Combustion device control method based on Simulink and Fluent cooperation

Abstract

The invention provides a combustion device control method based on Simulink and Fluent cooperation, and the method comprises the following steps: importing a combustion device mesh file into Fluent, initializing a computational domain, setting a time step length and a total step length, and starting Fluent solution; when the calculation is converged or reaches the set number of iterations, the Fluent UDF traverses a combustion device grid to calculate the average temperature, and data is sent to a server side of the Simulink S function module; the temperature data is converted into a temperature time sequence value, a difference value between the temperature data and a preset characteristic temperature curve is calculated, the difference value is input into a fuzzy PID controller to obtain a natural gas inlet flow variable quantity, a control parameter is output, and the natural gas inlet flow variable quantity is packaged and sent to a client of a Fluent UDF through a Simulink S function module; and adjusting the natural gas inlet flow so as to update the boundary condition of Fluent, then calculating the next time step length, and repeatedly solving until the total iteration time is reached. The control accuracy of the combustion device is improved.

Description

Technical Field

[0001] The invention relates to a combustion device control method, in particular to a combustion device control method based on the collaboration of Simulink and Fluent, belonging to the technical field of industrial combustion device control. Background Art

[0002] The research and application of combustion device control methods have become an important topic in the fields of chemical industry, energy, etc. Traditional combustion device control is generally carried out by transfer function modeling, which often relies on simplified mathematical models. However, since the combustion process involves complex multi-physical field coupling phenomena such as flow, chemical reaction and heat transfer, the traditional transfer function model is difficult to accurately describe the actual operating state of the combustion device and cannot fully reflect the complex thermal process inside the combustion device, resulting in limited control accuracy. In addition, the existing temperature control system is still at the level of combining PID control with field control. PID control is simple and the technology is mature, but for complex combustion devices, it is impossible to stably control the temperature and requires on-site manual assistance. The optimization of PID control parameters mostly relies on empirical values ​​or trial and error, which makes it difficult to obtain the optimal control effect, and the optimization process is time-consuming.

[0003] Therefore, there is an urgent need to develop a combustion device control method that can accurately describe the actual operating status of the combustion device, realize multi-parameter coordinated control, and have parameter optimization function. Summary of the invention

[0004] Based on the above background, the purpose of the present invention is to provide a combustion device control method based on the collaboration of Simulink and Fluent to improve the accuracy of combustion device control.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A combustion device control method based on Simulink and Fluent collaboration, the method comprising the following steps:

[0007] Import the combustion device mesh file into Fluent, set up the multi-physics field model including turbulence model, combustion model and radiation model, configure the initial conditions including initial temperature and gas gravity acceleration, establish the closed equation group of mass conservation equation, momentum conservation equation and energy conservation equation, initialize the calculation domain and set the time step and total step, and start Fluent solution;

[0008] When the calculation converges or reaches the set number of iterations, the Fluent UDF traverses the combustion device grid to calculate the average temperature, and uses the UDP protocol to send the data to the server of the Simulink S function module, while suspending Fluent to wait for the response of the server;

[0009] The Simulink S-function module receives and parses the data packet, converts the temperature data into a temperature time series value, calculates the difference between the temperature data and the preset characteristic temperature curve, inputs the difference into the fuzzy PID controller to obtain the change in the natural gas inlet flow rate and outputs the control parameter, and packages the change in the natural gas inlet flow rate through the Simulink S-function module and sends it to the client of the Fluent UDF;

[0010] The Fluent UDF analyzes the received natural gas inlet flow rate changes and adjusts the natural gas inlet flow rate accordingly, so that the boundary conditions of Fluent are updated, and then the calculation of the next time step is performed, and the solution is repeated until the total iteration time is reached.

[0011] Preferably, the computational domain is initialized and the time step and total step are set, specifically including:

[0012] Set the initial temperature T of the combustion device 0 , total iteration time t a , time step Δt, according to the convergence of steady-state simulation, set the maximum number of iterations per time step; initialize the average temperature T of the combustion device 0 , use the following formula to calculate the average temperature,

[0013]

[0014] Where, T m is the average temperature of all grids in the combustion device, and n is the total number of grids.

[0015] Preferably, before the Simulink S-function module receives and parses the data packet, the method further comprises:

[0016] Initializing proportional parameter K of fuzzy PID controller in Simulink p , integration parameter K i and the differential parameter K d , with a uniform time step Δt.

[0017] Preferably, when the calculation converges or reaches a set number of iterations, the Fluent UDF traverses the combustion device grid to calculate the average temperature, specifically including:

[0018] The numerical calculation time of Fluent is t, and the time of the last convergence is t-Δt. When the current time step of Fluent converges or reaches the set number of iterations, the Fluent UDF calculates the average temperature T of the current combustion device based on the converged temperature field. m (t), T m (t) Write the text and save it.

[0019] Preferably, the Simulink S-function module receives and parses the data packet, converts the temperature data into a temperature time series value, and calculates the difference between the temperature data and a preset characteristic temperature curve, specifically including:

[0020] The Simulink S function module obtains the average temperature T of the current combustion device m (t), calculate the average temperature T m (t) The preset temperature T corresponding to the preset characteristic temperature curve at the calculation time t d (t), e(t) = T d (t)-T m (t), and calculate the change rate of the deviation value ec(t), ec(t) = e(t) - e(t - Δt), based on which the natural gas inlet flow change Δq(t) is obtained through the fuzzy PID controller.

[0021] Preferably, the difference is input into a fuzzy PID controller to obtain a change in the natural gas inlet flow rate and output a control parameter, specifically including:

[0022] According to the temperature data and the preset characteristic temperature curve, the correlation coefficient is calculated using the following formula:

[0023]

[0024] Where, T d is the preset characteristic temperature curve temperature value, is the time average of the characteristic temperature, is the spatial average temperature T m Average over time;

[0025] Compare the calculated value of the correlation coefficient with the preset value of the correlation coefficient 0.95. If the calculated value of the correlation coefficient is greater than the preset value of the correlation coefficient, the proportional parameter K of the fuzzy PID controller is output. p , integration parameter K i and the differential parameter K d As a control parameter;

[0026] Otherwise, calculate the deviation value e(t) and the rate of change of the deviation value ec(t), and input them into the fuzzy PID controller to generate ΔK p , ΔK i and ΔK d , corrected K p , K i and K d , get the new natural gas inlet flow change, calculate the new average temperature T m(t), calculate the new correlation coefficient value and compare it with the preset correlation coefficient value, until the correlation coefficient value is greater than the preset correlation coefficient value, output the current fuzzy PID controller proportional parameter K p , integration parameter K i and the differential parameter K d as a control parameter.

[0027] As a preferred method, the natural gas inlet flow rate is adjusted accordingly, so that the boundary conditions of Fluent are updated, specifically including:

[0028] If t <t a -Δt, the natural gas inlet flow rate q(t) in the boundary condition of Fluent is calculated and adjusted using the following formula: q(t) = q(t-Δt) + Δq(t).

[0029] As a preferred method, the total iteration time is reached when t=t a -Δt.

[0030] Preferably, after calculating the natural gas inlet flow rate q(t) in the boundary condition of Fluent, the following is further included:

[0031] The natural gas inlet flow rate q(t) is substituted into the preset air-fuel ratio control piecewise function to obtain the air-fuel ratio at the calculation moment. The air inlet flow rate is calculated and adjusted according to the air-fuel ratio to update the boundary conditions of Fluent.

[0032] Compared with the prior art, the present invention has the following advantages:

[0033] The present invention discloses a combustion device control method based on the collaboration of Simulink and Fluent. The method constructs a numerical model of the combustion device based on computational fluid dynamics, and uses the numerical model to replace the transfer function for control, which is more in line with the thermal state in the actual combustion device production process and improves reliability. The method obtains the optimal value of the key control parameter by repeatedly iterating the control parameters, and optimizes the control effect of the actual combustion device. In the collaborative control, not only the natural gas inlet flow rate is controlled, but also the air flow rate is controlled to ensure the atmosphere in the combustion device. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0035] Figure 1It is the principle diagram of the collaborative control of Simulink and Fluent in the present invention;

[0036] Figure 2 It is a flow chart of the air flow control strategy in the present invention;

[0037] Figure 3 It is a geometric model diagram of the combustion device in the present invention;

[0038] Figure 4 It is the collaborative control flow chart of Simulink and Fluent in the present invention;

[0039] Figure 5 It is the temperature distribution diagram of the combustion device when the monitoring point reaches 1230°C in the present invention;

[0040] Figure 6 It is the temperature distribution diagram of the combustion device when the monitoring point reaches 1050°C in the present invention;

[0041] Figure 7 It is the temperature distribution diagram of the combustion device when the monitoring point reaches 600°C in the present invention;

[0042] Figure 8 It is the temperature distribution diagram of the combustion device when the monitoring point reaches 410°C in the present invention;

[0043] Fig. 9 It is the temperature distribution diagram of the combustion device when the monitoring point reaches 270°C in the present invention;

[0044] Fig.10 It is the temperature distribution diagram of the combustion device when the monitoring point reaches 210°C in the present invention. DETAILED DESCRIPTION

[0045] The technical solution of the present invention is further described in detail below through specific embodiments and in conjunction with the accompanying drawings. It should be understood that the implementation of the present invention is not limited to the following embodiments, and any form of modification and / or change made to the present invention will fall within the protection scope of the present invention.

[0046] In the present invention, unless otherwise specified, all parts and percentages are weight units, and the equipment and raw materials used can be purchased from the market or are commonly used in the art. The methods in the following embodiments, unless otherwise specified, are conventional methods in the art. The components or equipment in the following embodiments, unless otherwise specified, are universal standard parts or components known to those skilled in the art, and their structures and principles are known to those skilled in the art through technical manuals or conventional experimental methods.

[0047] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following detailed description, for the convenience of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present invention.

[0048] A combustion device control method based on Simulink and Fluent collaboration, the method comprising the following steps:

[0049] Import the combustion device mesh file into Fluent, set up the multi-physics field model including turbulence model, combustion model and radiation model, configure the initial conditions including initial temperature and gas gravity acceleration, establish the closed equation group of mass conservation equation, momentum conservation equation and energy conservation equation, initialize the calculation domain and set the time step and total step, and start Fluent solution;

[0050] When the calculation converges or reaches the set number of iterations, the Fluent UDF traverses the combustion device grid to calculate the average temperature, and uses the UDP protocol to send the data to the server of the Simulink S function module, while suspending Fluent to wait for the response of the server;

[0051] The Simulink S-function module receives and parses the data packet, converts the temperature data into a temperature time series value, calculates the difference between the temperature data and the preset characteristic temperature curve, inputs the difference into the fuzzy PID controller to obtain the change in the natural gas inlet flow rate and outputs the control parameter, and packages the change in the natural gas inlet flow rate through the Simulink S-function module and sends it to the client of the Fluent UDF;

[0052] The Fluent UDF analyzes the received natural gas inlet flow rate changes and adjusts the natural gas inlet flow rate accordingly, so that the boundary conditions of Fluent are updated, and then the calculation of the next time step is performed, and the solution is repeated until the total iteration time is reached.

[0053] Based on computational fluid dynamics, a numerical model of the combustion device is constructed on the Fluent software platform. The UDP protocol is written using a user-defined function (UDF) to interact with Simulink for data collaboration. When the temperature field and flow field calculated by the numerical model of the combustion device converge, the UDF is called to update the boundary conditions to control the natural gas flow according to the temperature of the combustion device. The numerical model approximates the integral and differential terms in the control equations of fluid mechanics into discrete algebraic forms, making them a set of algebraic equations. These discrete algebraic equations are then solved by computer to obtain numerical solutions at discrete time / space points. The numerical model has significant advantages in describing the actual thermal state of the combustion device. By establishing an appropriate combustion device model, the detailed internal temperature distribution, the influence of different thermal parameters and boundary conditions on the thermal process can be obtained. The control of the fuzzy PID control system in Simulink can obtain optimized control parameters and effectively improve the operation effect of the combustion device.

[0054] like Figure 1 and Figure 2 As shown in the figure, the principle of this method mainly includes three parts: Simulink and Fluent collaborative control, control parameter optimization and air flow control.

[0055] Simulink and Fluent collaborative control requires importing the combustion device mesh file, selecting the control equation, setting boundary conditions and physical parameters such as the natural gas inlet flow rate, and initializing the calculation domain. Set the time step and total step, and start Fluent calculation and solution.

[0056] According to the parameters such as the structure and size of the combustion device, establish Figure 3 The numerical model of the combustion device shown is shown. The changes of the flow field and temperature field in the combustion device during operation are calculated, and the reliability of the numerical model is verified by using the actual measured data. Table 1 shows the structural dimension parameters of the combustion device of this embodiment.

[0057] Table 1 Structural dimension parameters of the combustion device

[0058]

[0059]

[0060] Table 2 shows the boundary condition settings of the numerical model of the combustion device in this embodiment.

[0061] Table 2 Boundary condition settings of the combustion device numerical model

[0062]

[0063] like Figure 4 As shown, the initial temperature of the combustion device is set to T0 =300K, total iteration time t a =50000s, set the time step Δt = 1s, the total number of steps is 50000, and the maximum number of iterations per time step Δt is 500. When the calculation starts, the average temperature T of the combustion device will be initialized in the UDF. m (0) = T 0 , T m is the average temperature of all grids. n is the total number of grids, calculated as follows:

[0064]

[0065] The calculated temperature value T m (0) Send to Simulink. According to the transfer function of the combustion device, a fuzzy PID controller is constructed in Simulink to obtain the initial value of the control parameter K p (0) = 0.172, K i (0) = 0.049, K d (0) = -3.214, and the time step and Fluent are uniformly set to Δt = 1s.

[0066] Simulink calculates the temperature value T m (0) and preset temperature T d The deviation of (0) e(0)=0 and the rate of change of the deviation ec(0)=0 are transmitted to the fuzzy PID controller.

[0067] Start Fluent numerical calculation, set the current time as t, and the last converged time as t-Δt. The DEFIND_EXECUTE_AT_END macro in UDF will only be called when the maximum number of iterations is reached or convergence occurs. When Fluent calculates convergence at the current time step, UDF will calculate the current average temperature T of the combustion device based on the converged temperature field. m (t), and T m (t) The results of the current time step are written into text and saved locally.

[0068] Simulink obtains temperature T m (t), calculate the deviation value e(t) = T d (t)-T m (t), the change rate of the deviation value ec(t) = e(t)-e(t-Δt), and the fuzzy PID controller is called to obtain the change in the natural gas inlet flow rate Δq(t). The UDF receives the change and adjusts the natural gas inlet flow rate q(t) = q(t-Δt) + Δq(t) in the boundary condition.

[0069] When t < ta - Δt, a new natural gas inlet flow rate is adopted to calculate the next time step.

[0070] When t = ta - Δt, Fluent / Simulink ends. The UDF issues a Boolean instruction to control Matlab to end Simulink. The UDF receives the closed instruction from Matlab to calculate the last time step. The calculation ends after convergence.

[0071] The optimization process of the control parameters involved in obtaining the change in the natural gas inlet flow rate and outputting the control parameters by the above-mentioned fuzzy PID controller is described as follows.

[0072] Establish the transfer function between the inlet natural gas flow rate q(t) and the average temperature T m (t) of the combustion device, and establish a fuzzy PID controller in the Matlab Simulink software to obtain K p 、K i and K d .

[0073] Using K p 、K i and K d , the natural gas inlet flow rate q(t) is obtained and input into the numerical model of the combustion device to calculate the spatially averaged temperature T m (t) of the combustion device, and compared with the preset characteristic temperature T d (t). If it is greater than the preset value of the correlation coefficient, then K p 、K i and K d are output as control parameters;

[0074] If it is less than the preset value, then calculate the deviation value e(t) = T d (t) - T m (t), the change rate of the deviation value ec(t) = e(t) - e(t - Δt), input e(t), ec(t) into the fuzzy PID controller to generate ΔK p 、ΔK i and ΔK d , correct K p 、K i and K d , obtain the new natural gas inlet flow rate, return to the numerical model of the combustion device, calculate the new spatially averaged temperature T m (t) of the combustion device until the compliant K p 、K i and K d are output.

[0075] The preset value of the above-mentioned correlation coefficient is 0.950, and the calculation formula of the correlation coefficient is,

[0076]

[0077] Among them, T d is the preset characteristic temperature curve temperature value, is the time average of the characteristic temperature, is the temporal average of the spatially average temperature.

[0078] In this method, after calculating the natural gas inlet flow rate q(t) in the boundary condition of Fluent, the method further includes substituting the natural gas inlet flow rate q(t) into a preset air-fuel ratio control piecewise function to obtain the air-fuel ratio at the calculation moment, calculating the air inlet flow rate according to the air-fuel ratio and adjusting it, so that the boundary condition of Fluent is updated. The control method of the air flow rate is specifically described as follows.

[0079] The air-fuel ratio β is controlled by a piecewise function, and the air flow rate is dynamically adjusted according to the natural gas flow rate to ensure that the internal atmosphere of each combustion device is appropriate.

[0080] In this embodiment, the main chemical equation for natural gas combustion is:

[0081] If natural gas is completely burned, the main chemical equation is:

[0082] CH 4 +2O 2 +7.52N 2 →CO 2 +2H 2 O+7.52N 2

[0083] If natural gas is not completely burned, the main equation is:

[0084] CH 4 +1.5O 2 +5.64N 2 →CO+2H 2 O+5.64N 2

[0085] The atmosphere change of the combustion device mainly depends on the O 2 The combustion device temperature is below 310℃, which is a general oxidizing atmosphere. 2 The content is 10%; the combustion device temperature is between 310℃-880℃, which is a strong oxidizing atmosphere. 2 The content is 5%; when the temperature of the combustion device is greater than 880℃ and less than the maximum temperature, the CO content is 5%, which is a reducing atmosphere.

[0086] In this embodiment, the air-fuel ratio (volume ratio) can be set as follows under each stage atmosphere:

[0087]

[0088] After UDF obtains the change in natural gas flow rate fed back by Simulink, it calculates the new natural gas inlet flow rate and then determines the combustion device temperature T m range.

[0089] If T m ≤310℃, air-fuel ratio β=12.02; if 310℃≤T m <880℃, air-fuel ratio β=16.09; if 880℃≤T m , air-fuel ratio β=8.53; update the air inlet flow rate through the corresponding air-fuel ratio, and modify and adjust the boundary conditions to ensure the atmosphere of the combustion device in each stage.

[0090] like Figure 5 As shown in the figure, the blue part is air and natural gas, which are sprayed in from the bottom burner, flow through the combustion device to the top and return to the flue at the tail of the combustion device, and finally flow out from the flue. From the temperature cloud diagrams of Front Back and Left Right, it can be observed that the temperature of the bottom part close to the burner can reach as high as 2019℃, but the overall temperature of the bottom is still lower than the temperature in the middle of the combustion device. The temperature of the entire combustion device shows a distribution with high in the middle and low at both ends. In the Up Down temperature cloud diagram, it can be seen that the temperature distribution of the Up section is relatively uniform. Figure 6-Figure 10 As shown, the temperature cloud diagrams at other temperatures also show the above results, indicating that during the entire control process, the obtained temperature distribution of the combustion device is in line with reality and relatively uniform.

[0091] It can be seen that the overall temperature distribution of the combustion device is relatively uniform, and only some parts are directly exposed to the flame and cause the temperature to be too high. Figure 6 For example, regardless of the cross section or the longitudinal section, the temperature range of 1220℃-1310℃ (yellow-green) accounts for the majority. Figure 5 It can be seen that the numerical results of the combustion device temperature under the fuzzy PID controller show a good control effect.

[0092] Therefore, the method disclosed in this embodiment is more accurate than the traditional control system method and can better meet actual needs.

[0093] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core ideas of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A combustion device control method based on Simulink and Fluent collaboration, characterized in that: The method comprises the following steps: Import the combustion device mesh file into Fluent, set up the multi-physics field model including turbulence model, combustion model and radiation model, configure the initial conditions including initial temperature and gas gravity acceleration, establish the closed equation group of mass conservation equation, momentum conservation equation and energy conservation equation, initialize the calculation domain and set the time step and total step, and start Fluent solution; When the calculation converges or reaches the set number of iterations, the Fluent UDF traverses the combustion device grid to calculate the average temperature, and uses the UDP protocol to send the data to the server of the Simulink S function module, while suspending Fluent to wait for the response of the server; The Simulink S-function module receives and parses the data packet, converts the temperature data into a temperature time series value, calculates the difference between the temperature data and the preset characteristic temperature curve, inputs the difference into the fuzzy PID controller to obtain the change in the natural gas inlet flow rate and outputs the control parameter, and packages the change in the natural gas inlet flow rate through the Simulink S-function module and sends it to the client of the Fluent UDF; The Fluent UDF analyzes the received natural gas inlet flow rate changes and adjusts the natural gas inlet flow rate accordingly, so that the boundary conditions of Fluent are updated, and then the calculation of the next time step is performed, and the solution is repeated until the total iteration time is reached.

2. The combustion device control method based on Simulink and Fluent collaboration according to claim 1 is characterized in that: Initialize the computational domain and set the time step and total step size, including: Set the initial temperature T0 of the combustion device and the total iteration time t a , time step Δt, according to the convergence of steady-state simulation, set the maximum number of iterations per time step; initialize the average temperature T0 of the combustion device, and calculate the average temperature using the following calculation formula, Where, T m is the average temperature of all grids in the combustion device, and n is the total number of grids.

3. The combustion device control method based on Simulink and Fluent collaboration according to claim 2 is characterized in that: Before the data packet is received and parsed by the Simulink S-function module, it also includes: Initializing proportional parameter K of fuzzy PID controller in Simulink p , integration parameter K i and the differential parameter K d , with a uniform time step Δt.

4. The combustion device control method based on Simulink and Fluent collaboration according to claim 2 is characterized in that: When the calculation converges or reaches the set number of iterations, the Fluent UDF traverses the combustion device grid to calculate the average temperature, including: The numerical calculation time of Fluent is t, and the time of the last convergence is t-Δt. When the current time step of Fluent converges or reaches the set number of iterations, the Fluent UDF calculates the average temperature T of the current combustion device based on the converged temperature field. m (t), T m (t) Write the text and save it.

5. The combustion device control method based on the collaboration of Simulink and Fluent according to claim 4 is characterized in that: The Simulink S-function module receives and parses the data packet, converts the temperature data into a temperature time series value, and calculates the difference between the temperature data and the preset characteristic temperature curve, specifically including: The Simulink S function module obtains the average temperature T of the current combustion device m (t), calculate the average temperature T m (t) The preset temperature T corresponding to the preset characteristic temperature curve at the calculation time t d (t), e(t) = T d (t)-T m (t), and calculate the change rate of the deviation value ec(t), ec(t) = e(t) - e(t - Δt), based on which the natural gas inlet flow change Δq(t) is obtained through the fuzzy PID controller.

6. The combustion device control method based on the collaboration of Simulink and Fluent according to claim 5 is characterized in that: The difference is input into the fuzzy PID controller to obtain the change of the natural gas inlet flow rate and output the control parameters, specifically including: According to the temperature data and the preset characteristic temperature curve, the correlation coefficient is calculated using the following formula: Where, T d is the preset characteristic temperature curve temperature value, is the time average of the characteristic temperature, is the spatial average temperature T m Average over time; Compare the calculated value of the correlation coefficient with the preset value of the correlation coefficient 0.

95. If the calculated value of the correlation coefficient is greater than the preset value of the correlation coefficient, the proportional parameter K of the fuzzy PID controller is output. p , integration parameter K i and the differential parameter K d As a control parameter; Otherwise, calculate the deviation value e(t) and the rate of change of the deviation value ec(t), and input them into the fuzzy PID controller to generate ΔK p , ΔK i and ΔK d , corrected K p , K i and K d , get the new natural gas inlet flow change, calculate the new average temperature T m (t), calculate the new correlation coefficient value and compare it with the preset correlation coefficient value, until the correlation coefficient value is greater than the preset correlation coefficient value, output the current fuzzy PID controller proportional parameter K p , integration parameter K i and the differential parameter K d as a control parameter.

7. The combustion device control method based on Simulink and Fluent collaboration according to claim 2 is characterized in that: The natural gas inlet flow rate is adjusted accordingly, so that the boundary conditions of Fluent are updated, including: If <t a -Δt, the following formula is used to calculate the natural gas inlet flow rate q(t) in the boundary conditions of Fluent and make adjustments: q(t)=q(t-Δt)+Δq(t).

8. The combustion device control method based on the collaboration of Simulink and Fluent according to claim 7 is characterized in that: The total iteration time is reached when t = t a -Δt.

9. The combustion device control method based on the collaboration of Simulink and Fluent according to claim 7 is characterized in that: After calculating the natural gas inlet flow rate q(t) in the boundary conditions of Fluent, it also includes: The natural gas inlet flow rate q(t) is substituted into the preset air-fuel ratio control piecewise function to obtain the air-fuel ratio at the calculation moment. The air inlet flow rate is calculated and adjusted according to the air-fuel ratio to update the boundary conditions of Fluent.

Images