CN112417709A - Dynamic modal analysis method based on schlieren image - Google Patents
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Abstract
The invention provides a schlieren image-based dynamic modal analysis method, which comprises the steps of building a schlieren measurement system, setting exposure time, obtaining a flow field pressure signal after incoming flow static pressure is constant, obtaining schlieren data, preprocessing and carrying out relevant transformation on the schlieren data, and carrying out dynamic modal decomposition so as to judge. The invention performs data acquisition, data processing and verification. The invention makes up the defect of qualitative measurement in the schlieren observation means, has lower cost and higher precision compared with quantitative observation means such as a particle image velocimetry and the like, can accurately capture the characteristic frequency and the corresponding mode in the supersonic flow field, and provides an effective means with low cost and high precision for supersonic flow observation.
Description
Technical Field
The invention relates to the technical field of supersonic flow measurement, in particular to a flow modal analysis method.
Background
Accurate description of perturbing behavior in complex flows presents a significant challenge to physical testing, as well as algorithms that extract and quantify such behavior. At the same time, many applications, such as flow in scramjet engines, can greatly benefit from a more thorough understanding of the underlying flow instability mechanisms.
The dynamic modal decomposition is a data-driven algorithm for extracting dynamic information from an unsteady experimental measurement or numerical simulation flow field, and can be used for analyzing the main characteristics of complex unsteady flow or building a low-order flow field dynamic model. The essence of the dynamic mode decomposition is that the flow evolution is regarded as a linear dynamic process, and the low-order mode representing flow field information and the corresponding characteristic value thereof are obtained by performing characteristic analysis on the flow field snapshot in the whole process. The method is characterized in that the mode obtained by decomposition has single frequency and growth rate, so that the method has great advantage in analyzing dynamic linear and periodic flow. In addition, the method can directly represent the flow evolution process through the characteristic values of all the modes, so that an additional control equation does not need to be established. Compared with the current flow field reduction based on system identification (by using time series and input/output samples) and feature extraction (by using space samples), the method has the unique advantage of space-time coupling modeling.
However, at present, the dynamic modal analysis of the test flow field is mainly based on flow field data obtained by quantitative measurement means such as a particle image velocimetry technology. However, the technology has the disadvantages of high cost, complex operation, poor popularity and poor applicability to high-speed flow fields.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a dynamic modal analysis method based on schlieren images. The invention aims to solve the problem that quantitative analysis is difficult to perform by means of schlieren observation, is used for accurately capturing characteristic frequency and corresponding mode in an ultrasonic flow field, and provides an effective means with low cost and high precision for quantitative observation of the ultrasonic flow field.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
the method comprises the following steps: setting up a schlieren measuring system, setting exposure time u, and setting sampling frequency to be at least twice of the flow dominant frequency;
step two: setting supersonic flow conditions in a wind tunnel test, obtaining a flow field pressure signal p after the static pressure of incoming flow is constant, and acquiring schlieren data of k sampling points;
step three: preprocessing the schlieren data of k sampling points: converting the schlieren image into a gray value, extracting the numerical value of each pixel point to form an m multiplied by n matrix, wherein m is the number of longitudinal pixel points of the schlieren image, and n is the number of transverse pixel points of the schlieren image;
step four: converting the m × n matrix into a column vector x containing l elements, where l is m × n;
step five: constructing a column vector X of k sampling moments into a matrix X of l multiplied by k;
step six: performing dynamic modal decomposition on the matrix X to obtain characteristic frequency f, corresponding modal phi and amplitude A, and adjusting the number of sampling points k in the first step until the amplitude A is constant;
step seven: performing Fourier transform on the pressure signal p in the test process to obtain characteristic frequency f ', and ending the dynamic modal analysis based on the schlieren image when the deviation between the characteristic frequency f and the characteristic frequency f' is less than or equal to 5%; and when the deviation between the characteristic frequency f and the characteristic frequency f 'is more than 5%, returning to the step I to re-determine the exposure time u until the deviation between the characteristic frequency f and the characteristic frequency f' in the pressure signal is less than or equal to 5%.
The exposure time u is the inverse of the sampling frequency.
The invention has the advantages of providing a set of complete design method, and carrying out data acquisition, data processing and verification finally. The invention makes up the defect of qualitative measurement in the schlieren observation means, has lower cost and higher precision compared with quantitative observation means such as a particle image velocimetry and the like, can accurately capture the characteristic frequency and the corresponding mode in the supersonic flow field, and provides an effective means with low cost and high precision for supersonic flow observation.
Drawings
Fig. 1 is a design flowchart of a schlieren image-based kinetic modality analysis method.
Fig. 2 is the schlieren data of the supersonic flow field obtained by the experiment.
Fig. 3 is a characteristic mode of flow in a supersonic flow field.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The examples are as follows:
according to a design flow chart of a dynamic modal analysis method based on the schlieren image in figure 1, obtaining a schlieren image of an ultrasonic flow field obtained by a schlieren measurement system in figure 2 through a wind tunnel test, obtaining a characteristic mode of flow in the ultrasonic flow field in figure 3 by dynamic modal decomposition,
the method of the embodiment comprises the following steps:
the method comprises the following steps: setting up a schlieren measuring system, setting exposure time u, and setting sampling frequency to be at least twice of the flow dominant frequency;
step two: setting supersonic flow conditions in a wind tunnel test, obtaining a flow field pressure signal p after the static pressure of incoming flow is constant, and acquiring schlieren data of k sampling points;
step three: preprocessing the schlieren data of k sampling points: converting the schlieren image into a gray value, extracting the numerical value of each pixel point to form an m multiplied by n matrix, wherein m is the number of longitudinal pixel points of the schlieren image, and n is the number of transverse pixel points of the schlieren image;
step four: converting the m × n matrix into a column vector x containing l elements, where l is m × n;
step five: constructing a column vector X of k sampling moments into a matrix X of l multiplied by k;
step six: performing dynamic modal decomposition on the matrix X to obtain characteristic frequency f, corresponding modal phi and amplitude A, and adjusting the number of sampling points k in the first step until the amplitude A is constant;
step seven: performing Fourier transform on the pressure signal p in the test process to obtain characteristic frequency f ', and ending the dynamic modal analysis based on the schlieren image when the deviation between the characteristic frequency f and the characteristic frequency f' is less than or equal to 5%; and when the deviation between the characteristic frequency f and the characteristic frequency f 'is more than 5%, returning to the step I to re-determine the exposure time u until the deviation between the characteristic frequency f and the characteristic frequency f' in the pressure signal is less than or equal to 5%.
The exposure time u is the inverse of the sampling frequency.
The kinetic modal analysis results obtained by the present invention are shown in table 1 in comparison with the pressure signal ratio:
| pressure signal acquisition characteristic frequency (Hz) | Feature frequency (Hz) based on schlieren image acquisition | |
| Mode 1 | 33.85 | 33.88 |
| Mode 2 | 67.70 | 67.56 |
| Mode 3 | 115.10 | 113.19 |
Table 1 shows the comparison between the pressure signal obtained by the test and the characteristic frequency obtained by the dynamic modal decomposition method, and it can be seen from table 1 that the deviation between the characteristic frequency (second column) of the different modes of the flow field obtained by the method of the present invention and the characteristic frequency (first column) obtained based on the pressure signal is less than 2%, and the deviation of the analysis of the dominant mode (mode 1) is even less than 1 ‰, and the frequency information obtained by the method has high accuracy, so the present invention can effectively analyze the flow mode of the supersonic flow field.
Claims (2)
1. A dynamic modal analysis method based on schlieren images is characterized by comprising the following steps:
the method comprises the following steps: setting up a schlieren measuring system, setting exposure time u, and setting sampling frequency to be at least twice of the flow dominant frequency;
step two: setting supersonic flow conditions in a wind tunnel test, obtaining a flow field pressure signal p after the static pressure of incoming flow is constant, and acquiring schlieren data of k sampling points;
step three: preprocessing the schlieren data of k sampling points: converting the schlieren image into a gray value, extracting the numerical value of each pixel point to form an m multiplied by n matrix, wherein m is the number of longitudinal pixel points of the schlieren image, and n is the number of transverse pixel points of the schlieren image;
step four: converting the m × n matrix into a column vector x containing l elements, where l is m × n;
step five: constructing a column vector X of k sampling moments into a matrix X of l multiplied by k;
step six: performing dynamic modal decomposition on the matrix X to obtain characteristic frequency f, corresponding modal phi and amplitude A, and adjusting the number of sampling points k in the first step until the amplitude A is constant;
step seven: performing Fourier transform on the pressure signal p in the test process to obtain characteristic frequency f ', and ending the dynamic modal analysis based on the schlieren image when the deviation between the characteristic frequency f and the characteristic frequency f' is less than or equal to 5%; and when the deviation between the characteristic frequency f and the characteristic frequency f 'is more than 5%, returning to the step I to re-determine the exposure time u until the deviation between the characteristic frequency f and the characteristic frequency f' in the pressure signal is less than or equal to 5%.
2. The schlieren image-based kinetic modal analysis method of claim 1, wherein:
the exposure time u is the inverse of the sampling frequency.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114813037A (en) * | 2022-04-21 | 2022-07-29 | 中国船舶科学研究中心 | Method for analyzing frequency distribution characteristics of cavitation flow structure |
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| CN112067239A (en) * | 2020-07-27 | 2020-12-11 | 中国航天空气动力技术研究院 | Method for automatically judging establishment of supersonic wind tunnel flow field based on schlieren video |
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| CN103884486A (en) * | 2014-02-27 | 2014-06-25 | 中国科学院力学研究所 | System and method for schlieren measurement imaging |
| CN106682278A (en) * | 2016-12-06 | 2017-05-17 | 西安交通大学 | Supersonic flow field predicting accuracy determination device and method based on image processing |
| CN107977494A (en) * | 2017-11-20 | 2018-05-01 | 中国运载火箭技术研究院 | Gas handling system characteristic predicting method and system under hypersonic aircraft back-pressure |
| US20200073908A1 (en) * | 2018-03-06 | 2020-03-05 | Dalian University Of Technology | Sparse component analysis method for structural modal identification when the number of sensors is incomplete |
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| CN114813037A (en) * | 2022-04-21 | 2022-07-29 | 中国船舶科学研究中心 | Method for analyzing frequency distribution characteristics of cavitation flow structure |
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