Design method of noise reduction fairing

Abstract

The invention discloses a design method of a noise reduction fairing, which comprises the following steps: determining a performance demand parameter of the noise reduction fairing, and constructing an initial noise reduction model based on a noise spectrum of a target application scene and an air duct structure defect; structural parameters are designed according to the performance requirement parameters; establishing a fluid-solid-sound coupled parameterized model of the layered composite structure and the flow guide assembly, and performing iterative optimization to generate an optimized configuration of the noise reduction fairing; a fairing prototype is manufactured based on the optimized configuration, actual measurement data are collected, simulation errors are corrected through an empirical regression model, and boundary conditions of a parameterized model are updated; the corrected noise reduction fairing is disassembled into modular assemblies, and an assembly process based on modularization is designed; in the assembling process of the modular assembly, a digital positioning system is adopted to be matched with an assembly interface; through the collaborative design of the layered composite structure and the diversion assembly and the module assembly design of the manufacturing link, the balance of broadband noise suppression and heat dissipation efficiency is realized, and the overall performance is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fairings, and particularly to a design method for a noise-reducing fairing. Background Art

[0002] With the popularization of high-power LED displays in outdoor advertising, command and monitoring, stage performances and other scenarios, the contradiction between their heat dissipation requirements and noise control has become increasingly prominent. The increase in the power density of LED modules has led to the need for large-flow fans or complex air ducts in the heat dissipation system. However, the broadband noise (500 Hz - 2 kHz) generated by high-speed airflows and mechanical vibrations seriously interferes with quiet and sensitive environments such as indoor meetings and medical examinations. Traditional heat dissipation solutions often maintain heat dissipation efficiency by increasing the air volume or simplifying the air duct structure, but this results in a noise level exceeding the standard by >65 dB, restricting the scene adaptability of high-end display devices.

[0003] Existing noise-reducing fairing technologies mostly adopt single-function designs: either wrapping the noise source with porous sound-absorbing materials (such as glass wool) to reduce high-frequency noise, or using a flow deflector to optimize the air flow path to reduce turbulent noise. However, the sound-absorbing materials significantly increase the air duct resistance (drag coefficient >0.5), forcing the heat dissipation system to increase the fan speed, which instead exacerbates the low-frequency vibration noise; while the structural reinforcement of the flow deflector can suppress turbulence, its rigid connection easily conducts vibration energy and lacks active adaptation to broadband noise. The core problem is that existing technologies regard noise reduction and heat dissipation as independent goals and lack a collaborative design mechanism, resulting in noise reduction measures significantly sacrificing heat dissipation efficiency and being unable to meet the rigid requirements of high-power LED devices for a "low-noise and high-efficiency" heat dissipation system.

[0004] In view of this, it is necessary to improve the fairing technology in the existing technology to solve the technical problem that it is difficult to balance the noise reduction and heat dissipation performance of high-power devices. Summary of the Invention

[0005] The purpose of the present invention is to provide a design method for a noise-reducing fairing to solve the above technical problems.

[0006] To achieve this purpose, the present invention adopts the following technical solutions: A design method for a noise-reducing fairing, comprising: Determining the performance requirement parameters of the noise-reducing fairing, and constructing an initial noise reduction model based on the noise spectrum of the target application scenario and the defects of the air duct structure; Designing the structural parameters of the layered composite structure and the flow deflector assembly according to the performance requirement parameters; Establishing a parametric model of the fluid-structure-acoustic coupling of the layered composite structure and the flow deflector assembly, and using a multi-objective optimization algorithm to iteratively optimize the structural parameters to generate an optimized configuration of the noise-reducing fairing; Manufacture a fairing prototype based on the optimized configuration, collect measured data and correct the simulation error through an empirical regression model, and update the boundary conditions of the parametric model; Split the corrected noise reduction fairing into modular components, and design a modular-based assembly process, where the modular components include an acoustic absorption module, a flow guiding module, and a heat conduction module; During the assembly process of the modular components, use a digital positioning system to match the component interfaces and fill the composite medium layer of acoustic absorption and heat conduction.

[0007] Optionally, to determine the performance requirement parameters of the noise reduction fairing, construct an initial noise reduction model based on the noise spectrum and duct structure defects of the target application scenario, specifically including: Based on the noise source distribution and spectrum characteristics of the target application scenario, locate the noise hot spots through an acoustic array and extract the main noise frequency bands to generate a noise energy distribution map; Conduct a flow field simulation and modal analysis on the duct structure of the target application scenario, identify the noise amplification areas caused by turbulence and resonance, and establish a duct defect characteristic model; Combine the noise energy distribution map and the duct defect characteristic model to define the quantitative performance requirement parameters of the noise reduction fairing. The quantitative performance requirement parameters include the target noise reduction frequency band, the maximum allowable wind resistance coefficient, and the heat conduction efficiency threshold; Based on the performance requirement parameters, construct an initial noise reduction model and associate it with the duct defect characteristic model; the initial noise reduction model includes noise propagation paths, sound absorption and insulation materials, equivalent impedance, and flow guiding structure control equations; Verify the effectiveness of the initial noise reduction model through benchmark tests, and correct the deviation of the initial noise reduction model by using the combined simulation of acoustic transmission loss and computational fluid dynamics, and output the calibrated initial noise reduction model.

[0008] Optionally, the layered composite structure includes an acoustic absorption layer, a damping layer, and a heat conduction layer, and the flow guiding component includes flow guiding ribs and a support frame; Among them, the acoustic absorption layer, the damping layer, and the heat conduction layer are arranged from the outer layer to the inner layer in sequence; The flow guiding ribs are distributed in a spiral form on the inner wall of the support frame, and the support frame is provided with a plurality of honeycomb-shaped strengthening holes.

[0009] Optionally, to establish a parametric model of the fluid-structure-acoustic coupling of the layered composite structure and the flow guiding component, use a multi-objective optimization algorithm to iteratively optimize the structural parameters to generate an optimized configuration of the noise reduction fairing, specifically including: Define the key design parameters of the layered composite structure and the flow guiding component. The key design parameters include the porosity of the acoustic absorption layer, the density gradient of the damping layer, the inclination angle of the flow guiding ribs, and the honeycomb aperture of the support frame, and associate them with the input variables of the fluid-structure-acoustic coupling model; Construct a parametric model based on the key design parameters, equivalent the hierarchical composite structure to a coupled domain of porous medium-viscoelastic material, embed the flow guiding component into the hydrodynamic equation and the structural vibration equation, and establish a three-dimensional coupled solver for the flow field-acoustic field-structural field; Use the cubic sampling method to generate a design space sample set, batch calculate the noise reduction amount and wind resistance coefficient of each sample in the space sample set through the three-dimensional coupled solver, and output the response surface of the multi-objective performance; Train an adaptive surrogate model based on the response surface, use the interpolation algorithm to predict the performance of unsampled points, and construct the Pareto front set; Optionally, after training the adaptive surrogate model based on the response surface, using the interpolation algorithm to predict the performance of unsampled points, and constructing the Pareto front set, it further includes: Use the objective optimization algorithm to iteratively optimize the Pareto front set, introduce a dynamic weight strategy to balance the noise reduction and heat dissipation objectives, and screen out the optimal parameter combination that meets the constraint conditions; Verify the optimal parameter combination with a preset multi-physical field evaluation model, jointly evaluate through the acoustic transmission loss index TL and the turbulent kinetic energy dissipation rate TDR, and generate the optimized configuration and the corresponding confidence index.

[0010] Optionally, after manufacturing the fairing prototype based on the optimized configuration, collecting the measured data and correcting the simulation error through an empirical regression model, and updating the boundary conditions of the parametric model, specifically including: Based on the optimized configuration, use additive manufacturing and composite material lamination process to manufacture the fairing prototype, and calibrate the actual processing error of the key design parameters; Build a test platform, collect noise spectrum, air duct flow velocity distribution and heat dissipation surface temperature gradient data under simulated working conditions, and generate a measured multi-source data set; Compare the measured data set with the simulation results of the multi-physical field evaluation model, extract the deviation matrix of the noise reduction amount, wind resistance coefficient and thermal conductivity, and mark the coupling area of the high-frequency sound absorption performance error and the low-frequency turbulence prediction error.

[0011] Optionally, after marking the coupling area of the high-frequency sound absorption performance error and the low-frequency turbulence prediction error, it further includes: Construct an empirical regression model based on the deviation matrix, train an error correction function through the random forest algorithm, and predict the dynamic compensation coefficients of the equivalent impedance of the sound absorption layer and the inclination angle of the flow guiding rib; Inversely map the dynamic compensation coefficients to the boundary conditions of the parametric model, update the porous medium permeability tensor and the turbulent wall function, and generate a corrected fluid-structure-acoustic coupling model; Verify the confidence level of the corrected fluid-structure-acoustic coupling model. If the prediction error of the noise reduction amount ≤ 1.5 dB and the error of the drag coefficient ≤ 0.05, lock the final model parameters; otherwise, iteratively execute the update and optimization of the boundary conditions.

[0012] Optionally, split the corrected noise reduction fairing into modular components and design a modular-based assembly process, which specifically includes: Based on the configuration of the corrected noise reduction fairing, split the fairing into an acoustic absorption module, a flow guiding module, and a heat conduction module according to the functional partition, and define the geometric and mechanical coupling constraint conditions of the interfaces between the modules; For the interface characteristics of the modular components, design an assembly connection structure, including a positioning card slot, a bolt group with adjustable pre-tightening force, and a heat conduction medium filling channel, and generate a module code and an assembly relationship matrix; According to the assembly relationship matrix, formulate a modular assembly process flow, including the sequence and tolerance control threshold of the lamination and curing of the acoustic absorption module, the angle calibration of the flow guiding module, and the hot pressing and bonding of the heat conduction module; Optimize the matching accuracy and assembly path of the assembly connection structure, and output modular process files suitable for equipment with different power levels.

[0013] Optionally, during the assembly process of the modular components, use a digital positioning system to match the component interfaces and fill the composite medium layer of acoustic absorption and heat conduction, which specifically includes: Based on the assembly relationship matrix of the modular components, use a digital positioning system that integrates laser scanning and detection vision to scan the geometric morphology and spatial position of the module interfaces, and generate a three-dimensional matching coordinate system; According to the three-dimensional matching coordinate system, plan the filling path of the composite medium layer of acoustic absorption and heat conduction, and design a gradient density distribution structure, where the proportion of the acoustic absorption medium decreases from the module interface to the core area, and the proportion of the heat conduction medium increases; Use an injection molding device to simultaneously inject an acoustic absorption microsphere-silicone composite material and a graphene-ceramic heat conduction slurry according to the filling path, and form an interlayer interlocking structure through an in-situ curing process; Perform a joint acoustic-thermal performance measurement on the filled composite medium layer to determine whether the sound absorption coefficient reaches the preset coefficient value and the thermal conductivity reaches the preset thermal conductivity threshold. If so, determine that the assembly is qualified; otherwise, perform local supplementary injection to correct the medium.

[0014] Compared with the prior art, the present invention has the following beneficial effects: First, define the performance requirements based on the noise spectrum of the target scenario and the duct defects, and construct an initial model. Design the structural parameters of the composite structure including the sound-absorbing layer, damping layer, and heat-conducting layer, and the diversion component including the diversion ribs and support frame. Then, establish a fluid-structure-acoustic coupling parametric model, and use the multi-objective optimization algorithm to iteratively optimize the structural parameters to generate an optimized configuration. Based on the optimization results, produce a prototype and collect measured data, and correct the simulation error through an empirical regression model to update the model boundary conditions. Split the fairing into modular components of sound absorption, diversion, and heat conduction, and complete the adaptive assembly by combining the digital positioning system and composite medium filling to form a closed-loop design process. This method realizes the balance between broadband noise suppression and heat dissipation efficiency through the collaborative design of the layered composite structure and the diversion component, combined with parametric modeling and model correction of simulation errors in the design process, and modular assembly design in the manufacturing process, while reducing the processing difficulty of complex structures and improving the overall performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] The structures, ratios, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limited conditions under which the present invention can be implemented. Therefore, they do not have a substantial technical meaning. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.

[0017] Figure 1 It is one of the flow diagrams of the design method of the noise reduction fairing for this embodiment; Figure 2 It is the second flow diagram of the design method of the noise reduction fairing for this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to make the invention purpose, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the following described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. It should be noted that when a component is considered to be "connected" to another component, it can be directly connected to the other component or there may be an intermediate component present.

[0020] The technical solution of the present invention will be further described below in conjunction with the drawings and specific embodiments.

[0021] Combined Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a design method for a noise reduction fairing, including: S1. Determine the performance requirement parameters of the noise reduction fairing, and construct an initial noise reduction model based on the noise spectrum of the target application scenario and the duct structure defect; Through noise spectrum analysis and duct defect modeling, locate the core problems to be solved by the noise reduction fairing (such as high-frequency airflow whistling, low-frequency vibration), and avoid design redundancy. The construction of the initial model combines acoustic, fluid and structural parameters, provides a quantitative benchmark for subsequent design, and reduces repeated costs.

[0022] S2. According to the performance requirement parameters, design the structural parameters of the layered composite structure and the flow guiding component; the collaborative design of the layered composite structure (sound absorption, damping, heat conduction layer) and the flow guiding component (flow guiding rib, honeycomb support frame) realizes the physical decoupling of noise suppression and heat dissipation efficiency, and avoids the performance conflict of traditional single-functional materials.

[0023] S3. Establish a parametric model of the fluid-structure-acoustic coupling of the layered composite structure and the flow guiding component, and use a multi-objective optimization algorithm to iteratively optimize the structural parameters to generate an optimized configuration of the noise reduction fairing; The fluid-structure-acoustic coupling model combines a multi-objective optimization algorithm, breaks through the limitations of traditional empirical design, achieves the best between broadband noise reduction and low wind resistance, and improves the design accuracy.

[0024] S4. Manufacture a fairing prototype based on the optimized configuration, collect measured data and correct the simulation error through an empirical regression model, and update the boundary conditions of the parametric model; Through the comparative analysis of the measured data and the simulation results, use an empirical regression model (such as Gaussian process regression) to correct the boundary conditions, and significantly improve the model prediction accuracy.

[0025] S5. Split the corrected noise reduction fairing into modular components, and design a modular-based assembly process, where the modular components include an acoustic absorption module, a flow guiding module, and a heat conduction module; The modular design (separation of acoustic absorption, flow guiding, and heat conduction modules) supports rapid customization and mass production, reduces the processing difficulty of complex structures, and realizes cross-device compatibility through interface standardization.

[0026] S6. During the assembly process of the modular components, use a digital positioning system to match the component interfaces and fill the composite medium layer of acoustic absorption and heat conduction.

[0027] The digital positioning system ensures seamless docking between modules, and the gradient filling of the acoustic absorption-heat conduction composite medium further optimizes the local acoustic and thermal performance, avoiding interface thermal resistance and acoustic reflection problems.

[0028] The working principle of the present invention is as follows: First, define the performance requirements based on the noise spectrum and duct defects of the target scenario and construct an initial model, and design the structural parameters of the composite structure including the acoustic absorption layer, damping layer, and heat conduction layer, and the flow guiding component including the flow guiding ribs and support frames; then establish a fluid-structure-acoustic coupling parametric model, use a multi-objective optimization algorithm to iteratively optimize the structural parameters, and generate an optimized configuration; based on the optimization results, make a prototype and collect measured data, and correct the simulation error through an empirical regression model to update the model boundary conditions; split the fairing into acoustic absorption, flow guiding, and heat conduction modular components, and complete the adaptive assembly by combining a digital positioning system and composite medium filling to form a closed-loop design process; this method realizes the balance of broadband noise suppression and heat dissipation efficiency through the collaborative design of the layered composite structure and the flow guiding component, and combines parametric modeling and model correction of simulation errors in the design process, as well as modular assembly design in the manufacturing process, reduces the processing difficulty of complex structures, and improves the overall performance.

[0029] In this embodiment, specifically, step S1 specifically includes: S11. Based on the noise source distribution and spectral characteristics of the target application scenario, locate the noise hot spot area through an acoustic array and extract the main noise frequency band to generate a noise energy distribution map; Use acoustic array technology (forming a beamforming algorithm through the spatial distribution of multiple microphones to enhance the sound source signal in a specific direction and suppress environmental noise interference) to locate the spatial distribution of the noise source; combine the short-time Fourier transform (STFT) to decompose the time-frequency domain signal, extract the instantaneous spectrum of the non-stationary noise, analyze the main noise frequency band (such as 1 / 3 octave segmentation), and generate a noise energy distribution map. The noise energy distribution map is a two-dimensional heat map mapping of the sound pressure level energy in the frequency domain - spatial domain.

[0030] Through the joint spatial-frequency domain analysis, the core frequency band and location of the noise reduction target are clarified, avoiding the local deviation of traditional single-point measurement, and providing high-precision data support for subsequent designs.

[0031] S12. Conduct a flow field simulation and modal analysis on the air duct structure of the target application scenario to identify the noise amplification areas caused by turbulence and resonance, and establish a duct defect characteristic model. Based on the Navier-Stokes equations of computational fluid dynamics, solve the distribution of flow field velocity, pressure, and turbulent kinetic energy to identify high-turbulence areas (such as the vortex shedding area at a right-angle elbow). Through modal analysis, extract the natural frequencies and vibration modes of the structure, and solve the characteristic equation: [K] - ω²[M] = 0 to determine the resonance frequency band of the duct wall (such as the modal dense area near 800 Hz), and establish a duct defect characteristic model. For example, the defect area is marked as a coupling area with a turbulence intensity > 15% or a modal participation factor > 0.8.

[0032] S13. Combine the noise energy distribution map and the duct defect characteristic model to define the quantitative performance requirement parameters of the noise reduction fairing. The quantitative performance requirement parameters include the target noise reduction frequency band, the maximum allowable wind resistance coefficient, and the heat conduction efficiency threshold. Through multi-physical field coupling correlation analysis and calculation of the transfer function matrix of the sound-fluid-solid energy transfer path, map the noise energy map and the duct defect model to performance targets: The target noise reduction frequency band covers the main noise frequency band ± 1 / 3 octave, such as 500 Hz - 2 kHz. The maximum allowable wind resistance coefficient is inversely deduced from the Darcy-Weisbach formula Cd = 2ΔP / (ρv²) to obtain the flow velocity limit; the heat conduction efficiency threshold is based on Fourier's law combined with the heat dissipation power requirement.

[0033] S14. Based on the performance requirement parameters, construct an initial noise reduction model and correlate it with the duct defect characteristic model. The initial noise reduction model includes the noise propagation path, sound absorption and insulation materials, equivalent impedance, and flow guiding structure control equations. Construct a multi-physical field coupling initial model, integrating the noise propagation path (acoustic wave reflection and transmission), equivalent impedance of sound absorption and insulation materials (acoustic absorption characteristics of porous media), and flow guiding structure control equations (aerodynamic laws). Embed the duct defect characteristics (such as turbulence intensity threshold and resonance frequency) into the model boundary conditions through parametric scripts to achieve the dynamic correlation between the simulation environment and the actual working conditions.

[0034] The initial model integrates the multi-field coupling effects of sound, flow, and solid, and highly accurately simulates the real working state of the fairing, providing a reliable benchmark for subsequent optimization and significantly shortening the design iteration cycle.

[0035] S15. Verify the effectiveness of the initial noise reduction model through benchmark testing, correct the deviation of the initial noise reduction model by using the combined simulation of acoustic transmission loss and computational fluid dynamics, and output the calibrated initial noise reduction model.

[0036] Through the acoustic transmission loss (TL) test and CFD combined simulation, compare the model prediction values with the measured data (such as noise reduction amount, flow velocity distribution). Adopt the residual optimization algorithm to inversely correct the material parameters (such as damping coefficient, porous medium permeability) and grid convergence conditions in the model, reduce the deviation between the simulation and the actual situation, and output the calibrated high-confidence model.

[0037] Solve the problem of model inaccuracy caused by material anisotropy and processing errors, improve the engineering applicability of the design scheme, ensure that the performance of the subsequent prototype is highly consistent with the simulation results, and reduce the trial production risk.

[0038] In this embodiment, specifically, the layered composite structure includes an acoustic absorption layer, a damping layer and a heat conduction layer, and the flow guiding component includes flow guiding ribs and a support frame; this part of the structure is specifically described as: The acoustic absorption layer is located on the outermost layer of the fairing, uses a micro-perforated aluminum plate, directly contacts the external airflow noise source, and preferentially absorbs high-frequency noise; The damping layer is sandwiched between the acoustic absorption layer and the heat conduction layer, uses gradient density polyurethane foam, suppresses medium and low-frequency vibration noise and blocks the sound bridge transmission; The heat conduction layer is attached to the inner wall of the fairing, uses graphene-silica gel composite material (thermal conductivity ≥ 5W / m·K), and conducts the heat in the equipment heating area to the air flow in the air duct.

[0039] Flow guiding component setting: The flow guiding ribs are distributed on the inner wall of the support frame in a spiral form (tilt angle 10° - 20°), guiding the airflow to turn smoothly and reducing the vortex noise caused by turbulent separation; The support frame adopts a hexagonal honeycomb aluminum structure, which serves as the load-bearing skeleton of the fairing. The honeycomb holes are filled with acoustic absorption media (such as polyurethane microspheres), which has both lightweight and broadband acoustic absorption functions.

[0040] In this embodiment, specifically, step S3 specifically includes: S31. Define the key design parameters of the layered composite structure and the flow guiding component. The key design parameters include the porosity of the acoustic absorption layer, the density gradient of the damping layer, the tilt angle of the flow guiding ribs, and the honeycomb pore diameter of the support frame, and associate them with the input variables of the fluid-structure-acoustic coupling model; Through parametric modeling technology, map the physical structure features into adjustable variables, such as porosity, density gradient, inclination angle, pore size, and combine with the multi-physics coupling interface to define the bidirectional transfer function of material properties and boundary conditions in COMSOL, associate the parameters of the sound absorption layer, damping layer, flow guiding ribs, and honeycomb frame with the input variables of the fluid-structure-acoustic coupling model, and realize the dynamic binding of design parameters and simulation models.

[0041] By parametrically defining the key influencing factors covering noise reduction and heat dissipation (such as porosity regulating the sound absorption frequency band and inclination angle suppressing turbulence), avoid the parameter blind spots of traditional empirical design, and improve the pertinence of subsequent optimization.

[0042] S32, construct a parametric model based on the key design parameters, equivalent the layered composite structure to a coupled domain of porous medium-viscoelastic material, embed the flow guiding component into the fluid mechanics equation and the structural vibration equation, and establish a three-dimensional coupled solver for the flow field-acoustic field-structural field; Based on the porous medium-viscoelastic coupling theory (the Biot equation describes the propagation of sound waves in porous materials, and the Maxwell model characterizes the viscoelastic energy dissipation of the damping layer), equivalent the layered composite structure to a multi-physics field domain; the flow guiding component is embedded into the model through the Reynolds-averaged Navier-Stokes equation (fluid mechanics) and the Euler-Bernoulli beam equation (structural vibration), and a full coupled solver for fluid-acoustic-solid is established.

[0043] The full coupled model accurately quantifies the interaction effects of sound energy attenuation, airflow resistance, and structural vibration, provides a high-fidelity simulation basis for multi-objective optimization, and avoids the simplification errors of traditional single-field models.

[0044] S33, use the cubic sampling method to generate a sample set of the design space, batch calculate the noise reduction amount and wind resistance coefficient of each sample in the space sample set through the three-dimensional coupled solver, and output the response surface of multi-objective performance; Use the Latin hypercube sampling method to evenly distribute sample points in the N-dimensional design space, ensure that each parameter combination has no overlap and covers the entire domain, and generate 200-500 groups of design parameter samples; batch calculate the noise reduction amount (TL) and wind resistance coefficient (Cd) of each sample through a distributed computing cluster (HPC parallel calls the coupled solver), construct a multi-objective response surface, and interpolate to generate a continuous performance distribution cloud map.

[0045] Efficiently explore the design space with the minimum sample size, and the response surface intuitively shows the trade-off relationship between noise reduction and heat dissipation, providing high-density data support for surrogate model training.

[0046] S34, train an adaptive surrogate model based on the response surface, use the interpolation algorithm to predict the performance of unsampled points, and construct a frontier solution set; Based on the interpolation algorithm, Gaussian process regression is combined with the spatial correlation function to predict the performance of unsampled points \(y(x)=\mu + Z(x)\), where \(Z(x)\) is a Gaussian random field. An adaptive surrogate model is trained, and sampling points are dynamically added according to the prediction variance; the Pareto sorting (non-dominated solution screening, defining the front surface of the solution set) is used to construct the noise reduction - heat dissipation double-objective front solution set.

[0047] S35. The target optimization algorithm is used to iteratively optimize the front solution set. A dynamic weight strategy is introduced to balance the noise reduction and heat dissipation objectives, and the optimal parameter combination that meets the constraint conditions is screened. The target optimization algorithm (reference point-guided multi-objective genetic algorithm, maintaining population diversity) is adopted. A dynamic weight strategy is introduced. At the initial stage of iteration, the weight biases towards noise reduction, \(w1 = 0.7\), \(w2 = 0.3\), and in the later stage, it biases towards heat dissipation, then \(w1 = 0.4\), \(w2 = 0.6\). The optimal parameter combination that simultaneously satisfies \(TL\geq15dB\) and \(Cd\leq0.3\) is screened (constraint domination sorting and crowding distance calculation).

[0048] The dynamic weight avoids premature convergence of the algorithm and ensures that the solution set simultaneously approaches the global optimum of noise reduction and heat dissipation; the parameter combination can directly guide engineering manufacturing and reduce the later adjustment cost.

[0049] S36. The optimal parameter combination is verified by a preset multi-physical field evaluation model. Through the joint evaluation of the acoustic transmission loss index TL and the turbulent kinetic energy dissipation rate TDR, an optimized configuration and the corresponding confidence index are generated.

[0050] Based on the acoustic transmission loss index TL and the turbulent kinetic energy dissipation rate TDR, \(TDR = (\varepsilon / \rho)^{1 / 3}L / u^3\), quantifying the airflow energy loss, a multi-physical field evaluation model is constructed (\(TL\geq15dB\) and \(TDR\leq12\%\) are the qualified thresholds); the confidence index of the optimized configuration is calculated through Monte Carlo sampling (randomly perturbing the parameters by \(\pm5\%\) to generate 100 groups of samples) (the pass rate \(\geq95\%\) is determined to pass).

[0051] The dual-index verification ensures the reliability of the optimization results in terms of both acoustic and fluid performance; the confidence index quantifies the design reliability and reduces the mass production risk.

[0052] In this embodiment, specifically, step S4 specifically includes: S41. Based on the optimized configuration, an additively manufactured and composite laminated fairing prototype is made using additive manufacturing and composite lamination processes, and the actual machining errors of the key design parameters are calibrated. The additive manufacturing technology (such as selective laser sintering) is used to form complex flow guiding structures and honeycomb frameworks layer by layer to ensure high-precision geometric restoration; combined with the composite material lamination process (such as the lamination of vacuum-injected polyurethane foam and prepreg of micro-perforated aluminum plates), the integrated forming of the layered composite structure is realized. The actual processing errors of key parameters such as porosity and inclination angle are calibrated by a coordinate measuring machine and a laser scanner, and an error distribution database is established.

[0053] Additive manufacturing breaks through the limitations of traditional processing on complex structures, and composite material lamination ensures lightweight and functional integration; error calibration quantifies the influence of process fluctuations, provides reference data for subsequent model correction, and avoids design failures caused by error accumulation.

[0054] S42, Build a test platform, collect noise spectrum, air duct flow velocity distribution and heat dissipation surface temperature gradient data under simulated working conditions, and generate a measured multi-source data set; Build a multi-sensor synchronous test platform, integrate an acoustic array (microphone), a hot-wire anemometer (flow velocity measurement) and an infrared thermal imager (temperature field monitoring), and under the simulated working conditions driven by a variable-frequency fan, collect noise spectrum (1 / 3 octave), air duct flow velocity distribution (turbulence intensity) and heat dissipation surface temperature gradient data in real time to generate a spatio-temporally synchronous multi-source data set.

[0055] The synchronous acquisition of multi-physical field data reveals the dynamic coupling relationship between noise reduction, heat dissipation and flow field, provides a comprehensive measured basis for model verification, and makes up for the one-sidedness of single-dimensional testing.

[0056] S43, Compare the measured data set with the simulation results of the multi-physical field evaluation model, extract the deviation matrices of noise reduction amount, wind resistance coefficient and thermal conductivity, and mark the coupling areas of the sound absorption performance error in the high-frequency band and the low-frequency turbulence prediction error.

[0057] By using a data alignment algorithm (timestamp synchronization and spatial coordinate mapping), compare the measured data with the simulation results, calculate the absolute deviations of the noise reduction amount, wind resistance coefficient and thermal conductivity; adopt a joint frequency-domain and spatial-domain analysis method to mark the coupling areas of insufficient sound absorption performance in the high-frequency band (such as deviation > 3 dB above 2 kHz) and inaccurate low-frequency turbulence prediction (such as flow velocity error > 10% below 500 Hz).

[0058] The deviation matrix accurately locates the weak links of the simulation model (such as high-frequency impedance mismatch of sound absorption materials and low-frequency fluid-structure coupling deviation of flow guiding structures), and provides guidance for targeted correction.

[0059] S44, Based on the deviation matrix, build an empirical regression model, train an error correction function through the random forest algorithm, and predict the dynamic compensation coefficients of the equivalent impedance of the sound absorption layer and the inclination angle of the flow guiding ribs; Based on the deviation matrix, the error correction function is trained using the random forest algorithm (ensemble learning of multiple decision trees). The input parameters include the porosity of the sound-absorbing layer, the inclination angle of the flow guiding ribs, and the measured turbulence intensity. The output includes the equivalent impedance compensation coefficient of the sound-absorbing layer (such as +5% to -3%) and the dynamic adjustment amount of the inclination angle of the flow guiding ribs (such as ±2°), establishing a non-linear mapping relationship between parameters and errors.

[0060] S45, inversely map the dynamic compensation coefficient to the boundary conditions of the parametric model, update the permeability tensor of the porous medium and the turbulent wall function, and generate a corrected fluid-structure-acoustic coupling model; Embed the dynamic compensation coefficient into the parametric model, update the permeability tensor of the porous medium (correct the equivalent relationship between the flow resistance and acoustic impedance of the sound-absorbing layer) and the turbulent wall function (adjust the velocity gradient near the wall of the flow guiding ribs), reconstruct the boundary conditions of the fluid-structure-acoustic coupling model, and generate a corrected model that matches the measured data.

[0061] Through inverse mapping, a closed-loop optimization of "measured data → model parameters" is achieved, solving the process error problems (such as uneven porosity distribution of materials) that are difficult to cover by traditional forward simulation, and enhancing the adaptability of the model to the actual production environment.

[0062] S46, perform confidence verification on the corrected fluid-structure-acoustic coupling model. If the prediction error of the noise reduction amount ≤ 1.5 dB and the error of the wind resistance coefficient ≤ 0.05, lock the final model parameters; otherwise, iteratively execute the update and optimization of the boundary conditions.

[0063] Adopt a double-threshold verification rule (noise reduction amount error ≤ 1.5 dB, wind resistance coefficient error ≤ 0.05) to evaluate the confidence of the corrected model; if not up to standard, additional directional sampling (such as dense sampling at high frequencies) is added based on the residual distribution, and the boundary conditions are iteratively updated until the model prediction results pass the Monte Carlo test (qualified rate ≥ 95%).

[0064] Confidence verification ensures that the corrected model has both accuracy and stability. The iterative optimization mechanism dynamically adapts to complex working conditions, provides reliable parameter locking for mass production design, and reduces the later modification cost.

[0065] In this embodiment, specifically, step S5 specifically includes: S51, based on the corrected noise reduction fairing configuration, according to the functional partition, split the fairing into a sound-absorbing module, a flow guiding module, and a heat conduction module, and define the geometric and mechanical coupling constraint conditions of the interfaces between the modules; Based on topology optimization technology (dynamically dividing functional regions according to the distribution of acoustic energy flow density and heat flow density), the fairing is split into an acoustic absorption module (for high-frequency noise suppression), a flow guiding module (for smooth airflow turning), and a heat conduction module (for efficient heat dissipation). Through interface coupling analysis (finite element contact mechanics simulation), the geometric fit tolerances (such as clearance ≤ 0.2 mm) and mechanical constraints (such as pre-tightening force 5 - 10 N·m) between modules are defined to ensure the overall stiffness and sealing performance after module combination.

[0066] The functional partition design realizes the physical decoupling of acoustic, thermal, and flow performance, and the modular split supports quick replacement and customized adaptation; the interface constraint definition avoids assembly stress concentration and acoustic-thermal leakage, ensuring the collaborative working performance of multiple modules.

[0067] S52, aiming at the interface characteristics of modular components, design the assembly connection structure, including positioning card slots, bolt groups with adjustable pre-tightening force, and heat conduction medium filling channels, and generate the module code and assembly relationship matrix; Design the positioning card slots and bolt groups with adjustable pre-tightening force to achieve high-precision positioning and anti-vibration connection between modules; reserve heat conduction medium filling channels (micro-channel structure guides the flow of graphene slurry) at the interface to ensure thermal continuity between modules. Assign a unique ID to each module through an intelligent coding system (such as QR code / RFID tag), associate material parameters with assembly priorities, and generate the assembly relationship matrix (such as "acoustic absorption module A1 → flow guiding module D3 → heat conduction module C2").

[0068] S53, according to the assembly relationship matrix, formulate the modular assembly process flow, including the sequence and tolerance control thresholds of the lamination and curing of the acoustic absorption module, the angle calibration of the flow guiding module, and the thermocompression bonding of the heat conduction module; Formulate a staged curing process: the acoustic absorption module uses vacuum lamination and curing (60 °C / 2 h), the flow guiding module calibrates the inclination angle through laser marking (error ≤ 0.5 °), and the heat conduction module uses thermocompression bonding (pressure 1 MPa / 150 °C) to ensure that the interface thermal conductivity ≥ 5 W / m·K. Define the tolerance control thresholds (such as module clearance ≤ 0.3 mm, inclination angle deviation ± 1 °), and dynamically monitor key parameters through a digital process management system (such as MES system).

[0069] S54, optimize the matching accuracy and assembly path of the assembly connection structure, and output modular process files suitable for equipment with different power levels.

[0070] Based on digital twin technology (closed-loop feedback of virtual assembly simulation and physical trial assembly data), optimize the matching accuracy of module interfaces (such as chamfer correction of card slots and adjustment of bolt pre-tightening sequence); use path planning algorithms (A* algorithm for obstacle avoidance) to design differentiated assembly paths for multi-power-level devices (such as installing the heat conduction module first for small devices and the flow guiding module first for large devices), and output modular process files (including tooling fixture design drawings and assembly sequence tables) suitable for different scenarios.

[0071] In this embodiment, specifically, step S6 specifically includes: S61, Based on the assembly relationship matrix of modular components, use a digital positioning system that integrates laser scanning and detection vision to scan the geometric morphology and spatial pose of the module interface, and generate a three-dimensional matching coordinate system; Adopt the technology of fusing lidar scanning and machine vision. Through high-precision laser point clouds, capture the geometric morphology (such as curvature and aperture) of the module interface, and combine with vision feature point matching algorithms (such as SIFT key point extraction) to calibrate the spatial pose (translation and rotation angle) of the module in real time, generating a three-dimensional matching coordinate system with millimeter-level accuracy to ensure the physical alignment of the interfaces between modules and the uniform distribution of mechanical loads.

[0072] The fusion positioning technology breaks through the cumulative error limitation of traditional mechanical positioning, realizes sub-millimeter-level precise docking of complex curved interfaces, avoids problems such as sound leakage or sudden change in thermal resistance caused by misalignment, and significantly improves assembly consistency.

[0073] S62, According to the three-dimensional matching coordinate system, plan the filling path of the sound-absorbing and heat-conducting composite medium layer, and design a gradient density distribution structure, where the proportion of the sound-absorbing medium decreases from the module interface to the core area, and the proportion of the heat-conducting medium increases; Based on the three-dimensional matching coordinate system, design the gradient filling path of the sound-absorbing and heat-conducting composite medium through a topology optimization algorithm (combining the distribution of sound energy flow density and heat flow density): Sound-absorbing medium gradient, high proportion (70% microspheres - silica gel) at the interface to absorb the reflected noise of the assembly gap, decreasing to 30% towards the core area; Heat-conducting medium gradient, high proportion (40% graphene - ceramic slurry) in the core area to strengthen heat conduction, decreasing to 10% towards the interface.

[0074] Import the path planning results into fluid dynamics simulation software to verify the filling uniformity.

[0075] The gradient density design takes into account acoustic sealing at the interface and efficient heat dissipation in the core area. Compared with homogeneous filling, the noise reduction amount is increased by 10% - 15%, the thermal resistance is reduced by 20%, and at the same time, the usage of high-cost heat-conducting materials is reduced.

[0076] S63. An injection molding device is adopted to simultaneously inject the sound-absorbing microsphere-silica gel composite material and the graphene-ceramic thermal conductive paste according to the filling path, and an interlayer interlocking structure is formed through an in-situ curing process. A multi-axis linkage injection molding device is adopted. Through an independently controlled microchannel system, the sound-absorbing microsphere-silica gel composite material and the graphene-ceramic thermal conductive paste are simultaneously injected according to the planned path. The ultraviolet-thermal dual-curing process (UV light irradiation initiates the cross-linking of silica gel, and the ceramic paste is cured by heating at 80°C) is used to form a chemical bonding interlocking structure at the interface to enhance the interlayer bonding strength.

[0077] Simultaneous injection avoids the risk of interlayer peeling during step-by-step filling. The dual-curing process realizes high-strength interfacial bonding of heterogeneous materials, solves the problem of thermal expansion mismatch of traditional adhesives, and ensures long-term reliability.

[0078] S64. The sound-thermal performance of the filled composite dielectric layer is monitored to determine whether the sound absorption coefficient reaches the preset coefficient value and the thermal conductivity reaches the preset thermal conductivity threshold. If so, the assembly is determined to meet the standard; if not, local supplementary injection is carried out to correct the dielectric.

[0079] Through the impedance tube sound absorption test (in accordance with the ISO 10534-2 standard) and the transient plane heat source method thermal conductivity test (in accordance with the ISO 22007-2 standard), the sound absorption coefficient (target ≥ 0.8) and thermal conductivity (target ≥ 4 W / m·K) of the composite dielectric layer are quantified. If not up to the standard, the microchannel supplementary injection process is used to directionally correct the defective area (such as injecting the sound-absorbing microsphere paste into the area with insufficient porosity) until the performance meets the standard.

[0080] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A design method for a noise reduction fairing, characterized in that, Including: Determine the performance requirement parameters of the noise reduction fairing, and construct an initial noise reduction model based on the noise spectrum of the target application scenario and the duct structure defects; According to the performance requirement parameters, design the structural parameters of the layered composite structure and the flow guiding component; Establish a parametric model of the fluid-structure-acoustic coupling of the layered composite structure and the flow guiding component, and use a multi-objective optimization algorithm to iteratively optimize the structural parameters to generate an optimized configuration of the noise reduction fairing; Manufacture a prototype of the fairing based on the optimized configuration, collect measured data and correct the simulation error through an empirical regression model, and update the boundary conditions of the parametric model; Split the corrected noise reduction fairing into modular components, and design a modular-based assembly process, where the modular components include an acoustic absorption module, a flow guiding module, and a heat conduction module; During the assembly process of the modular components, use a digital positioning system to match the component interfaces and fill the composite medium layer of acoustic absorption and heat conduction.

2. The design method of the noise reduction fairing according to claim 1, wherein The determination of the performance requirement parameters of the noise reduction fairing and the construction of the initial noise reduction model based on the noise spectrum of the target application scenario and the duct structure defects specifically include: Based on the noise source distribution and spectral characteristics of the target application scenario, locate the noise hot spots through an acoustic array and extract the main noise frequency band to generate a noise energy distribution map; Conduct a flow field simulation and modal analysis on the duct structure of the target application scenario, identify the noise amplification areas caused by turbulence and resonance, and establish a duct defect characteristic model; Combining the noise energy distribution map and the duct defect characteristic model, define the quantitative performance requirement parameters of the noise reduction fairing, and the quantitative performance requirement parameters include the target noise reduction frequency band, the maximum allowable wind resistance coefficient, and the heat conduction efficiency threshold; Based on the performance requirement parameters, construct an initial noise reduction model and associate it with the duct defect characteristic model; the initial noise reduction model includes noise propagation paths, sound absorption and insulation materials, equivalent impedance, and flow guiding structure control equations; Verify the effectiveness of the initial noise reduction model through benchmark tests, and use a joint simulation of acoustic transmission loss and computational fluid dynamics to correct the deviation of the initial noise reduction model, and output a calibrated initial noise reduction model.

3. The design method of the noise reduction fairing according to claim 1, characterized in that, The layered composite structure includes an acoustic absorption layer, a damping layer, and a heat conduction layer, and the flow guiding component includes flow guiding ribs and a support frame; Among them, the acoustic absorption layer, the damping layer, and the heat conduction layer are arranged from the outer layer to the inner layer in sequence; The flow guiding ribs are distributed in a spiral form on the inner wall of the support frame, and the support frame is provided with a plurality of honeycomb-shaped strengthening holes.

4. The design method of the noise reduction fairing according to claim 3, characterized in that, The establishment of the parametric model of the fluid-structure-acoustic coupling of the layered composite structure and the flow guiding component, and the use of a multi-objective optimization algorithm to iteratively optimize the structural parameters to generate an optimized configuration of the noise reduction fairing specifically include: Define the key design parameters of the layered composite structure and the flow guiding component, and the key design parameters include the porosity of the acoustic absorption layer, the density gradient of the damping layer, the inclination angle of the flow guiding ribs, and the honeycomb aperture of the support frame, and associate them with the input variables of the fluid-structure-acoustic coupling model; Construct a parametric model based on the key design parameters, equivalent the layered composite structure to a coupled domain of porous medium-viscoelastic material, embed the flow guiding component into the hydrodynamic equation and the structural vibration equation, and establish a three-dimensional coupled solver for the flow field - sound field - structural field; Generate a design space sample set using the cubic sampling method, batch calculate the noise reduction amount and wind resistance coefficient of each sample in the space sample set through the three-dimensional coupled solver, and output the response surface of the multi-objective performance; Train an adaptive surrogate model based on the response surface, use the interpolation algorithm to predict the performance of unsampled points, and construct the Pareto front set.

5. The design method of the noise reduction fairing according to claim 4, characterized in that, After training the adaptive surrogate model based on the response surface, using the interpolation algorithm to predict the performance of unsampled points, and constructing the Pareto front set, it further includes: Use the objective optimization algorithm to iteratively optimize the Pareto front set, introduce a dynamic weight strategy to balance the noise reduction and heat dissipation objectives, and screen out the optimal parameter combination that meets the constraint conditions; Verify the optimal parameter combination using a preset multi-physics field evaluation model, jointly evaluate through the acoustic transmission loss index TL and the turbulent kinetic energy dissipation rate TDR, and generate the optimized configuration and the corresponding confidence index.

6. The design method of the noise reduction fairing according to claim 5, characterized in that, Manufacture a fairing prototype based on the optimized configuration, collect the measured data and correct the simulation error through an empirical regression model, and update the boundary conditions of the parametric model. Specifically, it includes: Based on the optimized configuration, use additive manufacturing and composite material lamination process to manufacture a fairing prototype, and calibrate the actual processing error of the key design parameters; Build a test platform, collect noise spectrum, air duct flow velocity distribution and heat dissipation surface temperature gradient data under simulated working conditions, and generate a measured multi-source data set; Compare the measured data set with the simulation results of the multi-physics field evaluation model, extract the deviation matrix of the noise reduction amount, wind resistance coefficient and thermal conductivity, and mark the coupling area of the high-frequency sound absorption performance error and the low-frequency turbulence prediction error.

7. The design method of the noise reduction fairing according to claim 6, wherein After marking the coupling area of the high-frequency sound absorption performance error and the low-frequency turbulence prediction error, it further includes: Construct an empirical regression model based on the deviation matrix, train an error correction function through the random forest algorithm, and predict the dynamic compensation coefficient of the equivalent impedance of the sound absorption layer and the inclination angle of the flow guiding rib; Inverse map the dynamic compensation coefficient to the boundary conditions of the parametric model, update the porous medium permeability tensor and the turbulent wall function, and generate a corrected fluid-structure-acoustic coupling model; Conduct a confidence verification on the corrected fluid-structure-acoustic coupling model. If the noise reduction prediction error ≤ 1.5 dB and the wind resistance coefficient error ≤ 0.05, lock the final model parameters, otherwise iteratively execute the update and optimization of the boundary conditions.

8. The design method of the noise reduction fairing according to claim 1, characterized in that Split the corrected noise reduction fairing into modular components and design a modular-based assembly process. Specifically, it includes: Based on the corrected noise reduction fairing configuration, according to the functional partition, split the fairing into a sound absorption module, a flow guiding module and a heat conduction module, and define the geometric and mechanical coupling constraint conditions of the interfaces between the modules; Design an assembly connection structure for the interface characteristics of the modular components, including positioning card slots, bolt groups with adjustable pre-tightening force and heat conduction medium filling channels, and generate a module coding and assembly relationship matrix; According to the assembly relationship matrix, formulate a modular assembly process flow, including the sequence and tolerance control thresholds of acoustic module lamination and curing, flow guide module angle calibration, and heat conduction module hot pressing and bonding; Optimize the matching accuracy and assembly path of the assembly connection structure, and output modular process documents suitable for equipment with different power levels.

9. The design method of the noise reduction fairing according to claim 8, characterized in that, During the assembly process of the modular components, use a digital positioning system to match the component interfaces and fill the composite medium layer of sound absorption and heat conduction, specifically including: Based on the assembly relationship matrix of the modular components, use a digital positioning system that integrates laser scanning and detection vision to scan the geometric morphology and spatial position of the module interfaces, and generate a three-dimensional matching coordinate system; According to the three-dimensional matching coordinate system, plan the filling path of the sound absorption and heat conduction composite medium layer, and design a gradient density distribution structure, where the proportion of the sound absorption medium decreases from the module interface to the core area, and the proportion of the heat conduction medium increases; Use injection molding equipment to synchronously inject the sound absorption microsphere-silicone composite material and the graphene-ceramic heat conduction slurry according to the filling path, and form an interlayer interlocking structure through an in-situ curing process; Conduct joint acoustic-thermal performance measurement on the filled composite medium layer, and judge whether the sound absorption coefficient reaches the preset coefficient value and the thermal conductivity reaches the preset thermal conductivity threshold. If so, it is determined that the assembly meets the standard; otherwise, local supplementary injection is carried out to correct the medium.

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