A piston drive parameter decision-making method based on risk assessment

Abstract

The present invention belongs to the technical field of hypersonic test equipment, and discloses a piston drive parameter decision-making method based on risk assessment. The piston drive parameter decision-making method includes: determining a combination of drive parameters that affect the drive characteristics of a piston driver, extracting mutually independent basic random variables, and determining the distribution type and parameter distribution of each basic random variable; constructing a drive parameter matrix and testing the distribution characteristics of the simulated sample points constructed by each basic random variable; establishing a theoretical model for calculating piston drive characteristics and conducting random tests using the Monte Carlo method; statistically analyzing the results of the random tests; evaluating the risk type and risk probability of the drive parameter combination; judging the acceptable degree of the risk type and the corresponding risk probability, and determining whether to use this set of drive parameter combinations to conduct tests. The piston drive parameter decision-making method improves the fault tolerance of the driver operation and reduces the safety risks brought by random factors during the operation process.

Description

Technical Field

[0001] The present invention belongs to the technical field of hypersonic test equipment, and particularly relates to a piston drive parameter decision-making method based on risk assessment. Background Art

[0002] In pulse wind tunnel equipment such as shock tunnels, gun tunnels, and expansion tunnels driven by pistons, the piston driver, as the energy source of the ultra-high-speed airflow in the pulse wind tunnel, is the core component for the operation of the pulse wind tunnel. The accuracy and stability of the piston drive parameters are crucial for the operation performance and safety of the pulse wind tunnel.

[0003] Traditional piston drive parameter methods often perform parameter budgeting based on theoretical models corrected by experiments before the experiment, and cannot fully consider the randomness of various factors in actual operation. Since the actual drive characteristics of the piston driver are affected by various factors such as filling parameters, diaphragm rupture pressure, sealing effect, and friction state, and have obvious random characteristics, it may cause the actual piston drive parameters to deviate from the theoretical model, resulting in experimental results falling short of expectations, experimental failures, and even safety accidents.

[0004] Currently, there is an urgent need to develop a piston drive parameter decision-making method based on risk assessment. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a piston drive parameter decision-making method based on risk assessment to overcome the defects of the prior art.

[0006] The piston drive parameter decision-making method based on risk assessment of the present invention determines whether the piston drive parameters are feasible by constructing a random parameter matrix affecting the piston drive characteristics, then conducting simulated random experiments, and analyzing and evaluating the results of the random experiments.

[0007] The piston drive parameter decision-making method based on risk assessment of the present invention includes the following steps:

[0008] S10. Determine the drive parameter combination affecting the drive characteristics of the piston driver, extract independent basic random variables, and determine the distribution type and parameter distribution of each basic random variable;

[0009] S20. Construct a drive parameter matrix and test the distribution characteristics of the simulated sample points constructed by each basic random variable;

[0010] S30. Establish a theoretical model for calculating the piston drive characteristics and conduct random experiments using the Monte Carlo method;

[0011] S40. Statistically analyze the results of the random experiments;

[0012] Perform statistics and analysis, and the obtained results of the random experiment are piston drive parameters characterizing the piston drive characteristics, including the compressed gas pressure, the compressed gas temperature, the maximum piston speed during the compression process, and the piston impact speed;

[0013] S50. Evaluate the risk type and risk probability of the drive parameter combination;

[0014] The risk types of the piston driver include the risk of piston damage, the risk of misfire, the risk of excessive piston movement speed, and the risk of insufficient compressed gas; evaluate the risk type and the corresponding risk probability of the drive parameter combination; if the risk probability is lower than the pre-set risk probability, it is evaluated as a low risk, and if no risk occurs, it is evaluated as no risk; if the risk probability is higher than the pre-set risk probability, it is evaluated as a high risk, and return to S10 for iteration;

[0015] S60. Judge the acceptability of the risk type and the corresponding risk probability, and determine whether to conduct an experiment using the group drive parameter combination; if an experiment is conducted using the group drive parameter combination, then conduct a wind tunnel experiment, otherwise, return to S10 for iteration.

[0016] Furthermore, the drive parameter combination affecting the drive characteristics of the piston driver described in S10 includes the initial pressure of the gas in the gas storage tank, the initial temperature of the gas in the gas storage tank, the initial pressure of the gas in the compression pipe, the initial temperature of the gas in the compression pipe, the specific heat ratio of the gas in the compression pipe, the molecular weight of the gas in the compression pipe, the diaphragm rupture pressure, and the friction force during the piston movement process; the methods for determining the distribution type and parameter distribution of each basic random variable include: consulting previous test data and instrument calibration data, obtaining the operation experience of the operators, and making assumptions.

[0017] Furthermore, the specific methods for constructing the drive parameter matrix described in S20 and testing the distribution characteristics of the simulated sample points constructed for each basic random variable include:

[0018] S21. Use Latin hypercube sampling to generate a vector of simulated sample points X 1 、X 2 ,...X n ;

[0019] S22. Test the distribution characteristics of the simulated sample points constructed for each basic random variable to confirm whether the distribution characteristics meet the expectations;

[0020] S23. If all meet the expectations, then construct a random parameter matrix X = [X 1 ; X 2 ;...X n affecting the drive characteristics of the piston driver;

[0021] S24. If the distribution of the simulated sample points of one or more basic random variables does not meet the expectation, return to S21. Regenerate the vector of simulated sample points of the corresponding basic random variables and perform the distribution characteristic test again until all meet the expectation.

[0022] Further, the method for testing the distribution characteristics of the simulated sample points constructed for each basic random variable and confirming whether the distribution characteristics meet the expectation includes one of the following methods or a combination of two or more methods:

[0023] a. Judge the distribution characteristics of the sample points by plotting the statistical histograms of the simulated sample points constructed for each basic random variable;

[0024] b. Calculate the mean and standard deviation of the simulated sample points constructed for each basic random variable, with the mean deviation not exceeding 0.5% and the standard deviation deviation not exceeding 10%;

[0025] c. K-S test method;

[0026] d. Anderson-Darling test method.

[0027] Further, the theoretical model for calculating the piston driving characteristics described in S30 adopts a CFD-FSI coupled numerical simulation model or a gas-piston dynamics differential equation model corrected by combining experiments.

[0028] Further, the random test results described in S40 are as follows: The obtained random test results are the piston driving parameters characterizing the piston driving characteristics, including the compressed gas pressure, the compressed gas temperature, the compressed gas length, the maximum piston speed during the compression process, and the piston impact speed.

[0029] Further, the determination criteria for the risk types described in S50 are as follows:

[0030] If the piston impact speed exceeds the designed safety threshold, it will cause the piston to hard land, corresponding to the risk of piston damage;

[0031] If the peak value of the compressed gas pressure is less than the film bursting pressure, it will cause the diaphragm to fail to open normally and the compressed gas cannot be released, corresponding to the risk of misfire;

[0032] If the maximum piston speed during the compression process exceeds the designed safety threshold, it will cause damage to the piston seal and out-of-control movement, corresponding to the risk of excessive piston movement;

[0033] If the compressed gas length is less than the safety threshold, it will cause the downstream gas to be forced to stop without being fully compressed, corresponding to the risk of insufficient compressed gas; The risk of insufficient compressed gas includes the risks of insufficient pressure and temperature.

[0034] Further, the calculation method for the risk probability of the evaluated drive parameter combination in S50 includes the following steps:

[0035] S51. Statistically count the number of random trials N 0 , the number of times N 1 of the risk of piston damage, the number of times N 2 of the risk of misfire, the number of times N 3 of the risk of the piston moving too fast, and the number of times N 4 of the risk of insufficient compressed gas, as well as the number of times N 5 when all the risks of piston damage, misfire, the piston moving too fast, and insufficient compressed gas occur;

[0036] S52. The probabilities of the risks of piston damage, misfire, the piston moving too fast, and insufficient compressed gas occurring are PR i = N i / N 0 , (i = 1, 2, 3, 4);

[0037] S53. The low - risk probability PR s = N5 / N 0 .

[0038] The piston drive parameter decision - making method based on risk assessment according to the present invention takes into account the random characteristics of the relevant parameters that affect the actual drive characteristics of the piston driver. By simulating random trials, it evaluates the risk types and risk probabilities that may be caused by the relevant parameter combinations, provides quantitative data support for piston drive parameter decision - making, improves the operation fault - tolerance ability of the piston driver, reduces the safety risks brought by random characteristics during operation, and has engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of the piston drive parameter decision - making method based on risk assessment according to the present invention;

[0040] Figure 2a is a distribution frequency histogram of the initial gas pressure P tank of the gas storage tank in Embodiment 1;

[0041] Figure 2b is a distribution frequency histogram of the initial gas temperature T tank of the gas storage tank in Embodiment 1;

[0042] Figure 2c is a distribution frequency histogram of the initial gas pressure P tube of the compression pipe in Embodiment 1;

[0043] Figure 2d is a distribution frequency histogram of the initial gas temperature T tubeFrequency histogram of the distribution;

[0044] Figure 2e For the specific heat ratio GAMA of the compressed tube gas in Example 1 tube Frequency histogram of the distribution;

[0045] Figure 2f For the molecular weight M of the compressed tube gas in Example 1 tube Frequency histogram of the distribution;

[0046] Figure 2g For the frequency histogram of the diaphragm bursting pressure Popen in Example 1;

[0047] Figure 2h For the frequency histogram of the friction force Friction during the piston movement process in Example 1;

[0048] Figure 3a For the statistical results of the compressed gas pressure obtained in Example 1;

[0049] Figure 3b For the statistical results of the compressed gas temperature obtained in Example 1;

[0050] Figure 3c For the statistical results of the maximum piston speed during the compression process obtained in Example 1;

[0051] Figure 3d For the statistical results of the piston impact speed obtained in Example 1;

[0052] Figure 4 For the probability of typical risks occurring in Example 1. Detailed implementation manners

[0053] The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0054] Embodiment: As Figure 1 shown, the piston drive parameter decision method based on risk assessment described in this embodiment includes the following steps:

[0055] S10. Determine the combination of drive parameters that affect the drive characteristics of the piston driver, extract independent basic random variables, and determine the distribution type and parameter distribution of each basic random variable;

[0056] Determine the combination of drive parameters that affect the drive characteristics of the piston driver by referring to previous test data and instrument calibration data, obtaining the operation experience of operators, and making reasonable assumptions, extract independent basic random variables, and determine the distribution type and parameter distribution of each basic random variable;

[0057] The basic random variables in this embodiment and the distribution types of each basic random variable are as follows:

[0058] The initial pressure of the gas in the gas storage tank (MPa) follows a uniform distribution: P tank ~U(2.0, 2.2);

[0059] The initial temperature of the gas in the gas storage tank (K) follows a uniform distribution: T tank ~U(292, 308);

[0060] The initial pressure of the gas in the compression pipe (MPa) follows a uniform distribution: P tube ~U(0.4915, 0.505);

[0061] The initial temperature of the gas in the compression pipe (K) follows a uniform distribution: T tube ~U(292, 308);

[0062] The specific heat ratio of the gas in the compression pipe follows a standard normal distribution: GAMA tube ~N(1.667, 0.003);

[0063] The molecular weight of the gas in the compression pipe follows a standard normal distribution: M tube ~N(4, 0.2);

[0064] The diaphragm bursting pressure (MPa) follows a standard normal distribution: Popen ~ N(40, 3);

[0065] The friction force during the piston movement process (N) follows a standard normal distribution: Friction ~ N(6600, 500);

[0066] S20. Construct a driving parameter matrix and check the distribution characteristics of the simulated sample points constructed for each basic random variable;

[0067] Construct a random parameter matrix that affects the driving characteristics of the piston driver, check the distribution characteristics of the simulated sample points constructed for each random variable, and confirm whether the distribution characteristics meet the expectations; if they meet the expectations, proceed to the next step; otherwise, reconstruct the random parameter matrix and re-check the distribution characteristics;

[0068] The basic random variables of this embodiment and the distribution characteristics of the simulated sample points of each basic random variable are shown in Figures 2a to 2h , meeting the expectations, proceed to the next step;

[0069] S30. Establish a theoretical model for calculating the piston driving characteristics and conduct random experiments using the Monte Carlo method;

[0070] This embodiment conducts 100,000 random experiments;

[0071] S40. Statistically analyze the results of the random experiments;

[0072] Statistical analysis is carried out, and the results of the random test obtained are piston drive parameters characterizing the piston drive characteristics, including the compressed gas pressure, the compressed gas temperature, the compressed gas length, the maximum piston speed during the compression process, and the piston impact speed.

[0073] The statistical results of the distributions of the compressed gas pressure, the compressed gas temperature, the maximum piston speed during the compression process, and the piston impact speed obtained in this embodiment are shown in Figures 3a to 3d ;

[0074] S50. Evaluate the risk type and risk probability of the drive parameter combination;

[0075] The risk types of the piston driver include the risk of piston damage, the risk of misfire, the risk of excessive piston movement speed, and the risk of insufficient compressed gas; evaluate the risk type and the corresponding risk probability of the drive parameter combination; if the risk probability is lower than the pre-set risk probability, it is evaluated as low risk, and if no risk occurs, it is evaluated as no risk; if the risk probability is higher than the pre-set risk probability, it is evaluated as high risk, and return to S10 for iteration.

[0076] The probabilities of typical risks occurring in this embodiment are shown in Figure 4 ;

[0077] S60. Judge the acceptable degree of the risk type and the corresponding risk probability, and determine whether to use the group drive parameter combination to carry out the test; if the group drive parameter combination is used to carry out the test, then carry out the wind tunnel test, otherwise, return to S10 for iteration.

[0078] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and embodiments. For those familiar with the field, without departing from the principle of the present invention, all the features disclosed in the present invention, or all the steps in the disclosed methods or processes, except for mutually exclusive features and / or steps, can be combined in any way. The present invention is not limited to the specific details and the illustrated examples here.

Claims

1. A piston drive parameter decision method based on risk assessment, characterized in that: The following steps are involved: S10. Determine the driving parameter combination that affects the driving characteristics of the piston driver, extract independent basic random variables, and determine the distribution type and parameter distribution of each basic random variable; S20. construct a driving parameter matrix and test the distribution characteristics of the simulated sample points constructed by each basic random variable; S30. Establish a theoretical model for calculating piston drive characteristics and conduct random experiments using the Monte Carlo method; S40. Conduct statistics and analysis on the results of randomized trials; The random test results obtained by statistics and analysis are piston driving parameters that characterize the piston driving characteristics, including the gas pressure after compression, the gas temperature after compression, the maximum speed of the piston during the compression process, and the piston impact speed; S50. Assess the risk type and risk probability of the driving parameter combination; The risk types of piston actuators include the risk of piston damage, risk of misfire, risk of piston over-speed movement, and risk of insufficient compressed gas; assess the risk type of the drive parameter combination and the corresponding risk probability; If the risk probability is lower than the pre-set risk probability, it is assessed as low risk; if no risk occurs, it is assessed as no risk; if the risk probability is higher than the pre-set risk probability, it is assessed as high risk, and returns to S10 for iteration; The criteria for determining the risk types are as follows: If the piston impact speed exceeds the designed safety threshold, it will cause the piston to land hard, which corresponds to the risk of piston damage; If the peak pressure of the compressed gas is lower than the membrane rupture pressure, the diaphragm will not open normally and the compressed gas will not be released, which corresponds to the risk of a gun failure. If the maximum speed of the piston during the compression process exceeds the designed safety threshold, it will cause damage to the piston seal and uncontrolled movement, corresponding to the risk of excessive piston movement. The length of the compressed gas is less than the safety threshold, resulting in the downstream gas not being fully compressed and forced to stop, which corresponds to the risk of insufficient compressed gas; the risk of insufficient compressed gas includes the risk of insufficient pressure and temperature; The calculation method for evaluating the risk probability of a driving parameter combination comprises the following steps: S51. Count the number of random tests N0, the number of piston damage risk N1, the number of gun failure risk N2, the number of piston too fast movement risk N3, and the number of compressed gas shortage risk N4, as well as the number of piston damage risk, gun failure risk, piston too fast movement risk, and compressed gas shortage risk N5; S52. The probability of piston damage risk, gun failure risk, piston overspeed risk and compressed gas shortage risk is PR respectively. i =N i / N0, (i=1,2,3,4); S53. Low risk probability PR s =N5 / N0; S60. Determine the acceptability of the risk type and the corresponding risk probability, and determine whether to use the group drive parameter combination to carry out the test; if the group drive parameter combination is used to carry out the test, then carry out the wind tunnel test, otherwise, return to S10 for iteration.

2. The risk assessment-based piston drive parameter decision method according to claim 1, characterized in that: The driving parameter combination affecting the driving characteristics of the piston driver described in S10 includes the initial pressure of the gas in the gas tank, the initial temperature of the gas in the gas tank, the initial pressure of the gas in the compression pipe, the initial temperature of the gas in the compression pipe, the specific heat ratio of the gas in the compression pipe, the molecular weight of the gas in the compression pipe, the diaphragm rupture pressure and the friction force during the piston movement process; the method for determining the distribution type and parameter distribution of each basic random variable includes: consulting previous test data and instrument calibration data, obtaining the operating experience of the operator and making assumptions.

3. The risk assessment-based piston drive parameter decision method according to claim 2, characterized in that: The specific method of constructing the driving parameter matrix described in S20 and testing the distribution characteristics of the simulated sample points constructed by each basic random variable includes: S21. Use Latin hypercube sampling to generate simulated sample point vectors X1, X2, ...X of each basic random variable. n ; S22. Test the distribution characteristics of the simulated sample points constructed by each basic random variable to confirm whether the distribution characteristics meet expectations; S23. If all meet expectations, a random parameter matrix X=[X1; X2; ...X n ]; S24. If the simulated sample point distribution of one or several basic random variables does not meet expectations, return to S21. Regenerate the simulated sample point vector of the corresponding basic random variables and re-test the distribution characteristics until all meet expectations.

4. The risk assessment-based piston drive parameter decision method according to claim 3, characterized in that: The method for testing the distribution characteristics of the simulated sample points constructed from each basic random variable in S22 to confirm whether the distribution characteristics meet expectations includes one of the following methods or a combination of two or more methods: a. Determine the distribution characteristics of the sample points by drawing the statistical histogram of the simulated sample points constructed by each basic random variable; b. Calculate the mean and standard deviation of the simulated sample points constructed by each basic random variable. The mean deviation shall not exceed 0.5% and the standard deviation shall not exceed 10%; cK-S test method; d.Anderson-Darling test method.

5. The risk assessment-based piston drive parameter decision method according to claim 4, characterized in that: The establishment of the piston driving characteristic calculation theoretical model described in S30 adopts a CFD-FSI coupled numerical simulation model or a gas-piston dynamics differential equation model combined with experimental correction.

6. The risk assessment-based piston drive parameter decision method according to claim 5, characterized in that: The random test results described in S40 are: the obtained random test results are piston drive parameters that characterize the piston drive characteristics, including compressed gas pressure, compressed gas temperature, compressed gas length, maximum piston speed during compression, and piston impact speed.

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