CN111709128A - Shell structure high-frequency sound radiation forecasting method based on local optimization - Google Patents

Shell structure high-frequency sound radiation forecasting method based on local optimization Download PDF

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CN111709128A
CN111709128A CN202010478851.2A CN202010478851A CN111709128A CN 111709128 A CN111709128 A CN 111709128A CN 202010478851 A CN202010478851 A CN 202010478851A CN 111709128 A CN111709128 A CN 111709128A
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成玮
王桦瑀
陈雪峰
韩圣明
张鹏
杨志勃
张兴武
高琳
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Xian Jiaotong University
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Abstract

The invention discloses a shell structure high-frequency sound radiation forecasting method based on local optimization, which comprises the steps of firstly establishing a structure model of a shell structure based on size parameters of the shell structure, then dividing the structure model into grids, and carrying out subsystem division on the structure based on the grid model; the method comprises the steps of establishing a boundary coupling relation of each subsystem and a fluid acoustic environment around a structure, obtaining an internal loss factor of a high-energy contribution subsystem of the subsystem by adopting a transient attenuation method, establishing an energy balance equation of a shell structure acoustic radiation model by locally correcting model parameters, further reducing the test quantity of the model parameters while ensuring the enough prediction precision, improving the acoustic radiation prediction precision while reducing the test quantity of the model parameters, and reducing the modeling period.

Description

Shell structure high-frequency sound radiation forecasting method based on local optimization
Technical Field
The invention relates to the field of mechanical vibration reduction and noise reduction, in particular to a shell structure high-frequency sound radiation forecasting method based on local optimization.
Background
The realization of rapidity is a pursuit target of large-size shell structure equipment such as current underwater aircrafts, ships, airplanes and the like, the structural quality is developed to be light weight, the power of power equipment is increased, the problems of equipment vibration and radiation noise are increasingly highlighted, and the quality performance and the service performance of the equipment are reduced. The accurate prediction of the sound radiation of the shell structure equipment can fully reveal the distribution of a noise sound field, so as to scientifically guide the vibration and noise reduction of the submarine, and has important engineering significance for improving the equipment performance. The vibration excitation source of the large-size shell equipment has the characteristics of large amplitude and wide spectrum, sound radiation prediction needs to be carried out in a wide frequency band, an effective result cannot be obtained by adopting a single analysis method, and a structural radiation sound field is generally predicted by adopting statistical energy in a high frequency band. The key link of calculating by adopting the statistical energy analysis method is to estimate the high-frequency model parameters of a complex structure system, however, in the practical engineering problem, the estimation of the model parameters by adopting a theoretical method has larger errors due to the complexity and irregularity of the structure, so that the model parameters need to be obtained by adopting an experimental method, but the measurement of the model parameters is extremely difficult due to the huge volume of shell structure equipment such as submarines, ships and the like and the numerous subsystems, so that the parameters can be estimated only by adopting the theoretical method, and the prediction precision of the medium-frequency radiation sound field is lower.
Disclosure of Invention
Aiming at the problems of difficult subsystem division and difficult model parameter test of large-size shell structure high-frequency sound radiation prediction, the invention provides a shell structure high-frequency sound radiation prediction method based on local optimization.
In order to achieve the purpose, the invention adopts the following technical scheme:
a shell structure high-frequency sound radiation forecasting method based on local optimization comprises the following steps:
step 1), establishing a geometric model of the shell structure according to geometric information of the shell structure;
step 2), carrying out meshing division on the geometric model of the shell structure to obtain a shell structure mesh model, and carrying out subsystem division on the shell structure based on the shell structure mesh model to obtain a plurality of subsystems;
step 3), establishing a boundary coupling relation of each subsystem and a fluid acoustic environment around the shell structure, and acquiring an internal loss factor of the high-energy contribution subsystem of the subsystem by adopting a transient attenuation method;
and 4), establishing a shell model energy balance equation based on the local correction model parameters, solving the shell model energy balance equation through the internal loss factors obtained in the step 3), and obtaining a shell structure radiation sound field to realize shell structure high-frequency sound radiation forecast.
Further, the step 1) is specifically as follows: and measuring the geometric information of the shell structure, carrying out structural analysis on the geometric information of the shell structure, and establishing a geometric model of the shell structure according to an analysis result.
Further, the geometric information includes the geometric shape and the form and position size of each component in the structure.
Further, in the step 2), the shell structure is divided into subsystems, specifically, a single-curvature plate is adopted to divide the shell structure along the axial direction of the shell structure grid model to obtain a plurality of subsystems.
Further, an internal loss factor of the high-energy contribution subsystem is obtained by adopting a transient attenuation method:
Figure BDA0002516661020000021
Figure BDA0002516661020000022
in the formula:Tn-internal loss attenuation ratio ηi-the internal loss factor of subsystem i; a. theTn(t1) -the magnitude of the decay rate curve; omegac-the center frequency.
Further, the energy balance equation of the shell model established based on the local correction model parameters is as follows:
Figure BDA0002516661020000031
in the formula: omegacη as center frequencyiη is the loss factor in the subsystemijCoupling loss factors of the ith subsystem and the jth subsystem; n isiIs the modal density of subsystem i; piIs the input power of system i; eiIs the average energy of subsystem i.
Further, when the high-frequency sound radiation of the shell structure is obtained, the high-frequency band vibration source is adopted for excitation.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention relates to a shell structure high-frequency sound radiation forecasting method based on local optimization, which comprises the steps of firstly establishing a structure model of a shell structure based on size parameters of the shell structure, then dividing the structure model into grids, and carrying out subsystem division on the structure based on the grid model; the method comprises the steps of establishing a boundary coupling relation of each subsystem and a fluid acoustic environment around a structure, obtaining an internal loss factor of a high-energy contribution subsystem of the subsystem by adopting a transient attenuation method, establishing an energy balance equation of a shell structure acoustic radiation model by locally correcting model parameters, further reducing the test quantity of the model parameters while ensuring the enough prediction precision, improving the acoustic radiation prediction precision while reducing the test quantity of the model parameters, and reducing the modeling period.
Drawings
Fig. 1 is a structure of a test bed of a geometric model of a shell structure according to an embodiment of the present invention.
FIG. 2 is a schematic structural diagram of a subsystem division of a cylindrical shell by using a single-curvature plate according to an embodiment of the present invention; FIG. 2(a) is a schematic diagram of the division result of the shell structure grid model along the axial direction by using 1 single-curvature plate; FIG. 2(b) is a schematic diagram of the division result of the shell structure grid model along the axial direction by using 3 single-curvature plates; FIG. 2(c) is a schematic diagram of the division result of the shell structure grid model along the axial direction by using 5 single-curvature plates; FIG. 2(d) is a schematic diagram of the division result along the radial direction (circumferential direction) of the shell structure mesh model by using 2 single-curvature plates; fig. 2(e) is a schematic diagram of the division result along the radial direction (circumferential direction) of the shell structure mesh model by using 4 single-curvature plates.
Fig. 3 is a comparison graph of sound radiation prediction results of different division methods of the cylindrical shell subsystem in the embodiment of the invention.
FIG. 4 is a schematic structural diagram of a sub-system division of a conical shell by using a single-curvature plate according to an embodiment of the present invention; fig. 4(a) is a schematic diagram of the division result of the conical shell structure grid model along the axial direction by using 1 single-curvature plate; FIG. 4(b) is a schematic diagram of the division result of the conical shell structure grid model along the axial direction by using 2 single-curvature plates; the figure (c) is a schematic diagram of the division result of the conical shell structure grid model along the axial direction by adopting 5 single-curvature plates; and (d) is a schematic diagram of a division result along the radial direction (circumferential direction) of the conical shell structure grid model by adopting 2 single-curvature plates, and (e) is a schematic diagram of a division result along the radial direction (circumferential direction) of the conical shell structure grid model by adopting 4 single-curvature plates.
FIG. 5 is a comparison graph of sound radiation prediction results of different division methods of the cylindrical shell cone subsystem in the embodiment of the invention.
Fig. 6 is a schematic diagram of a model of the influence of different parameters on the sound radiation prediction result according to an embodiment of the present invention.
FIG. 7 is a comparison of the internal loss factor change versus acoustic radiation prediction results in an embodiment of the present invention.
FIG. 8 is a comparison of coupling loss factor change versus acoustic radiation prediction results in an embodiment of the present invention.
FIG. 9 is a comparison of loss factor change versus acoustic radiation prediction results for subsystems of different energy contributions in an embodiment of the present invention.
Fig. 10 is a diagram of a high energy contribution internal dissipation factor experimental test structure in an embodiment of the present invention.
FIG. 11 is a graph of the high energy contribution internal dissipation factor test results in an embodiment of the present invention.
FIG. 12 is a model diagram of the high frequency acoustic radiation of the experimental bench according to the embodiment of the present invention.
Fig. 13 is a graph of the prediction result of high-frequency sound radiation of the large-size shell structure based on local optimization in the embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
the method comprises the steps of using a three-dimensional geometric model of a shell structure, constructing a shell structure grid model through grid division, carrying out subsystem division on the shell structure grid model based on the grid model, establishing a boundary coupling relation of each subsystem and a fluid acoustic environment around the shell structure, obtaining an internal loss factor of a subsystem high-energy contribution subsystem by adopting a transient attenuation method, establishing a shell structure acoustic radiation model energy balance equation through local correction of model parameters, obtaining more accurate acoustic response solution, and improving the radiation sound field prediction precision while reducing model parameter tests compared with the traditional high-frequency acoustic radiation prediction method. The method specifically comprises the following steps:
step 1), measuring geometric information of a shell structure, carrying out structural analysis on the geometric information of the shell structure, and establishing a geometric model of the shell structure according to an analysis result; as shown in fig. 1, a laboratory bench of geometric models of the shell structure was constructed.
The geometric information comprises the geometric shape and the form and position size of each part in the structure; the structural analysis object comprises a bearing member, a vibration transmission member and a mass member;
step 2), carrying out meshing division on the geometric model of the shell structure to obtain a shell structure mesh model, and carrying out subsystem division on the shell structure based on the shell structure mesh model to obtain a plurality of subsystems;
the subsystem division is carried out on the shell structure, and specifically, a single-curvature plate is adopted to carry out division along the axial direction of a shell structure grid model to obtain a plurality of subsystems.
Step 3), establishing a boundary coupling relation of each subsystem and a fluid acoustic environment around the shell structure, and acquiring an internal loss factor of the high-energy contribution subsystem of the subsystem by adopting a transient attenuation method;
acquiring the internal loss factor of the high-energy contribution subsystem by adopting a transient attenuation method in the step 3):
the acceleration response signal after the shell structure is knocked by the force hammer is a real function x (t), and the corresponding Hilbert is transformed into:
Figure BDA0002516661020000061
in the formula: denotes convolution.
The inverse transformation is as follows:
Figure BDA0002516661020000062
the analytic signal of the resulting real function is then:
Figure BDA0002516661020000063
the complex expression of u (t) is:
u(t)＝A(t)ejθt
in the formula:
Figure BDA0002516661020000064
in response to the envelope of signal x (t), the instantaneous phase being
Figure BDA0002516661020000065
So that any real function can be re-expressed asAnd x (t) ═ a (t) cos θ (t), and the envelope of the response signal is logarithmized to obtain an attenuation curve, the absolute value of the slope of the curve is the structural damping λ, the structural damping λ is divided by the frequency to obtain a damping ratio ζ λ/ω, and the damping ratio divided by 2 is the average loss factor η ζ/2 in the frequency band of the tested structure.
Obtaining an internal loss factor of the high-energy contribution subsystem by adopting a transient attenuation method:
Figure BDA0002516661020000071
Figure BDA0002516661020000072
in the formula:Tn-internal loss attenuation ratio ηi-the internal loss factor of subsystem i; a. theTn(t1)——t1Amplitude of the time decay Rate Curve, ATn(t2)——t2Amplitude of the time decay rate curve; omegac-the center frequency.
And 4), establishing a shell model energy balance equation based on the local correction model parameters, solving the shell model energy balance equation through the internal loss factors obtained in the step 3), and obtaining a shell structure radiation sound field to realize shell structure high-frequency sound radiation forecast. And when the high-frequency sound radiation of the shell structure is obtained, exciting by adopting the excitation of a vibration source of a high frequency band.
Compared with the traditional method, the radiation sound field of the shell structure acquired by the method proves the advantages and effectiveness of the method.
The energy balance equation of the shell model established based on the local correction model parameters is as follows:
Figure BDA0002516661020000073
in the formula: omegacη as center frequencyiη is the loss factor in the subsystemijCoupling loss factors of the ith subsystem and the jth subsystem; n isiFor the module of subsystem iDensity of states; piIs the input power of system i; eiIs the average energy of subsystem i.
As shown in fig. 2(a) -2 (e), the cylindrical shell is divided by using a single-curvature plate along the axial direction of the shell structure grid model and along the circumferential direction of the shell structure grid model; then forecasting is carried out on the divided subsystems, and the result is shown in fig. 3, wherein c2 represents the forecasting result of the subsystem after being divided by adopting 2 single-curvature plates along the circumferential direction, c4 represents the forecasting result of the subsystem after being divided by adopting 4 single-curvature plates along the circumferential direction, x1 represents the forecasting result of the subsystem after being divided by adopting one single-curvature plate along the axial direction of the shell structure grid model, x3 represents the forecasting result of the subsystem after being divided by adopting 3 single-curvature plates along the axial direction of the shell structure grid model, x5 represents the forecasting result of the subsystem after being divided by adopting 5 single-curvature plates along the axial direction of the shell structure grid model, and the result is shown in fig. 3, when the number of the sub-system modes is enough, the sub-system division is more detailed, the prediction effect is better, when 2 sub-systems are divided along the circumferential direction, the prediction result generates larger errors, and the errors are reduced after the number of the circumferential division is increased; when the subsystems are divided along the axial direction, the error and the variance of the forecast result are small; therefore, when the cylindrical shell divides the subsystem, the calculation precision of the result of dividing the subsystem along the axial direction is higher than that of dividing the subsystem along the circumferential direction; under the condition that the modal density is sufficient, the calculation accuracy can be improved by increasing the number of the subsystems divided along the circumferential direction.
As shown in fig. 4(a) -4 (e), the prediction test is performed after the conical shell is divided into subsystems by using single-curvature plates, and the test result is shown in fig. 5, where c2 represents the prediction result of the subsystems after the conical shell is divided along the circumferential direction of the conical shell structure grid model by using 2 single-curvature plates; c4 shows the forecasting result of the subsystem after 4 single-curvature plates are divided along the circumferential direction of the conical shell structure grid model; x1 represents the forecasting result of the subsystem after the single-curvature plate is adopted to divide along the axial direction of the conical shell structure grid model; x2 represents the prediction result of the subsystem after the subsystem is divided by adopting 2 single-curvature plates along the axial direction of the conical shell structure grid model, x5 represents the prediction result of the subsystem after the subsystem is divided by adopting 5 single-curvature plates along the axial direction of the conical shell structure grid model, and the results shown in fig. 5 show that the more detailed the subsystem is divided, the higher the prediction precision is; in fig. 5, when 2 subsystems are divided in the circumferential direction, the prediction result has a large error, and the error is reduced after the number of the circumferential divisions is increased; when the subsystems are divided in the axial direction, the error and variance of the prediction result are small. Therefore, when the conical shell structure divides the subsystems, the calculation accuracy of the result of dividing the subsystems along the axial direction is higher than that of dividing the subsystems along the circumferential direction, and under the condition that the modal density is sufficient, the calculation accuracy can be improved by increasing the number of the subsystems divided along the circumferential direction. c represents the circumferential direction and x represents the axial direction.
Fig. 6 is a schematic diagram of a model of the influence of different parameters on the sound radiation prediction result, where the model includes: and 4 SEA subsystems, namely a single-curvature plate is adopted to simulate a cylindrical subsystem, two flat plates simulate circular cover plates at two ends, and a cavity subsystem is arranged inside the two flat plates.
Fig. 7 is a comparison graph of the reported results of the changes of the loss factors in all the subsystems in the model of fig. 6, in which the standard value of the loss factor in the subsystem is 0.005, the error loss factors are respectively set to 0.004, 0.003 and 0.002, and the internal loss factors of all the subsystems are changed. Fig. 7 shows that when the internal loss factor of the subsystem in the model has an error within the order of magnitude, the sound pressure level prediction result has a larger error. Therefore, the internal loss factor has a large influence on the prediction result.
Fig. 8 is a comparison graph of the results of the changes of the coupling loss factors of all the subsystems in the model of fig. 6, in which the standard value of the coupling loss factor of the subsystem is 0.0005, the error coupling loss factors are respectively set to 0.0004, 0.0003 and 0.0002, and the internal loss factors of all the subsystems are changed. It can be known from fig. 8 that when the coupling loss factor of the subsystem in the model has an error within the order of magnitude, the error of the sound pressure level prediction result is small. Therefore, the coupling loss factor has little influence on the prediction result.
Fig. 9 is a comparison graph of reported results of changes in the loss factors of the individual subsystems in the model of fig. 6, where the standard internal loss factor of the subsystem is set to 0.005, the error internal loss factor is set to 0.001, and the internal loss factors of the individual subsystems are changed respectively. From fig. 9, it can be known that the error of the result is the largest when the subsystem with the error fails to measure the input end, and the error of the forecast result decreases as the error subsystem gradually moves away from the energy end. Therefore, the internal loss factor of the high-energy quantum system is the dominant factor for predicting the error of the result.
Fig. 10(a) is a diagram of a loss factor test scheme in a base plate subsystem (subsystem 1), in which the base plate subsystem is suspended in a free state, a force hammer is used as an excitation source to excite a base plate in a pulse mode, a force sensor included in the force hammer acquires a transient excitation force signal, a PCB acceleration sensor is adhered to the surface of the base plate subsystem to acquire a vibration test point response acceleration signal of the subsystem, the force sensor and the acceleration sensor output voltage signals to a multichannel data acquisition instrument COCO80, and data are analyzed according to a transient attenuation method loss factor test theory; fig. 10(b) is a diagram of a scheme for testing the dissipation factor in the base support plate subsystem (subsystem 2), and the testing principle is the same as above.
FIG. 11 is a diagram showing the result of the high-energy-contribution internal loss factor test, which is obtained by sequentially exciting different excitation points and analyzing the attenuation rate of the corresponding acceleration response signal of each excitation point for multiple times to obtain an average internal loss factor η of each subsystem i according to the transient excitation test process shown in FIG. 6i. Fig. 12 shows a high-frequency acoustic radiation model of a housing structure of a laboratory bench, which includes 1 double-curvature plate subsystem, 33 single-curvature plate subsystems, 6 flat plate subsystems, and 10 acoustic cavity subsystems. According to the comparison graph of the sound radiation prediction results of the shell structure shown in fig. 13, the maximum relative error of the prediction results after the model parameters are locally corrected is 7.06% compared with the sound pressure level of the experimental value, and the variance between the prediction results and the sound pressure level of the experimental value is small; the maximum relative error of the sound pressure level of the prediction result of the uncorrected model parameter is 10.23%, and the variance between the maximum relative error and the sound pressure level of the experimental value is large. Therefore, the method provided by the invention is proved to be capable of improving the forecasting precision of the radiation sound field while reducing the model parameter tests.

Claims (7)

1. A shell structure high-frequency sound radiation forecasting method based on local optimization is characterized by comprising the following steps:
step 1), establishing a geometric model of the shell structure according to geometric information of the shell structure;
step 2), carrying out meshing division on the geometric model of the shell structure to obtain a shell structure mesh model, and carrying out subsystem division on the shell structure based on the shell structure mesh model to obtain a plurality of subsystems;
step 3), establishing a boundary coupling relation of each subsystem and a fluid acoustic environment around the shell structure, and acquiring an internal loss factor of the high-energy contribution subsystem of the subsystem by adopting a transient attenuation method;
and 4), establishing a shell model energy balance equation based on the local correction model parameters, solving the shell model energy balance equation through the internal loss factors obtained in the step 3), and obtaining a shell structure radiation sound field to realize shell structure high-frequency sound radiation forecast.
2. The shell structure high-frequency sound radiation forecasting method based on local optimization according to claim 1, wherein the step 1) is specifically as follows: and measuring the geometric information of the shell structure, carrying out structural analysis on the geometric information of the shell structure, and establishing a geometric model of the shell structure according to an analysis result.
3. The method for forecasting high-frequency acoustic radiation of shell structure based on local optimization according to claim 2, wherein the geometric information includes geometric shapes and form and position dimensions of each component in the structure.
4. The method for forecasting the high-frequency sound radiation of the shell structure based on the local optimization as claimed in claim 1, wherein the step 2) of sub-system division of the shell structure is implemented by dividing a single-curvature plate along the axial direction of a mesh model of the shell structure to obtain a plurality of sub-systems.
5. The shell structure high-frequency sound radiation forecasting method based on local optimization according to claim 1, characterized in that an internal loss factor of the high-energy contribution subsystem is obtained by a transient attenuation method:
Figure FDA0002516661010000011
Figure FDA0002516661010000021
in the formula:Tn-internal loss attenuation ratio ηi-the internal loss factor of subsystem i; a. theTn(t1) -the magnitude of the decay rate curve; omegac-the center frequency.
6. The shell structure high-frequency sound radiation forecasting method based on local optimization according to claim 1, characterized in that the shell model energy balance equation established based on the local modification model parameters is as follows:
Figure FDA0002516661010000022
in the formula: omegacη as center frequencyiη is the loss factor in the subsystemijCoupling loss factors of the ith subsystem and the jth subsystem; n isiIs the modal density of subsystem i; piIs the input power of system i; eiIs the average energy of subsystem i.
7. The shell structure high-frequency sound radiation forecasting method based on local optimization according to claim 1, characterized in that when the shell structure high-frequency sound radiation is obtained, a high-frequency band vibration source is adopted for excitation.
CN202010478851.2A 2020-05-29 2020-05-29 Shell structure high-frequency sound radiation forecasting method based on local optimization Pending CN111709128A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113779778A (en) * 2021-08-24 2021-12-10 中国舰船研究设计中心 Method for optimizing task channel test points of ship system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080114496A1 (en) * 2006-11-15 2008-05-15 Thorsten Bartosch Methods of analyzing the vibro-acoustic optimization potential and optimizing the vibro-acoustic behavior of a structure
CN105590003A (en) * 2016-01-05 2016-05-18 浙江大学 Interior noise analysis and prediction method of high speed train

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080114496A1 (en) * 2006-11-15 2008-05-15 Thorsten Bartosch Methods of analyzing the vibro-acoustic optimization potential and optimizing the vibro-acoustic behavior of a structure
CN105590003A (en) * 2016-01-05 2016-05-18 浙江大学 Interior noise analysis and prediction method of high speed train

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
唐荣江等: "重型商用车内高频噪声的预测与分析", 《机械设计与制造》 *
李海虹等: "机床电主轴多结构耦合损耗因子的试验研究", 《振动与冲击》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113779778A (en) * 2021-08-24 2021-12-10 中国舰船研究设计中心 Method for optimizing task channel test points of ship system
CN113779778B (en) * 2021-08-24 2024-04-05 中国舰船研究设计中心 Ship system task channel test point optimization method

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