A variable stiffness spring simulation design method for a high-pressure common rail pump

Abstract

This invention relates to the field of diesel engine fuel supply technology, specifically to a simulation design method for a variable stiffness spring used in a high-pressure common rail pump. The method includes: establishing a multiphysics coupling model based on simulation software; establishing a thermodynamic module; simulating plunger motion; simulating valve core motion; establishing a dynamic fuel pressure change model; calculating spring stiffness; calculating target adjustment parameters; optimizing the spring stiffness coefficient: adjusting the spring stiffness coefficient using a genetic algorithm; setting and initializing the population, then selecting an operation method and performing crossover operations to obtain offspring individual data; outputting optimal parameters; calculating optimal target parameters: introducing the optimal parameters into the simulation software to reduce the maximum collision velocity V of the valve core and improve the system volumetric efficiency L; by using the variable stiffness spring of this invention, the collision velocity can be effectively reduced, the impact stress between the valve core and the valve seat can be reduced, fatigue damage between valve core components can be reduced, and the service life of the high-pressure common rail pump valve assembly can be improved.

Description

Technical Field

[0001] This invention relates to the field of diesel engine fuel supply technology, and more specifically to a method for simulating the design of a variable stiffness spring for a high-pressure common rail pump. Background Technology

[0002] As a core component of the fuel supply system, the high-pressure common rail pump of a diesel engine plays a crucial role in the operation of the common rail system and the entire diesel engine. Currently, both the high-pressure common rail pump in a diesel engine fuel supply system and the unit pump in a mechanical fuel supply system are plunger pumps with valve distribution structures. Among them, the structural parameters of the plunger pump's inlet and outlet valves significantly affect the performance indicators of the high-pressure pump and even the entire diesel engine.

[0003] As a positive displacement pump, the piston pump's valve core moves under the combined force of its own gravity, spring force, and hydraulic force. During the opening and closing process of the valve core, its response speed largely determines the volumetric efficiency of the reciprocating pump; in addition, the collision between the valve core and the valve seat and limiting mechanism during movement generates impact stress, which directly affects the durability of the pump and valve assembly.

[0004] Of the three forces acting on the valve core, the spring force is provided by the return spring. Based on the relationship between spring force and compression, springs can be classified as linear springs and nonlinear springs. Linear springs have a constant stiffness coefficient, and their deformation is linearly related to the applied force; nonlinear springs, on the other hand, have their stiffness coefficient dynamically adjusted with changes in deformation.

[0005] Currently, the return springs of the inlet and outlet valves of high-pressure pumps mostly use linear springs as specified in national standards. Linear springs have a simple structure and low manufacturing cost, making them suitable for low-to-medium frequency and relatively stable operating conditions, but they have limitations in response adaptability. Nonlinear springs, with their optimized opening and closing characteristics, can better handle high-frequency opening and closing and complex operating conditions, reducing pressure shocks, but their design and manufacturing costs are relatively high, and their performance stability needs to be strictly controlled. For example, the speed regulating spring in a diesel engine's mechanical governor is a typical nonlinear spring, and its design and manufacturing require precise control.

[0006] In summary, nonlinear springs (variable stiffness springs) have demonstrated broad application potential in diesel engine fuel supply technology. Applying nonlinear springs to high-pressure pump valves can significantly improve valve spool response speed and reduce high-pressure fuel backflow, thereby improving pump volumetric efficiency. Simultaneously, nonlinear springs can reduce the collision speed between the valve spool and the limiting mechanism and valve seat during valve opening and closing, thus reducing impact stress and improving the service life and reliability of pump and valve components. However, due to high design and manufacturing costs, the application of nonlinear springs in the domestic high-pressure pump technology field is not yet widespread; currently, most pump and valve springs are still dominated by linear springs.

[0007] Therefore, to improve the volumetric efficiency of high-pressure pumps and reduce valve core impact stress, replacing traditional linear springs with nonlinear springs (variable stiffness springs) is a practical solution. Based on this, this invention proposes a simulation design method for variable stiffness springs in high-pressure common rail pumps to meet the requirements of high performance and high reliability. Summary of the Invention

[0008] To overcome the aforementioned technical problems, the present invention aims to provide a simulation design method for variable stiffness springs in high-pressure common rail pumps, which solves the problems that most pump valve springs in domestic high-pressure pumps are standard parts and linear springs, which can easily lead to problems such as excessive valve seat speed and slow valve core response speed in some application scenarios, resulting in untimely valve core seat and reduced volumetric efficiency.

[0009] The objective of this invention can be achieved through the following technical solutions:

[0010] A simulation design method for variable stiffness springs used in high-pressure common rail pumps includes the following steps:

[0011] S1. Establish a multiphysics coupling model based on simulation software, including mechanical, hydraulic, and thermodynamic modules; couple these modules to calculate mechanical parameters and use them as the basis for optimizing spring stiffness coefficients;

[0012] S2. Establish a thermodynamic module: Collect experimental data on fuel temperature and spring stiffness, and fit the collected experimental data to obtain the temperature sensitivity coefficient. A nonlinear correction function is obtained by coupling the spring displacement with temperature. Temperature sensitivity coefficient and nonlinear correction function Used to calculate the spring stiffness of the coupled variable stiffness spring. Nonlinear correction function c1, c2, and c3 are the fitting parameters, all of which are greater than 0 and less than or equal to 1. c1 + c2 + c3 = 1, where x and T are calculated after normalization.

[0013] S3. Simulate plunger motion: Perform operational state analysis and force analysis on the plunger, simulate the plunger motion mode, decompose the influence parameters of the plunger motion, and construct the plunger motion equation;

[0014] S4. Simulate valve core motion: Perform operational state analysis and force analysis on the valve core, fit the valve core's motion velocity, decompose the influencing parameters of the valve core's motion, and construct the valve core's motion equation;

[0015] S5. Establish a dynamic fuel pressure change model: Analyze the impact of fuel pressure fluctuations using the plunger motion equation obtained in S3 and the valve core motion equation obtained in S4, and use this model to evaluate the stability of the parameters of the plunger motion equation and the valve core motion equation.

[0016] S6. Calculate spring stiffness : Define the spring stiffness coefficients a and b; calculate according to the following formula:

[0017] Define initial values ​​a0 and b0, and calculate the initial spring stiffness.

[0018] ; x represents the deformation of the spring. The main reason for using an inverse proportional function is that when the independent variable x is small, the dependent variable... Compared to other functions, its decay rate is faster. Applying this characteristic to spring stiffness allows the spring to respond quickly under low load conditions, effectively reducing valve core return hysteresis and improving the system's dynamic performance. Furthermore, this characteristic maintains stable opening and closing behavior under high load conditions, ensuring system reliability and efficiency.

[0019] S7. Calculate the target adjustment parameters: Based on the simulation software, obtain the maximum collision velocity V of the valve core and the system volumetric efficiency L;

[0020] S8. Optimize Spring Stiffness Coefficients: Adjust spring stiffness coefficients a and b using a genetic algorithm; set and initialize the population, then select an operation method and perform crossover to obtain offspring data; perform mutation on the offspring data to increase population diversity; update the population and repeat the iteration until the algorithm converges, then output the optimal parameters. and ;

[0021] S9. Calculate the optimal objective parameters: Use the optimal parameters obtained in S8... and The introduction of simulation software AMESim reduces the maximum collision velocity V of the valve core and improves the system volumetric efficiency L.

[0022] Further details should be noted: the simulation software is AMESim; the mechanical module is used to simulate the movement of the plunger and valve core, calculating dynamic displacement, velocity, and acceleration; the hydraulic module is used to simulate fuel flow and pressure fluctuations; and the thermodynamic module is used to simulate the effect of temperature on the nonlinear behavior of the spring.

[0023] It should be further explained that:

[0024] The formula for calculating the maximum collision velocity V of the valve core in S7 is as follows:

[0025] , It's the quality of the valve core. It is the acceleration of the valve core; , It is the distance between the valve core and its formation.

[0026] The formula for calculating the system volumetric efficiency L in S7 is as follows:

[0027] , It is the actual output flow rate. It is the theoretical output flow rate; ,in It is the cross-sectional area of ​​the plunger. It is the plunger's stroke distance. It is the piston drive frequency; , It is the speed of the plunger's movement.

[0028] It should be further explained that the specific steps of the plunger motion equation are as follows:

[0029] S31. Plunger Analysis: Perform operational and force analysis on the plunger. The plunger's motion is affected by spring force, fuel pressure, and damping force. Then, simulate the plunger's motion mode.

[0030] S32. Parameters affecting the decomposition of plunger motion:

[0031] Analysis of the plunger's motion revealed that the parameters affecting the plunger's motion include: plunger mass, damping coefficient, spring force, plunger pressure area, damping force, and fuel pressure.

[0032] S33. Constructing the plunger motion equation: Through mechanical analysis of the above parameters, the plunger motion equation is obtained as follows:

[0033] ;

[0034] in, For the plunger mass, The linear plunger damping coefficient is... The nonlinear damping coefficient is... Indicates damping force ; For piston spring displacement The spring force generated at that time For fuel pressure, This represents the area of ​​the plunger under pressure. This represents the fuel pressure force generated by the fuel. ; The driving force of the plunger is obtained and set through simulation experiments.

[0035] Among them, the spring force of the plunger .

[0036] It should be further explained that the specific steps of the valve core motion equation are as follows:

[0037] S41. Valve core analysis: Perform operational and force analysis on the valve core. The movement of the valve core is affected by the combined action of spring force, fuel pressure, and damping force. Then, simulate the movement mode of the valve core.

[0038] S42. Parameters affecting the decomposition of valve core movement:

[0039] Analysis of the valve core's motion revealed that the parameters affecting its motion include: valve core mass, damping coefficient, spring force, damping force, and fuel pressure.

[0040] S43. Constructing the valve core motion equation: Through mechanical analysis of the above parameters, the valve core motion equation is obtained as follows:

[0041] ;

[0042] in, For valve core quality, The linear plunger damping coefficient is... The nonlinear damping coefficient is... Indicates damping force ; For valve core spring displacement The spring force generated at that time For fuel pressure, This represents the area of ​​the plunger under pressure. This represents the fuel pressure force generated by the fuel. .

[0043] Among them, the spring force of the valve core .

[0044] It should be further explained that;

[0045] In S5, the impact of fuel pressure fluctuations is analyzed using the plunger motion equation obtained in S3 and the valve core motion equation obtained in S4.

[0046] The pressure fluctuation formula is constructed as follows:

[0047] ,in It is the bulk elastic modulus of fuel, reflecting the compressibility of fuel; It is the volume of the high-pressure chamber; It is the fuel flow rate entering the high-pressure chamber. This refers to the flow rate of fuel discharged from the high-pressure chamber.

[0048] It should be further explained that the specific steps of the genetic algorithm to adjust the spring stiffness coefficients a and b are as follows:

[0049] S81 defines the objective function:

[0050] The goal is to adjust the spring parameters a and b using a genetic algorithm to ensure the high-pressure common rail system meets performance requirements. The optimization problem can be defined as an objective function, in the following form:

[0051] in: The objective function consists of the maximum collision velocity and volumetric efficiency; a and b are the parameters that need to be optimized.

[0052] S82 constructs the objective function:

[0053] objective function Where w1 and w2 are weighting coefficients;

[0054] Define the range of values ​​for a and b, and set the constraints.

[0055] S83 Initial Population:

[0056] Each individual represents a set of spring parameters (a, b), which can be represented by a vector: [a, b].

[0057] The population is a collection of all individuals, with a size of Np, representing the population size; then the parameters are initialized.

[0058] An initial population is randomly generated, with individuals uniformly distributed within the design space; the objective function value is calculated for each individual in the population. ;

[0059] S84 Selection Operation Method:

[0060] The objective function value calculated based on S83 is the fitness value assigned to each individual in the population, and the specific formula is as follows:

[0061] Select an operation method and choose individuals from the population based on their fitness values ​​to generate the parent individuals for the next generation;

[0062] S85 crossover operation:

[0063] Using a two-point crossover or uniform crossover method, two parent individuals are randomly selected to generate two offspring.

[0064] S86 mutation operation

[0065] The parameters of offspring individuals are randomly perturbed to increase population diversity, and a low mutation probability of 0.01 to 0.1 is set to prevent premature convergence.

[0066] S87 Population Update: Merge parent and offspring, sort by fitness, and select the top n individuals as the next generation; the algorithm stops when the change in the population's objective function value is less than a preset threshold or when the maximum number of iterations is reached, and outputs the optimal parameters. and .

[0067] In S83, the operating method is either roulette wheel selection or tournament selection.

[0068] The beneficial effects of this invention are:

[0069] This invention discloses a simulation design method for a variable stiffness spring in a high-pressure common rail pump. It couples the motion states of the plunger and valve core in the high-pressure common rail system, analyzes the system force state of the variable stiffness spring, and constructs mechanical, hydraulic, and thermodynamic modules based on simulation software according to the system's operating state. This is used to evaluate the impact of the variable stiffness spring's stiffness on the system. An optimization algorithm is then used to find the optimal parameters to reduce the maximum collision speed and improve the system's volumetric efficiency, while simultaneously reducing the impact of fuel pressure fluctuations on the system. Attached Figure Description

[0070] The invention will now be further described with reference to the accompanying drawings.

[0071] Figure 1 This is a schematic diagram of a variable stiffness spring simulation design method for a high-pressure common rail pump according to the present invention. Detailed Implementation

[0072] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0073] Example 1:

[0074] Please see Figure 1 As shown in the figure, this embodiment is a simulation design method for a variable stiffness spring used in a high-pressure common rail pump. The specific steps are as follows:

[0075] S1. Establish a multiphysics coupling model based on the simulation software AMESim, including mechanical, hydraulic and thermodynamic modules;

[0076] The mechanical module is used to simulate the movement of the plunger and valve core, and calculate dynamic displacement, velocity and acceleration; the hydraulic module is used to simulate fuel flow and pressure fluctuations; and the thermodynamic module is used to simulate the effect of temperature on the nonlinear behavior of the spring.

[0077] S2. Establish a thermodynamic module: Collect experimental data on fuel temperature and spring stiffness, and fit the collected experimental data to obtain the temperature sensitivity coefficient. A nonlinear correction function is obtained by coupling the spring displacement with temperature. Temperature sensitivity coefficient and nonlinear correction function Used to calculate the spring stiffness of the coupled variable stiffness spring. Nonlinear correction function c1, c2, and c3 are the fitting parameters; the nonlinear correction function is used to represent the nonlinear behavior and multi-physics coupling effects of the material, and the fitting parameters should be adjusted based on the results of multiple simulations.

[0078] S3. Simulate plunger motion: Perform operational state analysis and force analysis on the plunger, simulate the plunger motion mode, decompose the influence parameters of the plunger motion, and construct the plunger motion equation;

[0079] S4. Simulate valve core motion: Perform operational state analysis and force analysis on the valve core, fit the valve core's motion velocity, decompose the influencing parameters of the valve core's motion, and construct the valve core's motion equation;

[0080] S5. Establish a dynamic fuel pressure change model: Analyze the impact of fuel pressure fluctuations using the plunger motion equation obtained in S3 and the valve core motion equation obtained in S4, and use this model to evaluate the stability of the parameters of the plunger motion equation and the valve core motion equation.

[0081] S6. Calculate spring stiffness : Define the spring stiffness coefficients a and b; calculate according to the following formula:

[0082] Define initial values ​​a0 and b0, and calculate the initial spring stiffness.

[0083] ;

[0084] S7. Calculate the target adjustment parameters: Based on the simulation software AMESim, obtain the maximum collision velocity V of the valve core and the system volumetric efficiency L, where... , It's the quality of the valve core. It is the acceleration of the valve core; , It is the actual output flow rate. It is the theoretical output flow rate; , It is the distance between the valve core and its formation.

[0085] ,in It is the cross-sectional area of ​​the plunger. It is the plunger's stroke distance. It is the piston drive frequency; , It is the speed of the plunger's movement;

[0086] S8. Optimize Spring Stiffness Coefficients: Adjust spring stiffness coefficients a and b using a genetic algorithm; set and initialize the population, then select an operation method and perform crossover to obtain offspring data; perform mutation on the offspring data to increase population diversity; update the population and repeat the iteration until the algorithm converges, then output the optimal parameters. and ;

[0087] S9. Calculate the optimal objective parameters: Use the optimal parameters obtained in S8... and The introduction of simulation software AMESim reduces the maximum collision velocity V of the valve core and improves the system volumetric efficiency L.

[0088] Since the valve core repeatedly impacts the valve seat during operation, the variable stiffness spring in this invention can effectively reduce the impact speed, reduce the impact stress between the valve core and the valve seat, reduce fatigue damage between the valve core assembly, and improve the service life of the high-pressure common rail pump valve assembly.

[0089] Example 2:

[0090] Based on Example 1, this embodiment further illustrates the specific steps of the plunger motion equation as follows:

[0091] S31. Plunger Analysis: Perform operational and force analysis on the plunger. The plunger's motion is affected by spring force, fuel pressure, and damping force. Then, simulate the plunger's motion mode.

[0092] In the high-pressure common rail system, the plunger is driven by a cam to reciprocate. Its main task is to compress the fuel and inject it into the high-pressure chamber to provide high-pressure fuel to the injector.

[0093] The plunger's thrust is set to be [value] during its movement. Fuel pressure is generated during the movement. The higher the pressure, the greater the reaction force on the plunger, and the greater the resistance to plunger movement. At the same time, pressure fluctuations will cause instability in the plunger movement, which will affect the movement of the valve core.

[0094] The spring force generated by the plunger spring also affects the plunger's movement. and damping force The above factors combined will affect the stability of the plunger movement and the resulting fuel pressure. To exert influence;

[0095] S32. Parameters affecting the decomposition of plunger motion:

[0096] Analysis of the plunger's motion revealed that the parameters affecting the plunger's motion include: plunger mass, damping coefficient, spring force, plunger pressure area, damping force, and fuel pressure.

[0097] S33. Constructing the plunger motion equation: Through mechanical analysis of the above parameters, the plunger motion equation is obtained as follows:

[0098] ;

[0099] in, For the plunger mass, The linear plunger damping coefficient is... The nonlinear damping coefficient is... Indicates damping force ; For piston spring displacement The spring force generated at that time For fuel pressure, This represents the area of ​​the plunger under pressure. The fuel pressure force generated by the fuel. ; This indicates the driving force of the plunger, which is obtained and set through simulation experiments.

[0100] spring force of the plunger ;

[0101] Fuel pressure force fuel pressure and the pressure area of ​​the plunger Jointly determined, and damping force Caused by friction or other dissipation effects between the plunger and the liquid, it is related to the plunger speed, which in turn affects the volumetric efficiency L.

[0102] The actual output flow rate of a high-pressure oil pump is less than its theoretical output flow rate. The ratio of the actual output flow rate to the theoretical output flow rate of a high-pressure oil pump is the volumetric efficiency of the high-pressure oil pump.

[0103] Example 3:

[0104] Based on any of the above embodiments, this embodiment further illustrates the specific steps of the valve core motion equation as follows:

[0105] S41. Valve core analysis: Perform operational and force analysis on the valve core. The movement of the valve core is affected by the combined action of spring force, fuel pressure, and damping force. Then, simulate the piston movement.

[0106] The valve core's movement is powered by fuel pressure. The higher the fuel pressure, the greater the maximum impact speed the valve core can achieve. The maximum impact speed needs to be reduced by adjusting the spring's stiffness coefficient.

[0107] S42. Parameters affecting the decomposition of valve core movement:

[0108] Analysis of the valve core's motion revealed that the parameters affecting its motion include: valve core mass, damping coefficient, spring force, damping force, and fuel pressure.

[0109] S43. Constructing the valve core motion equation: Through mechanical analysis of the above parameters, the valve core motion equation is obtained as follows:

[0110] ;

[0111] in, For valve core quality, The linear plunger damping coefficient is... The nonlinear damping coefficient is... Indicates damping force ; For valve core spring displacement The spring force generated at that time For fuel pressure, This represents the area of ​​the plunger under pressure. This represents the fuel pressure force generated by the fuel. ;

[0112] The maximum collision velocity V is an important output of the valve core's motion equation, reaching its maximum when the valve core reaches the seat surface. The calculation of the maximum collision velocity V depends on the valve core's dynamic equation and is obtained through numerical integration or simulation calculation.

[0113] The magnitude of the maximum collision speed V is affected by fuel pressure, spring stiffness, damping effect, etc. The collision speed can be reduced by optimizing these parameters. By reasonably designing the parameters a and b of the variable stiffness spring, the acceleration of the valve core can be effectively slowed down, thereby reducing the maximum collision speed V.

[0114] Among them, the spring force of the valve core .

[0115] Example 4:

[0116] Based on any of the above embodiments, this embodiment further illustrates that the fuel pressure dynamic change model specifically includes:

[0117] The impact of fuel pressure fluctuations is analyzed using the plunger motion equation obtained in S3 and the valve core motion equation obtained in S4.

[0118] Fuel pressure is a core parameter of the high-pressure common rail system, directly affecting the injection characteristics of the injectors. The flow rate and pressure fluctuation formulas describe the dynamic changes in fuel pressure over time. The pressure fluctuation formula is as follows:

[0119] ,in It is the bulk elastic modulus of fuel, reflecting the compressibility of fuel; It is the volume of the high-pressure chamber; It is the fuel flow rate entering the high-pressure chamber. It is the fuel flow rate discharged from the high-pressure chamber; used to dynamically capture the effects of plunger movement, valve core opening and closing, pressure fluctuations, etc., on fuel pressure; when When the pressure approaches 1, the overall fuel pressure of the system tends to stabilize. The reciprocating motion of the plunger directly affects the volume of the high-pressure chamber, thereby changing the fuel pressure; and the fuel pressure is an important force driving the valve core motion. The pressure fluctuation formula establishes the relationship between plunger motion, fuel pressure, and valve core dynamic behavior, and is used to evaluate the stability of the parameters of the plunger motion equation and the valve core motion equation.

[0120] Example 5:

[0121] Based on any of the above embodiments, this embodiment further illustrates the specific steps of adjusting the spring stiffness coefficients a and b using a genetic algorithm. A genetic algorithm (GA) is a global optimization algorithm based on natural selection and genetic mechanisms, which is very suitable for solving high-dimensional, nonlinear, and multi-objective optimization problems. In a high-pressure common rail system, a genetic algorithm can be used to optimize the spring parameters a and b to improve system performance, such as reducing the maximum collision velocity of the valve core, reducing pressure fluctuations, and improving volumetric efficiency.

[0122] The spring stiffness coefficients (a and b) are optimized using a genetic algorithm. A population is set and initialized, then an operation method is selected and crossover is performed to obtain offspring data. Mutation is applied to the offspring data to increase population diversity. The population is updated and iterated repeatedly until the algorithm converges, at which point the optimal parameters are output. and .

[0123] The specific steps are as follows:

[0124] S81 defines the objective function:

[0125] The goal is to adjust the spring parameters a and b using a genetic algorithm to ensure the high-pressure common rail system meets performance requirements. The optimization problem can be defined as an objective function, in the following form:

[0126] ;

[0127] in: The objective function consists of the maximum collision velocity and volumetric efficiency; a and b are the parameters that need to be optimized.

[0128] S82 constructs the objective function:

[0129] objective function Where w1 and w2 are weighting coefficients;

[0130] The goal is to minimize the maximum collision velocity V of the valve core and maximize the system volumetric efficiency L.

[0131] Define the range of values ​​for a and b, and set constraints, for example:

[0132] The spring force must not exceed the material strength limit of the valve core or plunger.

[0133] The system's dynamic response time meets specific requirements.

[0134] S83 Initial Population:

[0135] Define population representation

[0136] Each individual represents a set of spring parameters (a, b), which can be represented by a vector: [a, b].

[0137] The population is the set of all individuals, with a size of Np, representing the population size. Then, the parameters are initialized.

[0138] An initial population is randomly generated, with individuals uniformly distributed within the design space. For each individual in the population,

[0139] Calculate its objective function value ;

[0140] S84 Selection Operation Method:

[0141] The objective function value calculated based on S83 is the fitness value assigned to each individual in the population, and the specific formula is as follows:

[0142] Using roulette wheel selection or tournament selection, individuals are selected from the population based on their fitness values ​​to generate the parent individuals of the next generation.

[0143] S85 crossover operation:

[0144] Using a two-point crossover or uniform crossover method, two parent individuals are randomly selected to generate two offspring.

[0145] S86 mutation operation

[0146] The parameters of offspring individuals are randomly perturbed to increase population diversity, and a low mutation probability of 0.01 to 0.1 is set to prevent premature convergence.

[0147] S87 Population Update: Merge parent and offspring, sort by fitness, and select the top n individuals as the next generation; the algorithm stops when the change in the population's objective function value is less than a preset threshold or when the maximum number of iterations is reached, and outputs the optimal parameters. and .

[0148] It should be further noted that the above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients or thresholds in the formulas are set by those skilled in the art based on the actual situation.

[0149] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0150] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A method for simulating the design of variable stiffness springs for high-pressure common rail pumps, characterized in that, Includes the following steps: S1. Establish a multiphysics coupling model based on simulation software, including mechanical module, hydraulic module, and thermodynamic module; S2. Establish a thermodynamic module: Collect experimental data on fuel temperature and spring stiffness, and fit the collected experimental data to obtain the temperature sensitivity coefficient. A nonlinear correction function is obtained by coupling the spring displacement with temperature. Temperature sensitivity coefficient and nonlinear correction function Used to calculate the spring stiffness of the coupled variable stiffness spring. Nonlinear correction function c1, c2, and c3 are the fitting parameters, respectively. S3. Simulate plunger motion: Perform operational state analysis and force analysis on the plunger, simulate the plunger motion mode, decompose the influence parameters of the plunger motion, and construct the plunger motion equation; S4. Simulate valve core motion: Perform operational state analysis and force analysis on the valve core, fit the valve core's motion velocity, decompose the influencing parameters of the valve core's motion, and construct the valve core's motion equation; S5. Establish a dynamic fuel pressure change model: Analyze the impact of fuel pressure fluctuations using the plunger motion equation obtained in S3 and the valve core motion equation obtained in S4, and use this model to evaluate the stability of the parameters of the plunger motion equation and the valve core motion equation. S6. Calculate spring stiffness Set the spring stiffness coefficients a and b; Calculate according to the following formula: Define initial values ​​a0 and b0, and calculate the initial spring stiffness. ; S7. Calculate the target adjustment parameters: Based on the simulation software, obtain the maximum collision velocity V of the valve core and the system volumetric efficiency L; S8. Optimize Spring Stiffness Coefficients: Adjust spring stiffness coefficients a and b using a genetic algorithm; set and initialize the population, then select an operation method and perform crossover to obtain offspring data; perform mutation on the offspring data to increase population diversity; update the population and repeat the iteration until the algorithm converges, then output the optimal parameters. and ; S9. Calculate the optimal objective parameters: Use the optimal parameters obtained in S8... and The introduction of simulation software reduces the maximum collision velocity V of the valve core and improves the system volumetric efficiency L.

2. The method for simulating the design of a variable stiffness spring for a high-pressure common rail pump according to claim 1, characterized in that, The simulation software is AMESim; the mechanical module is used to simulate the movement of the plunger and valve core, and calculate dynamic displacement, velocity and acceleration; the hydraulic module is used to simulate fuel flow and pressure fluctuations; and the thermodynamic module is used to simulate the effect of temperature on the nonlinear behavior of the spring.

3. The method for simulating the design of a variable stiffness spring for a high-pressure common rail pump according to claim 1, characterized in that, The formula for calculating the maximum collision velocity V of the valve core in S7 is as follows: , It's the quality of the valve core. It is the acceleration of the valve core; , It is the distance between the valve core and its formation. The formula for calculating the system volumetric efficiency L is as follows: , It is the actual output flow rate. It is the theoretical output flow rate; ,in It is the cross-sectional area of ​​the plunger. It is the plunger's stroke distance. It is the piston drive frequency; , It is the speed of the plunger's movement.

4. The method for simulating the design of a variable stiffness spring for a high-pressure common rail pump according to claim 1, characterized in that, The specific steps of the plunger motion equation are as follows: S31. Plunger Analysis: Perform operational and force analysis on the plunger. The plunger's motion is affected by spring force, fuel pressure, and damping force. Then, simulate the plunger's motion mode. S32. Parameters affecting the decomposition of plunger motion: Analysis of the plunger's motion revealed that the parameters affecting the plunger's motion include: plunger mass, damping coefficient, spring force, plunger pressure area, damping force, and fuel pressure. S33. Constructing the plunger motion equation: Through mechanical analysis of the above parameters, the plunger motion equation is obtained as follows: ; in, For the plunger mass, The linear plunger damping coefficient is... The nonlinear damping coefficient is... Indicates damping force ; For piston spring displacement The spring force generated at that time For fuel pressure, This represents the area of ​​the plunger under pressure. This represents the fuel pressure force generated by the fuel. ; This indicates the driving force of the plunger, which is obtained and set through simulation experiments.

5. The method for simulating the design of a variable stiffness spring for a high-pressure common rail pump according to claim 4, characterized in that, spring force of the plunger .

6. The method for simulating the design of a variable stiffness spring for a high-pressure common rail pump according to claim 4, characterized in that, The specific steps of the valve core motion equation are as follows: S41. Valve core analysis: Perform operational and force analysis on the valve core. The movement of the valve core is affected by the combined action of spring force, fuel pressure, and damping force. Then, simulate the movement mode of the valve core. S42. Parameters affecting the decomposition of valve core movement: Analysis of the valve core's motion revealed that the parameters affecting its motion include: valve core mass, damping coefficient, spring force, damping force, and fuel pressure. S43. Constructing the valve core motion equation: Through mechanical analysis of the above parameters, the valve core motion equation is obtained as follows: ; in, For valve core quality, The linear plunger damping coefficient is... The nonlinear damping coefficient is... Indicates damping force ; For valve core spring displacement The spring force generated at that time For fuel pressure, This represents the area of ​​the plunger under pressure. This represents the fuel pressure force generated by the fuel. .

7. The method for simulating the design of a variable stiffness spring for a high-pressure common rail pump according to claim 6, characterized in that, spring force of valve core .

8. The method for simulating the design of a variable stiffness spring for a high-pressure common rail pump according to claim 5, characterized in that, The impact of fuel pressure fluctuations is analyzed using the plunger motion equation obtained in S3 and the valve core motion equation obtained in S4. The pressure fluctuation formula is constructed as follows: ,in It is the bulk elastic modulus of fuel, reflecting the compressibility of fuel; It is the volume of the high-pressure chamber; It is the fuel flow rate entering the high-pressure chamber. This refers to the flow rate of fuel discharged from the high-pressure chamber.

9. The method for simulating the design of a variable stiffness spring for a high-pressure common rail pump according to claim 1, characterized in that, The specific steps for adjusting the spring stiffness coefficients a and b using a genetic algorithm are as follows: S81 defines the objective function: The goal is to adjust the spring parameters a and b using a genetic algorithm to ensure the high-pressure common rail system meets performance requirements. The optimization problem can be defined as an objective function, in the following form: , in: The objective function consists of the maximum collision velocity and volumetric efficiency; a and b are the parameters that need to be optimized. S82 Constructing the Objective Function: Objective Function Where w1 and w2 are weighting coefficients; Define the range of values ​​for a and b, and set the constraints. S83 Initial Population: Each individual represents a set of spring parameters (a, b), which can be represented by a vector: [a, b]. The population is a collection of all individuals, with a size of Np, representing the population size; then the parameters are initialized. An initial population is randomly generated, with individuals uniformly distributed within the design space; the objective function value is calculated for each individual in the population. ; S84 Selection Operation Method: The objective function value calculated based on S83 is the fitness value assigned to each individual in the population, and the specific formula is as follows: Select an operation method and choose individuals from the population based on their fitness values ​​to generate the parent individuals for the next generation; S85 crossover operation: Using a two-point crossover or uniform crossover method, two parent individuals are randomly selected to generate two offspring. S86 mutation operation The parameters of offspring individuals are randomly perturbed to increase population diversity, and a low mutation probability of 0.01 to 0.1 is set to prevent premature convergence. S87 Population Update: Merge parent and offspring, sort by fitness, and select the top n individuals as the next generation; the algorithm stops when the change in the population's objective function value is less than a preset threshold or when the maximum number of iterations is reached, and outputs the optimal parameters. and .

10. A method for simulating the design of a variable stiffness spring for a high-pressure common rail pump according to claim 9, characterized in that, The operating method is either roulette wheel selection or tournament selection.

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