Engineering design optimization method for electromagnetic valve

Abstract

The invention discloses an electromagnetic valve engineering design optimization method, and relates to the technical field of electromagnetic valve engineering design. According to the method, boundary condition parameters are clearly designed by obtaining high-pressure fluid control demand data, and an optimization constraint condition set is established, so that the design accuracy is ensured. Designing parameters are converted into optimization variables through gene coding, a fitness function is constructed through a genetic algorithm, and a solution set is generated; in a particle swarm optimization stage, design parameters are initialized to be particles, a multi-objective optimization function is defined, and a fitness function solution set is gradually optimized by utilizing an inertia weight and a group optimal guide search direction. The performance of the electromagnetic valve is verified through finite element analysis and fluid dynamics simulation, whether the control requirement is met or not is analyzed, and design parameters are finely adjusted according to the simulation result. The problems that the electromagnetic valve is insufficient in performance and unreasonable in design in the high-pressure fluid control process are effectively solved.

Description

Technical Field

[0001] The invention relates to the technical field of solenoid valve engineering design, and in particular to an engineering design optimization method for a solenoid valve. Background Art

[0002] With the continuous development of industrial automation and intelligence, high-pressure fluid control systems play an increasingly important role in various engineering applications, especially in the fields of petrochemicals, aerospace, and new energy. These industries have put forward higher requirements on the performance of solenoid valves, especially in high-pressure environments. Solenoid valves not only need to have fast response and high reliability, but also need to meet the needs of precise control. Therefore, the design optimization of solenoid valves is particularly critical, aiming to improve their performance through scientific methods to achieve higher safety and efficiency.

[0003] Traditional solenoid valve design optimization methods mostly use empirical design and trial and error methods to verify the design scheme through physical experiments. These methods usually rely on the experience and intuition of engineers, resulting in an inefficient design process and a long cycle. In addition, traditional methods often cannot consider multiple optimization objectives at the same time, such as flow, response time and reliability, which limits the improvement of solenoid valve performance. In addition, relying on a single optimization method cannot effectively deal with the complex variables and nonlinear characteristics in the high-pressure fluid control environment, resulting in the final product may not meet the actual application requirements. These shortcomings have prompted engineers to seek more scientific and efficient optimization methods to achieve engineering design innovations for solenoid valves. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an engineering design optimization method for a solenoid valve to solve the problems of insufficient performance and unreasonable design of the solenoid valve in the process of high-pressure fluid control.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for engineering design optimization of a solenoid valve, comprising the following steps: S1. Acquire high-pressure fluid control demand data, determine the design boundary condition parameters of the solenoid valve, and establish a set of constraint conditions for engineering design optimization of the solenoid valve; S2. Genetically encode the design boundary condition parameters of the solenoid valve and input them into the genetic algorithm as optimization variables to construct a fitness function, and generate a fitness function solution set through selection, crossover and mutation operations; S3. Initialize the design parameters as particles in a particle swarm, define a multi-objective optimization function, update the position and velocity of the particles through a particle swarm optimization algorithm, guide the search direction of the particle swarm through inertia weights and group optimality, and gradually adjust the particle swarm to optimize the fitness function solution set; S4. Verify the performance of the solenoid valve under working conditions through finite element analysis and fluid dynamics simulation, analyze whether it meets the requirements of high-pressure fluid control, and fine-tune the design boundary condition parameters of the solenoid valve according to the simulation results.

[0006] Furthermore, the high-pressure fluid control demand data includes the working pressure range, flow demand, response time and ambient temperature of the solenoid valve.

[0007] Furthermore, the specific process of determining the design boundary condition parameters of the solenoid valve and establishing a set of constraint conditions for the solenoid valve engineering design optimization is as follows: Based on the high-pressure fluid control demand data, the maximum working pressure, minimum response time and maximum flow coefficient of the solenoid valve are determined as the constraint conditions for the solenoid valve engineering design optimization.

[0008] Furthermore, the design boundary condition parameters of the solenoid valve are genetically encoded and input into the genetic algorithm as optimization variables. The specific process of constructing the fitness function is as follows: the design boundary condition parameters of the solenoid valve are numerically represented, and these parameters are converted into genotypes that can be processed by the genetic algorithm using real number coding; the encoded gene set is input into the genetic algorithm as the optimization variable to construct the initial population; the working pressure, response time and flow coefficient of the solenoid valve are set as the optimization targets to construct the fitness function.

[0009] Furthermore, the specific process of generating a set of solutions to the fitness function through selection, crossover and mutation operations is as follows: each individual in the initial population is evaluated based on the constructed fitness function to calculate its fitness value; individuals with high fitness values ​​are selected from the initial population, crossover and mutation operations are performed on the selected individuals, and the crossover rate and mutation rate are set within the standard range to obtain a set of solutions to the fitness function.

[0010] Furthermore, the design parameters are initialized as particles in the particle swarm, and the specific process of defining the multi-objective optimization function is as follows: the design boundary condition parameters of the solenoid valve are used as the initial particles in the particle swarm and randomly initialized to form a particle swarm; the multi-objective optimization function is defined according to the optimization objectives and the constraints of the solenoid valve engineering design optimization; the fitness value of each particle under the multi-objective optimization function is calculated, and the historical optimal position of each particle is recorded.

[0011] Furthermore, the specific process of guiding the search direction of the particle swarm by inertia weight and group optimality and gradually adjusting the particle swarm to achieve the optimal solution of the objective function is as follows: initialize the particle speed and position through the particle swarm optimization algorithm to ensure that each particle is evenly distributed in the search space; set the inertia weight to ensure that the particles can search within a suitable range; use the current speed of each particle, its historical optimal position and the global optimal position to calculate the particle update speed; iteratively update the speed and position of each particle, evaluate its fitness value, set the fitness threshold as the convergence condition, and compare and update the particle's historical optimal position and the group's global optimal position in each iteration until the preset convergence condition is met or the maximum number of iterations is reached.

[0012] Furthermore, the performance of the solenoid valve under working conditions is verified through finite element analysis and fluid dynamics simulation. The specific process of analyzing whether it meets the requirements of high-pressure fluid control is as follows: the structure of the solenoid valve is analyzed through finite element analysis tools, and the stress distribution and deformation under high-pressure conditions are evaluated. The fluid flow inside the solenoid valve is simulated through fluid dynamics software, and the flow characteristics, pressure distribution, flow coefficient and flow loss of the fluid are analyzed to evaluate the flow control capability and response time of the solenoid valve; the finite element analysis and fluid dynamics simulation results are compared with the predetermined high-pressure fluid control requirements to evaluate whether the optimized solenoid valve meets the design indicators, including the maximum working pressure, minimum response time and maximum flow coefficient.

[0013] The present invention has the following beneficial effects:

[0014] (1) This engineering design optimization method for a solenoid valve can accurately determine the design boundary condition parameters of the solenoid valve and establish a reasonable set of constraint conditions by obtaining high-pressure fluid control demand data, thereby ensuring the reliability and safety of the solenoid valve in practical applications. This process provides a solid foundation for subsequent optimization. The design boundary condition parameters of the solenoid valve are genetically encoded and input into the genetic algorithm as optimization variables to construct a fitness function. By utilizing the powerful search capabilities of the genetic algorithm, the optimal solution can be effectively found in a wider solution space. The combination of selection, crossover and mutation operations can promote the diversity of the population, accelerate the convergence process, and thus improve the optimization efficiency.

[0015] (2) This method for optimizing the engineering design of a solenoid valve can achieve a comprehensive search of the design space by initializing the design parameters as particles in a particle swarm and defining a multi-objective optimization function. By using a particle swarm optimization algorithm, the collective intelligence characteristics of the particle swarm make the optimization process more flexible and efficient by adjusting the position and velocity of the particles, thereby improving the performance indicators of the solenoid valve. The performance of the solenoid valve under working conditions is verified by finite element analysis and fluid dynamics simulation, which can identify potential problems in advance during the design phase and reduce the cost and time of subsequent experimental verification. The simulation results are analyzed and the design boundary condition parameters are fine-tuned to ensure that the final designed solenoid valve meets the requirements of high-pressure fluid control, thereby enhancing its reliability and efficiency in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of an engineering design optimization method for a solenoid valve. DETAILED DESCRIPTION

[0017] The embodiment of the present application effectively solves the problems of insufficient performance and unreasonable design of the solenoid valve in the high-pressure fluid control process through an engineering design optimization method of the solenoid valve.

[0018] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0019] Obtain high-pressure fluid control demand data, determine the design boundary condition parameters of the solenoid valve, and establish a set of constraint conditions for the solenoid valve engineering design optimization.

[0020] The design boundary condition parameters of the solenoid valve are genetically encoded and input into the genetic algorithm as optimization variables to construct a fitness function. Through selection, crossover and mutation operations, a set of fitness function solutions is generated.

[0021] Initialize the design parameters for the particles in the particle swarm, define the multi-objective optimization function, update the position and velocity of the particles through the particle swarm optimization algorithm, guide the search direction of the particle swarm through the inertia weight and the group optimal, and gradually adjust the particle swarm to optimize the solution set of the fitness function.

[0022] The performance of the solenoid valve under working conditions is verified through finite element analysis and fluid dynamics simulation, and whether it meets the requirements of high-pressure fluid control is analyzed. The design boundary condition parameters of the solenoid valve are fine-tuned according to the simulation results.

[0023] See also Figure 1 The embodiment of the present invention provides a technical solution: an engineering design optimization method for a solenoid valve, comprising the following steps: S1. Acquire high-pressure fluid control demand data, determine the design boundary condition parameters of the solenoid valve, and establish a constraint condition set for the engineering design optimization of the solenoid valve; S2. Genetically encode the design boundary condition parameters of the solenoid valve, input them into the genetic algorithm as optimization variables, construct a fitness function, and generate a fitness function solution set through selection, crossover and mutation operations; S3. Initialize the design parameters as particles in a particle swarm, define a multi-objective optimization function, update the position and velocity of the particles through a particle swarm optimization algorithm, guide the search direction of the particle swarm through inertia weights and group optimality, and gradually adjust the particle swarm to optimize the fitness function solution set; S4. Verify the performance of the solenoid valve under working conditions through finite element analysis and fluid dynamics simulation, analyze whether it meets the requirements of high-pressure fluid control, and fine-tune the design boundary condition parameters of the solenoid valve according to the simulation results.

[0024] In this embodiment, S1. First, high-pressure fluid control demand data are collected, which include the working pressure of the fluid (the pressure of the liquid or gas in the pipeline or equipment), the flow demand (the volume or mass of the fluid passing through the cross section per unit time), the response time (the time required for the solenoid valve to start opening or closing after receiving the control signal), and the ambient temperature (the temperature that affects the material and fluid performance of the solenoid valve). Based on these requirements, the design boundary condition parameters of the solenoid valve are determined, such as the valve body material, size, and working environment. These parameters will constitute a set of constraints for the engineering design of the solenoid valve to ensure that specific performance requirements are met in practical applications. S2. The design boundary condition parameters are genetically encoded, that is, these parameters are converted into a format suitable for genetic algorithm processing. The parameters are represented in real number encoding form, and the generated genotype represents the possible characteristics of the solenoid valve design. Then, these encoded gene sets are input into the genetic algorithm as optimization variables, and a fitness function is constructed, which is a mathematical function used to evaluate the performance of each design scheme. The design objectives of the fitness function usually include performance indicators such as working pressure, response time and flow coefficient. By performing selection (selecting the best performing individual), crossover (combining the characteristics of two or more individuals to generate new individuals) and mutation (randomly changing certain genes of individuals to introduce diversity) operations on multiple design schemes, a fitness function solution set is generated. S3. In this step, the design parameters are initialized as particles in the particle swarm, and each particle represents a possible design scheme. Define a multi-objective optimization function, which is used to optimize multiple objectives (such as working pressure, response time, and flow coefficient) at the same time. Through the particle swarm optimization algorithm, the position and speed of the particles are updated, and the particles move in the solution space to find the optimal solution. By introducing inertia weight (controlling the degree of dependence of the particle on its previous state) and group optimality (the best solution among all current particles), the search direction of the particle swarm is guided, so that the particle swarm is gradually adjusted to optimize the fitness function solution set. S4. In this step, the performance of the solenoid valve under preset working conditions is verified by finite element analysis (a numerical calculation technology used to analyze the mechanical properties of the solenoid valve under different working conditions) and fluid dynamics simulation (simulating the flow state of the fluid inside the solenoid valve). Analyze whether the solenoid valve meets the needs of high-pressure fluid control. If it is found that the performance of the solenoid valve is not ideal under certain conditions, the design boundary condition parameters of the solenoid valve are fine-tuned according to the simulation results to ensure that the solenoid valve can work effectively and meet the design requirements under various working conditions. Genetic coding is a method of converting design variables into digital representations that can be used for optimization algorithms. In the engineering design of solenoid valves, genetic coding converts the design boundary condition parameters (such as valve body material, size, etc.) into a format suitable for genetic algorithm processing, usually using real number coding, that is, expressing the parameters as real values. Genetic coding allows the algorithm to evaluate and optimize multiple design options by simulating the process of natural selection.The fitness function is a mathematical function used to evaluate the quality of each design solution in an optimization problem. In solenoid valve optimization, the fitness function evaluates the performance of the solenoid valve design solution by considering multiple performance indicators (such as working pressure, response time, flow coefficient, etc.). The higher the fitness value, the better the solution meets the design requirements. Genetic algorithm is a heuristic search algorithm that simulates natural selection and genetics principles to find the optimal solution. The basic steps of the algorithm include operations such as selection, crossover, and mutation. Genetic algorithms iteratively evolve individuals (possible solutions) in the population to gradually improve the quality of the solution and are often used to solve complex optimization problems. Particle Swarm Optimization (PSO) is a swarm intelligence optimization technology inspired by the foraging behavior of bird flocks. Each "particle" represents a potential solution, and adjusts its position and speed by tracking its own best experience and the best experience of the group, gradually approaching the global optimal solution. PSO is suitable for multi-objective optimization problems and can quickly find a satisfactory solution in the solution space. Finite Element Analysis (FEA) is a numerical method used to solve complex physical problems. This method divides an object into multiple small finite elements (small, discrete parts) and analyzes the mechanical properties of the object under different working conditions by solving the equations of these finite elements. In solenoid valve design, FEA is used to verify the strength and deformation of the valve body under the action of high-pressure fluid. Computational FluidDynamics (CFD) is a technology that uses computers to simulate fluid motion. Through numerical methods, CFD can analyze the flow, pressure distribution and temperature changes of fluids under various conditions. In solenoid valve design, CFD is used to evaluate the flow characteristics of fluids in the valve body to ensure the effectiveness and reliability of the valve under working conditions. Constraints refer to restrictive conditions that must be met in the optimization problem, usually including physical limitations and performance requirements of design parameters. In the engineering design of solenoid valves, constraints may include maximum working pressure, minimum flow requirements, material properties, etc. These conditions ensure the feasibility of the final design solution in practical applications. Multi-objective optimization refers to the process of optimizing multiple objective functions simultaneously in the same optimization problem. This method is often used in engineering design because the design solution often requires a trade-off in multiple aspects such as performance, cost, and reliability. In solenoid valve optimization, multiple indicators such as operating pressure, response time and flow coefficient may need to be optimized simultaneously.

[0025] Specifically, the specific process of determining the design boundary condition parameters of the solenoid valve and establishing a set of constraint conditions for the solenoid valve engineering design optimization is as follows: Based on the high-pressure fluid control demand data, the maximum working pressure, minimum response time and maximum flow coefficient of the solenoid valve are determined as the constraint conditions for the solenoid valve engineering design optimization.

[0026] In this embodiment, the maximum working pressure is determined: according to the control requirements and fluid characteristics, the maximum working pressure that the solenoid valve must be able to withstand is determined. This parameter is one of the most important constraints in the design, ensuring that the solenoid valve will not fail or be damaged under extreme conditions. The setting of the maximum working pressure not only takes into account the pressure characteristics of the fluid, but also requires the evaluation of the safety standards of the system and the potential risks of the use environment. This parameter will directly affect the material selection and structural design of the solenoid valve. Determine the minimum response time: The minimum response time refers to the time required for the solenoid valve to start changing the fluid flow state from receiving the control signal. This parameter is critical for applications that require fast response (such as industrial automation and fluid control systems). By analyzing the system requirements, determine the response capability that the solenoid valve must have in a specific application. Ensure that the solenoid valve can complete the switching operation within the set time to meet the system's real-time control requirements for fluid flow. Determine the maximum flow coefficient: The flow coefficient is an important parameter that measures the amount of fluid allowed to pass through the solenoid valve at a given pressure. Maximizing the flow coefficient means that the solenoid valve should be designed to achieve maximum flow capacity when the fluid flows efficiently.

[0027] Specifically, the design boundary condition parameters of the solenoid valve are genetically encoded and input into the genetic algorithm as optimization variables. The specific process of constructing the fitness function is as follows: the design boundary condition parameters of the solenoid valve are numerically represented, and these parameters are converted into genotypes that can be processed by the genetic algorithm using real number coding; the encoded gene set is input into the genetic algorithm as the optimization variable to construct the initial population; the working pressure, response time and flow coefficient of the solenoid valve are set as the optimization targets to construct the fitness function.

[0028] In this implementation scheme, the design boundary condition parameters are numerically represented: the design boundary condition parameters of the solenoid valve include the maximum working pressure, the minimum response time and the maximum flow coefficient, etc. These parameters need to be converted into numerical values ​​during the design process for calculation and optimization. Maximum working pressure (P_max): set to 10MPa, minimum response time (T_min): set to 100ms, maximum flow coefficient (Cv max): set to 5.0. Through numerical representation, these physical quantities can be directly used for subsequent encoding and calculation. The real number coding form is converted into a genotype. The purpose of genetic coding is to convert the design parameters of the solenoid valve into a form that can be processed by the genetic algorithm. Use real number coding to represent each parameter. Assumption: x 1 Indicates the maximum working pressure (P_max), x 2 Indicates the minimum response time (T_min), x 3 Represents the maximum flow coefficient (Cv_max), expressed as: Genotype = [x 1 ,x 2 ,x3 ]=[P max ,T min ,Cv max ], the encoded gene set is used as the optimization variable and input into the genetic algorithm: the encoded gene set is used as the individuals of the initial population and input into the genetic algorithm. For example, if an initial population is defined to contain N individuals, it is expressed as: initial population = {individual 1 ,individual 2 ,…,individual N}; Construct fitness function: The purpose of fitness function is to quantify the performance of individuals under optimization objectives. Here, the working pressure, response time and flow coefficient of the solenoid valve are taken as optimization objectives, and the fitness function can be expressed as: F(x) = w 1 ·f 1 (x)+w 2 ·f 2 (x)+w 3 ·f 3 (x), where F(x) is the fitness function value, which indicates the comprehensive performance of an individual on the optimization target. The higher the value, the more the individual meets the optimization target. 1 ,w 2 ,w 3 : Weight coefficient, used to balance the importance of each optimization goal. Set according to actual needs, w 1 : represents the weight of work pressure in the overall goal, represents the weight of response time in the overall goal, w 3 : represents the weight of the flow coefficient in the overall target, f 1 (x): The working pressure objective function of the solenoid valve, which calculates the working pressure performance of the individual. Usually compared with the design target, the smaller the value, the closer the working pressure is to the target. 2 (x): The response time objective function of the solenoid valve, which calculates the response time performance of the individual. Compared with the target response time, the smaller the value, the more the response time meets the requirements. 3 (x): Flow coefficient objective function of the solenoid valve, calculate the flow coefficient performance of the individual. Compared with the target flow coefficient, the closer the value is to the target value, the better the flow performance of the individual. 1 (x): Work stress objective function, which measures whether the individual's work stress is within the design requirements; f 2 ( x): Response time objective function, which measures whether the individual response time meets the minimum requirement. f 3 (x): Flow coefficient objective function, which measures whether the individual flow coefficient is within the maximum range.

[0029] Specifically, the specific process of generating a set of solutions to the fitness function through selection, crossover and mutation operations is as follows: each individual in the initial population is evaluated based on the constructed fitness function to calculate its fitness value; individuals with high fitness values ​​are selected from the initial population, crossover and mutation operations are performed on the selected individuals, and the crossover rate and mutation rate are set within the standard range to obtain a set of solutions to the fitness function.

[0030] In this embodiment, based on the constructed fitness function, each individual in the initial population is evaluated. The fitness value of each individual is calculated. The fitness value usually reflects the performance of the individual on the optimization target. The higher the value, the better. The calculation of the fitness value can be completed by formula F(x). The specific method is shown in the above content. Individuals with high fitness values ​​are selected from the initial population to retain excellent genes. The selection strategy can adopt the roulette selection method. Crossover operation: Crossover operation is performed on the selected individuals to generate new individuals. Crossover is to combine the genetic information of two parents to produce offspring individuals. The crossover method can adopt single-point crossover, double-point crossover, etc. Crossover rate: The probability of the crossover operation is set, usually between 0.6 and 0.9. That is, only individuals within the crossover probability range will crossover. Mutation operation: Mutation operation is performed on the new individuals generated by the crossover to introduce new genetic information and increase the diversity of the population. Mutation can prevent the algorithm from falling into a local optimum. The mutation rate is set in a lower range (such as 0.01 to 0.05), that is, only a few individuals will mutate. Mutation method: It can be a small random adjustment of the individual's genes. Generate a fitness function solution set: Through selection, crossover and mutation operations, update the population and generate a new fitness function solution set as the input for the next round of iteration. Selection probability P select (x): The probability of individual x being selected, usually calculated by the following formula: Where n is the number of individuals in the population, and F(i) is the fitness value of the i-th individual. cross : Used to control the probability of crossover operation. If P cross =0.7, then there is a 70% chance of crossover. mutate : Control the probability of mutation operation. If P mutate =0.05, then there is a 5% probability of mutation. Offspring individual x': A new individual produced through crossover and mutation, whose genes may come from a combination of multiple parent individuals.

[0031] Specifically, the design parameters are initialized as particles in the particle swarm, and the specific process of defining the multi-objective optimization function is as follows: the design boundary condition parameters of the solenoid valve are used as the initial particles in the particle swarm and randomly initialized to form a particle swarm; the multi-objective optimization function is defined according to the optimization objectives and the constraints of the solenoid valve engineering design optimization; the fitness value of each particle under the multi-objective optimization function is calculated, and the historical optimal position of each particle is recorded.

[0032] In this embodiment, the particle swarm is randomly initialized: the design boundary condition parameters of the solenoid valve are used as the initial particles in the particle swarm. Each particle represents a possible solution, including different design parameter combinations of the solenoid valve (such as working pressure, flow coefficient and response time, etc.). The purpose of random initialization is to explore the design space and ensure that the distribution of the initial particles can cover the entire design space to avoid being limited to the local optimal solution. For example, assuming that the maximum working pressure of the solenoid valve is 100 bar, the minimum response time is 0.1 seconds, and the flow coefficient is 10, multiple particles can be randomly generated within these ranges. According to the optimization objectives and the constraints of the solenoid valve engineering design optimization, a multi-objective optimization function is defined. The optimization objectives are usually multiple, such as minimizing the response time, maximizing the flow coefficient, and ensuring stability under a certain pressure. The fitness value of each particle under the multi-objective optimization function is calculated. The fitness value reflects the quality of the particle and is usually calculated by evaluating the performance of the particle on all objectives. The calculation formula of the fitness value can be evaluated by the weighted sum method. The particle will continuously adjust its position and speed according to the fitness value and the historical optimal position, in order to find the optimal solution that meets the optimization objectives of the solenoid valve engineering design. The core of this method is to enable particles to effectively search in the design space through collective cooperation and information sharing to find the global optimal solution.

[0033] Specifically, the specific process of guiding the search direction of the particle swarm by inertia weight and group optimality and gradually adjusting the particle swarm to achieve the optimal solution of the objective function is as follows: initialize the particle speed and position through the particle swarm optimization algorithm to ensure that each particle is evenly distributed in the search space; set the inertia weight to ensure that the particles can search within a suitable range; use the current speed of each particle, its historical optimal position and the global optimal position to calculate the particle update speed; update the speed and position of each particle by iteratively, evaluate its fitness value, set the fitness threshold as the convergence condition, and compare and update the particle's historical optimal position and the group's global optimal position in each iteration until the preset convergence condition is met or the maximum number of iterations is reached.

[0034] In this implementation scheme, in the particle swarm optimization algorithm (PSO), the speed and position of the particles need to be randomly initialized first. Each particle represents a possible solution, the speed determines the speed of the particle moving in the search space, and the position represents the current solution. Ensuring that each particle is evenly distributed in the search space helps to improve the algorithm's exploration ability and reduce the trapping of local optimal solutions. Set the inertia weight: Inertia Weight is an important parameter in the particle swarm optimization algorithm, which is used to control the movement of particles in the search space. By setting a suitable inertia weight, the search range of particles during the exploration process can be ensured. A high inertia weight gives particles greater mobility and the ability to explore a wider search space, while a low inertia weight prompts particles to converge to the optimal solution faster. The inertia weight is generally set in the algorithm to a value that decreases linearly or nonlinearly with the number of iterations. Calculate the update speed of the particle. The particle update speed calculation formula is: v i (t+1)=w·v i (t)+c 1 ·r 1 ·(pbest i -x i (t))+c 2 ·r 2 ·(gbest-x i (t)); where: v i (t): velocity of particle i at time t. w: inertia weight. c 1 and c 2 : are the individual learning factor and the social learning factor, respectively, which are usually between [0,2] and are used to adjust the sensitivity of the particle to its own optimal position and the group's optimal position. 1 and r 2 : A random number between [0,1] used to increase the randomness of the algorithm. pbest i : The best historical position of particle i. gbest: The global best position (the position of the particle with the best fitness value among all particles). i(t): The current position of particle i at time t. Fitness Threshold refers to the standard that the fitness value should reach when the algorithm converges. Usually in optimization problems, if the fitness value changes less than the preset threshold or reaches a specific value, the algorithm is considered to have converged. In each iteration, the historical optimal position of the particle and the global optimal position of the group are compared and updated to dynamically adjust the optimization process. Convergence conditions are met or the maximum number of iterations is reached: The iterative process will continue until the preset convergence conditions are met or the maximum number of iterations is reached. The latter is to prevent the algorithm from executing indefinitely. Through these steps, the particle swarm optimization algorithm can effectively search the design space and find the optimal solution that meets the optimization objectives of the solenoid valve engineering design.

[0035] Specifically, the performance of the solenoid valve under working conditions is verified through finite element analysis and fluid dynamics simulation, and the specific process of analyzing whether it meets the needs of high-pressure fluid control is as follows: the structure of the solenoid valve is analyzed through finite element analysis tools, and the stress distribution and deformation under high-pressure conditions are evaluated; the fluid flow inside the solenoid valve is simulated through fluid dynamics software, and the flow characteristics, pressure distribution, flow coefficient and flow loss of the fluid are analyzed to evaluate the flow control capability and response time of the solenoid valve; the finite element analysis and fluid dynamics simulation results are compared with the predetermined high-pressure fluid control requirements to evaluate whether the optimized solenoid valve meets the design indicators, including the maximum working pressure, minimum response time and maximum flow coefficient.

[0036] In this embodiment, finite element analysis (FEA) evaluates the structural performance of the solenoid valve under high pressure conditions, especially the stress distribution and deformation. This is crucial to ensure the mechanical stability and durability of the solenoid valve. The three-dimensional model of the solenoid valve is meshed using finite element analysis software (such as ANSYS, ABAQUS, etc.) and divided into several small units. The model is analyzed using corresponding boundary conditions and loads (such as working pressure) to calculate stress, strain and displacement. The analysis results will show which parts of the solenoid valve may have high stress concentration under different working conditions, thereby evaluating its safety and structural reliability. Computational FluidDynamics (CFD) evaluates the performance of the solenoid valve in fluid control, mainly analyzing the fluid flow characteristics, pressure distribution, flow coefficient and flow loss to ensure that the solenoid valve can effectively control the fluid under actual working conditions. Fluid dynamics software is used to model and simulate the fluid flow inside the solenoid valve. Set boundary conditions (such as inlet flow rate, outlet pressure) and physical properties (such as fluid type, temperature, etc.) to simulate the flow field. Calculate the velocity field, pressure field, flow coefficient, etc. of the fluid inside the solenoid valve, and analyze the flow loss. The flow coefficient is a key indicator that describes the flow control capability of the valve, and is usually closely related to the valve design and flow state. Compare the results obtained from the finite element analysis and fluid dynamics simulation with the predetermined high-pressure fluid control requirements to determine whether the optimized solenoid valve meets the design indicators. Compare the stress and deformation data obtained in the finite element analysis to determine whether it is within the safety range and whether it meets the maximum working pressure requirements. Compare the flow coefficient and response time obtained in the fluid dynamics simulation to ensure that the performance of the solenoid valve can meet the design requirements of minimum response time and maximum flow coefficient. Make necessary design improvements based on the results.

[0037] Specifically, the specific process of fine-tuning the design boundary condition parameters of the solenoid valve according to the simulation results is as follows: through finite element analysis and fluid dynamics simulation results, if the performance of the solenoid valve does not fully meet the high-pressure fluid control requirements, the design boundary condition parameters of the solenoid valve are fine-tuned through the gradient descent algorithm. The gradient of the solenoid valve performance index to each design parameter is calculated, and the maximum working pressure, minimum response time and flow coefficient are gradually adjusted along the gradient descent direction of the performance error until the simulation results of the solenoid valve meet the preset high-pressure fluid control requirements.

[0038] In this implementation scheme, the gradient descent algorithm: This algorithm is used to gradually adjust the design parameters of the solenoid valve so that the performance index is close to the target value. The core idea of ​​gradient descent is to calculate the partial derivative (gradient) of the objective function with respect to each design parameter, and move the design parameter in the direction of gradient descent until it converges to a better solution. Calculate the gradient of the solenoid valve performance index to each design parameter: For each design parameter (such as maximum working pressure, minimum response time, flow coefficient), calculate its impact on the performance error, and measure this impact through the partial derivative (ie gradient). The performance error function of the solenoid valve is E(θ), where θ represents the design parameter (such as a set of maximum working pressure, minimum response time, and flow coefficient), and the update formula of gradient descent is as follows: Among them, θ new : Updated design parameters, θ old : Current design parameters, η: learning rate, controls the step size of each adjustment, The gradient of the performance error function with respect to the design parameter θ indicates the sensitivity of the performance error to each parameter. The maximum working pressure indicates the solenoid valve's ability to withstand high pressure conditions, which affects the valve's reliability. The minimum response time indicates the shortest time required for the solenoid valve to go from open to closed, which affects the control system's response speed. The flow coefficient measures the amount of fluid allowed to pass through the solenoid valve, which affects the fluid regulation capability. Performance error: The performance error is the difference between the simulation result and the target value. The gradient descent algorithm gradually adjusts the design parameters based on this error to minimize the error.

[0039] In summary, this application has at least the following effects:

[0040] An engineering design optimization method for a solenoid valve is proposed. The method obtains high-pressure fluid control demand data, clarifies the design boundary condition parameters, and establishes a set of optimization constraints to ensure design accuracy. Genetic coding is used to convert design parameters into optimization variables, and a fitness function is constructed through a genetic algorithm to generate a solution set. In the particle swarm optimization stage, the design parameters are initialized as particles, and a multi-objective optimization function is defined. The inertia weight and the group optimal guidance search direction are used to gradually optimize the fitness function solution set. The performance of the solenoid valve is verified by finite element analysis and fluid dynamics simulation, and whether the control requirements are met is analyzed, and the design parameters are fine-tuned according to the simulation results. The problem of insufficient performance and unreasonable design of the solenoid valve in the process of high-pressure fluid control is effectively solved.

[0041] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0043] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0045] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0046] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the engineering design of a solenoid valve, characterized in that: The following steps are involved: S1. Obtain high-pressure fluid control demand data, determine the design boundary condition parameters of the solenoid valve, and establish a set of constraint conditions for the solenoid valve engineering design optimization; S2. Genetically encode the design boundary condition parameters of the solenoid valve and input them into the genetic algorithm as optimization variables to construct a fitness function. Through selection, crossover and mutation operations, a solution set of the fitness function is generated; S3. Initialize the design parameters for the particles in the particle swarm, define the multi-objective optimization function, update the position and velocity of the particles through the particle swarm optimization algorithm, guide the search direction of the particle swarm through the inertia weight and the group optimal, and gradually adjust the particle swarm to optimize the solution set of the fitness function; S4. Verify the performance of the solenoid valve under working conditions through finite element analysis and fluid dynamics simulation, analyze whether it meets the requirements of high-pressure fluid control, and fine-tune the design boundary condition parameters of the solenoid valve based on the simulation results.

2. The engineering design optimization method of a solenoid valve according to claim 1, characterized in that: The high-pressure fluid control demand data includes the working pressure range, flow demand, response time and ambient temperature of the solenoid valve.

3. The engineering design optimization method of a solenoid valve according to claim 2, characterized in that: The specific process of determining the design boundary condition parameters of the solenoid valve and establishing the constraint condition set for the solenoid valve engineering design optimization is as follows: Based on the high-pressure fluid control demand data, the maximum working pressure, minimum response time and maximum flow coefficient of the solenoid valve are determined as the constraints for the solenoid valve engineering design optimization.

4. The engineering design optimization method of a solenoid valve according to claim 3, characterized in that: The design boundary condition parameters of the solenoid valve are genetically encoded and input into the genetic algorithm as optimization variables. The specific process of constructing the fitness function is as follows: The design boundary condition parameters of the solenoid valve are numerically represented and converted into genotypes that can be processed by the genetic algorithm using real number coding. The encoded gene set is used as an optimization variable and input into the genetic algorithm to construct the initial population; The working pressure, response time and flow coefficient of the solenoid valve are set to optimize the objectives and construct the fitness function.

5. The engineering design optimization method of a solenoid valve according to claim 4, characterized in that: The specific process of generating a set of fitness function solutions through selection, crossover and mutation operations is as follows: Based on the constructed fitness function, each individual in the initial population is evaluated and its fitness value is calculated; Individuals with high fitness values ​​are selected from the initial population, crossover and mutation operations are performed on the selected individuals, and the crossover rate and mutation rate are set within the standard range, and the fitness function solution is aggregated.

6. The engineering design optimization method of a solenoid valve according to claim 5, characterized in that: The specific process is as follows: Initialize the design parameters as particles in the particle swarm, and define the specific process of the multi-objective optimization function as follows: The design boundary condition parameters of the solenoid valve are used as initial particles in the particle swarm and randomly initialized to form a particle swarm; Define the multi-objective optimization function according to the optimization objectives and the constraints of solenoid valve engineering design optimization; The fitness value of each particle under the multi-objective optimization function is calculated, and the historical optimal position of each particle is recorded.

7. The engineering design optimization method of a solenoid valve according to claim 6, characterized in that: The specific process is as follows: The search direction of the particle swarm is guided by the inertia weight and the group optimality, and the particle swarm is gradually adjusted to achieve the optimal solution of the objective function. Initialize particle speed and position through particle swarm optimization algorithm to ensure that each particle is evenly distributed in the search space; Set the inertia weight to ensure that particles can search within a suitable range; Using the current speed of each particle, its historical optimal position and the global optimal position, calculate the particle's update speed; The speed and position of each particle are iteratively updated, its fitness value is evaluated, the fitness threshold is set as the convergence condition, and the historical optimal position of the particle and the global optimal position of the group are compared and updated in each iteration until the preset convergence condition is met or the maximum number of iterations is reached.

8. The engineering design optimization method of a solenoid valve according to claim 7, characterized in that: The specific process is as follows: The performance of the solenoid valve under working conditions is verified through finite element analysis and fluid dynamics simulation, and the specific process of analyzing whether it meets the needs of high-pressure fluid control is as follows: The structure of the solenoid valve is analyzed by finite element analysis tools to evaluate the stress distribution and deformation under high pressure conditions. The fluid flow inside the solenoid valve is simulated by fluid dynamics software to analyze the flow characteristics, pressure distribution, flow coefficient and flow loss of the fluid, so as to evaluate the flow control capability and response time of the solenoid valve. The finite element analysis and fluid dynamics simulation results are compared with the predetermined high-pressure fluid control requirements to evaluate whether the optimized solenoid valve meets the design indicators, including maximum operating pressure, minimum response time and maximum flow coefficient.

9. The engineering design optimization method of a solenoid valve according to claim 8, characterized in that: The specific process of fine-tuning the design boundary condition parameters of the solenoid valve according to the simulation results is as follows: Through finite element analysis and fluid dynamics simulation results, if the performance of the solenoid valve does not fully meet the high-pressure fluid control requirements, the design boundary condition parameters of the solenoid valve are fine-tuned through the gradient descent algorithm.

10. The engineering design optimization method of a solenoid valve according to claim 9, characterized in that: The specific process is as follows: The specific process of fine-tuning the design boundary condition parameters of the solenoid valve through the gradient descent algorithm is as follows: The gradient of the solenoid valve performance index to each design parameter is calculated, and the maximum working pressure, minimum response time and flow coefficient are gradually adjusted along the gradient descending direction of the performance error until the simulation result of the solenoid valve meets the preset high-pressure fluid control requirements.

Images