CN115408953A - Self-ventilation motor fan pneumatic noise simulation evaluation method - Google Patents
Self-ventilation motor fan pneumatic noise simulation evaluation method Download PDFInfo
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Abstract
The invention relates to the technical field of motor noise, in particular to the technical field of motor fan pneumatic noise, and specifically relates to a self-ventilation motor fan pneumatic noise simulation evaluation method. The invention aims to provide a motor fan aerodynamic noise simulation evaluation method with short calculation period and strong applicability, which comprises the steps of firstly obtaining input information such as a motor original geometric model, a rotating speed, a boundary and the like, then carrying out steady-state flow field analysis and data extraction, taking an extracted result as a proxy model input parameter, and calculating the aerodynamic noise sound power level of a motor centrifugal fan; and after the sound power level estimated value is obtained, carrying out standard-exceeding risk level evaluation with a standard limit value or a client requirement, and producing a sample machine and carrying out test testing after the evaluation is passed. And if the evaluation is not passed, adjusting the design scheme, including adjusting relevant parameters such as the outer diameter of the fan, the number of the fan blades, the width of the fan blades, the shape of the fan blades and the like, and analyzing and predicting again until the scheme passes the noise risk level evaluation. The method is simple and easy to implement, and the evaluation period is short.
Description
Technical Field
The invention relates to the technical field of motor noise, in particular to the technical field of self-ventilation motor fan pneumatic noise, and specifically relates to a self-ventilation motor fan pneumatic noise simulation evaluation method.
Background
With the technical development and the improvement of the speed grade of the train, higher requirements are provided for the rotating speed and the power density of the motor, and the rotating speed and the power of the fan are correspondingly increased, so that the self-ventilation traction motor always faces the problem of exceeding the noise standard, and therefore, the accurate noise evaluation in the motor design stage also becomes one of the main problems to be considered by designers.
According to GB/T25123.2-2018 and GB/T25123.4-2015, whether the traction motors are asynchronous traction motors or permanent magnet traction motors, noise evaluation is mostly carried out under no load, and the evaluation parameter is the sound power level. The problem that the noise of the self-ventilation motor exceeds the standard under the high-speed working condition is prominent, the noise during high-speed operation mainly comes from pneumatic noise generated by a fan, and the electromagnetic noise and the mechanical noise are small and can be ignored. Therefore, in evaluating the noise of the self-ventilating motor, only the aerodynamic noise caused by the fan is generally considered.
A simple and convenient method for evaluating the aerodynamic noise of the fan in the current stage of engineering design adopts a general formula for evaluation, and a noise evaluation formula for geometrically similar fans is introduced in 'analysis and control of motor noise' written by Chengyangschool and the like at home; the noise prediction problem of a ventilator in the same structural form or in the same series is solved by using the sound level of ratio A in fan handbook compiled by Changkong. The standard AMCA301-2014 of the foreign American society for ventilation and air conditioning specifies that the existing noise test data of the fan is used for estimating the sound power level of each frequency band when other fans with similar geometrical relations exist at different rotating speeds, outer diameters or working points. The standard and the method are only suitable for quick estimation of the pneumatic noise of the single fan or have a small application range, and the estimation applicability of the sound power level of the pneumatic noise of the motor fan when the test is carried out according to the related standard of the motor noise test cannot be ensured.
Aiming at the aerodynamic noise of the motor fan, a finite element simulation method with mature technology can be adopted for calculation, a motor flow field model and an acoustic model need to be established, steady-state and transient flow fields and acoustic solving calculation need to be carried out, the accuracy is high, and the applicability is wide. When the method is used for calculating the noise of the motor fan, the consumed time is long (generally 2-3 weeks), more hardware resources are occupied, and the requirement of a faster calculation period in a motor design stage cannot be met; meanwhile, the design process of the centrifugal fan of the motor usually needs multi-scheme comparison or multi-round sub-optimization, and the characteristics of long single calculation period and low efficiency of finite element simulation enable the optimization design of the motor to be slow in working process and low in efficiency.
In summary, the existing general empirical formula cannot be reliably applied to the rapid calculation of the aerodynamic noise of the motor fan, and the finite element method has an excessively large demand for calculation resources, so that the simulation evaluation and optimization process of the aerodynamic noise of the motor centrifugal fan is difficult, and therefore, a self-ventilation motor fan aerodynamic noise simulation evaluation method which is short in calculation period, small in error (plus or minus 3 dBA) and strong in applicability needs to be developed.
Disclosure of Invention
The invention aims to provide a self-ventilation motor fan pneumatic noise simulation evaluation method which is short in calculation period and strong in applicability.
The invention is realized by adopting the following technical scheme: a self-ventilation motor fan pneumatic noise simulation evaluation method comprises the following steps:
1) Analyzing a motor steady-state flow field and extracting the flow field simulation result data corresponding to each rotating speed and each rotating speed from the flow field simulation result after analyzing the motor steady-state flow field: how to analyze the steady-state flow field of the motor and extract data belongs to the common knowledge of technicians in the field, and specifically, the steady-state flow field of the motor is analyzed by a finite volume method, and the calculation flow comprises the following steps: a. simplifying a geometric model of the motor and establishing a flow field model: the original model has a plurality of parts, unnecessary fillets and chamfers are eliminated, after adjacent parts are combined, the simplified solid model of the motor comprises but is not limited to a rotating shaft, a fan, a rotor core, a conducting bar-end ring or magnetic steel, a stator core, a coil, a transmission end cover, a non-transmission end cover and a base, or the part of the model (for example, for a fully-closed motor, the simplified model only comprises the fan, the two end covers and the base), after the simplified structural model is obtained, a fluid domain is extracted, the fluid domain is properly extended at an inlet and an outlet, and the extension distance is equivalent to the size of the motor. Because the rotor rotates at a certain rotating speed, the part of the fluid domain, which wraps the solid rotating region, needs to be divided to provide a rotating load, at the moment, the fluid domain is generally divided into a static region and a rotating region, and each part comprises a certain number of sub-fluid domains; b. after a flow field model is obtained, a grid is divided by adopting an unstructured network: after a flow field model is obtained, adopting unstructured grids, and obtaining a computational domain grid model with better grid quality by giving the size of a body grid and a surface grid of each part of a computational domain, division parameters of an inlet and outlet boundary layer and encryption parameters at a bent or narrow area, wherein the orthogonality of all the grids is larger than 1E-3, and the average value is larger than 0.75; c. after the grid division is finished, carrying out solution setting, and after the setting is finished, starting to solve until the calculation is converged: how to perform the solution setup in detail is well known to those skilled in the art: specifying a material model and a turbulence model for solution (such as an ideal gas and k-epsilon turbulence model), giving load and boundary conditions, mainly including but not limited to rotation speed, inlet and outlet pressure boundaries and the like, specifying a solution format (such as coupling solution, second-order accuracy and the like), and setting convergence judgment criteria (such as calculating a one-step flow change rate smaller than 1E-4). After the setting is finished, the solution is started until the calculation is converged;
2) Sample data evaluation: giving a group of initial sample data consisting of M sample points (the sample points are sample points consisting of different motors or different fans or different rotating speeds), wherein each sample point consists of N input parameters and one output parameter, the N input parameters are the rotating speed extracted in the step 1) and flow field simulation result data corresponding to the rotating speed, the 1 output parameter is a pneumatic noise power level test value of the same type of motor fan at the corresponding rotating speed (the test value can also be called a test accumulation value, namely a noise power level value acquired when the same type of motor fan carries out noise test), normalizing each input parameter of the group of samples to form an array A, the array A is M rows and N columns, solving the Euclidean distance between every two samples in the array A to form an array B, and the maximum Euclidean distance of N-dimensional space consisting of the input variables after the sample input parameter normalization is that
The sample evaluation criteria are as follows:
in the formula: mean (B) is the average value of the array B, min (B) is the minimum value of the array B, max (B) is the maximum value of the array B, and when the sample data does not meet the sample evaluation standard of the formula (1), the sample points are added, replaced or removed until the sample evaluation standard of the formula (1) is met;
3) Establishing a proxy model and evaluating the proxy model: selecting a method for constructing a proxy model (such as an elliptic base neural network, a polynomial response surface, a kriging, a support vector regression, a radial basis function and an improvement method thereof), evaluating the constructed proxy model by adopting an LOO cross verification method, namely removing one sample point data, constructing the proxy model by using the other sample point data, predicting on the removed sample point by using the model and calculating the prediction error of the output parameter of the sample point, when the prediction error of the motor pneumatic noise of each sample point is within [ -3,3] dBA and the fitting degree of the final proxy model is more than 0.9, evaluating the proxy model, and otherwise, reselecting other methods to construct the proxy model;
4) And (3) noise evaluation: substituting the flow field simulation results extracted in the step 1) at each rotating speed into the proxy model passing the evaluation to obtain the aerodynamic noise sound power level estimated value L of the motor fan W According to the formula
Performing an evaluation in which
For the limit value of the aerodynamic noise sound power level of the motor fan at the corresponding rotating speed, delta L W For the aerodynamic noise sound power level error of the motor fan under the corresponding rotating speed, max (Delta L) W ) The maximum value of the aerodynamic noise sound power level error of the motor fan under each rotating speed is as max (delta L) W ) At ≦ 0, noise assessment passed, max (Δ L) W )>At 0, the noise estimate does not pass, and the design is modified until the noise estimate passes.
The beneficial effects produced by the invention are as follows: the invention provides a self-ventilation motor fan pneumatic noise simulation evaluation method, which is particularly suitable for a self-ventilation motor centrifugal fan. The method is simple and easy to implement, and by combining the processes of motor steady-state flow field analysis calculation, sample data evaluation, proxy model establishment, noise evaluation and the like, compared with the existing finite element method and the general empirical formula method which are respectively used, the contradiction between quick calculation and high accuracy is effectively solved, the noise simulation evaluation speed of the motor fan is improved, the rationality and accuracy of an evaluation result are maintained, the fan pneumatic noise simulation evaluation period in the self-ventilation motor design process is shortened (the evaluation of the scheme can be completed in about 1 day), and the method can be further used for developing multi-fan scheme comparison optimization in the motor pneumatic noise design stage.
Drawings
FIG. 1 is a flow chart of a self-ventilating motor fan aerodynamic noise simulation evaluation;
FIG. 2 is a flow chart of proxy model building;
FIG. 3 is a simplified solid state field of the motor;
fig. 4 is a simplified fluid domain of the motor.
Detailed Description
Example 1: the method for simulating and evaluating the aerodynamic noise of the centrifugal fan of the permanent magnet self-ventilation motor comprises the following steps as shown in figure 1:
1) Analyzing the steady-state flow field of the motor and extracting the flow field simulation result data corresponding to each rotating speed and each rotating speed from the subsequent flow field simulation result: the motor steady-state flow field analysis adopts a finite volume method, and the calculation flow comprises the following steps: a. simplifying a geometric model of the motor and establishing a flow field model, obtaining a simplified structure model as shown in figure 3, and extracting a fluid domain as shown in figure 4; b. after a flow field model is obtained, a grid is divided by adopting an unstructured network: after a flow field model is obtained, adopting unstructured grids, and obtaining a computational domain grid model with better grid quality by giving the size of a body grid and a surface grid of each part of a computational domain, division parameters of an inlet and outlet boundary layer and encryption parameters at a bent or narrow area, wherein the orthogonality of all the grids is larger than 1E-3, and the average value is larger than 0.75; c. after the grid division is finished, carrying out solution setting, and after the setting is finished, starting to solve until the calculation is converged: how to perform the solution setup in detail is well known to those skilled in the art: specifying a material model and a turbulence model (such as an ideal gas and a k-epsilon turbulence model) for solving, giving load and boundary conditions, mainly including but not limited to rotating speed, inlet and outlet pressure boundaries and the like, specifying a solving format (such as coupling solving, second-order precision and the like), setting a convergence judgment standard (such as calculating a one-step flow change rate to be less than 1E-4), and starting to solve until the calculation is converged after the setting is completed; obtaining flow field simulation result data, namely each rotating speed omega and motor circulation volume flow Q corresponding to each rotating speed omega through post-processing and related calculation m Fan circulation volume flow Q fs Full pressure increment P from fan inlet to outlet a Static pressure increment P from fan inlet to fan outlet s And the pneumatic resistance moment M suffered in the rotation process of the fan fs Effective total pressure efficiency of fan in motor
Full pressure efficiency of fan
Wherein: extracting motor circulation volume flow Q m In time, the unit of the inlet and outlet of the motor is m 3 S, extracting fan circulation volume flow Q fs In the meantime, the integral section is taken in the fan flow passage in m 3 S, extracting the full pressure increment P from the inlet to the outlet of the fan a And static pressure increase P s When the fan is used, the integral cross section of the inlet and the outlet is selected to be perpendicular to the main flow direction of airflow, is close to the inlet and outlet boundaries of a fan flow passage by about 2mm, has the unit of Pa, and extracts the pneumatic resistance moment M received in the rotation process of the fan fs When the fan is used, all rotating surfaces of the fan are selected as integral surfaces, the torque direction is axial, and the unit is N × m;
2) As shown in fig. 2, the sample data is evaluated: given a set of initial sample data (too much data, so it is not illustrated, this specific embodiment is only an example of the simulation evaluation method, how many sample points should be actually determined according to the type of the motor and the type of the fan, and it is recommended that the number of sample points should be greater than 5N to ensure the sufficiency of the sample), each sample point has 8 input parameters and one output parameter, 8 input parameters are the flow field simulation result data of the rotating speed extracted in step 1) and the corresponding rotating speed, 1 output parameter is the aerodynamic noise power level test value of the motor fan of the same type at the corresponding rotating speed, the input parameters of the set of samples are normalized to form an array a, the array a is 68 rows by 8 columns, the euclidean distance between two samples in the array a is solved to form an array B (length is 2278), and the sample evaluation standard is as follows:
in the formula: mean (B) is the average value of the array B, min (B) is the minimum value of the array B, max (B) is the maximum value of the array B, relevant parameters are calculated according to the formula (1), the calculation result is shown in table 1, after analysis, the distance of 3 sample points in 68 sample points does not meet the standard, three sample points are removed, and the rest 65 sample points form a sample to be re-evaluated and evaluatedThe estimation result is shown in table 2, which accords with the standard of formula (1), and a proxy model can be constructed;
TABLE 1 initial sample evaluation
TABLE 2 update sample evaluation
3) Establishing a proxy model and evaluating the proxy model: an elliptic base neural network model is adopted, an LOO cross verification method is adopted for the constructed proxy model to evaluate, namely, one sample point data is removed, the other sample point data is used for constructing the proxy model, then the model is used for predicting on the removed sample point and calculating the prediction error of the output parameter (the pneumatic noise sound power level test value corresponding to the sample point) of the sample point, the prediction error of the motor pneumatic noise of each sample point is in the range of (-2.6, 2.7) dBA, the fitting degree of the final proxy model is 0.981, and the proxy model passes evaluation;
4) And (3) noise evaluation: substituting the flow field simulation results extracted in the step 1) at each rotating speed into an agent model passing evaluation to obtain a motor fan pneumatic noise sound power level estimated value L W According to the formula
Performing an evaluation in which
For the limit value of the aerodynamic noise sound power level of the motor fan at the corresponding rotating speed, delta L W For the aerodynamic noise sound power level error of the motor fan under the corresponding rotating speed, max (Delta L) W ) The maximum value of the pneumatic noise sound power level error of the motor fan at each rotating speed is obtained. The motor fan aerodynamic noise overproof risk ratings are shown in table 3,
TABLE 3 Motor Fan noise out of limits Risk Classification
The evaluation results and the test results are shown in table 4.
TABLE 4 Motor Fan aerodynamic noise assessment (three speeds are listed for illustration)
Example two: in the design initial stage of a certain self-ventilation motor fan, the manufacturing cost of the fan is considered, a conical front disk and a conical rear disk are adopted and are marked as a fan A, and the pneumatic noise of the fan is evaluated and the result shows that the fan does not pass; readjusting the front and rear disks of the fan, changing the front and rear disks into arc-shaped front and rear disks, recording as the fan B, and re-evaluating without passing; and adjusting the outer diameter and the width of the fan blades again to design a C fan, wherein the fan passes the quick evaluation of the aerodynamic noise performance of the fan. The final results were compared with the experimental results and the calculated results are shown in Table 5.
TABLE 5 comparison of the levels of risk of excessive aerodynamic noise of different fans
| Fan (Ref. TM. Fan) | max(ΔL W )/dBA | Evaluation conclusion | Test results |
| A | 5.9 | Out of limits | —— |
| B | 2.8 | The exceeding risk is larger | —— |
| C | -1.8 | Less overproof risk | Not exceeding standard |
The design processes, technical routes and evaluation methods according to the claimed invention are not limited to the above embodiments, and all the modifications and variations can be made without departing from the spirit of the claimed invention. For example:
1) The technical route of constructing the self-ventilation motor pneumatic noise agent model by adopting the flow field input and the noise output in the invention can also achieve the aim of rapidly estimating the motor noise by increasing, reducing or replacing the input and output parameters.
2) In the process of steady state analysis of the motor flow field and data extraction, the purpose of obtaining the input parameters of the proxy model can be achieved by dividing different grids, adopting different turbulence models and intercepting sections at other positions.
3) The purpose of constructing the proxy model can be achieved by adopting different methods (including polynomial response surfaces, kriging, support vector regression, radial basis functions, improvement methods thereof and the like).
Claims (8)
1. A self-ventilation motor fan pneumatic noise simulation evaluation method is characterized by comprising the following steps:
1) Analyzing a motor steady-state flow field and extracting each rotating speed and flow field simulation result data corresponding to each rotating speed from a subsequent flow field simulation result; 2) Sample data evaluation: given a set of initial samples consisting of M sample pointsThe method comprises the steps that each sample point comprises N input parameters and one output parameter, the N input parameters are the rotating speed extracted in the step 1) and flow field simulation result data corresponding to the rotating speed, the 1 output parameter is a pneumatic noise sound power level test value of a motor fan of the same type at the corresponding rotating speed, each input parameter of a group of samples is normalized to form an array A, the array A is M rows and N columns, the Euclidean distance between every two samples in the array A is solved to form an array B, and the maximum Euclidean distance of an space formed by input variables after the input parameters of the samples are normalized is N
The sample evaluation criteria are as follows:
in the formula: mean (B) is the average value of the array B, min (B) is the minimum value of the array B, max (B) is the maximum value of the array B, and when the sample data does not meet the sample evaluation standard of the formula (1), the sample points are added, replaced or removed until the sample evaluation standard of the formula (1) is met; 3) Establishing a proxy model and evaluating the proxy model: selecting a method for constructing a proxy model and evaluating the constructed proxy model by adopting an LOO cross verification method, namely removing one sample point data, constructing the proxy model by using the other sample point data, then predicting on the removed sample point by using the model and calculating the estimated error of the output parameter of the sample point, and when the estimated error of the motor aerodynamic noise of each sample point is [ -3,3]When the fitting degree of the final agent model is within dBA and is above 0.9, the agent model is evaluated, otherwise, other methods are reselected to construct the agent model; 4) And (3) noise evaluation: substituting the flow field simulation results extracted in the step 1) at each rotating speed into an agent model passing evaluation to obtain a motor fan pneumatic noise sound power level estimated value L W According to the formula
Carrying out an evaluation in which
Is a motor fan pneumatic noise sound power level limit value, delta L, at a corresponding rotating speed W For the aerodynamic noise sound power level error of the motor fan under the corresponding rotating speed, max (Delta L) W ) The maximum value of the aerodynamic noise sound power level error of the motor fan under each rotating speed is as max (delta L) W ) At 0 or less, the noise evaluation passes, max (. DELTA.L) W )>At 0, the noise estimate does not pass, and the design is refined until the noise estimate passes.
2. The self-ventilation motor fan aerodynamic noise simulation evaluation method according to claim 1, wherein in the step 1), a finite volume method is adopted for motor steady-state flow field analysis, and the calculation process comprises the following steps: a. simplifying a geometric model of the motor and establishing a flow field model; b. after a flow field model is obtained, dividing grids by adopting an unstructured network; c. and after the grid division is finished, carrying out solution setting, and after the setting is finished, starting to solve until the calculation is converged.
3. The self-ventilating motor fan aerodynamic noise simulation assessment method according to claim 2, characterized in that in step 2), M >5N.
4. The method for simulating and evaluating the aerodynamic noise of the fan of the self-ventilating motor as recited in claim 3, wherein N in the step 2) is 8,8 input parameters are 8 pieces of flow field simulation result data extracted in the step 1), namely the unit of the rotational speed of the omega motor is rad/s, and the circulating volume flow Q of the motor is Q m Fan circulation volume flow rate Q fs Full pressure increment P from fan inlet to outlet a Static pressure increment P from fan inlet to fan outlet s The pneumatic resistance moment M suffered by the fan in the rotating process fs Effective total pressure efficiency of fan in motor
Full pressure efficiency of fan
5. The self-ventilating motor fan aerodynamic noise simulation evaluation method according to claim 4, wherein a fan circulation volume flow Q is extracted fs The integral cross section should be cut within the fan flow path.
6. The self-ventilating motor fan aerodynamic noise simulation assessment method according to claim 5, wherein the fan inlet to outlet full pressure increase P is extracted a And fan inlet to outlet static pressure increase P s The inlet and outlet integral cross-sections are chosen to be perpendicular to the main flow direction of the gas flow.
7. The self-ventilating motor fan aerodynamic noise simulation assessment method according to claim 6, wherein the fan inlet to outlet full pressure increase P is extracted a And fan inlet to outlet static pressure increase P s When the fan is used, the selection of the integral cross sections of the inlet and the outlet is required to be 2mm close to the inlet and the outlet boundaries of the fan flow channel.
8. The self-ventilating motor fan aerodynamic noise simulation assessment method according to claim 7, wherein a motor circulation volume flow Q m Is the circulating volume flow at the inlet or outlet of the motor.
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| CN117371255A (en) * | 2023-12-06 | 2024-01-09 | 华中科技大学 | Construction method and application of rotary sound source radiation noise prediction model |
| CN117371255B (en) * | 2023-12-06 | 2024-02-20 | 华中科技大学 | Construction method and application of rotary sound source radiation noise prediction model |
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