CN116579252A - Particle swarm optimization-based vortex mechanical exhaust hole optimization method and system - Google Patents

Particle swarm optimization-based vortex mechanical exhaust hole optimization method and system Download PDF

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CN116579252A
CN116579252A CN202310625627.5A CN202310625627A CN116579252A CN 116579252 A CN116579252 A CN 116579252A CN 202310625627 A CN202310625627 A CN 202310625627A CN 116579252 A CN116579252 A CN 116579252A
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王澈
吴建华
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Xian Jiaotong University
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Abstract

A vortex machinery exhaust hole optimizing method and system based on particle swarm algorithm, the method includes setting the center of exhaust hole and the parameter of particle swarm algorithm, and initializing the particle swarm variable randomly; constructing the geometric shape of the exhaust hole by using contour point control parameters corresponding to the position variables of the particle swarm; judging whether the geometric shape of the exhaust hole meets the limiting condition, if not, carrying out geometric processing, and if so, carrying out simulation of the scroll compressor, and calculating to obtain an objective function; repeating iteration, using a particle swarm algorithm to update contour point control parameters to obtain new particle swarm position variables, calculating adaptation values of all particles according to an objective function, comparing each adaptation value with a globally optimal particle variable, and updating the speed and position of each particle swarm; and when the iteration times or the objective function reach the requirements, outputting the control parameters and the geometric results of the profile points of the exhaust holes. The application can achieve the purposes of improving the running efficiency of the compressor and reducing the design period of the compressor.

Description

Particle swarm optimization-based vortex mechanical exhaust hole optimization method and system
Technical Field
The application belongs to the technical field of vortex machinery, and particularly relates to a vortex machinery exhaust hole optimization method and system based on a particle swarm algorithm.
Background
The vortex compressor is characterized in that a vortex scroll is vertically arranged on an movable scroll, the movable scroll is meshed through the revolution of the movable scroll, a plurality of changed crescent working cavities are formed, fluid working media are enabled to change in pressure in the crescent working cavities, and fluid in the working cavities is discharged into a system through an exhaust hole in the center of the movable scroll.
In actual operation, the unreasonable design of the shape of the exhaust hole can cause over-compression and under-compression of the compressor, and the indication efficiency of the compressor is greatly influenced. The existing vent hole shape design method has the following defects:
1. a circular structure is generally adopted, so that the area of a central working cavity cannot be fully utilized; 2. the noncircular special-shaped exhaust hole structure design depends on experience of a designer, and a mode of multiple groups of tangent circular arcs is generally adopted, so that the applicability to variable curvature lines is limited; 3. performance evaluation in the process of optimal design is based on experimental assessment, repeated measurement of compressor performance is needed, and the process is time-consuming, laborious and complex.
Disclosure of Invention
The application aims to solve the problems in the prior art, and provides a vortex mechanical exhaust hole optimizing method and system based on a particle swarm algorithm, which are used for generating exhaust holes with arbitrary geometric shapes according to spline curves under contour point control parameters, and simultaneously considering internal flow influence in the operation of a compressor, and improving the geometric shape design of the exhaust holes based on an objective function of a particle swarm algorithm construction performance parameter, so that the operation efficiency of the compressor is improved, and the design period of the compressor is reduced.
In order to achieve the above purpose, the present application has the following technical scheme:
a vortex mechanical exhaust hole optimization method based on a particle swarm optimization comprises the following steps:
setting the center of the exhaust hole and parameters of a particle swarm algorithm, randomly initializing position variables of the particle swarm, and randomly initializing a particle swarm velocity variable corresponding to a group of contour point control parameters forming the geometric shape of each exhaust hole;
constructing the geometric shape of the exhaust hole by using contour point control parameters corresponding to the position variables of the particle swarm;
judging whether the geometric shape of the constructed exhaust hole meets the limiting condition, if not, carrying out geometric treatment, and if so, carrying out vortex compressor simulation, and calculating to obtain an objective function;
repeating iteration, using a particle swarm algorithm to update contour point control parameters to obtain new particle swarm position variables, calculating adaptation values of all particles according to an objective function of the running condition of the compressor, comparing each adaptation value with a globally optimal particle variable, and updating the speed and position of each particle swarm;
and when the iteration times or the objective function reach the requirements, outputting the control parameters and the geometric results of the profile points of the exhaust holes.
Preferably, the center of the exhaust hole is O, and the position variable of the m groups of particle groups is randomly initialized to be x (0) = (x) 1 ,x 2 ,x 3 ,…,x m ) Each particle variable is x k K=1, …, m, randomly initializing the group velocity variable to v (0) = (v) 1 ,v 2 ,v 3 ,…,v m )；
The number of the contour points forming each exhaust hole geometric shape is n, and the contour points are P respectively 1 ,P 2 ,P 3 ,…,P n The contour point control parameter is the distance L from the contour point to the center, and a particle variable x k ＝(L 1 ,L 2 ,L 3 ,…,L n )。
Preferably, the included angles between two adjacent contour points and the center of the exhaust hole satisfy the following relation:
∠P i OP i+1 =2pi/n, the corresponding spread angle of the contour point is θ i ＝2πi/n。
Preferably, in the step of constructing the geometric shape of the exhaust hole by using the contour point control parameters corresponding to the position variables of the particle swarm, the geometric shape of the exhaust hole is constructed:
each exhaust hole is provided withThe shape is generated by two contour points which are adjacent in sequence according to a spline curve, wherein the two contour points P i ，P i+1 Arbitrary point P on the curve between j The coordinates of (2) are:
wherein L is j For the arbitrary point P j Distance from center, θ j For the arbitrary point P j Corresponding spreading angles.
Preferably, the spline curve has an equation L i (θ)＝a i θ 3 +b i θ 2 +c i θ+d i The undetermined coefficient in the equation is a i ，b i ，c i ，d i I=1, …, n, distance L from center of a given contour point according to a cubic spline curve equation i Spread angle theta corresponding to contour point i And (5) obtaining.
Preferably, the step of judging whether the geometric shape of the exhaust hole obtained by construction meets the limiting condition, wherein the limiting condition is a contour point control parameter L i The contour point is away from the static disc molded line distance t and the exhaust hole center point and the static disc molded line distance L' satisfy the following relation: l (L) i <L′-t；
In the step of performing geometric processing, the geometric processing method is that L is made i ＝L′-t。
Preferably, the scroll compressor simulation includes modeling compressor profile and exhaust port geometry, thermodynamics, and dynamics, and the objective function for calculating the adaptation value includes, but is not limited to, p-V curve for compressor operation, gas force, torque, indicated efficiency, isentropic efficiency.
Preferably, the expression for updating the speed and position of each particle group is as follows:
v k (t+1)＝ω×v k (t)+c 1 ×rand 1 (t)×(pBest k -x(t))+c 2 ×rand 2 (t)(gBest-x k (t))
x k (t+1)＝x k (t)+v k (t+1)
in the formula, v k (t+1) is a new particle group velocity, gBest is a global optimum particle variable, pBest k For the optimal position of the current particle, ω is the inertia factor of the population of primary particles, v k (t) is the primary particle group velocity, c 1 ,c 2 Are learning factors, rand 1 (t),rand 2 (t) two random numbers of 0 to 1, x (t) is the primary particle group position variable, x k (t) and x k (t+1) is the position of the new particle group and the original particle group, respectively.
A particle swarm algorithm-based scroll machine exhaust hole optimization system, comprising:
the particle swarm initialization module is used for setting the center of the exhaust hole and parameters of a particle swarm algorithm, randomly initializing position variables of the particle swarm, and randomly initializing particle swarm velocity variables corresponding to a group of contour point control parameters forming the geometric shape of each exhaust hole;
the exhaust hole geometric shape construction module is used for constructing exhaust hole geometric shapes according to contour point control parameters corresponding to the position variables of the particle swarm;
the objective function calculation module is used for judging whether the geometric shape of the constructed exhaust hole meets the limiting condition, carrying out geometric processing if the geometric shape does not meet the limiting condition, and carrying out vortex compressor simulation if the geometric shape meets the limiting condition, and calculating to obtain an objective function;
the particle swarm updating module is used for repeatedly iterating, updating the contour point control parameters by using a particle swarm algorithm to obtain new particle swarm position variables, calculating the adaptation values of all particles according to an objective function of the running condition of the compressor, comparing each adaptation value with a globally optimal particle variable, and updating the speed and position of each particle swarm;
and the output module is used for outputting the control parameters and the geometric results of the profile points of the exhaust hole when the iteration times or the objective function reach the requirements.
An electronic device, comprising: a memory storing at least one instruction; and the processor executes the instructions stored in the memory to realize the vortex mechanical exhaust hole optimization method based on the particle swarm algorithm.
A computer readable storage medium storing a computer program which when executed by a processor implements the particle swarm algorithm-based scroll machine exhaust hole optimization method.
Compared with the prior art, the application has at least the following beneficial effects:
according to the application, the spline curve is used for replacing the vent hole formed by the traditional arc section, and the vent hole with any geometric shape can be generated under the control of the contour point parameter, so that the vent hole geometric shape design which meets the actual requirements better is obtained, and the vent hole geometric shape is optimized by using an optimization algorithm conveniently. Meanwhile, the application considers the internal flow influence in the operation of the compressor through simulation, and constructs an optimization method for improving the geometric shape design of the exhaust hole based on the particle swarm algorithm by taking the actual operation characteristics and the performance parameters of the compressor as target functions, thereby achieving the purposes of improving the operation efficiency of the compressor and reducing the design period of the compressor.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application, and that other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for optimizing exhaust holes of a vortex machine based on a particle swarm algorithm according to an embodiment of the application;
FIG. 2 is a schematic illustration of vent geometry constructed from control parameters in accordance with an embodiment of the application;
FIG. 3 is a schematic diagram of the geometry of an exhaust hole constructed after updating the contour point control parameters according to an embodiment of the present application;
FIG. 4 is a schematic diagram of the geometric constraints and processing methods according to an embodiment of the present application;
FIG. 5 is a p-V diagram comparison schematic of compressors before and after vent optimization in accordance with an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. Based on the embodiments of the present application, one of ordinary skill in the art may also obtain other embodiments without undue burden.
According to the vortex mechanical exhaust hole optimization method based on the particle swarm algorithm, exhaust holes with any geometric shapes are generated according to spline curves under contour point control parameters, the geometric shapes of the exhaust holes which meet actual requirements are designed, meanwhile, the influence of internal flow in operation of a compressor is considered, the geometric shapes of the exhaust holes are designed based on an objective function of the particle swarm algorithm construction performance parameters, the operation efficiency of the compressor is improved, and the design period is shortened. As shown in fig. 1, the method comprises the following steps:
step 101, setting a vent hole center O, setting parameters of a particle swarm algorithm, randomly initializing m groups of particle swarm position variables x (0) = (x 1, x2, x3, …, xm), each particle variable xk, k=1, …, m, corresponding to a group of contour point control parameters constituting each vent hole geometry, and randomly initializing particle swarm velocity variables v (0) = (v 1, v2, v3, …, vm);
step 102, constructing m exhaust hole geometries 2 from the control parameters by using spline curves, wherein fig. 2 shows the exhaust hole geometries constructed from the control parameters, and fig. 3 shows the exhaust hole geometries constructed after updating the contour point control parameters;
step 103, judging whether the geometry of the exhaust hole meets the limiting condition, if some exhaust hole does not meet the limiting condition, processing 103a the geometry;
104, calculating to obtain a required objective function according to the simulation of the scroll compressor;
step 105, repeating iteration, judging that the iteration times or the objective function reach the requirement, if the requirement is met, entering step 107, and if the requirement is not met, entering step 106;
step 106, updating the contour point control parameter by using the particle swarm algorithm to obtain a new particle swarm position variable x (t+1): evaluating all particles according to the objective function to obtain a global optimal particle variable gBest and an adaptive value, and storing the optimal position pBest of the current particle k And the adaptation value, the speed and the position of each particle swarm are updated through the particle swarm algorithm setting and formula:
v k (t+1)＝ω×v k (t)+c 1 ×rand 1 (t)×(pBest k -x(t))+c 2 ×rand 2 (t)(gBest-x k (t)) and x k (t+1)＝x k (t)+v k (t+1), then step 102 is entered;
in the formula, v k (t+1) is a new particle group velocity, gBest is a global optimum particle variable, pBest k For the optimal position of the current particle, ω is the inertia factor of the population of primary particles, v k (t) is the primary particle group velocity, c 1 ,c 2 Are learning factors, rand 1 (t),rand 2 (t) two random numbers of 0 to 1, x (t) is the primary particle group position variable, x k (t) and x k (t+1) is the position of the new particle group and the original particle group, respectively.
As shown in fig. 2 to 4, the number of the set of contour points is n, P1, P2, P3, … Pn, and the contour point control parameter is a distance L between the contour point and the center, and the one particle variable xk= (L1, L2, L3, …, ln).
In one possible implementation, the angles between two adjacent points and the center of the exhaust hole satisfy the following relationship:
the angle piopi+1=2pi/n and the corresponding spread angle θi=2pi/n of the contour point Pi.
In one possible embodiment, each vent geometry is generated from a spline curve from two contour points that are adjacent in succession, wherein the two contour points P i ，P i+1 Arbitrary point P on the curve between j The coordinates of (2) are:
wherein L is j For the arbitrary point P j Distance from center, θ j For the arbitrary point P j Corresponding spreading angles.
In one possible embodiment, the spline curve has an equation of L i (θ)＝a i θ 3 +b i θ 2 +c i θ+d i The undetermined coefficient in the equation is a i ，b i ，c i ，d i I=1, …, n, distance L from center of a given contour point according to a cubic spline curve equation i Spread angle theta corresponding to contour point i And (5) obtaining.
As shown in FIG. 4, the limiting condition is a contour point control parameter L i The distance t between the profile point and the static disc molded line 1 and the distance L' between the center point of the exhaust hole and the static disc molded line satisfy the following relation: l (L) i <L′-t；
In the step of performing geometric processing, the geometric processing method is that L is made i ＝L′-t。
The scroll compressor simulation includes modeling compressor profile and exhaust port geometry, thermodynamics, and dynamics, and the objective function for calculating the adaptation value includes, but is not limited to, p-V curve for compressor operation, gas force, torque, indicated efficiency, isentropic efficiency.
As shown in FIG. 3, the method of the embodiment of the application replaces the vent hole formed by the traditional arc section with the spline curve, and can produce vent holes with arbitrary geometric shapes under the control of the contour point parameters, thereby obtaining the geometric shape design of the vent hole which meets the actual requirements better, and being convenient for optimizing the geometric shape of the vent hole by using an optimization algorithm.
Meanwhile, the method of the embodiment of the application considers the internal flow influence in the operation of the compressor through simulation, and constructs an optimization method for improving the geometric design of the exhaust hole by taking the actual operation characteristics and performance parameters of the compressor as target functions based on a particle swarm algorithm, as shown in fig. 5, reduces the over-compression 4 of the compressor, and can achieve the purposes of improving the operation efficiency of the compressor and reducing the design period of the compressor.
The application also provides a vortex mechanical exhaust hole optimizing system based on a particle swarm algorithm, which comprises the following steps:
the particle swarm initialization module is used for setting the center of the exhaust hole and parameters of a particle swarm algorithm, randomly initializing position variables of the particle swarm, and randomly initializing particle swarm velocity variables corresponding to a group of contour point control parameters forming the geometric shape of each exhaust hole;
the exhaust hole geometric shape construction module is used for constructing exhaust hole geometric shapes according to contour point control parameters corresponding to the position variables of the particle swarm;
the objective function calculation module is used for judging whether the geometric shape of the constructed exhaust hole meets the limiting condition, carrying out geometric processing if the geometric shape does not meet the limiting condition, and carrying out vortex compressor simulation if the geometric shape meets the limiting condition, and calculating to obtain an objective function;
the particle swarm updating module is used for repeatedly iterating, updating the contour point control parameters by using a particle swarm algorithm to obtain new particle swarm position variables, calculating the adaptation values of all particles according to an objective function of the running condition of the compressor, comparing each adaptation value with a globally optimal particle variable, and updating the speed and position of each particle swarm;
and the output module is used for outputting the control parameters and the geometric results of the profile points of the exhaust hole when the iteration times or the objective function reach the requirements.
Another embodiment of the present application is directed to an electronic device, including a memory storing at least one instruction; and the processor executes the instructions stored in the memory to realize the vortex mechanical exhaust hole optimization method based on the particle swarm algorithm.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the vortex mechanical exhaust hole optimization method based on the particle swarm optimization when being executed by a processor.
The instructions stored in the memory may be divided into one or more modules/units, which are stored in a computer-readable storage medium and executed by the processor to perform the particle swarm algorithm-based scroll machine exhaust hole optimization method of the present application, for example. The one or more modules/units may be a series of computer readable instruction segments capable of performing a specified function, which describes the execution of the computer program in a server.
The electronic equipment can be a smart phone, a notebook computer, a palm computer, a cloud server and other computing equipment. The electronic device may include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that the electronic device may also include more or fewer components, or may combine certain components, or different components, e.g., the electronic device may also include input and output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be an internal storage unit of the server, such as a hard disk or a memory of the server. The memory may also be an external storage device of the server, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the server. Further, the memory may also include both an internal storage unit and an external storage device of the server. The memory is used to store the computer readable instructions and other programs and data required by the server. The memory may also be used to temporarily store data that has been output or is to be output.
It should be noted that, because the content of information interaction and execution process between the above module units is based on the same concept as the method embodiment, specific functions and technical effects thereof may be referred to in the method embodiment section, and details thereof are not repeated herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims (10)

1. The vortex mechanical exhaust hole optimization method based on the particle swarm optimization is characterized by comprising the following steps of:
setting the center of the exhaust hole and parameters of a particle swarm algorithm, randomly initializing position variables of the particle swarm, and randomly initializing a particle swarm velocity variable corresponding to a group of contour point control parameters forming the geometric shape of each exhaust hole;
constructing the geometric shape of the exhaust hole by using contour point control parameters corresponding to the position variables of the particle swarm;
judging whether the geometric shape of the constructed exhaust hole meets the limiting condition, if not, carrying out geometric treatment, and if so, carrying out vortex compressor simulation, and calculating to obtain an objective function;
repeating iteration, using a particle swarm algorithm to update contour point control parameters to obtain new particle swarm position variables, calculating adaptation values of all particles according to an objective function of the running condition of the compressor, comparing each adaptation value with a globally optimal particle variable, and updating the speed and position of each particle swarm;
and when the iteration times or the objective function reach the requirements, outputting the control parameters and the geometric results of the profile points of the exhaust holes.
2. The method for optimizing exhaust holes of scroll machine based on particle swarm optimization according to claim 1, wherein the center of the exhaust hole is O, and the position variable of randomly initializing m groups of particle swarm is x (0) = (x) 1 ,x 2 ,x 3 ,…,x m ) Each particle variable is x k K=1, …, m, randomly initializing the group velocity variable to v (0) = (v) 1 ,v 2 ,v 3 ,…,v m )；
The number of the contour points forming each exhaust hole geometric shape is n, and the contour points are P respectively 1 ,P 2 ,P 3 ,…,P n The contour point control parameter is the distance L from the contour point to the center, and a particle variable x k ＝(L 1 ,L 2 ,L 3 ,…,L n )。
3. The optimization method of the vortex mechanical exhaust hole based on the particle swarm optimization according to claim 2, wherein the included angles between two adjacent contour points and the center of the exhaust hole satisfy the following relation: angle P i OP i+1 =2pi/n, the corresponding spread angle of the contour point is θ i ＝2πi/n。
4. The method for optimizing exhaust hole of scroll machine based on particle swarm optimization according to claim 3, wherein said step of constructing exhaust hole geometry from contour point control parameters corresponding to the position variables of the particle swarm is performed by:
each vent geometry is generated from spline curves by two contour points which are adjacent in sequence, wherein the two contour points P i ，P i+1 Arbitrary point P on the curve between j The coordinates of (2) are:
wherein L is j For the arbitrary point P j Distance from center, θ j For the arbitrary point P j Corresponding spreading angles.
5. The method for optimizing exhaust holes of scroll machine based on particle swarm optimization according to claim 4, wherein the spline curve equation is L i (θ)＝a i θ 3 +b i θ 2 +c i θ+d i The undetermined coefficient in the equation is a i ，b i ，c i ，d i I=1,..n, distance L from center by given contour point according to cubic spline curve equation i Spread angle theta corresponding to contour point i And (5) obtaining.
6. The method for optimizing exhaust hole of scroll machine based on particle swarm optimization according to claim 1, wherein said step of judging whether the geometry of the exhaust hole obtained by construction satisfies a constraint condition, said constraint condition being a contour point control parameter L i The contour point is away from the static disc molded line distance t and the exhaust hole center point and the static disc molded line distance L' satisfy the following relation: l (L) i <L′-t；
In the step of performing geometric processing, the geometric processing method is that L is made i ＝L′-t。
7. The method for optimizing exhaust holes of a scroll machine based on a particle swarm optimization according to claim 1, wherein the simulation of the scroll compressor comprises modeling geometrical characteristics, thermodynamics and dynamics of a compressor profile and exhaust holes, and the objective function for calculating the adaptation value comprises a p-V curve, gas force, torque, indication efficiency and isentropic efficiency of the compressor operation.
8. The method for optimizing exhaust holes of scroll machine based on particle swarm optimization according to claim 1, wherein the expression for updating the speed and position of each particle swarm is as follows:
v k (t+1)＝ω×v k (t)+c 1 ×rand 1 (t)×(pBest k -x(t))+c 2 ×rand 2 (t)(gBest-x k (t))
x k (t+1)＝x k (t)+v k (t+1)
in the formula, v k (t+1) is a new particle group velocity, gBest is a global optimum particle variable, pBest k For the optimal position of the current particle, ω is the inertia factor of the population of primary particles, v k (t) is the primary particle group velocity, c 1 ，c 2 Are learning factors, rand 1 (t)，rand 2 (t) two random numbers of 0 to 1, x (t) is the primary particle group position variable, x k (t) and x k (t+1) is the position of the new particle group and the original particle group, respectively.
9. A particle swarm algorithm-based scroll machine exhaust hole optimization system, comprising:
the particle swarm initialization module is used for setting the center of the exhaust hole and parameters of a particle swarm algorithm, randomly initializing position variables of the particle swarm, and randomly initializing particle swarm velocity variables corresponding to a group of contour point control parameters forming the geometric shape of each exhaust hole;
the exhaust hole geometric shape construction module is used for constructing exhaust hole geometric shapes according to contour point control parameters corresponding to the position variables of the particle swarm;
the objective function calculation module is used for judging whether the geometric shape of the constructed exhaust hole meets the limiting condition, carrying out geometric processing if the geometric shape does not meet the limiting condition, and carrying out vortex compressor simulation if the geometric shape meets the limiting condition, and calculating to obtain an objective function;
the particle swarm updating module is used for repeatedly iterating, updating the contour point control parameters by using a particle swarm algorithm to obtain new particle swarm position variables, calculating the adaptation values of all particles according to an objective function of the running condition of the compressor, comparing each adaptation value with a globally optimal particle variable, and updating the speed and position of each particle swarm;
and the output module is used for outputting the control parameters and the geometric results of the profile points of the exhaust hole when the iteration times or the objective function reach the requirements.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the particle swarm algorithm-based scroll machine exhaust hole optimization method according to any of claims 1 to 7.
CN202310625627.5A 2023-05-30 2023-05-30 Particle swarm optimization-based vortex mechanical exhaust hole optimization method and system Pending CN116579252A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117744283A (en) * 2024-02-20 2024-03-22 陕西空天信息技术有限公司 Design method, device, equipment and computer storage medium for compressor

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117744283A (en) * 2024-02-20 2024-03-22 陕西空天信息技术有限公司 Design method, device, equipment and computer storage medium for compressor
CN117744283B (en) * 2024-02-20 2024-04-30 陕西空天信息技术有限公司 Design method, device, equipment and computer storage medium for compressor

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