Optimization method and optimization system of hydraulic servo valve and storage medium

Abstract

The invention provides an optimization method and system of a hydraulic servo valve and a storage medium. The optimization method of the hydraulic servo valve specifically comprises the following steps that boundary conditions and constraint conditions of the hydraulic servo valve are obtained, and a mathematical model is established; an initial three-dimensional model is established, the working state of the initial three-dimensional model is simulated, and a simulation result is obtained; determining an optimization variable, obtaining a preset optimization target, and establishing a function relation between the optimization variable and the preset optimization target; performing iterative mating on the optimization variable by using a genetic algorithm, continuously obtaining a mating result, and stopping iteration if a preset condition is met; and screening an iteration result, outputting an optimal value of the optimization variable, and constructing an optimal three-dimensional model. And establishing a three-dimensional model and performing simulation to obtain a function relationship, finding an optimal value of an optimization variable through a genetic algorithm, obtaining an optimal structure of the servo valve, and efficiently obtaining the optimal structure of the hydraulic servo valve.

Description

Technical Field

[0001] This invention relates to the field of hydraulic servo valve technology, and in particular to an optimization method, optimization system and storage medium for hydraulic servo valves. Background Technology

[0002] Servo valves, as crucial components of hydraulic servo control systems, are widely used in industrial production, aerospace, and military fields. Their performance directly impacts the control accuracy, response speed, reliability, and lifespan of the entire hydraulic control system. With increasing demands on the performance of hydraulic servo valves, the hydraulic force acting on the valve core has become a significant factor affecting their performance. Spool valves are widely used in hydraulic servo valves as power stage hydraulic amplifiers; however, existing technologies often suffer from excessive hydraulic force. As hydraulic control systems evolve, the requirements for servo valve frequency response and flow rate increase. To improve the overall performance of servo valves, it is necessary to optimize the design of spool valves, reduce the hydraulic force acting on the valve core, and balance the radial force.

[0003] In the process of optimizing the design of spool valves, theoretical formulas and engineers' design experience are generally relied upon to conduct extensive simulations or to test a small number of valve cores with representative parameters to determine the optimal valve core optimization variables. However, this design method has many drawbacks. It requires a large amount of computational and experimental resources, lacks reliable optimization methods to guide the calculation and experimentation process, resulting in high design costs, long project cycles, and insignificant optimization effects. Summary of the Invention

[0004] The main objective of this invention is to propose an optimization method, optimization system, and storage medium for hydraulic servo valves, aiming to improve optimization efficiency and ensure optimization results.

[0005] To achieve the above objectives, this invention proposes an optimization method for a hydraulic servo valve, specifically including the following steps: obtaining the boundary conditions and constraints of the hydraulic servo valve; performing theoretical analysis on the hydraulic servo valve and establishing a mathematical model; establishing an initial three-dimensional model based on the hydraulic servo valve; simulating the working state of the initial three-dimensional model and obtaining simulation results; determining optimization variables based on the simulation results; obtaining a preset optimization objective and establishing a functional relationship between the optimization variables and the preset optimization objective based on the mathematical model; using a genetic algorithm to iteratively mate the optimization variables and continuously obtaining mate results, stopping the iteration when the mate results meet preset conditions; filtering all iteration results and outputting the optimal values ​​of the optimization variables; and constructing the optimal three-dimensional model of the hydraulic servo valve based on the optimal values ​​of the optimization variables. Taking a hydraulic servo valve as the research object, this paper determines the boundary conditions and constraints under the normal operating conditions of the hydraulic servo valve, conducts theoretical analysis and establishes a mathematical model of the hydraulic servo valve, builds a three-dimensional model and performs simulation to obtain various types of force conditions of the hydraulic servo valve, obtains the values ​​of optimization variables, clarifies the preset optimization objective and establishes the functional relationship between the optimization variables, and finally obtains the optimal value of the optimization objective through a genetic algorithm and finds the corresponding optimization variable according to the functional relationship, thereby obtaining the optimal structure of the servo valve. This design establishes a multi-objective optimization solution, combining physics and mathematics, to efficiently obtain the optimal structure of the hydraulic servo valve to reduce hydraulic forces and ensure better performance of the optimized servo valve.

[0006] In one embodiment, simulating the working state of the initial 3D model specifically includes the following steps: obtaining the flow channel model of the liquid flow in the working state and the main structural model involved in the flow channel model in the initial 3D model; and performing coupled simulation calculations on the flow channel model and the main structural model in the working state based on boundary conditions and constraints. Analyzing the hydrodynamic forces of the liquid flow requires corresponding research on the actual structure and size of the servo valve. Therefore, it is necessary to extract the flow channel model of the liquid flow within the valve core and the important main structures through which the liquid flows. Simulating the flow channel model and the main structure can comprehensively obtain complete data on the liquid flow, and by changing some structures, the magnitude of the hydrodynamic force can be controlled, thereby improving the performance of the servo valve, making it more convenient to use, and significantly improving optimization efficiency.

[0007] In one embodiment, before simulating the hydraulic servo valve under working conditions based on boundary and constraint conditions, the following steps are included: performing polyhedral mesh generation on the flow channel model and tetrahedral mesh generation on the main structure model; calculating and storing the generated meshes respectively. Polyhedral mesh generation and tetrahedral mesh generation achieve different levels of precision, and the computational load for these two types of meshes also differs. Therefore, it is necessary to balance the ratio of precision to computational load to improve computational efficiency.

[0008] In one embodiment, determining the optimization variables based on simulation results specifically includes the following steps: determining the design variables and their dimensional ranges based on the simulation results; judging whether the dimensional ranges of the design variables meet preset requirements based on the valve core's structural parameters; if the dimensional range does not meet the preset requirements, narrowing the dimensional range until it does; if the dimensional range meets the preset requirements, then determining each design variable and its corresponding dimensional range as optimization variables. Many design parameters affect the liquid flow within the valve core cavity, and each parameter has a limit range of variation. Therefore, it is necessary to analyze the design parameters and their varying dimensional ranges as a whole to ensure data reliability and further improve optimization efficiency.

[0009] In one embodiment, obtaining a preset optimization target and establishing a functional relationship between the optimization variables and the preset optimization target based on a mathematical model includes the following steps: constructing a mathematical-physical model of the optimization variables and the valve core motion time response characteristics based on the established mathematical model; obtaining the transfer function corresponding to the optimization variables based on the mathematical-physical model; obtaining the preset optimization target, performing simulation calculations on the preset optimization target based on the transfer function, and obtaining the functional relationship between the preset optimization target and the optimization variables. By confirming the preset optimization target and obtaining the transfer function of the intermediate state through the construction of a mathematical-physical model, the functional relationship between the optimization variables and the preset optimization target parameters can be obtained. This allows for a direct reflection of the optimization effect achievable by adjusting the optimization variables, significantly improving optimization efficiency.

[0010] In one embodiment, a genetic algorithm is used to iteratively mate the optimization variables and continuously obtain mating results. The iteration stops when the mating results meet preset conditions. Specifically, this includes the following steps: randomly generating multiple individuals based on each optimization variable within constraints; obtaining the corresponding optimization target based on the generated individuals and the preset optimization target's functional relationship with the optimization variables; using the obtained optimization target as the first-generation population; using crossover and mutation in the genetic algorithm to mate the first-generation population to obtain a new population; continuously iterating the genetic algorithm mating process on the new population until a preset number of iterations is reached, or the new population reaches a preset number, at which point the iteration stops. Since the adjustment direction of the optimization variables is not uniform, obtaining the optimal optimization variables requires a large amount of computation. By using a genetic algorithm combined with a population for iteration, the overall operation is more systematic, and the iteration can be automatically exited by setting the number of iterations or the number of new populations, making it convenient and easy to use.

[0011] In one embodiment, after filtering all iteration results and outputting the optimal values ​​of the optimization variables, and establishing the optimal three-dimensional model of the hydraulic servo valve based on the optimal variable values, an optimization accuracy verification process is also included. This process specifically includes the following steps: Simulation calculations are performed on the flow channel model and main structure model of the optimal three-dimensional model of the hydraulic servo valve under working conditions based on boundary and constraint conditions to obtain simulation results; the simulation results of the initial three-dimensional model and the optimal three-dimensional model for the preset optimization objective are compared. If the simulation results of the optimal three-dimensional model are better than those of the initial three-dimensional model, the accuracy optimization is successful. By performing simulation calculations on the optimal three-dimensional model, analyzing the simulation results of the optimal three-dimensional model for the preset optimization objective, and comparing them with the results of the initial three-dimensional model, reliable optimization data is obtained, intuitively reflecting the optimization results of this optimization method.

[0012] This invention also proposes an optimization system for a hydraulic servo valve. This system utilizes the hydraulic servo valve optimization method described above to optimize the valve, including a data analysis module, a model building and simulation module, a data processing module, a genetic iteration module, and a screening and reconstruction module. Specifically, the data analysis module obtains the boundary conditions and constraints of the hydraulic servo valve, performs theoretical analysis, and establishes a mathematical model. The model building and simulation module establishes an initial three-dimensional model based on the hydraulic servo valve, simulates the working state of this initial three-dimensional model, and obtains simulation results. The data processing module determines optimization variables based on the simulation results, obtains preset optimization objectives, and establishes a functional relationship between the optimization variables and the preset optimization objectives based on the mathematical model. The genetic iteration module uses a genetic algorithm to iteratively mate the optimization variables and continuously obtains mating results; iteration stops when the mating results meet preset conditions. The screening and reconstruction module filters all iteration results and outputs the optimal values ​​of the optimization variables, constructing the optimal three-dimensional model of the hydraulic servo valve based on these optimal values. This hydraulic servo valve optimization system utilizes the hydraulic servo valve optimization method described above to optimize the servo valve, achieving the same beneficial effects as the aforementioned optimization method, which will not be elaborated upon here.

[0013] The present invention also proposes a storage medium storing a computer program configured to execute the optimization method for the hydraulic servo valve described above during runtime. This storage medium possesses the same beneficial effects as the optimization method for the hydraulic servo valve described above, which will not be elaborated upon here.

[0014] The technical solution of this invention establishes a mathematical model and a three-dimensional model and performs simulation to clarify the analysis object, i.e., the preset optimization target, and establish the functional relationship between the optimization variables. Finally, the optimal value of the optimization target is obtained through a genetic algorithm, and the corresponding optimization variable is found according to the functional relationship, thereby obtaining the optimal structure of the servo valve. This design establishes a multi-objective optimization solution, combining physics and mathematics, so as to efficiently obtain the optimal structure of the hydraulic servo valve to reduce hydraulic force and thus improve the overall performance of the servo valve. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of the optimization method for the hydraulic servo valve provided by the present invention; Figure 2 The flowchart of step S2 in the optimization method of the hydraulic servo valve provided by the present invention; Figure 3 The process flow of step S3 in the hydraulic servo valve optimization method provided by the present invention Figure 1 ; Figure 4 A flowchart of step S21 in the optimization method of the hydraulic servo valve provided by the present invention; Figure 5 The process flow of step S3 in the hydraulic servo valve optimization method provided by the present invention Figure 2 ; Figure 6 The flowchart of step S4 in the optimization method of the hydraulic servo valve provided by the present invention; Figure 7 The process flow of step S5 in the hydraulic servo valve optimization method provided by the present invention Figure 1 ; Figure 8 The process flow of step S5 in the hydraulic servo valve optimization method provided by the present invention Figure 2 ; Figure 9 A schematic diagram of the servo valve core in the hydraulic servo valve optimization method provided by the present invention; Figure 10 The steady-state hydrodynamics of the valve core before and after optimization in the hydraulic servo valve optimization method provided by this invention. Figure 1 ; Figure 11The steady-state hydrodynamics of the valve core before and after optimization in the hydraulic servo valve optimization method provided by this invention. Figure 2 ; Figure 12 A schematic diagram of the steady-state hydrodynamics of the valve core before and after optimization in the optimization method of the hydraulic servo valve provided by the present invention; Figure 13 A schematic diagram of the optimized system for the hydraulic servo valve provided by the present invention; Figure 14 A schematic diagram of the storage medium provided by the present invention.

[0017] Explanation of icon numbers: 100. Valve core; 11. Flow into metering area; 12. Flow out of metering area; 200. Optimization system for hydraulic servo valves; 21. Data analysis module; 22. Model building and simulation module; 23. Data processing module; 24. Genetic iteration module; 25. Screening and reconstruction module; 300. Storage medium; 31. Computer program.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. 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 a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that if directional indicators (such as up, down, left, right, front, back, etc.) are involved in the embodiments of this invention, these directional indicators are only used to explain the relative positional relationships and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly. Unless otherwise explicitly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0021] Furthermore, if the embodiments of the present invention involve descriptions using terms such as "first," "second," etc., these descriptions are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Furthermore, the use of "and / or" or "and / or" throughout the text includes three parallel options; for example, "A and / or B" includes option A, option B, or options where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0022] As a crucial component of hydraulic servo control systems, the performance of servo valves directly impacts the control accuracy, response speed, reliability, and lifespan of the entire system. Spool valves are widely used in hydraulic servo valves as power-stage hydraulic amplifiers, characterized by high flow and pressure gain and large output flow rates. However, their drawback lies in their significant hydraulic force. Conventional servo valve structure optimization design typically relies on theoretical formulas and engineers' design experience, involving extensive simulations or experimental testing with a small number of representative valve cores to determine optimal valve core structure parameters. This design method requires substantial computational and experimental resources, and lacks reliable optimization guidance during the calculation and experimentation process, leading to high design costs, long project cycles, and limited optimization results. Therefore, efficiently obtaining the optimal structure of a hydraulic servo valve to reduce hydraulic force while ensuring optimization effectiveness is urgently needed.

[0023] Please combine Figure 1 and Figure 9 To achieve the above objectives, this invention proposes an optimization method for a hydraulic servo valve, specifically including the following steps: S1: Obtain the boundary conditions and constraints of the hydraulic servo valve, perform theoretical analysis on the hydraulic servo valve, and establish a mathematical model; S2: Establish an initial three-dimensional model based on the hydraulic servo valve, simulate the working state of the initial three-dimensional model, and obtain the simulation results; S3: Determine the optimization variables based on the simulation results, obtain the preset optimization objective, and establish the functional relationship between the optimization variables and the preset optimization objective based on the mathematical model; S4: Use a genetic algorithm to iteratively mate the optimization variables and continuously obtain the mating results. Stop iterating when the mating results meet the preset conditions. S5: Filter all iteration results and output the optimal values ​​of the optimization variables. Construct the optimal three-dimensional model of the hydraulic servo valve based on the optimal values ​​of the optimization variables.

[0024] It should be noted that the maximum equivalent stress within the hydraulic servo valve is located on the guide surface in the middle of the valve core and the fluid inflow chamber contact surfaces on both sides. Therefore, the flow of fluid within the chamber can be adjusted by changing the size and position of the hydraulic chamber inlet and outlet, thereby achieving hydraulic force optimization. Specifically, taking the hydraulic servo valve as the research object, the boundary conditions and constraints are determined under the normal operating state of the hydraulic servo valve. A three-dimensional model is established and simulation is performed to obtain various types of force conditions on the hydraulic servo valve. Specifically, under the normal operating state of the servo valve, the forces acting on the servo valve include: medium pressure, valve seat support force, and the gravity of components such as the valve body, valve sleeve, and valve core. The forces acting on the valve core include: driving force, resistance, and hydraulic force. Based on these external forces, a mathematical model is established to determine the constraint forces, i.e., constraint conditions, on the entire servo valve. At the same time, it is also necessary to determine the temperature of the medium inside the servo valve and the pressure conditions of the four valves, i.e., the boundary conditions of the servo valve.

[0025] Specifically, the hydraulic servo valve has five chambers. The middle chamber is connected to an external hydraulic pump via the high-pressure port P. Adjacent chambers are connected to external hydraulic pistons via working ports A and B. The two outermost chambers are connected to an external oil tank via the low-pressure port T. The flow paths of the two low-pressure ports are interconnected within the valve body, with only one T-port connection point externally. When the hydraulic servo valve is open, the position of the valve core determines the working state of the main valve. When the valve core is working, it tends to move to the right. At this time, oil enters the main valve from the high-pressure port P, then flows out of the main valve from the working port B and enters the actuator. Conversely, oil flows from the actuator into the main valve via the working port A, flows out of the main valve from the low-pressure port T, and finally returns to the oil tank. Under normal operating conditions, the forces acting on the valve core are mainly: driving force F. m Resistance F k and hydraulic F f Therefore, it is necessary to determine the temperature, medium properties, overall constraints of the servo valve, and inlet / outlet boundary conditions within the hydraulic servo valve.

[0026] A mathematical model for the hydraulic servo valve, established through theoretical analysis, is combined with an initial three-dimensional model based on the valve's actual structure and dimensions. Simulation of this initial three-dimensional model yields numerical values ​​of various forces acting on the valve core and optimization variables, clarifying the analysis object—the preset optimization objective. Based on the mathematical model, a functional relationship is established between the preset optimization objective and the optimization variables. Specifically, the preset optimization objective comprises the dynamic characteristic indicators of the servo valve, including the number of oscillations N and the rise time t. r Overshoot M p The optimization variables include six structural parameters, specifically the shoulder length L1 in the middle of the valve core and the shoulder length L2 at the end of the valve core, and the guide surface height. h Length of the central guide surface l 1 Length of the guide surfaces on both sides l 1 , Curvature of the guide slope θ .

[0027] Finally, the optimal value of the optimization objective is obtained through a genetic algorithm, and the corresponding optimization variable is found according to the functional relationship, thereby obtaining the optimal structure of the servo valve.

[0028] Specifically, the optimization method of the genetic algorithm involves randomly generating individuals as the initial population within constraints, and assigning corresponding fitness to the individuals based on the function value of the preset optimization objective and the fitness allocation rules of the algorithm. In this optimization program, the method for selecting mating individuals involves selection, crossover, and mutation to obtain a new population. After obtaining the new population, one standard cycle of the genetic algorithm is completed.

[0029] It should be noted that the fitness assignment rule is as follows: after the original population is generated, the fitness of each individual in the population should be evaluated. The smaller the objective function value of the problem to be solved, the better. For constrained problems, the augmented function of the original objective can be used as the evaluation function.

[0030]

[0031] and F i (X) represents the augmented function and objective function of the optimization problem; C g is the penalty factor for violating constraints; m is the number of constraints; ; Take suitability The above formula can be used to evaluate the fitness of each individual in the original population.

[0032] Understandably, this optimization method establishes a multi-objective optimization solution that combines physics and mathematics to efficiently obtain the optimal structure of the hydraulic servo valve to reduce hydraulic forces and ensure better performance of the servo valve after optimization.

[0033] Please see Figure 2 In one embodiment, simulating the working state of the initial 3D model specifically includes the following steps: S21: Obtain the flow channel model of the liquid flow in the working state in the initial three-dimensional model and the main structural model involved in the flow channel model; S22: Coupled simulation calculation of the flow channel model and main structure model under working conditions based on boundary conditions and constraints.

[0034] It should be noted that analyzing the hydrodynamic forces of liquid flow requires obtaining the actual structure and size of the servo valve. Therefore, it is necessary to extract the flow channel model of the liquid flow inside the valve core and the important main structural model through which the liquid flows. The important main structures include the shell, valve sleeve and valve core, which have an important impact on the analysis results. In addition, the extracted three-dimensional model needs to be simplified and the minor structural features that do not affect the calculation results need to be optimized, such as: rounded corners, chamfers, small holes and grooves. Therefore, simulation calculations of the flow channel model and the main structure model can obtain complete data on liquid flow, and by changing some structures, the magnitude of hydraulic force can be controlled, thereby improving the performance of the servo valve, making it more convenient to use, and significantly improving optimization efficiency.

[0035] Please see Figure 4 In one embodiment, before performing simulation calculations on the hydraulic servo valve under working conditions based on boundary and constraint conditions, the following steps are also included: S211: Perform polyhedral meshing on the flow channel model and tetrahedral meshing on the main structure model; S212: Calculate and store the divided grids separately.

[0036] It should be noted that polyhedral meshing and tetrahedral meshing achieve different levels of precision. Tetrahedral meshing results in more accurate calculations, and the computational load also differs between the two types of meshing. Relatively speaking, the main structural model requires a smaller mesh area, while the flow channel model requires a larger mesh area and has a more complex structure. To improve computational efficiency while maintaining computational accuracy, tetrahedral meshing is used for the main structural model, and polyhedral meshing is used for the flow channel model. This balances the ratio of precision to computational load, thereby improving computational efficiency.

[0037] Please see Figure 3 In one embodiment, determining the optimization variables based on simulation results specifically includes the following steps: S31: Determine the design variables and the size range of each design variable based on the simulation results; S32: Based on the structural parameters of the valve core, determine whether the size range of each design variable meets the preset requirements. If the size range does not meet the preset requirements, reduce the size range until it meets the preset requirements. S33: If the size range meets the preset requirements, then each design variable and its corresponding size range are determined as optimization variables.

[0038] It should be noted that the optimization variables are specifically six structural parameters, also known as design variables. During the adjustment of these six structural parameters, the overall dimensions of the valve core must remain unchanged. Then, based on the processing requirements of the hydraulic servo valve and the interrelationships between structural dimensions, the size range of these six design variables is initially determined. Based on the initially determined size range, some boundary values ​​of the dimensions are selected for simulation calculations. The calculation results are analyzed, and it is judged whether the size range is reasonable. If the structural strength is not met, the size range is reduced, and the size range of the six design variables is redefined. This process is repeated to ensure its rationality. Finally, the design variables and their corresponding reasonable size ranges are used as optimization variables for subsequent calculations.

[0039] Understandably, many design parameters affect the flow of liquid within the valve core cavity, and each parameter has a limit range of variation. Therefore, it is necessary to analyze the design parameters and the dimensional range of each design parameter variation as a whole to ensure the reliability of the data and further improve work efficiency.

[0040] Please see Figure 5 In one embodiment, obtaining a preset optimization objective and establishing a functional relationship between the optimization variables and the preset optimization objective based on a mathematical model includes the following steps: S34: Construct a mathematical and physical model of the optimization variables and the time response characteristics of the valve core movement based on the established mathematical model; S35: Based on this mathematical physics model, the transfer function corresponding to the optimization variable is obtained; S36: Obtain the preset optimization objective, perform simulation calculations on the preset optimization objective based on the transfer function, and obtain the functional relationship between the preset optimization objective and the optimization variables.

[0041] It should be noted that the transfer function parameters directly affect the performance of the valve core. The preset optimization target, i.e., the dynamic characteristics, is an important indicator for measuring the performance of the valve core. This is achieved by using the number of oscillations N and the rise time t of the valve core. r Overshoot M p The three objectives are used as objective functions to measure the performance of the hydraulic servo valve. The smaller the three objectives are, the better the performance of the hydraulic servo valve. Therefore, the preset optimization objective function can be simplified to: .

[0042] Understandably, by identifying the preset optimization target and obtaining the transfer function of the intermediate state through the construction of a mathematical-physical model, the functional relationship between the optimization variables and the preset optimization target parameters can be obtained. This allows for a direct reflection of the optimization effect that can be achieved by adjusting the optimization variables, thus significantly improving optimization efficiency.

[0043] Please see Figure 6 In one embodiment, a genetic algorithm is used to iteratively mate the optimization variables and continuously obtain mating results. The iteration stops when the mating results meet a preset condition. Specifically, the steps include: S41: Randomly generate multiple individuals within the constraints based on each optimization variable; S42: Based on the generated individuals and the functional relationship between the preset optimization objective and the optimization variables, the corresponding optimization objective output is obtained as the first generation population; S43: Use crossover and mutation in the genetic algorithm to obtain a new population from the first generation population; S44: Perform continuous iterations of genetic algorithm mating on the new population until the preset number of iterations is reached, or the new population reaches the preset number, at which point the iteration stops.

[0044] It should be noted that the preset conditions can be the number of iterations or the size of the new population. When continuously iterating the genetic algorithm mating of the new population, the number of iterations is usually set. If the set number of iterations is not reached, the iteration will continue to generate a new population. When the number of iterations or the number of new populations meets the set size of the new population, the iteration will exit. The two are based on the time order, and the loop will exit if the condition is met first.

[0045] Specifically, when new individuals are generated through mating using a genetic algorithm, the values ​​of the new individuals will gradually stabilize. That is, after a certain number of iterations, the new individuals generated by further iterations will not produce the optimal value of the optimization variable. Therefore, the maximum number of iterations and the maximum population size generated by the iterations can be set based on the number of trials or empirical results, and the optimal value of the optimization variable can be selected from the maximum number of iterations or the maximum population size.

[0046] It can be seen that, as a learnable method, genetic algorithms can greatly improve the efficiency of obtaining the optimal value of optimization variables based on empirical results.

[0047] Understandably, the direction of the optimization variable adjustment is not uniform, and it can be changed within a reasonable size range. To obtain the optimal optimization variable, a lot of calculations are required. By using a genetic algorithm combined with a population for iteration, the overall operation is more systematic, and the iteration can be automatically exited by setting the number of iterations or the number of new populations, which is convenient to use and easy to operate.

[0048] Please see Figure 7In one implementation, filtering all iteration results and outputting the optimal value of the optimization variable specifically includes the following steps: S51: Compare the values ​​of the optimization objective function of all new populations in the mating results, and select the target population with the smallest optimization objective function value; S52: Based on the functional relationship between the preset optimization objective and the optimization variables, obtain the optimization variables of the target population and output them as the optimal values ​​of the optimization variables.

[0049] It should be noted that the optimization variables are intuitively obtained from the mating results through the optimization objective function value corresponding to the functional relationship. Therefore, the population with the smallest optimization objective function value is selected as the target population. Based on this target population, the best three-dimensional model of the servo valve needs to be obtained. Therefore, the optimization variables of the target population need to be obtained again according to the functional relationship. This optimization variable is the optimal value of the optimization variable.

[0050] Please see Figure 8 In one embodiment, after filtering all iteration results and outputting the optimal values ​​of the optimization variables, and establishing the optimal three-dimensional model of the hydraulic servo valve based on the optimal variable values, an optimization accuracy verification process is also included, specifically including the following steps: S53: The optimal three-dimensional model of the hydraulic servo valve in the working state, including the flow channel model and the main structure model, is simulated based on boundary conditions and constraints to obtain simulation results; S54: Compare the simulation results of the initial 3D model and the optimal 3D model for the preset optimization target. If the simulation results of the optimal 3D model are better than those of the initial 3D model, then the accuracy optimization is successful.

[0051] Understandably, by simulating the optimal 3D model, analyzing the simulation results of the optimal 3D model for the preset optimization objective, and comparing them with the results of the initial 3D model, reliable optimization data can be obtained, which intuitively reflects the optimization results of the optimization method.

[0052] Please combine Figures 9-12 Specifically, the valve core shoulder position is selected in the servo valve. L 1 and L 2 Central guide surface height h Length of the central guide surface l 1 Length of the guide surfaces on both sides l 2 , Curvature of the guide slope θ During valve core operation, the main force acting on the valve core that needs to be considered is the driving force. F m Viscous resistance F kSteady-state hydrodynamics F S Transient hydrodynamics F t Hydraulic servo valves are generally driven by hydraulic fluid. Whether the driving force is from the direct force applied by the actuator or the pressure difference between the hydraulic fluids at both ends of the main valve core, it has no impact on the force analysis of the main valve core. Viscous resistance. F k The shear force acting on the valve core is due to fluid viscosity and is sufficiently small compared to the hydrodynamic force; therefore, the viscous resistance can be ignored. Steady-state hydrodynamic force. F S This is caused by the momentum change of the fluid flowing into and out of the valve chamber. Taking the inflow into the metering zone as an example, the fluid flows at an inflow angle... a 1 Inflow speed v 1 Inflow into the metering area, outflow a 2 outflow velocity v 2 Flowing out of the metering zone. Based on the momentum theorem, the axial steady-state hydrodynamic force acting on the valve core within the metering zone is expressed as:

[0053] The flow rate q through the throttling orifice can be expressed using the orifice flow rate formula as follows:

[0054] in, ρ For the density of the oil, C d Where A is the flow coefficient and A is the orifice area. This refers to the pressure difference at the throttling orifice.

[0055] After merging the analysis of the two metering regions, the entire valve core is analyzed, and the global steady-state hydrodynamic force Fs is expressed as:

[0056] in, , The inflow angle and outflow angle of the inflow into the metering zone; , The inflow angle and outflow angle of the inflow into the metering zone; x This refers to the valve core displacement; w The throttling area A is related to the valve core displacement. x The functional relationship coefficients can be expressed as:

[0057] For the pressure drop of a single metering zone, the inflow pressure drop and outflow pressure drop The relational expression is:

[0058] Transient hydrodynamics F t It is the reaction force acting on the valve core caused by the change in fluid velocity within the servo valve chamber. Within a single metering zone, the set flow rate change rate is... The transient hydrodynamic force acting on the entire valve core is expressed as:

[0059] L 1 , L 2 This represents the distance between the inlet and outlet within the flow metering area. .

[0060] According to Newton's second law, inertial force F a Represented as M s a s ,in M s For valve core quality; a s Let be the acceleration of the valve core. Therefore, the equation of motion for the valve core can be expressed as:

[0061] Make the following settings: ;

[0062] Driven by valve core force F m As the input for the valve core movement process, the valve core displacement x As the output of the valve core's motion process, the transfer function can be expressed as:

[0063] Based on the transfer function of the valve core movement process, M s Valve core quality, K f Steady-state hydrodynamic transfer function parameters B f The transient hydraulic transfer function parameters directly affect the performance of the valve core. F t , K f With steady-state hydrodynamics F s Regarding the inflow angle of the fluid within the valve in the metering zone...a 1 and outflow angle a 2 This will directly affect steady-state hydrodynamics. F s In summary, the valve core's operating performance can be adjusted by changing its volume V. S , Valve internal damping length , and inflow angle of fluid in the metering zone within the valve. a 1 and outflow angle a 2 To make adjustments.

[0064] Based on the established relationship between the valve core optimization variables and the valve core motion transfer function, a mathematical description is established for the multi-objective optimization problem of the valve core structure. For hydraulic servo valves, dynamic characteristics are an important indicator for evaluating their performance; the number of valve core oscillations is used as a measure. N Ascent time t r Overshoot M p The objective function measures the performance of the hydraulic servo valve, aiming to minimize these three objectives. Therefore, the objective function can be expressed as: .

[0065] Based on the genetic optimization algorithm, in terms of structural parameters L 1 , L 2 , h , l 1 , l 2 , θ Individuals are randomly generated within the constraints, and an initial population is selected. The constraints on the design parameters are determined based on the structural strength, structural interference relationship, and flow field analysis of the optimized valve core structure. The design parameter constraints are as follows:

[0066] Transmission function through valve core M S ,B f ,K f , Based on the time-domain response results of the valve core displacement simulation, the dynamic characteristic indexes are calculated. N , t r , M pThe first-generation population is obtained and its fitness is calculated. Specifically, in this embodiment of the invention, the genetic algorithm is set to iterate 500 times, the population size is 200, the crossover probability is 0.8, and the mutation probability is 0.2. Then, simulating the biological evolution process in genetics, the second-generation population is obtained through selection, crossover, and mutation, and the fitness of the second-generation population is calculated. This process is repeated iteratively until the number of new populations reaches a certain size. The minimum objective function value and the corresponding optimization variable are found in the new population, and finally, the optimal solution, i.e., the optimal valve core structure, is output.

[0067] The optimal main valve model was obtained through the optimization process in the previous step, with specific parameter values ​​of L1=6.35 mm, L2=5.65 mm, and h=0.82 mm. l 1 =1.22 mm l 2 =2.25 mm θ =40°, and based on the S2 simulation calculation method, the optimized three-dimensional model of the hydraulic servo valve was simulated and analyzed, and the calculation results were extracted. The results show that the steady-state hydraulic force acting on the valve core is significantly improved after optimization, with a maximum improvement of 30.32%. Under the operating conditions of an inlet pressure of 10 MPa and a valve core displacement of x=1 mm, the overshoot improved by an average of 45.95% under different valve core displacements. The maximum improvement occurred at a displacement of 1 mm, with an improvement of 52.86%; the minimum improvement occurred at 0.2 mm, with an improvement of 39.83%, which is also sufficiently significant.

[0068] Please see Figure 13 The present invention also proposes a hydraulic servo valve optimization system 200, which optimizes the hydraulic servo valve using the hydraulic servo valve optimization method described above. Since the hydraulic servo valve optimization system 200 adopts all the technical solutions of all embodiments of the above-described hydraulic servo valve optimization method, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be described in detail here.

[0069] The hydraulic servo valve optimization system 200 includes a data analysis module 21, a model building and simulation module 22, a data processing module 23, a genetic iteration module 24, and a screening and reconstruction module 25. Specifically, the data analysis module 21 is used to obtain the boundary conditions and constraints of the hydraulic servo valve, perform theoretical analysis on the hydraulic servo valve, and establish a mathematical model. The model building and simulation module 22 is used to build an initial three-dimensional model based on the hydraulic servo valve, simulate the working state of the initial three-dimensional model, and obtain simulation results. The data processing module 23 is used to determine the optimization variables based on the simulation results, obtain the preset optimization target, and establish the functional relationship between the optimization variables and the preset optimization target based on the mathematical model; The genetic iteration module 24 is used to use a genetic algorithm to iteratively mate the optimization variables and continuously obtain the mating results. The iteration stops when the mating results meet the preset conditions. The filtering and reconstruction module 25 is used to filter all iteration results and output the optimal values ​​of the optimization variables, and construct the optimal three-dimensional model of the hydraulic servo valve based on the optimal values ​​of the optimization variables.

[0070] The present invention also proposes a storage medium 300, wherein the storage medium 300 stores a computer program 31, wherein the computer program 31 is configured to execute the optimization method of the hydraulic servo valve as described above when running.

[0071] This storage medium has the same beneficial effects as the optimization method for hydraulic servo valves described above, which will not be elaborated here.

[0072] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. An optimization method for a hydraulic servo valve, characterized in that, Specifically, the steps include the following: Obtain the boundary conditions and constraints of the hydraulic servo valve, perform theoretical analysis on the hydraulic servo valve, and establish a mathematical model; An initial three-dimensional model is established based on the hydraulic servo valve. The working state of the initial three-dimensional model is simulated, and the simulation results are obtained. Based on simulation results, the optimization variables are determined, the preset optimization objectives are obtained, and a functional relationship between the optimization variables and the preset optimization objectives is established based on a mathematical model. The genetic algorithm is used to iteratively mate the optimization variables and continuously obtain the mating results. The iteration stops when the mating results meet the preset conditions. Filter all iteration results and output the optimal values ​​of the optimization variables. Construct the optimal three-dimensional model of the hydraulic servo valve based on the optimal values ​​of the optimization variables.

2. The optimization method for the hydraulic servo valve as described in claim 1, characterized in that, The simulation of the working state of the initial 3D model includes the following steps: Obtain the flow channel model and the main structural model involved in the flow channel model of the liquid flow in the working state in the initial three-dimensional model; Coupled simulation calculations are performed on the flow channel model and the main structure model under working conditions based on boundary conditions and constraints.

3. The optimization method for the hydraulic servo valve as described in claim 2, characterized in that, Before performing coupled simulation calculations of the hydraulic servo valve under working conditions based on boundary conditions and constraints, the following steps are also included: The flow channel model is meshed with polyhedral meshes, and the main structure model is meshed with tetrahedral meshes. The calculations are performed and stored for each of the divided grids.

4. The optimization method for the hydraulic servo valve as described in claim 1, characterized in that, Determining optimization variables based on simulation results includes the following steps: The design variables and their size ranges are determined based on the simulation results. Based on the structural parameters of the valve core, determine whether the dimensional range of each design variable meets the preset requirements. If the dimensional range does not meet the preset requirements, reduce the dimensional range until it meets the preset requirements. If the size range meets the preset requirements, then each design variable and its corresponding size range are determined as optimization variables.

5. The optimization method for the hydraulic servo valve as described in claim 1, characterized in that, Obtaining the preset optimization objective and establishing the functional relationship between the optimization variables and the preset optimization objective based on the mathematical model includes the following steps: A mathematical-physical model of the optimization variables and the time response characteristics of the valve core motion is constructed based on the established mathematical model. Based on this mathematical physics model, the transfer function corresponding to the optimization variable is obtained; Obtain the preset optimization objective, perform simulation calculations on the preset optimization objective based on the transfer function, and obtain the functional relationship between the preset optimization objective and the optimization variables.

6. The optimization method for the hydraulic servo valve as described in claim 5, characterized in that, The genetic algorithm is used to iteratively mate the optimization variables and continuously obtain the mating results. The iteration stops when the mating results meet the preset conditions. The specific steps include the following: Multiple individuals are randomly generated within the constraints based on each optimization variable; Based on the generated individuals and the functional relationship between the preset optimization objective and the optimization variables, the corresponding optimization objective is obtained; The obtained optimization objective is used as the first generation population; A new population is obtained by crossover and mutation mating in the genetic algorithm on the first generation population; The genetic algorithm is continuously iterated over the new population until a preset number of iterations is reached, or the new population reaches a preset size, at which point the iteration stops.

7. The optimization method for the hydraulic servo valve as described in claim 5, characterized in that, The process of filtering all iteration results and outputting the optimal value of the optimization variable includes the following steps: By comparing the values ​​of the optimization objective function for all new populations in the mating results, the target population with the smallest optimization objective function value is selected. The optimal variables of the target population are obtained based on the functional relationship between the preset optimization objective and the optimization variables, and the optimal values ​​of the optimization variables are output as the output.

8. The optimization method for the hydraulic servo valve as described in claim 1, characterized in that, After filtering all iteration results and outputting the optimal values ​​of the optimization variables, and establishing the optimal 3D model of the hydraulic servo valve based on the optimal variable values, the optimization accuracy verification process is also included, specifically including the following steps: The optimal three-dimensional model of the hydraulic servo valve under working conditions, including the flow channel model and the main structure model, is simulated based on boundary conditions and constraints to obtain simulation results. Compare the simulation results of the initial 3D model and the optimal 3D model for the preset optimization target. If the simulation results of the optimal 3D model are better than those of the initial 3D model, then the accuracy optimization is successful.

9. An optimization system for a hydraulic servo valve, comprising optimizing the hydraulic servo valve using the optimization method for the hydraulic servo valve as described in any one of claims 1-8, characterized in that: It includes modules for data analysis, model building and simulation, data processing, genetic iteration, and screening and reconstruction. The data analysis module is used to obtain the boundary conditions and constraints of the hydraulic servo valve, perform theoretical analysis on the hydraulic servo valve, and establish a mathematical model. The model building and simulation module is used to build an initial three-dimensional model based on the hydraulic servo valve, simulate the working state of the initial three-dimensional model, and obtain simulation results. The data processing module is used to determine optimization variables based on simulation results, obtain preset optimization objectives, and establish a functional relationship between optimization variables and preset optimization objectives based on a mathematical model. The genetic iteration module is used to use a genetic algorithm to iteratively mate the optimization variables and continuously obtain the mating results. The iteration stops when the mating results meet the preset conditions. The filtering and reconstruction module is used to filter all iteration results and output the optimal values ​​of the optimization variables, and construct the optimal three-dimensional model of the hydraulic servo valve based on the optimal values ​​of the optimization variables.

10. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the optimization method of the hydraulic servo valve according to any one of claims 1-8 when it is run.

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