A method and platform for optimizing the design of a submersible mixer

Abstract

The application provides a submersible mixer optimization design method and an optimization design platform, and the method comprises the following steps: three-dimensional modeling, numerical simulation, automatic three-dimensional modeling, automatic numerical simulation, and optimization design variable; the three-dimensional modeling software and the finite element analysis software are integrated by using the Isight optimization platform to build the submersible mixer optimization design integrated platform, the submersible mixer is optimized and designed based on the DOE sampling and the numerical optimization mixed strategy, and the automatic cyclic modeling, numerical analysis and optimization process are realized through the Isight optimization platform. The submersible mixer optimization design platform can reasonably select the mixer model, the installation form and the pool size according to the needs, and the operation effect of more than half the effort is achieved. The application greatly reduces the rebound caused by the collision between the jet flow and the pool wall by using the effective axial advancing distance method, causes the backflow, vortex and other conditions, and improves the accuracy of the detection performance.

Description

Technical Field

[0001] This invention belongs to the field of submersible mixer technology, and particularly relates to an optimization design method and optimization design platform for submersible mixers. Background Technology

[0002] Submersible mixers, as a new type of high-efficiency submersible mixing and flow-generating device, are commonly used in wastewater treatment plants and industrial processes to mix liquids containing suspended solids. Their function is to homogenize the mixture and suspend the suspended solids from the bottom. They are also used in various pools and oxidation ditches to generate strong, low-tangential flows, creating water flow and promoting water circulation within the pool through mixing. Furthermore, they are used in landscape maintenance equipment to create water flow through mixing, thereby improving water quality, increasing the oxygen content in the water, and effectively suspending suspended solids from the bottom.

[0003] The flow field characteristics of a mixing tank are influenced by multiple factors, primarily including the hub ratio, the installation position and angle of the mixer, blade clearance, and blade placement angle. In practical engineering applications, the shape of the tank, the inlet and outlet locations, and the collisions between the jet and the tank walls, which can cause rebounds, backflow, and vortices, must be fully considered. These factors directly affect the efficiency, effectiveness, and stability of the entire mixing system. To minimize short-circuit circulation and dead zones, and to avoid impacts with the tank that reduce fluid velocity, an optimized design method and platform for submersible mixers are needed. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a submersible mixer optimization design method and platform. Utilizing the Isight optimization platform, which integrates 3D modeling software and finite element analysis software, an integrated platform for submersible mixer optimization design is established. The internal flow field characteristics of the submersible mixer are studied, and the submersible mixer is optimized based on a hybrid strategy of DOE sampling and numerical optimization. The Isight optimization platform enables automated cyclic modeling, numerical analysis, and optimization processes. Using this submersible mixer optimization design platform, the mixer model, installation method, and pool size can be rationally selected according to needs, achieving twice the result with half the effort.

[0005] The present invention achieves the above-mentioned technical objectives through the following technical means.

[0006] A submersible mixer optimization design method includes the following steps:

[0007] Step S1, 3D Modeling: Determine the basic structure of the submersible mixer, initially establish a 3D model of the submersible mixer and a 3D model of the water tank in the 3D modeling software, place the submersible mixer model in the 3D model of the water tank, and adjust the position of the submersible mixer model according to the shape of the water tank.

[0008] Step S2, Numerical Simulation: Import the submersible mixer model from Step S1 into the finite element analysis software to obtain the average flow velocity v-average and effective axial propulsion distance Ly inside the water tank of the submersible mixer model;

[0009] Step S3, Automatic 3D Modeling: Integrate the 3D modeling software from Step S1 into the Isight software. Input the modeling process parameters from the 3D modeling software in Step S1 into the Isight software for integration. The modeling process parameters include A, B, C, AY, AZ, LX, and LW. Among them, parameter A is the distance from the rotation center of the submersible mixer impeller to the side wall of the pool, parameter B is the distance from the rotation center of the submersible mixer impeller to the bottom wall of the pool, parameter C is the distance from the back of the submersible mixer to the mounting wall. A, B, and C are defined by the distance of the submersible mixer in the pool along the xyz coordinate system. AY is the submersible mixer relative to the mounting wall in the pool. AZ is the tilt angle of the submersible mixer to the adjacent pool wall in the pool. AY and AZ are defined by the angle of rotation around the y-axis and the angle of rotation around the z-axis, respectively. LX is the length of the mounting wall of the submersible mixer, and LW is the depth of the pool.

[0010] Step S4, Automatic Numerical Simulation: Integrate the finite element analysis software from Step S2 into the Isight software. Input the average flow velocity v-average and effective axial propulsion distance Ly inside the water tank of the submersible mixer model obtained from the finite element analysis software in Step S2 into the Isight software for integration. Perform automatic modeling using the modeling process parameters input in Step S3. Import the newly created model into the finite element analysis software and perform numerical simulation on the submersible mixer blade rotation speed, average flow velocity v-average inside the water tank, and effective axial propulsion distance Ly. Extract the input and output parameters of each model group for visualization.

[0011] Step S5: Optimize Design Variables: Using the Isight software integrated in Step S4, the average flow velocity v-average and effective axial advance distance Ly extracted in Step S4 are analyzed using a hybrid strategy of DOE sampling and numerical optimization. The DOE sampling method, number of sample points, design variables, and post-processing objectives are set. Then, the optimization algorithm, design variables, constraints, and optimization objectives are set, with the average flow velocity v-average and effective axial advance distance Ly as the optimization objectives. The design variables include A, B, C, AY, AZ, LX, and LW. Design variables are selected as needed, and optimization analysis is performed to maximize the average v-average and effective axial advance distance Ly within the pool.

[0012] In the above scheme, Creo Parametric 6.0 is selected as the 3D modeling software in step S1.

[0013] In the above scheme, the finite element analysis software selected in step S2 is ANSYS Workbench 2020.

[0014] Furthermore, in step S2, ANSYS Workbench 2020 finite element analysis software is selected, specifically including the following steps:

[0015] S21. Perform polyhedral unstructured mesh generation on the water body and impeller of the pool;

[0016] S22. The contact surface between the submersible mixer and the water tank is densified, specifically including densifying the impeller blades, the impeller water body, and the contact surface between the impeller and the water tank.

[0017] S23. The submersible mixer model uses Realizable k-ε, and the near-wall surface uses Scalable WallFunctions.

[0018] S24. The impeller water body is set as the rotating region, the pool water body is set as the stationary region, and the residual convergence accuracy is set to 0.0001.

[0019] S25. Perform steady-state simulation on the computational domain of the submersible mixer and output the velocity flow field analysis results of the submersible mixer, including: the average flow velocity v-average inside the pool and the effective axial advance distance Ly.

[0020] In the above scheme, step S5, which employs a hybrid strategy of DOE sampling and numerical optimization, integrates the DOE algorithm with the parameter optimization algorithm using the Task Plan component in the Isight software, including the following steps:

[0021] S51. In the Isight software, uniformly sample the design space using the DOE component to obtain the most effective design area in the entire design space. Select the Optimal Latin Hypercubic sampling method and set the number of sample points.

[0022] S52. In the Isight software, select the design variables;

[0023] S53. In the Isight software, through the General page of the Optimization component, select the NLPQLP optimization algorithm, and on the Factors page, use the optimal solution of DOE as the initial position point for optimization, and set the upper and lower limits of design variables A, B, C, AY, AZ, LX, LW, and rotorvelocity respectively.

[0024] S54. In the Isight software, through the Constents page of the Optimization component, set the constraints of the optimization model, and set the upper and lower limits of the effective axial advance distance Ly respectively. Through the Objectives page of the Optimization component, set the optimization objectives to maximize the average v-average inside the pool and maximize the effective axial advance distance Ly.

[0025] Furthermore, the specific selection of design variables in step S52 is as follows:

[0026] If only the appropriate mixing tank size needs to be selected, the design variables are set to LX and LW; if only the appropriate mixer model needs to be selected, the design variables are set to rotorvelocity; if only the appropriate installation method needs to be selected, the design variables are set to A, B, C, AY, and AZ. If the appropriate mixing tank size, mixer model, and installation method need to be selected simultaneously, the design variables are set to A, B, C, AY, AZ, LX, LW, and rotorvelocity. The upper and lower limits and initial values ​​are set respectively, and then the post-processing objectives are set to maximize v-average and maximize the effective axial advance distance Ly.

[0027] In the above scheme, the constraints in step S5 include: 100mm≤A≤1000mm; 100mm≤B≤750mm; 200≤C≤500mm; 0≤AY≤90°; 0≤AZ≤90°.

[0028] In the above scheme, the constraints in step S5 also include: 1800mm≤LX≤2000mm; 1000mm≤LW≤1500mm; 2500mm≤LZ≤5000mm.

[0029] In the above scheme, the constraints in step S5 also include:

[0030] 1.60m≤Ly≤2.3m; 800rpm≤rotorvelocity≤1500rpm.

[0031] A platform for implementing the submersible mixer optimization design method includes an automatic 3D modeling module, an automatic numerical simulation module, and an optimization design variable module;

[0032] The automatic 3D modeling is used to integrate 3D modeling software with Isight software. The modeling process parameters of the 3D modeling software are input into Isight software for integration. The modeling process parameters include A, B, C, AY, AZ, LX, and LW. Among them, parameter A is the distance from the rotation center of the submersible mixer impeller to the side wall of the pool, parameter B is the distance from the rotation center of the submersible mixer impeller to the bottom wall of the pool, parameter C is the distance from the back of the submersible mixer to the mounting wall, A, B, and C are defined by the distance of the submersible mixer in the pool along the xyz coordinate system, AY is the submersible mixer in the pool relative to the mounting wall, AZ is the tilt angle of the submersible mixer to the adjacent pool wall, AY and AZ are defined by the angle of rotation around the y-axis and the angle of rotation around the z-axis, respectively, LX is the length of the mounting wall of the submersible mixer, and LW is the depth of the pool.

[0033] The automatic numerical simulation is used to integrate finite element analysis software with Isight software. The average flow velocity v-average and effective axial propulsion distance Ly inside the water tank of the submersible mixer model obtained from the finite element analysis software are input into Isight software for integration. Automatic modeling is performed using the input modeling process parameters. The newly created model is then imported into the finite element analysis software to perform numerical simulations on the submersible mixer blade rotation speed, average flow velocity v-average inside the water tank, and effective axial propulsion distance Ly. The input and output parameters of each model are extracted for visualization.

[0034] The optimized design variables are used to extract the average flow velocity (v-average) and effective axial propulsion distance (Ly) inside the pool using the integrated Isight software. A hybrid strategy of DOE sampling and numerical optimization is adopted, setting the DOE sampling method, number of sample points, design variables, and post-processing objectives. Then, the optimization algorithm, design variables, constraints, and optimization objective function are set, with the average flow velocity (v-average) and effective axial propulsion distance (Ly) inside the pool as the optimization objectives. The design variables include A, B, C, AY, AZ, LX, and LW. Design variables are selected as needed and optimization analysis is performed to maximize the average flow velocity (v-average) and effective axial propulsion distance (Ly) inside the pool.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] This invention, through the effective axial propulsion distance method, greatly reduces the rebound caused by the collision between the jet and the pool wall, which can lead to backflow, vortices, and other issues, thereby improving the accuracy of detection performance.

[0037] This invention utilizes the Isight software optimization platform, integrating Creo Parametric 6.0 3D modeling software and ANSYS Workbench 2020 finite element analysis software, to build an integrated platform for the optimization design of submersible mixers. It studies the internal flow field characteristics of the submersible mixer and optimizes its design based on a hybrid strategy of DOE sampling and numerical optimization. The Isight optimization platform enables automated cyclic modeling, numerical analysis, and optimization processes. Using this submersible mixer optimization design platform, the mixer model, installation method, and pool size can be rationally selected according to needs, achieving twice the result with half the effort.

[0038] This invention can identify the optimal mixer model, installation method, and tank size based on requirements. During each cycle of analysis, the design parameter input and performance parameter output can be displayed in real time through Isight software, facilitating monitoring by designers and saving a significant amount of repetitive labor during the optimization of flow components. The submersible mixer optimization design platform initially built based on Isight software has significant practical value in improving the efficiency, effectiveness, and stability of the entire mixing and processing system. Attached Figure Description

[0039] Figure 1 This is a data flow diagram of an optimized submersible mixer based on Isight software according to one embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of the impeller water body according to one embodiment of the present invention;

[0041] Figure 3 This is a diagram showing the position of the submersible mixer according to one embodiment of the present invention in a water tank where A=900, B=C=500, and AZ=AY=0;

[0042] Figure 4 This is a front view of a submersible mixer according to an embodiment of the present invention in a water tank;

[0043] Figure 5 This is a side view of a submersible mixer according to an embodiment of the present invention in a water tank;

[0044] Figure 6 This is a schematic diagram of the interface built by integrating Creo Parametric 6.0 and ANSYS Workbench 2020 with the Isight software according to one embodiment of the present invention.

[0045] Figure 7 This is a design variable diagram set according to requirements in one embodiment of the present invention. Detailed Implementation

[0046] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0047] like Figure 1 The diagram shows the data flow chart for submersible mixer optimization based on the Isight software. According to the framework structure of the submersible mixer optimization design platform, the data flow chart for submersible mixer optimization is constructed, and the Isight optimization platform is used to integrate 3D modeling software and finite element analysis software.

[0048] In one embodiment of this invention, the Isight optimization platform integrates Creo Parametric 6.0 3D modeling software and ANSYS Workbench 2020 finite element analysis software. Parametric modeling is key to optimized design. This invention uses Creo Parametric 6.0 3D modeling software for hydraulic design of the submersible mixer. This is a 3D CAD software for product design with an ANSYS Workbench 2020 interface, allowing the 3D submersible mixer water volume to be directly imported into ANSYS Workbench 2020 for calculation. This invention presents a new method for evaluating the performance of submersible mixers—the effective axial thrust distance method. This method constrains the effective axial thrust distance while finding its maximum value. Specifically, it reduces the collision between the jet and the pool wall by constraining the effective distance ly of the submersible mixer's axial thrust on the water within the effective working area of ​​the water flow (maintaining a flow velocity greater than or equal to 0.3 m / s), while ensuring that the average flow velocity inside the pool is ≥0.1 m / s. This invention, through the effective axial propulsion distance method, greatly reduces the rebound caused by the collision between the jet and the pool wall, which can lead to backflow, vortices, and other issues, thereby improving the accuracy of detection performance.

[0049] This invention utilizes the Isight software optimization platform, integrating Creo Parametric 6.0 3D modeling software and ANSYS Workbench 2020 finite element analysis software, to build an integrated platform for the optimization design of submersible mixers. It studies the internal flow field characteristics of the submersible mixer and optimizes its design based on a hybrid strategy of DOE sampling and numerical optimization. The Isight optimization platform enables automated cyclic modeling, numerical analysis, and optimization processes. Using this submersible mixer optimization design platform, the mixer model, installation method, and pool size can be rationally selected according to needs, achieving twice the result with half the effort.

[0050] In one embodiment of this invention, a three-bladed submersible mixer and a cuboid water tank are used as the hydraulic performance optimization research objects. The core idea is to use the submersible mixer parameters before optimization as the initial condition, the effective axial propulsion distance as the constraint condition, and maximizing the average flow velocity inside the water tank as the objective. Parametric modeling is performed using Creo Parametric 6.0 3D modeling software. The submersible mixer model is imported into ANSYS Workbench 2020 finite element analysis software. Numerical analysis is performed using the FluidFlow (Fluent) module. The submersible mixer is optimized based on a hybrid strategy of DOE sampling and numerical optimization. The optimal solution of the design variable DOE is obtained through DOE sampling calculation. Optimization is then performed, using the optimal solution of the design variable DOE as the initial position point for optimization. The Isight optimization platform is used to automatically iterate through the modeling, numerical analysis, and optimization processes. Once a set of optimal key geometric parameter values ​​for the submersible mixer is obtained, the iteration stops, and the optimization is complete.

[0051] Directly using geometric parameters as design variables for complex three-dimensional mixers leads to an excessive number of parameters and prolonged computation time. Therefore, appropriately reducing the number of design parameters significantly impacts the efficiency of optimization design. The purpose of optimization design is to satisfy the expected value of the objective function and find the optimal value of the design variables within their range. Therefore, correctly and appropriately determining the design variables, constraints, and optimization objectives of the submersible mixer model, and establishing the objective function, is fundamental to successful optimization design. This invention selects A, B, C, AY, AZ, LX, LW, and rotorvelocity as design parameters.

[0052] A submersible mixer optimization design method includes the following steps:

[0053] 3D Modeling: Determine the basic structure of the submersible mixer, and initially establish a 3D model of the submersible mixer and the water tank in 3D modeling software. Place the submersible mixer model in the 3D model of the water tank, and adjust the position of the submersible mixer model according to the shape of the water tank.

[0054] Specifically: Determine the basic structure of the submersible mixer, and create an optimized 3D model of the submersible mixer in Creo Parametric 6.0 3D modeling software, such as... Figure 2 The image shows the water volume of the impeller. The impeller has 3 blades and an outer diameter of 100 mm. Figure 3The diagram shows the submersible mixer positioned in a water tank at A = 900mm, B = C = 500mm, and AZ = AY = 0. The water tank is rectangular, with dimensions of 2500mm (length), 1800mm (width), and 1000mm (height). (If the water tank is spherical, the coordinate system is changed to cylindrical coordinates. This platform can be applied to water tanks of any shape.) The resulting file, t1.txt, records the Creo Parametric 6.0 design process.

[0055] In one embodiment of the present invention, a three-blade submersible mixer and a cuboid water tank are used as examples. The initial values ​​of the main parameters are shown in Table 1:

[0056] Table 1: Overview of submersible mixer parameters before optimization

[0057]

[0058]

[0059] Based on the above parameters, the design variables, optimization objectives, and constraints for the optimal mathematical model of the submersible mixer can be established as follows:

[0060] X=[A, B, C, AY, AZ, LX, LW, rotorvelocity]

[0061] Maxf1 = v-average

[0062] Maxf2 = Ly

[0063] 100mm≤A≤1000mm; 100mm≤B≤750mm; 200≤C≤500mm; 0≤AY≤90°; 0≤AZ≤90°;

[0064] 1800mm≤LX≤2000mm; 1000mm≤LW≤1500mm; 2500mm≤LZ≤5000mm;

[0065] 1.60m≤Ly≤2.3m; 800rpm≤rotorvelocity≤1500rpm;

[0066] Where A is the distance from the impeller rotation center of the submersible mixer to the side wall of the pool, B is the distance from the impeller rotation center of the submersible mixer to the bottom wall of the pool, and C is the distance from the back of the submersible mixer to the mounting wall. A, B, and C are defined by the distance of the submersible mixer in the pool along the xyz coordinate system, respectively. AY is the submersible mixer in the pool relative to the mounting wall, and AZ is the tilt angle of the submersible mixer to the adjacent pool wall. AY and AZ are defined by the angle of rotation around the y-axis and the angle of rotation around the z-axis, respectively. LX is the length of the mounting wall of the submersible mixer; LW is the pool depth; LZ is the length of the adjacent pool wall; rotorvelocity is the rotational speed of the submersible mixer blades; v-average is the average flow velocity inside the pool; Ly is the effective axial propulsion distance; Maxf1 and Maxf2 are the optimization objective functions of the submersible mixer.

[0067] Numerical simulation: The submersible mixer model was imported into the finite element analysis software to obtain the average flow velocity v-average and effective axial propulsion distance Ly inside the water tank of the submersible mixer model.

[0068] Specifically, a submersible mixer model was imported into ANSYS Workbench 2020 finite element analysis software. Numerical analysis was performed using the Fluid Flow (Fluent) module to study the internal flow field characteristics of the three-bladed submersible mixer. A mesh was used to create a polyhedral unstructured mesh for the water in the pool and the impeller, with finer meshing applied to the impeller blades and the contact surface between the impeller and the water. Steady-state simulations were performed on the computational domain of the submersible mixer model. The turbulence model used was Realizable k-e, and Scalable Wall Functions were used near the wall. The impeller water was set as the rotating region, and the pool water as the stationary region. The residual convergence accuracy was set to 0.0001. The velocity-flow field analysis results were extracted from the submersible mixer and used as the output file for integrated optimization.

[0069] Automatic 3D Modeling: Integrate the 3D modeling software from step S1 into the Isight software. Input the modeling process parameters from the 3D modeling software in step S1 into the Isight software for integration. The modeling process parameters include A, B, C, AY, AZ, LX, and LW. Wherein, A is the distance from the rotation center of the submersible mixer impeller to the side wall of the pool, B is the distance from the rotation center of the submersible mixer impeller to the bottom wall of the pool, C is the distance from the back of the submersible mixer to the mounting wall, AY is the submersible mixer relative to the mounting wall in the pool, AZ is the tilt angle of the submersible mixer to the adjacent pool wall in the pool, LX is the length of the mounting wall of the submersible mixer, and LW is the depth of the pool.

[0070] Automatic numerical simulation: The finite element analysis software from step S2 is integrated into the Isight software. The average flow velocity v-average and effective axial propulsion distance Ly inside the water tank of the submersible mixer model obtained from the finite element analysis software in step S2 are input into the Isight software for integration. Automatic modeling is performed using the modeling process parameters input in step S3. The newly created model is then imported into the finite element analysis software to perform numerical simulations on the submersible mixer blade rotation speed, average flow velocity v-average inside the water tank, and effective axial propulsion distance Ly.

[0071] Specifically, in the Isight software, such as Figure 4 The diagram shows the interface built using Isight software integrating Creo Parametric 6.0 and ANSYS Workbench 2020. The Simcode and ANSYS Workbench 2020 components are used to perform flow field analysis and performance prediction within the submersible mixer tank using numerical simulation methods. This connects the calculation programs to drive Creo Parametric 6.0 3D modeling software and ANSYS Workbench 2020 finite element analysis software, enabling automatic iterative modeling, numerical analysis, and optimization. The steps include:

[0072] S41. Create the batch command RunCreo.bat to run the software:

[0073] "E:\Creo6.0\Creo 6.0.2.0\Parametric\bin\parametric.bat"t1.txt

[0074] Configure the Simcode component. On the Command page, click the Find Program… button and select the batch command RunCreo.bat in the corresponding path. The Simcode component uses batch commands to automatically perform cyclic modeling using the file-driven Creo Parametric 6.0 through the RunCreo.bat script.

[0075] S42. Configure the ANSYS Workbench 2020 components in the Isight software and drive the ANSYS Workbench 2020 software to perform numerical simulations;

[0076] S43. Read the input file and output file, and extract the design variables A, B, C, AY, AZ, LX, LW, rotorvelocity, optimization objective value v-average maximization, effective axial thrust distance Ly maximization, and constraint value Ly from the submersible mixer optimization problem.

[0077] Optimizing Design Variables: Using the integrated Isight software, the extracted average flow velocity (v-average) and effective axial advance distance (Ly) parameters inside the pool are analyzed using a hybrid strategy of DOE sampling and numerical optimization. The DOE sampling method, number of sample points, design variables, and post-processing objectives are set. Then, the optimization algorithm, design variables, constraints, and objective function are set. Using the average flow velocity (v-average) and effective axial advance distance (Ly) inside the pool as the objective functions, optimization analysis is performed on design variables A, B, C, AY, AZ, LX, and LW to maximize the average flow velocity (v-average) and effective axial advance distance (Ly) inside the pool. The hybrid strategy of DOE sampling and numerical optimization improves optimization efficiency while leveraging the high efficiency of numerical algorithms for complex optimization models. The Task Plan component integrates the DOE algorithm with the parameter optimization algorithm for global optimization design. First, the DOE sampling method, number of sample points, design variables, and response are set. Then, the optimization algorithm, design variables, constraints, and optimization objectives are set, specifically including the following steps:

[0078] S51. In the Isight software, through the General page of the DOE component, select Optimal Latin Hypercubic sampling and set the number of sample points; and according to customer requirements, select appropriate design variables on the Factors page, such as... Figure 5 The diagram shows the design variables set according to customer needs. If the customer only needs to select the appropriate mixing tank size, the design variables are set to LX and LW; if the customer only needs to select the appropriate mixer model, the design variable is set to rotorvelocity; if the customer only needs to select the appropriate installation method, the design variables are set to A, B, C, AY, and AZ. If the customer needs to select the appropriate mixing tank size, mixer model, and installation method simultaneously, the design variables are set to A, B, C, AY, AZ, LX, LW, and rotorvelocity, with their upper and lower limits and initial values ​​set respectively.

[0079] S52. In the Isight software, through the Objectives page of the DOE component, set the post-processing objectives to maximize the average v-average inside the pool and maximize the effective axial advance distance Ly.

[0080] S53. In the Isight software, through the General page of the Optimization component, select the NLPQLP optimization algorithm. On the Factors page, use the optimal solution of the DOE as the initial position point for optimization, and set the upper and lower limits for design variables A, B, C, AY, AZ, LX, LW, and rotorvelocity respectively;

[0081] S54. In the Isight software, through the Constents page of the Optimization component, set the constraint condition of the optimization model to the effective axial thrust distance Ly. The effective axial thrust distance refers to the effective distance that the submersible mixer pushes the water along the axial direction within the effective working area of ​​water flow and mixing (under the condition of maintaining a flow velocity greater than or equal to 0.3m / s).

[0082] S55. In the Isight software, through the Objectives page of the Optimization component, set the optimization objectives to maximize the average v-average inside the pool and maximize the effective axial propulsion distance Ly. Run the optimization calculation in the Isight software to obtain the optimal solution.

[0083] In one embodiment 1, 2, or 3 of the present invention, the constraint condition of the optimization model is set as follows: the upper limit of the effective axial thrust distance Ly is 2.3m, and the lower limit is 1.6m.

[0084] Example 1

[0085] In Embodiment 1 of this invention, a suitable submersible mixer is selected according to requirements. The design variable is the rotor velocity of the submersible mixer blades, with an initial value of 900 rpm, an upper limit of 1000 rpm, and a lower limit of 800 rpm. The initial values ​​of the distance A between the submersible mixer and the adjacent wall of the installation surface in the water tank are 900 mm, the initial value of the distance B between the submersible mixer and the water tank surface are 500 mm, the initial value of the distance C between the submersible mixer and the installation wall of the water tank are 500 mm, the initial value of the tilt angle AY relative to the installation wall in the water tank is 0, and the initial value of the tilt angle AZ relative to the adjacent wall in the water tank is 0. The initial value of the length LX of the installation wall of the submersible mixer is 1800 mm, the initial value of the water tank depth LW is 1000 mm, the initial value of the length of the adjacent wall LZ is 2500 mm, and the number of sample points is set to 8.

[0086] In Example 1, the comparison of parameters before and after optimization is shown in Table 2. The optimal solution for the design variable (DOE) of the submersible mixer blade rotation speed (rotorvelocity) is 1500. Therefore, the initial position point for optimizing the blade rotation speed (rotorvelocity) is set to 1500 rpm, with an upper limit of 1000 rpm and a lower limit of 800 rpm. Other parameters remain unchanged. When the submersible mixer achieves optimal mixing effect, the blade rotation speed (rotorvelocity) is 1500 rpm. After optimization, the effective axial propulsion distance is increased by 5.341% while maintaining an average flow velocity ≥0.1 m / s inside the pool.

[0087] Table 2 Comparison of main parameters before and after optimization in Example 1

[0088] Before optimization After optimization Design variable Blade rotor velocity 900 1500 Constraint condition Effective axial propulsion distance Ly 1.6338461 1.7211073 Optimization objective Average flow velocity inside pool v-average 0.2115207 0.3499602

[0089] Example 2

[0090] In Embodiment 2 of the present invention, a suitable installation method is selected according to requirements. The design variables are A, B, C, AY, and AZ. The initial value of the distance A between the mixer and the adjacent wall of the installation surface in the water tank is 900mm, with an upper limit of 1000mm and a lower limit of 100mm; the initial value of the distance B between the mixer and the water tank surface is 500mm, with an upper limit of 750mm and a lower limit of 100mm; the initial value of the distance C from the installation wall is 500mm, with an upper limit of 500mm and a lower limit of 200mm; the initial value of the tilt angle AY relative to the installation wall in the water tank is 0, with an upper limit of 90° and a lower limit of 0; the initial value of the tilt angle AZ relative to the adjacent wall in the water tank is 0, with an upper limit of 90° and a lower limit of 0. The initial value of the length LX of the submersible mixer installation wall is 1800mm, the initial value of the water tank depth LW is 1000mm, the initial value of the adjacent wall length LZ is 2500mm, and the initial value of the submersible mixer blade rotation speed (rotorvelocity) is 900rpm. The number of sample points is set to 7.

[0091] In Example 2, the comparison of parameters before and after optimization is shown in Table 3. The optimal solutions for the design variables DOE of the submersible mixer A, B, C, AY, and AZ are 665mm, 163mm, 350mm, 1°, and 18°, respectively. Therefore, the initial position of the submersible mixer in the pool is optimized as follows: distance A from the adjacent wall of the installation surface is set to 665mm, with an upper limit of 1000mm and a lower limit of 100mm; distance B from the pool surface is set to 163mm, with an upper limit of 750mm and a lower limit of 100mm; distance C from the installation wall of the pool is set to 350mm, with an upper limit of 500mm and a lower limit of 200mm; the initial position of the tilt angle AY relative to the installation wall in the pool is set to 1°, with an upper limit of 90° and a lower limit of 0; and the initial position of the tilt angle AZ relative to the adjacent wall in the pool is set to 18°, with an upper limit of 90° and a lower limit of 0. Other parameters remain unchanged; when the submersible mixer achieves optimal mixing effect, the distance A between the submersible mixer and the adjacent wall of the mounting surface in the pool is 665mm; the distance B between the submersible mixer and the pool surface is 163mm; the distance C between the submersible mixer and the mounting wall of the pool is 350mm; the tilt angle AY relative to the mounting wall in the pool is 1°; and the tilt angle AZ relative to the adjacent wall in the pool is 18°. After optimization, the effective axial propulsion distance increased by 19.113% at the design point while maintaining an average flow velocity ≥0.1m / s inside the pool.

[0092] Table 3 Comparison of main parameters before and after optimization in Example 2

[0093]

[0094]

[0095] Example 3

[0096] In Embodiment 3 of the present invention, to select a suitable size for the mixing tank according to requirements, the design variables are LX and LW. The initial value of the distance A between the mixer and the adjacent tank wall is 900mm, the initial value of the distance B between the mixer and the tank surface is 500mm, the initial value of the distance C between the mixer and the installation wall is 500mm, the initial value of the tilt angle AY relative to the installation wall in the tank is 0, the initial value of the tilt angle AZ relative to the adjacent tank wall in the tank is 0, the initial value of the length LX of the submersible mixer installation wall is 1800mm, with an upper limit of 2300mm and a lower limit of 1800mm; the initial value of the tank depth LW is 1000mm, with an upper limit of 1500mm and a lower limit of 1000mm; the initial value of the length of the adjacent tank wall LZ is 2500mm, the initial value of the submersible mixer blade rotation speed is 900rpm, and the number of sample points is set to 7.

[0097] In Example 3, the comparison of parameters before and after optimization is shown in Table 4. The optimal solutions for the design variables DOE of the pool LX and LW are 1800mm and 1167mm, respectively. Therefore, the initial value of the submersible mixer mounting wall length LX is set to 1800mm, with an upper limit of 2300mm and a lower limit of 1800mm; the initial value of the pool depth LW is set to 1167mm, with an upper limit of 1500mm and a lower limit of 1000mm; the initial value of the adjacent pool wall length LZ remains 2500mm. Other parameters remain unchanged. When the submersible mixer achieves optimal mixing effect, the submersible mixer mounting wall length LX is 1800mm, the pool depth LW is 1000mm, and the initial value of the adjacent pool wall length LZ is 2500mm. After optimization, the effective axial propulsion distance is increased by 26.888% while maintaining an average flow velocity ≥0.1m / s inside the pool.

[0098] Table 4 compares the main parameters involved in Example 3 before and after optimization.

[0099]

[0100] This invention can identify the optimal mixer model, installation method, and tank size based on requirements. During each cycle of analysis, the design parameter input and performance parameter output can be displayed in real time through Isight software, facilitating monitoring by designers and saving a significant amount of repetitive labor during the optimization of flow components. The submersible mixer optimization design platform initially built based on Isight software has significant practical value in improving the efficiency, effectiveness, and stability of the entire mixing and processing system.

[0101] It should be understood that although this specification describes various embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0102] The detailed descriptions listed above are merely specific illustrations of feasible embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. All equivalent embodiments or modifications made without departing from the spirit of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the design of a submersible mixer, characterized in that, Includes the following steps: Step S1, 3D Modeling: Determine the basic structure of the submersible mixer, initially establish a 3D model of the submersible mixer and a 3D model of the water tank in the 3D modeling software, place the submersible mixer model in the 3D model of the water tank, and adjust the position of the submersible mixer model according to the shape of the water tank. Step S2, Numerical Simulation: Import the submersible mixer model from Step S1 into the finite element analysis software to obtain the average flow velocity v-average and effective axial propulsion distance Ly inside the water tank of the submersible mixer model; Step S3, Automatic 3D Modeling: Integrate the 3D modeling software from Step S1 into the Isight software. Input the modeling process parameters from the 3D modeling software in Step S1 into the Isight software for integration. The modeling process parameters include A, B, C, AY, AZ, LX, and LW. Wherein, A is the distance from the rotation center of the submersible mixer impeller to the side wall of the pool, B is the distance from the rotation center of the submersible mixer impeller to the bottom wall of the pool, C is the distance from the back of the submersible mixer to the mounting wall, AY is the submersible mixer relative to the mounting wall in the pool, AZ is the tilt angle of the submersible mixer to the adjacent pool wall in the pool, LX is the length of the mounting wall of the submersible mixer, and LW is the depth of the pool. Step S4, Automatic Numerical Simulation: Integrate the finite element analysis software from Step S2 into the Isight software. Input the average flow velocity v-average and effective axial propulsion distance Ly inside the water tank of the submersible mixer model obtained from the finite element analysis software in Step S2 into the Isight software for integration. Perform automatic modeling using the modeling process parameters input in Step S3. Import the newly created model into the finite element analysis software and perform numerical simulation on the submersible mixer blade rotation speed, average flow velocity v-average inside the water tank, and effective axial propulsion distance Ly. Step S5: Optimize Design Variables: Using the Isight software integrated in Step S4, the average flow velocity v-average and effective axial advance distance Ly extracted in Step S4 are analyzed using a hybrid strategy of DOE sampling and numerical optimization. The DOE sampling method, number of sample points, design variables, and post-processing objectives are set. Then, the optimization algorithm, design variables, constraints, and optimization objectives are set, with the average flow velocity v-average and effective axial advance distance Ly as the optimization objectives. The design variables include A, B, C, AY, AZ, LX, and LW. Design variables are selected as needed, and optimization analysis is performed to maximize the average v-average and effective axial advance distance Ly within the pool.

2. The submersible mixer optimization design method according to claim 1, characterized in that, In step S1, Creo Parametric 6.0 is selected as the 3D modeling software.

3. The submersible mixer optimization design method according to claim 1, characterized in that, In step S2, the finite element analysis software used is ANSYS Workbench 2020.

4. The submersible mixer optimization design method according to claim 3, characterized in that, In step S2, ANSYS Workbench 2020 finite element analysis software is selected, specifically including the following steps: S21. Perform polyhedral unstructured mesh generation on the water body and impeller of the pool; S22. Enrich the impeller blades, impeller water body, and the contact surface between the impeller and the water tank; S23. The submersible mixer model uses Realizable k-ε, and the near-wall surface uses Scalable Wall Functions. S24. Set the impeller water body as the rotating region and the pool water body as the stationary region, and set the residual convergence accuracy. S25. Perform steady-state simulation on the computational domain of the submersible mixer and output the velocity flow field analysis results of the submersible mixer, including: the average flow velocity v-average inside the pool and the effective axial advance distance Ly.

5. The submersible mixer optimization design method according to claim 1, characterized in that, In step S5, a hybrid strategy of DOE sampling and numerical optimization is adopted. The DOE algorithm and parameter optimization algorithm are integrated using the Task Plan component in the Isight software, including the following steps: S51. In the Isight software, uniformly sample the design space using the DOE component to obtain the most effective design area in the entire design space. Select the Optimal Latin Hypercubic sampling method and set the number of sample points. S52. In the Isight software, select the design variables; S53. In the Isight software, through the General page of the Optimization component, select the NLPQLP optimization algorithm, and on the Factors page, use the optimal solution of DOE as the initial position point for optimization, and set the upper and lower limits of design variables A, B, C, AY, AZ, LX, LW, and rotorvelocity respectively. S54. In the Isight software, through the Constents page of the Optimization component, set the constraints of the optimization model, and set the upper and lower limits of the effective axial advance distance Ly respectively. Through the Objectives page of the Optimization component, set the optimization objectives to maximize the average v-average inside the pool and maximize the effective axial advance distance Ly.

6. The submersible mixer optimization design method according to claim 5, characterized in that, The specific selection of design variables in step S52 is as follows: If only the appropriate mixing tank size needs to be selected, the design variables are set to LX and LW; if only the appropriate mixer model needs to be selected, the design variables are set to rotorvelocity; if only the appropriate installation method needs to be selected, the design variables are set to A, B, C, AY, and AZ. If the appropriate mixing tank size, mixer model, and installation method need to be selected simultaneously, the design variables are set to A, B, C, AY, AZ, LX, LW, and rotorvelocity. The upper and lower limits and initial values ​​are set respectively, and then the post-processing objectives are set to maximize v-average and maximize the effective axial advance distance Ly.

7. The submersible mixer optimization design method according to claim 1, characterized in that, The constraints in step S5 include: 100mm≤A≤1000mm; 100mm≤B≤750mm; 200≤C≤500mm; 0≤AY≤90°; 0≤AZ≤90°.

8. The submersible mixer optimization design method according to claim 1, characterized in that, The constraints in step S5 also include: 1800mm≤LX≤2000mm; 1000mm≤LW≤1500mm; 2500mm≤LZ≤5000mm.

9. The submersible mixer optimization design method according to claim 1, characterized in that, The constraints in step S5 also include: 1.60m≤Ly≤2.3m; 800rpm≤rotorvelocity≤1500rpm.

10. A platform for implementing the submersible mixer optimization design method according to any one of claims 1-9, characterized in that, It includes an automatic 3D modeling module, an automatic numerical simulation module, and an optimization design variable module; The automatic 3D modeling is used to integrate 3D modeling software with Isight software. The modeling process parameters of the 3D modeling software are input into Isight software for integration. The modeling process parameters include A, B, C, AY, AZ, LX, and LW. Among them, parameter A is the distance from the rotation center of the submersible mixer impeller to the side wall of the pool, parameter B is the distance from the rotation center of the submersible mixer impeller to the bottom wall of the pool, parameter C is the distance from the back of the submersible mixer to the mounting wall, AY is the submersible mixer relative to the mounting wall in the pool, AZ is the tilt angle of the submersible mixer to the adjacent pool wall in the pool, LX is the length of the mounting wall of the submersible mixer, and LW is the depth of the pool. The automatic numerical simulation is used to integrate finite element analysis software with Isight software. The average flow velocity v-average and effective axial propulsion distance Ly inside the water tank of the submersible mixer model obtained from the finite element analysis software are input into Isight software for integration. Automatic modeling is performed using the input modeling process parameters. The newly created model is then imported into the finite element analysis software to perform numerical simulations on the submersible mixer blade rotation speed, average flow velocity v-average inside the water tank, and effective axial propulsion distance Ly. The input and output parameters of each model are extracted for visualization. The optimized design variables are used in the integrated Isight software to extract the average flow velocity v-average and effective axial advance distance Ly parameters inside the pool. A hybrid strategy of DOE sampling and numerical optimization is adopted, setting the DOE sampling method, number of sample points, design variables, and post-processing objectives. Then, the optimization algorithm, design variables, constraints, and optimization objectives are set, with the average flow velocity v-average and effective axial advance distance Ly inside the pool as the optimization objective functions. The design variables include A, B, C, AY, AZ, LX, and LW. Design variables are selected as needed and optimization analysis is performed to maximize the average v-average and effective axial advance distance Ly inside the pool.

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