A model characterization and numerical simulation method for porous media of epiphytic fouling in farmland irrigation systems

Abstract

This invention discloses a method for characterizing and numerically simulating a porous media model of parasitic dirt in a farmland irrigation system. This method relates to the field of agricultural water conservancy technology and includes: nondestructive testing of parasitic dirt on emitters to obtain a permeable porous media model; based on this permeable porous media model, computational fluid dynamics methods are used to numerically simulate the flow within the farmland irrigation system under parasitic dirt conditions to determine the fluid flow characteristics within the system. This method innovatively considers parasitic dirt as a porous medium composed of numerous solid skeletons and interstitial pores. This method constructs a porous media model of parasitic dirt in emitters by combining nondestructive testing of dirt characteristics, CT scanning-inverse modeling techniques, and porous media modeling. A numerical simulation method based on this model is developed, demonstrating the advantages of high accuracy, reliable calculation results, and strong universality.

Description

Technical Field

[0001] The present invention relates to the field of agricultural water conservancy technology, and more particularly to a method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system. Background Art

[0002] Currently, emitter clogging is considered one of the major obstacles to the widespread application of drip irrigation technology. At the very least, it can significantly reduce the performance and efficiency of agricultural irrigation systems, and at worst, it can lead to the scrapping of entire irrigation systems. A key bottleneck lies in the unclear process and mechanism of emitter clogging. The internal structure of epiphytic fouling is complex and varied, primarily manifesting as a multi-component, physical, chemical, and biological compound fouling. Furthermore, the accumulation of fouling is significantly affected by hydrodynamic parameters. Increased water velocity increases near-wall shear forces, promoting the shedding of epiphytic biofilms. Low flow rates also reduce the accumulation of clogging material due to weaker adsorption and collision effects between water and the wall. In short, emitter clogging is a complex and unavoidable problem. Accurately characterizing the hydrodynamic parameters under which clogging material adheres to the emitter is crucial for understanding the clogging mechanism.

[0003] Numerous experts and scholars have systematically studied methods for analyzing the relationship between internal clogging materials and hydrodynamic parameters in emitters. For example, Li Yunkai et al. (Application Publication No. CN106096179A) from China Agricultural University disclosed a drip irrigation emitter flow channel structure design method and its fractal flow channel emitter product. This method uses a no-slip fixed boundary and combines it with average roughness to analyze hydrodynamic parameters such as shear force and flow velocity during internal flow, thereby improving the emitter's anti-clogging capability. Yu Liming et al. (Application Publication No. CN101667218) from the Beijing Hydraulic Science Research Institute disclosed an emitter anti-clogging design method that eliminates suspended particle accumulation sites within the flow channel. This method uses a computational fluid dynamics (CFD) solid-liquid two-phase flow model to characterize the interaction between particulate matter and hydrodynamic parameters. However, epiphytic fouling often consists of complex, irregularly shaped areas with internal pores. Both of these methods fail to account for the complexity of fouling structure and cannot accurately characterize the interaction between complex fouling morphology and hydrodynamic parameters.

[0004] Therefore, how to accurately describe the fluid flow characteristics under the porous media model of attached dirt in the farmland irrigation system is an urgent problem that technicians in this field need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a method for characterizing and numerically simulating a porous media model of epiphytic dirt in a farmland irrigation system. The permeable porous media model is used to characterize the internal clogging substances in the emitter flow channel, and a complete internal clogging substance testing and characterization method system is established to solve the problems existing in the background technology.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for characterizing and numerically simulating a porous media model of epiphytic fouling in a farmland irrigation system, comprising:

[0008] Non-destructive testing of emitter-attached fouling to obtain porosity, pore size distribution, and density of permeable porous media areas;

[0009] Construct a three-dimensional scanning model of the interior of attached dirt based on ultrasonic testing and CT scanning;

[0010] Based on the 3D scanning model of the interior of the attached dirt, the 3D morphology is reconstructed and the permeable porous medium model is obtained by combining the porosity, viscous resistance coefficient and inertial resistance coefficient.

[0011] Based on the permeable porous media model, with the help of computational fluid dynamics method, the internal flow characteristics of the farmland irrigation system under the condition of epiphytic fouling were obtained by numerical simulation.

[0012] Optionally, the non-destructive test includes a dirt internal porosity test, a pore size distribution test, and a density analysis of attached dirt.

[0013] Optionally, the internal porosity test of the dirt is based on the gas adsorption method. The capillary condensation phenomenon and the principle of volume equivalence are used to establish a capillary condensation model on the premise that the shape of the pore is cylindrical and tubular, and then the pore size distribution characteristics and pore volume of the sample are obtained; and then the porosity is analyzed and calculated.

[0014] Optionally, the pore size distribution test is performed according to the mercury intrusion method, by injecting mercury into the pores of the material at different pressures, measuring the volume of mercury at different pressures, and then calculating the pore size distribution based on the measured pore size and pore volume of the porous material.

[0015] Optionally, the density analysis of the attached dirt is performed based on energy spectrum analysis to analyze the composition of dry matter in the attached dirt, determine the element type and relative element content in the attached dirt, and then obtain the density of the permeable porous medium area.

[0016] Optionally, the specific process of constructing a three-dimensional scanning model of the interior of the attached dirt based on ultrasonic testing and CT scanning is as follows:

[0017] First, the software platform controls the 3D scanner to scan the sample's reflective reference points. This requires capturing 3D data from different angles, changing the object's placement, or adjusting the 3D scanner's camera direction to complete data acquisition by performing a full-scale scan of the object.

[0018] Secondly, using the automatic stitching method of the 3D scanner point cloud, after the sample is scanned, the system will automatically generate a 3D point cloud graphic of the sample to complete the data stitching and matching;

[0019] Finally, a three-dimensional scanning model of the interior of the attached dirt is constructed based on ultrasonic testing.

[0020] Optionally, the three-dimensional reconstruction specifically includes:

[0021] First, based on the internal 3D scanning model of the attached dirt, texture mosaicking is performed to convert the model data. The system software automatically converts the point cloud data directly into an STL file, and then the model is further repaired and improved using 3D software. Then, reverse engineering modeling of the CT 3D scanning point cloud data is completed in the 3D software, and finally a permeable porous medium model is obtained.

[0022] Optionally, porosity is determined using the following formula:

[0023]

[0024] Where: ρ1 and ρ2 are the densities of the blocking material and porous medium area, in kg / m 3 The density ρ1 of the blocking material area is obtained by the ratio of the mass to the volume of the blocking material area. The mass of the blocking material area is obtained by the dry weight test of the blocking material, and the volume is determined by the volume analysis function of the VG Studio software. The density ρ2 of the porous medium area is obtained by the conversion method. Determine, where: ρ i is the density of each component of the blocking material, in kg / m 3 ;p i is the percentage of each component of the blocking material; the element types and their content ratios in the dry matter of the blocking material are measured by an energy spectrometer.

[0025] Optionally, the viscous drag coefficient is determined using the following formula:

[0026]

[0027] The inertial drag coefficient is determined using the following formula:

[0028]

[0029] Where: ε is the porosity; D p is the equivalent diameter, in mm; the equivalent diameter of the blocking material area is defined by the average diameter of the particles inside the blocking material.

[0030] As can be seen from the above technical solutions, the present invention discloses a method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system, which has the following beneficial effects compared with the prior art:

[0031] 1. The present invention proposes a non-destructive testing method system for epiphytic dirt in farmland irrigation systems, and establishes an analytical system that integrates dirt porosity, void distribution, composition, density, and three-dimensional morphology testing, solving the problem of being unable to accurately obtain the characteristic parameters and three-dimensional morphology characteristics of epiphytic dirt.

[0032] 2. The present invention constructs a porous medium model of attached fouling, which fully considers the irregularities of the velocity and pressure of the fluid inside the porous medium, and determines the fluid flow characteristics from the perspective of defining relevant parameters. It has the advantages of accurate calculation, reliable results, and strong universality, and solves the problem of low accuracy of previous models.

[0033] 3. The present invention uses computational fluid dynamics methods to perform numerical simulation calculations on the internal flow of farmland irrigation systems under conditions of epiphytic fouling, thereby solving a number of problems such as complex internal structure, difficult observation, and difficulty in description. The system output results are true and reliable and can be used to guide production practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0035] Figure 1 The accompanying drawing is a flow chart of the method provided by the present invention;

[0036] Figure 2 The accompanying drawings are the surface diagrams created by the present invention;

[0037] Figure 3 The accompanying drawing is a schematic diagram of cross-sectional positions corresponding to the average hydrodynamic characteristics and differences provided by the present invention;

[0038] Figure 4a The accompanying drawing is a cloud diagram of velocity distribution characteristics in the hydrodynamic characteristics provided by the present invention;

[0039] Figure 4b The accompanying drawing is a cloud diagram of the pressure distribution characteristics in the hydrodynamic characteristics provided by the present invention;

[0040] Figure 4c The attached figure is a cloud diagram of the turbulent kinetic energy distribution characteristics in the hydrodynamic characteristics provided by the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] The embodiment of the present invention discloses a method for characterizing and numerically simulating a porous medium model of attached dirt in a farmland irrigation system. Figure 1 Shown, including:

[0043] Non-destructive testing of emitter-attached fouling to obtain porosity, pore size distribution, and density of permeable porous media areas;

[0044] Construct a three-dimensional scanning model of the interior of attached dirt based on ultrasonic testing and CT scanning;

[0045] Based on the 3D scanning model of the interior of the attached dirt, the 3D morphology is reconstructed and the permeable porous medium model is obtained by combining the porosity, viscous resistance coefficient and inertial resistance coefficient.

[0046] Based on the permeable porous media model, with the help of computational fluid dynamics method, the internal flow characteristics of the farmland irrigation system under the condition of epiphytic fouling were obtained by numerical simulation.

[0047] In a specific embodiment, the non-destructive test includes a porosity test inside the dirt, a pore size distribution test, and a density analysis of the attached dirt.

[0048] In a specific embodiment, the internal porosity test of the dirt is based on the gas adsorption method. The capillary condensation phenomenon and the principle of volume equivalence are used to establish a capillary condensation model under the premise that the shape of the pore is cylindrical and tubular, and then the pore size distribution characteristics and pore volume of the sample are obtained; and then the porosity is analyzed and calculated.

[0049] In a specific embodiment, the pore size distribution test is performed according to the mercury intrusion method, which is performed by injecting mercury into the pores of the material at different pressures, measuring the volume of mercury at different pressures, and then calculating the pore size and pore volume of the porous material.

[0050] In a specific embodiment, the density analysis of the entrained dirt is performed based on energy spectrum analysis to analyze the composition of the dry matter in the entrained dirt, determine the element type and relative element content in the entrained dirt, and then obtain the density of the permeable porous medium area.

[0051] In a specific embodiment, the specific process of constructing a three-dimensional scanning model of the interior of attached dirt based on ultrasonic detection and CT scanning is as follows:

[0052] First, the software platform controls the 3D scanner to scan the sample's reflective reference points. This requires capturing 3D data from different angles, changing the object's placement, or adjusting the 3D scanner's camera direction to complete data acquisition by performing a full-scale scan of the object.

[0053] Secondly, using the automatic stitching method of the 3D scanner point cloud, after the sample is scanned, the system will automatically generate a 3D point cloud graphic of the sample to complete the data stitching and matching; however, manual noise removal and smoothing of the scanned point cloud data is required later.

[0054] Finally, a three-dimensional scanning model of the interior of the attached dirt is constructed based on ultrasonic testing.

[0055] In a specific embodiment, the three-dimensional reconstruction is specifically:

[0056] First, based on the internal 3D scanning model of the attached dirt, texture mosaicking is performed to convert the model data. The system software automatically converts the point cloud data directly into an STL file, and then the model is further repaired and improved using 3D software. Then, reverse engineering modeling of the CT 3D scanning point cloud data is completed in the 3D software, and finally a permeable porous medium model is obtained.

[0057] In a specific embodiment, the porosity is measured using the following formula:

[0058]

[0059] Where: ρ1 and ρ2 are the densities of the blocking material and porous medium area, in kg / m 3 The density ρ1 of the blocking material area is obtained by the ratio of the mass to the volume of the blocking material area. The mass of the blocking material area is obtained by the dry weight test of the blocking material, and the volume is determined by the volume analysis function of the VG Studio software. The density ρ2 of the porous medium area is obtained by the conversion method. Determine, where: ρ i is the density of each component of the blocking material, in kg / m 3 ;p i is the percentage of each component of the blocking material; the element types and their content ratios in the dry matter of the blocking material are measured by an energy spectrometer.

[0060] In a specific embodiment, the viscous drag coefficient is determined using the following formula:

[0061]

[0062] The inertial drag coefficient is determined using the following formula:

[0063]

[0064] Where: ε is the porosity; D p is the equivalent diameter, in mm; the equivalent diameter of the blocking material area is defined by the average diameter of the particles inside the blocking material.

[0065] In a specific embodiment, the resulting permeable porous medium model is meshed, boundary conditions are set and a computational solver is selected, monitoring indicators and convergence accuracy are set and the model is initialized, and the data is post-processed after the simulation calculation is completed; the difference between the actual outlet flow (test flow) of the farmland irrigation system with attached fouling and the flow (simulated flow) obtained by numerical simulation is compared and analyzed to verify the accuracy of the inverse modeling method and the reliability of the test model; commonly used hydrodynamic characteristic indicators are selected to evaluate the water flow movement characteristics within the farmland irrigation system with attached fouling, and in order to quantify their impact values, typical sections are selected in the repeated structural units of the farmland irrigation system with attached fouling for data collection.

[0066] A specific example is introduced below to further explain the present invention.

[0067] This embodiment provides a method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system. The method is described with reference to an emitter in a biogas slurry underground drip irrigation system. The method includes the following steps:

[0068] Three drip irrigation pipes were selected from a field biogas slurry underground drip irrigation test platform. Each drip irrigation pipe was 30 meters long and the emitter spacing was 30 cm, with a total of 99 emitters. Emitter flow tests were conducted on each emitter. The percentage of the dynamic outflow of the emitter to the rated flow (i.e., 100% - CD) was calculated based on the blockage degree (CD) of each emitter. This was used as the standard for determining the different blockage degrees of the emitters. Usually, a single emitter flow rate of more than 95% of its initial flow rate is defined as no blockage, 80%-95% as slight blockage, 50%-80% as general blockage, 20%-50% as severe blockage, and less than 20% as complete blockage.

[0069] (1) Nondestructive testing of emitter attachment dirt

[0070] The mass of attached dirt was found to be (5.18±0.46)×10 -3 g, volume is (2.63±0.45) mm 3 , the difference between the two is the density (1.97±0.18) g / cm 3The composition of the dry matter in the attached dirt was determined by energy spectrum analysis, and the element types and relative element contents in the attached dirt were determined as shown in Table 1. The density of the permeable porous medium area was obtained to be 2.61 g / cm 3 .

[0071] Table 1 Composition and proportion of dry matter of epiphytic dirt in emitters

[0072]

[0073] Therefore, it is calculated that the porosity of the permeable porous medium area is 0.24.

[0074] Emitters with blockage levels of 10%, 20%, 30%, 40%, and 50% were selected for testing the spatial distribution characteristics of attached fouling. Three emitters were used for each blockage level, for a total of 15 emitters. After CT scanning, preliminary analysis using VGStudio software revealed that there was no significant difference in internal attached fouling between adjacent blockage levels. Therefore, emitters with blockage levels of 10%, 30%, and 50% were selected for reverse engineering modeling. This example uses a blockage level of 10% as an example; 30% and 50% are similar. The specific method is as follows:

[0075] The test used a microfocus industrial CT scanner for 3D non-destructive scanning of the original structure. After debugging, the instrument was optimized with the following scanning parameters: voltage 100kV, current 80mA, resolution 0.024mm, and detector pixel count 1500×1900. The device consists of a X-ray source, a sample stage, a detector, and a control system. The emitter sample was placed on the sample stage, and the X-ray source emitted X-rays to scan the emitter sample. The detector converted the X-rays that passed through the sample into visible light, which was then converted into an electrical signal by a photoelectric converter. The control system then input the resulting tomographic image of the emitter sample into a computer. Because the digital information contained in the 3D CT scan image is not suitable for direct analysis, the 3D model of the emitter sample was imported into the post-processing software VG Studio MAX 3.4 to identify and extract the emitter structural features contained in the image. Due to the difference in grayscale values ​​between the attached dirt inside the emitter and the flow channel material, the grayscale threshold of the 3D model was continuously adjusted to accurately segment the boundary between the two, thereby obtaining information on the spatial distribution of attached dirt within the emitter flow channel.

[0076] (2) Construction of a permeable porous media boundary model that can characterize blocking materials

[0077] The reverse modeling of the sprinkler is to process the point cloud data in Geomagic DesignX software, eliminate the sprinkler noise, smooth the point cloud to reduce the roughness of the point cloud's external shape, construct the surface, set the geometric shape capture accuracy to high, and keep the scanner accuracy as default; to reconstruct the coordinate system, you first need to create a base plane, then select the segmented area group, create each plane separately, and select the XYZ mode through "manual alignment", that is, a position point and three straight lines, the position point selects the intersection of the two planes, and the three straight lines select the straight lines perpendicular to each other with the position points; the patch repair and processing requires entering the patch template; through hole filling, fill the holes where the data is missing; smooth the model, adjust the smoothing degree to the middle position, set the intensity to maximum, set the allowable deviation to automatic, and let the unit point move within the allowable deviation range during the smoothing process; select automatic surface creation, select "uniformly distributed grid" on the patch, and set the "patch grid option" to automatic. At this time, you can adjust the cross position and finally complete the surface creation. Figure 2 As shown; Finally, the inverse modeling error analysis is performed. The deviation between the surface and the triangle is displayed in AccuracyAnalyzer(TM). The upper and lower limits of the deviation between the surface and the original data are set. The range within the allowable tolerance is indicated in green. Then, by placing the pointer on the green area, you can see whether the deviation value between the surface and the triangle is within the allowable range.

[0078] (2) Establishment and characterization of porous media models

[0079] The establishment of the porous media model of emitter attached dirt mainly requires three parameters: porosity, inertial resistance coefficient, and viscous resistance coefficient. The calculation method is as follows:

[0080] The porosity is determined by analyzing the fitting curve of the drip irrigation tape wall roughness. The specific calculation formula is as follows:

[0081]

[0082] Where:

[0083] ρ1 is the density of the rough boundary region, in kg / m 3 ;

[0084] ρ2 is the density of the porous medium region, in kg / m 3 .

[0085] The viscous resistance coefficient K and the inertial resistance coefficient C2 are calculated based on the porosity test results using the Ergun empirical formula. The calculation formula is as follows:

[0086]

[0087]

[0088] Where: ε is the porosity;

[0089] D p is the equivalent diameter in mm.

[0090] Equivalent diameter of blocking material (D p ) is formulated based on the fitting curve of the drip irrigation tape wall roughness.

[0091] The inertial drag coefficient and the viscous drag coefficient are obtained by analyzing the density of dirt attached to the emitter based on experimental data. The porosity ε is known, and D p = is the equivalent diameter of attached dirt, measured using a Malvern particle size analyzer. The average of three replicates was taken as the result, yielding a value of 9.74 μm. Finally, the Ergun empirical formula was used to determine the viscous and inertial resistance coefficients. In numerical simulations, the permeability coefficient of the permeable porous medium was calculated as 1 / K.

[0092] (3) Visual verification of internal flows

[0093] In ANSYS ICEM, the inlet, outlet, emitter wall, interface between the water area and the clogging material area, water area, and clogging material area of ​​the model were named "inlet," "outlet," "wall," "interface," "water," and "porous," respectively. The clogging material area was deleted, retaining only the water surface. The global size was set to 0.01, and the water area meshing was completed using a two-dimensional calculation. Quality metrics was selected to check the mesh quality. In this example, the number of meshes was 177,040, and the mesh quality was 0.409. The previous step was repeated to complete the meshing of the attached fouling, with the global size set to 0.005. The water domain mesh was opened, and the clogging material mesh was added to complete the assembly of the two meshes.

[0094] Complete the creation of water and biogas slurry phases in ANSYS FLUENT software, and set the biogas slurry density to 1019.75 kg / m 3 , viscosity is 0.0008158kg / m -s The Standard k-ε turbulence model was selected as the turbulence model, the Eulerian model was selected as the two-phase flow model, and the first phase was defined as water and the second phase as biogas slurry. The particle diameter of the biogas slurry was set to 0.0091595 mm. Under the unit area conditions, the porous medium region was set to porous, the porous zone and source terms were checked, the porosity was set to 0.24, and the inertial resistance coefficient (1.98×10 7 )m -1 , viscous resistance coefficient (1.51×10 -14 )m2 Under the boundary conditions, the corresponding interfaces between the two blocked material areas and the water area are selected. The inlet and outlet boundary definition type is pressure-inlet, the inlet pressure is set to 100 kPa, the outlet pressure is 1.766 kPa, and the biogas liquid volume fraction is 0.0588. The parameters of the convection term are all discretized as second-order upwind. The SIMPLE algorithm is used to process the coupling of pressure and velocity. The mass flow rate at the outlet is defined and monitored. The convergence accuracy is set to 10 -9 Initialization was completed by selecting Standard Initialization. The number of iterations was set to 10,000, and the simulation was performed. After the calculations were completed and the results converged, the CFD Post post-processing software generated a cloud map of the emitter's flow path and cross-sectional data.

[0095] Specifically, the porosity, inertial resistance coefficient and viscous resistance coefficient can be set in the following ranges: porosity is 0.2-0.3; inertial resistance coefficient is 0.5×10 -14 -3×10 -14 ; The viscous drag coefficient is 0.4×10 7 -3×10 7 ;

[0096] As long as the three parameters are within the above ranges, the method of the present invention can be implemented to obtain simulation calculation results.

[0097] To verify the accuracy of the inverse modeling method, the reliability of the model was tested by comparing the actual emitter outlet flow (test flow Qt) with the emitter flow obtained through numerical simulation (simulated flow Qs). The test flow refers to the actual outlet flow measured on a drip irrigation emitter blockage characteristic test platform; the simulated flow refers to the flow obtained by restoring the emitter to its original state with the blockage material attached to the 2D model and then performing a numerical simulation under clear water conditions with an inlet pressure of 100 kPa and an outlet pressure of 0 kPa. The measured test flow Qt was 1.93 L / h, and the simulated flow Qs was 2.05 L / h, with an average flow error of 6.21%, accurately representing the emitter's state after the blockage material adhered.

[0098] The numerical simulation uses four hydrodynamic characteristics, velocity, shear force, pressure, and turbulent kinetic energy, to evaluate the flow characteristics inside the emitter. In order to quantify their impact, a total of 34 typical sections were selected from the 33 repeating structural units of the emitter for data collection. The average hydrodynamic characteristics (velocity, shear force, pressure, and turbulent kinetic energy) and the corresponding cross-sectional locations of the differences are shown in Figure 2. Figure 3As shown in the figure (the red line position), the numbers are arranged in order from the water inlet to the water outlet. Figure 4a 、 Figure 4b 、 Figure 4c and as shown in Table 3.

[0099] Table 3 Average hydrodynamic characteristics and differences at cross sections

[0100]

[0101]

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0103] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for characterizing and numerically simulating a porous media model of epiphytic dirt in a farmland irrigation system, characterized in that: A method for characterizing internal clogging materials in emitter channels using a permeable porous media model was proposed, and a comprehensive system for testing and characterizing internal clogging materials was established. Specifically, the method includes: Non-destructive testing of emitter-attached fouling to obtain porosity, pore size distribution, and density of permeable porous media areas; Construct a three-dimensional scanning model of the interior of attached dirt based on ultrasonic testing and CT scanning; Based on the 3D scanning model of the interior of the attached dirt, the 3D morphology is reconstructed and the permeable porous medium model is obtained by combining the porosity, viscous resistance coefficient and inertial resistance coefficient. Based on the permeable porous media model, the computational fluid dynamics method is used to numerically simulate the internal flow of the farmland irrigation system under the condition of epiphytic fouling, and the internal fluid flow characteristics of the farmland irrigation system are obtained. Porosity is measured using the following formula: Where: ρ1 and ρ2 are the densities of the blocking material and porous medium area, in kg / m 3 The density ρ1 of the blocking material area is obtained by the ratio of the mass to the volume of the blocking material area. The mass of the blocking material area is obtained by the dry weight test of the blocking material, and the volume is determined by the volume analysis function of the VG Studio software. The density ρ2 of the porous medium area is obtained by the conversion method. Determine, where: ρ i is the density of each component of the blocking material, in kg / m 3 ;p i is the percentage of each component of the blocking material; the element types and their content ratios in the dry matter of the blocking material are measured by an energy spectrometer.

2. The method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system according to claim 1, characterized in that: The non-destructive test includes a dirt internal porosity test, a pore size distribution test, and a density analysis of attached dirt.

3. The method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system according to claim 2, characterized in that: The dirt internal porosity test is based on the gas adsorption method. The capillary condensation phenomenon and the principle of volume equivalence are used to establish a capillary condensation model under the premise that the shape of the pores is cylindrical and tubular, thereby obtaining the pore size distribution characteristics and pore volume of the sample; and then analyzing and calculating its porosity.

4. The method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system according to claim 2, characterized in that: The pore size distribution test is performed according to the mercury intrusion method, which involves injecting mercury into the pores of a material at different pressures, measuring the volume of mercury at different pressures, and then calculating the pore size and pore volume of the porous material.

5. The method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system according to claim 2, characterized in that: The density analysis of the attached dirt is performed based on energy spectrum analysis, which analyzes the composition of the dry matter in the attached dirt, determines the element type and relative element content in the attached dirt, and then obtains the density of the permeable porous medium area.

6. The method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system according to claim 1, characterized in that: The specific process of constructing a 3D scanning model of the interior of attached dirt based on ultrasonic testing and CT scanning is as follows: First, the software platform controls the 3D scanner to scan the sample's reflective reference points. This requires capturing 3D data from different angles, changing the object's placement, or adjusting the 3D scanner's camera direction to complete data acquisition by performing a full-scale scan of the object. Secondly, using the automatic stitching method of the 3D scanner point cloud, after the sample is scanned, the system will automatically generate a 3D point cloud graphic of the sample to complete the data stitching and matching; Finally, a three-dimensional scanning model of the interior of the attached dirt is constructed based on ultrasonic testing.

7. The method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system according to claim 6, characterized in that: The three-dimensional shape reconstruction is specifically as follows: First, based on the internal 3D scanning model of the attached dirt, texture mosaicking is performed to convert the model data. The system software automatically converts the point cloud data directly into an STL file, and then the model is further repaired and improved using 3D software. Then, reverse engineering modeling of the CT 3D scanning point cloud data is completed in the 3D software, and finally a permeable porous medium model is obtained.

8. The method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system according to claim 1, characterized in that: The viscous drag coefficient is determined using the following formula: The inertial drag coefficient is determined using the following formula: Where: ε is the porosity; D p is the equivalent diameter, in mm; the equivalent diameter of the blocking material area is defined by the average diameter of the particles inside the blocking material.

9. The method for characterizing and numerically simulating a porous medium model of epiphytic dirt in a farmland irrigation system according to claim 1, characterized in that: The porosity range is 0.2-0.3; the inertial resistance coefficient range is 0.5×10 -14 -3×10 -14 ; The range of viscous drag coefficient is 0.4×10 7 -3×10 7 .

Images