Micro array nozzle homogenization and deceleration method and system based on fluid simulation analysis

Abstract

The invention relates to the technical field of fluid simulation, and discloses a micro array nozzle homogenization and deceleration method and system based on fluid simulation analysis, and the method comprises the steps: building a three-dimensional fluid simulation model of a coupling temperature field, a flow field and a self-oscillation field, detecting the outlet flow velocity, a pressure pulsation signal and a temperature signal of each micro nozzle; and a multivariable fuzzy PID controller is adopted, outlet speed uniformity, average speed and pressure pulse rate are taken as control targets, controllable parameters are dynamically adjusted, and active suppression and compensation of dynamic disturbance are realized. And meanwhile, real-time monitoring data is compared with a simulation result, a simulation model is continuously corrected by utilizing deviation, so that the simulation model infinitely approaches physical reality, a high-reliability control parameter set is generated, and the problem of control failure caused by model mismatch is fundamentally solved. Through a periodic online calibration mechanism, the flow distribution of each nozzle is adjusted in a self-adaptive manner, and automatic compensation of performance drift is realized, so that the targets of homogenization and deceleration are stably achieved for a long time.

Description

Technical Field

[0001] This application relates to the field of fluid simulation technology, specifically to a method and system for homogenization and deceleration of micro-array nozzles based on fluid simulation analysis. Background Technology

[0002] Micro-array nozzles are core components in many high-precision industrial processes, and their performance directly determines the quality of the final process. For example, in applications such as chemical mechanical polishing slurry delivery for semiconductor chips, jet impingement cooling for high-power electronic devices, fuel atomization for aerospace engines, and precise powder deposition in 3D printing, it is required that the medium flowing out of each micro-nozzle in the array has a highly consistent velocity (i.e., homogenization), and the overall flow rate needs to be stabilized within a specific low range (i.e., deceleration) to achieve uniform, gentle, and controllable interaction.

[0003] In existing technologies, it is difficult to coordinate homogenization and deceleration objectives. Due to factors such as simplified boundary conditions, there are often deviations between simulation models and actual objects, resulting in poor performance of model-based design and control parameters in practical applications. At the same time, because it is impossible to perceive and compensate for performance drift caused by equipment aging, environmental changes, etc. in real time, it is difficult to maintain long-term operational stability and consistency.

[0004] Therefore, there is an urgent need for a microarray nozzle homogenization and deceleration method that can fundamentally and synergistically resolve the contradiction between homogenization and deceleration, effectively suppress dynamic disturbances, and enable model self-evolution. Summary of the Invention

[0005] In view of this, the embodiments of this application provide a method and system for homogenization and deceleration of micro-array nozzles based on fluid simulation analysis, so as to solve the problem of contradiction between homogenization and deceleration in traditional technology, and achieve the purpose of effectively suppressing dynamic disturbances and model self-evolution.

[0006] In a first aspect, one embodiment of this application provides a method for homogenization and deceleration of a micro-array nozzle based on fluid simulation analysis. The method includes: establishing a three-dimensional fluid simulation model of the micro-array nozzle, wherein the simulation model uses a transient simulation algorithm to couple the temperature field, flow field, and self-excited oscillation field; wherein the micro-array nozzle includes multiple micro-nozzles, and the self-excited oscillation field is implemented by writing a momentum source term formula through a user-defined function; based on the simulation model, optimizing and analyzing offline optimization parameters to obtain an optimized parameter combination that satisfies the homogenization and deceleration objectives of the micro-array nozzle flow; based on the optimized parameter combination, detecting the outlet velocity, pressure pulsation signal, and temperature signal of each micro-nozzle, and using a multivariable fuzzy PID controller with outlet velocity uniformity, average velocity, and pressure pulse rate as control objectives, dynamically adjusting controllable parameters, including the total inlet pressure, medium temperature, and inlet flow distribution ratio; comparing the detection results with the simulation prediction results, and correcting the simulation model according to the deviation value to form a homogenization and deceleration control parameter set; and based on the homogenization and deceleration control parameter set, periodically calibrating to adaptively distribute the flow of each micro-nozzle to complete the homogenization and deceleration processing of the micro-array nozzle.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, offline optimization parameters are optimized and analyzed based on simulation models to obtain optimal parameter combinations that enable the flow of the micro-array nozzles to meet the objectives of homogenization and deceleration. This includes: performing single-factor variable analysis on the offline optimization parameters through simulation models, extracting the performance indicators of each micro-nozzle, and establishing a variable-performance mapping database; constructing a multi-objective optimization model based on the variable-performance mapping database with the optimization objectives of exit velocity uniformity, average velocity, and pressure pulse rate; and using the multi-objective optimization model to select parameter combinations with interaction intensity exceeding a predetermined threshold, thereby generating optimal parameter combinations.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, the offline optimization parameters include the structural parameters of the self-excited oscillation cavity, the inlet pressure fluctuation frequency, and the surface roughness parameters of the nozzle flow channel. A simulation model is used to perform single-factor variable analysis on the offline optimization parameters, extracting the performance indicators of each micro-nozzle and establishing a variable-performance mapping database. This includes: adjusting the structural parameters of the self-excited oscillation cavity, the inlet pressure fluctuation frequency, and the surface roughness parameters of the nozzle flow channel sequentially and individually using the simulation model; extracting the velocity standard deviation, average velocity, and pressure pulse rate of each nozzle on the exit section of the micro-array nozzle as performance indicators based on the transient simulation results after each single-parameter adjustment; and recording each set of parameter values ​​and corresponding performance indicators to establish a variable-performance mapping database.

[0009] In conjunction with the first aspect, in certain implementations of the first aspect, the detection results are compared with the simulation prediction results, and the simulation model is corrected based on the deviation value to form a homogenized deceleration control parameter set. This includes: performing point-by-point deviation analysis between the detected outlet velocity, pressure pulsation signal, and temperature signal of each micro-nozzle and the simulation prediction results on the indicators of outlet velocity uniformity, average velocity, and pressure pulse rate, and calculating the relative deviation value of each indicator; when the relative deviation value of any indicator among outlet velocity uniformity, average velocity, and pressure pulse rate exceeds the corresponding predetermined threshold, the simulation model is adaptively corrected; based on the corrected simulation model, controllable parameter optimization analysis and multivariable fuzzy PID control are re-executed, and deviation detection and model correction are iteratively performed until the relative deviation values ​​of all indicators are lower than the corresponding predetermined thresholds, and finally generating a homogenized deceleration control parameter set.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, the simulation model is adaptively modified, including the modification of boundary conditions; wherein the modification of boundary conditions includes at least one of inlet turbulence intensity, wall roughness, and near-wall mesh parameters.

[0011] In conjunction with the first aspect, in some implementations of the first aspect, based on the homogenized deceleration control parameter set, the flow rate of each micro-nozzle is adaptively allocated through periodic calibration to complete the homogenization and deceleration processing of the micro-array nozzles. This includes: setting periodic calibration trigger conditions; collecting the outlet velocity distribution data of each micro-nozzle in each calibration cycle and comparing it with the expected performance of the homogenized deceleration control parameter set; dynamically adjusting the flow distribution coefficient of each micro-nozzle through a flow weight allocation algorithm based on the comparison deviation, and feeding the updated flow distribution coefficient back to the multivariable fuzzy PID controller to adjust the flow rate of each micro-nozzle until the outlet velocity distribution meets the preset homogenization standard and the average velocity reaches the preset deceleration standard.

[0012] In conjunction with the first aspect, in some implementations of the first aspect, the periodic calibration triggering conditions include: reaching a preset system operating time interval; and / or: the temperature change detected by the ambient temperature sensor exceeds a predetermined tolerance; and / or: the deviation between the real-time detected outlet velocity distribution data of each micro-nozzle and the expected performance of the homogenized deceleration control parameter set exceeds a preset threshold.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, the self-excited oscillation field is implemented by writing momentum source term formulas through user-defined functions, including: the self-excited oscillation field simulates the periodic oscillations generated by the Helmholtz resonator by writing a preset momentum source term method through user-defined functions; wherein, the source term expression defined by the momentum source term method includes the oscillation amplitude coefficient, fluid density, reference velocity, and reference length.

[0014] In conjunction with the first aspect, in some implementations of the first aspect, a multivariable fuzzy PID controller is employed, with outlet velocity uniformity, average velocity, and pressure pulse rate as control objectives, dynamically adjusting controllable parameters. This includes: establishing a multi-parameter fuzzy rule base with outlet velocity uniformity, average velocity, and pressure pulse rate as input variables and controllable parameters as output variables; defining corresponding membership functions for each input variable and output variable; mapping real-time detected input variables to the adjustment amount of output variables based on a fuzzy inference mechanism; and employing a multi-objective coordination strategy to weighted decouple the adjustment amount of output variables to achieve the control objectives.

[0015] Secondly, one embodiment of this application provides a micro-array nozzle homogenization and deceleration system based on fluid simulation analysis. The system includes: a simulation modeling module for establishing a three-dimensional fluid simulation model of the micro-array nozzle. The simulation model employs a transient simulation algorithm to couple the temperature field, flow field, and self-excited oscillation field. The micro-array nozzle includes multiple micro-nozzles, and the self-excited oscillation field is implemented by writing a momentum source term formula using a user-defined function; a parameter optimization module for performing offline optimization analysis based on the simulation model to obtain an optimized parameter combination that satisfies the homogenization and deceleration objectives of the micro-array nozzle flow; and a real-time control module for controlling the flow based on the optimized parameter combination. The system detects the outlet flow rate, pressure pulsation signal, and temperature signal of each micro-nozzle and uses a multivariable fuzzy PID controller to dynamically adjust controllable parameters, including the total inlet pressure, medium temperature, and inlet flow distribution ratio, with the controllable parameters being the outlet velocity uniformity, average velocity, and pressure pulse rate. A model correction module compares the detection results with the simulation prediction results and corrects the simulation model based on the deviation value, forming a homogenized deceleration control parameter set. An online calibration module, based on the homogenized deceleration control parameter set, periodically calibrates to adaptively distribute the flow rate of each micro-nozzle, completing the homogenization and deceleration processing of the micro-array nozzles.

[0016] The micro-array nozzle homogenization and deceleration method based on fluid simulation analysis provided in this application first establishes a high-fidelity simulation model coupling the temperature field, transient flow field, and self-excited oscillation field. Based on this model, the optimization objectives are outlet velocity uniformity, average velocity, and pressure pulse rate. This allows for the systematic and coordinated optimization of multiple mutually constraining objectives such as homogenization and deceleration during the design phase, resulting in an optimized parameter combination and overcoming the limitation of traditional methods with a single objective. During actual operation, the flow field state is monitored in real time, and a multivariable fuzzy PID controller is used to dynamically adjust controllable parameters, thereby achieving active suppression and compensation for dynamic disturbances. Simultaneously, the real-time monitoring data is compared with simulation predictions, and the deviation is used to continuously correct the simulation model itself, making it infinitely close to physical reality and generating a highly reliable set of control parameters, fundamentally solving the problem of control failure caused by model mismatch. Finally, through a periodic online calibration mechanism, the flow distribution of each nozzle is adaptively adjusted to achieve automatic compensation for performance drift, thus achieving homogenization and deceleration objectives in a long-term and stable manner. Attached Figure Description

[0017] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 The diagram shown is a flowchart illustrating a microarray nozzle homogenization and deceleration method based on fluid simulation analysis provided in an embodiment of this application.

[0019] Figure 2 The diagram shown is a schematic representation of an exemplary embodiment of this application, illustrating the process of optimizing offline optimization parameters based on a simulation model to obtain an optimized parameter combination that satisfies the homogenization and deceleration targets of the flow at the microarray nozzle.

[0020] Figure 3 The diagram shown is a schematic representation of an exemplary embodiment of this application, illustrating the process of performing single-factor variable analysis on offline optimization parameters using a simulation model, extracting performance indicators of each micro-nozzle, and establishing a variable-performance mapping database.

[0021] Figure 4 The diagram shown is an exemplary embodiment of this application, illustrating the process of comparing detection results with simulation prediction results and correcting the simulation model based on the deviation value to form a set of uniform deceleration control parameters.

[0022] Figure 5The diagram shows a schematic of the process of homogenizing and decelerating the micro-array nozzles by periodically calibrating a set of homogenized deceleration control parameters based on an exemplary embodiment of this application to adaptively allocate the flow rate of each micro-nozzle.

[0023] Figure 6 The diagram shown is a schematic representation of an exemplary embodiment of this application, which uses a multivariable fuzzy PID controller to dynamically adjust controllable parameters with the controllable parameters being outlet speed uniformity, average speed, and pressure pulse rate as control objectives.

[0024] Figure 7 The diagram shown is a schematic representation of a micro-array nozzle homogenization and deceleration system based on fluid simulation analysis provided in an exemplary embodiment of this application.

[0025] Figure 8 The diagram shown is a structural schematic of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Figure 1 The diagram shown is a flowchart illustrating a microarray nozzle homogenization and deceleration method based on fluid simulation analysis provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the method for homogenization and deceleration of micro-array nozzles based on fluid simulation analysis includes the following steps.

[0028] Step 100: Establish a three-dimensional fluid simulation model of the micro-array nozzle. The simulation model uses a transient simulation algorithm to couple the temperature field, flow field, and self-excited oscillation field.

[0029] For example, a three-dimensional digital model is used to simulate the flow of fluid inside a microarray nozzle.

[0030] For example, the temperature field is the temperature distribution in space; the flow field is the distribution of flow parameters such as velocity and pressure in space.

[0031] For example, a self-excited oscillation field refers to a stable pressure / velocity fluctuation field generated by the fluid itself in a specific structure (such as a cavity) that does not depend on external periodic excitation.

[0032] Specifically, a three-dimensional fluid simulation model including all micro-nozzles is established to ensure that the simulation can capture the mutual interference between nozzles and the complex flow phenomena within the flow channel. Because temperature changes affect fluid viscosity and density, thereby altering the flow field, and the flow field in turn affects the temperature distribution, a transient simulation algorithm is used to simultaneously solve for the temperature field, flow field, and self-excited oscillation field, achieving multi-field coupled analysis.

[0033] The self-excited oscillation field is implemented by writing the momentum source term formula through user-defined functions. Specifically, the self-excited oscillation field simulates the periodic oscillation generated by the Helmholtz resonator by writing the preset momentum source term method through user-defined functions. The source term expression defined by the momentum source term method is: S ; Where S is the source term added to the momentum equation, A is the oscillation amplitude coefficient, ρ is the fluid density, U_ref and L_ref are the reference velocity and reference length, and f(t, L_ref) is a periodic function incorporating time t and reference length L_ref. This method can efficiently simulate periodic perturbations similar to those generated by a Helmholtz resonator, thereby actively controlling the stability of the flow field.

[0034] Step 101: Based on the simulation model, perform optimization analysis on the offline optimization parameters to obtain an optimal parameter combination that satisfies the homogenization and deceleration targets of the microarray nozzle flow.

[0035] For example, offline optimization parameters are typically structure-related parameters that are optimized through simulation analysis before the system is actually run. Offline optimization parameters include structural parameters of the self-excited oscillation cavity, inlet pressure fluctuation frequency, and surface roughness parameters of the nozzle flow channel.

[0036] Step 102: Based on the optimized parameter combination, detect the outlet flow rate, pressure pulsation signal and temperature signal of each micro-nozzle, and use a multivariable fuzzy PID controller to dynamically adjust the controllable parameters with the controllable parameters of outlet velocity uniformity, average velocity and pressure pulse rate as control objectives.

[0037] The controllable parameters include the total inlet pressure, the medium temperature, and the inlet flow distribution ratio.

[0038] After the system is installed with optimized parameter combinations and started, sensors detect the flow velocity, pressure pulsation, and temperature signals at the outlet of each micro-nozzle in real time. Then, these measured data characterizing the current flow field state (i.e., outlet velocity uniformity, average velocity, and pressure pulse rate) are used as inputs and passed to a multivariable fuzzy PID controller. This controller has built-in intelligent inference rules, which can simultaneously handle the coupling relationship between multiple control objectives and dynamically calculate the optimal adjustment amount. It adjusts the three controllable parameters of total inlet pressure, medium temperature, and inlet flow distribution ratio in real time, thereby actively suppressing disturbances in the flow and enabling the outlet flow field of the entire array to quickly and stably achieve the goals of homogenization and deceleration.

[0039] Step 103: Compare the detection results with the simulation prediction results, and correct the simulation model according to the deviation value to form a set of uniform deceleration control parameters.

[0040] Specifically, the real-time monitored outlet flow velocity, pressure pulsation signals, and temperature signals of each micro-nozzle are compared and analyzed with the simulation predictions. Based on the discrepancies between the two, key parameters in the simulation model are corrected to make the model more closely resemble physical reality. Subsequently, a set of homogenized deceleration control parameters is generated using the corrected model.

[0041] Step 104: Based on the homogenized deceleration control parameter set, the flow rate of each micro-nozzle is adaptively allocated through periodic calibration to complete the homogenization and deceleration processing of the micro-array nozzles.

[0042] It should be understood that calibration cycles can be automatically initiated under various conditions (such as timed triggering, drastic changes in ambient temperature, or performance deviations exceeding limits). Within each calibration cycle, the system collects real-time flow velocity data from each nozzle and compares it with the expected performance of the homogenization and deceleration control parameter set. Then, using a flow weight allocation algorithm, it dynamically calculates a new round of flow allocation coefficients based on the deviation between the actual velocity and the target velocity of each nozzle. These updated coefficients are immediately fed back to the multivariable fuzzy PID controller, which quickly redirects the flow by fine-tuning controllable parameters such as the inlet flow allocation ratio, thereby compensating for deviations and suppressing unevenness. This process iterates until the flow velocity distribution once again meets the homogenization and deceleration criteria.

[0043] The micro-array nozzle homogenization and deceleration method based on fluid simulation analysis provided in this application first establishes a high-fidelity simulation model coupling the temperature field, transient flow field, and self-excited oscillation field. Based on this model, the optimization objectives are outlet velocity uniformity, average velocity, and pressure pulse rate. This allows for the systematic and coordinated optimization of multiple mutually constraining objectives such as homogenization and deceleration during the design phase, resulting in an optimized parameter combination and overcoming the limitation of traditional methods with a single objective. During actual operation, the flow field state is monitored in real time, and a multivariable fuzzy PID controller is used to dynamically adjust controllable parameters, thereby achieving active suppression and compensation for dynamic disturbances. Simultaneously, the real-time monitoring data is compared with simulation predictions, and the deviation is used to continuously correct the simulation model itself, making it infinitely close to physical reality and generating a highly reliable set of control parameters, fundamentally solving the problem of control failure caused by model mismatch. Finally, through a periodic online calibration mechanism, the flow distribution of each nozzle is adaptively adjusted to achieve automatic compensation for performance drift, thus achieving homogenization and deceleration objectives in a long-term and stable manner.

[0044] Figure 2 The diagram illustrates a process flow of an exemplary embodiment of this application, whereby an offline optimization parameter combination is performed based on a simulation model to obtain an optimized parameter combination that satisfies the homogenization and deceleration objectives of the flow at the microarray nozzle. Figure 1 This application extends from the embodiments shown. Figure 2 The illustrated embodiment will be described in detail below. Figure 2 The illustrated embodiments and Figure 1 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0045] like Figure 2 As shown in the embodiment of this application, the method for homogenizing and decelerating micro-array nozzles based on fluid simulation analysis optimizes offline optimization parameters based on the simulation model to obtain an optimized parameter combination that makes the flow of the micro-array nozzles meet the homogenization and deceleration objectives, including the following steps.

[0046] Step 200: Perform single-factor variable analysis on the offline optimization parameters using a simulation model, extract the performance indicators of each micro-nozzle, and establish a variable-performance mapping database.

[0047] It should be understood that, while keeping other parameters unchanged, each offline optimization parameter is adjusted individually in turn. For each adjustment, a transient simulation is run, and key performance indicators are extracted from the simulation results. Finally, the parameter adjustment value and the corresponding performance indicator results are associated and recorded to build a variable-performance mapping database.

[0048] For example, a variable-performance mapping database is used to store data tables or knowledge bases that show the relationship between different combinations of parameters and their corresponding output performance.

[0049] Step 201: Based on the variable-performance mapping database, construct a multi-objective optimization model with the optimization objectives of outlet velocity uniformity, average velocity, and pressure pulse rate.

[0050] The mathematical expression of this multi-objective optimization model is to find an optimal set of offline optimization parameters X to minimize the following three objective functions: Minimize: [ F1(X), F2(X), F3(X) ]; Wherein, F1(X) = velocity standard deviation, used to quantify the uniformity of the outlet velocity. The smaller the value, the better the uniformity. F2(X) = |average velocity - target velocity|, used to measure the deceleration effect, bringing it closer to the preset target. The smaller the value, the more precise the deceleration control. In F2(X) = |μ - V_target|, V_target represents the preset target deceleration velocity. F3(X) = pressure pulsation rate: used to evaluate the stability of the flow. The smaller the value, the weaker the pressure pulsation, and the more stable the flow. In F3(X) = (P_max - P_min) / P_mean, P_max, P_min, and P_mean are the maximum, minimum, and average pressure values ​​at a representative location, respectively.

[0051] This multi-objective optimization model collaboratively optimizes three performance indicators: outlet velocity uniformity (pursuing homogenization), average velocity (achieving deceleration), and pressure pulse rate (ensuring stability). By constructing this model, the engineering design objectives of "homogenization and deceleration" can be expressed as a multi-objective mathematical optimization problem, providing a scientific decision-making basis for the next step of systematic parameter selection.

[0052] Step 202: Using a multi-objective optimization model, select parameter combinations whose interaction strength exceeds a predetermined threshold and generate optimized parameter combinations.

[0053] For example, interaction strength is used to measure the strength of the coupling effect between parameters.

[0054] Preferably, the predetermined threshold can be set to 2.0 or 2.5 to balance sensitivity and robustness. Of course, other thresholds can also be set according to the actual situation, usually falling between 1.5 and 3.0, and the specific value needs to be determined based on the experimental design and technical requirements.

[0055] Specifically, by analyzing the interaction strength between different parameters, all possible parameter pairs are traversed. When the interaction strength of any parameter pair exceeds a preset threshold, the parameter combination is considered to have a significant interaction effect. This allows the optimal parameter combination with the most significant impact on performance to be selected as the benchmark for subsequent control.

[0056] Figure 3 The diagram illustrates an exemplary embodiment of this application, showing a process for performing single-factor variable analysis on offline optimization parameters using a simulation model, extracting performance indicators for each micro-nozzle, and establishing a variable-performance mapping database. In this application... Figure 2 This application extends from the embodiments shown. Figure 3 The illustrated embodiment will be described in detail below. Figure 3 The illustrated embodiments and Figure 2 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0057] like Figure 3 As shown in the embodiment of this application, the method for homogenization and deceleration of micro-array nozzles based on fluid simulation analysis involves performing single-factor variable analysis on offline optimization parameters through a simulation model, extracting performance indicators of each micro-nozzle, and establishing a variable-performance mapping database, including the following steps.

[0058] Step 300: Using the simulation model, adjust the structural parameters of the self-excited oscillation cavity, the inlet pressure fluctuation frequency, and the surface roughness parameters of the nozzle flow channel individually in sequence.

[0059] For example, the structural parameters of the self-excited oscillation cavity can be adjusted by changing the cavity length or cavity volume defined in the user-defined function, in order to simulate Helmholtz resonators with different physical structures.

[0060] For example, the adjustment of the inlet pressure fluctuation frequency is achieved by changing the frequency value of the periodic pressure fluctuation signal in the inlet boundary conditions.

[0061] For example, the surface roughness parameters of the nozzle channel can be adjusted by changing the equivalent sand grain roughness height defined in the wall boundary conditions.

[0062] Specifically, single-factor variable analysis was performed on the structural parameters of the self-excited oscillation cavity, the inlet pressure fluctuation frequency, and the surface roughness parameters of the nozzle flow channel. Only one parameter was changed at a time, while the other parameters remained constant, and transient simulations were run.

[0063] Step 301: For each transient simulation result after single-parameter adjustment, extract the velocity standard deviation, average velocity, and pressure pulse rate of each nozzle on the exit section of the microarray nozzle as performance indicators.

[0064] For each transient simulation result after a single-parameter adjustment, once the system reaches dynamic stability, the standard deviation of the velocity at each nozzle exit, the average velocity, and the pressure pulse rate are extracted, and performance indicators are calculated accordingly. Specifically, the standard deviation of the average velocity of all nozzles over the statistical time period is used to quantify the uniformity of the exit velocity. The average velocity is quantified by the arithmetic mean of the average velocities of all nozzles over the statistical time period and is used to measure the deceleration effect. The pressure pulse rate is quantified by the ratio of the peak-to-peak value to the average value of the pressure signal from the main nozzle or a specified nozzle and is used to measure flow stability.

[0065] Step 302: Record each set of parameter values ​​and corresponding performance metrics to establish a variable-performance mapping database.

[0066] Each parameter adjustment, parameter identifier, parameter value, and corresponding set of calculated performance index values ​​are associated and recorded to form multiple data records. These records are then compiled into a variable-performance mapping database to reveal the impact trend of single parameter changes on system performance.

[0067] The microarray nozzle homogenization and deceleration method based on fluid simulation analysis provided in this application systematically extracts key performance indicators from the transient simulation results after single-parameter adjustment and establishes a variable-performance mapping database, thereby achieving precise quantification of the outlet velocity uniformity, deceleration effect, and flow stability of the microarray nozzle. It not only reveals the independent influence of each offline optimization parameter on performance, but also provides structured data support for subsequent multi-objective optimization models, ultimately laying a solid foundation for actively suppressing dynamic disturbances and achieving long-term stable control.

[0068] Figure 4 The diagram illustrates an exemplary embodiment of this application, showing a process for comparing detection results with simulation prediction results and correcting the simulation model based on the deviation value to form a uniform deceleration control parameter set. Figure 3 This application extends from the embodiments shown. Figure 4 The illustrated embodiment will be described in detail below. Figure 4 The illustrated embodiments and Figure 3 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0069] like Figure 4 As shown in the embodiment of this application, the method for homogenization and deceleration of a micro-array nozzle based on fluid simulation analysis compares the detection results with the simulation prediction results, and corrects the simulation model according to the deviation value to form a set of homogenization and deceleration control parameters, including the following steps.

[0070] Step 400: Perform point-by-point deviation analysis between the detected outlet flow rate, pressure pulsation signal, and temperature signal of each micro-nozzle and the simulation prediction results on the indicators of outlet velocity uniformity, average velocity, and pressure pulse rate, and calculate the relative deviation value of each indicator.

[0071] The relative deviation is the difference between the measured value and the simulated prediction value, expressed as a percentage or ratio relative to the measured value, as shown in the following formula: Relative deviation = |Measured value - Simulated value| / |Measured value|; Specifically, instead of employing a point-by-point deviation analysis method, the measured data of each micro-nozzle is compared one by one with the corresponding simulation prediction results. For example, the measured exit velocity of the i-th nozzle is compared with the simulation prediction velocity of that nozzle to ensure the precision and accuracy of the analysis.

[0072] Among these parameters, outlet velocity uniformity reflects the consistency of the velocity distribution across the entire nozzle array. Average velocity is directly related to the degree to which the deceleration target is achieved. Pressure pulse rate characterizes the stability and pulsation level of the flow.

[0073] It should be understood that using relative deviation can eliminate the influence of different physical dimensions and numerical magnitudes, making the deviations of different performance indicators comparable, and providing a unified, dimensionless measurement standard for judging whether the deviation is significant.

[0074] Step 401: When the relative deviation of any one of the indicators of outlet velocity uniformity, average velocity, and pressure pulse rate exceeds the corresponding predetermined threshold, the simulation model is adaptively corrected.

[0075] For example, the relative deviation threshold for outlet velocity uniformity can be set to 1% - 2%. For instance, when the relative deviation between the measured flow velocity uniformity and the simulation prediction exceeds 2%, the model is considered mismatched and needs correction. The relative deviation threshold for average velocity can be set to 0.5% - 1.5%. The relative deviation threshold for pressure pulse rate can be set to 3% - 5%.

[0076] Specifically, the simulation model itself (such as boundary conditions) is automatically adjusted based on the relative deviation values ​​of each index to make its predictions closer to the actual physical process. The boundary condition correction includes at least one of the following: inlet turbulence intensity, wall roughness, and near-wall mesh parameters.

[0077] Step 402: Based on the corrected simulation model, re-execute the controllable parameter optimization analysis and multivariable fuzzy PID control, and iteratively perform deviation detection and model correction until the relative deviation values ​​of all indicators are lower than the corresponding predetermined thresholds, and finally generate the homogenized deceleration control parameter set.

[0078] Among them, the homogenization and deceleration control parameter set is a set of control commands obtained after offline optimization and online correction, which can ensure that the array nozzle achieves the best homogenization and deceleration effect.

[0079] Specifically, the system uses the modified simulation model to re-execute the controllable parameter optimization analysis, and finds a new and better combination of controllable parameters in order to achieve the optimal solution for the homogenization and deceleration objectives on the modified model.

[0080] The newly optimized parameter combination is applied to the real-time control of the actual system. The multivariable fuzzy PID controller is restarted, and the controllable parameters are dynamically adjusted by taking the real-time detected outlet velocity uniformity, average velocity and pressure pulse rate as inputs, so as to actively suppress disturbances in the flow.

[0081] After re-executing control, the system immediately returns to step 400 to collect real-time data again and perform a new round of more rigorous point-by-point deviation analysis with the prediction results of the current simulation model. If the analysis finds that the relative deviation values ​​of performance indicators still exceed their predetermined thresholds, step 401 is triggered again to further adaptively correct the simulation model, thereby improving its predictive ability again, until the relative deviation values ​​of all key performance indicators are stably lower than their respective predetermined thresholds.

[0082] When the termination condition is met, the system fixes the optimal control parameters that have been verified through multiple iterations and generates a uniform deceleration control parameter set.

[0083] By using an iterative closed loop, the gap between the digital model and the physical entity is dynamically narrowed, ensuring that the model-based control strategy has extremely high reliability and adaptability. The resulting parameter set provides a core guarantee for the system to achieve long-term, stable, and high-performance operation.

[0084] Figure 5 The diagram illustrates a process in an exemplary embodiment of this application, where a set of homogenized deceleration control parameters is used to adaptively allocate the flow rate of each micro-nozzle through periodic calibration, thereby completing the homogenization and deceleration processing of a micro-array nozzle. Figure 4 This application extends from the embodiments shown. Figure 5 The illustrated embodiment will be described in detail below. Figure 5 The illustrated embodiments and Figure 4 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0085] like Figure 5 As shown in the embodiment of this application, the micro-array nozzle homogenization and deceleration method based on fluid simulation analysis completes the homogenization and deceleration processing of the micro-array nozzle by periodically calibrating and adaptively allocating the flow rate of each micro-nozzle based on the homogenization and deceleration control parameter set, including the following steps.

[0086] Step 500: Set the periodic calibration trigger conditions.

[0087] The periodic calibration trigger conditions include: reaching a preset system operation time interval; and / or: the temperature change detected by the ambient temperature sensor exceeds a predetermined tolerance; and / or: the deviation between the real-time detected outlet velocity distribution data of each micro-nozzle and the expected performance of the homogenization deceleration control parameter set exceeds a preset threshold.

[0088] Step 501: During each calibration cycle, collect the outlet velocity distribution data of each micro-nozzle and compare it with the expected performance of the homogenized deceleration control parameter set.

[0089] Step 502: Based on the comparison deviation, dynamically adjust the flow distribution coefficient of each micro-nozzle through the flow weight allocation algorithm.

[0090] The flow weighting allocation algorithm refers to dynamically allocating the proportion of the total flow to each nozzle based on the deviation between the actual performance of each nozzle and the target flow rate, using a coefficient ω_i (i=1,2,...,n). The algorithm formula is: ; Where v_target is the target velocity, v_actual_i is the actual velocity of the i-th nozzle, and k is the normalization coefficient, which is a dynamically generated calculation result for each calculation. This ensures that the total flow of the entire system remains constant after adjusting the flow distribution coefficients of each nozzle, thus avoiding the loss of control over the total flow of the system due to the distribution adjustment.

[0091] Step 503: Feed the updated flow distribution coefficient back to the multivariable fuzzy PID controller to adjust the flow of each micro-nozzle until the outlet velocity distribution meets the preset homogenization standard and the average velocity reaches the preset deceleration standard.

[0092] The system uses the updated flow distribution coefficients dynamically calculated by the flow weight allocation algorithm in step 502 as key inputs and feeds them back to the multivariable fuzzy PID controller in real time. This controller integrates these coefficients with real-time monitored parameters such as outlet velocity uniformity, average velocity, and pressure pulse rate. Through its built-in fuzzy rule base and multi-objective coordination strategy, it intelligently decouples and outputs precise adjustment commands for controllable parameters, driving the outlet velocity distribution of the microarray nozzles to meet preset homogenization standards (typically requiring the standard deviation of each nozzle velocity to be lower than 1% to 3% of the overall average velocity), while ensuring that the overall average velocity reaches a preset deceleration standard (typically requiring its relative deviation from the target velocity to be stable within ±0.5% to ±2%). This allows the system to maintain long-term, adaptive operation within its optimal performance range.

[0093] Figure 6The diagram shown is a schematic representation of an exemplary embodiment of this application, illustrating the use of a multivariable fuzzy PID controller to dynamically adjust controllable parameters with outlet speed uniformity, average speed, and pressure pulse rate as control objectives. In this application... Figure 5 This application extends from the embodiments shown. Figure 6 The illustrated embodiment will be described in detail below. Figure 6 The illustrated embodiments and Figure 5 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0094] like Figure 6 As shown in the embodiment of this application, the micro-array nozzle homogenization and deceleration method based on fluid simulation analysis adopts a multivariable fuzzy PID controller, with the outlet velocity uniformity, average velocity and pressure pulse rate as control objectives, and dynamically adjusts controllable parameters, including the following steps.

[0095] Step 600: Establish a multi-parameter fuzzy rule base with outlet speed uniformity, average speed and pressure pulse rate as input variables and controllable parameters as output variables.

[0096] It should be understood that the rule base consists of a series of fuzzy rules in the form of "IF-THEN," which are determined based on fluid dynamics knowledge and expert experience. For example: IF velocity uniformity deviation is negative (i.e., very non-uniform) AND average velocity deviation is positive (i.e., velocity is too high), THEN significantly reduce the total inlet pressure (output is negative) and appropriately adjust the flow distribution ratio. By establishing a rule base, complex multi-objective control problems are transformed into rule-based logical reasoning, providing the controller with a clear decision-making basis, enabling it to systematically, rather than in isolation, handle multiple coupled control objectives.

[0097] Step 601: Define the corresponding membership functions for each input variable and output variable.

[0098] The membership function defines the degree to which each input and output variable belongs to a fuzzy linguistic variable (such as "negative large", "negative small", "zero", "positive small", "positive large"). Its shape is usually triangular or trapezoidal. For example, a velocity deviation of 0.2 m / s may have a membership degree of 0.8 for "positive small" and 0.2 for "zero".

[0099] It should be understood that the membership function enables the controller to understand and handle the uncertainty and continuous changes in measurement, transforming precise sensor signals into semantic information that the fuzzy logic system can process, laying the foundation for subsequent fuzzy inference, and enhancing the control system's adaptability to nonlinear characteristics.

[0100] Step 602: Based on the fuzzy inference mechanism, the real-time detected input variables are mapped to the adjustment amount of the output variables.

[0101] Specifically, the real-time detected input variables are substituted into a multi-parameter fuzzy rule base for inference. Each rule calculates an output fuzzy set based on the membership degree of the input. Finally, the output fuzzy sets of all activated rules are superimposed to convert them into a precise control output.

[0102] It should be understood that this fuzzy inference mechanism realizes the intelligent mapping from multi-dimensional and imprecise operating condition information to precise control commands, and can quickly respond to dynamic disturbances and output smooth control commands.

[0103] Step 603: A multi-objective coordination strategy is adopted to decouple the adjustment amounts of the output variables by weighting, so as to achieve the control objective.

[0104] Specifically, because there is a coupling relationship between the three control objectives (uniformity, deceleration, and stability) (for example, adjusting the inlet pressure will simultaneously affect the average velocity and uniformity), the adjustment amounts of the three output variables are weighted, prioritized, and decoupled. For example, when the pressure pulse rate (stability) is severely out of control, the algorithm will assign it a higher weight, temporarily relax the precise control of the average velocity, and prioritize the suppression of oscillations.

[0105] It should be understood that this strategy is key to achieving multi-objective collaborative optimization. It intelligently handles the inherent contradictions between control objectives, ensuring that the system always adjusts towards the overall optimum under complex operating conditions.

[0106] Figure 7 The diagram shown is a schematic representation of a microarray nozzle homogenization and deceleration system based on fluid simulation analysis, provided in an exemplary embodiment of this application. Figure 7 As shown, the micro-array nozzle homogenization and deceleration system based on fluid simulation analysis provided in this application embodiment includes: a simulation modeling module 70, a parameter optimization module 71, a real-time control module 72, a model correction module 73, and an online calibration module 74.

[0107] The simulation modeling module 70 is used to establish a three-dimensional fluid simulation model of the micro-array nozzles. The simulation model uses a transient simulation algorithm to couple the temperature field, flow field, and self-excited oscillation field. The micro-array nozzles include multiple micro-nozzles, and the self-excited oscillation field is implemented by writing the momentum source term formula through a user-defined function. The parameter optimization module 71 is used to optimize and analyze the offline optimization parameters based on the simulation model to obtain an optimized parameter combination that satisfies the homogenization and deceleration objectives of the flow in the micro-array nozzles. The real-time control module 72 is used to detect the outlet velocity, pressure pulsation signal, and temperature signal of each micro-nozzle based on the optimized parameter combination. It uses a multivariable fuzzy PID controller to dynamically adjust the controllable parameters, including the inlet total pressure, medium temperature, and inlet flow distribution ratio, with the controllable parameters being outlet velocity uniformity, average velocity, and pressure pulse rate as control objectives. The model correction module 73 is used to compare the detection results with the simulation prediction results and correct the simulation model according to the deviation value to form a homogenization and deceleration control parameter set. The online calibration module 74 is used to adaptively allocate the flow rate of each micro-nozzle through periodic calibration based on the homogenization deceleration control parameter set, thereby completing the homogenization and deceleration processing of the micro-array nozzles.

[0108] The micro-array nozzle homogenization and deceleration system based on fluid simulation analysis provided in this application solves three core problems in traditional methods through the coordinated work of the simulation modeling module 70, parameter optimization module 71, real-time control module 72, model correction module 73, and online calibration module 74: difficulty in coordinating homogenization and deceleration targets, model mismatch leading to control failure, and long-term performance drift.

[0109] It should be understood that the operation and functions of the relevant modules and units mentioned in the micro-array nozzle homogenization and deceleration system based on fluid simulation analysis can be referred to the above. Figures 1 to 6 The method for homogenization and deceleration of microarray nozzles based on fluid simulation analysis is provided, and will not be elaborated here to avoid repetition.

[0110] Below, for reference Figure 8 This describes an electronic device according to embodiments of the present application. Figure 8 The diagram shown is a structural schematic of an electronic device provided in an exemplary embodiment of this application.

[0111] like Figure 8 As shown, the electronic device 800 includes one or more processors 801 and memory 802.

[0112] The processor 801 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 800 to perform desired functions.

[0113] The memory 802 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 801 may execute the program instructions to implement the microarray nozzle homogenization and deceleration methods based on fluid simulation analysis described in the various embodiments of this application above, and / or other desired functions. Various contents, such as offline optimization parameters, may also be stored in the computer-readable storage medium.

[0114] In one example, the electronic device 800 may also include an input device 803 and an output device 804, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0115] The input device 803 may include, for example, a keyboard, a mouse, etc.

[0116] The output device 804 can output various information to the outside, including a set of equalization deceleration control parameters. The output device 804 may include, for example, a display, a communication network, and remote output devices connected thereto.

[0117] Of course, for the sake of simplicity, Figure 8 Only some of the components of the electronic device 800 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 800 may include any other suitable components depending on the specific application.

[0118] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the fluid simulation analysis-based microarray nozzle homogenization and deceleration method according to various embodiments of this application described in the "Exemplary Methods" section above.

[0119] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0120] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the fluid simulation analysis-based microarray nozzle homogenization and deceleration method according to various embodiments of this application described in the "Exemplary Methods" section above.

[0121] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0122] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0123] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0124] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0125] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0126] The above description has been given for illustrative and descriptive purposes. Furthermore, this description is not intended to limit the embodiments of this application to the forms of the invention herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for microarray nozzle homogenization and deceleration based on fluid simulation analysis, characterized in that, The method comprises the following steps: a three-dimensional fluid simulation model of the micro-array nozzle is established, the simulation model adopts a transient simulation algorithm to couple a temperature field, a flow field and a self-excited oscillation field, wherein the micro-array nozzle comprises a plurality of micro-nozzles, and the self-excited oscillation field is realized by writing a momentum source term formula through a user-defined function; based on the simulation model, offline optimization parameters are optimized and analyzed to obtain an optimization parameter combination that makes the micro-array nozzle flow meet homogenization and speed reduction targets; based on the optimization parameter combination, outlet flow velocities, pressure pulsation signals and temperature signals of the micro-nozzles are detected, and a multivariable fuzzy PID controller is adopted to take the outlet velocity uniformity, the average velocity and the pressure pulse rate as control targets, and to dynamically adjust controllable parameters, wherein the controllable parameters comprise an inlet total pressure, a medium temperature and an inlet flow distribution ratio; the detection results are compared with simulation prediction results, and the simulation model is corrected according to deviation values to form a homogenization and speed reduction control parameter set; based on the homogenization and speed reduction control parameter set, the flow of each micro-nozzle is adaptively distributed through periodic calibration to complete the homogenization and speed reduction processing of the micro-array nozzle.

2. The method of claim 1, wherein, The method of optimizing and analyzing offline optimization parameters based on the simulation model to obtain an optimization parameter combination that makes the micro-array nozzle flow meet homogenization and speed reduction targets comprises the following steps: single-factor variable analysis is performed on the offline optimization parameters through the simulation model, performance indexes of the micro-nozzles are extracted, and a variable-performance mapping database is established; based on the variable-performance mapping database, a multi-objective optimization model is constructed with the outlet velocity uniformity, the average velocity and the pressure pulse rate as optimization targets; through the multi-objective optimization model, a parameter combination with an interaction intensity exceeding a predetermined threshold is screened out to generate the optimization parameter combination.

3. The method of claim 2, wherein, The offline optimization parameters comprise self-excited oscillation cavity structure parameters, inlet pressure fluctuation frequencies and surface roughness parameters of nozzle flow channels, and the single-factor variable analysis performed on the offline optimization parameters through the simulation model to extract performance indexes of the micro-nozzles and establish a variable-performance mapping database comprises the following steps: the self-excited oscillation cavity structure parameters, the inlet pressure fluctuation frequencies and the surface roughness parameters of the nozzle flow channels are sequentially and individually adjusted through the simulation model; for transient simulation results after each single-parameter adjustment, velocity standard deviations, average velocities and pressure pulse rates of each nozzle at an outlet cross section of the micro-array nozzle are extracted as performance indexes; each group of parameter values and corresponding performance indexes are recorded to establish the variable-performance mapping database.

4. The method of claim 2, wherein, The method of comparing detection results with simulation prediction results and correcting the simulation model according to deviation values to form a homogenization and speed reduction control parameter set comprises the following steps: point-by-point deviation analysis is performed on the detected outlet flow velocities, pressure pulsation signals and temperature signals of the micro-nozzles and the simulation prediction results in terms of the outlet velocity uniformity, the average velocity and the pressure pulse rate, and relative deviation values of each index are calculated. when the relative deviation value of any one of the outlet velocity uniformity, the average velocity, and the pressure pulse rate exceeds a corresponding predetermined threshold value, adaptively correcting the simulation model; based on the corrected simulation model, re-executing controllable parameter optimization analysis and multivariable fuzzy PID control, and iteratively performing deviation detection and model correction until the relative deviation values of all indicators are lower than the corresponding predetermined threshold values, and finally generating the set of homogenization and deceleration control parameters.

5. The method of claim 4, wherein, The adaptive correction of the simulation model includes correction of boundary conditions. The correction of the boundary conditions includes at least one of inlet turbulence intensity, wall roughness, and near-wall grid parameters.

6. The method of claim 1, wherein, The homogenization and deceleration of the micro-array nozzle based on the set of homogenization and deceleration control parameters are completed by periodic calibration to adaptively allocate the flow rates of the micro-nozzles, including: setting a periodic calibration trigger condition; in each calibration period, collecting outlet flow rate distribution data of the micro-nozzles and comparing with expected performance of the set of homogenization and deceleration control parameters; based on the comparison deviation, dynamically adjusting the flow allocation coefficients of the micro-nozzles through a flow weight allocation algorithm, feeding the updated flow allocation coefficients back to the multivariable fuzzy PID controller to adjust the flow rates of the micro-nozzles until the outlet flow rate distribution meets the preset homogenization standard and the average flow rate meets the preset deceleration standard.

7. The method of claim 6, wherein, The periodic calibration trigger condition includes: reaching a preset system running time interval; and / or; the temperature change detected by the ambient temperature sensor exceeds a predetermined tolerance; and / or; the deviation between the real-time detected outlet flow rate distribution data of the micro-nozzles and the expected performance of the set of homogenization and deceleration control parameters exceeds a preset threshold value.

8. The method according to any one of claims 1 to 6, characterized in that, The self-excited oscillation field is realized by writing a momentum source term formula with a user-defined function, including: The self-excited oscillation field simulates the periodic oscillation generated by a Helmholtz resonator through a user-defined function to write a preset momentum source term method; wherein the source term expression defined by the momentum source term method includes an oscillation amplitude coefficient, a fluid density, a reference velocity, and a reference length.

9. The method according to any one of claims 1 to 6, characterized in that, The multivariable fuzzy PID controller is used to dynamically adjust the controllable parameters with outlet velocity uniformity, average velocity, and pressure pulse rate as control targets, including: establishing a multi-parameter fuzzy rule base with the outlet velocity uniformity, the average velocity, and the pressure pulse rate as input variables and the controllable parameters as output variables; defining corresponding membership functions for each input variable and output variable; based on a fuzzy inference mechanism, mapping real-time detected input variables to adjustment amounts of output variables; using a multi-objective coordination strategy to weight and decouple the adjustment amounts of the output variables to achieve the control targets.

10. A microarray nozzle homogenization and deceleration system based on fluid simulation analysis for implementing the method of any one of claims 1 to 9, characterized in that, including: a simulation modeling module for establishing a three-dimensional fluid simulation model of the micro-array nozzle, the simulation model using a transient simulation algorithm to couple a temperature field, a flow field, and a self-excited oscillation field, wherein the micro-array nozzle includes a plurality of micro-nozzles, and the self-excited oscillation field is realized by writing a momentum source term formula with a user-defined function; a parameter optimization module, configured to perform optimization analysis on offline optimization parameters based on the simulation model to obtain an optimized parameter combination that makes the micro-array nozzle flow meet the homogenization and deceleration targets; a real-time control module, configured to detect outlet flow velocity, pressure pulsation signals and temperature signals of each micro-nozzle based on the optimized parameter combination, and to use a multivariable fuzzy PID controller to dynamically adjust controllable parameters including inlet total pressure, medium temperature and inlet flow distribution ratio, with outlet velocity uniformity, average velocity and pressure pulse rate as control targets; a model correction module, configured to compare the detection results with simulation prediction results, and to correct the simulation model according to deviation values to form a homogenization and deceleration control parameter set; an online calibration module, configured to periodically calibrate to adaptively distribute the flow of each micro-nozzle based on the homogenization and deceleration control parameter set, and to complete the homogenization and deceleration processing of the micro-array nozzle.

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