CN106845111A - A kind of aero-engine noise Forecasting Methodology based on multiple regression - Google Patents

Abstract

The present invention proposes a kind of aero-engine noise Forecasting Methodology based on multiple regression,The method is acquisition aero-engine internal noise time-domain signal and aeroengine operation status parameter matrix,Aero-engine internal noise time-domain signal is pre-processed,Obtain the third-octave spectrum and overall sound pressure level of aero-engine internal noise,Third-octave spectrum and aeroengine operation status parameter matrix according to aero-engine internal noise,Aero-engine internal noise sound pressure level multiple regression equation is set up using least square method,Real-time Collection aeroengine operation status parameter,Using the working status parameter under aero-engine certain working condition as aero-engine internal noise sound pressure level multiple regression equation input,Then obtain the aero-engine internal noise third-octave spectrum and overall sound pressure level of corresponding linear analysis under the working condition,The present invention is by controlling residual error,Ensure that the accuracy of wave load analysis result.

Description

A kind of aero-engine noise Forecasting Methodology based on multiple regression

Technical field

The invention belongs to aviation power field of engineering technology, and in particular to a kind of aero-engine based on multiple regression is made an uproar Sound prediction method.

Background technology

With the update of aero-engine, aeroengine combustor buring chamber interior noise problem also constantly shows especially out, So as to combustion chamber acoustics design is also faced with important breakthrough, thus forces and combustion chamber acoustic loads forecasting technique is ground Study carefully.The research that combustion chamber acoustic load is estimated is all considerably less at home and abroad, carries out the research of this respect for determining chamber structure Acoustic mode, its acoustic excitation sources is found, preferably recognize combustion chamber burning situation, calculate fatigue life, design and conform to Engine asked etc. has great importance.Therefore, the foundation of aero-engine internal noise loading spectrum has become aviation hair The key of the designs such as motivation chamber structure intensity.

As aero-engine constantly updates, aeroengine combustor buring chamber interior noise problem just shows especially out, So as to the acoustics design of combustion chamber also faces great breakthrough, thus force and combustion chamber acoustic loads Predicting Technique is goed deep into Research.US and European begins to launch fan noise and produces ground with the mechanism of transmission and control method from the sixties in 20th century Study carefully, also made great progress in fan discrete noise Predicting Technique and control technology, but be not directed to aero-engine combustion Burn the Predicting Technique of room burner inner liner inside broadband noise.The invention is a kind of aero-engine noise load based on multiple regression Spectrum method for building up, Main Basiss aero-engine internal noise load test test data, reasonable selection participates in multiple regression Aeroengine operation status parameter, fully with reference to multivariate regression models, establish first effective aero-engine linearly with Nonlinear regression model (NLRM) formula, forms the curing for setting up the airborne noise lotus spectrum of aeroplane engine.

The content of the invention

In view of the shortcomings of the prior art, the present invention proposes a kind of aero-engine noise prediction side based on multiple regression Method.

Technical solution of the present invention is as follows：

A kind of aero-engine noise Forecasting Methodology based on multiple regression, comprises the following steps：

Step 1：Obtain aero-engine internal noise time-domain signal and aeroengine operation status parameter matrix X_n×k, n It is aeroengine operation status number, k is selected aeroengine operation status number of parameters；

Step 2：Aero-engine internal noise time-domain signal is pre-processed, aero-engine internal noise is obtained Third-octave is composed and overall sound pressure level；

Step 2.1：FFT is carried out to aero-engine internal noise time-domain signal and obtains aero-engine internal noise Frequency-region signal；

Step 2.2：Third-octave analysis of spectrum and overall sound pressure level point are carried out to aero-engine internal noise frequency-region signal Analysis, obtains the third-octave spectrum and overall sound pressure level of aero-engine internal noise；

Step 3：Third-octave spectrum and aeroengine operation status parameter matrix according to aero-engine internal noise X_n×k, aero-engine internal noise sound pressure level multiple regression equation is set up using least square method；

Step 4：Real-time Collection aeroengine operation status parameter, by the work under aero-engine certain working condition State parameter then obtains correspondence under the working condition as the input of aero-engine internal noise sound pressure level multiple regression equation Linear analysis aero-engine internal noise third-octave spectrum and overall sound pressure level.

Aero-engine internal noise sound pressure level multiple regression equation in the step 3 includes：Inside aero-engine Noise sound multiple linear regression equations and aero-engine internal noise sound pressure level nonlinear multivariate regression equations；

The aero-engine internal noise sound pressure level multiple linear regression equations include：Aero-engine internal noise is carried Lotus spectrum sound pressure level multiple linear regression equations, aero-engine internal noise overall sound pressure level multiple linear regression equations；

The aero-engine internal noise sound pressure level nonlinear multivariate regression equations include：Aero-engine internal noise Loading spectrum sound pressure level nonlinear multivariate regression equations, aero-engine internal noise overall sound pressure level nonlinear multivariate regression equations.

The process of setting up of the aero-engine internal noise sound pressure level multiple linear regression equations is comprised the following steps：

A1：Tentatively set up aero-engine internal noise sound pressure level multiple linear regression equations；

The aero-engine internal noise sound pressure level multiple linear regression equations of the preliminary foundation are as follows：

Wherein,It is i-th linear fit sound pressure level of engine behavior, i=1,2 ... at p-th centre frequency N, p=1,2 ... m, m are that third-octave composes centre frequency number,It is k-th linear regression side at p-th centre frequency Journey coefficient, X_ikIt is the value of corresponding k-th working status parameter of i-th engine behavior；

A2：Using least square method to aero-engine internal noise sound pressure level multiple linear regression side at each centre frequency Journey coefficientEstimated, wherein,

A2.1：Set up the residual sum of squares (RSS) Q's of the linear fit sound pressure level of engine behavior using least square method Computing formula；

The computing formula of the residual sum of squares (RSS) Q of the linear fit sound pressure level of the engine behavior is as follows：

Wherein, ε_piIt is the residual error of the linear fit sound pressure level of engine behavior, Y_piFor i-th at p-th centre frequency The sound pressure level of individual engine behavior；

A2.2：The extreme value of the residual sum of squares (RSS) Q of the linear fit sound pressure level of engine behavior is asked for, so that it is determined that respectively Aero-engine internal noise sound pressure level multiple linear regression equations coefficient at centre frequency

A3：According to aero-engine internal noise sound pressure level multiple linear regression equations coefficient at each centre frequencyIt is determined that Aero-engine internal noise sound pressure level multiple linear regression equations；

A4：Using the third-octave spectrum and aeroengine operation status parameter of aero-engine internal noise as aviation The input of engine interior noise sound multiple linear regression equations, determines aero-engine internal noise loading spectrum sound pressure level Multiple linear regression equations；

A5：Using the overall sound pressure level of aero-engine internal noise and aeroengine operation status parameter as aeroplane engine The input of machine internal noise sound pressure level multiple linear regression equations, determines aero-engine internal noise overall sound pressure level multiple linear Regression equation.

The process of setting up of the aero-engine internal noise sound pressure level nonlinear multivariate regression equations is comprised the following steps：

B1：Tentatively set up aero-engine internal noise sound pressure level nonlinear multivariate regression equations；

The aero-engine internal noise sound pressure level nonlinear multivariate regression equations of the preliminary foundation are as follows：

Wherein,It is i-th nonlinear fitting sound pressure level of engine behavior at p-th centre frequency, i=1, 2 ... n, p=1,2 ... m,It is k-th Nonlinear regression equation coefficient at p-th centre frequency, X_ikIt is i-th engine The value of corresponding k-th working status parameter of working condition；

B2：By aero-engine internal noise sound pressure level nonlinear multivariate regression equations linearization process, linearized Aero-engine internal noise sound pressure level multiple regression equation afterwards；

Aero-engine internal noise sound pressure level multiple regression equation after the linearisation is as follows：

B3：Using least square method to aero-engine internal noise sound pressure level Multiple Non Linear Regression at each centre frequency Equation coefficientEstimated, wherein,

B3.1：The residual sum of squares (RSS) Q ' of the nonlinear fitting sound pressure level of engine behavior is set up using least square method Computing formula；

The computing formula of the residual sum of squares (RSS) Q ' of the nonlinear fitting sound pressure level of the engine behavior is as follows：

Wherein, ε '_piIt is the residual error of the nonlinear fitting sound pressure level of engine behavior, Y_piAt p-th centre frequency I-th sound pressure level of engine behavior；

B3.2：The extreme value of the residual sum of squares (RSS) Q ' of the nonlinear fitting sound pressure level of engine behavior is asked for, so that really Aero-engine internal noise sound pressure level nonlinear multivariate regression equations coefficient at fixed each centre frequency

B4：According to aero-engine internal noise sound pressure level nonlinear multivariate regression equations coefficient at each centre frequency Determine aero-engine internal noise sound pressure level nonlinear multivariate regression equations；

B5：Using the third-octave spectrum and aeroengine operation status parameter of aero-engine internal noise as aviation The input of engine interior noise sound nonlinear multivariate regression equations, determines aero-engine internal noise loading spectrum acoustic pressure Level nonlinear multivariate regression equations；

B6：Using the overall sound pressure level of aero-engine internal noise and aeroengine operation status parameter as aeroplane engine The input of machine internal noise sound pressure level nonlinear multivariate regression equations, determines that aero-engine internal noise overall sound pressure level is polynary non- Equation of linear regression.

The aeroengine operation status parameter includes：Import section stagnation temperature T^*, import section stagnation pressure P^*, air mass flow G_B, excess air coefficient α.

Beneficial effects of the present invention：

The present invention proposes a kind of aero-engine noise Forecasting Methodology based on multiple regression, of the present invention polynary The Return Law, by controlling residual error, it is ensured that the accuracy of wave load analysis result；The characteristic parameter of institute's research object can be selected to make It is one of aeroengine operation status parameter, participates in multiple regression, obtains in the aero-engine relevant with the research object Portion's noise sound multiple regression equation, so as to be carried to study the relation between the research object and engine interior acoustic loads For foundation；Effectively establish and made an uproar inside aero-engine internal noise sound pressure level multiple linear regression equations, aero-engine Several nonlinear multivariate regression equations of arbitrarily downgrading, aero-engine internal noise loading spectrum sound pressure level multiple linear regression equations and boat Empty engine interior noise overall sound pressure level multiple linear regression equations, can predict that frequency spectrum sound pressure level can predict overall sound pressure level again；Knot Close aero-engine noise load test test data and effectively establish aero-engine noise loading spectrum, and form solidification Algorithm, can be widely used in solution correlation engineering practical problem.

Brief description of the drawings

Fig. 1 is the flow of the aero-engine noise Forecasting Methodology based on multiple regression in the specific embodiment of the invention Figure；

Fig. 2 is that aviation engine interior noise sound multiple linear regression equations are built in the specific embodiment of the invention The flow chart of vertical process；

Fig. 3 is aviation engine interior noise sound nonlinear multivariate regression equations in the specific embodiment of the invention Set up the flow chart of process.

Specific embodiment

The specific embodiment of the invention is described in detail below in conjunction with the accompanying drawings.

The present invention proposes a kind of aero-engine noise Forecasting Methodology based on multiple regression, as shown in figure 1, including following Step：

Step 1：Obtain aero-engine internal noise time-domain signal and aeroengine operation status parameter matrix X_n×k, n It is aeroengine operation status number, k is selected aeroengine operation status number of parameters.

In present embodiment, aeroengine operation status parameter includes：Import section stagnation temperature T^*, import section stagnation pressure P^*、 Air mass flow G_B, excess air coefficient α, i.e. aeroengine operation status number of parameters n=4.

Step 2：Aero-engine internal noise time-domain signal is pre-processed, aero-engine internal noise is obtained Third-octave is composed and overall sound pressure level.

Step 2.1：FFT is carried out to aero-engine internal noise time-domain signal and obtains aero-engine internal noise Frequency-region signal.

Step 2.2：Third-octave analysis of spectrum and overall sound pressure level point are carried out to aero-engine internal noise frequency-region signal Analysis, obtains the third-octave spectrum A of aero-engine internal noise_m×nAnd overall sound pressure level, wherein, m is that third-octave composes center frequently Rate number.

Step 3：Third-octave spectrum and aeroengine operation status parameter matrix according to aero-engine internal noise X_n×k, aero-engine internal noise sound pressure level multiple regression equation is set up using least square method.

In present embodiment, aero-engine internal noise sound pressure level multiple regression equation includes：Inside aero-engine Noise sound multiple linear regression equations and aero-engine internal noise sound pressure level nonlinear multivariate regression equations.

Aero-engine internal noise sound pressure level multiple linear regression equations include：Aero-engine internal noise loading spectrum Sound pressure level multiple linear regression equations, aero-engine internal noise overall sound pressure level multiple linear regression equations.

In present embodiment, aero-engine internal noise sound pressure level multiple linear regression equations set up process such as Fig. 2 It is shown, comprise the following steps：

A1：Tentatively set up aero-engine internal noise sound pressure level multiple linear regression equations.

In present embodiment, the preliminary aero-engine internal noise sound pressure level multiple linear regression equations such as formula set up (1) shown in：

Wherein,It is i-th linear fit sound pressure level of engine behavior, i=1,2 ... at p-th centre frequency N, p=1,2 ... m,It is k-th equation of linear regression coefficient at p-th centre frequency, X_ikIt is i-th engine work shape The value of corresponding k-th working status parameter of state.

A2：Using least square method to aero-engine internal noise sound pressure level multiple linear regression side at each centre frequency Journey coefficientEstimated, wherein,

A2.1：Set up the residual sum of squares (RSS) Q's of the linear fit sound pressure level of engine behavior using least square method Computing formula.

In present embodiment, the computing formula of the residual sum of squares (RSS) Q of the linear fit sound pressure level of engine behavior is such as Shown in formula (2)：

Wherein, ε_piIt is the residual error of the linear fit sound pressure level of engine behavior, Y_piFor i-th at p-th centre frequency The sound pressure level of individual engine behavior.

A2.2：The extreme value of the residual sum of squares (RSS) Q of the linear fit sound pressure level of engine behavior is asked for, so that it is determined that respectively Aero-engine internal noise sound pressure level multiple linear regression equations coefficient at centre frequency

In present embodiment, the calculating of the extreme value of the residual sum of squares (RSS) Q of the linear fit sound pressure level of engine behavior Shown in formula such as formula (3)：

Aero-engine internal noise sound pressure level multiple linear regression equations coefficient at each centre frequency for determiningSuch as formula (4) shown in：

Wherein,

A3：According to aero-engine internal noise sound pressure level multiple linear regression equations coefficient at each centre frequencyIt is determined that Aero-engine internal noise sound pressure level multiple linear regression equations.

In present embodiment, shown in aero-engine internal noise sound pressure level multiple linear regression equations such as formula (5)：

Wherein,

A4：Using the third-octave spectrum and aeroengine operation status parameter of aero-engine internal noise as aviation The input of engine interior noise sound multiple linear regression equations, determines aero-engine internal noise loading spectrum sound pressure level Multiple linear regression equations.

A5：Using the overall sound pressure level of aero-engine internal noise and aeroengine operation status parameter as aeroplane engine The input of machine internal noise sound pressure level multiple linear regression equations, determines aero-engine internal noise overall sound pressure level multiple linear Regression equation.

Aero-engine internal noise sound pressure level nonlinear multivariate regression equations include：Aero-engine internal noise load Spectrum sound pressure level nonlinear multivariate regression equations, aero-engine internal noise overall sound pressure level nonlinear multivariate regression equations.

In present embodiment, the process of setting up of aero-engine internal noise sound pressure level nonlinear multivariate regression equations is such as schemed Shown in 3, comprise the following steps：

B1：Tentatively set up aero-engine internal noise sound pressure level nonlinear multivariate regression equations.

In present embodiment, the preliminary aero-engine internal noise sound pressure level nonlinear multivariate regression equations such as formula set up (6) shown in：

Wherein,It is i-th nonlinear fitting sound pressure level of engine behavior at p-th centre frequency, i=1, 2 ... n, p=1,2 ... m,It is k-th Nonlinear regression equation coefficient at p-th centre frequency.

B2：By aero-engine internal noise sound pressure level nonlinear multivariate regression equations linearization process, linearized Aero-engine internal noise sound pressure level multiple regression equation afterwards.

In present embodiment, aero-engine internal noise sound pressure level multiple regression equation such as formula (7) institute after linearisation Show：

B3：Using least square method to aero-engine internal noise sound pressure level Multiple Non Linear Regression at each centre frequency Equation coefficientEstimated, wherein,

B3.1：The residual sum of squares (RSS) Q ' of the nonlinear fitting sound pressure level of engine behavior is set up using least square method Computing formula.

In present embodiment, the computing formula of the residual sum of squares (RSS) Q ' of the nonlinear fitting sound pressure level of engine behavior As shown in formula (8)：

Wherein, ε '_piIt is the residual error of the nonlinear fitting sound pressure level of engine behavior, Y_piAt p-th centre frequency I-th sound pressure level of engine behavior.

B3.2：The extreme value of the residual sum of squares (RSS) Q ' of the nonlinear fitting sound pressure level of engine behavior is asked for, so that really Aero-engine internal noise sound pressure level nonlinear multivariate regression equations coefficient at fixed each centre frequency

In present embodiment, the meter of the extreme value of the residual sum of squares (RSS) Q ' of the nonlinear fitting sound pressure level of engine behavior Calculate shown in formula such as formula (9)：

Aero-engine internal noise sound pressure level nonlinear multivariate regression equations coefficient at each centre frequency for determiningSuch as Shown in formula (10)：

Wherein,

B4：According to aero-engine internal noise sound pressure level nonlinear multivariate regression equations coefficient at each centre frequency Determine aero-engine internal noise sound pressure level nonlinear multivariate regression equations.

In present embodiment, shown in aero-engine internal noise sound pressure level nonlinear multivariate regression equations such as formula (11)：

Wherein,

B5：Using the third-octave spectrum and aeroengine operation status parameter of aero-engine internal noise as aviation The input of engine interior noise sound nonlinear multivariate regression equations, determines aero-engine internal noise loading spectrum acoustic pressure Level nonlinear multivariate regression equations.

B6：Using the overall sound pressure level of aero-engine internal noise and aeroengine operation status parameter as aeroplane engine The input of machine internal noise sound pressure level nonlinear multivariate regression equations, determines that aero-engine internal noise overall sound pressure level is polynary non- Equation of linear regression.

Step 4：Real-time Collection aeroengine operation status parameter, by the work under aero-engine certain working condition State parameter then obtains correspondence under the working condition as the input of aero-engine internal noise sound pressure level multiple regression equation Linear analysis aero-engine internal noise third-octave spectrum and overall sound pressure level.

In present embodiment, the working status parameter under aero-engine working condition is input in step 3 and is obtained Aero-engine internal noise loading spectrum sound pressure level multiple linear regression equations, then obtain corresponding linear under the working condition The aero-engine internal noise third-octave spectrum of analysis；

Working status parameter under aero-engine working condition is input in the aero-engine obtained in step 3 Portion's noise overall sound pressure level multiple linear regression equations, then obtain under the working condition in the aero-engine of corresponding linear analysis Portion's noise overall sound pressure level；

Working status parameter under aero-engine working condition is input in the aero-engine obtained in step 3 Portion's acoustic loads spectrum sound pressure level nonlinear multivariate regression equations, then obtain the aviation of corresponding nonlinear analysis under the working condition Engine interior noise third-octave is composed；

Working status parameter under aero-engine working condition is input in the aero-engine obtained in step 3 Portion's non-property regression equation of the polynary line of noise overall sound pressure level, then obtain the aeroplane engine of corresponding nonlinear analysis under the working condition Machine internal noise overall sound pressure level.

Claims (5)

1. a kind of aero-engine noise Forecasting Methodology based on multiple regression, it is characterised in that comprise the following steps：

Step 1：Obtain aero-engine internal noise time-domain signal and aeroengine operation status parameter matrix X_n×k, n is boat Empty engine behavior number, k is selected aeroengine operation status number of parameters；

Step 2：Aero-engine internal noise time-domain signal is pre-processed, the 1/3 of aero-engine internal noise is obtained Octave is composed and overall sound pressure level；

Step 2.1：FFT is carried out to aero-engine internal noise time-domain signal and obtains aero-engine internal noise frequency domain Signal；

Step 2.2：Third-octave analysis of spectrum and overall sound pressure level analysis are carried out to aero-engine internal noise frequency-region signal, is obtained To the third-octave spectrum and overall sound pressure level of aero-engine internal noise；

Step 3：Third-octave spectrum and aeroengine operation status parameter matrix X according to aero-engine internal noise_n×k, Aero-engine internal noise sound pressure level multiple regression equation is set up using least square method；

Step 4：Real-time Collection aeroengine operation status parameter, by the working condition under aero-engine certain working condition Parameter then obtains corresponding line under the working condition as the input of aero-engine internal noise sound pressure level multiple regression equation Property analysis aero-engine internal noise third-octave spectrum and overall sound pressure level.

2. the aero-engine noise Forecasting Methodology based on multiple regression according to claim 1, it is characterised in that described Aero-engine internal noise sound pressure level multiple regression equation in step 3 includes：Aero-engine internal noise sound pressure level is more First equation of linear regression and aero-engine internal noise sound pressure level nonlinear multivariate regression equations；

The aero-engine internal noise sound pressure level multiple linear regression equations include：Aero-engine internal noise loading spectrum Sound pressure level multiple linear regression equations, aero-engine internal noise overall sound pressure level multiple linear regression equations；

The aero-engine internal noise sound pressure level nonlinear multivariate regression equations include：Aero-engine internal noise load Spectrum sound pressure level nonlinear multivariate regression equations, aero-engine internal noise overall sound pressure level nonlinear multivariate regression equations.

3. the aero-engine noise Forecasting Methodology based on multiple regression according to claim 2, it is characterised in that described The process of setting up of aero-engine internal noise sound pressure level multiple linear regression equations is comprised the following steps：

A1：Tentatively set up aero-engine internal noise sound pressure level multiple linear regression equations；

The aero-engine internal noise sound pressure level multiple linear regression equations of the preliminary foundation are as follows：

${\hat{Y}}_{pi}={\widehat{\mathrm{\β}}}_{p0}+{\widehat{\mathrm{\β}}}_{p1}{X}_{i1}+{\widehat{\mathrm{\β}}}_{p2}{X}_{i2}+\mathrm{...}+{\widehat{\mathrm{\β}}}_{pk}{X}_{ik};$

Wherein,It is i-th linear fit sound pressure level of engine behavior at p-th centre frequency, i=1,2 ... n, n are Aeroengine operation status number, p=1,2 ... m, m are that third-octave composes centre frequency number,For p-th center frequently K-th equation of linear regression coefficient at rate, X_ikIt is corresponding k-th working status parameter of i-th engine behavior Value, k is selected aeroengine operation status number of parameters；

A2：Using least square method to aero-engine internal noise sound pressure level multiple linear regression equations system at each centre frequency NumberEstimated, wherein,

A2.1：The calculating of the residual sum of squares (RSS) Q of the linear fit sound pressure level of engine behavior is set up using least square method Formula；

The computing formula of the residual sum of squares (RSS) Q of the linear fit sound pressure level of the engine behavior is as follows：

$Q=\underset{i=1}{\overset{n}{\Σ}}{\mathrm{\ϵ}}_{pi}^2=\underset{i=1}{\overset{n}{\Σ}}{(Y_{pi}-{\hat{Y}}_{pi})}^2=\underset{i=1}{\overset{n}{\Σ}}{(Y_{pi}-({\widehat{\mathrm{\β}}}_{p0}+{\widehat{\mathrm{\β}}}_{p1}{X}_{i1}+{\widehat{\mathrm{\β}}}_{p2}{X}_{i2}+\mathrm{...}+{\widehat{\mathrm{\β}}}_{pi}{X}_{ik}\left)\right)}^2;$

Wherein, ε_piIt is the residual error of the linear fit sound pressure level of engine behavior, Y_piFor i-th is sent out at p-th centre frequency The sound pressure level of motivation working condition；

A2.2：The extreme value of the residual sum of squares (RSS) Q of the linear fit sound pressure level of engine behavior is asked for, so that it is determined that each center Aero-engine internal noise sound pressure level multiple linear regression equations coefficient at frequency

A3：According to aero-engine internal noise sound pressure level multiple linear regression equations coefficient at each centre frequencyDetermine aviation Engine interior noise sound multiple linear regression equations；

A4：Using the third-octave spectrum and aeroengine operation status parameter of aero-engine internal noise as aeroplane engine The input of machine internal noise sound pressure level multiple linear regression equations, determines that aero-engine internal noise loading spectrum sound pressure level is polynary Equation of linear regression；

A5：Using the overall sound pressure level of aero-engine internal noise and aeroengine operation status parameter as in aero-engine The input of portion's noise sound multiple linear regression equations, determines aero-engine internal noise overall sound pressure level multiple linear regression Equation.

4. the aero-engine noise Forecasting Methodology based on multiple regression according to claim 2, it is characterised in that described The process of setting up of aero-engine internal noise sound pressure level nonlinear multivariate regression equations is comprised the following steps：

B1：Tentatively set up aero-engine internal noise sound pressure level nonlinear multivariate regression equations；

The aero-engine internal noise sound pressure level nonlinear multivariate regression equations of the preliminary foundation are as follows：

${{\hat{Y}}^{\′}}_{pi}={{\widehat{\mathrm{\β}}}^{\′}}_{p0}{X}_{i1}^{{{\widehat{\mathrm{\β}}}^{\′}}_{p1}}{X}_{i2}^{{{\widehat{\mathrm{\β}}}^{\′}}_{p2}}\mathrm{...}{X}_{ik}^{{{\widehat{\mathrm{\β}}}^{\′}}_{pk}};$

Wherein,It is i-th nonlinear fitting sound pressure level of engine behavior at p-th centre frequency, i=1,2 ... n, N is aeroengine operation status number, and p=1,2 ... m, m are that third-octave composes centre frequency number,For in p-th K-th Nonlinear regression equation coefficient at frequency of heart, X_ikIt is corresponding k-th working condition of i-th engine behavior The value of parameter, k is selected aeroengine operation status number of parameters；

B2：By aero-engine internal noise sound pressure level nonlinear multivariate regression equations linearization process, after being linearized Aero-engine internal noise sound pressure level multiple regression equation；

Aero-engine internal noise sound pressure level multiple regression equation after the linearisation is as follows：

$ln{{\hat{Y}}^{\′}}_{pi}=ln{{\widehat{\mathrm{\β}}}^{\′}}_{p0}+{{\widehat{\mathrm{\β}}}^{\′}}_{p1}\ln X_{i1}+{{\widehat{\mathrm{\β}}}^{\′}}_{p2}\ln X_{i2}+\mathrm{...}+{{\widehat{\mathrm{\β}}}^{\′}}_{pk}\ln X_{ik};$

B3：Using least square method to aero-engine internal noise sound pressure level nonlinear multivariate regression equations at each centre frequency CoefficientEstimated, wherein,

B3.1：The meter of the residual sum of squares (RSS) Q' of the nonlinear fitting sound pressure level of engine behavior is set up using least square method Calculate formula；

The computing formula of the residual sum of squares (RSS) Q' of the nonlinear fitting sound pressure level of the engine behavior is as follows：

$Q^{\′}=\underset{i=1}{\overset{n}{\Σ}}{{\mathrm{\ϵ}}^{\′}}_{pi}^2=\underset{i=1}{\overset{n}{\Σ}}{(Y_{pi}-{{\hat{Y}}^{\′}}_{pi})}^2=\underset{i=1}{\overset{n}{\Σ}}{(Y_{pi}-(\ln {{\widehat{\mathrm{\β}}}^{\′}}_{p0}+{{\widehat{\mathrm{\β}}}^{\′}}_{p1}\ln X_{i1}+{{\widehat{\mathrm{\β}}}^{\′}}_{p2}\ln X_{i2}+\mathrm{...}+{{\widehat{\mathrm{\β}}}^{\′}}_{pi}\ln X_{ik}\left)\right)}^2;$

Wherein, ε '_piIt is the residual error of the nonlinear fitting sound pressure level of engine behavior, Y_piFor i-th at p-th centre frequency The sound pressure level of engine behavior；

B3.2：The extreme value of the residual sum of squares (RSS) Q' of the nonlinear fitting sound pressure level of engine behavior is asked for, so that it is determined that respectively Aero-engine internal noise sound pressure level nonlinear multivariate regression equations coefficient at centre frequency

B4：According to aero-engine internal noise sound pressure level nonlinear multivariate regression equations coefficient at each centre frequencyIt is determined that Aero-engine internal noise sound pressure level nonlinear multivariate regression equations；

B5：Using the third-octave spectrum and aeroengine operation status parameter of aero-engine internal noise as aeroplane engine The input of machine internal noise sound pressure level nonlinear multivariate regression equations, determines that aero-engine internal noise loading spectrum sound pressure level is more First Nonlinear regression equation；

B6：Using the overall sound pressure level of aero-engine internal noise and aeroengine operation status parameter as in aero-engine The input of portion's noise sound nonlinear multivariate regression equations, determines aero-engine internal noise overall sound pressure level nonlinear multivariable Regression equation.

5. the aero-engine noise Forecasting Methodology based on multiple regression according to any one of Claims 1-4, its feature It is that the aeroengine operation status parameter includes：Import section stagnation temperature T^*, import section stagnation pressure P^*, air mass flow G_B、 Excess air coefficient α.