Compressor dynamics simulation optimization method combined with numerical optimization
Through the combined numerical optimization of compressor dynamic simulation optimization method, a multi-body dynamic model is established and the parameters are optimized using genetic algorithms, which solves the problem of increased vibration when designing the compressor shaft system moving parts in the prior art, and achieves a more stable and reliable compressor operation.
Patent Information
- Application Number
- CN202411519629.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-29
- Publication Date
- 2025-03-14
AI Technical Summary
The prior art lacks comprehensive consideration of dynamic characteristics when designing compressor shaft system moving parts, which may lead to increased vibration after optimization or inability to effectively reduce vibration in key parts.
The compressor dynamics simulation optimization method with combined numerical optimization is adopted. By establishing a multi-body dynamics model, using genetic algorithms to optimize key parameters, combined with Matlab and RecurDyn software for simulation and optimization.
It can reduce the dynamic response of the compressor according to different working conditions, improve the stability and reliability of the compressor, and significantly reduce the vibration in key parts.
Smart Images
Figure CN119623239A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of dynamics analysis and optimization of reciprocating piston compressors, and more specifically, to a compressor dynamics simulation optimization method combined with numerical optimization. Background Art
[0002] For reciprocating piston compressors, the vibration value of key parts of the unit largely determines the fatigue life and safety reliability of its components during operation. In particular, the vibration values of the upper crankcase, the lower end of the crosshead slideway, the cylinder head, etc. Compressor users monitor the vibration values of these parts to evaluate whether the operation of the unit is stable and reliable. Therefore, a series of relatively strict technical requirements are set during the design, processing, assembly and installation of the unit to ensure that the vibration values of key parts meet the requirements.
[0003] From the actual performance of the compressor operation, in the process of compressor unit design, especially after the operating conditions of the compressor are determined, the values of a series of parameters such as the reciprocating inertia mass of the compressor shaft system moving parts, the bearing clearance, etc. largely determine the vibration value of key parts during the operation of the unit. Therefore, how to perform dynamic analysis and optimization of the compressor shaft system moving parts during the design process is a very important issue.
[0004] Generally speaking, in the design process of shaft system moving parts, they are usually regarded as rigid parts, and the main methods used are to design appropriate matching clearances, balance the reciprocating inertia of the shaft system, minimize the reciprocating alternating force and torque, etc., to reduce the impact, unbalanced force or torque brought to the compressor unit during operation to achieve the purpose of dynamic optimization. However, this method does not consider the impact of the dynamic characteristics of the compressor shaft system moving parts, and it is possible that the optimized compressor will experience increased vibration (such as resonance). Secondly, the optimization method lacks comprehensiveness. Due to the processing or installation process of the compressor in the design, it is basically impossible to make each part of the shaft system moving parts balanced or minimize the impact force in the rigid body balancing method. Therefore, only the force balance of the main shaft system components can be guaranteed, and the possibility of further reducing the vibration of the unit is abandoned.
[0005] With the development of dynamics and numerical optimization technology, the multi-body dynamics method can be used to more accurately calculate the dynamic response of the compressor shaft system moving parts during the working process; the optimization algorithms such as genetic algorithms can be used to perform fast and comprehensive multi-objective optimization calculations on a certain relationship to find the global optimal parameter configuration. Therefore, in order to better achieve the goal of compressor unit dynamics optimization, the multi-body dynamics combined with genetic algorithm simulation method can be used to optimize the design parameters of the compressor shaft system moving parts. Summary of the invention
[0006] The purpose of the present invention is to provide a compressor dynamics simulation optimization method combined with numerical optimization, which can reduce the dynamic response of the compressor during operation and improve the stability and reliability of the compressor operation according to the actual different working conditions of the compressor.
[0007] The technical solution adopted by the present invention to solve this technical problem is: to provide a compressor dynamics simulation optimization method combined with numerical optimization, comprising the following steps: Step 1: Establish a multi-body dynamics model of the compressor shaft system moving parts; Step 2: setting the key parameters affecting vibration in the multi-body dynamics model of the compressor shaft system moving parts in step 1 as parameterized variables of input and output; Step 3: Use genetic algorithm to optimize the parameterized variables in step 2, save and output the m file; Step 4: Use Matlab software to open the m file saved in step 3 and run it to obtain the Simulink module of multi-body dynamics joint simulation, enter the Simulink resource library, and establish an iterative optimization process; Step 5: Find the final optimized simulation parameters by repeatedly performing the iterative optimization process described in step 4.
[0008] Preferably, in the compressor dynamics simulation optimization method combined with numerical optimization, in step 1, the specific steps of establishing a multi-body dynamics model of the compressor shaft system moving parts include: According to the structural parameters of the compressor shaft system moving parts, a 3D model of the compressor shaft system moving parts was established using 3D modeling software. The 3D model was imported into RecurDyn multi-body dynamics software, and the finite element method was used to flexibly process the components that require dynamic optimization, including the crankshaft, flywheel and motor shaft.
[0009] Preferably, in the compressor dynamics simulation optimization method combined with numerical optimization, the flexible processing includes dividing the three-dimensional model of the compressor shaft system moving parts into a grid and setting the component coupling.
[0010] Preferably, in the compressor dynamics simulation optimization method combined with numerical optimization, in the step 2, the independent variable parameters in the multi-body dynamics model of the compressor shaft system moving parts, such as reciprocating inertial mass, bearing fit clearance and lubricating oil pressure, are set as multi-body dynamics analysis input parameters; The dependent variable parameters of the joint optimization, such as the torque at the motor output end and the stress at the crankshaft input end, are set as the output parameters of the multi-body dynamics analysis.
[0011] Preferably, in the compressor dynamics simulation optimization method of joint numerical optimization, in step three, the specific steps of optimizing the parameterized variables in step two using a genetic algorithm are as follows: S1: Determine the initial conditions, including the reciprocating inertia mass, bearing clearance and lubricating oil pressure parameters of the compressor unit, and obtain the objective function, design variables and constraints required for optimization; S2: Initialize the population; based on the design variables and constraints set in S1, generate an initial data population, and substitute the individuals in this population into the multi-body dynamics model of the compressor shaft system moving parts step by step; S3: Solve the multi-body dynamics model of the compressor shaft system moving parts; calculate the mass matrix, constraint Jacobian matrix and generalized force matrix of the dynamic system and assemble them into the multi-body dynamics system equations, then reduce the order of the system equations and solve them to obtain the constraint forces and moments of the key parts that affect the constraint conditions; S4: Evaluate individuals; according to the compressor dynamics evaluation method, the constraint force and torque of the key parts described in S3 are converted into a fitness function, so as to calculate the fitness of the chromosomes in the individuals, find the best chromosome, and record the best chromosome fitness and average fitness in each generation of evolution; S5: Iterative optimization, select individuals with high fitness functions in the population, and continuously perform crossover and mutation operations to generate new populations, find the minimum and maximum fitness values and their positions in the population, replace the best chromosome in the last evolution, and record the fitness value, repeating the cycle until the iterative evolution ends.
[0012] Preferably, in the compressor dynamics simulation optimization method combined with numerical optimization, the key parts in steps S3 and S4 include the main bearing, the crosshead and the coupling.
[0013] The present invention has at least the following beneficial effects: 1. This method can reduce the dynamic response of the compressor during operation and improve the stability and reliability of the compressor operation according to the actual different working conditions of the compressor.
[0014] 2. The parameters optimized by the optimization process based on genetic algorithm can greatly reduce the vibration of key parts of the compressor unit and improve the safety and stability of the compressor unit.
[0015] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 A flowchart of a compressor dynamics simulation optimization method combined with numerical optimization; Figure 2 A schematic diagram of a simulink module for multi-body dynamics joint simulation in a compressor dynamics simulation optimization method for joint numerical optimization; Figure 3 Flowchart of the genetic algorithm in a compressor dynamics simulation optimization method combined with numerical optimization. DETAILED DESCRIPTION
[0017] The present invention is described in detail and completely below in conjunction with the accompanying drawings. A person of ordinary skill in the art will be able to implement the present invention based on these descriptions. Before describing the present invention in conjunction with the accompanying drawings, it should be particularly pointed out that the technical solutions and technical features provided in each part of the present invention, including the following description, can be combined with each other without conflict.
[0018] In addition, the embodiments of the present invention involved in the following description are generally only a part of the embodiments of the present invention, rather than all the embodiments. Therefore, based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.
[0019] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments, and its specific implementation process is as follows: like Figure 1-3 As shown, the present invention provides a compressor dynamics simulation optimization method combined with numerical optimization, comprising the following steps: Step 1: Establish a multi-body dynamics model of the compressor shaft system moving parts; Step 2: setting the key parameters affecting vibration in the multi-body dynamics model of the compressor shaft system moving parts in step 1 as parameterized variables of input and output; Step 3: Use genetic algorithm to optimize the parameterized variables in step 2, save and output the m file; Step 4: Use Matlab software to open the m file saved in step 3 and run it to obtain the Simulink module of multi-body dynamics joint simulation, enter the Simulink resource library, and establish an iterative optimization process; like Figure 2 As shown in the figure, Ki, Kp and Ke are input independent variables, and Tx (out.Tx) is the output dependent variable. After inputting different independent variables, the Simulink module of multi-body dynamics joint simulation will calculate different dependent variables.
[0020] Step 5: Find the final optimized simulation parameters by repeatedly performing the iterative optimization process described in step 4.
[0021] Furthermore, in the compressor dynamics simulation optimization method combined with numerical optimization, in step 1, the specific steps of establishing a multi-body dynamics model of the compressor shaft system moving parts include: According to the structural parameters of the compressor shaft system moving parts, a 3D model of the compressor shaft system moving parts was established using 3D modeling software. The 3D model was imported into RecurDyn multi-body dynamics software, and the finite element method was used to flexibly process the components that require dynamic optimization, including the crankshaft, flywheel and motor shaft.
[0022] In this embodiment, a preferred implementation scheme is to compare the actual test results (such as weight, elastic modulus, etc.) of the shaft system moving parts to obtain accurate physical parameters of each moving part.
[0023] Furthermore, in the combined numerical optimization method for compressor dynamics simulation, the flexible processing includes meshing the three-dimensional model of the compressor shaft system moving parts and setting component coupling.
[0024] Furthermore, in the compressor dynamics simulation optimization method of the combined numerical optimization, in the step 2, the independent variable parameters in the multi-body dynamics model of the compressor shaft system moving parts, such as the reciprocating inertia mass value, the bearing fit clearance value and the lubricating oil pressure value, are set as multi-body dynamics analysis input parameters; The dependent variable parameters of the joint optimization, such as the torque value at the motor output end and the stress value at the crankshaft input end, are set as the output parameters of the multi-body dynamics analysis.
[0025] In the above technical solution, the reciprocating inertial mass is set to Ki, the bearing fit clearance is set to Kp, the lubricating oil pressure is set to Ke, and the torque at the motor output end is set to Tx.
[0026] Furthermore, the compressor dynamics simulation optimization method combined with numerical optimization is as follows: Figure 3 As shown, in step three, the specific steps of using genetic algorithm to optimize the parameterized variables in step two are as follows: S1: Determine the initial conditions; determine the initial conditions of the mechanism dynamics optimization model, including the reciprocating inertia mass, bearing fit clearance and lubricating oil pressure parameters of the compressor unit, and obtain the objective function, design variables and constraints required for optimization; in this embodiment, a preferred implementation scheme is that the constraint condition is to avoid vibration of the compressor.
[0027] S2: Initialize the population; based on the design variables and constraints set in S1, generate an initial data population, and substitute the individuals in this population into the multi-body dynamics model of the compressor shaft system moving parts step by step; S3: Solve the multi-body dynamics model of the compressor shaft system moving parts; calculate the mass matrix, constraint Jacobian matrix and generalized force matrix of the dynamic system and assemble them into the multi-body dynamics system equations, then reduce the order of the system equations and solve them to obtain the constraint forces and moments of the key parts that affect the constraint conditions; S4: Evaluate the individual; according to the compressor dynamics evaluation method, the compressor dynamics evaluation method includes safety factor calculation and fatigue life calculation, etc., the constraint force and torque of the key parts described in S3 are converted into a fitness function, and the chromosome fitness in the individual is calculated to find the best chromosome, and the best chromosome fitness and average fitness in each generation of evolution are recorded; S5: Iterative optimization, select individuals with high fitness functions in the population, and continuously perform crossover and mutation operations to generate new populations, find the minimum and maximum fitness values and their positions in the population, replace the best chromosome in the last evolution, and record the fitness value, repeating the cycle until the iterative evolution ends.
[0028] The parameters optimized through the above optimization process can greatly reduce the vibration of key parts of the compressor unit and improve the safety and stability of the compressor unit.
[0029] Furthermore, in the combined numerical optimization method for compressor dynamics simulation, the key parts in steps S3 and S4 include a main bearing, a crosshead and a coupling.
[0030] The method can reduce the dynamic response of the compressor during operation according to different actual working conditions of the compressor, and improve the stability and reliability of the compressor operation.
[0031] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.
Claims
1. A compressor dynamics simulation optimization method combined with numerical optimization, characterized in that: The steps include: Step 1: Establish a multi-body dynamics model of the compressor shaft system moving parts; Step 2: setting the key parameters affecting vibration in the multi-body dynamics model of the compressor shaft system moving parts in step 1 as parameterized variables of input and output; Step 3: Use genetic algorithm to optimize the parameterized variables in step 2, save and output the m file; Step 4: Use Matlab software to open the m file saved in step 3 and run it to obtain the Simulink module of multi-body dynamics joint simulation, enter the Simulink resource library, and establish an iterative optimization process; Step 5: Find the final optimized simulation parameters by repeatedly performing the iterative optimization process described in step 4.
2. The compressor dynamics simulation optimization method combined with numerical optimization according to claim 1, characterized in that: In the step 1, the specific steps of establishing the multi-body dynamics model of the compressor shaft system moving parts include: According to the structural parameters of the compressor shaft system moving parts, a 3D model of the compressor shaft system moving parts was established using 3D modeling software. The 3D model was imported into RecurDyn multi-body dynamics software, and the finite element method was used to flexibly process the components that require dynamic optimization, including the crankshaft, flywheel and motor shaft.
3. The compressor dynamics simulation optimization method combined with numerical optimization according to claim 2, characterized in that: The flexible processing includes dividing the three-dimensional model of the compressor shaft system moving parts into grids and setting the component coupling.
4. The compressor dynamics simulation optimization method combined with numerical optimization according to claim 1, characterized in that: In the step 2, the independent variable parameters in the multi-body dynamics model of the compressor shaft system moving parts, such as reciprocating inertial mass, bearing fit clearance and lubricating oil pressure, are set as multi-body dynamics analysis input parameters; In the step 2, the dependent variable parameters of the joint optimization, such as the torque at the motor output end and the stress at the crankshaft input end, are set as the output parameters of the multi-body dynamics analysis.
5. The compressor dynamics simulation optimization method combined with numerical optimization according to claim 1, characterized in that: In step 3, the specific steps of using genetic algorithm to optimize the parameterized variables in step 2 are as follows: S1: Determine the initial conditions, including the reciprocating inertia mass, bearing clearance and lubricating oil pressure parameters of the compressor unit, and obtain the objective function, design variables and constraints required for optimization; S2: Initialize the population; based on the design variables and constraints set in S1, generate an initial data population, and substitute the individuals in this population into the multi-body dynamics model of the compressor shaft system moving parts step by step; S3: Solve the multi-body dynamics model of the compressor shaft system moving parts; calculate the mass matrix, constraint Jacobian matrix and generalized force matrix of the dynamic system and assemble them into the multi-body dynamics system equations, then reduce the order of the system equations and solve them to obtain the constraint forces and moments of the key parts that affect the constraint conditions; S4: Evaluate individuals; according to the compressor dynamics evaluation method, the constraint force and torque of the key parts described in S3 are converted into a fitness function, so as to calculate the fitness of the chromosomes in the individuals, find the best chromosome, and record the best chromosome fitness and average fitness in each generation of evolution; S5: Iterative optimization; select individuals with high fitness functions in the population, and continuously perform crossover and mutation operations to generate new populations, find the minimum and maximum fitness values and their positions in the population, replace the best chromosome in the last evolution, and record the fitness value, repeating the cycle until the iterative evolution ends.
6. The compressor dynamics simulation optimization method combined with numerical optimization according to claim 5, characterized in that: The key parts in steps S3 and S4 include a main bearing, a crosshead and a coupling.