A nebulizer design method and system based on a simulation model

Abstract

This invention relates to the field of atomizer design technology, and more particularly to an atomizer design method and system based on a simulation model. The method includes the following steps: acquiring application requirement data for the atomizer and drawing an initial atomizer design based on this data, while simultaneously constructing a simulation model; using the simulation model to calculate the dynamic airflow resistance index of the atomization channel, and evaluating the gradient degradation of atomization uniformity based on this index, thereby detecting the degradation trajectory of the atomizer's spray accuracy; identifying the attenuation of atomization transmission efficiency by analyzing the spray accuracy degradation trajectory, and evaluating the overall performance degradation trend of the atomizer; predicting design defect parameters of the atomizer, and optimizing the initial design for the identified defects to obtain improved atomizer design data; this invention achieves more efficient atomization performance through optimization of the atomizer design.

Description

Technical Field

[0001] This invention relates to the field of atomizer design technology, and in particular to an atomizer design method and system based on a simulation model. Background Technology

[0002] The primary function of an atomizer is to transform liquids into tiny droplets through high pressure, airflow, or physical means, achieving uniform distribution or rapid evaporation. Especially in fields like precision spraying or pharmaceutical atomization, the atomization effect, spray accuracy, and airflow distribution of the atomizer directly impact the application's effectiveness. Traditional atomizer design methods largely rely on experience and experimentation, resulting in a complex and costly process. Numerous factors need to be considered during the design process, such as the size and distribution of atomized particles, spray angle, and flow stability. These factors not only require repeated testing and debugging in experiments, but the complex interrelationships between many parameters often make it difficult for traditional methods to effectively predict and address design flaws. This leads to long atomizer development cycles and makes it difficult to guarantee product consistency and long-term stability. With the advancement of computer simulation technology, simulation-based design methods have become an important trend in atomizer design. By establishing accurate simulation models, engineers can simulate the performance of the atomizer in a virtual environment, identify design problems in advance, and make corresponding optimizations. However, traditional atomizer design suffers from inaccurate analysis of atomizer defects and performance. Summary of the Invention

[0003] Therefore, it is necessary to provide a simulation model-based atomizer design method and system to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a simulation model-based atomizer design method includes the following steps:

[0005] Step S1: Obtain atomizer application requirement data; draw the initial atomizer design based on the atomizer application requirement data, and build an atomizer simulation model based on the initial atomizer design;

[0006] Step S2: Evaluate the dynamic airflow resistance index of the atomizing pipe based on the atomizer simulation model, and evaluate the atomization uniformity gradient degradation data based on the dynamic airflow resistance index of the atomizing pipe; detect the dynamic decay trajectory of multidimensional spray accuracy based on the atomization uniformity gradient degradation data and the dynamic airflow resistance index of the atomizing pipe.

[0007] Step S3: Identify the atomization transmission efficiency decay based on the dynamic decay trajectory of multi-dimensional spray accuracy; evaluate the atomizer performance decay based on the atomization transmission efficiency decay.

[0008] Step S4: Predict the atomizer nozzle diameter degradation trend based on the atomizer performance degradation; detect atomizer design defect parameters based on the predicted atomizer performance degradation and atomizer nozzle diameter degradation trend; optimize the initial atomizer design based on the atomizer design defects to obtain atomizer design data.

[0009] This invention employs simulation modeling and evaluation throughout the entire process, from application requirements to design optimization. By acquiring detailed application requirement data and drawing initial designs, a simulation model of the atomizer that matches actual operating conditions is constructed. This lays the foundation for subsequent performance evaluation and optimization. By evaluating the airflow channel blockage index, bottlenecks affecting atomization can be accurately identified, thereby achieving reasonable optimization of airflow distribution. Further analysis of airflow channel blockage and uniformity gradient degradation data allows for real-time monitoring of atomization uniformity changes, effectively preventing performance degradation caused by uneven spraying. Detection of spray accuracy degradation trajectories enables timely identification and adjustment of spray accuracy decline, ensuring spray stability during long-term use. Analysis of transmission efficiency decay assesses the atomizer performance degradation trend, providing a scientific basis for equipment maintenance and replacement. Combining performance degradation data allows for early detection of design flaws and targeted design optimization. Based on the optimized design, more efficient and durable atomizer design data is obtained, improving equipment lifespan and operating efficiency. Overall, this process makes atomizer design more precise. Therefore, this invention optimizes the traditional simulation-based atomizer design method, solving the problems of inaccurate atomizer defect analysis and performance analysis inherent in traditional simulation-based atomizer design methods. It improves the accuracy of both atomizer defect and performance analysis.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain atomizer application requirement data;

[0012] Step S12: Determine the atomizer application scenario environment data based on the atomizer application requirement data;

[0013] Step S13: Draw the initial design of the atomizer based on the atomizer application scenario environment data and atomizer requirement data;

[0014] Step S14: Construct an atomizer simulation model based on the initial atomizer design.

[0015] This invention's atomizer design method ensures high efficiency and accuracy throughout the entire process, from application requirements to simulation modeling. By acquiring atomizer application requirement data, it provides a deep understanding of the various operational requirements of the atomizer in practical applications, such as atomization effect and spray accuracy, ensuring that subsequent designs can meet these requirements. By transforming these requirements into specific application scenario environmental data, it effectively considers the impact of the external environment on the atomization effect, such as temperature, humidity, and airflow velocity, ensuring that the design can adapt to different working conditions. Based on this, combined with application scenario environmental data and requirement data, an initial design of the atomizer is drawn up, making the design process targeted and practical, fundamentally improving the performance and stability of the atomizer. By constructing a simulation model, the initial design is systematically evaluated, enabling the early detection of potential problems and reducing failures or performance deficiencies in actual production. This process makes the design process more scientific and systematic, optimizing the working efficiency and reliability of the atomizer, and avoiding the high costs and time consumption of blind design and experimental verification.

[0016] Preferably, step S13 includes the following steps:

[0017] Step S131: Set the temperature measurement range of the temperature sensor to 0-100℃, the minimum temperature change to 0.05℃, and the temperature sampling frequency to 10Hz;

[0018] Step S132: Set the humidity sensor's humidity measurement range to 0% to 100% RH, the minimum humidity change to 0.1% RH, and the humidity sampling frequency to 5 Hz;

[0019] Step S133: Use temperature and humidity sensors to collect atomizer operating environment parameters for the atomizer application scenario;

[0020] Step S134: Determine atomization efficiency based on atomizer application requirement data; determine atomization accuracy based on atomizer application requirement data;

[0021] Step S135: Evaluate the impact of atomization accuracy on atomization accuracy based on atomizer operating environment parameters;

[0022] Step S136: Draw the initial design of the atomizer based on the impact of atomization accuracy and atomization efficiency.

[0023] This invention efficiently acquires environmental parameters for atomizer application scenarios by precisely setting the measurement range, minimum variation, and sampling frequency of the temperature and humidity sensors. This data provides a reliable foundation for subsequent atomizer design, ensuring that the impact of environmental factors (such as temperature and humidity) on atomization performance can be accurately assessed. By collecting this environmental data, the atomizer's operating environment can be monitored in real time, providing a basis for optimizing atomization efficiency and accuracy. Determining atomization efficiency and accuracy based on application requirement data allows for designs that better adapt to actual needs, achieving higher performance standards. Evaluating the impact of environmental parameters on atomization accuracy allows for the early identification of potential performance effects from environmental changes, effectively guiding adjustments to the initial design. By comprehensively considering the impact of accuracy and efficiency, the most suitable initial design scheme is formulated, ensuring the atomizer achieves optimal performance in specific application scenarios, optimizing the design process and improving the reliability and practicality of the atomizer.

[0024] Preferably, step S14 includes the following steps:

[0025] Step S141: Draw the internal structural parameters of the atomizer according to the initial design of the atomizer;

[0026] Step S142: Analyze the internal topology data of the atomizer based on the internal structural parameters of the atomizer;

[0027] Step S143: Identify the internal connectivity of the atomizer based on the internal topology data of the atomizer;

[0028] Step S144: Collect atomizer nozzle style data according to the initial atomizer design;

[0029] Step S145: Construct the atomizer's three-dimensional geometric structure parameters based on the atomizer nozzle style data and the internal connectivity of the atomizer;

[0030] Step S146: Construct a simulation model of the atomizer based on the atomizer's three-dimensional geometric parameters and internal topology data.

[0031] This invention, by drawing internal structural parameters based on the initial atomizer design, accurately establishes the preliminary design framework of the atomizer, ensuring that subsequent designs better adapt to actual needs. Based on this, analyzing internal topological data provides a deeper understanding of the atomizer's internal flow paths and connectivity, offering data support for subsequent performance optimization. Identifying the atomizer's internal connectivity further clarifies the fluid transport direction and path, helping to discover potential structural bottlenecks and optimize the design. By collecting nozzle pattern data, the interaction between the atomizer and the environment can be determined, ensuring the spray effect meets design requirements. Combining nozzle pattern data with internal connectivity data to construct three-dimensional geometric parameters provides a more intuitive representation of the atomizer's three-dimensional spatial layout, offering precise reference for design optimization. Combining the three-dimensional geometry and internal topological data to construct a simulation model allows for accurate predictions of atomizer performance, ensuring optimal atomization under different operating conditions. This process ensures that every step from design to simulation to optimization is scientifically based, greatly improving the accuracy and reliability of atomizer design.

[0032] Preferably, the calculation of the dynamic airflow resistance index of the atomizing pipe in step S2 includes:

[0033] The size distribution of atomized particles ranging from 0.1μm to 1000μm was collected based on the fineness of the atomized particles.

[0034] The floating time of atomized particles is calculated based on the size distribution of the atomized particles;

[0035] Calculate the probability of atomized particle recirculation during the floating time of atomized particles when the ambient wind speed is 0.5 m / s.

[0036] The atomized particle backflow accumulation area is collected based on the probability of atomized particle backflow;

[0037] Mark the concentrated accumulation area of ​​atomized particles in the atomized particle backflow accumulation area;

[0038] The degree of droplet aggregation of atomized particles is estimated based on the area where atomized particles are concentrated.

[0039] The probability of impurity accumulation inside the equipment is predicted based on the concentrated area of ​​atomized particle accumulation and the degree of atomized particle droplet coagulation.

[0040] The degree of blockage in the atomizing pipeline is measured based on the probability of impurity accumulation inside the equipment exceeding 10% and the degree of atomized droplet coagulation.

[0041] The dynamic airflow resistance index of the atomizing pipe is calculated based on the degree of congestion in the atomizing pipe.

[0042] This invention comprehensively understands the distribution characteristics of atomized particles by collecting particle size distribution data ranging from 0.1 μm to 1000 μm, providing accurate data support for subsequent analysis. Statistical analysis of particle size distribution and float duration helps assess the stability of particles in air suspension, thus affecting the persistence of atomization effects. Calculating the backflow probability of atomized particles during float duration effectively analyzes backflow phenomena under given ambient wind speeds, affecting the uniformity and stability of atomization effects. Collecting and marking backflow accumulation areas allows for accurate location of particle accumulation areas during atomization, providing clear targets for further design optimization. Droplet aggregation analysis of accumulation areas predicts particle aggregation effects within specific regions, thus affecting the atomizer's spray accuracy. Based on the prediction of accumulation areas and droplet aggregation, the probability of impurity accumulation inside the device can be estimated, assessing potential equipment clogging risks. When the impurity accumulation probability exceeds 10%, the airflow channel blockage index is evaluated, enabling timely detection of airflow channel blockage risks and helping designers optimize the structure to improve the long-term stability and service life of the atomizer.

[0043] Preferably, the atomization uniformity gradient degradation data evaluation in step S2 includes:

[0044] The characteristics of uneven pressure distribution in the atomizing pipeline are measured based on the dynamic airflow resistance index of the atomizing pipeline.

[0045] The pressure fluctuation inside the atomizer is determined based on the characteristics of uneven pressure distribution in the pipeline.

[0046] The stability of the flow rate in the atomization pipeline is detected by observing pressure fluctuations inside the atomizer.

[0047] Identifying heterogeneity of atomization density based on the flow stability of atomization pipelines;

[0048] Calculate the motion inertia of the atomized particles based on the fineness of the atomized particles;

[0049] Predicting excessively fast settling velocity of atomized particles based on their motion inertia;

[0050] The gradient degradation data of atomization uniformity was assessed based on data on excessively fast atomized particle settling velocity and atomization density heterogeneity.

[0051] This invention, by measuring the non-uniformity of pressure distribution in pipelines, can accurately identify abnormal changes in the airflow channel, and thus analyze the impact of airflow non-uniformity on atomization. Analyzing the non-uniformity of pipeline pressure distribution helps to further understand the internal pressure fluctuations of the atomizer, revealing potential airflow fluctuation problems that affect atomization stability. Detecting the flow stability of the atomization pipeline using internal pressure fluctuations provides an important basis for judging the performance of the atomizer and identifying the potential threat of flow instability to atomization uniformity. Based on flow stability, it can effectively identify the heterogeneity of atomization density, assess the changes in atomization concentration in different regions during atomization, and thus affect the consistency of spray effect. By calculating the motion inertia of atomized particles, it can predict the motion state of particles and reveal their behavior characteristics in the airflow. Predicting the risk of excessively fast settling velocity based on particle motion inertia can promptly detect phenomena such as uneven atomization or particles failing to suspend, reducing the impact of poor atomization. Comprehensively considering the heterogeneity of particle settling velocity and atomization density, it comprehensively evaluates the gradient degradation data of atomization uniformity, providing a precise basis for the optimized design and debugging of the atomizer, ensuring efficient and stable spray effect.

[0052] Preferably, the multidimensional spray accuracy dynamic decay trajectory described in step S2 includes:

[0053] Identify the atomization concentration change state based on the dynamic airflow resistance index of the atomization pipeline;

[0054] Calculate the atomizer spray deviation angle based on the atomization uniformity gradient degradation data;

[0055] Predict the trend of atomizer spray range reduction based on atomizer spray deviation angle;

[0056] Calculate the atomizer jet heterogeneity based on the trend of atomizer jet range reduction;

[0057] Predict excessive local concentration data of the atomizer based on the atomizer concentration change status and atomizer jet heterogeneity;

[0058] Based on the large amount of data on local concentration in the atomizer, the dynamic decline trajectory of multi-dimensional spray accuracy is detected.

[0059] This invention effectively assesses changes in atomization concentration by identifying the dynamic airflow resistance index of the atomizing channel, thereby promptly detecting blockages or partial blockages in the airflow channel and preventing deviations in atomization performance. Calculating the spray deviation angle based on atomization uniformity gradient degradation data helps analyze the causes of deviations during spraying and accurately quantifies the trend of spray angle changes. This data is crucial for predicting the shrinkage trend of the spray range and can identify problems such as uneven spraying or reduced spray volume. Based on the shrinkage trend of the spray range, assessing the spray heterogeneity of the atomizer provides precise adjustments to spray accuracy and uniformity in atomizer design, thereby improving overall spray performance. Combining atomizer concentration changes and spray heterogeneity data identifies situations where the spray concentration is excessively high in localized areas, thus avoiding localized over-concentration or under-concentration. By detecting large amounts of localized excessive concentration in the atomizer, the trajectory of spray accuracy degradation is further revealed, providing a scientific basis for atomizer performance optimization and accuracy recovery, ensuring the stability and efficiency of the spray system in long-term use.

[0060] Preferably, step S3 includes the following steps:

[0061] Step S31: Identify the attenuation of atomization transmission efficiency based on the dynamic decay trajectory of multi-dimensional spray accuracy;

[0062] Step S32: Assess the growth trend of atomization transmission energy consumption based on the atomization transmission efficiency decay;

[0063] Step S33: Evaluate the durability of the atomizer based on the growth trend of atomization transmission energy consumption and the decay of atomization transmission efficiency, and obtain atomizer durability data;

[0064] Step S34: Assess the performance degradation of the atomizer based on atomizer durability data and the growth trend of atomization transmission energy consumption.

[0065] This invention effectively monitors the decay trend of atomization transmission efficiency by identifying the dynamic decline trajectory of multi-dimensional spray accuracy, providing data support for assessing overall performance degradation. This analysis helps identify early signs of atomizer performance instability, allowing for timely preventative measures and ensuring the system's continued stability in various application environments. Assessing the growth trend of atomization transmission energy consumption based on atomization transmission efficiency decay allows for early detection of signs of reduced energy efficiency and provides a basis for optimizing energy use, avoiding unnecessary energy waste. By combining energy consumption growth trends and transmission efficiency decay data, the durability of the atomizer can be assessed, accurately grasping its lifespan and performance degradation patterns, providing users with scientific maintenance cycles and equipment replacement recommendations. Furthermore, assessing atomizer performance degradation based on durability data and energy consumption trends further ensures the reliability of the equipment during long-term use.

[0066] Preferably, step S4 includes the following steps:

[0067] Step S41: Predict the deterioration trend of the atomizer nozzle diameter based on the atomizer performance degradation.

[0068] Step S42: Calculate the atomizer aging life parameters based on the atomizer nozzle diameter degradation trend;

[0069] Step S43: Detect atomizer design defect parameters based on atomizer aging life parameters and atomizer nozzle diameter deterioration trend;

[0070] Step S44: Optimize the initial design of the atomizer based on atomizer design defects and atomizer aging life parameters to obtain atomizer design data.

[0071] This invention predicts the degradation trend of the atomizer's nozzle diameter, enabling early identification of performance decline issues during long-term use and providing data support for subsequent maintenance and optimization. Calculating the atomizer's aging life parameters based on this trend helps accurately estimate the equipment's lifespan and provides a scientific basis for replacement or repair, avoiding premature or delayed maintenance interventions. Combining atomizer aging life parameters and nozzle diameter degradation trends to detect design flaws helps identify weaknesses in the original design, allowing for proactive design improvements and enhancing the atomizer's long-term performance stability. By optimizing the initial atomizer design and addressing design flaws, the reliability and durability of the equipment can be effectively improved, while reducing maintenance and operating costs, ensuring efficient and stable operation throughout its entire lifespan.

[0072] The present invention also provides a simulation model-based atomizer design system for executing the simulation model-based atomizer design method described above. The simulation model-based atomizer design system includes:

[0073] The simulation model building module acquires atomizer application requirement data; draws the initial atomizer design based on the atomizer application requirement data; and builds the atomizer simulation model based on the initial atomizer design.

[0074] The accuracy degradation trajectory detection module is used to evaluate the dynamic airflow resistance index of the atomizing pipeline based on the atomizer simulation model, and evaluate the atomization uniformity gradient degradation data based on the dynamic airflow resistance index of the atomizing pipeline; and detect the multidimensional spray accuracy dynamic decay trajectory based on the atomization uniformity gradient degradation data and the dynamic airflow resistance index of the atomizing pipeline.

[0075] The performance degradation assessment module is used to identify the degradation of atomization transmission efficiency based on the dynamic degradation trajectory of multi-dimensional spray accuracy; and to assess the performance degradation of the atomizer based on the degradation of atomization transmission efficiency.

[0076] The initial design optimization module is used to predict the degradation trend of the atomizer nozzle diameter based on the atomizer performance degradation; detect atomizer design defect parameters based on the predicted atomizer performance degradation and the atomizer nozzle diameter degradation trend; and optimize the initial atomizer design based on the atomizer design defects to obtain atomizer design data.

[0077] This invention involves simulation modeling and evaluation throughout the entire process, from application requirements to design optimization. By acquiring detailed application requirement data and drawing initial designs, a simulation model of the atomizer that matches actual operating conditions is constructed. This lays the foundation for subsequent performance evaluation and optimization. By evaluating the airflow channel blockage index, bottlenecks affecting atomization can be accurately identified, thereby achieving reasonable optimization of airflow distribution. Further analysis of airflow channel blockage and uniformity gradient degradation data allows for real-time monitoring of atomization uniformity changes, effectively preventing performance degradation caused by uneven spray. Detection of spray accuracy degradation trajectories enables timely identification and adjustment of spray accuracy decline, ensuring spray stability during long-term use. Analysis of transmission efficiency decay assesses the atomizer performance degradation trend, providing a scientific basis for equipment maintenance and replacement. Combining performance degradation data allows for early detection of design flaws and targeted design optimization. Based on the optimized design, more efficient and durable atomizer design data is obtained, improving equipment lifespan and operating efficiency. Overall, this process makes atomizer design more precise. Therefore, this invention optimizes the traditional simulation-based atomizer design method, solving the problems of inaccurate atomizer defect analysis and performance analysis inherent in traditional simulation-based atomizer design methods. It improves the accuracy of both atomizer defect and performance analysis. Attached Figure Description

[0078] Figure 1 This is a flowchart illustrating the steps of a simulation model-based atomizer design method.

[0079] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0080] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4.

[0081] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0082] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0083] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0084] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0085] To achieve the above objectives, please refer to Figures 1 to 3 A simulation-based atomizer design method includes the following steps:

[0086] Step S1: Obtain atomizer application requirement data; draw the initial atomizer design based on the atomizer application requirement data, and build an atomizer simulation model based on the initial atomizer design;

[0087] Step S2: Evaluate the dynamic airflow resistance index of the atomizing pipe based on the atomizer simulation model, and evaluate the atomization uniformity gradient degradation data based on the dynamic airflow resistance index of the atomizing pipe; detect the dynamic decay trajectory of multidimensional spray accuracy based on the atomization uniformity gradient degradation data and the dynamic airflow resistance index of the atomizing pipe.

[0088] Step S3: Identify the atomization transmission efficiency decay based on the dynamic decay trajectory of multi-dimensional spray accuracy; evaluate the atomizer performance decay based on the atomization transmission efficiency decay.

[0089] Step S4: Predict the atomizer nozzle diameter degradation trend based on the atomizer performance degradation; detect atomizer design defect parameters based on the predicted atomizer performance degradation and atomizer nozzle diameter degradation trend; optimize the initial atomizer design based on the atomizer design defects to obtain atomizer design data.

[0090] In this embodiment of the invention, reference Figure 1 As shown in the example, the atomizer design method based on a simulation model includes the following steps:

[0091] Step S1: Obtain atomizer application requirement data; draw the initial atomizer design based on the atomizer application requirement data, and build an atomizer simulation model based on the initial atomizer design;

[0092] In this embodiment of the invention, application requirement data for the atomizer is acquired. This data includes, but is not limited to, the operating conditions of the atomizer application scenario, ambient temperature and humidity, the physical properties of the liquid (such as viscosity, surface tension, and density), and the desired atomization effect (such as droplet size distribution and spray distance). For example, in spraying applications, the atomizer atomizes the liquid into particles of 10μm to 50μm, capable of covering a certain surface area. Temperature and humidity sensors are used to monitor environmental parameters. The temperature sensor's measurement range is set to 0-100℃, with a minimum temperature change of 0.05℃ and a sampling frequency of 10Hz; the humidity sensor's measurement range is 0% to 100%RH, with a minimum humidity change of 0.1%RH and a humidity sampling frequency of 5Hz. Based on the above application requirement data, an initial design for the atomizer is drawn. This design includes the atomizer's geometry, nozzle type, and key parameters such as the shape and size of the airflow channel. Using CAD (Computer-Aided Design) software, such as AutoCAD or SolidWorks, an initial 3D design drawing of the atomizer is drawn, and the design drawing is converted into a format suitable for simulation calculations. Subsequently, a simulation model of the atomizer is constructed using CFD (Computational Fluid Dynamics) simulation software, such as ANSYS Fluent or COMSOL Multiphysics, based on the preliminary design. The construction of the simulation model needs to consider the dimensions of the airflow channel, the type of nozzle, the physical properties of the liquid, and the fluid dynamics behavior to obtain the atomizer simulation model.

[0093] Step S2: Evaluate the dynamic airflow resistance index of the atomizing pipe based on the atomizer simulation model, and evaluate the atomization uniformity gradient degradation data based on the dynamic airflow resistance index of the atomizing pipe; detect the dynamic decay trajectory of multidimensional spray accuracy based on the atomization uniformity gradient degradation data and the dynamic airflow resistance index of the atomizing pipe.

[0094] In this embodiment of the invention, a pre-constructed atomizer simulation model is used to evaluate the airflow channel blockage index. This step involves analyzing fluid flow, calculating pressure loss and velocity distribution in the airflow channel, and then evaluating the atomizer's airflow channel blockage index. This index reflects the degree of flow restriction in the airflow channel due to atomized particle deposition or unreasonable structural design. Using CFD simulation software, pressure probes and velocity sensors are installed in the airflow channel to obtain detailed fluid pressure distribution and velocity data. It is assumed that when the pressure drop in the airflow channel exceeds a certain threshold, it is calibrated as airflow channel blockage, and the blockage index is calculated. Based on the airflow channel blockage index, atomization uniformity gradient degradation data is evaluated. Atomization uniformity gradient refers to the change in the spatial non-uniformity of atomized particle distribution over time during spraying. By analyzing the droplet distribution in the simulation model, the spatial distribution of atomized particles at different time points is calculated to further obtain its gradient change trend. For example, a baseline uniformity is set, and the droplet distribution in each region during spraying is compared with the baseline to obtain degradation data. Based on atomization uniformity gradient degradation data and airflow channel blockage index, a multidimensional dynamic degradation trajectory of spray accuracy was detected. This trajectory indicates the trend of gradual decrease in spray accuracy during use due to airflow blockage and atomization uniformity degradation. By continuously tracking pressure changes and droplet distribution changes in the airflow channel, their impact on spray accuracy was analyzed, and a trend graph of accuracy degradation was plotted.

[0095] Step S3: Identify the atomization transmission efficiency decay based on the dynamic decay trajectory of multi-dimensional spray accuracy; evaluate the atomizer performance decay based on the atomization transmission efficiency decay.

[0096] In this embodiment of the invention, the decay of atomization transmission efficiency is identified based on the aforementioned multidimensional dynamic decay trajectory of spray accuracy. Transmission efficiency reflects the atomizer's ability to convert liquid into atomized particles and spray them effectively. By continuously monitoring the spray accuracy degradation trajectory, the relationship between spray accuracy and droplet distribution is analyzed, further evaluating the droplet loss rate and distribution non-uniformity during spraying. When the spray accuracy drops to a certain critical value, it indicates that the atomization transmission efficiency has decayed. For example, during spraying, the total transmission efficiency can be calculated by analyzing the volumetric flow rate and number of sprayed droplets. Based on the decay of atomization transmission efficiency, the performance degradation of the atomizer is evaluated. By comparing the spray accuracy and transmission efficiency of the atomizer in different working cycles, the gradual decline in the atomizer's performance is analyzed. The performance degradation manifests as a decrease in atomization efficiency, a narrowing of the spray range, and non-uniformity of atomized particles. Combining the trend of transmission efficiency changes, the overall performance degradation of the atomizer within a specific time period is predicted.

[0097] Step S4: Predict the atomizer nozzle diameter degradation trend based on the atomizer performance degradation; detect atomizer design defect parameters based on the predicted atomizer performance degradation and atomizer nozzle diameter degradation trend; optimize the initial atomizer design based on the atomizer design defects to obtain atomizer design data.

[0098] In this embodiment of the invention, design flaw parameters of the atomizer are predicted and detected based on the atomizer's performance degradation. By analyzing the causes of performance degradation, potential design flaws are identified. For example, if the atomizer's airflow channel is significantly blocked, it indicates an unreasonable nozzle design or an excessively narrow internal channel, leading to poor airflow and affecting atomization. In this case, a thorough analysis of the atomizer design reveals deficiencies in its structural design or material selection. Based on these design flaws, the initial atomizer design is optimized. By modifying the atomizer's geometric parameters, nozzle style, or internal channel structure, the airflow performance can be improved, blockages reduced, and atomization accuracy enhanced. The optimized design data can be used to reconstruct the atomizer's simulation model for further verification and evaluation, resulting in optimized atomizer design data.

[0099] Preferably, step S1 includes the following steps:

[0100] Step S11: Obtain atomizer application requirement data;

[0101] Step S12: Determine the atomizer application scenario environment data based on the atomizer application requirement data;

[0102] Step S13: Draw the initial design of the atomizer based on the atomizer application scenario environment data and atomizer requirement data;

[0103] Step S14: Construct an atomizer simulation model based on the initial atomizer design.

[0104] In this embodiment of the invention, specific requirements data for atomizer applications are obtained through on-site surveys and experiments. These data mainly include the required performance indicators of the atomizer and the specific requirements of its working environment. In specific implementation, the application field and environment of the atomizer are determined, such as spraying, sterilization, and liquid cooling. For spraying applications, the droplet size range, spray angle, and atomization accuracy requirements need to be clearly defined. For example, for sprayed coatings, the droplet size requirement is 15-50 μm, and the spray accuracy error is controlled within ±5%. During data collection, high-precision measuring equipment such as a laser particle size analyzer is used to measure the droplet particle size distribution, and environmental sensors are used to acquire data such as temperature, humidity, and airflow rate. Temperature and humidity sensors are used, with a temperature range set to -10℃ to 50℃ and a humidity range of 0% to 100% RH. An anemometer measures wind speeds from 0.1 m / s to 15 m / s. By analyzing the application requirements data and combining it with the on-site environment, the environmental data for the atomizer application scenario is determined. In specific operation, data such as temperature, humidity, and airflow rate need to be obtained from the on-site or laboratory environment. For example, by deploying a sensor array, changes in ambient wind speed can be measured in different areas. Airflow rates range from 0.5 m / s to 10 m / s, and these variations significantly impact the atomizer's performance. For different humidity conditions, humidity sensors need to record humidity variations in real time, such as from 50% RH to 90% RH, to predict the environment's influence on atomized particle movement. Furthermore, the sensors should be able to measure ambient temperature in real time, with a temperature fluctuation range set from -20°C to 60°C, which significantly affects droplet evaporation rate and atomizer efficiency. All this data constitutes complete application scenario environmental data, which is used to create the initial atomizer design. During implementation, the application requirements and scenario environmental data are input into design software, and CAD software such as SolidWorks and AutoCAD are used for modeling. The design needs to clearly define the atomizer's core structure, including nozzle type, number of nozzles, spray angle, and liquid flow rate. For example, for spraying requirements, the nozzle type chosen is a rotary atomizing nozzle or a pressure atomizing nozzle, with a nozzle angle set to 30° to ensure spray uniformity. The liquid flow rate needs to be set according to the application requirements of the atomizer, with a flow rate of 50-200 mL / min selected. At this stage, simulation analysis tools, such as ANSYS Fluent, are needed to perform preliminary flow field and droplet trajectory simulations to ensure that the designed airflow channel and nozzle meet the atomizer's performance requirements. In addition, the uniformity of droplet distribution and particle size also need to be simulated to ensure compliance with atomization accuracy requirements. Based on these preliminary design data, an atomizer design scheme that meets the initial requirements is derived.A CFD simulation model of the atomizer is built using software such as ANSYS Fluent and COMSOL Multiphysics. The CAD design file is imported, and mesh generation technology is used to model the atomizer, dividing it into airflow channels, nozzle sections, and liquid flow regions. Then, the internal physical properties of the atomizer are defined, such as the density and viscosity of the gas fluid, and the surface tension and flow rate of the liquid. By setting boundary conditions, including inlet flow rate, inlet pressure, and temperature, the simulation is initiated. Specifically, the inlet flow rate is set to 1-2 L / min, the outlet pressure is kept at atmospheric pressure, and the airflow velocity range is set to 0.5 m / s to 5 m / s. Simultaneously, based on the liquid characteristics, the liquid surface tension range can be set between 25-40 mN / m. Through simulation, the generation, distribution, and trajectory of atomized particles can be predicted, thereby evaluating parameters such as atomization effect, droplet distribution uniformity, and spray accuracy.

[0105] Preferably, step S13 includes the following steps:

[0106] Step S131: Set the temperature measurement range of the temperature sensor to 0-100℃, the minimum temperature change to 0.05℃, and the temperature sampling frequency to 10Hz;

[0107] Step S132: Set the humidity sensor's humidity measurement range to 0% to 100% RH, the minimum humidity change to 0.1% RH, and the humidity sampling frequency to 5 Hz;

[0108] Step S133: Use temperature and humidity sensors to collect atomizer operating environment parameters for the atomizer application scenario;

[0109] Step S134: Determine atomization efficiency based on atomizer application requirement data; determine atomization accuracy based on atomizer application requirement data;

[0110] Step S135: Evaluate the impact of atomization accuracy on atomization accuracy based on atomizer operating environment parameters;

[0111] Step S136: Draw the initial design of the atomizer based on the impact of atomization accuracy and atomization efficiency.

[0112] In this embodiment of the invention, a suitable measurement range, accuracy, and sampling frequency are set for the temperature sensor. Specifically, a suitable high-precision temperature sensor, such as a PT100 or thermocouple, is selected to ensure its measurement range covers temperature fluctuations encountered in atomizer applications. The temperature measurement range is set to 0 to 100°C to meet the application requirements of most industrial environments. To ensure high measurement accuracy, a minimum temperature change of 0.05°C is set to ensure the accuracy of temperature data in dynamically changing environments. Then, the sampling frequency of the temperature sensor is set to 10Hz, ensuring 10 data acquisitions per second to effectively capture rapid temperature changes. The temperature sensor is installed in the most representative location in the atomizer's operating environment, preferably near the atomizer's air inlet. The operating parameters of the humidity sensor are set to obtain accurate humidity data. A suitable humidity sensor, such as a capacitive humidity sensor, is selected due to its high accuracy and stability. The humidity measurement range is set to 0% to 100%RH to cover various humidity conditions encountered in atomizer applications. A minimum humidity change of 0.1%RH is set to ensure sufficiently fine humidity data that reflects minute environmental changes. The humidity sensor's sampling frequency is set to 5Hz, ensuring 5 data acquisitions per second. This provides continuous humidity data during actual operation, reflecting the impact of ambient humidity on atomization performance. The humidity sensor's installation location should consider the airflow characteristics around the atomizer, ideally near the nozzle. The installed temperature and humidity sensors collect real-time environmental data for the atomizer's application scenario. The data acquisition system transmits the output signals from the temperature and humidity sensors to a computer or processing unit. This system needs high-precision acquisition capabilities, simultaneously acquiring and recording temperature and humidity changes in real time. By configuring the data acquisition system and setting appropriate time intervals and synchronization parameters, the consistency and accuracy of temperature and humidity data are ensured. During acquisition, the temperature sensor samples 10 times per second, and the humidity sensor samples 5 times per second. Using data storage and analysis software (such as LabVIEW or MATLAB), environmental data is viewed and analyzed in real time, and atomization efficiency is determined based on the atomizer's application requirements. In practice, the atomization efficiency is calculated using theoretical formulas or simulation analysis tools (such as ANSYS Fluent) by analyzing factors such as the atomizer's nozzle design, liquid flow rate, airflow rate, and application scenario. Atomization efficiency is closely related to nozzle design and airflow parameters. With a fixed liquid flow rate, increasing the airflow rate improves atomization efficiency. Next, atomization accuracy, i.e., the uniformity of droplet distribution, is determined based on application requirements. Through experimental testing or simulation, data such as droplet size distribution, spray angle, and uniformity under different spray modes are obtained to determine the required spray accuracy.For example, in spraying applications, it is required that the sprayed droplets be uniformly distributed within a specific area, and the droplet size should be within the range of 15-50 μm. This process obtains atomization efficiency and atomization accuracy parameters that match the atomizer's requirements. Real-time data collected by temperature and humidity sensors is used to analyze the impact of temperature and humidity changes on droplet formation and movement. For example, increased temperature leads to faster droplet evaporation, thus affecting the uniformity of droplet distribution; in high humidity environments, the droplet evaporation rate is slower, causing droplets to accumulate in the spray area, affecting the atomization effect. Based on these environmental parameters, combined with existing atomizer performance models, the impact of temperature and humidity changes on atomization accuracy is evaluated through simulation analysis, experimental verification, or theoretical derivation. Multiple experiments are conducted to obtain spray accuracy variation data under different environmental conditions, further analyzing the specific impact of temperature and humidity changes on atomization accuracy. For example, in an environment with a temperature of 40℃ and a humidity of 60%RH, the atomization accuracy change is small, while at a temperature of 10℃ and a humidity of 90%RH, the atomization accuracy change is large. Based on the impact of atomization accuracy and atomization efficiency assessed in step S135, the initial design of the atomizer is drawn up. In practice, based on the environmental data and performance requirements analyzed earlier, computer-aided design (CAD) software, such as SolidWorks, is used to draw the preliminary design drawings of the atomizer. During the design process, according to the atomization efficiency requirements of the atomizer, an appropriate nozzle type and size are selected, and the nozzle layout is adjusted to ensure uniform droplet distribution. Parameters such as the spray angle, liquid flow rate, and airflow velocity also need to be adjusted based on the atomization accuracy assessment results to ensure that the atomization accuracy meets application requirements. For example, for higher atomization efficiency requirements, an adjustable airflow rate nozzle structure is designed, or a multi-nozzle system is set up to increase the spray coverage area.

[0113] Preferably, step S14 includes the following steps:

[0114] Step S141: Draw the internal structural parameters of the atomizer according to the initial design of the atomizer;

[0115] Step S142: Analyze the internal topology data of the atomizer based on the internal structural parameters of the atomizer;

[0116] Step S143: Identify the internal connectivity of the atomizer based on the internal topology data of the atomizer;

[0117] Step S144: Collect atomizer nozzle style data according to the initial atomizer design;

[0118] Step S145: Construct the atomizer's three-dimensional geometric structure parameters based on the atomizer nozzle style data and the internal connectivity of the atomizer;

[0119] Step S146: Construct a simulation model of the atomizer based on the atomizer's three-dimensional geometric parameters and internal topology data.

[0120] In this embodiment of the invention, the internal structural parameters of the atomizer are drawn based on the initial design data. Specifically, computer-aided design (CAD) software (such as AutoCAD or SolidWorks) is used to accurately draw the internal structure of the atomizer based on the preliminary design drawings. This determines the overall shape of the atomizer, the location and dimensions of the airflow channels, nozzle system, and liquid input channels, as well as the layout of important components. The airflow channels are designed to effectively guide airflow into the atomization area. The liquid input channels are designed to ensure that the liquid flow rate matches the overall spray effect of the atomizer. During the drawing process, the rationality of the dimensions of each component, pipe angles, and interfaces is ensured to avoid structural conflicts and ensure that the internal structure of the atomizer is consistent with the subsequent analysis and simulation model data. During the design process, the geometric parameters of each component are recorded, such as the nozzle diameter, air inlet diameter, length and curvature of the flow channel, etc. Based on the internal structural parameters of the atomizer, the internal topology data of the atomizer is analyzed. The three-dimensional model data of the atomizer's internal structure is imported into the CAD design software and analyzed using topology analysis tools (such as ANSYS Discovery or Autodesk Meshmixer). Topology analysis focuses on the connections between components and the flow paths of airflow channels. Specifically, it involves identifying the connection methods of key components such as nozzles, airflow channels, and liquid inlets / outlets, analyzing whether airflow can pass evenly through each channel, and evaluating hydrodynamic performance, such as the presence of dead zones or uneven flow. Furthermore, it considers the pressure drop of the atomizer, the flow direction of airflow, and their impact on atomization, identifying potential flow bottlenecks and optimizing them. In this process, topology connection data between each component is calculated, and based on the internal topology data of the atomizer, the connectivity status within the atomizer is identified. This process relies on the topology data obtained in previous steps, and uses computer simulation tools (such as ANSYS Fluent or OpenFOAM) to perform connectivity analysis on the internal flow channels of the atomizer. By simulating the flow paths of airflow and liquid, it determines whether there are any disconnections or poor connections in the internal airflow and liquid channels. Specifically, by setting simulation boundary conditions, the flow of fluid in each channel is analyzed, especially the connection between the nozzle and the airflow channel, ensuring that the airflow can pass smoothly and be evenly distributed to the atomization area. If problems are found in the connections of certain channels (such as partial blockage or obstruction), the atomizer design needs to be adjusted to optimize the channel structure, ensuring thorough mixing of airflow and liquid to improve atomization. Based on the initial atomizer design, the style data of the atomizer nozzles should be collected. During implementation, a suitable nozzle style should be selected according to the atomizer's design requirements, and detailed measurements should be taken.Nozzle style data includes the nozzle's geometry (e.g., circular, elliptical), nozzle diameter, spray angle, nozzle internal shape, and its hydrodynamic characteristics. This data is obtained by creating actual nozzle samples and measuring them using precision instruments (e.g., laser scanners), or directly through computer modeling. The collected nozzle style data will be used for subsequent simulation analysis and design optimization. For example, the nozzle diameter directly affects the size of the atomized droplets, while the spray angle affects the spray coverage. Based on the aforementioned nozzle style data and the internal connectivity of the atomizer, the three-dimensional geometric parameters of the atomizer are constructed. This step relies on CAD design software (e.g., SolidWorks, CATIA) to perform three-dimensional modeling of the atomizer. Combining the nozzle style data, airflow channel layout, and connectivity, the nozzle is accurately modeled using the nozzle style data to ensure that the nozzle diameter, shape, and spray angle meet design requirements. Next, based on the internal connectivity data of the atomizer, the accurate layout of the airflow channels and liquid flow paths is determined to ensure that the airflow can smoothly pass through the nozzle and match the liquid flow. Combining these parameters, the overall three-dimensional geometric model of the atomizer is formed. The model needs to accurately describe the geometric dimensions, connection methods, and relative positions of each part. Based on the atomizer's three-dimensional geometry and internal topology data, a simulation model of the atomizer is constructed. This step uses fluid dynamics simulation tools (such as ANSYS Fluent or OpenFOAM) to simulate the atomizer's operation. The three-dimensional geometric model constructed in step S145 is imported, and the model's physical properties, such as the liquid's viscosity and density, and the airflow velocity and temperature, are defined. Then, boundary conditions are set in the simulation tool, including the nozzle's air intake velocity, liquid flow rate, and nozzle diameter. By calculating the fluid flow, atomization process, and gas-liquid mixing effect, the atomizer's performance data under different operating conditions is obtained.

[0121] Preferably, the calculation of the dynamic airflow resistance index of the atomizing pipe in step S2 includes:

[0122] The size distribution of atomized particles ranging from 0.1μm to 1000μm was collected based on the fineness of the atomized particles.

[0123] The floating time of atomized particles is calculated based on the size distribution of the atomized particles;

[0124] Calculate the probability of atomized particle recirculation during the floating time of atomized particles when the ambient wind speed is 0.5 m / s.

[0125] The atomized particle backflow accumulation area is collected based on the probability of atomized particle backflow;

[0126] Mark the concentrated accumulation area of ​​atomized particles in the atomized particle backflow accumulation area;

[0127] The degree of droplet aggregation of atomized particles is estimated based on the area where atomized particles are concentrated.

[0128] Predict the probability of impurity accumulation inside the equipment based on the concentrated area of ​​atomized particle accumulation and the degree of atomized particle droplet coagulation.

[0129] The degree of blockage in the atomizing pipeline is measured based on the probability of impurity accumulation inside the equipment exceeding 10% and the degree of atomized droplet coagulation.

[0130] The dynamic airflow resistance index of the atomizing pipe is calculated based on the degree of congestion in the atomizing pipe.

[0131] In this embodiment of the invention, the size distribution of atomized particles is collected. A laser particle size analyzer (such as a Malvern Mastersizer 3000) is used to measure the particle size distribution after spraying from the atomizer. The measurement range is set to 0.1 μm to 1000 μm to ensure that the entire size distribution of the atomized particles is captured. The humidity and temperature of the measurement environment need to be strictly controlled to avoid affecting the particle measurement results. Test samples are collected through the nozzle of the atomizer in a standard experimental environment to ensure that each sample is uniform and stable. The particle size analyzer analyzes the sample particles using the principle of laser scattering and records the particle size distribution data. Based on the number of particles in different size ranges, a complete particle distribution curve is generated. After obtaining the size distribution data of the atomized particles, the particle float time is statistically analyzed. A high-precision camera system (such as a high-speed camera, with a frame rate set to 500 frames / second) is used to record the float process of the atomized particles in real time. The experiment is conducted under standard wind speed conditions, with the wind speed set to 0.5 m / s, to simulate the floating of atomized particles in the air. By filming the entire process of the particles from spraying to landing, the float time of the particles is calculated. The floating time of particles is closely related to factors such as particle size, air velocity, and ambient humidity. By tracking the floating time of particles of different sizes, the floating time distribution data of particles is obtained. For particles within different size ranges, their floating times are statistically analyzed, and the suspension stability of the atomized particles is evaluated based on this data. Under laboratory wind speed conditions, with the ambient wind speed set to 0.5 m / s, the backflow probability of atomized particles is calculated. Computational fluid dynamics (CFD) simulation software (such as ANSYS Fluent) is used to simulate the interaction between airflow and atomized particles. Based on the particle size distribution, floating time, and airflow velocity, the software predicts the particle trajectory and then calculates the backflow probability of particles of different sizes. In particular, small particles (such as particles smaller than 10 μm) are more prone to backflow due to the influence of airflow, resulting in a higher backflow probability. Through simulation analysis, the initial position of the particles is compared with their current position to determine whether the particles return to the nozzle area, thereby evaluating the backflow probability. The backflow probability of all particles is calculated under different conditions and summarized into a backflow probability distribution. Based on the backflow probability data, the backflow accumulation regions of atomized particles are further collected. This step involves a detailed simulation of the airflow and atomized particle backflow path using CFD simulation tools (such as OpenFOAM). The simulation settings include the definition of particle backflow boundaries and accumulation zones. Backflowing particles accumulate in areas of airflow deceleration or local vortex. Based on the simulation results, the locations of the backflow regions are marked, and the particle accumulation trends in these regions are analyzed. Specific markers are used to distinguish backflow accumulation regions in the atomizer's internal structural diagram. Building upon the backflow accumulation regions, further analysis identifies concentrated accumulation areas of atomized particles, and the backflow regions are further refined and annotated using graphics processing software based on the distribution of particle backflow.The accumulation trend of backflow particles at certain locations is analyzed, particularly around the nozzle and at airflow bends, where larger particle accumulations are more likely to occur. Statistical analysis determines the particle density in these areas, and these concentrated accumulation regions are represented by different colors or symbols on a graph. This step further verifies the potential for airflow channel blockage in the atomizer design by calculating the number of particles in the accumulation regions and their impact on airflow. After identifying the concentrated accumulation regions, agglomeration analysis of atomized particle droplets is performed. Using CFD simulation tools (such as ANSYS Fluent), particle collisions and droplet agglomeration phenomena in the concentrated accumulation regions are simulated. The agglomeration effect mainly occurs in areas with high particle density, where particles collide and merge into larger droplets. Based on the simulation model, the collision frequency, agglomeration time, and droplet formation rate in these regions are calculated. The simulation results show the changes in particles within the accumulation regions and help further analyze the impact of agglomeration on atomizer performance, especially the degree of airflow channel blockage. Based on the concentrated accumulation regions and droplet agglomeration data, the probability of impurity accumulation inside the device is further predicted. By quantitatively analyzing the impact of droplet agglomeration, the impurity accumulation regions generated by agglomerated particles inside the device are determined. Fluid dynamics simulation tools were used to analyze contaminant accumulation regions caused by particle backflow and deposition, calculating the probability of impurity accumulation. This process also considered the flow characteristics of different regions within the equipment, such as variations in airflow velocity and flow non-uniformity. By predicting the size of the accumulation region and the impurity concentration, it was determined which parts of the equipment were contaminated, thus affecting the long-term performance of the atomizer. When the probability of impurity accumulation within the equipment exceeded 10%, droplet agglomeration analysis was used to evaluate the dynamic airflow resistance index of the atomizing pipe. This step used fluid dynamics analysis software (such as Fluent) to calculate the pressure loss in the airflow channel and analyze particle deposition in different regions. If the accumulated impurities caused blockage of the airflow channel exceeding a set threshold (e.g., 10%), the dynamic airflow resistance index of the atomizing pipe would be evaluated as high. Factors such as particle flow path, backflow probability, and accumulation density were considered during the calculation.

[0132] Preferably, the atomization uniformity gradient degradation data evaluation in step S2 includes:

[0133] The characteristics of uneven pressure distribution in the atomizing pipeline are measured based on the dynamic airflow resistance index of the atomizing pipeline.

[0134] The pressure fluctuation inside the atomizer is determined based on the characteristics of uneven pressure distribution in the pipeline.

[0135] The stability of the flow rate in the atomization pipeline is detected by observing pressure fluctuations inside the atomizer.

[0136] Identifying heterogeneity of atomization density based on the flow stability of atomization pipelines;

[0137] Calculate the motion inertia of the atomized particles based on the fineness of the atomized particles;

[0138] Predicting excessively fast settling velocity of atomized particles based on their motion inertia;

[0139] The gradient degradation data of atomization uniformity was assessed based on data on excessively fast atomized particle settling velocity and atomization density heterogeneity.

[0140] In this embodiment of the invention, the pressure distribution non-uniformity of the atomizer is measured based on the blockage index of the atomizer's airflow channels. The blockage index of the airflow channels is calculated using CFD simulation tools (such as ANSYS Fluent) to simulate the flow of the atomizer's airflow under different operating conditions. By simulating the pressure changes in each airflow channel in the environment, relevant data on the pressure distribution non-uniformity can be obtained. To ensure the accuracy of the results, the airflow model needs to be input with actual parameters such as flow rate, nozzle size, and pipe bending angle, and pressure measurements are taken at multiple sensor locations to record pressure changes. After obtaining the pipe pressure distribution non-uniformity data, the pressure fluctuations inside the atomizer are analyzed using fluid dynamics simulation software (such as OpenFOAM). Pressure fluctuations affect the stability of the atomizer, especially the atomization effect. By adding sensor points to the simulation, the pressure fluctuations inside the entire atomizer are monitored to obtain the pressure change amplitude and frequency in different areas. In a laboratory environment, sensors are used to directly collect real-time pressure data inside the pipe. Through statistical analysis of the pressure fluctuation data, the frequency range of pressure changes is determined, and its impact on atomizer performance is evaluated. If the pressure fluctuations exceed the set range, it is considered to affect the airflow stability of the atomizer, leading to a degradation of the atomization effect. After analyzing the pressure fluctuations inside the atomizer, the flow stability of the atomization pipeline is detected using changes in pressure fluctuations. A flow sensor (such as an electromagnetic flowmeter) is used to monitor real-time changes in flow within the atomizer pipeline to ensure flow stability. During the experiment, flow sensors are installed at the inlet and outlet of the atomizer, and the flow rate changes over time are recorded. By comparing flow fluctuation data with pressure fluctuation data, the relationship between pressure fluctuations and flow instability is determined. If the flow fluctuation amplitude is too large, it indicates that the stability of the airflow within the pipeline is affected, leading to uneven atomization. Flow stability curves are generated by statistically analyzing flow stability data over different time periods. After detecting flow instability in the atomization pipeline, the heterogeneity of atomization density is identified by analyzing the flow data. Fluid dynamics simulation software (such as COMSOL Multiphysics) is used to analyze the impact of flow fluctuations on the airflow density distribution within the atomizer. Based on the density changes of the airflow under different flow rates, the uniformity of atomized particle distribution is calculated. In this process, the atomization effect of the atomizer under different flow rates is simulated, especially when the flow fluctuation is large, to analyze whether the airflow density distribution is uniform. If the density is significantly lower or higher in certain areas, it indicates heterogeneity in the atomization density of those areas. By statistically analyzing the spatial distribution of atomization density, regions with uneven airflow density are identified. The kinetic inertia is calculated based on the fineness of the atomized particles. A laser particle size analyzer is used to measure the particle size distribution of the atomized particles, ranging from 0.1 μm to 1000 μm. Based on the fineness and density of the particles, the kinetic inertia of particles with different sizes is calculated.Smaller particles (e.g., less than 5 μm) have lower inertia, making them more susceptible to airflow influence, while larger particles have higher inertia and are less affected by airflow. By analyzing particle inertia, the trajectory of particles in the airflow is predicted, and the settling velocity of atomized particles is predicted based on their inertia. Larger particles, due to their higher inertia, move faster in the airflow and have a larger settling velocity. By calculating the Reynolds number of the particles and considering parameters such as particle size, airflow rate, and air viscosity, the settling velocities of particles with different sizes are calculated. The case of excessively fast settling velocities is analyzed to determine if particles will settle too quickly and affect the atomization effect. Particles with excessively fast settling velocities can lead to uneven atomization. Based on the data on excessively fast particle settling velocities and the heterogeneity of atomization density, the degradation data of atomization uniformity gradient is evaluated. In this process, by analyzing the changes in particles with excessively fast settling velocities and atomization density, the non-uniformity of particle distribution at different locations is assessed. Statistical analysis methods are used to calculate the gradient of particle distribution and determine whether uniformity degradation exists. Based on experimental data and simulation results, a gradient degradation curve for atomization uniformity is generated.

[0141] Preferably, the multidimensional spray accuracy dynamic decay trajectory described in step S2 includes:

[0142] Identify the atomization concentration change state based on the dynamic airflow resistance index of the atomization pipeline;

[0143] Calculate the atomizer spray deviation angle based on the atomization uniformity gradient degradation data;

[0144] Predict the trend of atomizer spray range reduction based on atomizer spray deviation angle;

[0145] Calculate the atomizer jet heterogeneity based on the trend of atomizer jet range reduction;

[0146] Predict excessive local concentration data of the atomizer based on the atomizer concentration change status and atomizer jet heterogeneity;

[0147] Based on the large amount of data on local concentration in the atomizer, the dynamic decline trajectory of multi-dimensional spray accuracy is detected.

[0148] In this embodiment of the invention, the blockage index of the atomizer's airflow channel is used to evaluate the airflow distribution and flow resistance changes within the airflow channel, thereby determining the atomization concentration variation state of the atomizer. Airflow simulation is performed using CFD (Computational Fluid Dynamics) simulation software (such as ANSYS Fluent). By inputting the geometric parameters and operating parameters (such as flow rate and pressure) of the airflow channel, the blockage index of the airflow channel is calculated. This blockage index reflects whether the airflow is smooth and can indicate areas of local airflow stagnation or unstable flow within the channel. Changes in atomization concentration are monitored in real time under different airflow velocities and pressure variations. Through continuous monitoring data, concentration change curves under different operating conditions are obtained, thereby identifying situations of uneven concentration or unstable concentration changes. After obtaining atomization uniformity gradient degradation data, the atomizer's injection deviation angle is calculated based on this data. The atomizer's nozzle design and injection direction will generate a certain injection angle based on the airflow characteristics of the airflow channel, injection pressure, and the atomizer's geometric parameters. The deviation between the actual injection direction and the predetermined direction is obtained through simulation or experimental measurement. By utilizing the airflow characteristics of the atomizer and the degradation data of spray uniformity, combined with the spray angle and nozzle operating status, the spray deviation angle is calculated. A high-precision laser scanner or optical sensor is used to detect the spray deviation to ensure accurate reflection of the atomizer's spray accuracy. If significant deviations occur in atomization uniformity in certain areas, the spray angle deviation will also increase, affecting the overall performance of the atomizer. Based on the measured spray deviation angle, the trend of atomizer spray range reduction is predicted. The atomizer's spray range shrinks with increasing spray angle or abnormal changes in the internal airflow. During the atomizer design phase, fluid dynamics simulations (such as using COMSOL Multiphysics) are used to simulate the impact of spray angle changes on the atomizer's spray range. By analyzing the diffusion range of atomized particles and changes in the spray area under different spray angles and pressures, a model relating the spray range to the spray angle is established. Based on this model, the trend of spray angle deviation reducing the atomizer's spray range is predicted. During experiments, optical imaging techniques (such as high-speed cameras or beam trackers) are used to record changes in the atomizer's spray range under different operating conditions, thereby obtaining data on the trend of spray range reduction. After obtaining the trend of reduced spray range, this trend was used to further calculate the spray heterogeneity of the atomizer. Spray heterogeneity mainly refers to the difference in spray concentration in different areas of the atomizer. During the experiment, multiple concentration sensors were installed at different locations on the atomizer (such as around the nozzle, the atomizer outlet, etc.) to monitor the distribution of atomized concentration in real time. Based on the measured data, the concentration uniformity within the spray area was calculated. Combined with the trend of reduced spray range, spray heterogeneity also increases as the spray angle deviation increases.By employing data analysis methods (such as coefficient of variation and standard deviation), the jet heterogeneity of the atomizer under different operating conditions is calculated, and its impact on spray performance is evaluated. If the concentration changes drastically in certain areas, it indicates uneven atomization in those areas. Based on the atomizer's concentration variation and jet heterogeneity, areas with excessively high local jet concentrations are identified. Through comprehensive analysis of jet heterogeneity data and real-time changes in atomization concentration, a concentration sensor is used to perform precise measurements within the atomizer's operating area. If the concentration in a certain area significantly exceeds a preset concentration standard, it is considered that the area has an excessive jet concentration problem. Data processing algorithms (such as threshold-based anomaly detection algorithms) are used to compare concentration values ​​across multiple measurement points to determine which areas exhibit excessive concentration. In practical operation, by controlling the airflow distribution or adjusting the nozzle's operating parameters, the dynamic decay trajectory of multidimensional spray accuracy is detected based on data from areas with excessive local concentrations. A spray accuracy monitoring system is used to record the concentration changes during the atomizer's spray process in real time via sensors. During the experiment, changes in spray accuracy under different spray conditions are monitored, especially the long-term tracking of the atomizer's spray accuracy. Based on monitoring data, the changes in spray accuracy over time were analyzed, and the dynamic degradation trajectory of multidimensional spray accuracy was determined. Combined with data on excessively high concentrations, the specific trajectory of atomizer spray accuracy degradation was further inferred, including localized decreases in spray accuracy and reductions in spray range.

[0149] Preferably, step S3 includes the following steps:

[0150] Step S31: Identify the attenuation of atomization transmission efficiency based on the dynamic decay trajectory of multi-dimensional spray accuracy;

[0151] Step S32: Assess the growth trend of atomization transmission energy consumption based on the atomization transmission efficiency decay;

[0152] Step S33: Evaluate the durability of the atomizer based on the growth trend of atomization transmission energy consumption and the decay of atomization transmission efficiency, and obtain atomizer durability data;

[0153] Step S34: Assess the performance degradation of the atomizer based on atomizer durability data and the growth trend of atomization transmission energy consumption.

[0154] As an example of the present invention, reference is made to Figure 2 As shown, step S3 in this example includes:

[0155] Step S31: Identify the attenuation of atomization transmission efficiency based on the dynamic decay trajectory of multi-dimensional spray accuracy;

[0156] In this embodiment of the invention, when identifying the decay of atomization transmission efficiency, the multi-dimensional dynamic decay trajectory of spray accuracy obtained in the previous steps is used, combined with real-time changes in airflow, pressure, and the size and concentration of atomized particles, to evaluate the transmission efficiency of the atomizer. Dedicated flow monitoring instruments and pressure sensors are used to collect real-time data on the state of the atomizer's airflow and atomized particles. By analyzing this data, the relationship between the gradual decrease in spray accuracy and atomization transmission efficiency after prolonged use is identified. Especially under conditions of significant airflow pressure changes, the spray efficiency inside the atomizer is significantly affected. Through in-depth analysis of this data, the decay pattern of atomization transmission efficiency under different operating conditions is identified, and the trend of efficiency decline with extended use time is predicted. Flow rate analysis techniques are used to capture the decay trend of atomization transmission efficiency.

[0157] Step S32: Assess the growth trend of atomization transmission energy consumption based on the atomization transmission efficiency decay;

[0158] In this embodiment of the invention, the growth trend of atomization transmission energy consumption is assessed by analyzing relevant data based on the decay of atomization transmission efficiency. A dynamic power meter and heat flux sensor are used to monitor the energy consumption of the atomizer in real time, obtaining accurate energy consumption data. Combining the atomizer's spray accuracy and transmission efficiency, the energy consumption change resulting from the decrease in efficiency can be determined. Specifically, by changing operating conditions (such as airflow and pressure) and measuring energy consumption under different loads, the growth trend of energy consumption is fitted using experimental data. Based on this growth trend, the specific changes in energy consumption of the atomizer during different usage cycles are determined, further providing a basis for equipment optimization and maintenance. Especially under certain high-load operating conditions, energy consumption increases exponentially; this data can be used to predict when the equipment's energy consumption will reach a critical point.

[0159] Step S33: Evaluate the durability of the atomizer based on the growth trend of atomization transmission energy consumption and the decay of atomization transmission efficiency, and obtain atomizer durability data;

[0160] In this embodiment of the invention, based on the aforementioned energy consumption growth trend and transmission efficiency decay, long-term experimental monitoring data is used for estimation. By conducting load tests on the atomizer at different cycles in practical applications, its performance changes are monitored, and changes in parameters such as airflow, pressure, energy consumption, and atomization accuracy are recorded. These experimental data are combined with the transmission efficiency decay and energy consumption trend to create a model, and the durability performance of the device after long-term operation is predicted through a computational model. The main tools used include power consumption analyzers and atomized particle concentration analyzers, which can accurately measure and record the long-term usage status of the device. Through systematic analysis of this data, the durability of the atomizer under different operating environments is evaluated, and its durability data is obtained.

[0161] Step S34: Assess the performance degradation of the atomizer based on atomizer durability data and the growth trend of atomization transmission energy consumption.

[0162] In this embodiment of the invention, based on collected atomizer durability data and energy consumption growth trends, the performance degradation of the atomizer is assessed to obtain a more accurate assessment. A monitoring system continuously tracks the atomizer's operating status, recording long-term changes in key performance indicators (such as spray accuracy, particle size distribution, and airflow pressure). In experiments, different loads and operating cycles are set, and through multiple measurements and data acquisitions, the performance degradation trend of the device after a specific usage period can be identified. Using this experimental data, a mathematical model predicts the device's performance degradation path. At this point, high-precision sensors (such as flow meters, differential pressure gauges, and temperature and humidity sensors) are used to comprehensively measure various operating parameters of the device, and regression analysis and accelerated life testing are employed to quantitatively analyze the device's performance degradation. For example, by analyzing the relationship between performance changes and energy consumption, the rate of performance degradation under certain high-load or long-term use conditions can be clearly identified, thereby estimating its remaining service life.

[0163] Preferably, step S4 includes the following steps:

[0164] Step S41: Predict the deterioration trend of the atomizer nozzle diameter based on the atomizer performance degradation.

[0165] Step S42: Calculate the atomizer aging life parameters based on the atomizer nozzle diameter degradation trend;

[0166] Step S43: Detect atomizer design defect parameters based on atomizer aging life parameters and atomizer nozzle diameter deterioration trend;

[0167] Step S44: Optimize the initial design of the atomizer based on atomizer design defects and atomizer aging life parameters to obtain atomizer design data.

[0168] As an example of the present invention, reference is made to Figure 3 As shown, step S4 in this example includes:

[0169] Step S41: Predict the deterioration trend of the atomizer nozzle diameter based on the atomizer performance degradation.

[0170] In this embodiment of the invention, predicting the degradation trend of the atomizer's nozzle diameter based on the atomizer's performance decline requires combining performance change data during equipment use. This is achieved using devices such as pressure sensors, flow meters, and spray accuracy monitors to continuously track the atomizer's spray effect. These devices can collect real-time information on changes in nozzle diameter and spray particle size, monitoring the trend of spray accuracy changes. After prolonged use, wear and clogging of the atomizer nozzle will lead to a gradual reduction in nozzle diameter. By periodically measuring the nozzle diameter and considering the influence of factors such as spray pressure changes, airflow velocity, and ambient humidity, data analysis methods are used to predict the degradation trend of the nozzle diameter. The data analysis model can predict changes in nozzle diameter under different usage environments and cycles based on historical data.

[0171] Step S42: Calculate the atomizer aging life parameters based on the atomizer nozzle diameter degradation trend;

[0172] In this embodiment of the invention, based on the degradation trend of the atomizer's nozzle diameter, calculating the atomizer's aging life parameters requires considering the correlation between the nozzle diameter and atomization effect. A spray accuracy analyzer is used to periodically monitor the diameter, distribution, and accuracy of the spray, recording changes in the nozzle diameter during use. Using existing experimental data, a mathematical model is established between changes in the atomizer's nozzle diameter and performance degradation through multivariate regression analysis and curve fitting. Based on this model, the rate of change of the nozzle diameter under specific operating conditions (such as temperature, humidity, and airflow rate) is calculated, thereby deriving the device's aging life parameters. Through long-term usage data monitoring, combined with the device's cumulative operating time, the remaining service life of the atomizer can be estimated.

[0173] Step S43: Detect atomizer design defect parameters based on atomizer aging life parameters and atomizer nozzle diameter deterioration trend;

[0174] In this embodiment of the invention, by combining aging life parameters with the degradation trend of the spray nozzle diameter, and comparing the changes in the atomizer's spray accuracy and performance under different operating conditions, potential problems in the design can be identified. For example, under certain environmental conditions, the spray nozzle diameter degrades too quickly, leading to a significant decrease in atomizer performance. This is due to improper material selection or structural problems in certain components. Using testing equipment (such as a spray particle distribution analyzer and an airflow analyzer), the atomizer's operating status is monitored in real time, and data such as spray accuracy, spray nozzle diameter, and energy consumption are recorded for different usage cycles. These data are compared with design parameters to identify the probability of design defects. If design defect parameters are found, such as insufficient wear resistance of the nozzle material or an excessively narrow spray nozzle diameter, data analysis is used to determine the specific impact of these defects on equipment performance, identifying areas for design improvement. At this point, simulation tools can be used for more detailed structural analysis and flow field simulation to further confirm the design defects.

[0175] Step S44: Optimize the initial design of the atomizer based on atomizer design defects and atomizer aging life parameters to obtain atomizer design data.

[0176] In this embodiment of the invention, initial design optimization is performed based on atomizer design flaws and aging life parameters. By analyzing atomizer performance degradation data, nozzle diameter deterioration trends, and design flaw parameters, directions for atomizer improvement are determined. For example, to address nozzle material aging issues, materials with higher wear resistance are selected, or the nozzle geometry is optimized to reduce wear. Furthermore, analysis of aging life parameters reveals that certain components require structural reinforcement to improve durability. The overall atomizer design is optimized, and computer-aided design (CAD) tools and finite element analysis (FEA) software are used to simulate the new design, verifying the performance of the optimized structure under various operating conditions. The optimized design also requires multiple experimental verifications, including pressure tests, airflow tests, and spray tests, to ensure that the design improvements effectively enhance atomizer performance and extend its service life. The atomizer design data obtained through these improvement measures is then analyzed.

[0177] The present invention also provides a simulation model-based atomizer design system for executing the simulation model-based atomizer design method described above. The simulation model-based atomizer design system includes:

[0178] The simulation model building module is used to obtain atomizer application requirement data; draw the initial design of the atomizer based on the atomizer application requirement data; and build the atomizer simulation model based on the initial design of the atomizer.

[0179] The accuracy degradation trajectory detection module is used to evaluate the dynamic airflow resistance index of the atomizing pipeline based on the atomizer simulation model, and evaluate the atomization uniformity gradient degradation data based on the dynamic airflow resistance index of the atomizing pipeline; and detect the multidimensional spray accuracy dynamic decay trajectory based on the atomization uniformity gradient degradation data and the dynamic airflow resistance index of the atomizing pipeline.

[0180] The performance degradation assessment module is used to identify the degradation of atomization transmission efficiency based on the dynamic degradation trajectory of multi-dimensional spray accuracy; and to assess the performance degradation of the atomizer based on the degradation of atomization transmission efficiency.

[0181] The initial design optimization module is used to predict the degradation trend of the atomizer nozzle diameter based on the atomizer performance degradation; detect atomizer design defect parameters based on the predicted atomizer performance degradation and the atomizer nozzle diameter degradation trend; and optimize the initial atomizer design based on the atomizer design defects to obtain atomizer design data.

[0182] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for designing an atomizer based on a simulation model, characterized in that, Includes the following steps: Step S1: Obtain atomizer application requirement data; draw the initial atomizer design based on the atomizer application requirement data, and build an atomizer simulation model based on the initial atomizer design; Step S2: Evaluate the dynamic airflow resistance index of the atomizing pipe based on the atomizer simulation model, and evaluate the atomization uniformity gradient degradation data based on the dynamic airflow resistance index of the atomizing pipe; detect the multidimensional spray accuracy dynamic decay trajectory based on the atomization uniformity gradient degradation data and the dynamic airflow resistance index of the atomizing pipe, wherein the calculation of the dynamic airflow resistance index of the atomizing pipe includes: The size distribution of atomized particles ranging from 0.1µm to 1000µm was collected based on the fineness of the atomized particles. The floating time of atomized particles is calculated based on the size distribution of the atomized particles; Calculate the probability of atomized particle recirculation during the floating time of atomized particles when the ambient wind speed is 0.5 m / s. The atomized particle backflow accumulation area is collected based on the probability of atomized particle backflow; Mark the concentrated accumulation area of ​​atomized particles in the atomized particle backflow accumulation area; The degree of droplet aggregation of atomized particles is estimated based on the area where atomized particles are concentrated. Predict the probability of impurity accumulation inside the equipment based on the concentrated area of ​​atomized particle accumulation and the degree of atomized particle droplet coagulation. The degree of blockage in the atomizing pipeline is measured based on the probability of impurity accumulation inside the equipment exceeding 10% and the degree of atomized droplet coagulation. The dynamic airflow resistance index of the atomizing pipe is calculated based on the degree of congestion in the atomizing pipe. This also includes calculating the collision frequency, condensation time, and droplet formation rate of particles in the atomizing particle accumulation area based on the simulation model. The simulation results will show the changes of particles in the accumulation area. Step S3: Identify the atomization transmission efficiency decay based on the dynamic decay trajectory of multi-dimensional spray accuracy; evaluate the atomizer performance decay based on the atomization transmission efficiency decay. Step S4: Predict the atomizer nozzle diameter degradation trend based on the atomizer performance degradation; detect atomizer design defect parameters based on the predicted atomizer performance degradation and atomizer nozzle diameter degradation trend; optimize the initial atomizer design based on the atomizer design defects to obtain atomizer design data.

2. The atomizer design method based on simulation model according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain atomizer application requirement data; Step S12: Determine the atomizer application scenario environment data based on the atomizer application requirement data; Step S13: Draw the initial design of the atomizer based on the atomizer application scenario environment data and atomizer requirement data; Step S14: Construct an atomizer simulation model based on the initial atomizer design.

3. The atomizer design method based on simulation model according to claim 2, characterized in that, Step S13 includes the following steps: Step S131: Set the temperature measurement range of the temperature sensor to 0-100°C, the minimum temperature change to 0.05°C, and the temperature sampling frequency to 10Hz; Step S132: Set the humidity sensor's humidity measurement range to 0% to 100%RH, the minimum humidity change to 0.1%RH, and the humidity sampling frequency to 5Hz; Step S133: Use temperature and humidity sensors to collect atomizer operating environment parameters for the atomizer application scenario; Step S134: Determine atomization efficiency based on atomizer application requirement data; determine atomization accuracy based on atomizer application requirement data; Step S135: Evaluate the impact of atomization accuracy on atomization accuracy based on atomizer operating environment parameters; Step S136: Draw the initial design of the atomizer based on the impact of atomization accuracy and atomization efficiency.

4. The atomizer design method based on simulation model according to claim 2, characterized in that, Step S14 includes the following steps: Step S141: Draw the internal structural parameters of the atomizer according to the initial design of the atomizer; Step S142: Analyze the internal topology data of the atomizer based on the internal structural parameters of the atomizer; Step S143: Identify the internal connectivity of the atomizer based on the internal topology data of the atomizer; Step S144: Collect atomizer nozzle style data according to the initial atomizer design; Step S145: Construct the atomizer's three-dimensional geometric structure parameters based on the atomizer nozzle style data and the internal connectivity of the atomizer; Step S146: Construct a simulation model of the atomizer based on the atomizer's three-dimensional geometric parameters and internal topology data.

5. The atomizer design method based on a simulation model according to claim 1, characterized in that, The atomization uniformity gradient degradation data evaluation in step S2 includes: The characteristics of uneven pressure distribution in the atomizing pipeline are measured based on the dynamic airflow resistance index of the atomizing pipeline. The pressure fluctuation inside the atomizer is determined based on the characteristics of uneven pressure distribution in the pipeline. The stability of the flow rate in the atomization pipeline is detected by observing pressure fluctuations inside the atomizer. Identifying heterogeneity of atomization density based on the flow stability of atomization pipelines; Calculate the motion inertia of the atomized particles based on the fineness of the atomized particles; Predicting excessively fast settling velocity of atomized particles based on their motion inertia; The gradient degradation data of atomization uniformity was assessed based on data on excessively fast atomized particle settling velocity and atomization density heterogeneity.

6. The atomizer design method based on simulation model according to claim 1, characterized in that, The multidimensional spray accuracy dynamic decay trajectory mentioned in step S2 includes: Identify the atomization concentration change state based on the dynamic airflow resistance index of the atomization pipeline; Calculate the atomizer spray deviation angle based on the atomization uniformity gradient degradation data; Predict the trend of atomizer spray range reduction based on atomizer spray deviation angle; Calculate the atomizer jet heterogeneity based on the trend of atomizer jet range reduction; Predict excessive local concentration data of the atomizer based on the atomizer concentration change status and atomizer jet heterogeneity; Based on the large amount of data on local concentration in the atomizer, the dynamic decline trajectory of multi-dimensional spray accuracy is detected.

7. The atomizer design method based on a simulation model according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Identify the attenuation of atomization transmission efficiency based on the dynamic decay trajectory of multi-dimensional spray accuracy; Step S32: Assess the growth trend of atomization transmission energy consumption based on the atomization transmission efficiency decay; Step S33: Evaluate the durability of the atomizer based on the growth trend of atomization transmission energy consumption and the decay of atomization transmission efficiency, and obtain atomizer durability data; Step S34: Assess the performance degradation of the atomizer based on atomizer durability data and the growth trend of atomization transmission energy consumption.

8. The atomizer design method based on simulation model according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Predict the deterioration trend of the atomizer nozzle diameter based on the atomizer performance degradation. Step S42: Calculate the atomizer aging life parameters based on the atomizer nozzle diameter degradation trend; Step S43: Detect atomizer design defect parameters based on atomizer aging life parameters and atomizer nozzle diameter deterioration trend; Step S44: Optimize the initial design of the atomizer based on atomizer design defects and atomizer aging life parameters to obtain atomizer design data.

9. A simulation model-based atomizer design system, characterized in that, For executing the simulation model-based atomizer design method as described in claim 1, the simulation model-based atomizer design system includes: The simulation model building module is used to obtain atomizer application requirement data; draw the initial design of the atomizer based on the atomizer application requirement data; and build the atomizer simulation model based on the initial design of the atomizer. The accuracy degradation trajectory detection module is used to evaluate the dynamic airflow resistance index of the atomizing pipeline based on the atomizer simulation model, and evaluate the atomization uniformity gradient degradation data based on the dynamic airflow resistance index of the atomizing pipeline; and detect the multidimensional spray accuracy dynamic decay trajectory based on the atomization uniformity gradient degradation data and the dynamic airflow resistance index of the atomizing pipeline. The performance degradation assessment module is used to identify the degradation of atomization transmission efficiency based on the dynamic degradation trajectory of multi-dimensional spray accuracy; and to assess the performance degradation of the atomizer based on the degradation of atomization transmission efficiency. The initial design optimization module is used to predict the degradation trend of the atomizer nozzle diameter based on the atomizer performance degradation; detect atomizer design defect parameters based on the predicted atomizer performance degradation and the atomizer nozzle diameter degradation trend; and optimize the initial atomizer design based on the atomizer design defects to obtain atomizer design data.

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