Airplane acoustic structure parameter simulation design method, electronic device and medium

Abstract

The present disclosure provides an aircraft acoustic structure parameter simulation design method, electronic equipment and medium, the method comprises: obtaining the independent variable parameters of the simulation design of the aircraft acoustic structure, the independent variable parameters include the wallboard structure parameters of the aircraft wallboard and the acoustic parameters of the sound-absorbing material inside the aircraft interior panel; the independent variable parameters are loaded as input conditions to the statistical energy analysis (SEA) model of the simulation software; a plurality of numerical simulation operations are carried out through the SEA model to obtain simulation results of sound quality parameters corresponding to different independent variable parameters; based on a preset sound quality target screening strategy, the simulation results are screened to obtain target independent variable parameters that meet the sound quality target requirements. The present disclosure upgrades the acoustic evaluation dimension, optimizes the wallboard structure parameters and the sound-absorbing material parameters jointly, thereby realizing cabin noise control and improving noise subjective comfort.

Description

Technical Field

[0001] This disclosure relates to the field of aircraft structural design technology, and in particular to a simulation design method for aircraft acoustic structural parameters, electronic equipment, and media. Background Technology

[0002] With the rapid iterative development of civil aviation and high-end aircraft technology, cabin acoustic comfort has become one of the core indicators for measuring the overall performance of an aircraft, and is also a key consideration for aircraft model design, airworthiness certification and market competitiveness enhancement.

[0003] During cruise, takeoff, landing, and maneuvering, aircraft are continuously subjected to the coupled excitation of multiple noise sources, including aerodynamic noise, engine noise, environmental control system noise, and structural vibration noise. These various noises are transmitted, transmitted, and reflected through the fuselage panels and enter the cabin, easily causing excessive cabin noise levels. Prolonged exposure to excessive noise not only severely affects the crew's concentration and passenger experience but can also induce acoustic fatigue damage to the fuselage panel structure, shorten the aircraft's structural lifespan, and even affect the stable operation of onboard equipment.

[0004] Therefore, how to efficiently and accurately optimize the acoustic structural parameters of aircraft to achieve cabin noise control has become a key research focus in the field of aircraft design at present. Summary of the Invention

[0005] In a first aspect, embodiments of this disclosure provide a simulation design method for aircraft acoustic structure parameters. The method includes: obtaining independent variable parameters for the simulation design of aircraft acoustic structures, the independent variable parameters including the structural parameters of the aircraft wall panels and the acoustic parameters of the sound-absorbing materials on the inner side of the aircraft interior panels; loading the independent variable parameters as input conditions into a statistical energy analysis (SEA) model of simulation software; performing multiple sets of numerical simulation operations through the SEA model to obtain simulation results of sound quality parameters corresponding one-to-one with each set of different independent variable parameters; and filtering the simulation results based on a preset sound quality target screening strategy to obtain target independent variable parameters that meet the sound quality target requirements.

[0006] In some embodiments, the step of performing multiple sets of numerical simulations using the SEA model to obtain simulation results of sound quality parameters corresponding one-to-one with each set of different independent variable parameters includes: obtaining a first scan range setting value for the wall panel structure parameters and a second scan range setting value for the acoustic parameters of the sound-absorbing material; based on the first scan range setting value and the second scan range setting value, taking values ​​for the wall panel structure parameters and the acoustic parameters of the sound-absorbing material according to a preset step size to obtain multiple combinations of independent variable parameters; and performing multiple sets of numerical simulations using the SEA model according to the multiple combinations of independent variable parameters to obtain simulation results of sound quality parameters corresponding one-to-one with each set of different independent variable parameters.

[0007] In some embodiments, the wall panel structural parameters include at least one of the metal layer material, metal layer thickness, and damping layer material parameters, and the acoustic parameters of the sound-absorbing material include at least one of density and flow resistance.

[0008] In some embodiments, the structural parameters of the wall panel include the thickness of the metal layer, and the first scanning range of the thickness of the metal layer is set to 0.45 mm to 0.55 mm; the acoustic parameters of the sound-absorbing material include the density of the sound-absorbing material, and the second scanning range of the density of the sound-absorbing material is set to 10 kg / m³ to 20 kg / m³.

[0009] In some embodiments, the sound quality parameters include at least one of loudness N, sharpness S, roughness R, fluctuation F, clarity index AI, and psychoacoustic annoyance PA.

[0010] In some embodiments, the calculation process of obtaining the simulation results of sound quality parameters through the SEA model includes: applying acoustic excitation to the aircraft, obtaining different noise spectra corresponding to acoustic parameters of different sound-absorbing materials and different wall panel structure parameters, and performing at least one of the following operations based on the noise spectrum; using the Zwicker loudness model, converting the noise spectrum of one-third octave band into the critical band, correcting the sub-band sound pressure level according to the transmission factor, calculating the characteristic loudness of each critical band from each characteristic loudness chart and integrating it to obtain the loudness N of the noise; and using the Aures sharpness model to obtain the sharpness S based on the loudness N, the characteristic loudness of each critical band, and the critical band weighting factor; and so on. Based on the critical frequency band, modulation frequency, and time masking depth, the roughness R is obtained using the Zwicker roughness calculation model; based on the critical frequency band, modulation frequency, and time masking depth, the fluctuation F is obtained using the Zwicker fluctuation calculation model; when the sound pressure level of each frequency band in the one-third octave band of the noise spectrum is between the upper and lower limits of the linguistic region, the percentage value of the sharpness index corresponding to the noise spectral line in each frequency band is obtained by looking up a table, and the percentage values ​​of each frequency band are accumulated to obtain the sharpness index AI; based on the loudness N, the sharpness S, the roughness R, and the fluctuation F, the psychoacoustic annoyance PA is obtained using the Zwicker psychoacoustic annoyance model.

[0011] In some embodiments, the step of filtering the simulation results based on a preset sound quality target screening strategy to obtain target independent variable parameters that meet the sound quality target requirements includes: obtaining a dual-target screening strategy for sound quality target screening, wherein the dual-target screening strategy is constructed based on the clarity index AI and the psychoacoustic annoyance level PA; and filtering the simulation results based on the dual-target screening strategy to obtain target independent variable parameters that meet the sound quality target requirements.

[0012] In some embodiments, the step of filtering the simulation results based on the dual-objective screening strategy to obtain target independent variable parameters that meet the sound quality target requirements includes: filtering sound quality parameters from the simulation results whose clarity index AI is greater than or equal to a preset threshold to obtain a candidate set; determining the result with the minimum psychoacoustic annoyance level PA from the candidate set as the target sound quality parameter; and determining the wall panel structure parameters and the acoustic parameters of the sound-absorbing material corresponding to the target sound quality parameter as the target independent variable parameters.

[0013] Secondly, embodiments of this disclosure provide an electronic device, including: one or more processors; and a memory storing one or more programs thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-described simulation design method for aircraft acoustic structural parameters.

[0014] Thirdly, embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the above-described simulation design method for aircraft acoustic structural parameters.

[0015] This disclosure provides a simulation design method, electronic device, and medium for aircraft acoustic structural parameters. The method includes: acquiring independent variable parameters for aircraft acoustic structural simulation design, including structural parameters of aircraft panels and acoustic parameters of sound-absorbing materials on the inner side of aircraft interior panels; loading the independent variable parameters as input conditions into a statistical energy analysis (SEA) model of simulation software; performing multiple sets of numerical simulations through the SEA model to obtain simulation results of sound quality parameters corresponding one-to-one with each set of different independent variable parameters; and filtering the simulation results based on a preset sound quality target screening strategy to obtain target independent variable parameters that meet the sound quality target requirements. This disclosure upgrades the acoustic evaluation dimension by jointly optimizing panel structural parameters and sound-absorbing material parameters, thereby achieving cabin noise control and improving subjective noise comfort. Attached Figure Description

[0016] Figure 1 A flowchart of a simulation design method for aircraft acoustic structure parameters provided in this disclosure embodiment; Figure 2 Another flowchart of a simulation design method for aircraft acoustic structural parameters provided in this disclosure embodiment; Figure 3 A schematic diagram of a cross-sectional view of the damped aluminum alloy laminate provided in the embodiments of this disclosure; Figure 4 Exploded view of the aircraft cabin SEA subsystem provided in this embodiment of the disclosure; Figure 5 API development flowchart provided for embodiments of this disclosure; Figure 6 A graph showing the variation trend of acoustic quality parameters with aluminum alloy layer thickness h1 provided in the embodiments of this disclosure; Figure 7 A graph showing the variation trend of acoustic quality parameters with glass wool density ρ provided in the embodiments of this disclosure; Figure 8 A multi-objective trade-off design diagram provided for embodiments of this disclosure; Figure 9 A schematic diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions of this disclosure will be described in detail below with reference to the accompanying drawings.

[0018] Exemplary embodiments will be described more fully below with reference to the accompanying drawings; however, these exemplary embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will enable those skilled in the art to fully understand the scope of this disclosure.

[0019] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0020] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0021] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded.

[0022] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0023] As the requirements for passenger comfort in civil aircraft continue to increase, cabin noise control has evolved from simply reducing sound pressure levels to refined sound quality management that enhances passengers' subjective experience.

[0024] Currently, cabin noise control mainly employs the following technical means: (1) Aircraft cabin panel noise reduction design method using A-weighted sound pressure level or total sound pressure level as evaluation index. This type of method establishes a statistical energy analysis (SEA) model of the aircraft cabin to analyze the influence of different panel structural parameters or acoustic package configurations on the cabin sound pressure level, with the sound pressure level reduction as the optimization target.

[0025] However, this method has a specific drawback: sound pressure level only reflects the magnitude of noise energy and cannot accurately characterize passengers' subjective auditory experience. For noises with the same A-weighted sound pressure level, passengers' annoyance can vary significantly due to differences in their spectral distribution and time-frequency domain variation characteristics. This fundamental flaw means that the combination of wall panel parameters designed optimally based on sound pressure level may not necessarily provide passengers with the best subjective acoustic experience.

[0026] (2) Studies on evaluating aircraft cabin noise using sound quality parameters (loudness, sharpness, etc.). Such studies usually calculate and subjectively evaluate the sound quality of measured cabin noise samples to analyze the noise quality of existing aircraft cabins, but lack a closed-loop design method that feeds back the sound quality evaluation indicators to the panel structure design parameters.

[0027] However, the specific shortcomings of this type of research are that existing studies often examine the effects of a single structural parameter (such as the thickness of the metal layer) or a single sound-absorbing material parameter (such as the density of glass wool) separately. They lack the technical means to incorporate the structural parameters of the wall panel and the sound-absorbing material parameters into the same design process for joint parametric analysis under engineering constraints (such as a fixed total thickness of the wall panel), and to automatically output recommended design schemes with psychoacoustic annoyance and clarity index as dual objectives.

[0028] To address the aforementioned shortcomings, this disclosure provides a simulation design method for aircraft acoustic structural parameters. Based on the statistical energy analysis (SEA) model, parameter simulation is performed. Through a reverse linkage constraint mechanism, the parameters of the fuselage panel structure and the sound-absorbing material are jointly parameterized and designed. A dual-objective screening decision logic is used to design and optimize the parameters of the fuselage panel structure (including metal skin and embedded damping laminate), the damping layer configuration, and the acoustic parameters of the sound-absorbing materials (such as glass wool) on the inner side of the interior panels, thereby achieving noise control in the aircraft cabin.

[0029] The main technical concepts of the simulation design method for aircraft acoustic structural parameters disclosed herein include: 1. Reverse Linkage Constraint Mechanism: Under the engineering constraint of keeping the total thickness of the side panel constant, the thickness of the metal layer and the damping layer are modified parametrically in a reverse linkage manner (i.e., when the metal layer is thickened, the damping layer is thinned by the same amount, and vice versa). Existing technologies are not constrained by the total thickness and can arbitrarily increase the thickness of the panel. The constraint mechanism disclosed in this paper directly corresponds to the engineering reality of weight reduction in aircraft structures, enabling the design scheme to systematically explore the sound insulation-damping performance space without increasing weight.

[0030] 2. Joint parametric design of wall panel structural parameters and sound-absorbing material parameters: The thickness of the side wall panel metal layer (h1), the thickness of the damping layer, and the density (ρ) of the sound-absorbing material on the inner side of the interior panel are incorporated into the same simulation design process to form a parameter combination matrix. The simulation software is then further developed through an application programming interface (API) to achieve automated batch solving. Current technologies lack this type of joint parametric analysis process.

[0031] 3. Dual-objective screening decision logic: With the dual objectives of reducing psychoacoustic annoyance (PA) and meeting the preset threshold of clarity index (AI), a two-step decision is adopted, which first screens candidate combinations with AI ≥ the threshold and then selects the one with the lowest PA. This approach combines subjective noise comfort in the cabin with the voice communication environment, and is superior to existing methods that only target a single sound pressure level.

[0032] 4. Multi-Objective Trade-off Design Map: Using psychoacoustic annoyance level (PA) as the vertical axis and clarity index (AI) as the horizontal axis, all calculation results of the parameter combination matrix are visualized as a two-dimensional design map. Threshold boundaries and recommended design intervals are marked, enabling designers to intuitively identify the design space and flexibly select the optimal parameter range. This map format itself is a novel design output tool not proposed in existing technologies.

[0033] 5. Fully automated process: The system completes database creation, model file association, laminate thickness parameter writing, sound-absorbing material density parameter writing, simulation solution calling, and one-third octave band sound pressure level spectrum reading of the target cabin cavity subsystem sequentially through API interface, without manual intervention, significantly improving design efficiency.

[0034] The technical solutions of this disclosure and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this disclosure will now be described with reference to the accompanying drawings.

[0035] This disclosure provides a simulation design method for aircraft acoustic structural parameters. The workflow of this method can be implemented by electronic devices, such as computers and handheld smart terminals. For ease of explanation, the embodiments of this disclosure are described with the computer as the subject of the method execution.

[0036] Figure 1 This is a schematic diagram of the simulation design method for aircraft acoustic structure parameters provided in this embodiment of the disclosure. Figure 2 Another schematic diagram of the simulation design method for aircraft acoustic structure parameters provided in this disclosure is shown below. Figure 1 as well as Figure 2 As shown, this disclosure provides a simulation design method for aircraft acoustic structure parameters, the method including steps S1-S4, as follows: S1. Obtain the independent variable parameters for the simulation design of the aircraft acoustic structure. The independent variable parameters include the structural parameters of the aircraft wall panels and the acoustic parameters of the sound-absorbing materials on the inner side of the aircraft interior panels.

[0037] In the simulation design of aircraft acoustic structure, the simulation results are different depending on the value of the independent variable parameter. In this disclosure, the target simulation result of the aircraft acoustic structure is the sound quality parameter in the aircraft cabin. Therefore, the independent variable parameter is mainly a parameter related to the noise in the aircraft cabin, such as the structural parameters of the aircraft wall panel and the acoustic parameters of the sound-absorbing material on the inner side of the aircraft interior panel.

[0038] In some embodiments, the wall panel structural parameters include at least one of the following: metal layer material, metal layer thickness, and damping layer material parameters.

[0039] Figure 3 This is a schematic diagram of a cross-sectional view of the damped aluminum alloy laminate provided in the embodiments of this disclosure, as shown below. Figure 3 As shown, the aircraft panel structure consists of three layers from top to bottom: an upper metal layer (such as aluminum alloy, with thickness h1), a viscoelastic damping layer (with thickness h1), and a lower metal layer (with thickness h1). d The lower metal layer (e.g., aluminum alloy, thickness h1) is indicated on the right; the total thickness h = 1.2 mm is indicated on the right side; each layer is distinguished by a different fill pattern and labeled with its material name (e.g., Al7075 / viscoelastic damping material). The total thickness of the metal layer and the damping layer is limited to h, i.e., h1 + h d +h1=h, as the metal layer thickness h1 changes, the damping layer thickness h d The two also change in opposite directions, exhibiting inverse linkage and mutual constraint. That is, under the constraint of a constant total sidewall thickness, the constraint relationship between the metal layer thickness and the damping layer thickness is as follows: when the metal layer thickness h1 increases, the damping layer thickness h... d Consequently, the thickness decreases; conversely, when the metal layer thickness h1 decreases, the damping layer thickness h... d As it increases, the thickness of the metal layer in the material thickness is used as an independent variable in this disclosure.

[0040] In addition, commonly used materials for the metal layer of aircraft panels include 2024, 6061, and 7075 series aluminum alloys, TC4 and TA15 titanium alloys, 304 and 321 stainless steel, AZ31 and AZ91 magnesium alloys, and high-strength alloy steels.

[0041] In addition, the material parameters of the damping layer of aircraft panels include material density, elastic modulus, shear modulus, Poisson's ratio, loss factor, damping layer thickness, bonding stiffness, temperature range, deformation limit, inherent damping coefficient, and interface coupling characteristics.

[0042] Optionally, in addition to the metal layer material, metal layer thickness, and damping layer material parameters, the wall panel structural parameters may also include at least one of the following parameters: plate elastic modulus, Poisson's ratio, material density, structural surface curvature, stiffening layout, stiffener cross-sectional dimensions, stiffener spacing, wall panel opening ratio, splicing gap size, frame constraint stiffness, number of skin layers and interlayer bonding characteristics, etc.

[0043] In this disclosure, the sound-absorbing material on the inner side of the aircraft interior trim panels is a functional noise reduction material that adheres to the cabin interior back panel and is laid between the cabin wall panels. It is primarily used to absorb flight aerodynamic noise, mechanical vibration noise, and cabin human voices, reducing sound wave reflection and transmission, and optimizing the cabin acoustic environment. Commonly used aerospace-grade sound-absorbing materials on the inner side of aircraft interior trim panels include ultrafine glass wool, melamine foam, flame-retardant polyurethane foam, phenolic foam, rock wool / slag wool, aramid or fiberglass composite sound-absorbing felt, and acoustic non-woven fabrics. The sound-absorbing material is attached to the inner side of the interior trim panels, dissipating sound energy through its porous structure. By combining different specifications and parameters to adapt to different frequency bands of noise, it reduces cabin noise levels and improves acoustic comfort.

[0044] In some embodiments, the acoustic parameters of the sound-absorbing material include at least one of density and flow resistance.

[0045] Density refers to the mass per unit volume of the sound-absorbing material. The density directly affects the porosity and structural stiffness, thereby changing the ability to dissipate mid-to-high frequency sound waves. The higher the density, the stronger the material compactness.

[0046] Among them, flow resistance refers to the degree of obstruction encountered by airflow when passing through the pores of a material, reflecting the ease of air penetration. It is the core parameter that determines the low-frequency sound absorption effect of a material, and flow resistance is adapted to the noise absorption requirements of the corresponding frequency band.

[0047] Optionally, in addition to density and flow resistance, the acoustic parameters of the sound-absorbing material may include at least one of the following parameters: material porosity, fiber pore size, material thickness, elastic stiffness, damping loss factor, air permeability, resonant sound absorption frequency range, multilayer material stacking arrangement, and material compression deformation coefficient.

[0048] For the various types of independent variable parameters mentioned above, the simulation results will change accordingly when the parameter values ​​change. Different types of independent variable parameters can be selected for simulation design based on the actual situation and specific needs.

[0049] S2. Load the independent variable parameters as input conditions into the statistical energy analysis (SEA) model of the simulation software.

[0050] SEA is a computational method for predicting the mid-to-high frequency energy flow between subsystems in a complex coupled system. It rapidly predicts the acoustic and vibration responses by performing subsystem modal statistical averaging.

[0051] Figure 4 An exploded view of the aircraft cabin SEA subsystem provided in this disclosure embodiment, such as... Figure 4 As shown, the three-dimensional exploded view displays the spatial relationships of the cockpit skin, passenger cabin skin, floor, bulkhead, side wall panels, various windows, interior panels, luggage compartment, and passenger cabin cavity subsystem in layers.

[0052] Specifically, this disclosure employs a statistical energy analysis acoustic-structural coupling model (SEA model). The three-dimensional model of the aircraft cabin section is divided into 119 SEA subsystems in SEA simulation software (using VA One as an example), including 110 structural subsystems and 9 cavity subsystems. The structural subsystems include: cockpit skin (4), cabin skin (4), floor (4), bulkheads (10), sidewalls (6), cockpit window (1), cabin exterior window (28), cabin interior window (28), interior trim panels (3), baggage compartments (6), and baggage compartment bulkheads (16). The sidewalls use embedded damping aluminum alloy laminates (such as Al7075), with the top and bottom layers being aluminum alloy and the middle layer being a viscoelastic damping material. The mid-surface of the damping layer coincides with the mid-surface of the laminate. The interior trim panels (polycarbonate material) are lined with glass wool. Structural subsystems are connected by lines, while structural and cavity subsystems are connected by surfaces.

[0053] S3. Perform multiple sets of numerical simulations using the SEA model to obtain simulation results of sound quality parameters that correspond one-to-one with the different independent variable parameters in each set.

[0054] In this disclosure, MATLAB establishes a connection with the simulation software through the Application Programming Interface (API) provided by VA One, and writes m-language scripts to automatically complete parameter writing, solution calling, and result reading.

[0055] Figure 5 The API development flowchart provided for the embodiments of this disclosure is as follows: Figure 5As shown, the main API functions include: pi_ClientOpenConnection (establish connection), pi_fInit (interface initialization), pi_fNeoDatabaseCreate (create database), pi_fNeoDatabaseFileSpec (associate model file), pi_fNeoDatabaseOpenReadWrite (open database), pi_fGeneralLaminateGetClassID (read laminate class name), pi_fGeneralLaminateGetLayer (select laminate layer), pi_fIsoLayerSetThickness (set aluminum alloy layer thickness), pi_fFiberGetClassID (read fiber material class name), pi_fFiberSetDensity (set glass wool density), pi_fDatabaseSolve (solve model), pi_fResultsAtFreq (read spectrum results), and pi_fNeoDatabaseClose (close database).

[0056] refer to Figure 5 The call chain of the 14 API functions is as follows: pi_ClientOpenConnection → pi_fInit → pi_fNeoDatabaseCreate → pi_fNeoDatabaseFileSpec → pi_fNeoDatabaseOpenReadWrite → laminate addressing (pi_fGeneralLaminateGetClassID / GetLayer) → pi_fIsoLayerSetThickness (write to h1) → fiberglass addressing (pi_fFiberGetClassID) → pi_fFiberSetDensity (write to ρ) → pi_fDatabaseSolve → pi_fResultsAtFreq → pi_fNeoDatabaseClose.

[0057] In this disclosure, the Sound Quality (SQ) parameter refers to the evaluation of the suitability of sound for a specific technical objective or task.

[0058] In some embodiments, in S3, the sound quality parameters include at least one of loudness N, sharpness S, roughness R, fluctuation F, clarity index AI, and psychoacoustic annoyance PA.

[0059] Loudness (N) is a psychoacoustic parameter used to describe the loudness of a sound. The unit is one, and it is calculated using the Zwicker model (ISO 532B).

[0060] Sharpness (S) is a parameter that describes the proportion of high-frequency components in the sound spectrum, reflecting the harshness of the sound. It is measured in acuum and is calculated using the Aures model.

[0061] Roughness (R) is a parameter that describes the subjective roughness caused by sound amplitude modulation (20-300Hz), and its unit is asper.

[0062] Fluctuation strength (F) is a parameter that describes the subjective sense of fluctuation caused by low-frequency modulation (<20Hz) of sound, and its unit is vacil.

[0063] The Articulation Index (AI) describes the degree to which background noise interferes with speech communication. It is obtained by looking up the percentage of each speech band noise spectrum line in a table and summing them up. The unit is %.

[0064] Among them, psychoacoustic annoyance (PA) is a quantitative index of noise annoyance calculated by comprehensively considering loudness, sharpness, roughness and fluctuation, and is calculated using the Zwicker psychoacoustic annoyance model.

[0065] In this disclosure, the sound quality calculation can be implemented using MATLAB. Based on the one-third octave band sound pressure level spectrum of the target cabin cavity subsystem, the following sound quality parameters are calculated according to the following model: loudness N (ISO 532B standard, Zwicker model, unit: sone), sharpness S (Aures model, unit: acum), roughness R (Zwicker model, unit: asper), fluctuation F (Zwicker model, unit: vacil), clarity index AI (lookup table method, unit: %), and psychoacoustic annoyance PA (Zwicker psychoacoustic annoyance model).

[0066] The 1 / 3 octave band is a frequency band divided into 1 / 3 octaves. It is the standard frequency resolution for SEA simulation output and sound quality calculation. The 1 / 3 octave band is a commonly used logarithmic frequency division method in acoustic and vibration analysis: an octave (frequency ratio 2:1) is divided into 3 equal segments, with the ratio of the upper and lower limits of each segment being 2^(1 / 3) ≈ 1.26, and the bandwidth being approximately 26% of the center frequency. When conducting sound quality tests, the sound absorption coefficient of each frequency band is measured at 1 / 3 octaves (20Hz–20 kHz) to clarify the sound absorption capabilities of different parameters for low frequencies (250–500 Hz), mid frequencies (500–2 kHz), and high frequencies (2–8 kHz), thereby helping to guide material selection and structural design.

[0067] In some embodiments, S3 performs multiple sets of numerical simulations using the SEA model to obtain simulation results of sound quality parameters corresponding to each set of different independent variable parameters. This includes: obtaining a first scan range setting value for the wall panel structure parameters and a second scan range setting value for the acoustic parameters of the sound-absorbing material; taking values ​​for the wall panel structure parameters and the acoustic parameters of the sound-absorbing material according to a preset step size based on the first scan range setting value and the second scan range setting value to obtain multiple combinations of independent variable parameters; and performing multiple sets of numerical simulations using the SEA model based on the multiple combinations of independent variable parameters to obtain simulation results of sound quality parameters corresponding to each set of different independent variable parameters.

[0068] In some embodiments, the wall panel structural parameters include the thickness of the metal layer, and the first scanning range of the thickness of the metal layer is set to 0.45mm to 0.55mm; wherein, the step size of the thickness of the metal layer can be, for example, 0.01mm, with a total of 11 values.

[0069] In some embodiments, the acoustic parameters of the sound-absorbing material include the density of the sound-absorbing material, which may be glass wool. Accordingly, the second scanning range of the density of the sound-absorbing material (such as the density ρ of glass wool) is set to 10 kg / m³ to 20 kg / m³. The value step of the density ρ of glass wool may be, for example, 1 kg / m³, for a total of 11 values.

[0070] In this disclosure, multiple combinations of independent variable parameters can be obtained based on different scanning ranges and value step sizes, thereby forming a parameter combination matrix. This matrix is ​​composed of the Cartesian product of all design parameter values, with each row corresponding to a set of parameter combinations to be simulated.

[0071] In some embodiments, the calculation process for obtaining the simulation results of sound quality parameters through the SEA model includes: applying acoustic excitation to the aircraft to obtain different noise spectra corresponding to acoustic parameters of different sound-absorbing materials and different panel structure parameters; performing at least one of the following operations based on the noise spectrum; using the Zwicker loudness model to convert the noise spectrum of one-third octave band into the critical band; correcting the sub-band sound pressure level according to the transmission factor; calculating and integrating the characteristic loudness of each critical band from each characteristic loudness chart to obtain the loudness N of the noise; and obtaining the sharpness using the Aures sharpness model based on the loudness N, the characteristic loudness of each critical band, and the critical band weighting factor. S; Roughness R is obtained using the Zwicker roughness calculation model based on the critical band, modulation frequency, and time masking depth; Fluctuation F is obtained using the Zwicker fluctuation calculation model based on the critical band, modulation frequency, and time masking depth; When the sound pressure level of each band in the noise one-third octave band spectrum is between the upper and lower limits of the linguistic region, the percentage value of the sharpness index corresponding to the noise spectral line in each band is obtained by looking up a table, and the percentage values ​​of each band are accumulated to obtain the sharpness index AI; Psychoacoustic annoyance PA is obtained using the Zwicker psychoacoustic annoyance model based on loudness N, sharpness S, roughness R, and fluctuation F.

[0072] Through the above simulation calculations, this disclosure can obtain various sound quality parameters such as loudness N, sharpness S, roughness R, fluctuation F, clarity index AI, and psychoacoustic annoyance PA.

[0073] S4. Based on the preset sound quality target screening strategy, the simulation results are screened to obtain the target independent variable parameters that meet the sound quality target requirements.

[0074] In this disclosure, by summarizing the simulation calculation results of all sound quality parameters corresponding to the parameter combination matrix, sound quality target screening is performed, and recommended design parameters or recommended design ranges are output based on the screening results.

[0075] In some embodiments, S4 filters the simulation results based on a preset sound quality target screening strategy to obtain target independent variable parameters that meet the sound quality target requirements, including: obtaining a dual-target screening strategy for sound quality target screening, wherein the dual-target screening strategy is constructed based on the clarity index AI and psychoacoustic annoyance level PA; and filtering the simulation results based on the dual-target screening strategy to obtain target independent variable parameters that meet the sound quality target requirements.

[0076] In some embodiments, the simulation results are screened based on the dual-objective screening strategy to obtain target independent variable parameters that meet the sound quality target requirements, including: screening sound quality parameters from the simulation results whose clarity index AI is greater than or equal to a preset threshold to obtain a candidate set; determining the result with the minimum psychoacoustic annoyance level PA from the candidate set as the target sound quality parameter; and determining the wall panel structure parameters and sound-absorbing material acoustic parameters corresponding to the target sound quality parameter as the target independent variable parameters.

[0077] In some embodiments, this disclosure obtains a multi-objective trade-off design atlas based on simulation results: with psychoacoustic annoyance level PA as the vertical axis and clarity index AI as the horizontal axis, all calculation results of the parameter combination matrix are visualized as a two-dimensional design atlas (two-dimensional scatter plot) to visualize the parameter design space and recommended design interval. By marking the threshold boundary line and recommended design interval, designers can intuitively identify the design space and flexibly select the optimal parameter interval.

[0078] For ease of understanding, this disclosure uses the simulation design of aircraft cabin SEA acoustic quality with sidewall aluminum alloy layer thickness and glass wool density as design parameters as a specific embodiment to explain the technical solution of this disclosure. This embodiment specifically includes the following steps: Step 1 (Establish SEA Model): In VA One, establish an SEA acoustic-structural coupling model for the aircraft compartment, divide it into 119 subsystems, and set the material properties, structural type, and connection method for each subsystem.

[0079] Step 2 (Applying excitation and setting parameters): Using the measured noise from the sidewall near the wing in the middle section of the aircraft cabin as the excitation source, a diffuse sound field excitation is set on each side of the sidewall panel at row 13. The design parameter range is set as follows: the total sidewall thickness h remains constant at 1.2mm; the aluminum alloy layer thickness h1 scan range is 0.45mm~0.55mm (step size 0.01mm, 11 values ​​in total); the glass wool density ρ scan range is 10~20kg / m³ (step size 1kg / m³, 11 values ​​in total). The total number of parameter combinations is 11 (aluminum alloy series) + 11 (glass wool series).

[0080] Step 3 (Batch Parametric Solving): MATLAB performs the following operations sequentially on each set of parameters through the API interface: connect and initialize → create database → associate model file → open database → address laminate object → write current h1 → address glass wool object → write current ρ → call solver → read one-third octave band sound pressure level spectrum of cabin cavity subsystem → close database.

[0081] Step 4 (Sound Quality Calculation): Calculate six sound quality parameters for each set of parameters in the cabin noise spectrum: loudness (N), sharpness (S), roughness (R), fluctuation (F), clarity index (AI), and psychoacoustic annoyance level (PA). The specific calculation basis is as follows: Loudness N describes the intensity of a sound, measured in sone. The loudness model proposed by Zwicker is used to calculate the loudness of a steady-state noise signal. The noise spectrum of one-third octave band is transformed into the critical band (i.e., the Bark domain). The sub-band sound pressure level is corrected according to the transmission factor. The characteristic loudness of each critical band is calculated from the characteristic loudness charts and integrated to obtain the total loudness of the noise. This disclosure uses a loudness calculation model programmed according to the international standard ISO 543B (equivalent to DIN 45631).

[0082] Sharpness (S) is a physical quantity describing the proportion of high-frequency components in the sound spectrum, reflecting people's subjective perception of high-frequency sounds, and is measured in acumen (acum). This disclosure uses the Aures sharpness model: (1.) in, For sharpness, For total loudness, The characteristic loudness of each critical frequency band is determined by the critical frequency band weighting factor as follows: (2.) Both roughness R and volatility F are used to reflect a person's subjective perception of the amplitude modulation of sound. Volatility is suitable for evaluating low-frequency modulation below 20Hz, and its unit is vacil. The Zwicker volatility calculation model is as follows: (3.) Roughness is suitable for evaluating sounds with modulation frequencies between 20Hz and 300Hz, and its unit is asper. The Zwicker roughness calculation model is as follows: (4.) In equations (3) and (4), For modulation frequency, It is the time masking depth, which is generally expressed by substituting the amplitude of the sound pressure level change in each critical frequency band. calculate.

[0083] The psychoacoustic annoyance level (PA) characterizes the level of annoyance associated with noise. The Zwicker psychoacoustic annoyance level is calculated by substituting psychoacoustic parameters such as loudness, roughness, sharpness, and fluctuation intensity into the psychoacoustic annoyance level model. The Zwicker psychoacoustic annoyance level model calculation formula is as follows: (5.) in, It is the cumulative percentage loudness, defined as the loudness exceeding a certain threshold over the total measurement time. The time occupied is 5%. Since the simulation does not have the concept of measuring duration, the loudness N is directly substituted into the calculation here. The impact of sharpness in the annoyance level model can be calculated using equation (6): (6.) The influence of noise roughness and fluctuation on annoyance level can be calculated according to formula (7): (7.) The Clarity Index (CI) describes the degree to which noise affects the clarity of human speech. The size of the CI is related to the background noise and its frequency characteristics. When the sound pressure level of each frequency band in the one-third octave band of the noise spectrum is between the upper and lower limits of the speech region, the percentage value of the CI corresponding to the noise spectral line in each frequency band can be obtained by looking up a table. The total CI value can be obtained by summing up the percentage values ​​of each frequency band.

[0084] Typical calculation results are as follows: Table 1. Effects of Aluminum Alloy Layer Thickness h1 on Sound Quality Parameters Figure 6 The acoustic quality parameters provided in this embodiment of the disclosure vary with the thickness h1 of the aluminum alloy layer, as shown in the following figure. Figure 6 As shown, there are 5 sub-figures, with the horizontal axis being h1 (0.45–0.55 mm) and the vertical axes being: (a) loudness N / sone, (b) sharpness S / acum, (c) roughness R / asper, (d) clarity index AI / %, and (e) psychoacoustic annoyance PA. Refer to Table 1 and... Figure 6 ★ represents the minimum psychoacoustic annoyance level (h1=0.54mm); ▲ represents the abrupt change point in the clarity index (h1=0.52mm, where the clarity index drops from 80.07% to 78.19%).

[0085] Table 2. Effects of glass wool density ρ on acoustic quality parameters Figure 7 The following is a graph showing the trend of sound quality parameters as a function of glass wool density ρ, provided in the embodiments of this disclosure. Figure 7 As shown, there are 5 sub-plots, with the horizontal axis representing ρ (10-20 kg / m³), and the vertical axes representing: (a) loudness N / sone, (b) sharpness S / acum, (c) roughness R / asper, (d) clarity index AI / %, and (e) psychoacoustic annoyance PA, respectively, demonstrating the monotonic changing trend and decreasing rate of change of each parameter. Refer to Table 2 and... Figure 7★ represents the extreme value within the parameter scanning range (both loudness and annoyance reach their minimum values ​​when ρ=20kg / m³).

[0086] Table 3 Comparison of the relative changes in the impact of the two types of design parameters on sound quality Referring to Table 3, 6.06% and 5.31% indicate that the thickness of the aluminum alloy layer has a significantly better effect on improving sharpness and roughness than the density of glass wool.

[0087] Step 5 (Dual-objective screening and output): Set a clarity index threshold (e.g., AI ≥ 79.80%), screen candidate combinations that meet the threshold from all parameter combinations, and then select the one with the lowest psychoacoustic annoyance level (PA) from the candidate set as the recommended design scheme.

[0088] Simultaneously, a multi-objective trade-off design graph (AI on the horizontal axis, PA on the vertical axis, with each parameter combination corresponding to a scatter point, and the graph marked with a threshold dashed line and a recommended design interval shaded) and a ranking of the sensitivity of each parameter to sound quality indicators are generated.

[0089] Figure 8 A multi-objective trade-off design diagram provided for embodiments of this disclosure, such as Figure 8 As shown, the multi-objective trade-off design graph is a scatter plot, with the horizontal axis representing the sharpness index AI (%) and the vertical axis representing the psychoacoustic annoyance level PA. The aluminum alloy thickness series is marked with circles, and the glass wool density series is marked with squares. The preset sharpness index threshold (79.80%) is used as the vertical dashed line, and the candidate set that meets the constraints to the right of the threshold is marked with a shaded area.

[0090] Optionally, the input excitation in this disclosure can be replaced with the numerical simulation results of wall acoustic load based on cruise conditions, or approximated by standard diffuse sound field excitation, while the simulation calculation process remains unchanged.

[0091] Optionally, the sound-absorbing material in this disclosure can be replaced with other fibrous or porous sound-absorbing materials (such as rock wool or polyurethane foam), with density or flow resistance as the parameterized scanning variable, while the calculation process and sound quality evaluation method remain unchanged.

[0092] Optionally, the external computing platform in this disclosure can be replaced with other computing platforms with API call capabilities, such as Python; the simulation software can be replaced with other acoustic and vibration simulation software with SEA calculation and API interface (such as AutoSEA, Actran, etc.).

[0093] Optionally, the loudness model in the sound quality calculation in this disclosure can be replaced with the Moore-Glasberg model (ISO532-2), while the core bi-objective screening logic remains unchanged.

[0094] Optionally, the panel structure parameters in this disclosure can be extended to damping layer material parameters (such as loss factor) and metal layer materials (such as titanium alloy laminates), while the overall process of parametric scanning and sound quality evaluation remains unchanged.

[0095] Compared with the prior art, this disclosure has the following beneficial effects: 1. Upgraded acoustic evaluation dimensions to directly address the issue of passengers' subjective experience: This disclosure expands the evaluation indicators from A-weighted sound pressure level to six sound quality parameters: loudness (N), sharpness (S), roughness (R), fluctuation (F), clarity index (AI), and psychoacoustic annoyance (PA). This can accurately reflect passengers' subjective auditory experience from multiple dimensions such as loudness, harshness, time-varying fluctuations, and speech interference, overcoming the inconsistency between subjective and objective evaluation caused by existing technologies that only use sound pressure level for evaluation.

[0096] 2. Performance redistribution under weight constraints, closely aligning with practical engineering design needs: This disclosure employs a "reverse linkage constraint" mechanism. Under the premise of a fixed total sidewall thickness (1.2mm), it systematically explores the impact of the ratio of metal layer to damping layer thickness on sound quality. Compared to existing methods that increase panel thickness without weight constraints to reduce noise, the constraint design of this disclosure is closer to the engineering reality of aircraft weight reduction and can be directly used to guide the lightweight design of aircraft panel structures.

[0097] 3. Joint optimization of wall panel structural parameters and sound-absorbing material parameters reveals complementary design principles: This disclosure incorporates the thickness of the aluminum alloy layer in the side wall panel and the density of glass wool into the same design process. Simulation calculations reveal the differentiated contributions of these two types of parameters to various sound quality indicators: the thickness of the aluminum alloy layer significantly improves sharpness (relative change of 6.06%) and roughness (5.31%) better than the density of glass wool (0.95% and 0.84%, respectively), while the improvement of the clarity index (0.55%) by the density of glass wool can optimize the voice communication environment. This complementary principle provides a quantitative basis for designers to select parameters.

[0098] 4. Dual-objective screening balances comfort and voice communication quality: This disclosure proposes a two-step decision-making logic of "first AI≥threshold screening, then min(PA) optimization", which avoids the problem that optimizing annoyance may lead to substandard voice clarity, and also avoids the problem that optimizing clarity may lead to increased annoyance. It achieves simultaneous improvement of subjective comfort in noise and voice communication environment.

[0099] 5. Full-process automation, significantly improving design efficiency: This disclosure achieves full-process automation of parameter writing, batch simulation solving, spectrum extraction, sound quality calculation and design recommendation output through API interface. There is no need for manual intervention to modify parameters and export data in the simulation software interface one by one. When there are many parameter combinations, it saves a lot of repetitive operation time and significantly shortens the design cycle.

[0100] 6. Multi-objective trade-off design graph provides intuitive decision support: This publication outputs a two-dimensional design graph with PA-AI as the dual axis. Designers can intuitively identify the recommended design range without having to look up the numerical results one by one, and can quickly obtain the corresponding optimal parameter combination according to different threshold settings, which greatly reduces the cognitive threshold of sound quality driven design.

[0101] The simulation design method for aircraft acoustic structure parameters provided in this disclosure has the following direct application scenarios and reasonably inferred extended applications: (1) Acoustic design of cabin wall panels for civil aircraft: The most direct application of this disclosure is in the acoustic design stage of the cabin wall panels during aircraft development, providing aircraft manufacturers and suppliers with parameter optimization tools oriented towards passenger subjective comfort, which can replace the existing pure sound pressure level optimization method.

[0102] (2) Acoustic evaluation of aircraft upgrades and modifications: When performing acoustic modifications on existing aircraft, the impact of panel parameter adjustments on sound quality can be quickly evaluated, reducing the test cost of acoustic modifications.

[0103] (3) Noise reduction design of cabins of other transportation vehicles: The method framework disclosed herein can be extended to the acoustic design of wall panels of other transportation vehicles such as high-speed rail carriages and ship cabins. The sound quality evaluation and dual-objective screening process can be reused by replacing the corresponding SEA model.

[0104] (4) Design of building acoustic partition walls: The design concept of combining parametric simulation and sound quality evaluation disclosed herein can be extended to the combined design of sound absorption and sound insulation of building partition walls. The evaluation index can be replaced by building acoustic parameters (such as STI language transmission index).

[0105] (5) Sound quality simulation design platform: The method framework disclosed herein (SEA modeling → API batch solution → sound quality calculation → multi-objective screening → spectrum output) can be used as a general sound quality driven structural design platform. Combined with simulation software and sound quality standards of other industries (such as automobiles and home appliances), it can be extended to a wider range of product noise sound quality design scenarios.

[0106] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0107] Figure 9 A schematic diagram of an electronic device provided in an embodiment of this disclosure, such as... Figure 9 As shown, in some embodiments, this disclosure provides an electronic device including: one or more processors 201; a memory 202 storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the aircraft acoustic structure parameter simulation design method described above; and one or more I / O interfaces 203 connected between the processors and the memory, configured to enable information interaction between the processors and the memory.

[0108] The processor 201 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 202 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 203 is connected between the processor 201 and the memory 202, enabling information exchange between the processor 201 and the memory 202, including but not limited to a data bus (Bus).

[0109] In some embodiments, the processor 201, memory 202, and I / O interface 203 are interconnected via bus 204, and thus connected to other components of the computing device.

[0110] In some embodiments, this disclosure provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for simulating and designing aircraft acoustic structural parameters.

[0111] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0112] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in connection with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in connection with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A simulation design method for aircraft acoustic structural parameters, characterized in that, The method includes: Obtain the independent variable parameters for the simulation design of the aircraft acoustic structure. The independent variable parameters include the structural parameters of the aircraft wall panels and the acoustic parameters of the sound-absorbing materials on the inner side of the aircraft interior panels. The independent variable parameters are loaded as input conditions into the statistical energy analysis (SEA) model of the simulation software. Multiple sets of numerical simulations were performed using the SEA model to obtain simulation results of sound quality parameters corresponding to each set of different independent variable parameters. Based on a preset sound quality target screening strategy, the simulation results are screened to obtain target independent variable parameters that meet the sound quality target requirements.

2. The simulation design method for aircraft acoustic structure parameters according to claim 1, characterized in that, The process of performing multiple sets of numerical simulations using the SEA model to obtain simulation results for sound quality parameters corresponding to each set of different independent variable parameters includes: Obtain the first scan range setting value of the wall panel structural parameters and the second scan range setting value of the acoustic parameters of the sound-absorbing material; Based on the first scanning range setting value and the second scanning range setting value, the wall panel structural parameters and the acoustic parameters of the sound-absorbing material are respectively set according to a preset step size to obtain multiple combinations of independent variable parameters; Based on the combination of multiple independent variable parameters, multiple sets of numerical simulations are performed using the SEA model to obtain simulation results of sound quality parameters corresponding to each set of different independent variable parameters.

3. The simulation design method for aircraft acoustic structure parameters according to claim 2, characterized in that, The structural parameters of the wall panel include at least one of the following: metal layer material, metal layer thickness, and damping layer material parameters; the acoustic parameters of the sound-absorbing material include at least one of the following: density and flow resistance.

4. The simulation design method for aircraft acoustic structure parameters according to claim 3, characterized in that, The wall panel structural parameters include the thickness of the metal layer, and the first scanning range of the thickness of the metal layer is set to 0.45mm to 0.55mm; The acoustic parameters of the sound-absorbing material include the density of the sound-absorbing material, and the second scanning range of the density of the sound-absorbing material is set to 10 kg / m³ to 20 kg / m³.

5. The simulation design method for aircraft acoustic structural parameters according to any one of claims 1-4, characterized in that, The sound quality parameters include at least one of loudness N, sharpness S, roughness R, fluctuation F, clarity index AI, and psychoacoustic annoyance PA.

6. The simulation design method for aircraft acoustic structure parameters according to claim 5, characterized in that, The calculation process for obtaining the simulation results of acoustic quality parameters through the SEA model includes: By applying acoustic excitation to the aircraft, different noise spectra corresponding to different acoustic parameters of different sound-absorbing materials and different wall panel structural parameters are obtained, and at least one of the following operations is performed based on the noise spectrum; The Zwicker loudness model is used to transform the noise spectrum of one-third octave into the critical band. The sub-band sound pressure level is corrected according to the transmission factor. The characteristic loudness of each critical band is calculated from each characteristic loudness chart and integrated to obtain the loudness N of the noise. Based on the loudness N, the characteristic loudness of each critical frequency band, and the critical frequency band weighting factor, the sharpness S is obtained using the Aures sharpness model. Based on the critical frequency band, modulation frequency, and time masking depth, the roughness R is obtained using the Zwicker roughness calculation model. Based on the critical frequency band, modulation frequency, and time masking depth, the volatility F is obtained using the Zwicker volatility calculation model. When the sound pressure level of each frequency band in the one-third octave band of the noise spectrum is between the upper and lower limits of the speech region, the percentage value of the sharpness index corresponding to the noise spectral line in each frequency band is obtained by looking up the table, and the percentage values ​​of each frequency band are added together to obtain the sharpness index AI. The psychoacoustic annoyance level PA is obtained using the Zwicker psychoacoustic annoyance level model based on the loudness N, the sharpness S, the roughness R, and the fluctuation F.

7. The simulation design method for aircraft acoustic structure parameters according to claim 5, characterized in that, The simulation results are filtered based on a preset sound quality target screening strategy to obtain target independent variable parameters that meet the sound quality target requirements, including: A dual-target screening strategy for sound quality target selection is obtained, wherein the dual-target screening strategy is constructed based on the clarity index AI and the psychoacoustic annoyance level PA; Based on the dual-objective screening strategy, the simulation results are screened to obtain the target independent variable parameters that meet the sound quality target requirements.

8. The simulation design method for aircraft acoustic structure parameters according to claim 7, characterized in that, The simulation results are filtered based on the dual-objective screening strategy to obtain target independent variable parameters that meet the sound quality target requirements, including: From the simulation results, select sound quality parameters whose sharpness index AI is greater than or equal to a preset threshold to obtain a candidate set; The result that minimizes the psychoacoustic annoyance level PA from the candidate set is taken as the target acoustic quality parameter; The wall panel structural parameters and the acoustic parameters of the sound-absorbing material corresponding to the target sound quality parameters are determined as the target independent variable parameters.

9. An electronic device, characterized in that, include: One or more processors; A memory having stored one or more programs that, when executed by one or more processors, cause the one or more processors to implement the simulation design method for aircraft acoustic structural parameters according to any one of claims 1 to 8.

10. A computer-readable medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the simulation design method for aircraft acoustic structural parameters according to any one of claims 1 to 8.

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