A method and system for structural optimization of a trash blower of a sugarcane harvester

Abstract

The application discloses a structure optimization method and system of a sugarcane harvester's impurity removal fan, and belongs to the technical field of agricultural equipment manufacturing. The method comprises the following steps: based on CFD numerical simulation, numerical simulation analysis of the internal flow field of the impurity removal fan and optimization of key components of the impurity removal fan are performed; discrete element model establishment and parameter calibration of sugarcane segments, sugarcane tails and sugarcane leaves are performed; based on CFD-DEM coupling simulation, fluid-solid coupling simulation analysis of the impurity removal process of the impurity removal fan is performed; based on CFD numerical simulation and fan test results, the aerodynamic performance of the fan before and after optimization is compared; based on CFD-DEM coupling simulation, the impurity removal effect of the fan before and after optimization is compared; and based on the comparison results, the optimization effect is verified, the optimization cost can be reduced, and the optimization effect can be improved.

Description

Technical Field

[0001] This invention relates to the field of agricultural equipment manufacturing technology, and in particular to a method and system for optimizing the structure of a cleaning fan for a sugarcane harvester. Background Technology

[0002] Sugarcane is the primary raw material for sugar production, and its cultivation is a vital source of income for farmers in major sugarcane-producing areas. However, traditional sugarcane planting and harvesting methods often rely heavily on manual labor, resulting in low efficiency and high costs, making them unsuitable for the demands of modern agricultural production. Therefore, exploring and promoting the effective application of agricultural mechanization technology in sugarcane cultivation is an important direction for improving planting efficiency and optimizing production inputs.

[0003] The application and development of sugarcane harvesters are limited by the still relatively high impurity content of sugarcane harvested mechanically. The impurity removal fan is a core component of sugarcane harvesters, directly affecting the impurity content of mechanized sugarcane, which in turn affects the selling price and the difficulty of subsequent sugar processing. Therefore, improving the performance of impurity removal fans in sugarcane harvesters is crucial. Currently, the structure of impurity removal fans in sugarcane harvesters is relatively fixed and lacks flexibility, leading to high costs for structural optimization and testing during equipment development. Summary of the Invention

[0004] This invention proposes a structural optimization method and system for the impurity removal fan of a sugarcane harvester to solve the problem of high costs associated with existing structural optimization and testing.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A structural optimization method for a sugarcane harvester's cleaning fan includes: numerical simulation analysis of the internal flow field of the cleaning fan and optimization of key components of the cleaning fan based on CFD numerical simulation; establishment and parameter calibration of discrete element models of sugarcane segments, sugarcane tails, and sugarcane leaves; fluid-structure interaction simulation analysis of the cleaning process of the cleaning fan based on CFD-DEM coupled simulation; comparison of the aerodynamic performance of the fan before and after optimization based on CFD numerical simulation combined with fan test results; comparison of the cleaning effect of the fan before and after optimization based on CFD-DEM coupled simulation; and verification of the optimization effect based on the comparison results.

[0007] Furthermore, the numerical simulation analysis of the internal flow field of the impurity removal fan includes: establishing a three-dimensional model of the impurity removal fan; analyzing the influence of fan speed on the distribution and motion of the flow field through CFD numerical simulation; and comparing the simulated and measured values ​​of fan flow rate, power and wind speed at different fan speeds based on fan tests to verify the accuracy of the simulation and the rationality of the impurity removal fan model.

[0008] Furthermore, the optimization of key components of the impurity removal fan includes: using the maximum outlet wind speed and total pressure efficiency of the fan as optimization indicators, performing multi-parameter combination optimization design on the air guide duct structure and impeller structure of the fan, and optimizing and improving the shape of the hub end cover; determining the optimal combination of each parameter to improve the aerodynamic performance of the impurity removal fan.

[0009] Furthermore, the establishment and parameter calibration of discrete element models for sugarcane segments, sugarcane tails, and sugarcane leaves include: obtaining physical parameters of sugarcane segments, sugarcane tails, and sugarcane leaves through physical experiments, obtaining contact parameters of sugarcane segments, sugarcane tails, and sugarcane leaves; and calibrating the contact parameters of sugarcane segments, sugarcane tails, and sugarcane leaves through angle of repose tests and inclined plane tests.

[0010] Furthermore, the fluid-structure interaction simulation analysis of the impurity removal process of the impurity removal fan based on CFD-DEM coupled simulation includes: simulating the actual working state of the impurity removal fan using the CFD-DEM coupled simulation method, comparing and analyzing the impurity removal effect of the impurity removal fan at different speeds; and comparing the coupled simulation results and field test results to verify the CFD-DEM coupling of the impurity removal fan.

[0011] Furthermore, the internal flow field of the impurity removal fan was simulated by CFD using the MRF model; the mesh was divided into a hybrid mesh with a mesh size of 4.5 × 10⁶; the inlet and outlet boundary conditions of the fan model were set as pressure inlet and outlet with atmospheric pressure; the boundary conditions of the wall were set as a no-slip wall; a second-order upwind discretization model was used; and the solution was obtained using the Simple algorithm.

[0012] Furthermore, the CFD-DEM coupling adopts the Euler-Euler coupling model; the field test is designed according to the performance test requirements of JB / T 6275-2007 "Test Methods for Sugarcane Harvesting Machinery"; the physical parameters include geometric dimensions and density; the contact parameters are obtained by consulting literature; and the fan test is designed with reference to GB / T10178-2006 Industrial Fan Field Performance Test Standard.

[0013] Furthermore, the optimization of the key components of the impurity removal fan includes: air guide ducts: the number of primary and secondary air guide ducts and their axial spacing; impeller: installation angle, hub radius, blade tip clearance, and blade width; hub end cap: end cap shape.

[0014] Furthermore, the optimization of key components of the impurity removal fan includes: reducing the number of primary air guide ducts from 5 to 4; reducing the number of secondary air guide ducts from 5 to 7; reducing the axial spacing from 45mm to 30mm; reducing the installation angle from 30° to 32°; reducing the hub radius from 85mm to 90.41mm; reducing the blade tip clearance from 25mm to 14.99mm; reducing the blade width from 196mm to 207mm; and optimizing the hub end cap from a flat end cap to a semi-circular streamlined end cap.

[0015] A structural optimization system for a sugarcane harvester's cleaning fan includes: a first module for numerical simulation analysis of the internal flow field of the cleaning fan and optimization of key components of the cleaning fan based on CFD numerical simulation; a second module for establishing and calibrating discrete element models of sugarcane segments, sugarcane tails, and sugarcane leaves; a third module for fluid-structure interaction simulation analysis of the cleaning process of the cleaning fan based on CFD-DEM coupled simulation; and a fourth module for comparing the aerodynamic performance of the fan before and after optimization based on CFD numerical simulation combined with fan test results, comparing the cleaning effect of the fan before and after optimization based on CFD-DEM coupled simulation, and verifying the optimization effect based on the comparison results.

[0016] By adopting the above technical solution, the present invention has the following beneficial effects:

[0017] 1. This invention uses CFD numerical simulation to perform numerical simulation analysis of the internal flow field of the impurity removal fan and optimize key components of the fan; it establishes discrete element models and calibrates parameters for sugarcane segments, sugarcane tails, and sugarcane leaves; it uses CFD-DEM coupled simulation to perform fluid-structure interaction simulation analysis of the impurity removal process of the fan; it compares the aerodynamic performance of the fan before and after optimization based on CFD numerical simulation combined with fan test results; it compares the impurity removal effect of the fan before and after optimization based on CFD-DEM coupled simulation; and it verifies the optimization effect based on the comparison results. This invention can reduce optimization costs and improve optimization effectiveness. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a structural optimization method for a cleaning fan in a sugarcane harvester proposed in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1The method for structural optimization of a cleaning fan in a sugarcane harvester includes: S1, performing numerical simulation analysis of the internal flow field of the cleaning fan and optimizing key components of the cleaning fan based on CFD numerical simulation; S2, establishing and calibrating discrete element models of sugarcane segments, sugarcane tails, and sugarcane leaves; S3, performing fluid-structure interaction simulation analysis of the cleaning process of the cleaning fan based on CFD-DEM coupled simulation; S4, comparing the aerodynamic performance of the fan before and after optimization based on CFD numerical simulation combined with fan test results, comparing the cleaning effect of the fan before and after optimization based on CFD-DEM coupled simulation, and verifying the optimization effect based on the comparison results.

[0021] Current research on impurity removal systems for sugarcane harvesters, both domestically and internationally, mainly focuses on the analysis of fan airflow characteristics, optimization of operating parameters, and structural optimization of the impurity removal system. Research primarily employs experimental methods or numerical simulations to optimize the structure and evaluate the performance of impurity removal fans, with CFD numerical simulation being the most widely used method for analyzing the internal flow field of the fan. However, as a core component of the impurity removal system in sugarcane harvesters, the impurity removal fan has a relatively complex internal structure. Existing research has explored the structural design of key components such as impellers, guide ducts, and air outlets, and their impact on fan performance to some extent, but several shortcomings remain: Firstly, most studies focus on optimizing single structural parameters, neglecting the potential coupling relationships between multiple structural parameters within the impurity removal fan, and lacking discussion on the coupling effects between multiple structural parameters. Secondly, in terms of impurity removal performance evaluation, existing research relies more on aerodynamic characteristics such as wind speed and air volume, while paying insufficient attention to core evaluation indicators such as impurity removal efficiency and impurity content, lacking systematic performance evaluation methods. Furthermore, some studies have failed to effectively combine numerical simulations with experimental data, resulting in low feasibility of the research results in practical applications.

[0022] (1) Numerical simulation analysis of the internal flow field of the impurity removal fan. A three-dimensional model of the impurity removal fan was established, and the influence of the fan speed on the distribution and motion of the flow field was analyzed by CFD numerical simulation method. Based on the fan test, the simulated values ​​and measured values ​​of fan flow rate, power and wind speed under different fan speeds were compared and analyzed, which verified the accuracy of CFD numerical simulation method and the rationality of the impurity removal fan model, and provided a theoretical basis for the optimization of the impurity removal fan.

[0023] (2) Optimization study of key components of the impurity removal fan. The CFD numerical simulation method was used to optimize the key components of the impurity removal fan. The maximum outlet wind speed and the total pressure efficiency of the fan were used as optimization indicators. The air guide duct structure and impeller structure of the fan were optimized by multiple parameters, and the shape of the hub end cover was optimized and improved. The optimal combination of each parameter was determined, which improved the aerodynamic performance of the impurity removal fan.

[0024] (3) Establishment and parameter calibration of discrete element models for sugarcane segments, sugarcane tails, and sugarcane leaves. Physical parameters such as geometric dimensions and density of sugarcane segments, sugarcane tails, and sugarcane leaves were obtained through physical experiments, and contact parameters of sugarcane segments, sugarcane tails, and sugarcane leaves were obtained by consulting literature. The contact parameters of sugarcane segments, sugarcane tails, and sugarcane leaves were calibrated through angle of repose tests and inclined plane tests.

[0025] (4) Fluid-structure interaction simulation analysis of the impurity removal process of the impurity removal fan. The CFD-DEM coupled simulation method was used to simulate the actual working state of the impurity removal fan, and the impurity removal effect of the impurity removal fan at different speeds was compared and analyzed. Field tests were also conducted, and the accuracy of the CFD-DEM coupled model of the impurity removal fan was verified by comparing and analyzing the CFD-DEM coupled simulation results with the field test results.

[0026] (5) Compare the performance of the impurity removal fan before and after optimization. Use CFD numerical simulation combined with fan test results to compare the aerodynamic performance of the fan before and after optimization; use CFD-DEM coupled simulation to compare the impurity removal effect of the fan before and after optimization. Verify the effectiveness of the fan optimization design.

[0027] The numerical simulation analysis of the internal flow field of the impurity removal fan includes: establishing a three-dimensional model of the impurity removal fan; analyzing the influence of fan speed on the distribution and motion of the flow field using CFD numerical simulation methods; and comparing the simulated and measured values ​​of fan flow rate, power and wind speed at different fan speeds based on fan tests to verify the accuracy of the simulation and the rationality of the impurity removal fan model.

[0028] CFD (Computational Fluid Dynamics) calculations are a technique that uses numerical methods to simulate phenomena such as fluid flow, heat transfer, and mass transfer. CFD calculations help engineers predict various phenomena during fluid flow processes. Through reasonable physical models and numerical methods, CFD can provide crucial technical support in the design phase, reducing the costs of experimentation and trial and error. It is widely used in many fields, including agriculture, aerospace, automotive, and chemical engineering.

[0029] The establishment of a numerical simulation model for the flow field of the impurity removal fan includes:

[0030] Construction and preprocessing of the physical model:

[0031] A 1:1 scale model of the wind turbine was created in the 3D modeling software SolidWorks (the wind turbine can be the target wind turbine that needs to be optimized). During the modeling process, some detailed features and complex internal structures were appropriately simplified. These details do not directly affect the overall performance of the wind turbine in the subsequent numerical simulation and optimization analysis.

[0032] Selection of mathematical model:

[0033] The airflow inside a dust collector fan is turbulent, and the flow field near the impeller exhibits strong rotational flow. For the analysis of rotating machinery, it is essential to properly delineate the dynamic and static boundaries. Therefore, the entire computational domain is divided into five parts: the inlet / outlet region, the fluid region, the fan casing wall, the impeller wall, and the impeller rotation region. Since dust collector fans are rotating machinery, choosing an appropriate mathematical model is crucial when performing CFD numerical calculations on rotating machinery, as the internal flow field typically involves complex flow characteristics. The MRF (Multiple Reference Frame) model is a commonly used model for calculating the fluid characteristics of rotating machinery. This model simplifies the solution process by introducing multiple reference frames, and as a steady-state calculation model, it avoids the need for simulation time steps, thereby improving computational efficiency. The internal flow field of the dust removal fan is a steady-state fluid, so the MRF model is used for CFD numerical simulation. The principle is to assume that the impeller is stationary and let the flow field rotate around the impeller, thereby transforming the unsteady problem into a steady problem for calculation, thus improving computational efficiency. For fluid machinery such as dust removal fans, the boundary layer effect is significant, and the internal flow field of the fan usually has complex turbulent characteristics. The standard k-ε model can handle the behavior of the boundary layer well. By introducing turbulent kinetic energy k and turbulent dissipation rate ε, the description of turbulence is simplified. Compared with more complex turbulence models, the standard k-ε model has a smaller computational load, is simpler to calculate, and can provide relatively accurate calculation results.

[0034] Mesh generation and boundary condition determination:

[0035] Mesh generation is a crucial step in CFD numerical simulation, determining the accuracy and efficiency of the computational results. Mesh generation is the process of discretizing the computational domain into a series of small cells. Through meshing, the fluid continuum is transformed into discrete elements, on which physical quantities such as fluid motion and heat transfer can be calculated. A well-designed mesh can improve computational accuracy, ensure numerical stability, and effectively shorten computation time. Selecting an appropriate mesh type and density, and optimizing mesh quality based on computational requirements and the complexity of the flow field, is essential for efficient and accurate fluid simulation. Mesh generation is mainly divided into three types: structured meshes, unstructured meshes, and hybrid meshes. Structured meshes typically use square or cubic elements with a regular arrangement, suitable for simple geometries and offering high computational efficiency. Unstructured meshes, on the other hand, are more flexible, often using triangular, tetrahedral, or hexahedral elements, suitable for complex geometries, but computation is more complex and relatively less efficient. Hybrid meshes combine the advantages of both structured and unstructured meshes, typically using structured meshes in simple areas such as fluid inlets and outlets, and unstructured meshes in complex areas, thus achieving a balance between computational efficiency and accuracy. Because the structure of the debris removal fan is relatively complex, a more adaptable unstructured mesh is used for mesh generation. To ensure computational accuracy, the mesh near the impeller is refined; to conserve computer resources, the mesh is sparser further away from the blades. To ensure the accuracy of the calculation results and prevent errors caused by meshes that are too coarse or too fine, mesh independence verification is performed before computation.

[0036] The verification results show that when the grid number is less than 4 × 10⁶, the evaluation indicators such as wind speed and flow rate change significantly with the increase of the grid number. This indicates that at this stage, the change in the grid number has a significant impact on the calculation results, and a sparser grid cannot accurately capture the complex flow field information inside the wind turbine. However, when the grid number is greater than 4 × 10⁶, the evaluation indicators tend to stabilize. Further increasing the grid number results in smaller changes in the calculated wind speed and flow rate. This indicates that the current grid number is sufficient to accurately describe the flow field inside the wind turbine. While further increasing the grid number can improve the calculation accuracy to some extent, it will significantly increase the calculation time and computational resource consumption. Considering both the accuracy of the results and the calculation time, a grid number of 4.5 × 10⁶ was determined for subsequent calculations.

[0037] The results of the optimized simulation and the experiment / test were compared. The error between the simulated value and the measured value was less than the acceptable threshold, such as 10%.

[0038] The optimization of key components of the impurity removal fan includes: using the maximum outlet wind speed and total pressure efficiency of the fan as optimization indicators, performing multi-parameter combination optimization design on the air guide duct structure and impeller structure of the fan, and optimizing and improving the shape of the hub end cover; determining the optimal combination of each parameter to improve the aerodynamic performance of the impurity removal fan.

[0039] Although there are many studies on the structural optimization of dust collector fans, research on multi-parameter combination optimization of the fan's air guide duct structure, as well as parameter combination optimization of the impeller's tip clearance, blade width, blade installation angle, and hub radius, remains relatively scarce. Therefore, this study uses the maximum outlet wind speed and total pressure efficiency of the fan as optimization indicators to conduct multi-parameter combination optimization design of the fan's air guide duct structure and impeller structure, and to optimize and improve the shape of the hub end cap; determining the optimal combination of each parameter to improve the aerodynamic performance of the dust collector fan.

[0040] The process of establishing and calibrating discrete element models of sugarcane segments, sugarcane tails, and sugarcane leaves includes: obtaining physical parameters of sugarcane segments, sugarcane tails, and sugarcane leaves through physical experiments; obtaining contact parameters of sugarcane segments, sugarcane tails, and sugarcane leaves through angle of repose tests and inclined plane tests; and calibrating the contact parameters of sugarcane segments, sugarcane tails, and sugarcane leaves through angle of repose tests and inclined plane tests.

[0041] The Discrete Element Method (DEM) is a numerical method used to simulate and analyze the motion and interactions of particulate materials.

[0042] The sugarcane material, including segments, tops, and leaves, was collected after being cut by the target blower. The density of the segments, tops, and leaves was determined using the drainage method and density formula, and their diameter, length, and width were measured.

[0043] Determination of material mechanical properties: Mechanical properties include Poisson's ratio, elastic modulus, coefficient of restitution, static friction coefficient, and dynamic friction coefficient. By consulting the literature, we can find the Poisson's ratio and elastic modulus of sugarcane segments, sugarcane tails, and sugarcane leaves; the coefficient of restitution, static friction coefficient, and dynamic friction coefficient between sugarcane and steel plates; and the coefficient of restitution, static friction coefficient, and dynamic friction coefficient between sugarcane segments.

[0044] The angle of repose test is one of the important methods for calibrating DEM simulation parameters. It is used to determine the contact mechanical parameters between particles, such as the static friction coefficient, rolling friction coefficient, and coefficient of restitution. By obtaining the true angle of repose of the granular material through the test, and adjusting the simulation parameters to make the simulated angle of repose consistent with the experimental results, the contact parameters can be accurately calibrated.

[0045] The fluid-structure interaction simulation analysis of the impurity removal process of the impurity removal fan based on CFD-DEM coupling simulation includes: simulating the actual working state of the impurity removal fan using the CFD-DEM coupling simulation method, comparing and analyzing the impurity removal effect of the impurity removal fan at different speeds; and comparing the coupling simulation results and field test results to verify the CFD-DEM coupling of the impurity removal fan.

[0046] CFD-DEM coupling is a numerical simulation technique that combines computational fluid dynamics (CFD) and discrete element method (DEM) to study the interaction between fluid and particle systems. There are two main coupling models in CFD-DEM coupling simulation: the Euler-Lagrange coupling model and the Euler-Euler coupling model. The Euler-Euler coupling model, which considers both the force exerted by the fluid on the particles and the reaction force exerted by the particles on the fluid, can more accurately simulate the interaction between fluid and particles. Therefore, the Euler-Lagrange coupling model is chosen for CFD-DEM coupling simulation calculations.

[0047] The internal flow field of the impurity removal fan was simulated by CFD using the MRF model. The mesh was divided into a hybrid mesh with a mesh size of 4.5 × 10⁶. The inlet and outlet boundary conditions of the fan model were set as pressure inlet and outlet with atmospheric pressure. The boundary conditions of the wall were set as no-slip wall. A second-order upwind discretization model was used. The solution was obtained by the Simple algorithm.

[0048] Boundary condition settings are crucial for CFD calculations, directly impacting the accuracy of the results. Their selection is based on flow physics characteristics, fluid type, experimental data, and engineering requirements. The inlet and outlet boundary conditions for the fan model are set as pressure inlets and outlets. In actual operation, the fan is connected to the outside atmosphere, hence the pressure inlets and outlets are set as pressure inlets and outlets, with all pressure values ​​at atmospheric pressure, to realistically simulate the pressure balance between the fan and the outside atmosphere, making the simulated airflow more closely resemble reality. The wall boundary conditions are set as no-slip walls. In actual fans, there is friction between the solid walls (such as the casing) and the fluid. The no-slip wall condition assumes that the fluid velocity at the wall is the same as the wall velocity, and the fluid velocity at a stationary wall is zero. This condition accurately describes the fluid flow behavior near the wall. A second-order upwind discretization model is used. The second-order upwind discretization scheme has higher accuracy than the first-order scheme, more accurately capturing the convection and diffusion characteristics of the airflow, reducing numerical diffusion errors, and thus more accurately calculating the distribution of physical quantities such as velocity and pressure within the fan. The Simple algorithm is used for solving the problem. This algorithm is a commonly used pressure-velocity coupling algorithm. By iteratively solving the pressure correction and velocity equations, it can effectively handle the coupling relationship between pressure and velocity in incompressible fluids and has good convergence and stability.

[0049] The CFD-DEM coupling adopted the Euler-Euler coupling model; the field test was designed according to the performance test requirements of JB / T 6275-2007 "Test Methods for Sugarcane Harvesting Machinery"; the physical parameters included geometric dimensions and density; and the contact parameters were obtained by consulting literature.

[0050] Field Trial Methods: The field trial was designed according to the performance test requirements of JB / T 6275-2007 "Test Methods for Sugarcane Harvesting Machinery". The performance test was conducted in an area with good sugarcane growth and uniform planting density. First, a stable zone, a testing zone, and a parking zone were demarcated. The testing zone was 5m long, with the stable zone in front of it and the parking zone behind it. Before the trial, unevenly growing sugarcane was manually removed to ensure uniform plant spacing. A 3mm×3mm mesh polyethylene net with a circumference of 2m and a length of 2m was installed at the blower outlet to collect sugarcane leaf impurities. The sugarcane segments falling from below the blower were collected using the collection frame provided with the sugarcane harvester.

[0051] The optimization of key components of the impurity removal fan includes: air guide ducts: the number of primary and secondary air guide ducts and their axial spacing; impeller: installation angle, hub radius, blade tip clearance, and blade width; hub end cap: end cap shape.

[0052] The optimization of key components of the impurity removal fan includes: reducing the number of primary air guide slots from 5 to 4; reducing the number of secondary air guide slots from 5 to 7; reducing the axial spacing from 45mm to 30mm; reducing the installation angle from 30° to 32°; reducing the hub radius from 85mm to 90.41mm; reducing the blade tip clearance from 25mm to 14.99mm; reducing the blade width from 196mm to 207mm; and optimizing the hub end cap from a flat end cap to a semi-circular streamlined end cap.

[0053] A structural optimization system for a sugarcane harvester's cleaning fan includes: a first module for numerical simulation analysis of the internal flow field of the cleaning fan and optimization of key components of the cleaning fan based on CFD numerical simulation; a second module for establishing and calibrating discrete element models of sugarcane segments, sugarcane tails, and sugarcane leaves; a third module for fluid-structure interaction simulation analysis of the cleaning process of the cleaning fan based on CFD-DEM coupled simulation; and a fourth module for comparing the aerodynamic performance of the fan before and after optimization based on CFD numerical simulation combined with fan test results, comparing the cleaning effect of the fan before and after optimization based on CFD-DEM coupled simulation, and verifying the optimization effect based on the comparison results.

[0054] CFD-DEM simulation model establishment:

[0055] First, the wind turbine model to be calculated is imported into ANSYS software. Model preprocessing, mesh generation, and steady-state wind field calculations are performed on the wind turbine model, following the same process as in Chapter 2. Second, based on the steady-state calculation results, the blade rotation domain Frame Motion is converted to Mesh Motion, transforming the steady-state calculation into a transient calculation. The meshed wind turbine model file is then imported into EDEM software. Discrete element models of the sugarcane segments, tails, and leaves, as well as the steel plate material, are set according to the calibrated contact parameters. The CFD solver FLUENT and the DEM solver EDEM are connected via a coupling interface, using a Lagrange (DPM) coupled model. Drag model, lift model, and time step are set. In EDEM post-processing, mass sensors are added at the wind turbine outlet and inlet to collect data on the mass of sugarcane discharged from the outlet and to calculate the mass of any remaining undischarged sugarcane. Based on the statistical results, the impurity content is calculated using a formula to analyze the impurity removal effect of the wind turbine under various operating conditions.

[0056] The structure of key components of the fan was optimized using CFD numerical simulation, and the impurity removal process of the impurity removal fan was simulated using CFD-DEM coupled simulation. The aerodynamic performance and impurity removal effect of the fan before and after optimization were compared and analyzed.

[0057] The results include:

[0058] Establishment and verification of numerical simulation model for impurity removal fan:

[0059] A numerical simulation model of the internal flow field of a dust removal fan was established, and a fan test was designed to verify the effectiveness of the CFD numerical simulation method. Based on the results of the CFD simulation and the fan test, the flow rate and power at fan speeds of 1400 r / min, 1600 r / min, and 1800 r / min, as well as the wind speed at specified measurement points, were compared. The results show that the errors between the simulated flow rate and power and the measured values ​​are all less than 10%. The trends of the simulated and measured wind speeds at the specified measurement points are basically consistent. This verifies the rationality of the fan numerical simulation model and the accuracy of the CFD numerical simulation method, providing a theoretical basis for the optimized design of the fan structure.

[0060] Optimized design of key components of the impurity removal fan:

[0061] The air guide duct structure and impeller structure of the impurity removal fan were optimized using CFD numerical simulation, and the shape of the hub end cover was improved. First, the air guide duct structure was optimized. Single-factor analysis determined the optimal range for the air guide duct structural parameters (number of primary air guide ducts, number of secondary air guide ducts, and axial spacing between air guide ducts). Based on the single-factor analysis results, response surface analysis was performed on the three parameters, ultimately determining the optimal combination of air guide duct structural parameters as: 4 primary air guide ducts, 7 secondary air guide ducts, and an axial spacing of 30 mm. Second, the impeller structure was optimized. Single-factor analysis determined the optimal range for the impeller structural parameters (blade installation angle, blade width, hub radius, and blade tip clearance). Based on the single-factor analysis results, response surface analysis was performed on the four parameters, ultimately determining the optimal combination of impeller structural parameters as: a blade installation angle of 32°, a hub radius of 99.41 mm, a blade tip clearance of 14.99 mm, and a blade width of 207.24 mm. Finally, the shape of the hub end cover was improved. By comparing and analyzing the effects of flat end caps, conical end caps, spherical end caps, and semi-circular streamlined end caps on the flow field distribution characteristics, a semi-circular streamlined end cap was ultimately selected. After overall optimization, the maximum outlet velocity of the fan is 40.02 m / s, and the total pressure efficiency is 76.45%, representing an increase of 23.74% in maximum outlet velocity and 14.95% in total pressure efficiency compared to the original fan.

[0062] Establishment of discrete element models for sugarcane segments, sugarcane tails, and sugarcane leaves, and calibration of contact parameters:

[0063] The density and geometric parameters of sugarcane segments, sugarcane tails, and sugarcane leaves were determined through physical experiments. Their contact parameters were determined by consulting literature, and discrete element model (DEM) models of sugarcane segments, sugarcane tails, and sugarcane leaves were established. Secondly, the contact parameters of the DEM models were calibrated using a combination of angle-of-repose experiments and orthogonal experiments, as well as inclined plane experiments and regression model prediction. The calibration results for the sugarcane material's coefficient of restitution, static friction coefficient, and dynamic friction coefficient were obtained: sugarcane segment-sugarcane segment 0.3170, 0.1428, 0.019; sugarcane segment-steel plate 0.2364, 0.1428, 0.0154; sugarcane tail-sugarcane tail 0.3708, 0.486, 0.0512; sugarcane tail-steel plate 0.2298, 0.244, 0.0756. The parameters for sugarcane leaf-sugarcane leaf are 0.0015, 0.4944, and 0.0636; sugarcane leaf-steel plate are 0.0043, 0.192, and 0.0084; sugarcane segment-sugarcane tail are 0.3031, 0.3797, and 0.0725; sugarcane segment-sugarcane leaf are 0.0043, 0.4729, and 0.0495; and sugarcane tail-sugarcane leaf are 0.0043, 0.4709, and 0.0587. These parameters provide accurate parameters for CFD-DEM coupled simulation calculations.

[0064] Establishment and verification of CFD-DEM coupled simulation model of impurity removal fan:

[0065] A coupled simulation model for impurity removal by a blower was established, and a field trial was designed to verify the effectiveness of the CFD-DEM coupled simulation method. Based on the results of the CFD-DEM coupled simulation experiment and the field trial, the impurity content of sugarcane after impurity removal by the blower was compared and analyzed at blower speeds of 1400 r / min, 1600 r / min, and 1800 r / min. The results show that the error between the simulated and experimental values ​​of the impurity content at different speeds is less than 1.7%. This verifies the accuracy of the CFD-DEM coupled simulation method and the rationality of the coupled model for impurity removal by the blower, providing theoretical support for further comparison of the impurity removal performance of the blower.

[0066] Comparison of fan performance before and after optimization:

[0067] Based on CFD numerical simulation, the maximum outlet velocity and total pressure efficiency of the blower before and after optimization were compared. The results show that the total pressure efficiency of the optimized blower increased by 14.95% compared to the unoptimized blower, and the maximum outlet velocity increased by 23.74%. Based on blower tests, the outlet velocity and flow rate of the blower before and after optimization were compared at a blower speed of 1800 r / min. The results show that the outlet velocity of the optimized blower increased by an average of 6.27 m / s compared to the unoptimized blower, and the flow rate increased by 2.87 m³ / s. Based on CFD-DEM coupled simulation, the impurity removal effect of the blower before and after optimization was compared. The results show that when the blower speed is 1800 r / min, the impurity content of the sugarcane after impurity removal by the original blower is 9.63%, while the impurity content of the sugarcane after impurity removal by the optimized blower is 5.86%, a reduction of 3.77%. Therefore, the optimized blower significantly improves both aerodynamic performance and impurity removal performance, further verifying the optimization effect of the impurity removal blower.

[0068] The innovations include:

[0069] (1) Optimized design of the air guide duct structure of the impurity removal fan

[0070] This study is the first to investigate the structural parameters of the air guide ducts in a dust removal fan—the number of primary air guide ducts, the number of secondary air guide ducts, and their axial spacing—and to complete an optimized design combining multiple parameters. Compared to the original fan, the optimized fan showed an 8.84% increase in maximum outlet velocity and an 8.39% increase in total pressure efficiency. This optimized scheme, compared to traditional designs, effectively improves the uniformity of the fan's flow field and enhances its aerodynamic performance without altering the overall fan structure. This optimization method fills a gap in research on the structural optimization of air guide ducts in dust removal fans, providing a new perspective and theoretical basis for the field of fan design.

[0071] (2) Optimization design of impeller structure of impurity removal fan

[0072] This paper presents the first systematic combined optimization study on key parameters of a dust removal fan impeller—blade installation angle, hub radius, tip clearance, and blade width. Compared to the unoptimized fan, the optimized fan achieved a 14.90% increase in maximum outlet velocity and a 6.54% increase in total pressure efficiency. This parameter optimization effectively improves the fan's aerodynamic performance. This optimization strategy provides a new design approach for enhancing the performance of dust removal fans.

[0073] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. A method for optimizing the structure of a cleaning fan in a sugarcane harvester, characterized in that, include: Based on CFD numerical simulation, numerical simulation analysis of the internal flow field of the impurity removal fan and optimization of key components of the impurity removal fan are carried out. Discrete element models of sugarcane segments, sugarcane tails, and sugarcane leaves were established and their parameters were calibrated. Based on CFD-DEM coupled simulation, a fluid-structure interaction simulation analysis of the impurity removal process of the impurity removal fan is carried out. Based on CFD numerical simulation combined with wind turbine test results, the aerodynamic performance of the wind turbine before and after optimization was compared. Based on CFD-DEM coupled simulation, the impurity removal effect of the wind turbine before and after optimization was compared. The optimization effect was verified based on the comparison results.

2. The structural optimization method for the impurity removal fan of the sugarcane harvester according to claim 1, characterized in that, The numerical simulation analysis of the internal flow field of the impurity removal fan includes: A three-dimensional model of the impurity removal fan was established, and the influence of the fan speed on the distribution and motion of the flow field was analyzed using CFD numerical simulation. Based on the fan test, the simulated values ​​and measured values ​​of fan flow rate, power and wind speed at different fan speeds were compared to verify the accuracy of the simulation and the rationality of the impurity removal fan model.

3. The structural optimization method for the impurity removal fan of the sugarcane harvester according to claim 2, characterized in that, The optimization of the key components of the impurity removal fan includes: Using the maximum outlet wind speed and total pressure efficiency of the fan as optimization indicators, the fan's air guide duct structure and impeller structure are optimized by multiple parameters, and the shape of the hub end cover is also optimized and improved. Determine the optimal combination of parameters to improve the aerodynamic performance of the impurity removal fan.

4. The structural optimization method for the impurity removal fan of the sugarcane harvester according to claim 3, characterized in that, The process of establishing and calibrating discrete element models and parameters for sugarcane segments, sugarcane tails, and sugarcane leaves includes: Physical parameters of sugarcane segments, sugarcane tails, and sugarcane leaves are obtained through physical experiments. Obtain contact parameters for sugarcane segments, sugarcane tails, and sugarcane leaves; The contact parameters of sugarcane segments, sugarcane tails, and sugarcane leaves were calibrated through the angle of accumulation test and the inclined plane test.

5. The structural optimization method for the impurity removal fan of the sugarcane harvester according to claim 4, characterized in that, The fluid-structure interaction simulation analysis of the impurity removal process of the impurity removal fan based on CFD-DEM coupled simulation includes: The CFD-DEM coupled simulation method was used to simulate the actual working state of the impurity removal fan, and the impurity removal effect of the impurity removal fan at different speeds was compared and analyzed. The results of coupled simulation and field test were compared to verify the CFD-DEM coupling of the impurity removal fan.

6. The structural optimization method for the impurity removal fan of the sugarcane harvester according to claim 5, characterized in that, The internal flow field of the impurity removal fan was simulated by CFD using the MRF model. The grid is divided into a hybrid grid with a grid size of 4.5 × 10⁻⁶. 6 ; The inlet and outlet boundary conditions of the wind turbine model are set as pressure inlet and outlet, with pressure values ​​of atmospheric pressure. The boundary conditions of the wall are set to no-slip wall; A second-order upwind discrete model is adopted; The solution is obtained using the Simple algorithm.

7. The structural optimization method for the impurity removal fan of the sugarcane harvester according to claim 6, characterized in that, The CFD-DEM coupling adopts the Euler-Euler coupling model. The field test was designed according to the performance test requirements of JB / T 6275-2007 "Test Methods for Sugarcane Harvesting Machinery"; The physical parameters include geometric dimensions and density; By reviewing the contact parameters described in the literature; The fan test was designed in accordance with GB / T10178-2006 Industrial Fan Field Performance Test Standard.

8. The method for optimizing the structure of the impurity removal fan of the sugarcane harvester according to claim 7, characterized in that, The optimization of the key components of the impurity removal fan includes: Air guide ducts: the number of primary and secondary air guide ducts, and their axial spacing; Impeller: Installation angle, hub radius, blade tip clearance, blade width; Hub end cap: End cap shape.

9. The method for optimizing the structure of the impurity removal fan of the sugarcane harvester according to claim 8, characterized in that, The optimization of the key components of the impurity removal fan includes: The number of primary air ducts has been reduced from 5 to 4; The number of secondary air guide ducts has been optimized from 5 to 7; The axial spacing has been optimized from 45mm to 30mm; The installation angle has been optimized from 30° to 32°; The wheel radius has been optimized from 85mm to 90.41mm; The blade tip clearance was optimized from 25mm to 14.99mm; The blade width has been optimized from 196mm to 207mm; The wheel hub end cap has been optimized from a flat end cap to a semi-circular streamlined end cap.

10. A structural optimization system for a cleaning fan in a sugarcane harvester, characterized in that, include: The first module is used for numerical simulation analysis of the internal flow field of the impurity removal fan and optimization of key components of the impurity removal fan based on CFD numerical simulation. The second module is used to establish discrete element models and calibrate parameters for sugarcane segments, sugarcane tails, and sugarcane leaves; The third module is used for fluid-structure interaction simulation analysis of the impurity removal process of the impurity removal fan based on CFD-DEM coupled simulation. The fourth module is used to compare the aerodynamic performance of the fan before and after optimization based on CFD numerical simulation combined with fan test results. It also compares the impurity removal effect of the fan before and after optimization based on CFD-DEM coupled simulation, and verifies the optimization effect based on the comparison results.

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