A low-speed turbulent boundary layer noise prediction method considering flow space correlation

Abstract

This invention discloses a method for predicting low-speed turbulent boundary layer noise considering the correlation of flow space, belonging to the field of near-wall noise environment for aircraft. This invention selects key flow field parameters for predicting low-speed turbulent boundary layer noise by analyzing the correlation characteristics of flow space; and obtains ωδ* / U for predicting the power spectrum of turbulent boundary layer noise in different segments. ∞ The values ​​were calculated, and a prediction model for the turbulent boundary layer noise power spectrum was constructed to predict the turbulent boundary layer noise power spectrum; the convection velocity U was analyzed and obtained. c This invention is a key parameter determining the spatial correlation of turbulent boundary layer noise, and it constructs a spatial correlation prediction model for turbulent boundary layer noise. By incorporating the convection velocity into the prediction model, the convection velocity of the turbulent boundary layer noise is predicted, thus obtaining the spatial phase distribution of the noise. This invention can simulate the time-frequency characteristics of noise and accurately reproduce the spatial correlation characteristics of low-speed turbulent boundary layer noise, improving the prediction accuracy of turbulent boundary layer noise.

Description

Technical Field

[0001] This invention belongs to the field of near-wall noise environment of aircraft, and specifically relates to a method for predicting low-speed turbulent boundary layer noise that takes into account the relationship between the flow space and the flow space. Background Technology

[0002] Aerodynamic noise is a crucial aspect to consider in the design of large passenger aircraft. Engine propulsion noise and turbulent boundary layer noise are the primary noise sources during cruise. With advancements in aero-engine technology, engine propulsion noise levels have been effectively controlled, leading to a more significant increase in the proportion of turbulent boundary layer noise in the total noise level. Prolonged turbulent boundary layer noise loads not only reduce the fatigue life of the skin structure but also transmit noise into the cabin through the skin, affecting the comfort of passengers and crew. As people's living standards improve, developed aviation nations worldwide are establishing increasingly stringent airworthiness regulations. For my country's domestically developed large passenger aircraft to compete internationally with advanced aircraft like Boeing and Airbus, they must adhere to equally stringent noise standards. Therefore, research into the generation mechanism and control strategies of high-speed turbulent boundary layer noise, and the development of effective methods for predicting turbulent boundary layer noise, are fundamental requirements for the acoustic design of large passenger aircraft.

[0003] Wavenumber-spectrum (WF) models are commonly used to simulate the near-wall turbulent boundary layer noise load environment of aircraft, and are widely applied to evaluate the flow-induced vibration and structural acoustic radiation of typical aircraft components. Commonly used WF models include the Chase model, Corcos model, and Efimtsov model, but different models are typically only applicable to turbulent boundary layer noise models within a specific flow velocity range. WF models are widely used in the study of the dynamic response and acoustic radiation characteristics of plate and shell structures under turbulent boundary layer noise excitation. De Rosa used WF theoretical models to study the numerical and analytical solutions of the structural dynamic response under turbulent boundary layer noise excitation. Joshi developed a dynamic optimization technique for stiffened flat plate structures based on turbulent boundary layer noise models and conducted corresponding experimental verification studies. Franco developed a similarity law theory for the coupled response of structures under turbulent boundary layer noise based on WF models. Turbulent boundary layer noise models provide a reliable load environment for the prediction of turbulent boundary layer noise and the evaluation of structural acoustic and vibration responses in large passenger aircraft.

[0004] However, existing turbulent boundary layer models primarily focus on the time-frequency characteristics of noise loads, providing only empirical ranges for the spatial phase distribution characteristics of noise loads. This leads to inaccurate predictions of turbulent boundary layer noise, affecting the accuracy of aircraft structural acoustic design and falling far short of the requirements for refined acoustic design of large passenger aircraft. Constructing a low-speed turbulent boundary layer noise model that considers the correlation of flow space and developing a high-precision method for spatial correlation modeling of turbulent boundary layer noise are of great significance for the acoustic design of large passenger aircraft. Summary of the Invention

[0005] To address the shortcomings of existing WF models in simulating the spatial correlation of turbulent boundary layer noise, which leads to inaccurate predictions of turbulent boundary layer noise, the main objective of this invention is to provide a low-speed turbulent boundary layer noise prediction method (NW-F method) that considers the spatial correlation of flow. This method can not only simulate the time-frequency characteristics of noise, but also accurately reproduce the spatial correlation characteristics of low-speed turbulent boundary layer noise, thereby improving the prediction accuracy of turbulent boundary layer noise.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] This invention discloses a method for predicting low-speed turbulent boundary layer noise considering the correlation between flow space and the flow space, comprising the following steps:

[0008] Step 1: Analyze the relevant characteristics of the flow space, select key flow field parameters for predicting low-speed turbulent boundary layer noise, and test and obtain these key flow field parameters. The key flow field parameters include the incoming flow state parameter, velocity U. ∞ Density ρ0 and local boundary layer displacement thickness δ * .

[0009] Step 2: Based on the key flow field parameters obtained in Step 1, analyze and obtain ωδ for predicting the turbulent boundary layer noise power spectrum in different segments. * / U ∞ The value is calculated, and a turbulent boundary layer noise power spectrum prediction model is constructed. Based on the turbulent boundary layer noise power spectrum prediction model, the turbulent boundary layer noise power spectrum is predicted.

[0010] Based on the key flow field parameters obtained in step one, ωδ is first calculated. * / U ∞ According to ωδ * / U ∞ Different values ​​are used to predict turbulent boundary layer noise, and a turbulent boundary layer noise power spectrum prediction model is constructed as shown in Equation (1):

[0011]

[0012] Wherein, the boundary layer displacement thickness δ * =δ / 8, where δ represents the boundary layer thickness. (Flow pressure) Local Reynolds number Re x =ρ0U ∞ x / μ.

[0013] Step 3: Analyze and obtain the convection velocity U cThe key parameter that determines the spatial correlation of turbulent boundary layer noise is the key parameter. Based on the key parameters of the flow field obtained in step one, a spatial correlation prediction model for turbulent boundary layer noise is constructed. According to the power spectrum of turbulent boundary layer noise predicted in step two, the convection velocity is introduced into the spatial correlation prediction model for turbulent boundary layer noise to predict the convection velocity of turbulent boundary layer noise and obtain the spatial phase distribution of turbulent boundary layer noise.

[0014] Analysis yielded the convection velocity U c These are the key parameters that determine the spatial correlation of turbulent boundary layer noise. Based on the key flow field parameters obtained in step one, a spatial correlation prediction model for turbulent boundary layer noise as shown in formula (2) is constructed:

[0015]

[0016] Among them, U c This represents the convective velocity, where a and K are constants, and different values ​​typically correspond to different aircraft configurations.

[0017] Furthermore, for turbulent boundary layer noise on a flat plate, the spatial correlation prediction model for turbulent boundary layer noise is as follows:

[0018]

[0019] Step 4: The spatial phase distribution of the turbulent boundary layer noise predicted in Step 3 can simulate the time-frequency characteristics of the noise and accurately reproduce the spatial correlation characteristics of low-speed turbulent boundary layer noise, thereby improving the prediction accuracy of turbulent boundary layer noise.

[0020] Beneficial effects:

[0021] 1. This invention discloses a method for predicting low-speed turbulent boundary layer noise considering the correlation of flow space. By analyzing the correlation characteristics of flow space, key flow field parameters for predicting low-speed turbulent boundary layer noise are selected. Based on obtaining the key flow field parameters, ωδ is analyzed to obtain the power spectrum of turbulent boundary layer noise for different segments. * / U ∞ The value is calculated, and a turbulent boundary layer noise power spectrum prediction model is constructed. Based on the turbulent boundary layer noise power spectrum prediction model, the turbulent boundary layer noise power spectrum is predicted; the convection velocity U is analyzed and obtained. c This invention is a key parameter determining the spatial correlation of turbulent boundary layer noise, and constructs a spatial correlation prediction model for turbulent boundary layer noise. Based on the predicted power spectrum of turbulent boundary layer noise, the convection velocity is input into the spatial correlation prediction model to predict the convection velocity of turbulent boundary layer noise, thus obtaining the spatial phase distribution of turbulent boundary layer noise. This invention can simulate the time-frequency characteristics of noise and accurately reproduce the spatial correlation characteristics of low-speed turbulent boundary layer noise, improving the prediction accuracy of turbulent boundary layer noise.

[0022] 2. The present invention discloses a method for predicting low-speed turbulent boundary layer noise considering the spatial correlation of flow, and constructs a spatial correlation prediction model for turbulent boundary layer noise applicable to flat plate turbulent boundary layer noise prediction, which can improve the accuracy and efficiency of predicting the correlation of turbulent boundary layer noise on flat plates. Attached Figure Description

[0023] Figure 1 This is a diagram showing the correspondence between the numerical computation domain and the experimental test area.

[0024] Figure 2 This is a velocity distribution diagram of a flat plate.

[0025] Figure 3 This is a comparison chart of the turbulent boundary layer noise model results and the experimental measurement results.

[0026] Figure 4 This is a comparison chart of the measured convection velocity results and the NW-F model results.

[0027] Figure 5 This is a flowchart of a low-speed turbulent boundary layer noise prediction method that considers the relationship between the flow space and the present invention. Detailed Implementation

[0028] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings and examples. The following examples are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0029] Turbulent boundary layer noise is a significant cause of induced vibration in aircraft structures and a reduction in structural fatigue life. The spatial correlation of turbulent boundary layer noise is a crucial factor determining the magnitude of structural vibration. Therefore, research on the power spectrum and spatial correlation of turbulent boundary layer noise is of great value for the safety design of aircraft structures. This embodiment uses flat plate flow as an example to predict the energy spectrum and spatial correlation of turbulent boundary layer noise in a flat plate. Figure 5 As shown in the figure, this embodiment discloses a method for predicting low-speed turbulent boundary layer noise that considers the relationship between the flow space and the flow space. The specific implementation steps are as follows:

[0030] Step 1: Obtaining Key Flow Field Parameters: To predict and assess near-wall turbulent boundary layer noise using the WF model, it is first necessary to obtain relevant flow field parameters, specifically including the incoming flow state parameter velocity (U). ∞ ), density (ρ0), and local boundary layer displacement thickness (δ) * ).

[0031] The method for obtaining incoming flow state parameters is illustrated using the evaluation of turbulent boundary layer noise in a flat plate as an example. The computational domain and experimental measurement domain of the flat plate model are shown in [reference needed]. Figure 1The input conditions for CFD (Computational Fluid Dynamics) numerical calculations require the incoming flow velocity (U) to be provided directly. ∞ The density (ρ0) and the local boundary layer thickness (δ) were obtained by analyzing the near-wall velocity distribution calculated numerically. Figure 2 ), and further use the formula δ * =δ / 8 is obtained. However, using wind tunnel testing, the incoming flow velocity (U) ∞ The density (ρ0) needs to be obtained through actual flow field measurements. The incoming flow velocity (U) ∞ Substituting the density (ρ0) into the formula Re x =ρ0U ∞ The local Reynolds number is obtained by calculating x / μ, and then substituted into δ. * =δ / 8 gives the local boundary layer displacement thickness (δ) * ).

[0032] Step 2: Prediction of turbulent boundary layer noise power spectrum: Calculate ωδ by combining the incoming flow velocity, local boundary layer displacement thickness, and analysis frequency range. * / U ∞ The power spectral density of turbulent boundary layer noise is calculated using a turbulent boundary layer noise power spectrum prediction model based on the distribution range of the calculation results.

[0033] for Figure 1 For the flat plate model, turbulent boundary layer noise measurement was performed using a microphone array, which was arranged on... Figure 1 The sensor array area is shown in the image. The flat plate is 1m long, and the microphone array starts at 0.25m. For specific calculations of the turbulent boundary layer noise power spectrum, ωδ needs to be calculated first. * / U ∞ According to ωδ * / U ∞ The power spectrum of turbulent boundary layer noise was predicted using a turbulent boundary layer noise power spectrum prediction model for different intervals of the calculated values.

[0034] Step 3: Spatial correlation prediction of turbulent boundary layer noise: By substituting the convection velocity into the spatial correlation prediction model of turbulent boundary layer noise according to different analysis frequencies, the convection velocity of turbulent boundary layer noise can be predicted, and the spatial phase distribution of turbulent boundary layer noise can be obtained.

[0035] The spatial correlation of turbulent boundary layer noise is determined by the convection velocity. A spatial correlation prediction model for turbulent boundary layer noise is used to predict the convection velocity of the flow. The values ​​of a and K in the spatial correlation prediction model for turbulent boundary layer noise can be different depending on the shape or layout of the aircraft. The specific values ​​can be calibrated using CFD numerical calculation methods or wind tunnel testing methods. In this embodiment, the calculation formula for the convection velocity of turbulent boundary layer noise is calibrated using the measured results of a flat plate model. The calibrated formula can be written as shown in formula (3).

[0036] Figure 3 To compare the model results of turbulent boundary layer noise with experimental measurements, from... Figure 3 The results show that the trend of the pulsating pressure power spectrum curve predicted by the developed NW-F model is in good agreement with the wind tunnel test results, which verifies the reliability of the developed NW-F model for predicting the pulsating pressure power spectrum. Figure 4 The measured results of turbulent boundary layer convection velocity under different flow conditions are compared with the results of the NW-F model. The error is no more than 10%, which is a significant improvement compared with traditional methods. This improves the prediction accuracy of pulsating pressure and the level of structural safety design to a certain extent.

[0037] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting low-speed turbulent boundary layer noise considering the spatial correlation of flow, characterized in that: Includes the following steps, Step 1: Analyze the relevant characteristics of the flow space, select key flow field parameters for predicting low-speed turbulent boundary layer noise, and test and obtain the key flow field parameters; the key flow field parameters include the incoming flow state parameter velocity. ,density and local boundary layer displacement thickness ; Step 2: Based on the key flow field parameters obtained in Step 1, analyze and obtain the power spectrum of turbulent boundary layer noise for different segment predictions. The value is determined, and a turbulent boundary layer noise power spectrum prediction model is constructed. Based on the turbulent boundary layer noise power spectrum prediction model, the turbulent boundary layer noise power spectrum is predicted. Based on the key flow field parameters obtained in step one, the calculation is performed first. ,according to Different values ​​are used to predict turbulent boundary layer noise, and a turbulent boundary layer noise power spectrum prediction model is constructed as shown in formula (1): (1) Among them, the boundary layer displacement thickness , Indicates boundary layer thickness; from flow pressure Local Reynolds number ; Step 3: Analyze and obtain the convection velocity. These are the key parameters that determine the spatial correlation of turbulent boundary layer noise. Based on the key flow field parameters obtained in step one, a spatial correlation prediction model for turbulent boundary layer noise is constructed. According to the power spectrum of turbulent boundary layer noise predicted in step two, the convection velocity is introduced into the spatial correlation prediction model for turbulent boundary layer noise to predict the convection velocity of turbulent boundary layer noise and obtain the spatial phase distribution of turbulent boundary layer noise. Analysis yielded the convection velocity It is a key parameter that determines the spatial correlation of turbulent boundary layer noise. Based on the key parameters of the flow field obtained in step one, a spatial correlation prediction model for turbulent boundary layer noise as shown in formula (2) is constructed: (2) in, This represents the convection velocity, where a and K are constants.

2. The method for predicting low-speed turbulent boundary layer noise considering flow space correlation as described in claim 1, characterized in that: For turbulent boundary layer noise on a flat plate, the spatial correlation prediction model for turbulent boundary layer noise is as follows: (3)。 3. A method for predicting low-speed turbulent boundary layer noise considering flow space correlation as described in claim 1 or 2, characterized in that: It also includes step four, which uses the spatial phase distribution of the turbulent boundary layer noise obtained in step three to simulate the time-frequency characteristics of the noise and accurately reproduce the spatial correlation characteristics of the low-speed turbulent boundary layer noise, thereby improving the prediction accuracy of the turbulent boundary layer noise.

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