A nozzle structure optimization method of a methanol in-direct injection engine

Abstract

The present application relates to nozzle structure design and liquid film separation optimization technical field, specifically, the present application relates to a kind of nozzle structure optimization method of methanol direct injection engine, the present application aims at solving the problem that the fixed radius of chamfer of the outlet of the orifice of traditional nozzle cannot be adjusted dynamically with the inherent surface roughness of the outlet edge, which leads to the real-time viscosity fluctuation of methanol, and affects the power performance of engine.The surface roughness of the outlet of the orifice is quantified by atomic force microscope nanoscale scanning, combined with the viscosity of methanol under different working temperature, a dynamic mapping table is constructed by numerical simulation to match the correction coefficient of liquid film contact angle, the viscosity of methanol is determined according to real-time temperature and the correction coefficient is queried, the liquid film adhesion compensation value is calculated by algorithm model, the design size of the chamfer radius of the outlet of the orifice is adjusted dynamically, the chamfer radius control is realized by using pulse laser ablation technology combined with real-time monitoring, the viscosity and roughness change are adapted, the uniformity of liquid film separation is improved, and the combustion efficiency is optimized.

Description

Technical Field

[0001] This invention relates to the field of nozzle structure design and liquid film separation optimization technology, and more specifically, to a method for optimizing the nozzle structure of a methanol direct injection engine. Background Technology

[0002] Nozzle structure design and liquid film separation optimization is an important technology, specifically applied to improving the spray performance of engine nozzles. The core principle is to optimize the nozzle structure by dynamically adapting the liquid film contact angle to meet the engine's core requirements for spray uniformity and combustion efficiency. During operation of a methanol direct injection engine, temperature changes cause real-time dynamic changes in methanol viscosity, and the nozzle exit edge has inherent surface roughness. These factors collectively affect the contact angle and adhesion between the liquid film and the nozzle surface. Because traditional nozzles have a fixed nozzle exit chamfer radius, they cannot dynamically adjust with changes in methanol viscosity and surface roughness, leading to uneven liquid film separation at the nozzle exit. This results in large variations in spray particle size and incomplete combustion, impacting engine power performance and fuel economy. To address this technical problem, we provide a nozzle structure optimization method for methanol direct injection engines. Summary of the Invention

[0003] The purpose of this invention is to provide a method for optimizing the nozzle structure of a methanol direct injection engine, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, one objective of this invention is to provide a method for optimizing the nozzle structure of a methanol direct injection engine, comprising the following steps:

[0005] S1. Use an atomic force microscope to perform a three-dimensional nanoscale scan on the surface of the nozzle exit edge to detect and quantify its roughness distribution and obtain the surface roughness.

[0006] S2. A dynamic mapping table is constructed based on surface roughness. The dynamic mapping table establishes the correspondence between liquid film contact angle correction coefficients between different surface roughnesses and different real-time methanol viscosities through numerical simulation. The real-time methanol viscosity changes in real time according to the engine operating temperature, and the dynamic mapping table stores the specific corresponding data.

[0007] S3. Determine the methanol viscosity based on the real-time engine temperature sensor data, query the dynamic mapping table to obtain the liquid film contact angle correction coefficient under the current operating conditions, optimize the nozzle outlet geometric parameters based on the liquid film contact angle correction coefficient, and adjust the design size of the nozzle outlet chamfer radius by calculating the compensation value of the liquid film adhesion by the liquid film contact angle correction coefficient.

[0008] S4. The nozzle is designed and manufactured using the adjusted nozzle outlet chamfer radius design dimensions, and the nozzle outlet chamfer radius is controlled by laser fine-tuning.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0010] This invention achieves nanometer-level precise quantification of the surface roughness of the nozzle exit edge using atomic force microscopy. Combined with real-time methanol viscosity at different operating temperatures, a dynamic mapping table covering all operating conditions is constructed to accurately match the liquid film contact angle correction coefficient, providing reliable data support for structural optimization. Based on a machine learning-trained algorithm model, the chamfer radius compensation value is derived from the liquid film mechanical equilibrium equation, adapting to changes in liquid film adhesion in real time. Geometric parameters are iteratively optimized and verified using parametric design tools to ensure the chamfer radius dynamically matches the spray uniformity requirements under current operating conditions. Pulsed laser ablation technology combined with machine vision and real-time monitoring achieves micrometer-level precise control of the chamfer radius, effectively suppressing liquid bridge tailing effects and uneven liquid film cutting. This enables dynamic adaptation of the nozzle structure to methanol viscosity and surface roughness, improving liquid film separation uniformity and spray particle size consistency, optimizing engine combustion efficiency, and balancing power performance and economy, providing a strong guarantee for the stable and efficient operation of methanol direct injection engines. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating the overall workflow of the present invention. Detailed Implementation

[0012] 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.

[0013] Please see Figure 1 As shown, this embodiment provides a method for optimizing the nozzle structure of a methanol direct injection engine, including the following steps:

[0014] S1. Use an atomic force microscope to perform a three-dimensional nanoscale scan on the surface of the nozzle exit edge to detect and quantify its roughness distribution and obtain the surface roughness.

[0015] S2. A dynamic mapping table is constructed based on surface roughness. The dynamic mapping table establishes the correspondence between liquid film contact angle correction coefficients between different surface roughnesses and different real-time methanol viscosities through numerical simulation. The real-time methanol viscosity changes in real time according to the engine operating temperature, and the dynamic mapping table stores the specific corresponding data.

[0016] S3. Determine the methanol viscosity based on the engine real-time temperature sensor data, query the dynamic mapping table to obtain the liquid film contact angle correction coefficient under the current operating conditions, optimize the nozzle outlet geometric parameters based on the liquid film contact angle correction coefficient, and adjust the design size of the nozzle outlet chamfer radius by calculating the compensation value of the liquid film adhesion by the liquid film contact angle correction coefficient.

[0017] S4. The nozzle is designed and manufactured using the adjusted nozzle outlet chamfer radius design dimensions, and the nozzle outlet chamfer radius is controlled by laser fine-tuning.

[0018] The detection and quantification of its roughness distribution involves using a high-resolution probe of an atomic force microscope to perform multi-directional three-dimensional scanning along the nozzle exit edge surface to acquire raw point cloud data of the surface morphology. A built-in algorithm is then used to filter and denoise the raw point cloud data to eliminate measurement errors, and the surface roughness value is calculated based on the point cloud data. Specifically:

[0019] Extract the peak-valley height difference of all bumps and depressions in the point cloud data within the scanning area, and statistically analyze the spatial distribution characteristics of the peak-valley height difference to generate a roughness distribution map. Output the surface roughness value as the quantization result based on the roughness distribution map.

[0020] A simulation platform with different temperature conditions was set up in a laboratory environment. The surface roughness values ​​of various nozzle samples were measured using an atomic force microscope, and the methanol viscosity at the corresponding temperature was recorded simultaneously. A liquid film separation model was established using computational fluid dynamics simulation software. The surface roughness value and methanol viscosity were input to simulate the flow behavior of the liquid film at the nozzle outlet edge and output the liquid film contact angle change data. Then, the correspondence between the surface roughness value, methanol viscosity and liquid film contact angle correction coefficient was fitted by regression analysis. Finally, the correspondence was stored as a dynamic mapping table in the form of a structured database. The dynamic mapping table uses the surface roughness value and methanol viscosity as index keys to directly associate the liquid film contact angle correction coefficient.

[0021] When establishing the correspondence between liquid film contact angle correction coefficients for different surface roughnesses and different real-time methanol viscosities through numerical simulation, multiphysics coupling simulation technology is adopted. The nozzle outlet geometric model is set in the fluid dynamics simulation software, and the actual surface morphology data corresponding to the surface roughness value is imported as the boundary condition. At the same time, the dynamic process of liquid film formation and separation is simulated by inputting methanol viscosity. The force distribution of the liquid film at the uneven texture is calculated, and the offset of the liquid film contact angle is derived based on the force distribution analysis. The offset is then converted into liquid film contact angle correction coefficients, and the simulation results are made consistent with the experimental calibration data through iterative optimization. Finally, a mapping relationship of liquid film contact angle correction coefficients covering the entire surface roughness range and the entire methanol viscosity condition is established.

[0022] Optimizing the nozzle exit geometry parameters based on the liquid film contact angle correction coefficient includes calculating the compensation value for liquid film adhesion based on the liquid film contact angle correction coefficient obtained from the dynamic mapping table. Specifically:

[0023] The liquid film contact angle correction coefficient is input into the preset algorithm model. The preset algorithm model derives the adjustment amount of the chamfer radius based on the liquid film mechanical balance equation. When the liquid film contact angle correction coefficient is greater than the reference value, the output command increases the chamfer radius to reduce the liquid bridge trailing effect. When the liquid film contact angle correction coefficient is less than the reference value, the output command decreases the chamfer radius to balance the liquid film cutting force. The optimization process combines engine operating data in real time to ensure that the chamfer radius design size dynamically adapts to the spray uniformity requirements under the current viscosity.

[0024] The algorithm model is trained through machine learning. Historical operating data is collected, including measured values ​​of spray particle size variation coefficient under different surface roughness values, methanol viscosity, and corresponding chamfer radii. A training dataset is constructed, and a neural network model is used to learn the nonlinear relationship between the chamfer radius adjustment and the liquid film contact angle correction coefficient. After training, the algorithm model receives the liquid film contact angle correction coefficient as input and outputs the chamfer radius compensation value. The chamfer radius compensation value is used to adjust the design dimensions in real time to ensure the uniformity of liquid film separation.

[0025] Adjusting the design dimensions of the nozzle exit chamfer radius by calculating the compensation value of the liquid film adhesion based on the liquid film contact angle correction factor involves converting the chamfer radius compensation value into the actual adjustment amount of the chamfer radius. The specific process is as follows:

[0026] Based on the chamfer radius compensation value and referring to the preset chamfer radius reference design drawing, modify the geometric parameters in the computer-aided design software.

[0027] Modifying geometric parameters in computer-aided design software includes using parametric design tools to map chamfer radius compensation values ​​to increments or decrements of the chamfer radius. The tool has a built-in verification module that compares the adjusted chamfer radius with the liquid film force simulation results in real time. If the simulation shows uneven liquid film separation, the chamfer radius compensation value is automatically iterated and optimized until the final design dimensions that make the spray uniform are output.

[0028] Controlling the chamfer radius at the nozzle exit through laser fine-tuning involves employing pulsed laser ablation technology. Based on the optimized chamfer radius design dimensions, a laser processing path instruction is generated. The laser head is controlled to scan the nozzle exit area. By adjusting the laser energy and focus point position, material is removed layer by layer to refine the chamfer shape. Simultaneously, an online microscope is used to monitor the chamfer curvature in real time. The feedback data is compared with the design dimensions, and the laser parameters are dynamically calibrated to ensure that the chamfer radius meets the optimization requirements.

[0029] Controlling the laser head to scan the nozzle exit area involves combining a machine vision system to capture real-time images of the nozzle exit, identify the edges of the chamfered area, calculate the laser ablation depth and range based on the design dimensions, generate adaptive path planning, continuously collect data on the heat-affected zone during laser processing, prevent overheating deformation through temperature sensors, and perform three-dimensional morphology re-inspection after completion to ensure that the chamfer radius control accuracy meets the spray performance standards.

[0030] It needs further explanation that, in order to accurately obtain the microscopic roughness characteristics of the nozzle exit edge surface and provide reliable basic data for the subsequent calculation of the liquid film contact angle correction coefficient, the detection and quantification of its roughness distribution needs to be completed step by step through high-precision scanning, data purification, and statistical analysis. The specific implementation process is as follows:

[0031] The core of detecting and quantifying roughness distribution lies in using atomic force microscopy (AFM) for precise scanning and data processing. AFM is a microscopic morphology characterization instrument with nanometer-level resolution. Its high-resolution probe, as the core component directly in contact with the sample, has a tip curvature radius of only a few nanometers, enabling it to sensitively perceive surface variations at the atomic level. The nozzle exit edge surface is a critical area connecting the nozzle orifice to the external space; its microscopic roughness directly affects the adhesion and separation of the methanol liquid film. The specific scanning process is as follows:

[0032] The nozzle is fixed on the precision stage of the atomic force microscope (AFM). The stage position is adjusted so that the nozzle exit edge is within the probe scanning range. A multi-directional three-dimensional scan is performed around the nozzle exit edge, covering a complete 360° circumference and scanning within 10 μm above and below the exit axis. The scanning step size is set to 5 nm to ensure a balance between resolution and scanning efficiency. During the scanning process, the probe undergoes minute displacements due to surface unevenness. The displacement signals are recorded in real time by a laser interferometer and converted into three-dimensional spatial coordinates, ultimately forming raw point cloud data containing millions of discrete coordinate points. This data directly reflects the original morphology of the surface, but it is mixed with measurement noise caused by environmental vibrations, probe thermal drift, etc. To remove noise interference, the raw point cloud data needs to be filtered and denoised using the built-in filtering algorithm of the AFM. Here, a Gaussian filtering algorithm is used. Its core is to smooth the random fluctuations in the data through neighborhood averaging, preserving the true surface unevenness features. The measurement error mainly refers to the false morphology information caused by random noise, rather than systematic errors. The filtering process only targets this type of noise and does not change the true peak and valley structure. The specific denoising process is as follows:

[0033] The algorithm traverses all original point cloud data within a preset neighborhood window (e.g., 5×5 data points), calculates the mean z-axis coordinate of each point within the window, and replaces the original coordinates of the center data point of the window with the mean (if the center coordinates deviate from the mean by more than 3 times the standard deviation, they are directly identified as outliers and replaced). After the traversal is completed, the purified point cloud data is obtained, which can realistically restore the microscopic morphology of the nozzle exit edge. Based on the purified point cloud data, the surface roughness value is calculated. The surface roughness value is the core indicator for quantifying the degree of surface unevenness. Here, the arithmetic mean deviation surface roughness value commonly used in industry is selected as the quantification parameter, which can comprehensively reflect the overall undulation characteristics of the surface microtexture. The specific calculation process is as follows:

[0034] First, the peak-valley height difference of all bump textures corresponding to the point cloud data within the scanned area is extracted. A peak refers to the point with the highest z-axis coordinate (the convex vertex) within a local area, and a valley is the point with the lowest z-axis coordinate (the concave trough) within a local area. The peak-valley height difference is the difference in z-axis coordinate between the peak vertex and the valley trough within the same micro-unit (e.g., within a 1μm × 1μm range). Then, the spatial distribution characteristics of all peak-valley height differences are statistically analyzed. All height difference data are classified and counted according to preset height difference intervals (e.g., 0-5nm, 5-10nm, 10-20nm, etc.), and the proportion of height differences within each interval is calculated to form statistical results reflecting the roughness distribution pattern. Based on this... The statistical results generate a roughness distribution map. The horizontal axis represents the peak-valley height difference interval, and the vertical axis represents the distribution density of the corresponding interval (as a percentage of the total data). This visually presents the concentration trend and dispersion of surface roughness. Finally, the surface roughness value is calculated based on the roughness distribution map. By taking the arithmetic mean of all peak-valley height differences, the surface roughness value (unit: nm) is obtained. This value is the quantification result of surface roughness. For example, if the average value of all peak-valley height differences is 8.5 nm, then the surface roughness value is 8.5 nm. This result accurately quantifies the micro-roughness of the nozzle exit edge, providing core input parameters for the subsequent construction of a dynamic mapping table.

[0035] After obtaining quantitative data on the surface roughness of the nozzle exit edge using atomic force microscopy, in order to establish the comprehensive influence of this roughness on the real-time viscosity of methanol on the liquid film contact angle, a complete data acquisition and simulation analysis process needs to be constructed in a laboratory environment, ultimately forming a dynamically mapped table that can be directly accessed. The specific implementation process is as follows:

[0036] First, a simulation platform for different temperature conditions was built in a laboratory environment. This platform is an experimental device that can accurately control the ambient temperature and simulate the actual operating temperature range of the engine. The temperature control range covers -40℃ to 120℃ (including typical operating conditions such as low-temperature cold start, normal temperature idling, and high-temperature high load). The platform has a built-in temperature sensor (accuracy ±0.1℃) to provide real-time temperature data feedback, and a constant temperature bath is provided to ensure that the methanol temperature is consistent with the set operating temperature. Subsequently, various nozzle samples were selected. These samples were prepared using different processing techniques to ensure that the surface roughness covers the common range in actual applications (surface roughness value = 2nm to 50nm). Each roughness corresponds to 3 parallel samples to ensure data reliability. The surface of the exit edge of each nozzle sample was 3D scanned using an atomic force microscope, with the scanning parameters consistent with those described above (tap mode, 5nm step size). The surface roughness value of each sample was measured and recorded. At the same time, methanol was injected into the constant temperature container of the platform. After the temperature stabilized at the set value, the methanol viscosity at the corresponding temperature was measured using a rotational viscometer (accuracy ±1%). The roughness value and methanol viscosity data were recorded simultaneously to ensure that the temperature reference of each set of data was completely consistent, thus forming the basic dataset.Next, a liquid film separation model is established using computational fluid dynamics (CFD) simulation software. CFD simulation software is a specialized software based on the fundamental equations of fluid mechanics, capable of numerically solving physical phenomena such as fluid flow, heat transfer, and mass transfer. The liquid film separation model is a simulation model that recreates the entire process of methanol forming a liquid film at the nozzle exit edge from the nozzle to its separation from the nozzle. The model needs to accurately replicate the geometric dimensions of the nozzle and the microscopic morphological features of the nozzle exit edge, while defining the physical properties and boundary conditions of methanol. The surface roughness values ​​and corresponding methanol viscosities from the basic dataset are imported into the model as input parameters. The surface roughness values ​​are mapped to the surface morphology boundary conditions in the model, i.e., generating microscopic textures based on the measured roughness distribution. The methanol viscosity is directly defined as a fluid property parameter. After starting the simulation, the software numerically calculates the flow behavior of the liquid film on the microscopic texture at the nozzle exit edge, including the adhesion, extension, force balance, and final separation process of the liquid film, focusing on capturing the flow behavior under different roughness and viscosity conditions. The change in the contact state between the liquid film and the nozzle surface ultimately outputs data on the change in the liquid film contact angle. This data represents the angle formed between the liquid film and the nozzle surface when the liquid film is stable, directly reflecting the adhesion characteristics of the liquid film. Different combinations of input parameters correspond to a set of independent contact angle change data. To establish a quantitative correlation among the three, regression analysis is needed to fit the relationship between surface roughness, methanol viscosity, and the liquid film contact angle correction coefficient. Regression analysis is a statistical method used to uncover the correlation between variables. Here, a multivariate nonlinear regression algorithm is used, which can effectively handle the complex nonlinear relationship between surface roughness, methanol viscosity, and contact angle. The liquid film contact angle correction coefficient is a quantitative parameter used to compensate for the influence of surface roughness and methanol viscosity. Its value is derived from the deviation between the simulated actual contact angle and the theoretical contact angle under an ideal smooth surface (surface roughness = 0). The larger the deviation, the larger the absolute value of the correction coefficient, which is used for subsequent precise adjustment of the nozzle geometry parameters. The specific fitting process is as follows:

[0037] All simulated data sets of surface roughness, methanol viscosity, and liquid film contact angle were collected. Surface roughness and methanol viscosity were used as independent variables, and the liquid film contact angle correction coefficient was used as the dependent variable. Least squares regression fitting was employed to obtain a mathematical expression describing the correlation between the three. During the fitting process, the coefficient of determination (R²) was calculated to verify the fitting effect, ensuring that R² ≥ 0.95 to guarantee the reliability of the correlation. Finally, the fitted correspondence was stored as a dynamic mapping table in the form of a structured database. A structured database is a database type that organizes and stores data in a fixed format, supporting efficient index queries and data retrieval. The dynamic mapping table is used because methanol viscosity changes in real time with engine operating temperature, and the data in the table can dynamically match parameter combinations under different operating conditions. Its core fields include surface roughness, methanol viscosity, and liquid film contact angle correction coefficient. Surface roughness and methanol viscosity are used as index keys, which are optimized by segmenting according to numerical ranges (e.g., roughness in intervals of 2 nm, viscosity in intervals of 0.1 mPa·s) to ensure quick location of the corresponding correction coefficient during queries. The specific storage process is as follows:

[0038] All fitted data sets are entered into the database according to the index key rules to establish a dual-field joint index. At the same time, a data verification mechanism is set up so that if the newly added data exceeds the existing index range, the supplementary fitting process will be automatically triggered. After storage, the dynamic mapping table can be directly called by the subsequent optimization process. By inputting the measured surface roughness value and the methanol viscosity under the current operating conditions, the corresponding liquid film contact angle correction coefficient can be quickly queried, providing an accurate basis for the optimization of the nozzle outlet geometry parameters.

[0039] In the numerical simulation stage of constructing the dynamic mapping table, in order to accurately capture the synergistic effect of surface roughness and methanol viscosity on the liquid film contact angle, multi-physics coupling simulation technology needs to be adopted. This technology can simultaneously integrate multiple physical fields such as fluid dynamics, surface tension effect, and solid-liquid interface interaction, avoiding the deviation caused by neglecting key influencing factors in single-physics simulation, and adapting to the complex dynamic behavior of the liquid film at the nozzle outlet edge. In specific implementation, the nozzle outlet geometric model is first built in the fluid dynamics simulation software. This model is a digital model that is a 1:1 replica of the actual nozzle design drawing. The nozzle diameter (e.g., 0.15mm), the outlet reference chamfer radius (e.g., 5μm), and the macroscopic geometry of the nozzle wall need to be accurately defined. At the same time, a structured mesh (minimum mesh size 0.1μm) is divided to ensure accurate adaptation to the micro-roughness morphology imported later. Subsequently, the actual surface morphology data corresponding to the surface roughness values ​​is imported as boundary conditions. The actual surface morphology data is the point cloud data obtained earlier through atomic force microscopy scanning, after filtering and denoising. It is converted into the microscopic texture of the mesh surface through the software's data interface. Boundary conditions are parameters that define the physical properties of the model boundary. Here, the microscopic morphology data is assigned to the wall boundary of the nozzle outlet edge to make the wall surface in the simulation completely consistent with the roughness of the actual nozzle surface. At the same time, the methanol viscosity at the corresponding temperature is input into the software as a fluid property parameter. It is also necessary to simultaneously define the basic physical parameters such as methanol density and surface tension coefficient to provide complete fluid property support for simulating liquid film behavior. After starting the simulation, the dynamic process of liquid film formation and separation is simulated. The liquid film formation stage begins with the high-pressure injection of methanol at the nozzle inlet. After flowing inside the nozzle, a continuous liquid film is formed at the outlet edge. Subsequently, the liquid film extends under the action of surface tension, viscous force, and wall adhesion. Finally, under the combined action of inertial force and aerodynamic force, it detaches from the nozzle and completes the separation. The time step of the entire simulation process is set to 1e-6 seconds to ensure that the dynamic changes of the liquid film at the microscale are captured. During the simulation, the multiphase flow module and solid-liquid interface module of the software are used to calculate the force distribution of the liquid film at the micro-texture of the nozzle outlet edge. Specifically, this includes the viscous shear force inside the liquid film, the van der Waals adhesion between the liquid film and the wall, the surface tension of the free surface of the liquid film, and the shear force at the gas-liquid interface. The combined effect of these forces directly determines the adhesion state and contact angle of the liquid film.Based on the calculated force distribution, the offset of the liquid film contact angle is analyzed and derived. The liquid film contact angle is the angle formed between the liquid film and the nozzle wall when the liquid film is stable. The contact angle of an ideal smooth surface (surface roughness value = 0) at standard viscosity (2 mPa·s) is the reference contact angle. However, on actual rough surfaces and at non-standard viscosities, changes in the force distribution will cause the contact angle to deviate from the reference value. This degree of deviation is the offset of the liquid film contact angle. If the actual contact angle is 75°, the offset is +15°, and if it is 45°, the offset is -15°. Then, the offset is converted into a liquid film contact angle correction coefficient. The correction coefficient is a dimensionless parameter that is quantified by a preset ratio (such as offset ÷ reference contact angle) and used to directly correlate the adjustment amount of the nozzle chamfer radius. For example, the correction coefficient corresponding to an offset of +15° is 1.25, and the correction coefficient corresponding to an offset of -15° is 0.75. To ensure the reliability of the simulation results, iterative optimization is needed to make the simulation results consistent with the experimental calibration data. The experimental calibration data consists of the surface roughness-methanol viscosity-liquid film contact angle data set measured in the laboratory environment mentioned earlier. During iterative optimization, the relative error between the simulated contact angle and the measured contact angle is first calculated. If the error is greater than 1%, the key parameters in the model are adjusted, the simulation is rerun, and this process of calculating the error, adjusting the parameters, and resimulating is repeated until the simulation error corresponding to all experimental calibration data is less than 1%, ensuring that the numerical simulation can accurately replicate the actual working conditions. Finally, based on the iteratively optimized model, the liquid film contact angle correction coefficients covering the entire surface roughness range (surface roughness value = 2nm-50nm) and the entire methanol viscosity condition (0.5mPa·s-5mPa·s, corresponding to engine operating temperature -40℃-120℃) are calculated in batches, establishing a complete mapping relationship between surface roughness, methanol viscosity, and liquid film contact angle correction coefficients, providing core data support for the construction of the dynamic mapping table.

[0040] After obtaining the liquid film contact angle correction coefficient under the current operating conditions through a dynamic mapping table, the next core step is to optimize the nozzle outlet geometry parameters based on this coefficient. The key lies in accurately calculating the compensation value of liquid film adhesion, and then dynamically adjusting the nozzle outlet chamfer radius to ensure liquid film separation effect and spray uniformity. The specific implementation process is as follows:

[0041] The core step in optimizing the nozzle exit geometry parameters based on the liquid film contact angle correction coefficient is to calculate the compensation value for liquid film adhesion based on the obtained liquid film contact angle correction coefficient. Liquid film adhesion is the adsorption force generated between the liquid film and the nozzle exit edge surface due to molecular interactions. Its magnitude directly affects the smoothness of liquid film separation. Excessive adhesion will make it difficult for the liquid film to detach, while insufficient adhesion will easily cause premature separation, both of which will damage the spray uniformity. The compensation value is a quantitative parameter used to offset the influence of abnormal liquid film adhesion. The adhesion is indirectly controlled by adjusting the nozzle chamfer radius, ultimately achieving the optimal liquid film separation state. The specific calculation process is as follows:

[0042] The retrieved liquid film contact angle correction coefficient is input into the preset algorithm model. This preset algorithm model is a dedicated calculation model that has been trained and solidified. Its core is based on the liquid film mechanical equilibrium equation, which is a mathematical relationship describing the force state of the liquid film at the nozzle exit edge. It comprehensively considers the balance relationship of surface tension, viscous force, adhesion force, and inertial force, and can accurately derive the geometric parameter adjustment amount required to achieve the ideal separation state of the liquid film. When the algorithm model runs, it uses the liquid film mechanical equilibrium equation as the core and combines it with the input liquid film contact angle correction coefficient to derive the adjustment amount of the nozzle exit chamfer radius. The nozzle exit chamfer radius is the fillet size of the nozzle exit edge, and its size directly affects the liquid film and the nozzle exit chamfer radius. The contact area and force distribution on the nozzle surface are key geometric parameters for controlling liquid film adhesion. The adjustment amount refers to the specific value that needs to be increased or decreased relative to the reference chamfer radius. The reference chamfer radius is the optimal radius under standard operating conditions, calibrated experimentally, and serves as a reference for adjustment. When the liquid film contact angle correction coefficient is greater than the reference value, it indicates that the current liquid film contact angle is greater than the ideal value, corresponding to excessive liquid film adhesion. In this case, a liquid bridge tailing effect is prone to occur. The liquid bridge tailing effect refers to the phenomenon where the liquid film fails to break in time during separation due to excessive adhesion, forming an extended trail similar to a liquid bridge, resulting in uneven spray particle size and poor fuel atomization. To address this situation, the algorithm model will output an instruction to increase the chamfer radius. A large chamfer radius reduces the contact area between the liquid film and the nozzle surface, thereby reducing adhesion and effectively suppressing the liquid bridge trailing effect. When the liquid film contact angle correction coefficient is less than the reference value, it indicates that the current liquid film contact angle is less than the ideal value, the liquid film adhesion is too small, and uneven cutting is likely to occur during liquid film separation, i.e., some areas of the liquid film break prematurely while others remain adhered, resulting in large spray dispersion. At this time, the model will output a command to reduce the chamfer radius. By reducing the chamfer radius, the contact length between the liquid film and the nozzle surface is increased, the liquid film cutting force is balanced, and the liquid film is evenly separated circumferentially along the nozzle exit. The entire optimization process needs to be combined with engine operating data in real time, including the engine's current speed, load, and intake air. Real-time operating parameters such as temperature and injection pressure directly affect the atomization requirements of methanol. For example, under high-speed and high-load conditions, a finer and more uniform spray is needed to ensure complete combustion, while under low-speed and low-load conditions, a balance needs to be struck between spray particle size and fuel economy. The algorithm model receives these operating condition data in real time and dynamically corrects the derived chamfer radius adjustment amount. For example, under high-speed conditions, if the initial output chamfer radius increase is 2μm, it will be corrected to 2.5μm after combining with the operating condition data to further improve the liquid film separation speed and adapt to the high-speed combustion requirements. Under low-speed conditions, the initial adjustment amount of 1μm may be corrected to 0.8μm to avoid excessive adjustment that would cause the spray to become too dispersed.This closed-loop process of calculating correction coefficients, deriving adjustment amounts, and correcting operating data ensures that the final output nozzle outlet chamfer radius design size can dynamically adapt to the spray uniformity requirements of current methanol viscosity and engine operating conditions, providing accurate geometric parameter basis for subsequent nozzle design and manufacturing.

[0043] To ensure the algorithm model accurately captures the complex nonlinear relationship between the chamfer radius adjustment and the liquid film contact angle correction coefficient, and to guarantee that the output adjustment can dynamically adapt to the liquid film separation requirements under different operating conditions, this algorithm model is built through machine learning training. Its core principle is to learn patterns from massive amounts of historical data, replacing the limitations of traditional manual formula derivation. The specific implementation process is as follows:

[0044] The first step in training the algorithm model is to collect historical operating condition data. This data forms the foundation for model learning and covers all operating conditions from actual engine operation and laboratory simulations. Specifically, it includes different surface roughness values ​​(covering a common range of 2nm-50nm, corresponding to nozzle samples with different processing techniques), different methanol viscosities (0.5mPa·s-5mPa·s, matching engine operating temperatures of -40℃-120℃), different nozzle exit chamfer radius design values ​​(adjustable range of 3μm-8μm), and, crucially, measured values ​​of the spray particle size variation coefficient. The spray particle size variation coefficient is a core indicator for measuring spray uniformity, reflecting the different particle sizes in the spray. The droplet dispersion is measured by a laser particle size analyzer. A smaller value indicates more uniform droplet size and better liquid film separation. This value is used as a supervisory signal to judge whether the chamfer radius design is reasonable. After collection, the data is preprocessed to remove outliers caused by measurement errors and equipment failures. Missing data is filled by interpolation of the mean of adjacent operating conditions. Finally, the surface roughness value-methanol viscosity-liquid film contact angle correction coefficient is used as the input feature, and the chamfer radius adjustment amount-spray particle size variation coefficient is used as the output label. The dataset is divided into a training set (for model learning) and a validation set (for validating model performance) in a 7:3 ratio to construct a structured training dataset. Subsequently, a neural network model was used as the training medium to learn the nonlinear relationship between the chamfer radius adjustment and the liquid film contact angle correction coefficient. The neural network model selected here is a multilayer perceptron (MLP) because it is good at handling high-dimensional nonlinear mapping problems. The model structure is designed as an input layer-hidden layer-output layer. The input layer has 1 neuron, corresponding to the liquid film contact angle correction coefficient; the hidden layer has 2 layers, each with 32 neurons, and the ReLU activation function is used to enhance the nonlinear fitting ability of the model; the output layer has 1 neuron, corresponding to the chamfer radius compensation value.During training, the liquid film contact angle correction coefficients from the training set are input into the model. The model calculates and outputs the predicted chamfer radius compensation value through forward propagation. The predicted value is then compared with the actual chamfer radius adjustment in the training set, and the mean squared error is used as the loss function to measure the deviation between the predicted and actual values. The error is backpropagated through the Adam optimizer, and the weights and bias parameters of each layer of the model are adjusted round by round to reduce the loss value. The number of training iterations is set to 1000 rounds. After each round, the model performance is evaluated using a validation set. If the loss value on the validation set no longer decreases after 50 consecutive rounds, an early stopping mechanism is triggered to stop training and prevent model corruption. Overfitting (i.e., only adapting to training data and unable to cope with new operating conditions) ensures the model has good generalization ability. After training, the algorithm model forms a stable input-output mapping relationship. When it receives the liquid film contact angle correction coefficient obtained by querying the dynamic mapping table under the current operating condition, it can quickly output a precise chamfer radius compensation value through internal weighted calculation. The chamfer radius compensation value is a specific adjustment value relative to the reference chamfer radius (a standard value calibrated by experiments, such as 5μm), with the unit being μm. A positive value indicates that the chamfer radius needs to be increased, and a negative value indicates that the chamfer radius needs to be decreased. Its magnitude directly corresponds to the compensation requirement of liquid film adhesion. This compensation value is transmitted to the subsequent design stage in real time to adjust the design size of the nozzle outlet chamfer radius. This ensures that regardless of whether the current operating condition is high viscosity and low temperature or low viscosity and high temperature, or whether the surface roughness is large or small, the chamfer radius can be precisely adjusted to balance the force on the liquid film at the nozzle outlet edge, ultimately achieving uniform liquid film separation. The spray particle size variation coefficient is controlled within a preset threshold (such as ≤15%), ensuring the atomization effect and combustion efficiency of the methanol direct injection engine.

[0045] After the algorithm model outputs accurate chamfer radius compensation values, this theoretical compensation parameter needs to be converted into actual adjustment amounts that can directly guide the design. By modifying the geometric parameters in the computer-aided design software, the optimized chamfer radius size of the nozzle outlet, which is suitable for the current working conditions, is finally determined. The specific implementation process is as follows:

[0046] The design size of the nozzle exit chamfer radius is adjusted by calculating the compensation value of the liquid film adhesion based on the liquid film contact angle correction coefficient. The core is to convert the chamfer radius compensation value into the actual adjustment amount of the chamfer radius. The chamfer radius compensation value is a theoretical correction value output by the algorithm model based on the liquid film contact angle correction coefficient, relative to the baseline chamfer radius. A positive value indicates that the chamfer radius needs to be increased, and a negative value indicates that it needs to be decreased. Its magnitude directly corresponds to the compensation requirement for liquid film adhesion. The actual adjustment amount is the specific dimensional change after converting the compensation value. It is equal in value and consistent in direction with the compensation value, requiring no additional conversion and can be directly used to modify design parameters, ensuring that the theoretical compensation requirement is accurately translated into design size adjustments. The specific conversion process is as follows:

[0047] First, clarify the physical meaning of the compensation value. For example, when the compensation value is +1.5μm, the actual adjustment is an increase of 1.5μm; when the compensation value is -0.9μm, the actual adjustment is a decrease of 0.9μm. During the conversion process, the accuracy of the compensation value (to 0.1μm) must be maintained to avoid dimensional errors affecting the liquid film separation effect. Then, based on the magnitude of the chamfer radius compensation value, modify the geometric parameters in the computer-aided design software according to the preset chamfer radius reference design drawing. The preset chamfer radius reference design drawing is a numerical value under standard operating conditions (surface roughness = 10nm, methanol viscosity 2mPa·s) calibrated experimentally. The design drawings include the complete geometry of the nozzle, with the core parameter being the baseline chamfer radius (e.g., 5μm). Related dimensions such as the orifice diameter and wall thickness are also labeled to ensure compatibility with other structures when modifying the chamfer radius. The computer-aided design software is a professional tool with 3D modeling and parametric editing capabilities. It can accurately edit geometric features and update the model shape in real time to adapt to the design requirements of high-precision parts like nozzles. Here, the geometric parameter specifically refers to the radius of the nozzle exit chamfer, which is the core data defining the size of the chamfer arc and directly determines the contact area and force distribution between the liquid film and the nozzle surface. The specific modification process is as follows:

[0048] First, open the computer-aided design software and load the preset chamfer radius baseline design drawing using the software's import function. Locate the feature item corresponding to the nozzle outlet chamfer in the model tree (usually labeled Chamfer1 or Chamfer 1). Next, check the current value of the baseline chamfer radius (e.g., 5μm). Calculate the target chamfer radius by combining the baseline value with the actual adjustment amount. Add the baseline value to the actual adjustment amount. For example, baseline value 5μm + adjustment amount + 1.5μm = target value 6.5μm, baseline value 5μm + adjustment amount - 0.9μm = target value 4.1μm. Then, double-click the chamfer feature item and enter the calculated target chamfer radius in the parameter editing window. The software will automatically update the chamfer shape in the 3D model and synchronously adjust the associated geometric constraints. Finally, use the software's real-time preview function to check whether the connection between the chamfer and the nozzle wall and outlet edge is smooth. Ensure that the modified geometric parameters meet the compensation requirements without affecting the overall structural integrity of the nozzle, providing accurate digital design basis for subsequent nozzle manufacturing.

[0049] After determining the actual adjustment amount of the chamfer radius, the core of modifying the geometric parameters in the computer-aided design software is to achieve precise mapping using parametric design tools, while ensuring that the adjustment effect meets the requirements of liquid film separation through a built-in verification mechanism, thus avoiding a decrease in spray uniformity due to improper parameter modification. The specific implementation process is as follows:

[0050] The first step in modifying geometric parameters in computer-aided design (CAD) software is to use parametric design tools to map chamfer radius compensation values ​​to increments or decrements in the chamfer radius. Parametric design tools are core modules in CAD software with parameter association and automatic update functions. They establish a binding relationship between geometric features and quantified parameters, eliminating the need for manual model reshaping; simply modifying parameters drives changes in model shape, adapting to the rapid optimization needs of high-precision parts. The chamfer radius compensation value is the theoretical correction parameter output by the algorithm model, while the increment or decrement is determined by the positive or negative attribute of the compensation value. A positive compensation value corresponds to an increment (size increase) in the chamfer radius, and a negative value corresponds to a decrement (size decrease). During the mapping process, the tool automatically preserves the accuracy of the compensation value, ensuring complete consistency between the theoretical compensation requirements and the geometric parameter modifications. The specific mapping process is as follows:

[0051] Open the parametric editing interface in the computer-aided design software, call the preset chamfer radius-compensation value mapping rule, and input the chamfer radius compensation value output by the algorithm into the parameter box. The tool will automatically recognize the positive or negative sign of the compensation value and convert it into the corresponding dimensional change. For example, a compensation value of +1.2μm maps to a chamfer radius increase of 1.2μm, and a compensation value of -0.8μm maps to a chamfer radius decrease of 0.8μm. Simultaneously, it automatically associates the geometric features of the nozzle outlet chamfer and updates the chamfer dimensions in the 3D model in real time, ensuring a precise match between the model shape and the adjusted parameters. The parametric design tool has a built-in verification module that compares the adjusted parameters in real time. The chamfer radius and the simulation results of liquid film force are used to verify the rationality of parameter modification. The verification module is a built-in effect verification function module of the tool. Its core is to call the liquid film force simulation model built above (consistent with the model used in numerical simulation to ensure uniform verification standards). Through simulation calculation, it judges whether the current chamfer radius can make the liquid film achieve a uniform separation state. The liquid film force simulation results refer to the force distribution, contact angle, and spray particle size variation coefficient of the simulated liquid film at the nozzle outlet edge. Among them, the spray particle size variation coefficient is the core judgment indicator. A value ≤15% is considered that the liquid film separation is uniform (the qualified threshold calibrated by experiments). The specific comparison process is as follows:

[0052] After the parameters are updated, the verification module automatically extracts the adjusted chamfer radius value and simultaneously calls the surface roughness value and methanol viscosity data under the current working conditions, inputting all three into the liquid film stress simulation model. After rapid calculation, the model outputs the liquid film stress distribution cloud map and the spray particle size variation coefficient. The verification module compares the variation coefficient with the qualified threshold. If the variation coefficient is ≤15%, the liquid film separation is determined to be uniform, and the current parameters do not need to be adjusted. If the variation coefficient is >15%, the simulation shows uneven liquid film separation, and the automatic iterative optimization process needs to be started. When the simulation shows uneven liquid film separation, the tool will automatically iteratively optimize the chamfer radius compensation value until the final design size that makes the spray uniform is output. Automatic iterative optimization refers to the process in which the tool cyclically adjusts the compensation value, mapping parameters, and simulation verification according to preset rules, without manual intervention, and can continuously optimize parameters until the performance requirements are met. The final design size refers to the chamfer radius value that makes the spray particle size variation coefficient ≤15% after iterative optimization, which is the core basis for subsequent nozzle manufacturing. The specific optimization process is as follows:

[0053] After the verification module determines uneven separation, it adjusts the chamfer radius compensation value according to the deviation direction of the simulation results. If the liquid film exhibits a tailing effect (large coefficient of variation and large contact angle), it indicates that the chamfer radius increment is insufficient, and the compensation value needs to be increased by 10%. If the liquid film is scattered (large coefficient of variation and small contact angle), it indicates that the chamfer radius reduction is insufficient, and the compensation value needs to be reduced by 10%. Each adjustment is controlled within 10% to avoid excessive adjustment leading to parameter loss of control. The adjusted compensation value is then mapped back to the chamfer radius increment or decrement through the parametric design tool, updating the geometric parameters of the 3D model. The verification module then starts a new round of liquid film force simulation and comparison, repeating the closed-loop process of adjusting compensation value-mapping parameters-simulation verification until the coefficient of variation of the spray particle size in the simulation output is ≤15%. At this point, the tool locks the current chamfer radius size and outputs the final design size that makes the spray uniform. This size not only accurately matches the liquid film adhesion compensation requirements under the current working conditions but also ensures the spray performance through multiple rounds of simulation verification, providing a reliable digital design basis for the subsequent nozzle manufacturing.

[0054] After determining the final design dimension of the nozzle exit chamfer radius using computer-aided design software, the digital design needs to be precisely converted into a physical structure using laser fine-tuning technology. This relies on the high-precision processing characteristics of pulsed laser ablation, combined with real-time monitoring and dynamic calibration, to ensure that the chamfer radius fully meets the optimization requirements. The specific implementation process is as follows:

[0055] The control of the nozzle exit chamfer radius is achieved through laser fine-tuning, primarily using pulsed laser ablation technology. This technology utilizes a high-energy-density short-pulse laser (pulse width typically on the nanosecond level) to act on the material surface, causing the local material to absorb energy instantaneously and undergo vaporization and sputtering, thereby achieving a precision machining method for micro-removal. Its advantages include an extremely small heat-affected zone (less than 1μm), which can avoid material deformation, and the machining accuracy can reach the nanometer level, making it perfectly suited for the trimming needs of micro-sized nozzle chamfers. In practice, the optimized chamfer radius design size (e.g., 6.5μm) is first imported into the laser processing control system. This size is the target value determined through multiple rounds of simulation and iterative optimization, and is directly used as the basis for processing. The control system automatically generates laser processing path instructions based on the design size and the macroscopic geometric parameters of the nozzle (e.g., aperture 0.15mm). The laser processing path instructions are a digital instruction set that includes the laser head movement trajectory, scanning sequence, and dwell time. The path is planned according to the arc contour of the chamfer and adopts a spiral layer-by-layer scanning strategy, progressing from the reference position at the nozzle exit edge towards the target chamfer radius to ensure a smooth processing process and uniform material removal. Subsequently, the control system drives the laser head to perform precise scanning of the nozzle exit area. The laser head is the core execution component of the laser processing equipment. It has a built-in focusing lens group that can focus the laser beam into a spot with a diameter of only a few micrometers. The focal point position can be adjusted by precision piezoelectric ceramic drive, with a positioning accuracy of ±0.05μm. During the scanning process, the amount of material removed is controlled by adjusting the laser energy and the focal point position in real time. The laser energy is calibrated to 1-5 mJ according to the processing requirements. The energy level determines the material removal depth of a single scan (usually 0.05-0.1 μm / scan). This avoids excessive energy causing excessive ablation or cracking of the material. The focal point position must be precisely aligned with the chamfered processing surface of the nozzle exit to ensure that the laser energy is concentrated on the target area. If the focal point is off, it will lead to deviation in processing dimensions.The laser head scans layer by layer along a preset path, removing a small amount of material after each layer is scanned, gradually shaping the chamfer to meet the design dimensions. The scanning speed is set at 10-20 mm / s to balance processing efficiency and precision. To monitor processing quality in real time, an online microscope is used to monitor the chamfer curvature in real time. The online microscope is a high-magnification optical monitoring device with micron-level resolution. Through a dedicated optical lens aimed at the nozzle exit area, it can capture the microscopic morphology image of the chamfer in real time and extract the actual curvature data of the chamfer through image algorithms. The chamfer curvature is a core parameter reflecting the degree of curvature of the chamfer arc, and its value is directly related to the chamfer radius. The greater the curvature, the smaller the radius, and vice versa. The monitoring data is transmitted to the laser processing control system in real time and compared with the preset... The design dimensions are compared, and the deviation between the actual curvature and the target curvature is calculated (the allowable deviation range is set to ±0.1μm). If the deviation exceeds the allowable range, the control system will initiate a dynamic calibration process, adjusting the laser parameters according to the direction of the deviation. For example, if the actual chamfer radius is too small (curvature too large), the laser energy or scanning speed will be reduced to decrease the amount of material removed per pass; if the actual radius is too large (curvature too small), the laser energy will be appropriately increased or the scanning dwell time will be extended to improve material removal efficiency. This closed-loop process of scanning, processing, real-time monitoring, deviation comparison, and dynamic calibration is repeated until the radius value corresponding to the chamfer curvature detected by the online microscope deviates from the optimized design dimension by less than ±0.1μm. At this point, the processing is deemed satisfactory, and the laser processing system automatically stops scanning. The entire process, through the high-precision removal characteristics of pulsed laser ablation, combined with the feedback mechanism of real-time monitoring and dynamic calibration, effectively avoids the defects of traditional machining in controlling micro-dimensions, ensuring that the chamfer radius at the nozzle exit accurately matches the design requirements, and providing reliable physical structural support for uniform liquid film separation and spray performance optimization.

[0056] To further improve the accuracy and stability of laser processing and avoid processing errors caused by clamping deviations and differences in material properties, when controlling the laser head to scan the nozzle exit area, it is necessary to combine a machine vision system to achieve dynamic positioning and adaptive processing. At the same time, processing quality is ensured through full-process monitoring and final re-inspection. The specific implementation process is as follows:

[0057] The core of controlling laser head scanning lies in establishing a closed loop of real-time perception, precise calculation, and dynamic adjustment using a machine vision system. This machine vision system is an integrated system composed of a high-definition industrial camera, a coaxial illumination module, and an image processing unit. It can capture images of the workpiece surface at a microscopic scale and extract key features, linking in real-time with the laser processing control system to achieve dynamic calibration of the processing path. Specifically, the machine vision system is first activated, and the high-definition industrial camera (resolution no less than 1024×768, frame rate 30fps) is aligned with the nozzle exit area. The coaxial illumination module emits a uniform white light source to avoid shadow interference and ensure that the image clearly presents the microscopic morphology of the nozzle exit. The camera continuously captures real-time images of the nozzle exit at a frequency of 5 frames per second and transmits them to the image processing unit. The unit has a built-in edge detection algorithm (such as Canny). The algorithm performs grayscale and noise reduction on the image to accurately identify the edge of the chamfered area. The edge of the chamfered area is the boundary line between the nozzle exit chamfer, the nozzle wall, and the external space. Its contour shape directly determines the processing range. The algorithm extracts pixels with abrupt grayscale changes in the image and fits them to form a continuous edge curve, clearly marking the boundary of the chamfered area that needs to be laser-processed, avoiding the processing range from exceeding the target area. Based on the identified chamfered area edge and the optimized chamfer radius design size, the laser processing control system further calculates the laser ablation depth and range. The laser ablation depth refers to the thickness of material to be removed in each scan, which is determined by the difference between the design size and the current actual chamfer radius of the nozzle. For example, if the design radius is 6.5μm and the current actual radius is 5.0μm, then the total ablation depth is 1.5μm, which is completed in 15 rounds (each round removes 0.5μm).(1μm), to avoid material cracking caused by excessive ablation in a single operation. The ablation range refers to the spatial area covered by the laser scan, defined by the identified chamfer edge and the arc contour of the designed chamfer, ensuring that the ablation range completely covers the area to be repaired, while avoiding contact with other critical structures of the nozzle. Subsequently, the system generates an adaptive path plan. Adaptive path planning refers to a processing path that can be dynamically adjusted based on real-time edge recognition results. Unlike a fixed path, it plans a spiral-progressive scanning trajectory according to the ablation depth and range. The trajectory starts from the outer edge of the chamfer area and gradually progresses towards the center. The path of each scan is finely adjusted based on the edge image after the previous processing. To compensate for minor deviations that may occur during processing and ensure that the final chamfer contour closely matches the design dimensions, continuous data collection of the heat-affected zone (HAZ) and prevention of overheating deformation are necessary during laser processing. The HAZ is the area around the laser's point of action that is not ablated but has a significantly elevated temperature. If the temperature in this area is too high, it can cause changes in the material's metallographic structure and thermal deformation, thereby compromising the chamfer accuracy. Therefore, a miniature infrared temperature sensor (temperature range 0-500℃, accuracy ±1℃) is mounted next to the laser head to collect the temperature data of the HAZ in real time and transmit it to the control system. The system has a preset temperature threshold (experimentally calibrated to 200℃). If the detected temperature approaches or exceeds the threshold, the system immediately activates. Adjustment mechanism: Appropriately reduce laser energy (e.g., from 3mJ to 2mJ) or extend the interval between adjacent scanning paths (e.g., from 10ms to 15ms). Once the temperature returns to a safe range, restore the original parameters. This dynamic temperature control ensures the material remains stable throughout the processing, eliminating the risk of overheating and deformation. After laser scanning, a three-dimensional morphology review is required to verify the processing quality. An atomic force microscope or laser confocal microscope (resolution ≤0.05μm) is used to perform a full-range three-dimensional scan of the processed nozzle exit chamfer to obtain the actual three-dimensional morphology data. Data analysis software is then used to extract the actual chamfer radius, curvature, and other relevant parameters. Key parameters are compared with the optimized design dimensions. If the deviation between the actual parameters and the design dimensions is ≤ ±0.1μm, and the chamfered surface is free of cracks, burrs, or other defects, the processing is deemed qualified and meets the spray performance standards. These standards require the chamfered structure to ensure uniform methanol film separation and a spray particle size variation coefficient ≤ 15%, ensuring complete combustion in the engine. If the deviation exceeds the allowable range or defects exist, the process returns to the laser processing stage. Based on the re-inspection results, the ablation parameters and path are adjusted, and laser fine-tuning is repeated until the three-dimensional morphology re-inspection is qualified. This ultimately achieves high-precision control of the nozzle outlet chamfer radius, providing a solid guarantee for the excellent spray performance of the methanol direct injection engine.

[0058] The above-mentioned method for optimizing the nozzle structure of a methanol direct injection engine is successful.

[0059] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the nozzle structure of a methanol direct injection engine, characterized in that: Includes the following steps: S1. Use an atomic force microscope to perform a three-dimensional nanoscale scan on the surface of the nozzle exit edge to detect and quantify its roughness distribution and obtain the surface roughness. S2. A dynamic mapping table is constructed based on surface roughness. The dynamic mapping table establishes the correspondence between the liquid film contact angle correction coefficient between different surface roughnesses and different real-time methanol viscosities through numerical simulation. The real-time methanol viscosity changes in real time according to the engine operating temperature. The dynamic mapping table stores the specific corresponding data. S3. Determine the methanol viscosity based on the real-time engine temperature sensor data, query the dynamic mapping table to obtain the liquid film contact angle correction coefficient under the current operating conditions, optimize the nozzle outlet geometric parameters based on the liquid film contact angle correction coefficient, and adjust the design size of the nozzle outlet chamfer radius by calculating the compensation value of the liquid film adhesion by the liquid film contact angle correction coefficient. S4. The nozzle is designed and manufactured using the adjusted nozzle exit chamfer radius design dimensions, and the nozzle exit chamfer radius is controlled by laser fine-tuning. The control of the nozzle exit chamfer radius through laser fine-tuning includes using pulsed laser ablation technology, generating laser processing path instructions based on the optimized chamfer radius design size, controlling the laser head to scan the nozzle exit area, removing material layer by layer to trim the chamfer shape by adjusting the laser energy and focus point position, and simultaneously using an online microscope to monitor the chamfer curvature in real time, comparing the feedback data with the design size, and dynamically calibrating the laser parameters to ensure that the chamfer radius meets the optimization requirements.

2. The nozzle structure optimization method for a methanol direct injection engine according to claim 1, characterized in that: The detection and quantification of its roughness distribution includes using a high-resolution probe of an atomic force microscope to perform multi-directional three-dimensional scanning along the nozzle exit edge surface to acquire raw point cloud data of the surface morphology. A built-in algorithm is then used to filter and denoise the raw point cloud data to eliminate measurement errors, and the surface roughness value is calculated based on the point cloud data. Specifically: Extract the peak-valley height difference of all bumps and depressions in the point cloud data within the scanning area, and statistically analyze the spatial distribution characteristics of the peak-valley height difference to generate a roughness distribution map. Output the surface roughness value as the quantization result based on the roughness distribution map.

3. The nozzle structure optimization method for a methanol direct injection engine according to claim 1, characterized in that: A simulation platform with different temperature conditions was set up in a laboratory environment. The surface roughness values ​​of various nozzle samples were measured using an atomic force microscope, and the methanol viscosity at the corresponding temperature was recorded simultaneously. A liquid film separation model was established using computational fluid dynamics simulation software. The surface roughness value and methanol viscosity were input to simulate the flow behavior of the liquid film at the nozzle outlet edge and output the liquid film contact angle change data. Then, the correspondence between the surface roughness value, methanol viscosity and liquid film contact angle correction coefficient was fitted by regression analysis. Finally, the correspondence was stored as a dynamic mapping table in the form of a structured database. The dynamic mapping table uses the surface roughness value and methanol viscosity as index keys to directly associate the liquid film contact angle correction coefficient.

4. The nozzle structure optimization method for a methanol direct injection engine according to claim 1, characterized in that: When establishing the correspondence between liquid film contact angle correction coefficients for different surface roughnesses and different real-time methanol viscosities in the numerical simulation, multiphysics coupling simulation technology is adopted. The nozzle outlet geometric model is set in the fluid dynamics simulation software, and the actual surface morphology data corresponding to the surface roughness value is imported as boundary conditions. At the same time, the dynamic process of liquid film formation and separation is simulated by inputting methanol viscosity. The force distribution of liquid film at the uneven texture is calculated, and the offset of liquid film contact angle is derived based on the force distribution analysis. The offset is then converted into liquid film contact angle correction coefficients, and the simulation results are made consistent with the experimental calibration data through iterative optimization. Finally, a mapping relationship of liquid film contact angle correction coefficients covering the entire surface roughness range and the entire methanol viscosity condition is established.

5. The nozzle structure optimization method for a methanol direct injection engine according to claim 1, characterized in that: The optimization of nozzle outlet geometry parameters based on the liquid film contact angle correction coefficient includes calculating the compensation value for liquid film adhesion based on the liquid film contact angle correction coefficient obtained from the dynamic mapping table, specifically: The liquid film contact angle correction coefficient is input into the preset algorithm model. The preset algorithm model derives the adjustment amount of the chamfer radius based on the liquid film mechanical balance equation. When the liquid film contact angle correction coefficient is greater than the reference value, the output command increases the chamfer radius to reduce the liquid bridge trailing effect. When the liquid film contact angle correction coefficient is less than the reference value, the output command decreases the chamfer radius to balance the liquid film cutting force. The optimization process combines engine operating data in real time to ensure that the chamfer radius design size dynamically adapts to the spray uniformity requirements under the current viscosity.

6. The nozzle structure optimization method for a methanol direct injection engine according to claim 5, characterized in that: The preset algorithm model is trained through machine learning. Historical operating data is collected, including measured values ​​of spray particle size variation coefficient under different surface roughness values, methanol viscosity, and corresponding chamfer radii. A training dataset is constructed, and a neural network model is used to learn the nonlinear relationship between the chamfer radius adjustment amount and the liquid film contact angle correction coefficient. After training, the algorithm model receives the liquid film contact angle correction coefficient input and outputs the chamfer radius compensation value. The chamfer radius compensation value is used to adjust the design size in real time to ensure the uniformity of liquid film separation.

7. The nozzle structure optimization method for a methanol direct injection engine according to claim 1, characterized in that: The process of adjusting the design dimension of the nozzle outlet chamfer radius by calculating the compensation value of the liquid film adhesion based on the liquid film contact angle correction coefficient includes converting the chamfer radius compensation value into the actual adjustment amount of the chamfer radius. The specific process is as follows: Based on the chamfer radius compensation value and referring to the preset chamfer radius reference design drawing, modify the geometric parameters in the computer-aided design software.

8. The nozzle structure optimization method for a methanol direct injection engine according to claim 7, characterized in that: Modifying the geometric parameters in the computer-aided design software includes using parametric design tools to map the chamfer radius compensation value to the increment or decrement of the chamfer radius. The tool has a built-in verification module that compares the adjusted chamfer radius with the liquid film force simulation results in real time. If the simulation shows uneven liquid film separation, the chamfer radius compensation value is automatically iterated and optimized until the final design dimension that makes the spray uniform is output.

9. The nozzle structure optimization method for a methanol internal direct injection engine according to claim 1, characterized in that: The control of the laser head to scan the nozzle exit area includes combining a machine vision system to capture real-time images of the nozzle exit, identify the edge of the chamfer area, calculate the laser ablation depth and range according to the design dimensions, generate adaptive path planning, continuously collect heat-affected zone data during laser processing, prevent overheating deformation through temperature sensors, and perform three-dimensional morphology re-inspection after completion to ensure that the chamfer radius control accuracy meets the spray performance standards.

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