CN110990962B - Intelligent optimization method of fan for auxiliary converter cabinet - Google Patents

Intelligent optimization method of fan for auxiliary converter cabinet Download PDF

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CN110990962B
CN110990962B CN201811140861.4A CN201811140861A CN110990962B CN 110990962 B CN110990962 B CN 110990962B CN 201811140861 A CN201811140861 A CN 201811140861A CN 110990962 B CN110990962 B CN 110990962B
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fan
converter cabinet
optimization scheme
geometric
neural network
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CN110990962A (en
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丁杰
彭宣霖
王永胜
陈俊
夏亮
曾亚平
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Zhuzhou CRRC Times Electric Co Ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
    • F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D29/00—Details, component parts, or accessories
    • F04D29/002—Details, component parts, or accessories especially adapted for elastic fluid pumps
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D29/00—Details, component parts, or accessories
    • F04D29/66—Combating cavitation, whirls, noise, vibration or the like; Balancing
    • F04D29/661—Combating cavitation, whirls, noise, vibration or the like; Balancing especially adapted for elastic fluid pumps
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D29/00—Details, component parts, or accessories
    • F04D29/70—Suction grids; Strainers; Dust separation; Cleaning
    • F04D29/701—Suction grids; Strainers; Dust separation; Cleaning especially adapted for elastic fluid pumps
    • F04D29/703—Suction grids; Strainers; Dust separation; Cleaning especially adapted for elastic fluid pumps specially for fans, e.g. fan guards

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Structures Of Non-Positive Displacement Pumps (AREA)

Abstract

The invention relates to an intelligent optimization method of a fan for an auxiliary converter cabinet, which comprises the following steps: s1, establishing a geometric and physical initial model of the original fan and the converter cabinet body; s2, selecting an optimization scheme according to the geometric physical initial model; s3, carrying out parametric modeling according to the selected optimization scheme; s4, carrying out orthogonal test on the parameters in the modeling; s5, establishing a comprehensive evaluation model of the fan vibration noise performance; s6, constructing a neural network, and analyzing the corresponding relation between the parameters and the vibration noise performance; s7, introducing a particle swarm intelligent algorithm, and determining an optimal optimization scheme in the neural network; and S8, performing simulation verification on the optimal optimization scheme. The invention not only can obtain the optimal optimization scheme of the auxiliary converter cabinet fan, reduce the aerodynamic noise of the cooling fan to the maximum extent, but also can analyze the internal relation between each geometric parameter and the flow field, the sound field and even the product performance in various optimization schemes, and is beneficial to the initial design of products and the technical transformation of other forms.

Description

Intelligent optimization method of fan for auxiliary converter cabinet
Technical Field
The invention belongs to the field of fans, and particularly relates to an intelligent optimization method of a fan for an auxiliary converter cabinet.
Background
In the field of rail transit, noise indexes are not only related to stability and reliability of a locomotive, but also closely related to comfort of passengers. The centrifugal fan is a high-speed rotating part in the auxiliary converter cabinet, and during the operation of the cabinet, an obvious noise phenomenon can be observed frequently, and the pneumatic noise of the fan air duct is taken as a main noise source and is always the key research direction for noise reduction and technical improvement.
The traditional optimization and improvement method is generally characterized in that a plurality of reasonable optimization schemes are firstly proposed through engineering practice and field experience, and then the optimization schemes are verified through simulation or experiment. The point-taking test mode does not obtain the corresponding relation between the geometric model and the noise index, and the optimal optimization scheme is difficult to obtain.
Therefore, an optimization method capable of obtaining the correspondence between the geometric model and the noise index and obtaining the optimal optimization scheme is needed.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent optimization method of a fan for an auxiliary converter cabinet, which can comprehensively consider various optimization means, reduce the pneumatic noise caused by the operation of the fan to the maximum extent and improve the distribution characteristic of the flow field of the fan.
In order to achieve the purpose, the invention provides an intelligent optimization method of a fan for an auxiliary converter cabinet, which comprises the following steps:
s1, establishing a geometric and physical initial model of the original fan and the converter cabinet body;
s2, selecting an optimization scheme according to the geometric physical initial model;
s3, carrying out parametric modeling according to the selected optimization scheme;
s4, carrying out orthogonal test on the parameters in the modeling;
s5, establishing a comprehensive evaluation model of the fan vibration noise performance;
s6, constructing a neural network, and analyzing the corresponding relation between the parameters and the vibration noise performance;
s7, introducing a particle swarm intelligent algorithm, and determining an optimal optimization scheme in the neural network;
and S8, performing simulation verification on the optimal optimization scheme.
In one embodiment, the step S1 further includes performing simulation calculation on the geometric initial model, and correcting the geometric initial model by using field test data.
In one embodiment, in step S2, the optimization scheme includes three types: and installing a resonant cavity, installing a rectifying filter screen and changing the distribution mode of the fan blades.
In one embodiment, the resonant cavity is tubular and forms an included angle with the wall of the converter cabinet, one end of the resonant cavity is fixed on the wall of the converter cabinet, the other end of the resonant cavity extends to the edge of the fan, and two resonant cavities are arranged and are uniformly distributed in the circumferential direction of the fan along the rotation direction of the fan.
In one embodiment, the fairing screen is disposed in an axial direction of the fan.
In one embodiment, the number of fan blades is an odd number.
In one embodiment, the parameterization in step S3 is modeled as: defining a cavity length of a resonant cavity as l and a cavity radius as r; defining a rectification structure by using the radius R of a rectification filter screen and the distance d between the rectification filter screen and a fan; the blade distribution is defined by the number λ of blades.
In one embodiment, in the step S4, a plurality of orders are defined for each parameter in the step S3, simulation calculation is performed respectively, and a database is built according to the simulation calculation result.
In one embodiment, in step S5, an objective function is constructed according to the flow field characteristics and the sound field characteristics under different models, and the performance of the wind turbine is accurately evaluated, where the objective function is expressed by the following formula:
Figure BDA0001815775490000021
in the formula (I)
Figure BDA0001815775490000022
Is an energy loss penalty function, the second term
Figure BDA0001815775490000023
Characterization of the degree of turbulence in the flow field, item III
Figure BDA0001815775490000024
Is a penalty function of acoustic power level, the fourth term
Figure BDA0001815775490000025
Is the average sound pressure level penalty function;
wherein e represents the energy loss of the inlet and the outlet, epsilon represents the vorticity average value of ten typical measuring points in the flow field, v represents the speed average value of ten typical measuring points in the flow field, X represents the sound power level, X represents the average sound pressure level of the inlet and the outlet measuring points, and the subscript 0 represents an index quantity corresponding to the original model; w is a1,w2,w3,w4Respectively representing the weights of the four penalty functions.
In one embodiment, in step S8, if the error between the solution obtained in the neural network and the simulation result is greater than the predetermined error value, steps S6 and S7 are repeated until the error is less than the predetermined error value, and the constructed neural network is considered to be accurate.
Compared with the prior art, the invention has the advantages that: the invention abandons the experimental optimization method of the traditional scheme re-verification scheme, and utilizes the strong fitting capability and generalization capability of the neural network to analyze the internal relation between the parameters of the geometric physical model and the flow field, the sound field and the noise index, thereby accurately and intelligently finding the optimal scheme in all the schemes based on the neural network.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
FIG. 1 is a flow chart of a method for intelligently optimizing a fan for an auxiliary converter cabinet according to the present invention;
FIG. 2 is a schematic structural diagram of a resonant cavity according to the present invention;
FIGS. 3 and 4 are comparative views of a fan without a fairing screen and with a fairing screen, respectively;
fig. 5 and 6 are comparative diagrams of a fan with an even number of blades and an odd number of blades, respectively.
In the drawings like parts are provided with the same reference numerals. The figures are not drawn to scale.
Detailed Description
The invention will be further explained with reference to the drawings. Therefore, the realization process of how to apply the technical means to solve the technical problems and achieve the technical effect can be fully understood and implemented. It should be noted that the technical features mentioned in the embodiments can be combined in any way as long as no conflict exists. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
The invention provides an intelligent optimization method of a fan for an auxiliary converter cabinet, which comprises the following steps of:
s1, establishing a geometric and physical initial model of the original fan and the converter cabinet body;
s2, selecting an optimization scheme according to the geometric physical initial model;
s3, carrying out parametric modeling according to the selected optimization scheme;
s4, carrying out orthogonal test on the parameters in the modeling;
s5, establishing a comprehensive evaluation model of the fan vibration noise performance;
s6, constructing a neural network, and analyzing the corresponding relation between the parameters and the vibration noise performance;
s7, introducing a particle swarm intelligent algorithm, and determining an optimal optimization scheme in the neural network;
and S8, performing simulation verification on the optimal optimization scheme.
Further, in step S1, the geometric-physical initial model is built according to the design drawing, and in addition, in step S1, after the geometric-physical initial model is built, simulation calculation needs to be performed on the geometric-physical initial model, and the geometric-physical initial model is corrected through field test data.
Further, in step S2, a commonly used optimization scheme includes: the number of the blades is increased or decreased, the shape of the blades is changed, the flow channel is optimized to reduce resistance, sound absorption materials are arranged inside the flow channel, shutters are arranged at the air outlet, a resonant cavity is arranged aiming at a main noise frequency band, and the like. Preferably, in the embodiment of the present invention, the optimization scheme includes three types: and installing a resonant cavity, installing a rectifying filter screen and changing the distribution mode of the fan blades.
Specifically, as shown in fig. 2, two resonant cavities are tubular and arranged, and the resonant cavities and the wall of the converter cabinet form an included angle, preferably, one end of each resonant cavity is fixed on the wall of the converter cabinet, that is, one end of each resonant cavity is sealed by the wall of the converter cabinet, and the other end extends to the edge of the fan, that is, the other end is a free end with an opening, and the two resonant cavities are uniformly distributed in the circumferential direction of the fan along the rotation direction of the fan; specifically, the opening direction of one resonant cavity forms an obtuse angle with the gravity direction, the opening direction of the other resonant cavity forms an acute angle with the gravity direction, and the two angles are complementary.
Aiming at the noise characteristics at the outlet, the invention designs a pair of tubular resonant cavities, so that the tubular resonant cavities can absorb the sound energy corresponding to the designed frequency, play a role of a reactive muffler and obviously reduce the noise vibration at the inlet and the outlet.
Specifically, as shown in fig. 4, the rectifying filter is disposed in the axial direction of the fan. Preferably, the rectifying filter screen can be directly arranged on the fan, and can also be arranged at a certain distance from the fan in the axial direction. The noise of the fan is directly influenced by the flow characteristics of the near field area of the fan, the rectifying filter screen has a rectifying effect on irregular vortex airflow entering the fan, large-scale vortex generated by the rotation of an inlet area in the fan is weakened, the noise of the fan is reduced, and the propagation of the noise is inhibited.
Specifically, as shown in fig. 5, in the original design, the number of blades is even, the blades are distributed on the same straight line, and the shapes are symmetrical, so that vibration energy is mutually transmitted and influenced, and resonance is easy to occur. As shown in fig. 6, the number of the fan blades is changed to be odd, and the vibration of the odd blades does not generate obvious superposition interference. Therefore, the flow field can be improved and the vibration can be reduced by changing the number of the blades and the distribution mode of the blades.
In one embodiment, the parameterization in step S3 is modeled as: defining a cavity length of a resonant cavity as l and a cavity radius as r; defining a rectification structure by using the radius R of a rectification filter screen and the distance d between the rectification filter screen and a fan; the blade distribution is defined by the number λ of blades.
In one embodiment, in the step S4, a plurality of orders of magnitude, preferably 3 to 5 orders of magnitude, are defined for each parameter in the step S3, and simulation calculations are performed on the orders of magnitude between the parameters, where the simulation calculations include fan duct flow field simulation and acoustic simulation, and then a database is established according to simulation calculation results. Specifically, the magnitude is a magnitude defining a parameter as a basic magnitude, an equal proportion or non-equal proportion increase or decrease is performed on the basis of the parameter magnitude, for example, the distance between the rectifier filter screen and the fan is d, one of the distances d is preset as the basic magnitude, one magnitude is increased to 1.2d or increased by another magnitude of 1.6d or decreased by a magnitude of 0.8d and the like on the basis of the basic magnitude, and the finally determined magnitude is determined according to actual test requirements.
In one embodiment, in step S5, an objective function is constructed according to the flow field characteristics and the sound field characteristics under different models, and the performance of the wind turbine is accurately evaluated, where the objective function is expressed by the following formula:
Figure BDA0001815775490000051
in the formula (I)
Figure BDA0001815775490000052
Is an energy loss penalty function, the second term
Figure BDA0001815775490000053
Characterization of the degree of turbulence in the flow field, item III
Figure BDA0001815775490000054
Is a penalty function of acoustic power level, the fourth term
Figure BDA0001815775490000055
Is the average sound pressure level penalty function;
wherein e representsThe energy loss of an outlet, epsilon represents the vorticity average value of ten typical measuring points in the flow field, v represents the speed average value of the ten typical measuring points in the flow field, X represents the sound power level, X represents the average sound pressure level of the inlet and outlet measuring points, and the subscript 0 represents an index quantity corresponding to the original model; w is a1,w2,w3,w4Respectively representing the weights of the four penalty functions.
Eight typical measuring points are uniformly arranged on the circumference of the fan, and the outlets of the two resonant cavities are respectively provided with one typical measuring point. It should be noted that the selection of typical measuring points in the flow field is not fixed and can be changed according to actual needs, but all are within the protection scope of the present invention.
In one embodiment, in step S6, a neural network is constructed by using a BP neural network or an extreme learning machine.
In one embodiment, in step S7, a Particle Swarm intelligence algorithm (PSO) is introduced to find an Optimization scheme with optimal performance in the neural network.
In one embodiment, in step S8, the specific steps are: and performing analog simulation verification on the optimal scheme, if the error between the solution obtained in the neural network and the simulation result is greater than a preset error value, repeatedly executing the steps S6 and S7, merging the simulation example of the optimal scheme into the database, reconstructing a new neural network until the error is less than the preset error value, determining that the constructed neural network is accurate, and obtaining the optimal optimization scheme. In one embodiment of the present invention, the predetermined error value is 5%.
While the present invention has been described with reference to the preferred embodiments as above, the description is only for the convenience of understanding the present invention and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. An intelligent optimization method for a fan for an auxiliary converter cabinet is characterized by comprising the following steps:
s1, establishing a geometric and physical initial model of the original fan and the converter cabinet body;
s2, selecting an optimization scheme according to the geometric physical initial model; the optimization scheme comprises three types:
installing two resonant cavities, wherein the two resonant cavities are tubular and form an included angle with the wall of the converter cabinet, one end of each resonant cavity is fixed on the wall of the converter cabinet, the other end of each resonant cavity extends to the edge of the fan, and the two resonant cavities are uniformly distributed in the circumferential direction of the fan along the rotation direction of the fan;
installing a rectifying filter screen, wherein the rectifying filter screen is arranged in the axial direction of the fan;
changing the distribution mode of fan blades, wherein the number of the fan blades is odd;
s3, carrying out parametric modeling according to the selected optimization scheme;
s4, carrying out orthogonal test on the parameters in the modeling;
s5, establishing a comprehensive evaluation model of the fan vibration noise performance; the method comprises the following steps of constructing a target function according to flow field characteristics and sound field characteristics under different models, and accurately evaluating the performance of the fan, wherein the target function is shown as the following formula:
Figure FDA0003166787550000011
in the formula (I)
Figure FDA0003166787550000012
Is an energy loss penalty function, the second term
Figure FDA0003166787550000013
Characterization of the degree of turbulence in the flow field, item III
Figure FDA0003166787550000014
Is a penalty function of acoustic power level, the fourth term
Figure FDA0003166787550000015
Is the average sound pressure level penalty function;
wherein e represents the energy loss of the inlet and the outlet, epsilon represents the vorticity average value of ten typical measuring points in the flow field, v represents the speed average value of ten typical measuring points in the flow field, X represents the sound power level, X represents the average sound pressure level of the inlet and the outlet measuring points, and the subscript 0 represents an index quantity corresponding to the original model; w is a1,w2,w3,w4Respectively representing the weights of the four penalty functions;
s6, constructing a neural network, and analyzing the corresponding relation between the parameters and the vibration noise performance;
s7, introducing a particle swarm intelligent algorithm, and determining an optimal optimization scheme in the neural network;
and S8, performing simulation verification on the optimal optimization scheme.
2. The intelligent optimization method for wind turbine of auxiliary converter cabinet according to claim 1, wherein the step S1 further includes performing simulation calculation on the geometric initial model, and correcting the geometric initial model through field test data.
3. The intelligent optimization method for the fan used for the auxiliary converter cabinet according to claim 1 or 2, wherein the parametric modeling in the step S3 is as follows: defining a cavity length of a resonant cavity as l and a cavity radius as r; defining a rectification structure by using the radius R of a rectification filter screen and the distance d between the rectification filter screen and a fan; the blade distribution is defined by the number λ of blades.
4. The intelligent optimization method for the wind turbine of the auxiliary converter cabinet according to claim 1 or 2, wherein in step S4, a plurality of magnitudes are defined for each parameter in step S3, simulation calculation is performed respectively, and a database is built according to the simulation calculation results.
5. The intelligent optimization method of the fan for the auxiliary converter cabinet as recited in claim 1 or 2, wherein in step S8, if the error between the solution obtained in the neural network and the simulation result is greater than a preset error value, the steps S6 and S7 are repeatedly executed until the error is less than the preset error value, and the constructed neural network is considered to be accurate.
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