Wind tunnel balance cooling method based on multi-source dynamic target optimization

Abstract

The invention discloses a wind tunnel balance cooling method based on multi-source dynamic target optimization, and relates to the field of measurement in a wind tunnel test, and the method comprises the steps: S1, obtaining the current temperature Twc of a wind tunnel body and the current temperature Tbc of a balance in real time through a data collection module based on the final target temperature Td of the balance, and carrying out the data fusion; s2, establishing a fuzzy control model, and correcting PID parameters based on the output quantity of a fuzzy controller; s3, substituting the corrected PID parameters into a PID control formula to obtain a valve opening control value u (t) at the outlet of the cooling pipeline so as to control the injection flow of the low-temperature gas of the balance; and S4, returning to the step S1 for rolling optimization until Td is consistent with Tbc. According to the wind tunnel balance cooling method based on multi-source dynamic target optimization, balance cooling and wind tunnel cooling are synchronously controlled, energy is saved, consumption is reduced, and efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of measurement in wind tunnel tests, and more particularly to a wind tunnel balance cooling method based on multi-source dynamic target optimization. Background Art

[0002] When conducting low-temperature tests in a wind tunnel, the consistency of the balance temperature with the ambient temperature of the wind tunnel body is fundamental to the accuracy of the wind tunnel test. Because the balance is located inside the model, cooling solely through the wind tunnel body will result in a slow temperature transfer process and a long cooling time, increasing operating costs and reducing efficiency.

[0003] The current common practice at home and abroad is to add a balance cooling system, routing cooling pipes to the tail end of the balance inside the model, using the final temperature as the target value and the current balance temperature as the output value. The difference between the final target value and the current balance temperature is used as the feedback value, and a PID manual tuning method is used to control the valve opening of the balance cooling system and the injection flow rate of the low-temperature gas to improve the balance cooling efficiency. This method is more efficient than cooling the wind tunnel body alone. However, due to the different cooling efficiencies of the wind tunnel body and the balance cooling system, and the need to consider the impact of temperature differences on the wind tunnel structure during the wind tunnel cooling process, manual staged cooling and temperature control are adopted. The balance cooling system, which only cools to the final target temperature, is difficult to control the balance and wind tunnel body to reach the target temperature at the same time, resulting in a waiting situation, increasing the time to maintain the low-temperature environment, resulting in increased operating costs and reduced operating efficiency.

[0004] At the same time, due to the nonlinearity and time lag of the balance cooling system, the cooling efficiency is not only affected by the valve opening, but also by the cooling pipe length and the pipe gas temperature, such as Figure 2 The conventional PID control method shown in the figure only uses the difference between the set temperature and the feedback temperature as input and uses a fixed K p , K i , K d The control parameters control the valve opening, so the overshoot is large and the control accuracy is low, which affects the test efficiency. Summary of the Invention

[0005] An object of the present invention is to solve at least the above problems and / or disadvantages and to provide at least the advantages which will be described hereinafter.

[0006] To achieve these objectives and other advantages of the present invention, a wind tunnel balance cooling method based on multi-source dynamic target optimization is provided, comprising:

[0007] S1. Final target temperature T based on the wind tunnel body and balance d , obtain the current temperature T of the wind tunnel in real time through the data acquisition module wc , current temperature of the balance Tbc , perform data fusion;

[0008] S2. Establish a fuzzy control model and modify the PID parameters based on the results of data fusion and the output of the fuzzy controller;

[0009] S3. Substitute the corrected PID parameters into the PID control formula to obtain the valve opening control value u(t) at the outlet of the cooling pipeline to control the injection flow rate of the balance low-temperature gas;

[0010] S4, return to S1 and perform rolling optimization until T d With T bc consistent.

[0011] Preferably, the formula for data fusion in S1 is:

[0012]

[0013] In the above formula, k is an empirical value, and the value of k is between -2 and 0.5. wd Set a target temperature for the wind tunnel volume.

[0014] Preferably, in S2, the process of correcting the PID parameters is:

[0015] S20, based on T bd 、T bc Calculate the error value E and error change rate E c ;

[0016] S21, establish a fuzzy control model, and transform E, E c As the input of the fuzzy controller, and based on the output of the fuzzy controller ΔK p , ΔK i , ΔK d Correct the PID parameters.

[0017] Preferably, in S20, the error value E and the error change rate E c They are obtained by the following formulas:

[0018]

[0019] Preferably, in S21, the PID parameters are corrected based on the following formula:

[0020]

[0021] In the above formula, K p1 , K i1 , K d1 They are PID parameter correction, K p0 , Ki0 , K d0 is the initial PID parameter value;

[0022] Among them, E, E c , ΔK p , ΔK i , ΔK d The domain of discourse is [-3,3].

[0023] Preferably, in S3, the valve opening control value u(t) is obtained by the following formula:

[0024]

[0025] In the above formula, E is the current error value, is the integral term of the error, E c is the current error change rate.

[0026] The present invention has at least the following beneficial effects:

[0027] First, the present invention introduces wind tunnel temperature data into the balance temperature control, realizing synchronous control of balance cooling and wind tunnel cooling, saving energy and reducing consumption, and improving efficiency.

[0028] Secondly, the present invention adopts the modified PID parameter to optimize the temperature control effect of the balance cooling and improve the test efficiency.

[0029] Other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of the process of cooling the wind tunnel balance of the present invention;

[0031] Figure 2 This is a flow chart of a conventional PID control method used in a balance cooling system in the prior art;

[0032] Figure 3 A line graph comparing the wind tunnel balance cooling method based on data fusion of the present invention and the existing conventional PID wind tunnel balance cooling method. DETAILED DESCRIPTION

[0033] The present invention will be described in further detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0034] The present invention provides a wind tunnel balance cooling method based on multi-source dynamic target optimization, according to the final target temperature T of the wind tunnel body and the balance d , the target temperature of the wind tunnel body is set to Twd , the current temperature of the wind tunnel body T wc , cave cooling efficiency η and other data, according to the actual situation, use the multi-source data fusion method to correct the balance cooling target value T in real time bd , thereby improving the synchronization of the cooling of the balance and the cooling of the wind tunnel body; at the same time, during the cooling process of the balance, the PID parameters are updated in real time by using the fuzzy control method to improve the cooling efficiency of the balance. The specific control steps are as follows:

[0035] a. Read and analyze the wind tunnel cooling status

[0036] Read the set target temperature T of the wind tunnel body in real time wd , the current temperature of the wind tunnel body T wc , current temperature of the balance T bc , and calculate the cave cooling rate in real time

[0037] b. Multi-source data fusion to generate balance cooling target temperature T bd

[0038] According to the final target temperature T of the balance d , the target temperature of the wind tunnel body is set to T wd , the current temperature of the wind tunnel body T wc , cave cooling efficiency η and other state parameters, perform data fusion, and calculate the target setting value of the balance cooling system, where,

[0039] According to the actual application situation, the principles of data fusion are as follows:

[0040] ①When η=0, T bd =T wc , that is, when the current temperature of the wind tunnel body remains unchanged, the balance cooling target temperature is consistent with the current body temperature;

[0041] ②η≠0 and -10 <T d -T wc <0, in this state, the final target temperature has been approached, and the overshoot needs to be reduced, which is determined by T d and T wc The two data sources are combined to obtain the target temperature of the balance cooling, and the cave cooling efficiency The larger the absolute value, the higher the final target temperature T d The larger the proportion;

[0042] ③η≠0 and T d -T wc ≤-10, by T wd and T wc The two data sources are used to summarize the target temperature of the balance cooling and the cave cooling efficiency. The larger the absolute value, the higher the target temperature T of the wind tunnel body. wd The larger the proportion.

[0043] The fusion formula can be summarized as:

[0044]

[0045] Due to the cooling, Less than 0, (T wd -T wc ) and (T d -T wc ) are all less than 0, the k setting value must be less than 0. Based on practical experience, the k value can be manually set between -2 and 0.5.

[0046] c. Establish fuzzy control model

[0047] Due to the limitation of the cooling pipe size and length of the balance temperature control system, pre-cooling time is required when cooling begins. However, due to the large proportion of convection heat transfer in the pipe and the close relationship with the external ambient temperature, the specific heat formula cannot be used to derive it. Based on the previous data accumulation, according to the data and experience, a model controller rule table that meets this design is established, and a fuzzy control model is established. The fuzzy controller uses the error value E and the error change rate E c As input quantity (where E = T bd -T bc , ), and then fuzzification is achieved by linear quantization to the domain of fuzzy input quantity.

[0048] Specifically, let the fuzzy input E, E c and output ΔK p , ΔK i , ΔK d The domain is [-3, 3], which means the change is divided into seven levels: {-3, -2, -1, 0, 1, 2, 3}, which can be divided into 7 levels: negative large (NL), negative medium (NM), negative small (NS), zero (ZE), positive small (PS), positive medium (PM), positive large (PL), E, E c The corresponding relationship with the fuzzy control domain is shown in Table 1 below.

[0049] Domain -3 -2 -1 0 1 2 3 Logo NL NM NS ZE PS PM PL E(℃) ＜-6 ≥-6 ≥-5 ≥-4 ≥-3 ≥-2 ≥-1 Ec(℃ / min) ＜-0.6 ≥-0.6 ≥-0.5 ≥-0.4 ≥-0.3 ≥-0.2 ≥-0.1

[0050] Table 1

[0051] The five input and output variables all correspond to fuzzy sets {NL, NM, NS, ZE, PS, PM, PL}, and their membership functions are trigonometric functions.

[0052] The process of fuzzy rule reasoning and defuzzification outputs three PID parameter corrections, namely ΔKp , ΔK i , ΔK d . Establish a fuzzy control rule table that meets this design and obtain the fuzzy control rules for the controlled quantity. Table 2 shows ΔK p Example of fuzzy control rule expression, ΔK i , ΔK d The fuzzy control rule table is similar to Table 2.

[0053]

[0054] Table 2

[0055] According to the fuzzy control rule table, through E, E c , calculate ΔK p , ΔK i , ΔK d ,Through the PID parameter correction amount, the PID parameters can be corrected in real time. The correction formula is as follows:

[0056]

[0057] In the above formula, K p0 Set to 200,K i0 Set to 50,K d0 Set to 20, because K p0 The value is large, and ΔK p The range of ΔK is [-3,3], which is relatively small, so according to experience, p Magnification 5 times, the change in the proportional coefficient in the formula is 5ΔK p , the same reasoning, the change of the integral coefficient is 2ΔK i , the change in the differential coefficient is ΔK d .

[0058] The updated parameter K p1 , K i1 , K d1 Substitute it into the following PID control formula to calculate the valve opening u(t) at time t. Use u(t) as the balance cooling target value to control the cooling pipeline outlet valve opening and control the injection flow of the low-temperature gas.

[0059]

[0060] In the above formula, E is the current error value, is the integral term of the error, which reflects the accumulation of the error over a period of time, E c is the current error change rate, which is the differential term of the error

[0061] d. Repeat the above steps to achieve rolling optimization;

[0062] Repeat steps a and b to update the target temperature in real time;

[0063] Repeat step c, according to the balance target temperature T bd and the current temperature of the balance T bc , calculate E, E C Then, according to the fuzzy control rules, the PID parameters are updated. According to the updated PID parameters, the PID method is used to further control the opening of the cooling pipe outlet valve until T d With T bc consistent.

[0064] Verification example:

[0065] like Figure 3 As shown in FIG, it is a comparative line chart of the wind tunnel balance cooling method based on data fusion of the present invention and the conventional PID wind tunnel balance cooling method. It should be noted that the test conditions of both are: cooling from 300K to 255K (T d ), the wind tunnel body is cooled in stages according to 285K, 270K, and 255K (T wd =[285,270,255]), maintaining the temperature for half an hour at each stage to ensure that all sections of the cavern reach thermal equilibrium and ensure the safety of the cavern sections.

[0066] from Figure 3 It can be seen from the line graph that the cooling curve of the present invention closely follows the cooling curve of the wind tunnel body, while the conventional PID cooling method causes a large temperature difference between the balance and the tunnel body during the cooling process, requiring a large amount of low-temperature gas to maintain the cooling effect. If the low-temperature gas is used up, the temperature will return and the cooling effect will fail. Compared with the existing conventional PID wind tunnel balance cooling method, the present invention is more energy-saving and more efficient.

[0067] The above solution is only an illustration of a preferred embodiment, but is not limited thereto. When implementing the present invention, appropriate replacements and / or modifications can be made according to user needs.

[0068] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and exemplary embodiments. They can be applied to a variety of fields suitable for the present invention. Further modifications will be readily apparent to those skilled in the art. Therefore, the present invention is not limited to the specific details and illustrations shown and described herein without departing from the general concept defined by the claims and their equivalents.

Claims

1. A wind tunnel balance cooling method based on multi-source dynamic target optimization, characterized in that: include: S1. Final target temperature T based on the wind tunnel body and balance d , obtain the current temperature T of the wind tunnel in real time through the data acquisition module wc , current temperature of the balance T bc , perform data fusion; S2. Establish a fuzzy control model and modify the PID parameters based on the results of data fusion and the output of the fuzzy controller; S3. Substitute the corrected PID parameters into the PID control formula to obtain the valve opening control value u(t) at the outlet of the cooling pipeline to control the injection flow rate of the balance low-temperature gas; S4, return to S1 and perform rolling optimization until T d With T bc consistent.

2. The wind tunnel balance cooling method based on multi-source dynamic target optimization according to claim 1, characterized in that: In S1, the formula for data fusion is: In the above formula, k is an empirical value, and the value of k is between -2 and 0.

5. wd Set a target temperature for the wind tunnel volume.

3. The wind tunnel balance cooling method based on multi-source dynamic target optimization according to claim 1, characterized in that: In S2, the process of correcting the PID parameters is as follows: S20, based on T bd 、T bc Calculate the current error value E and error change rate E c ; S21, establish fuzzy control model, and transform E, E c As the input of the fuzzy controller, and based on the output of the fuzzy controller ΔK p , ΔK i , ΔK d Correct the PID parameters.

4. The wind tunnel balance cooling method based on multi-source dynamic target optimization according to claim 3, characterized in that: In S20, the error value E and the error change rate E c They are obtained by the following formulas: E=T bd -T bc 5. The wind tunnel balance cooling method based on multi-source dynamic target optimization according to claim 3, characterized in that: In S21, the PID parameters are corrected based on the following formula: In the above formula, K p1 , K i1 , K d1 They are PID parameter correction, K p0 , K i0 , K d0 is the initial PID parameter value; Among them, E, E c , ΔK p , ΔK i , ΔK d The domain of discourse is [-3,3].

6. The wind tunnel balance cooling method based on multi-source dynamic target optimization according to claim 5, characterized in that: In S3, the valve opening control value u(t) is obtained by the following formula: In the above formula, E is the current error value, is the integral term of the error, which reflects the accumulation of the error over a period of time, E c is the current error change rate, that is, the differential term of the error.

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