponding equipment and parameter optimization. CFD-DEM coupling simulation can visualize the motion characteristics of particles in the grinding process, providing a theoretical basis for the optimization of corresponding equipment structure and process parameters. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] Figure 1 It is a flow chart of the numerical calculation method of tea grinding mechanical properties based on CFD-DEM thermal-mechanical coupling in an embodiment of the present invention.
[0032] Figure 2 These are electron microscope images of green tea powder samples at different grinding times in the embodiments of the present invention.
[0033] Figure 3 This is a flow chart of green tea powder particle profile information extraction in an embodiment of the present invention.
[0034] Figure 4a Schematic diagram of the outlines of four typical green tea powder particles in the embodiments of the present invention.
[0035] Figure 4bThis is a flow chart of establishing a green tea powder particle model in an embodiment of the present invention.
[0036] Figure 5 This is a diagram showing the process of physical accumulation of green tea powder particles and measurement of the angle of repose in an embodiment of the present invention.
[0037] Figure 6a Schematic diagram of the cylinder lifting during the DEM simulation stacking of green tea powder particles in an embodiment of the present invention.
[0038] Figure 6b This is a schematic diagram of a cone-shaped pile formed by lifting tea powder particles with a cylinder in an embodiment of the present invention.
[0039] Figure 6c It is a schematic diagram of measuring the angle of repose from slices in different directions in an embodiment of the present invention.
[0040] Figure 7a It is a three-dimensional response surface diagram of the repose angle of the green tea powder particles and their contact parameters in the embodiment of the present invention.
[0041] Figure 7b It is a top view of the response surface of the repose angle of the green tea powder particles and their contact parameters in the embodiment of the present invention.
[0042] Figure 8 This is a flow chart of temperature collection during the green tea powder grinding process in an embodiment of the present invention.
[0043] Fig. 9 This is a flow chart of the coupling operation of EDEM and FLUENT in an embodiment of the present invention. DETAILED DESCRIPTION
[0044] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.
[0045] like Figure 1 As shown in FIG. 1 , the numerical calculation method of tea grinding mechanical properties based on CFD-DEM thermal coupling is applied to green tea powder particles, including the following steps:
[0046] Step 1: Collect green tea powder particle samples at different grinding times, and take electron microscope images of the samples at different grinding times.
[0047] like Figure 2 As shown, green tea powder particle samples were collected at different grinding times, recorded as T10, T14, T18, T22, T26, and T30, for a total of 6 groups of samples; the instrument parameters for collecting electron microscope information of the samples were: acceleration voltage of 10Kv, standard beam current, and magnification of 1000×.
[0048] Step 2: Establish a tea powder particle shape model by measuring the aspect ratio (Ar) of tea powder particles in the electron microscopy image, and conduct a statistical analysis of the particle contour shape to simplify the particle shape model. Based on the simplified tea powder particle shape model, use EDEM software to construct a typical contour model of tea powder particles, and use the soft sphere model to fill the particle contour as much as possible.
[0049] As Figure 3 shown, to construct a contour model of particles based on the measured aspect ratio (Ar) of particles, it is first necessary to perform threshold preprocessing on the electron microscopy image of tea powder particles, which can facilitate the extraction of the contour of green tea powder particles; however, there is still noise in the particles after Threshold processing, and it is difficult to accurately identify the contour of the particles. Therefore, it is also necessary to perform Fill holes processing on the image to reduce the noise in the image information; finally, measure the size of the particle contour Measurement. After statistical analysis, the aspect ratio of the particle contour is mainly distributed in four ranges: Ar > c, c < Ar < b, b < Ar < a, and Ar < a.
[0050] Select N particles in each aspect ratio range and measure the average value of their contour sizes as the typical representative particle contour in this range. That is, the aspect ratio of the contour ratio of the selected typical representative particle is (X1 + X2 + … X N ) / N, and X i (i ∈ (1~N)) takes values in the four aspect ratio ranges respectively.
[0051] In the embodiments of the present invention, four typical contours of green tea powder particles are selected through statistical analysis. The aspect ratio ranges of the particle contours are Ar > 3, 3 < Ar < 2, 2 < Ar < 1.5, and Ar < 1.5 respectively. To ensure the reliability and applicability of the selected particles, 8 particles are selected in each of the four ranges to measure their contour sizes, as shown in Table 1, and the average value of the particle contour sizes is calculated as the typical particle in this range. Through the measurement and analysis of the particles in the electron microscopy image, four representative particle shapes are determined, that is, the aspect ratios Ar are 3.45, 2.27, 1.73, and 1.27 respectively. The specific particle models are as Figure 4a shown.
[0052] Table 1 Aspect ratios of typical green tea powder particles
[0053]
[0054] As Figure 4bAs shown, on the basis of step 2, based on four typical particle contour aspect ratios Ar, the approximate shape of the particles is drawn using SOLIDWORKS software, and the mesh file of the particle contour is exported through ANSYS software, which is convenient for importing into EDEM (Engineering Discrete Element Method) software for particle filling. In EDEM software, the particle contour is filled as much as possible using a soft ball model according to the drawn particle contour.
[0055] Step 3: Carry out physical experiments and discrete element method simulations of tea powder particle accumulation at different grinding times, measure the angle of repose of the powder cone pile, and calibrate the interaction parameters of the tea powder particles in the simulation based on the change law of the angle of repose in the physical experiment.
[0056] Among them, a physical experiment of particle stacking was conducted on tea powder samples at different grinding times, and the repose angle θ of the powder cone pile was measured. i Specifically, the injection method was used to measure the angle of repose of green tea powder particles after accumulation at different grinding times. The funnel was placed on a bracket, and the bottom of the funnel was kept at a certain distance from the horizontal plane. Green tea powder was slowly added from the top of the funnel, so that the tea powder particles fell due to their own gravity. The tea powder particles leaked from the bottom of the funnel formed a conical accumulation on the horizontal plane. The inclination angle of the cone of the conical pile was the angle of repose. Each sample was repeated three times for experimental measurement, which was recorded as θ. i (i∈[1,6]), particle stacking experiment such as Figure 5 shown.
[0057] The results of the angle of repose of tea powder samples after stacking at different grinding times are shown in Table 2.
[0058] Table 2 Angle of repose after stacking of tea powder samples at different grinding times
[0059]
[0060]
[0061] Note: The above data are the mean ± standard deviation of three repeated experiments; different letters in the same column indicate significant differences between the data (p<0.05).
[0062] Among them, the DEM simulation was based on the accumulation of tea powder samples at different grinding times, and the repose angle θ of the powder cone pile was measured. j; Specifically, due to the possible adhesion and agglomeration between green tea powder particles, the Hertz-Mindlin model with JKR (Johnson-Kendall Roberts) was selected to simulate the particle stacking experiment considering the influence of van der Waals force in the contact area. Based on DEM, the hollow cylinder method was used to simulate the free fall motion of tea powder particles in the funnel, and the angle of repose of the simulated stacking experiment of tea powder particles under different grinding times was determined. The height-to-diameter ratio of the hollow cylinder used in the physical experiment and the simulation was h=6. Specifically, the diameter of the cylinder was 8mm and the height was 48mm. The particle factory was set on the top plane of the hollow cylinder to continuously produce particles until the cylinder was full, and then the cylinder was slowly pulled down from the base at a specific speed v to allow the tea powder particles to flow, as shown in FIG. Figure 6a As described above, as the cylinder rises, the tea powder particles form a conical pile under the action of gravity, such as Figure 6b As shown, in order to ensure the stable and slow falling accumulation of the green tea powder particles, a small lifting speed of 0.01 m / s was used in the simulation.
[0063] After the tea powder particles are accumulated, the angle of repose is measured using the slice function and angle measurement tool in the post-processing component of the EDEM software. Specifically, Figure 6c As shown in the figure, the cone pile structure of tea powder particles is obtained from the experiment and each DEM simulation. Slices are made in the four directions of AA, BB, CC, and DD, and then the repose angle values on both sides of each cone pile are measured. Note that the repose angle corresponds to the slope of the cone pile. Therefore, 8 repose angles can be obtained in each particle stacking simulation experiment. Each sample is simulated by DEM three times. The simulation result of each tea powder sample is determined by calculating the average of the eight measurement results, that is, the repose angle obtained by the simulated stacking of tea powder particles is recorded as θ j .
[0064] By changing the rolling friction coefficient A and static friction coefficient B of the tea powder particles in the DEM simulation, the repose angle θ results of different simulation test sets were obtained. The response surface diagram of the repose angle was drawn based on the repose angle results obtained from different simulation test sets. At the same time, the relationship function between the interaction parameters of the tea powder particles and the repose angle was calculated: θ = k1-k2A+k3B+k4AB+k5A 2 +k6B 2 .
[0065] In the embodiment of the present invention, the DEM simulation input parameters are first configured based on the tea powder sample of Sample 1. As shown in Table 3, the coefficient of restitution of the tea powder particle-device is set to 0.3, and the rolling friction coefficient and static friction coefficient of the particle-device are 0.05 and 0.5 respectively.
[0066] Table 3 DEM input parameters
[0067]
[0068] By changing the rolling friction and static friction coefficients of the particles in the DEM simulation model, the repose angle results of different simulation test sets are obtained, and the response surface of the repose angle is plotted, such as Figure 7a , Figure 7b The model and each term were tested for significance, as shown in Table 4.
[0069] Table 4 ANOVA of selected responses and inputs
[0070]
[0071] A: rolling friction coefficient; B: static friction coefficient
[0072] The results showed that the model was significant (P<0.0001) and the determination coefficient R 2 , Adjusted coefficient of determination and the predicted coefficient of determination They are 0.9851, 0.9821 and 0.9770 respectively, all close to 1, and the predicted determination coefficient Adjusted coefficient of determination Relatively consistent The predicted value fits the actual value well, and the model has high reliability. Adeq precision=55.0085>4, indicating that the model can better reflect the actual situation, with high accuracy and strong reliability.
[0073] The functional relationship between the contact parameters of tea powder and the angle of repose can be calculated:
[0074] θ=46.11-18.08A+4.58B+10.56AB+797.21A 2 +3.21B 2
[0075] Wherein, A represents the rolling friction coefficient of the tea powder particles, B represents the static friction coefficient of the tea powder particles, and θ represents the angle of repose.
[0076] Measure the value θ at a certain repose angle i As the goal, and with the range of interaction parameters as constraints, a single-objective multivariate optimization model is established;
[0077] In the embodiment of the present invention, the repose angle is a function of the contact parameters. For a given repose angle value, there may be an infinite combination of interaction parameters that match the response.
[0078] Based on the functional relationship between the angle of repose and the particle interaction parameters, in order to select appropriate DEM modeling parameters, the angle of repose θ obtained by Sample 1 (physical stacking experiment of green tea powder sample at a certain time) isi =51.78° as the target and the range of internal interaction parameters as the constraint, a single-objective diversified optimization model is established:
[0079] θ(A,B)=θ i
[0080] constraint:
[0081] Based on the single-objective multivariate optimization model, the solution set of rolling friction coefficient A and static friction coefficient B of the interaction parameters of tea powder particles is calculated {A z ,B z}.
[0082] In the embodiment of the present invention, by verifying the obtained multiple optimal solutions, it is found that when A=0.06 and B=0.55 are closest to the actual angle of repose, the simulated angle of repose values are 52°, 51.65° and 51.82°, respectively, with an average value of 51.82° and a standard deviation of 0.143. The comparative analysis of the results of the simulation verification experiment and the physical experiment stacking shows that for the Sample1 sample particles, the relative error of the obtained angle of repose is very small, only 0.42%. In addition, there is no significant difference in the shape of the particle pile in the two tests. Therefore, it can be determined that the static friction coefficient of the Sample1 sample particles is 0.55 and the rolling friction coefficient is 0.06.
[0083] However, the obtained parameter values can only satisfy the model of Sample 1. In order to verify the applicability of the obtained static friction coefficient and rolling friction coefficient, and to verify the accuracy of the obtained parameter set, the solution set was brought into the simulation model of the tea powder sample group under other grinding times, and the values of the repose angle obtained by the simulation were compared with the actual physical stacking experimental results to verify the applicability of the parameter A and B solution sets.
[0084] In the present embodiment, some of the particle parameters in Table 3 are input into the simulation model of other groups, and then a simulated stacking experiment is performed. Figure 6c The measurement method shown in the figure measures the repose angle values of each group of simulations. The repose angle values obtained by simulation are compared with the actual stacking test results. The results show that the static friction coefficient and rolling friction coefficient determined by calibration are suitable for simulation experiments of different sample groups, as shown in Table 5.
[0085] Table 5 Angle of repose of green tea powder physical stacking and simulated stacking experiments
[0086]
[0087] Note: The above data are the mean ± standard deviation of three repeated experiments; different letters in the same column indicate significant differences between the data (p<0.05). 1 It means physical stacking experiment;2 Represents a simulated stacking experiment.
[0088] It can be found that when the contact parameter solution set of the tea powder particles is input and calibrated, the repose angles of the physical and simulated particle accumulation are almost equal, and the error is very small. Therefore, it can be determined that the rolling friction coefficient of the green tea powder particles is 0.06 and the static friction coefficient is 0.55.
[0089] Step 4: Collect the grinding temperature, and combine the tea powder particle model with the calibrated tea powder particle interaction parameters. Through the coupling interface, build a computer fluid dynamics and discrete element method CFD-DEM coupling simulation model for simulating the tea powder grinding process. Analyze and calculate the mechanical properties of the particles: particle collision frequency, inter-particle interaction force, inter-particle heat transfer and particle motion behavior.
[0090] Taking the grinding of green tea powder by ball mill as an example, the grinding temperature information of the ball mill is collected by infrared temperature sensor. The parameters in discrete element simulation software EDEM and computational fluid dynamics software FLUENT are configured respectively based on the established tea powder particle model and the contact parameters of the tea powder particles obtained by calibration, such as Figure 8 The coupling interface is shown in the figure, and the CFD-DEM coupling simulation model is built based on the DPM coupling interface. Parameters need to be configured in EDEM and FLUENT respectively. The contact parameters of tea powder particles are configured in EDEM, and the contact model of particles is set.
[0091] In the embodiment of the present invention, the model is first set in EDEM Creator: a new particle material is created according to the properties of the tea powder particles in Table 3 and the contact parameters between the particles are set; then Figure 4a Import the shapes of the four GTP particles established in and set the Size Distribution according to the data obtained by the laser particle size analyzer. EDEM will automatically calculate the mass, volume and other properties of the particles, providing a calculation basis for the subsequent study of the particle motion characteristics of tea powder particles such as collision and crushing; import the established three-dimensional model of the ball mill and set the material of the ball mill to stainless steel and set the speed of the ball mill according to the simulation requirements (select "Add Rotation Kinematic" from "AddMotion" to define the initial speed and acceleration of the ball mill); establish a particle factory to generate the required particles; add corresponding contact models for different contacts. To ensure the update of particle temperature, add temperature update Temperature-Update to the particle body force in the Physics group. Configure parameters in FLUENT: import the mesh file of the three-dimensional model of the ball mill, set the time Time to transient, and set the gravity acceleration to -9.81m in the z-axis direction. 2 / s, define the material properties of the geometric body; set the unit area conditions and boundary conditions according to the actual experimental conditions, open the energy equation in the model setting and set the turbulence model to the standard k-epsilon. The present invention takes into account the influence of temperature parameters in the grinding process, and the temperature parameter information comes from the infrared temperature sensor. The temperature information is collected by connecting the sensor to the Mitsubishi PLC (programmable logic controller) through RS485 communication (Modbus RTU), and the Mitsubishi PLC is connected to the touch screen through a port to complete the collection and storage of temperature data. However, since the material of the ball mill is stainless steel, the outer wall of the ball mill needs to be coated when collecting the temperature to improve the measurement accuracy of the infrared temperature sensor. The infrared sensor collects data every 100ms. In order to ensure the accuracy of the collected temperature information, the sliding average filtering method is used to average the 10 sets of data collected continuously and then output and save them in the memory, and the final value is displayed on the touch screen, such as Figure 8 shown.
[0092] The most important step is to establish the coupling interface between EDEM and FLUENT, and enable the Eulerian model of multiphase flow and the discrete phase in the Model in FLUENT, such as Fig. 9 On the basis of the above, a coupled simulation model of green tea powder grinding process was established based on CFD-DEM to analyze and calculate the mechanical properties of particles: particle collision frequency, inter-particle interaction force, inter-particle heat transfer and particle motion behavior, providing a basis for the subsequent optimization of ball mill structure and process parameters.
[0093] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A numerical calculation method for tea grinding mechanical properties based on CFD-DEM thermal coupling, characterized in that The steps include: Step 1: Collect electron microscope images of green tea powder particle samples at different grinding times; Step 2: Establish a tea powder particle model by measuring the aspect ratio of the tea powder particles in the electron microscope image; Step 3: Physical experiments and discrete element method simulations of tea powder particle accumulation were performed at different grinding times, and the repose angle of the powder cone pile was measured. Based on the change law of the repose angle in the physical experiment, the interaction parameters of the tea powder particles in the simulation were calibrated; the interaction parameters include the rolling friction coefficient A and the static friction coefficient B. The relationship between the interaction parameters of the tea powder particles and the repose angle θ is expressed as follows: θ=k1-k2A+k3B+k4AB+k5A 2 +k6B 2 Among them, k1 to k6 are polynomial coefficients; Single-objective diversified optimization model: θ(A,B)=θ i constraint: Among them, θ i It represents the angle of repose in physical experiments; Based on the single-objective multivariate optimization model, the solution set of rolling friction coefficient A and static friction coefficient B of the interaction parameters of tea powder particles is calculated; Step 4: Collect the grinding temperature, and combine the tea powder particle model with the calibrated tea powder particle interaction parameters. Through the coupling interface, build a computer fluid dynamics and discrete element method CFD-DEM coupling simulation model to simulate the tea powder grinding process and study the mechanical properties of tea powder particles.
2. The method for numerically calculating the mechanical properties of tea grinding based on CFD-DEM thermal coupling according to claim 1, characterized in that: In step 2, a tea powder particle shape model is established by measuring the aspect ratio of the tea powder particles in the electron microscope image, and a statistical analysis is performed on the particle contour shape to simplify the particle shape model. Based on the simplified tea powder particle shape model, a typical contour model of the tea powder particles is constructed.
3. The method for numerically calculating the mechanical properties of tea grinding based on CFD-DEM thermal coupling according to claim 2, characterized in that: The construction of the typical contour model of the tea powder particles comprises the following steps: First, the electron microscope image of tea powder particles was preprocessed to extract the outline of green tea powder particles; the image was filled; the particle outline size was measured, and after statistical analysis, a group of main distribution ranges of the aspect ratio of the tea powder particle outline were obtained; Then, a plurality of tea powder particles are selected within each aspect ratio range and the average value of their outline dimensions is measured as the typical representative particle outline within the range.
4. The method for numerically calculating the mechanical properties of tea grinding based on CFD-DEM thermal coupling according to claim 1, characterized in that: The physical experiment in step 3 is to measure the angle of repose of green tea powder particles after accumulation at different grinding times by injection method. A funnel is placed on a bracket, and the bottom of the funnel is kept at a certain distance from the horizontal plane. Tea powder particles are slowly added from the top of the funnel to make the tea powder particles fall due to their own gravity. The tea powder particles leaking from the bottom of the funnel form a conical accumulation on the horizontal plane. The inclination angle of the cone of the conical pile is the angle of repose of the physical experiment.
5. The method for numerically calculating the mechanical properties of tea grinding based on CFD-DEM thermal coupling according to claim 1, characterized in that: The simulation in step 3 adopts the hollow cylinder method to simulate the free fall motion of tea powder particles in the funnel. The tea powder particles are filled into the hollow cylinder placed on the base, and then the cylinder is lifted at a specific speed so that the tea powder particles flow under the action of their own gravity to form a conical pile on the base. The cone pile is sliced and the angle is measured based on the discrete element method to obtain the repose angle of the simulation.
6. The method for numerical calculation of tea grinding mechanical properties based on CFD-DEM thermal coupling according to claim 1, characterized in that: In the step 3, the repose angles of different simulation test sets are obtained by changing the interaction parameters of the tea powder particles in the discrete element method simulation, and the response surface diagram of the repose angle is drawn based on the repose angles obtained from the different simulation test sets. At the same time, the relationship function between the interaction parameters of the tea powder particles and the repose angle is calculated. Taking the repose angle of the physical experiment as the target and the range of the interaction parameters as the constraint, a single-objective multivariate optimization model is established, and based on the single-objective multivariate optimization model, the interaction parameters of the tea powder particles are calculated.
7. The method for numerically calculating mechanical properties of tea grinding based on CFD-DEM thermal coupling according to claim 1, characterized in that: The solution set was brought into the simulation of tea powder particle samples at other grinding times, and the applicability of the solution set was verified by comparing the repose angle obtained from the simulation with the repose angle from the physical experiment.
8. The method for numerical calculation of tea grinding mechanical properties based on CFD-DEM thermal coupling according to claim 1, characterized in that: In step 4, a CFD-DEM coupling simulation model is built based on the DPM coupling interface, and parameters of the discrete element simulation software and the computational fluid dynamics software are configured respectively. The interaction parameters of the tea powder particles are configured in the discrete element simulation software, and the shape of the tea powder particles and the grinder information are imported to establish models for different interactions, and temperature updates are added for the tea powder particles; time, gravity, and material properties are configured in the computational fluid dynamics software.
9. The method for numerical calculation of tea grinding mechanical properties based on CFD-DEM thermal coupling according to claim 8, characterized in that: The multiphase flow model and discrete phase are enabled in the computational fluid dynamics software to establish a coupling interface; the mechanical properties of the particles are analyzed and calculated: particle collision frequency, inter-particle interaction force, inter-particle heat transfer, and particle motion behavior.