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Physical attribute and data drive coupled flow acoustic mode decomposition and prediction method

A data-driven, modal decomposition technology, applied in electrical digital data processing, CAD numerical modeling, instruments, etc., can solve the problem of inability to distinguish flow field structures of different scales, difficult to meet the requirements of fine analysis of aeroacoustics, and inability to accurately separate Problems such as dynamic mode and acoustic mode can achieve accurate and efficient decomposition and fast prediction, saving calculation time and cost, and accurate physical properties

Active Publication Date: 2022-03-01
CALCULATION AERODYNAMICS INST CHINA AERODYNAMICS RES & DEV CENT
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AI Technical Summary

Problems solved by technology

However, DMD cannot distinguish flow field structures of different scales. In the near field, DMD is actually a mixture of dynamic modes and acoustic modes.
Therefore, although the data-driven method is computationally efficient, it cannot accurately separate the dynamic mode and the acoustic mode, and it is difficult to meet the fine analysis requirements of aeroacoustics

Method used

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  • Physical attribute and data drive coupled flow acoustic mode decomposition and prediction method
  • Physical attribute and data drive coupled flow acoustic mode decomposition and prediction method
  • Physical attribute and data drive coupled flow acoustic mode decomposition and prediction method

Examples

Experimental program
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Effect test

Embodiment 1

[0065] A flow-acoustic mode decomposition method coupled with physical properties and data-driven, such as figure 1 as shown,

[0066] S10. Carry out DMD dynamic mode decomposition to the velocity field u=(u, v) that experiment or numerical simulation obtains, obtain the normalized dynamic mode, specifically:

[0067] S101. Reorganize the unsteady flow velocity field data, such as figure 2 shown;

[0068] The velocity field u=(u,v) obtained by experiment or numerical simulation is the technical research object, and the time-averaged velocity removed from the flow field, and the velocity perturbation field Compose the velocity matrix in the following form:

[0069] (1)

[0070] (2)

[0071] Among them, u is the velocity vector, u is the flow velocity, v is the normal velocity, N is the time series number of the flow field segment minus 1, u i is the velocity vector of the i-th flow field segment, m is twice the number of grid points or flow field monitoring poi...

Embodiment 2

[0106] A flow-acoustic mode prediction method coupled with physical properties and data-driven, such as image 3 As shown, based on the acoustic mode velocity DM calculated in Example 1 ja and dynamic modal velocity DM jr predict any moment The acoustic mode velocity of u ja and the dynamical modal velocity u jr :

[0107]

[0108] ,

[0109] in, is the eigenvalue of the jth dynamic mode, yes of i power.

[0110] Therefore, based on the original velocity field data, the accurate and efficient flow acoustic mode decomposition is realized, and the rapid prediction of the flow acoustic mode can be performed.

[0111] In order to further verify the validity of the flow acoustic mode decomposition method and the prediction method of the flow acoustic mode of the present invention, taking the application in the actual two-dimensional cavity flow as an example, the following calculation example is done:

[0112] select time interval The time series data of flo...

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Abstract

The invention is suitable for the field of computational aero-acoustics and computational fluid mechanics, and provides a physical attribute and data drive coupled streaming acoustic mode decomposition and prediction method.The streaming acoustic mode decomposition method comprises the steps that firstly, a DMD method is adopted for conducting dynamic mode decomposition on an initial velocity field, and a normalized dynamic mode is obtained; carrying out Helmholtz decomposition on the obtained dynamic mode to obtain an acoustic mode speed and a dynamic mode speed; and the acoustic modal speed and the dynamic modal speed at any moment can be predicted based on the obtained acoustic modal speed and the dynamic modal speed, so that the time development process of the acoustic modal and the dynamic modal can be analyzed. According to the method, the advantages of Helmholtz decomposition based on physical attributes and a dynamic mode decomposition method based on data driving are coupled, accurate and efficient decomposition and rapid prediction of the dynamic mode and the acoustic mode of the velocity field in the unsteady flow field are achieved, and the calculation time and cost are saved while the accuracy of the physical attributes is guaranteed.

Description

technical field [0001] The invention relates to the fields of computational aeroacoustics and computational fluid dynamics, in particular to a flow acoustic mode decomposition and prediction method coupled with physical attributes and data drive. Background technique [0002] Acoustic mode decomposition is the core basic problem of aeroacoustics, and it is the key to flow-acoustic interaction mechanism, rapid prediction of noise, and accurate identification of sound source. Since Lighthill established the theory of sound comparison in 1952, people have been studying the separation of flow sound for a long time. Lighthill's acoustic analogy theory shows that the sound source is composed of quadrupole tensors composed of turbulent fluctuations, entropy disturbances, and viscosities. However, the Lighthill sound source contains flow-acoustic interaction items such as acoustic convection, scattering, reflection, etc. that do not produce aerodynamic noise. It is only the sound p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/28G06F111/10G06F113/08G06F119/10G06F119/14
CPCG06F30/28G06F2111/10G06F2113/08G06F2119/10G06F2119/14Y02T90/00
Inventor 韩帅斌李虎罗勇王益民马瑞轩刘旭亮武从海张树海
Owner CALCULATION AERODYNAMICS INST CHINA AERODYNAMICS RES & DEV CENT
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