阅读说明：本技术 一种基于飞行试验脉动压力数据的双谱分析转捩辨识方法 (Flight test pulsating pressure data-based double-spectrum analysis transition identification method ) 是由 姚世勇 段毅 黄建栋 杨攀 陈政 詹振霖 李思怡 徐炜 段会申 史文东 林萌 于 2021-05-25 设计创作，主要内容包括：一种基于飞行试验脉动压力数据的双谱分析转捩辨识方法,包括步骤如下：S1：采用移动平均方法对飞行器上压力传感器测得的瞬时压力p进行滤波,得到平均压力获取飞行器的脉动压力在时间域上的分布曲线；S2：对脉动压力在时间域上的分布曲线进行划分,截取若干个Δt时间段内的脉动压力数据；Δt的取值范围根据采样频率选取；S3：对Δt时间段内的脉动压力数据进行双谱分析,得到每个Δt时间段内的脉动压力双谱值；S4：根据不同时间段内的脉动压力双谱值大小,辨识飞行器转捩发生时刻。本发明能够较为准确地辨识出飞行试验中飞行器边界层的转捩时刻,对后续飞行器的弹道优化、热防护设计提供数据支撑。(A double-spectrum analysis transition identification method based on flight test pulsating pressure data comprises the following steps: s1: filtering the instantaneous pressure p measured by the pressure sensor on the aircraft by adopting a moving average method to obtain average pressure Obtaining pulsating pressure of an aircraft A profile over a time domain; s2: dividing a distribution curve of the pulsating pressure on a time domain, and intercepting pulsating pressure data in a plurality of delta t time periods; selecting the value range of delta t according to the sampling frequency; s3: performing double-spectrum analysis on the pulsating pressure data in the delta t time period to obtain a pulsating pressure double-spectrum value in each delta t time period; s4: according to noAnd simultaneously, identifying the occurrence moment of transition of the aircraft according to the pulse pressure double-spectrum value in the time period. The method can accurately identify the transition time of the aircraft boundary layer in the flight test, and provides data support for the trajectory optimization and thermal protection design of the subsequent aircraft.)

1. A method for recognizing transition of double-spectrum analysis based on flight test pulsating pressure data is characterized by comprising the following steps:

s1: filtering the instantaneous pressure p measured by the pressure sensor on the aircraft by adopting a moving average method to obtain average pressureObtaining pulsating pressure of an aircraftA profile over a time domain;

s2: dividing a distribution curve of the pulsating pressure on a time domain, and intercepting pulsating pressure data in a plurality of delta t time periods; selecting the value range of delta t according to the sampling frequency;

s3: performing double-spectrum analysis on the pulsating pressure data in the delta t time period to obtain a pulsating pressure double-spectrum value in each delta t time period;

s4: and identifying the occurrence moment of transition of the aircraft according to the pulsating pressure double-spectrum values in different time periods.

2. The method for recognizing the transition of the double-spectrum analysis based on the flight test pulsating pressure data as claimed in claim 1, wherein the pressure sensor on the aircraft is a piezoresistive Kulite sensor or a piezoelectric PCB sensor.

3. The method for recognizing transition based on two-spectrum analysis of flight test pulse pressure data according to claim 1 or 2, characterized in that in S1, smooth function in matlab software is used for moving average filtering, wherein a point range SPAN is determined according to sampling frequency in actual measurement, and the filtering method is Lowess.

4. The method for recognizing transition based on flight test pulse pressure data according to claim 3, wherein the specific method of S3 is as follows:

(a) carrying out wavelet decomposition on the pulsating pressure data in each delta t time period by using a discrete wavelet function wavedec in matlab software; wherein, the wavelet function wname in the wavedec function is selected according to the concrete form of the sample data;

(b) and performing cross-correlation analysis on the pulsating pressure data under each scale frequency to obtain the correlation magnitude of the pulsating pressure in each delta t time period, namely the magnitude of the bispectrum value.

5. The utility model provides an identification system of transition of two spectral analysis based on experimental pulsation pressure data of flight which characterized in that includes:

a first module for filtering the instantaneous pressure p measured by the pressure sensor on the aircraft by means of a moving average method to obtain an average pressureObtaining pulsating pressure of an aircraftA profile over a time domain; the distribution curve of the pulsating pressure in the time domain is carried outDividing, namely intercepting pulsating pressure data in a plurality of delta t time periods; selecting the value range of delta t according to the sampling frequency;

the second module is used for carrying out double-spectrum analysis on the pulsating pressure data in the delta t time period to obtain a pulsating pressure double-spectrum value in each delta t time period;

and the third module is used for identifying the occurrence time of the transition of the aircraft according to the pulsating pressure double-spectrum values in different time periods.

6. The system for double-spectrum analysis transition recognition based on flight test pulse pressure data as claimed in claim 5, wherein the pressure sensor on the aircraft is selected from a piezoresistive Kulite sensor or a piezoelectric PCB sensor.

7. The method for recognizing the transition of the double-spectrum analysis based on the flight test pulsating pressure data as claimed in claim 6, wherein in the first module, smooth function in matlab software is used for moving average filtering, wherein a point range SPAN is determined according to sampling frequency in actual measurement, and a filtering method is Lowess.

8. The flight test pulse pressure data-based double-spectrum analysis transition recognition system as claimed in claim 7, wherein the specific method for performing double-spectrum analysis on the pulse pressure data within the Δ t time period is as follows:

(a) carrying out wavelet decomposition on the pulsating pressure data in each delta t time period by using a discrete wavelet function wavedec in matlab software; wherein, the wavelet function wname in the wavedec function is selected according to the concrete form of the sample data;

(b) performing cross-correlation analysis on the pulsating pressure data under each scale frequency to obtain each^ΔAnd (3) the correlation magnitude of the pulsating pressure in the t time period, namely the magnitude of the bispectrum value.

Technical Field

The invention relates to a double-spectrum analysis transition identification method.

Background

Pulsating pressure is an important physical quantity in aircraft design. High speed aircraft are subject to strong pressure pulsations that can cause vibrations of the cabin components and cause structural problems, affecting the performance of the aircraft. According to the flight test result of the unit, the incoming flow Reynolds number of the aircraft is increased along with the reduction of the flight altitude, and the pulsating pressure amplitude is not continuously increased when the boundary layer on the surface of the aircraft is transited from laminar flow to turbulent flow. For transition identification of flight test pulsating pressure data, the prior published documents are rarely reported. However, the transition occurrence time cannot be accurately interpreted simply by the pressure pulsation amplitude.

Most of the existing flight test transition measurement technologies use a heat flow sensor and a temperature sensor to determine transition, but the heat flow and temperature sensors have low frequency response (generally, tens of Hz to hundreds of Hz), and cannot give consideration to the characteristic of measuring disturbance waves (generally, kHz) in a boundary layer.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: the invention provides a double-spectrum analysis transition identification method based on flight test pulsating pressure data, which can accurately identify the transition moment of an aircraft boundary layer in a flight test and provide data support for trajectory optimization and thermal protection design of a subsequent aircraft.

The technical scheme adopted by the invention is as follows: a double-spectrum analysis transition identification method based on flight test pulsating pressure data comprises the following steps:

s1: filtering the instantaneous pressure p measured by the pressure sensor on the aircraft by adopting a moving average method to obtain average pressureObtaining pulsating pressure of an aircraftA profile over a time domain;

s2: dividing a distribution curve of the pulsating pressure on a time domain, and intercepting pulsating pressure data in a plurality of delta t time periods; selecting the value range of delta t according to the sampling frequency;

s3: performing double-spectrum analysis on the pulsating pressure data in the delta t time period to obtain a pulsating pressure double-spectrum value in each delta t time period;

s4: and identifying the occurrence moment of transition of the aircraft according to the pulsating pressure double-spectrum values in different time periods.

The pressure sensor on the aircraft selects a piezoresistive Kulite sensor or a piezoelectric PCB sensor.

In S1, moving average filtering is carried out by utilizing smooth function in matlab software, wherein the point range SPAN is determined according to sampling frequency in actual measurement, and a filtering method adopts Lowess.

The specific method of S3 is as follows:

(a) carrying out wavelet decomposition on the pulsating pressure data in each delta t time period by using a discrete wavelet function wavedec in matlab software; wherein, the wavelet function wname in the wavedec function is selected according to the concrete form of the sample data;

(b) and performing cross-correlation analysis on the pulsating pressure data under each scale frequency to obtain the correlation magnitude of the pulsating pressure in each delta t time period, namely the magnitude of the bispectrum value.

A kind of identification system that transitions of double-spectrum analysis based on pulsating pressure data of flight test, including:

a first module for filtering the instantaneous pressure p measured by the pressure sensor on the aircraft by means of a moving average method to obtain an average pressureObtaining pulsating pressure of an aircraftA profile over a time domain; dividing a distribution curve of the pulsating pressure on a time domain, and intercepting pulsating pressure data in a plurality of delta t time periods; selecting the value range of delta t according to the sampling frequency;

the second module is used for carrying out double-spectrum analysis on the pulsating pressure data in the delta t time period to obtain a pulsating pressure double-spectrum value in each delta t time period;

and the third module is used for identifying the occurrence time of the transition of the aircraft according to the pulsating pressure double-spectrum values in different time periods.

The pressure sensor on the aircraft selects a piezoresistive Kulite sensor or a piezoelectric PCB sensor.

In the first module, moving average filtering is performed by utilizing a smooth function in matlab software, wherein a point range SPAN is determined according to sampling frequency in actual measurement, and a filtering method adopts Lowess.

The specific method for performing double-spectrum analysis on the pulsating pressure data in the delta t time period comprises the following steps:

(a) carrying out wavelet decomposition on the pulsating pressure data in each delta t time period by using a discrete wavelet function wavedec in matlab software; wherein, the wavelet function wname in the wavedec function is selected according to the concrete form of the sample data;

(b) and performing cross-correlation analysis on the pulsating pressure data under each scale frequency to obtain the correlation magnitude of the pulsating pressure in each delta t time period, namely the magnitude of the bispectrum value.

Compared with the prior art, the invention has the advantages that:

the invention provides a flight test pulse pressure data-based double-spectrum analysis transition identification method which is characterized in that transition time is not interpreted from a pulse pressure amplitude intuitively aiming at the distribution characteristic of the pulse pressure of an aircraft in the time domain. And identifying the transition moment of the boundary layer by utilizing the maximum correlation coefficient of a double-spectrum method according to the characteristics of disturbance waves in the development and evolution process of the boundary layer. The method is proved by heat flow and temperature data of flight tests and applied to transition moment identification of a plurality of appearance aircrafts.

Most of the existing flight test transition measurement technologies use a heat flow sensor and a temperature sensor to determine transition, but the heat flow and temperature sensors have low frequency response (generally, tens of Hz to hundreds of Hz), and cannot give consideration to the characteristic of measuring disturbance waves (generally, kHz) in a boundary layer. The high-frequency response characteristic of the pulsating pressure sensor not only can intuitively measure the amplitude change of the pressure in the boundary layer, but also can reflect the development and evolution characteristics of disturbance waves in the boundary layer.

Drawings

Fig. 1 is a flowchart of a transition identification method based on flight test pulse pressure data according to an embodiment of the present invention;

fig. 2 is a schematic diagram of a distribution curve of pulse pressure dependency pulse pressure of different time periods in a time domain in an aircraft test, which is disclosed by the embodiment of the invention.

Fig. 3 is a schematic diagram for dividing a distribution curve of the pulsating pressure of the aircraft in a time domain, which is disclosed by the embodiment of the invention.

FIG. 4 is a graph of the results of two-spectrum analysis of the pulsating pressure data.

Detailed Description

The invention is described with reference to the accompanying drawings.

And filtering the instantaneous pressure data measured according to the flight test to obtain the distribution of the pulsating pressure on a time domain. Dividing the pulsating pressure data into different time periods, carrying out multi-scale decomposition on the pulsating pressure data of different time periods by using a wavelet transformation method, and then carrying out cross-correlation analysis on each scale pulsating pressure after decomposition. And judging the occurrence moment of the transition of the boundary layer according to the double-spectrum values in different time periods.

As shown in fig. 1, a transition identification method based on flight test pulse pressure data through two-spectrum analysis includes the following steps:

s101: filtering the instantaneous pressure p measured by the pressure sensor on the aircraft by adopting a moving average method to obtain average pressureObtaining pulsating pressure of an aircraftDistribution over time domain.

The pressure sensor used for flight test transition measurement is generally a piezoresistive Kulite sensor and a piezoelectric PCB sensor.

In S101, moving average filtering is carried out by utilizing a smooth function in matlab software, wherein a point range SPAN is determined according to sampling frequency in actual measurement, and a filtering method adopts Lowess.

S102: dividing the distribution curve of the pulsating pressure on a time domain, and intercepting pulsating pressure data in a plurality of delta t time periods. The value range of the delta t is selected according to the sampling frequency (the sampling frequency is 200kHz, and the delta t is 0.1 s);

s103: performing double-spectrum analysis on the pulsating pressure data in the delta t time period;

wavelet transform is a time-frequency localization analysis method with the characteristic of multi-resolution analysis, which has lower time resolution and higher frequency resolution in a low-frequency part and higher time resolution and lower frequency resolution in a high-frequency part. The bispectrum method based on the Fourier transform cannot process unsteady signals measured in a flight test, the wavelet transform is suitable for analyzing unsteady signals and extracting local characteristics of the signals, and the bispectrum method based on the wavelet transform can select wavelet basis functions matched with the distribution characteristics of measured data according to the distribution characteristics of the measured data.

The specific method comprises the following steps:

(a) and carrying out wavelet decomposition on the pulsating pressure data in each delta t time period by using a discrete wavelet function wavedec in matlab software. The wavelet function wname in the wavedec function has no explicit requirement and can be selected according to the specific form of sample data.

(b) And performing cross-correlation analysis on the pulsating pressure data under each scale frequency to obtain the correlation magnitude of the pulsating pressure in each delta t time period, namely the magnitude of the bispectrum value.

S104: and identifying the occurrence moment of transition of the aircraft according to the pulsating pressure double-spectrum values in different time periods.

A kind of identification system that transitions of double-spectrum analysis based on pulsating pressure data of flight test, including:

a first module for filtering the instantaneous pressure p measured by the pressure sensor on the aircraft by means of a moving average method to obtain an average pressureObtaining pulsating pressure of an aircraftA profile over a time domain; dividing the distribution curve of pulsating pressure in time domain, and interceptingPulsatile pressure data over a number of Δ t time periods; selecting the value range of delta t according to the sampling frequency;

the second module is used for carrying out double-spectrum analysis on the pulsating pressure data in the delta t time period to obtain a pulsating pressure double-spectrum value in each delta t time period;

and the third module is used for identifying the occurrence time of the transition of the aircraft according to the pulsating pressure double-spectrum values in different time periods.

The pressure sensor on the aircraft selects a piezoresistive Kulite sensor or a piezoelectric PCB sensor.

In the first module, moving average filtering is performed by utilizing a smooth function in matlab software, wherein a point range SPAN is determined according to sampling frequency in actual measurement, and a filtering method adopts Lowess.

The specific method for performing double-spectrum analysis on the pulsating pressure data in the delta t time period comprises the following steps:

(a) carrying out wavelet decomposition on the pulsating pressure data in each delta t time period by using a discrete wavelet function wavedec in matlab software; wherein, the wavelet function wname in the wavedec function is selected according to the concrete form of the sample data;

(b) and performing cross-correlation analysis on the pulsating pressure data under each scale frequency to obtain the correlation magnitude of the pulsating pressure in each delta t time period, namely the magnitude of the bispectrum value.

Example (b):

a transition identification method based on flight test pulsating pressure data can accurately identify transition time of an aircraft boundary layer in a flight test from engineering application, and provides important technical support for improvement of precision of an engineering transition indication method.

According to the invention, based on the pulsating pressure measurement result of a certain appearance flight test, the double-spectrum results of different time periods are obtained, and the occurrence time of the judgment transition is identified according to the double-spectrum values.

A double-spectrum analysis transition identification method based on flight test pulsating pressure data comprises the following steps:

s101: the mean value filtering is used to filter the instantaneous pressure to obtain the average pressure, and the distribution of the pressure pulsation in the time domain is obtained, as shown in fig. 2.

S102: the pulsating pressure is divided in the time domain, and the pulsating pressure data (with the frequency of 200kHz) within 0.1s is intercepted, as shown in figure 3.

S103: performing bispectrum analysis on the pulse pressure data of the divided time periods, namely performing wavelet transformation on the pulse pressure data in each time period, performing cross-correlation analysis on the data under each scale frequency, and acquiring the bispectrum value of the pulse pressure in each time period, as shown in fig. 4.

S104: judging the transition moment, namely identifying the transition occurrence moment according to the pulse pressure double-spectrum values in different time periods.

The present invention has not been described in detail, partly as is known to the person skilled in the art.