A helicopter acoustic footprint efficient prediction method based on acoustic holography

Abstract

The application discloses a kind of based on acoustic holography's helicopter sound footprint high-efficiency prediction method, including by deducing the construction rotor global noise acoustic mode expansion model, the acoustic mode expansion model is characterized the intermapping relationship of time-domain sound pressure on holographic surface and acoustic mode, as the conversion bridge of acoustic mode domain and time domain, is the key of subsequent calculation acoustic mode coefficient and sound footprint prediction;Then for the construction of acoustic mode coefficient database, acoustic mode coefficient database is the important basis and basis for subsequent sound footprint prediction;Finally, based on the sound footprint prediction of database and acoustic mode expansion model, the sound footprint prediction method has the advantages of global, high prediction accuracy, low computing cost, has important application potential in low-noise flight path design and environmental impact assessment.

Description

Technical Field

[0001] This invention relates to the field of rotor aerodynamic noise, and more specifically to an efficient method for predicting the acoustic footprint of helicopters based on acoustic holography. Background Technology

[0002] Helicopters have advantages such as vertical takeoff and landing and high maneuverability, and are widely used in military and civilian fields. Helicopters mostly operate in low-to-medium altitude airspace, and the noise they generate has become one of the key factors restricting their application, especially limiting their use in military missions requiring acoustic stealth and in civilian applications in densely populated areas.

[0003] Noise reduction methods during the helicopter design phase include passive noise reduction methods (such as airfoil optimization or rotor tip design) and active noise reduction methods (Independent Blade Control (IBC), Active Flange Control (ACF), etc.). These methods are characterized by long development cycles and high R&D costs. However, once the helicopter design is finalized, it becomes inconvenient to continue using "design-based noise reduction" methods. "Operational noise reduction" is a ground-based noise reduction technology applicable to existing helicopters. This method achieves noise reduction in ground target areas by altering the propagation direction and radiation intensity of rotor noise through flight path planning and attitude adjustment. Since the core of optimization is predicting the noise on the observation plane under different flight procedures, efficient and high-precision sound footprint prediction methods are crucial.

[0004] Specifically, existing acoustic footprint prediction methods include two types: direct prediction and far-field noise prediction based on acoustic radiation spheres.

[0005] Direct noise reduction methods rely on the location of each observation point. Predicting noise in the target area requires complex integration calculations for each observation point. As the target area expands and the number of observation points increases, the computational cost increases significantly.

[0006] The far-field noise prediction method based on the acoustic radiation sphere is an engineering approach that approximates the noise between the second-order interpolation point on the acoustic radiation sphere and the ground observation point as a linear attenuation in the far field. Therefore, the radius of the acoustic radiation sphere in the database should be located in the far field. Thus, the acoustic radiation sphere method has certain limitations: (1) It can only be applied to far-field noise prediction and is not suitable for the near-field range where noise propagation has severe nonlinearity. (2) This method requires determining the interpolation point on the acoustic radiation sphere for each ground observation point. Then, for each observation point, the interpolation point on the acoustic radiation sphere and its sound pressure level are determined and linearly estimated. The computational time cost needs to be further reduced. Overall, the existing method has certain limitations in application, making it difficult to achieve the globality, accuracy, and efficiency of helicopter acoustic footprint prediction. Summary of the Invention

[0007] To address the limitations of existing direct prediction and acoustic radiation sphere methods, this invention introduces acoustic holography, a technique from the general acoustics field, into the prediction of helicopter acoustic footprints, proposing an efficient prediction method for helicopter acoustic footprints based on acoustic holography.

[0008] To achieve the above objectives, this invention provides a method for efficient prediction of acoustic footprints based on acoustic holography, comprising:

[0009] Step 1: Obtain the near-field time-domain sound pressure level of the helicopter under different flight conditions;

[0010] Step 2: Derive the acoustic mode expansion model of the rotor's global noise and construct a database of acoustic mode coefficients;

[0011] Step 3: Discretize the helicopter's flight trajectory and extract the acoustic modal coefficients of the corresponding feature parameters from the database based on the helicopter's flight parameters at discrete positions.

[0012] Step 4: Using the acoustic modal expansion model, evaluate the noise radiation to the ground at each discrete location in the acoustic modal domain; use noise quantization units to characterize the noise energy radiated to the observation plane throughout the entire flight process.

[0013] As a preferred technical solution, step 1 further includes:

[0014] Step 1.1: Based on the helicopter rotor speed and radius, determine the rotor passing frequency of the noise and determine the number of observation positions for the holographic surface;

[0015] Step 1.2: Obtain the near-field time-domain sound pressure of the helicopter under different flight conditions based on the direct noise prediction method and experimental methods.

[0016] As a preferred technical solution, step 2 further includes:

[0017] Step 2.1: Construct an acoustic mode expansion model of the rotor's global noise using near-field acoustic holography and Fourier acoustic analysis.

[0018] Step 2.2: Using the holographic surface temporal sound pressure under different flight conditions as input, the acoustic mode coefficients under the corresponding flight conditions are estimated using mathematical optimization methods;

[0019] Step 2.3: Map the acoustic modal coefficients under different flight conditions to the feature parameter dimensions of the database and store them to form a database.

[0020] As a preferred technical solution, in step 3, if there are no characteristic parameters in the database that directly correspond to the discrete position flight parameters, the acoustic mode coefficients are determined by interpolation.

[0021] As a preferred technical solution, step 4 further includes:

[0022] Step 4.1: For each discrete position of the flight trajectory, use the acoustic mode expansion model of rotor noise to evaluate the radiation of noise sources to the observation plane at each discrete position in the acoustic mode domain;

[0023] Step 4.2: Incorporate the effects of flight time and use quantitative units to measure the average noise energy radiated to the ground throughout the entire flight process.

[0024] The advantages of this invention over the prior art are:

[0025] (1) Compared with the limitation of the existing acoustic radiation sphere method which can only predict the far field, the acoustic footprint prediction method based on acoustic holography can achieve global prediction and can be applied to both the near field and the far field.

[0026] (2) Compared with the acoustic radiation sphere method, the proposed method is based on the noise expansion model of acoustic modes and assesses the acoustic exposure level of the ground area in the acoustic mode domain. This analytical model can characterize the rotor noise mechanism, so the proposed method has higher prediction accuracy.

[0027] (3) Compared with existing noise direct prediction methods and acoustic radiation sphere methods, the prediction method provided by this invention does not depend on the location of each observation point. Thanks to matrix operations in the acoustic modal domain, the computation time cost is lower. Attached Figure Description

[0028] Figure 1 This invention provides a technical approach for predicting helicopter acoustic footprints based on acoustic holography.

[0029] Figure 2 Acoustic footprint of a certain type of helicopter during its approach;

[0030] In the figure, the advance ratio μ = 0.15, and the descent angle γ = -2°, -4°, -6°, -8°. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] This invention provides an efficient method for predicting helicopter acoustic footprints based on acoustic holography, such as... Figure 1 As shown, the specific steps include the following:

[0033] The first step is to predict the holographic surface noise under different flight conditions.

[0034] Specifically, based on fundamental parameters such as helicopter rotor speed and radius, the rotor passage frequency of the noise is determined, and the number of observation positions for the holographic surface is determined. Then, the near-field time-domain sound pressure under different helicopter flight conditions is obtained using a direct noise prediction method, which serves as the holographic surface.

[0035] It should be noted that the direct noise prediction method, also known as the numerical prediction method, is based on the aerodynamic loads of the rotor blades. The aerodynamic forces on the blades are calculated using a rotor aerodynamic model, including but not limited to CFD, vortex particle, and free wake methods. In the time and frequency domains, typical noise calculation formulas are Farassat's Formulas 1A based on the FW-H equations, and Hanson's formula, respectively. This example uses Formulas 1A based on the FW-H equations to obtain the time-domain sound pressure level of the holographic surface.

[0036] The second step involves deriving the acoustic mode expansion model of the rotor's global noise and constructing a database of acoustic mode coefficients. Near-field acoustic holography and Fourier acoustic analysis are used to construct the acoustic mode expansion model of the rotor's global noise. This model maps the time-domain sound pressure of the holographic surface to the amplitude and coefficients of the acoustic modes in the acoustic modal domain. Using the time-domain sound pressure of the holographic surface under different flight conditions as input, mathematical optimization methods are used to estimate the acoustic mode coefficients under the corresponding flight conditions. The acoustic mode coefficients under different flight conditions are then mapped to the feature parameter dimensions of the database and stored to form the database.

[0037] Furthermore, based on the acoustic analogy equation of FW-H, and using near-field acoustic holography and Fourier acoustic analysis, the acoustic mode expansion model of rotor noise can be derived:

[0038]

[0039]

[0040] Where (m,n) is the representation index of acoustic modes. This represents the Legendre function. imφ This represents the periodicity of the acoustic mode along the azimuth angle. r, θ, and φ represent the distance, elevation angle, and azimuth angle of the observation point relative to the origin in the spherical coordinate system, respectively. k represents the wavenumber. C m,n (k) are the coefficients of the acoustic mode (m,n). This is a first-order spherical Hankel function, representing the variation of acoustic mode amplitude with radius. Let be a spherical harmonic function, expressed as equation (2), which describes the variation of the amplitude in the azimuth and elevation directions.

[0041] Therefore, the matrix relationship between the acoustic modal coefficients and the time-domain sound pressure of the holographic surface satisfies:

[0042] P(k)=Ψ(k)C(k) (3)

[0043] In the formula, P(k) represents the holographic surface time-domain sound pressure matrix, Ψ(k) represents the amplitude of the acoustic mode, and C(k) represents the acoustic mode coefficient.

[0044] Solving equation (3) using mathematical optimization methods, the estimated acoustic modal coefficients C(k) can be expressed as:

[0045] C(k)=(Ψ H (k)Ψ(k)) -1 Ψ H (k)P(k) (4)

[0046] In the formula, Ψ H (k) denotes the conjugate transpose of the matrix.

[0047] The third step is to discretize the helicopter's flight trajectory and, based on the helicopter's flight parameters at discrete positions, extract the corresponding acoustic mode coefficients C from the database. m,n (k). If there are no characteristic parameters in the database that directly correspond to the discrete position flight parameters, interpolation can be used to determine the acoustic mode coefficients.

[0048] The fourth step is to predict the flight trajectory / sound footprint of the flight process.

[0049] First, for each discrete position of the flight trajectory, the radiation of the noise source to the observation plane is evaluated in the acoustic modal domain using the rotor noise acoustic modal expansion model.

[0050] Furthermore, the influence of flight time is taken into account, and the average noise energy radiated to the ground throughout the entire flight process is measured using quantitative units.

[0051] The quantification units of sound footprint include: Sound Exposure Level (SEL) and Effective Perceived Noise Level (EPNL). This example uses Sound Exposure Level to measure the cumulative effect of sound radiation in time and space during flight, as shown in Equation (5).

[0052]

[0053] SPL(i) is at time t i The discrete sound pressure level. n is the number of samples selected during the observation period, which in this invention refers to the element segment of the helicopter flight trajectory. Δt is the time interval between elements, i.e., the running time of each trajectory element.

[0054] Depend on Figure 2 Simulation results show that for a ground area containing 1800 observation points, the computation time of the invented method is only one-fifth that of the acoustic radiation sphere method, demonstrating its high efficiency. Simulation results for the hovering and forward flight states of a certain rotor show that the prediction accuracy of noise radiated to the ground from a single discrete position is less than 1%.

[0055] This embodiment provides an efficient helicopter acoustic footprint prediction method based on acoustic holography. First, it derives and constructs an acoustic mode expansion model of the rotor's global noise. This model characterizes the mapping relationship between time-domain sound pressure and acoustic modes on the holographic surface. Serving as a bridge between the acoustic mode domain and the time domain, it is crucial for subsequent calculation of acoustic mode coefficients and acoustic footprint prediction. Next, an acoustic mode coefficient database is constructed. This database is an important foundation and basis for subsequent acoustic footprint prediction. Finally, acoustic footprint prediction is performed based on the database and the acoustic mode expansion model. This acoustic footprint prediction method has the advantages of global applicability, high prediction accuracy, and low computational cost, and has significant application potential in low-noise flight path design and environmental impact assessment.

[0056] It should be noted that the above examples only provide one embodiment of the present invention and should not be construed as limiting the scope of protection of the present invention. Other equivalent substitutions, modifications, and improvements based on the spirit and essence of the present invention should all be covered within the scope of protection of the present invention.

Claims

1. A method for efficient prediction of acoustic footprints based on acoustic holography, characterized in that, include: Step 1: Obtain the near-field temporal sound pressure of the helicopter under different flight conditions using noise numerical prediction or experimental methods, and use it as a holographic surface; Step 2: Derive the acoustic mode expansion model of the rotor's global noise and construct a database of acoustic mode coefficients; specifically including: Step 2.1: Construct the acoustic mode expansion model of the rotor's global noise using near-field acoustic holography and Fourier acoustic analysis; Step 2.2: Using the holographic surface time-domain sound pressure under different flight conditions as input, estimate the acoustic mode coefficients under the corresponding flight conditions using mathematical optimization methods; Step 2.3: Map the acoustic mode coefficients under different flight conditions to the feature parameter dimensions of the database and store them to form the database; Step 3: Discretize the helicopter's flight trajectory and extract the acoustic modal coefficients of the corresponding feature parameters from the database based on the helicopter's flight parameters at discrete positions. Step 4: Using the acoustic modal expansion model, evaluate the noise radiation to the ground at each discrete location in the acoustic modal domain; use noise quantization units to characterize the noise energy radiated to the observation plane throughout the entire flight process.

2. The efficient acoustic footprint prediction method according to claim 1, characterized in that, Step 1 further includes: Step 1.1: Based on the helicopter rotor speed and radius, determine the rotor passing frequency of the noise and determine the number of observation positions of the holographic surface; Step 1.2: Obtain the near-field time-domain sound pressure of the helicopter under different flight conditions according to the noise direct prediction method and experimental method.

3. The efficient acoustic footprint prediction method according to claim 1, characterized in that: In step 3, if there are no characteristic parameters in the database that directly correspond to the discrete position flight parameters, the acoustic mode coefficients are determined by interpolation.

4. The efficient acoustic footprint prediction method according to claim 1, characterized in that, Step 4 further includes: Step 4.1: For each discrete position of the flight trajectory, use the acoustic mode expansion model of rotor noise to evaluate the radiation of the noise source to the observation plane at each discrete position in the acoustic mode domain; Step 4.2: Incorporate the influence of flight time and use quantization units to measure the average noise energy radiated to the ground throughout the entire flight process.

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