Multi-physical field coupling modeling method based on nanosecond laser etching micro-groove drag reduction simulation

Abstract

This invention discloses a multi-physics coupled modeling method for simulating drag reduction in microgrooves using nanosecond laser etching, relating to the field of laser processing technology. It addresses the optimization problem of drag reduction in microgrooves during laser etching. This method simulates the drag reduction effect of microgrooves during laser etching by establishing coupled models of multiple physical fields, including heat transfer, fluid dynamics, and material removal. Specifically, adaptive mesh technology is used to accurately calculate the temperature and flow fields during the etching process. Simultaneously, an equivalent pulsed laser processing method is introduced to model the influence of different scan numbers and laser power, optimizing the etching process parameters. This method can efficiently predict the drag reduction effect under different parameters, providing a theoretical basis for microgroove design and laser etching process optimization, and has broad application prospects.

Description

Technical Field

[0001] This invention relates to the field of laser processing technology, and more specifically, to a multiphysics coupling modeling method based on nanosecond laser etching microgroove drag reduction simulation. Background Technology

[0002] Nanosecond pulsed lasers are widely used in the fabrication of microstructured materials and the modification of material surfaces. Due to their instantaneous high peak energy, they can effectively remove material debris from the surface of the material being processed. However, in finite element modeling and simulation, the intermittent heating characteristic of pulsed lasers increases computational complexity. To simplify calculations, the heating behavior is often simulated by treating the pulsed laser as an equivalent continuous laser source. While this method effectively improves computational efficiency, it introduces new errors during the equivalence process, affecting the accuracy of subsequent processing.

[0003] Therefore, optimizing the scanning process and preventing overheating damage caused by heat accumulation without sacrificing processing accuracy and efficiency has become a pressing technical challenge in efficient and stable laser processing.

[0004] To address the above problems, this invention proposes a solution. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention are based on a multi-physics coupling modeling method for simulation of drag reduction of nanosecond laser etching microgrooves, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a preferred embodiment, it includes:

[0008] Step 1: Establish a multiphysics finite element model;

[0009] Step 2: Correct the prediction model by equating pulsed laser to a continuous laser source and combining it with scanning data;

[0010] Step 3: Construct a computational domain model and perform fluid simulation analysis to assess the drag reduction effect of the microchannels;

[0011] Step 4: Conduct laser etching and wind tunnel tests to verify and calibrate the model.

[0012] In a preferred embodiment, in step 1, a governing equation is established, a geometric model is created, and the governing equation is applied to COMSOL to set the physical field and construct a numerical calculation model; finally, the interaction between the laser and the material and the microgroove formation process are simulated.

[0013] In a preferred embodiment, in step 2, the pulsed laser is equivalent to a continuous laser source, and real-time response data is collected during the initial scan. The depth error, heat-affected zone size error, and surface roughness error of the processed material generated after the pulsed laser is equivalent to a continuous laser are quantified and removed. Based on the feedback of material removal efficiency and heat accumulation after error removal, the final number of scans is corrected and determined.

[0014] In a preferred embodiment, a numerical simulation method suitable for microgroove drag reduction is determined, and the control equations to be solved are determined by physical phenomena occurring at the cruising speed of a small aircraft, and a turbulence model is introduced to close the control equations.

[0015] In a preferred embodiment, the simulated microgroove structure is imported into COMSOL, the computational domain boundary is defined, the height of the first mesh layer of the boundary layer is adjusted, and a reliable microgroove drag reduction model is established. Considering the interaction between the fluid and the microgroove, the flow field characteristics are simulated, and the pressure and velocity distribution on the surface of the microgroove are obtained.

[0016] In a preferred embodiment, the width, depth, and surface smoothness parameters of the microgroove are obtained by laser etching; pressure and velocity data at different locations of the microgroove are obtained by wind tunnel testing, and the friction coefficient and drag reduction rate are calculated based on the pressure and velocity data at different locations of the microgroove.

[0017] In a preferred embodiment, laser etching and wind tunnel tests are repeated multiple times to record the friction coefficient and drag reduction rate under different experimental conditions, identify differences, and adjust model parameters.

[0018] In a preferred embodiment, the timely response data includes the following:

[0019] Initial scan depth ΔD1: Used to record the actual depth of material removal;

[0020] Heat-affected zone size (HAZ): The size of the heat-affected zone of the material after the initial scan;

[0021] Feedback on the surface temperature of the processed material: Record the surface temperature of the processed material after the initial scan;

[0022] Surface roughness and morphology of the workpiece: Measure the surface roughness and morphology characteristics of the workpiece after the initial scan.

[0023] In a preferred embodiment, the material removal efficiency is obtained as follows:

[0024] The formula for correcting material removal efficiency is: N adjusted =N predicted ×(1-c·E removal )

[0025] In the formula, Eremoval Where is the material removal efficiency, and c is the correction factor.

[0026] In a preferred embodiment, the thermal accumulation feedback is obtained as follows:

[0027] The formula for thermal accumulation feedback correction is:

[0028] In the formula, T current T is the current surface temperature. limit This is the thermal damage threshold temperature.

[0029] This invention discloses a multi-physics coupled modeling method for simulating drag reduction in microgrooves using nanosecond laser etching, relating to the field of laser processing technology. It addresses the optimization problem of drag reduction in microgrooves during laser etching. This method simulates the drag reduction effect of microgrooves during laser etching by establishing coupled models of multiple physical fields, including heat transfer, fluid dynamics, and material removal. Specifically, adaptive mesh technology is used to accurately calculate the temperature and flow fields during the etching process. Simultaneously, an equivalent pulsed laser processing method is introduced to model the influence of different scan numbers and laser power, optimizing the etching process parameters. This method can efficiently predict the drag reduction effect under different parameters, providing a theoretical basis for microgroove design and laser etching process optimization, and has broad application prospects. Attached Figure Description

[0030] Figure 1 This is a flowchart of the multiphysics coupling modeling method based on nanosecond laser etching microgroove drag reduction simulation of the present invention. Detailed Implementation

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

[0032] Example

[0033] This invention discloses a multiphysics coupling modeling method based on nanosecond laser etching microgroove drag reduction simulation, such as... Figure 1 As shown, it includes:

[0034] Step 1: Establish a multiphysics finite element model;

[0035] First, starting from laser physics, thermodynamics, and fluid mechanics, the governing equations are established sequentially based on heat transfer, phase transition, and dynamics during the processing, as detailed below:

[0036] The energy conservation equation describing the temperature change during laser heating is as follows:

[0037]

[0038] Where ρ represents the density of the molten metal, Cp represents the specific heat capacity, T represents the temperature, t represents the time, and k represents the thermal conductivity. This indicates the direction of heat flow, and Q represents the laser energy input.

[0039] It should be noted that Q is determined by the laser power density, which is specifically obtained using the formula:

[0040]

[0041] Where P represents the laser power, r represents the radial distance from a point on the material surface to the center of the laser beam, w0 represents the spot radius, and η represents the absorption rate of the material to the laser.

[0042] The mass conservation equation describing the rate at which material is removed during laser etching is as follows:

[0043]

[0044] Where v represents the flow rate of the molten metal, and S represents the material loss caused by laser ablation;

[0045] It should be noted that S is determined by the vaporization rate of the material, and its specific value is obtained using the following formula:

[0046] S=ρv ablation

[0047] Among them, v ablation Indicates the vaporization rate of the material;

[0048] The momentum conservation equation describing the motion of molten metal within a microchannel is as follows:

[0049]

[0050] Where μ represents the internal friction of the molten metal, p represents the pressure of the fluid, and F represents external forces such as steam backflow and surface tension;

[0051] It should be noted that the acquisition of factors such as density, temperature, flow rate, and material loss data in this invention is based on conventional selection methods in the prior art, and will not be elaborated here.

[0052] Furthermore, using a cuboid as the basic geometric shape, 7075 aluminum alloy was selected, and its thermal conductivity, density, specific heat capacity, melting point, vaporization temperature, and other parameters were set to create a geometric model. The governing equations were then applied to the COMSOL module to define the physical fields and construct a numerical calculation model, as detailed below:

[0053] Thermal field: Apply laser heat source; Set ambient heat transfer conditions; Set phase transition temperature;

[0054] Fluid field: Calculate the motion of molten metal; set surface tension and vapor recoil force;

[0055] Solid mechanical fields: Calculating thermal stress;

[0056] Subsequently, an adaptive mesh generation technique was employed to optimize the mesh and improve the simulation accuracy of the laser etching region. For example, an ultrafine mesh was used in the laser etching region, while a coarser mesh was used in areas far from the etching region. The level set method was then used to accurately simulate the interaction between the laser and the material, as well as the microgroove formation process, based on the following formula:

[0057]

[0058] Where φ represents the interface location and γ represents the interface diffusion coefficient;

[0059] Step 2: Correct the prediction model by equating the pulsed laser to a continuous laser source and combining it with the initial scan data;

[0060] The intermittent heating characteristics of pulsed lasers lead to complex transient heat conduction, increasing computational complexity. Therefore, this invention equates pulsed lasers to continuous laser sources, simplifying the simulation process, reducing computation time in finite element simulations, and enabling simulation and optimization through multiple scans. This equivalent treatment maintains good simulation accuracy under most processing conditions, providing reasonable predictions of energy distribution and heat accumulation.

[0061] The equivalent heat source is defined as follows:

[0062]

[0063] In the formula, t period =1 / f is the pulse period.

[0064] This invention acquires data during the initial scan by using a short-duration laser to collect the material's instantaneous response data, which is then used as the initial scan feedback state and a reference for predicting the number of scans. The main data collected includes:

[0065] Initial scan depth ΔD1: Used to record the actual depth of material removal;

[0066] Heat-affected zone size (HAZ): The size of the heat-affected zone of the material after the initial scan;

[0067] Feedback on the surface temperature of the processed material: Record the surface temperature of the processed material after the initial scan;

[0068] Surface roughness and morphology of the workpiece: Measure the surface roughness and morphology characteristics of the workpiece after the initial scan.

[0069] Since the laser equivalent process may introduce errors in material removal depth and heat accumulation, the errors introduced by equipping the pulsed laser with continuous laser in this invention are quantified by the following parameters to correct the predicted number of scans:

[0070] Material removal depth error:

[0071] In the formula, and These represent the removal depth under pulsed laser and continuous laser conditions, respectively.

[0072] Heat-affected zone size error: ΔHAZ equiv =|ΔHAZ pulse -ΔHAZ continuous |;

[0073] In the formula, ΔHAZ pulse and ΔHAZ continuous These represent the sizes of the heat-affected zones for pulsed lasers and continuous lasers, respectively.

[0074] Surface roughness error of the workpiece material: ΔR equiv =|ΔR pulse -R continuous |;

[0075] In the formula, ΔR pulse and R continuous These represent the surface roughness of the material being processed by pulsed laser and continuous laser, respectively.

[0076] This invention ensures model accuracy by quantifying the error sources after pulsed laser light is converted into a continuous laser source. Through comprehensive analysis that eliminates depth errors, heat-affected zone size errors, and surface roughness errors of the processed material, it effectively compensates for the impact of equivalent errors on processing accuracy. This provides a correction factor for subsequent scan number prediction, improving the final processing accuracy.

[0077] The scan count prediction model of this invention is as follows:

[0078]

[0079] In the formula, D target The target depth, pre-set by processing requirements, is used to determine the required total removal depth. This, combined with the removal depth of the initial scan, is used to calculate the basic number of scans. HAZ target The target heat-affected zone size is set by process requirements and is used to assess the impact of heat accumulation on subsequent scans in conjunction with equivalence error evaluation, ensuring that the heat-affected zone size is within the target range.target The target surface roughness of the workpiece is preset according to processing requirements and quality standards. It is used to control the final surface roughness of the workpiece by incorporating surface roughness errors, thereby reducing surface unevenness caused by these errors. ΔD equiv To eliminate depth errors, the removal depths of pulsed lasers and equivalent continuous lasers are compared to quantify the equivalence error, which is used to compensate for deviations in removal depth during the equivalence process. ΔHAZ equiv To address the error in the size of the heat-affected zone (HAZ), the difference in HAZ size between pulsed and continuous lasers is compared through simulation or experimentation. This quantified equivalence error is used to adjust the number of scans based on the target range of the HAZ, thereby controlling heat accumulation and preventing material damage due to excessive temperature. ΔR equiv To address the surface roughness error of the processed material, the equivalent error is quantified by comparing the surface roughness of the processed material processed by pulsed laser and continuous laser. This quantification is used to correct the number of scans, ensuring that the surface roughness of the processed material meets the required standard. k1, k2, and k3 are weighting coefficients for removing depth error, heat-affected zone size error, and surface roughness error of the processed material, respectively. These coefficients are set according to actual conditions, thereby adjusting the influence of each equivalent error in the scan number prediction through weighted adjustments, ensuring that the processing effect meets actual requirements.

[0080] This allows for adjustments to the predicted number of scans based on removal efficiency and heat accumulation, optimizing thermal control during processing, reducing thermal damage from multiple scans, and ensuring consistent processing results through dynamic adjustment. It also enables fine-tuning based on actual conditions, improving the adaptability of the prediction model.

[0081] Meanwhile, during the scanning process, the number of scans is further adjusted based on material removal efficiency and heat accumulation feedback, ultimately determining the number of scans N. final Used for initial processing settings, and dynamically adjusted during subsequent processing to ensure that the processing depth and morphology meet expectations.

[0082] The material removal efficiency correction formula is: N adjusted =N predicted ×(1-c·E removal In the formula, E removal denoted as material removal efficiency, and denoted as the average material removal depth per scan. Higher removal efficiency indicates greater material removal depth per scan, thus reducing the total number of scans required. c is a correction factor, representing the degree of influence of removal efficiency on the total number of scans, determined based on experimental or processing experience. It is used to quantify the impact of material removal efficiency on the final number of scans.

[0083] The formula for thermal accumulation feedback correction is: In the formula, T currentThis represents the current surface temperature of the material during the current scanning phase. A higher temperature indicates greater heat accumulation in the material, requiring a reduction in the number of scans. (T) limit The thermal damage threshold temperature represents the highest temperature a material can withstand during processing; exceeding this temperature may cause damage to the material's surface or structure.

[0084] By determining the initial number of scans through a comprehensive predictive model, and then dynamically adjusting the process based on real-time feedback, the processing becomes more adaptable. This dynamic adjustment mechanism reduces the trial-and-error steps in laser processing, improving efficiency while ensuring quality, thus helping to lower processing costs and enhance the industrial applicability of laser processing.

[0085] Furthermore, the final number of scans ensured the accuracy of the processing depth and morphology, and was dynamically adjusted during actual processing to further optimize the processing effect. While ensuring the processing results, the efficiency of laser processing was improved, minimizing processing time while meeting processing quality requirements.

[0086] This invention, through reasonable setting of the number of scans and energy control, can maintain the uniformity of the morphology in multi-layer scanning and gradually achieve the required processing depth and morphology.

[0087] Specifically, the heat source distribution for each scan is as follows:

[0088] In the formula, It is the basic heat source for a single scan, g i (t) controls the laser switch state for each scan, where n is the current scan number.

[0089] Step 3: Construct a computational domain model and perform fluid simulation analysis to assess the drag reduction effect of the microchannel.

[0090] Currently, the main numerical simulation methods used for microgroove drag reduction include:

[0091] Direct numerical simulation: It can resolve all scales of turbulence with the highest accuracy, but the computational cost is extremely high, making it unsuitable for engineering applications;

[0092] Large eddy simulation: Uses a subgrid-scale model to handle small-scale turbulence, suitable for detailed analysis of turbulent flow fields;

[0093] Reynolds-averaged Navier-Stokes method: It uses a turbulence model with closed control equations, is suitable for microscale simulation, has high computational efficiency, and is applicable to engineering optimization design;

[0094] This invention employs the Reynolds-averaged Navier-Stokes method, which is suitable for microscale simulation, computationally efficient, and applicable to engineering optimization design, for simulation analysis. Considering that the fluid exhibits low-speed incompressible flow and turbulence dominates when the small aircraft cruises at Ma = 0.2-0.3, the incompressible Reynolds-averaged Navier-Stokes equations are used. Furthermore, a standard k-ωSST turbulence model, which accurately predicts wall turbulence characteristics, is employed for closure.

[0095] Specifically, the incompressible Reynolds-averaged Navier-Stokes equations are as follows:

[0096] Continuity equation:

[0097] Momentum equation:

[0098] Where u represents the average velocity component of the fluid. To represent the Reynolds stress term, a turbulence model must be used for closure.

[0099] Then, the microgroove structure obtained from the above simulation is imported into COMSOL. There is no need to extract cross-sectional features again. In order to save computational resources, the boundary of the computational domain is defined as follows:

[0100] Inlet boundary: Set uniform inflow velocity U ∞ This ensures that there is no interference when fluid enters the computational domain;

[0101] Outlet boundary: Zero gradient boundary conditions are used to ensure natural fluid outflow;

[0102] Wall boundary: The surface of the microgroove is designed with no slippage conditions to ensure accurate calculation of wall shear stress;

[0103] Symmetrical boundary: Set symmetric boundary conditions on both sides of the computational domain to avoid unnecessary computational errors.

[0104] Furthermore, in order to adapt to complex geometries and enhance turbulence resolution, unstructured tetrahedral meshes are used for the microgroove region, and layered hexahedral meshes are used for the wall boundary layer.

[0105] Meanwhile, in order to ensure that the microgroove region is in a fully turbulent state, the target y is first set. + And calculate the wall shear stress T. w :

[0106]

[0107] Calculate the turbulent friction velocity u T :

[0108]

[0109] Calculate the height Δy of the first layer of mesh:

[0110]

[0111] Using y + With a target value of 0.5-1.0, the height of the first layer of the boundary layer is adjusted to ensure that the microgroove region is in a fully turbulent state, and a reliable microgroove drag reduction model is established.

[0112] Next, enable the following physics modules in COMSOL:

[0113] Fluid Flow Module: Calculates the velocity and pressure distribution of fluids;

[0114] Solid Mechanics module: Analyzing the stress and deformation of the microgroove wall;

[0115] Fluid-structure interaction module: Enables fluid and solid interaction;

[0116] The ALE (Active Grid) module allows the grid to dynamically adjust as the wall deforms.

[0117] Furthermore, the pressure and shear force of the fluid on the microchannel are calculated, and the deformation of the microchannel under the action of the fluid is calculated by solving the solid mechanics equations. Then, the flow field characteristics are simulated by using the moving mesh ALE module and fluid-structure interaction technology, and the wall pressure P and the velocity components of the fluid-structure interface are solved by using the incompressible Reynolds-averaged Navier-Stokes equations.

[0118] Step 4: Conduct laser etching and wind tunnel tests to verify and calibrate the model.

[0119] First, a metal or semiconductor material is used as the etching substrate and a high-precision laser etching machine is used. Parameters such as laser wavelength, power, and scanning speed are input, and the sample is placed in the laser etching machine to perform the etching process.

[0120] The laser-etched sample is mounted on the sample stage of an SEM or AFM. The accelerating voltage of the scanning electron microscope is set, an appropriate scanning mode is selected, and the morphological image of the microgroove is acquired using secondary electron mode (SE). The width, depth, surface smoothness, and other parameters of the microgroove are measured, and the deviation from the designed shape is recorded.

[0121] Furthermore, the etched microgroove sample was installed on a wind tunnel testing platform, ensuring its position was fixed and aligned with the fluid flow direction. Pressure sensors were installed at the inlet and outlet of the microgroove, as well as on the groove walls, to record pressure data at different locations. A laser Doppler velocimeter was used to measure the velocity of the fluid inside the microgroove and record the flow velocity at different locations within the microgroove.

[0122] Finally, by measuring the wall shear stress and combining it with the fluid velocity distribution, the friction coefficient was calculated, and the calculated friction coefficient was compared with the theoretical value or simulation results to analyze the error. The total resistance of the fluid in the microchannel was calculated and compared with the resistance when the microchannel was not used to verify the drag reduction effect of the microchannel design, and the results were calibrated with the simulation predictions. At the same time, laser etching and wind tunnel tests were repeated multiple times to record data under different experimental conditions. The experimental data and simulation results were compared to find differences and adjust the model parameters.

[0123] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0124] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0125] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0126] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0128] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multiphysics coupling modeling method based on nanosecond laser etching microgroove drag reduction simulation, characterized in that, include: Step 1: Establish the governing equations, create a geometric model, apply the governing equations to COMSOL, set the physical field, and construct a numerical calculation model; finally, simulate the interaction between the laser and the material and the microgroove formation process; Step 2: Equivalent the pulsed laser to a continuous laser source and collect instantaneous response data during the initial scan. Data acquisition is performed during the initial scan, and the instantaneous response data of the material is collected through short-duration laser action. This data is used as the feedback state of the initial scan and serves as a reference for predicting the number of scans. Quantify and remove the depth error, heat-affected zone size error, and surface roughness error of the processed material caused by equivalent the pulsed laser to a continuous laser. Material removal depth error: ; In the formula, and These represent the removal depth under pulsed laser and continuous laser conditions, respectively; Error in the size of the heat-affected zone: ; In the formula, and These represent the sizes of the heat-affected zones for pulsed lasers and continuous lasers, respectively. Surface roughness error of the workpiece material: ; In the formula, and These represent the surface roughness of the material being processed by pulsed laser and continuous laser, respectively. The scan count prediction model is as follows: ; In the formula, The target depth is preset by the processing requirements to determine the total removal depth required, and the basic number of scans is calculated in combination with the removal depth of the initial scan. The target size of the heat-affected zone is set by process requirements and is used to assess the impact of heat accumulation on subsequent scans in conjunction with equivalence error, ensuring that the size of the heat-affected zone is within the target range. The target surface roughness of the material to be processed is preset according to processing requirements and quality standards. It is used to control the final surface roughness of the material to be processed in combination with the surface roughness error of the material to reduce the unevenness of the surface of the material to be processed due to errors. To eliminate depth errors, the removal depths of pulsed lasers and equivalent continuous lasers are compared to quantify the equivalence error, which is then used to compensate for deviations in removal depth during the equivalence process. To address the error in the size of the heat-affected zone (HAZ), the difference in HAZ size between pulsed lasers and continuous lasers is compared through simulation or experimentation. The equivalence error is quantified and used to adjust the number of scans in conjunction with the target range of the HAZ to control heat accumulation and prevent material damage caused by excessive temperature. To address the surface roughness error of the processed material, the equivalent error is quantified by comparing the surface roughness of the processed material processed by pulsed laser and continuous laser. This quantification is used to correct the number of scans, ensuring that the surface roughness of the processed material meets the required standard. k1, k2, and k3 are weighting coefficients for the removal depth error, the heat-affected zone size error, and the surface roughness error of the processed material, respectively. These coefficients are set according to actual conditions, thereby adjusting the influence of each equivalent error in the scan number prediction through weighted adjustments. This ensures that the processing effect meets actual requirements. Based on the feedback of material removal efficiency and heat accumulation after error removal, the final number of scans is corrected and determined. Simultaneously, during the scanning process, the number of scans is further corrected based on the feedback of material removal efficiency and heat accumulation. The final number of scans, Nfinal, is used for initial processing settings and dynamically adjusted in subsequent processing to ensure that the processing depth and morphology meet expectations. Step 3: Determine a numerical simulation method suitable for microgroove drag reduction, and determine the governing equations to be solved by analyzing the physical phenomena occurring at the cruising speed of a small aircraft. Introduce a turbulence model to close the governing equations. Then, import the simulated microgroove structure into COMSOL. There is no need to extract cross-sectional features again. To save computational resources, define the computational domain boundary: Inlet boundary: Set uniform inflow velocity This ensures that there is no interference when fluid enters the computational domain; Outlet boundary: Zero gradient boundary conditions are used to ensure natural fluid outflow; Wall boundary: The surface of the microgroove is designed with no slippage conditions to ensure accurate calculation of wall shear stress; Symmetrical boundary: Symmetrical boundary conditions are set on both sides of the computational domain to avoid unnecessary computational errors; The height of the first boundary layer mesh was adjusted to establish a reliable microgroove drag reduction model; the interaction between the fluid and the microgroove was considered to simulate the flow field characteristics and obtain the pressure and velocity distribution on the surface of the microgroove. Step 4: Obtain the width, depth, and surface smoothness parameters of the microgroove through laser etching; obtain pressure and velocity data at different locations of the microgroove through wind tunnel testing, calculate the friction coefficient and drag reduction rate based on the pressure and velocity data at different locations of the microgroove, calculate the total resistance of the fluid in the microgroove and compare it with the resistance when the microgroove is not used, verify the drag reduction effect of the microgroove design, and calibrate it with the simulation prediction results. Meanwhile, laser etching and wind tunnel tests were repeated multiple times to record data under different experimental conditions. The experimental data were compared with the simulation results to identify differences and adjust the model parameters. Real-time response data includes the following: Initial scan depth ΔD1: Used to record the actual depth of material removal; Heat-affected zone size (HAZ): The size of the heat-affected zone of the material after the initial scan; Feedback on the surface temperature of the processed material: Record the surface temperature of the processed material after the initial scan; Surface roughness and morphology of the workpiece: Measure the surface roughness and morphology characteristics of the workpiece after the initial scan.

2. The multiphysics coupling modeling method based on nanosecond laser etching microgroove drag reduction simulation according to claim 1, characterized in that: The material removal efficiency is obtained as follows: The formula for correcting material removal efficiency is: ; In the formula, Where is the material removal efficiency, and c is the correction factor.

3. The multiphysics coupling modeling method based on nanosecond laser etching microgroove drag reduction simulation according to claim 1, characterized in that: The thermal accumulation feedback is obtained as follows: The formula for thermal accumulation feedback correction is: ; In the formula, The current surface temperature, This is the thermal damage threshold temperature.

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