Piston driving parameter decision-making method based on risk assessment

Abstract

The invention belongs to the technical field of hypersonic speed test equipment, and discloses a piston driving parameter decision-making method based on risk assessment. The piston driving parameter decision-making method comprises the following steps: determining a driving parameter combination influencing the driving 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 driving parameter matrix, and checking the distribution characteristics of the simulation sample points constructed by the basic random variables; establishing a piston driving characteristic calculation theoretical model, and using a Monte Carlo method to carry out a random test; carrying out statistics and analysis on random test results; evaluating the risk type and the risk probability of the driving parameter combination; and judging the risk type and the acceptable degree of the corresponding risk probability, and determining whether to use the group of driving parameter combinations to carry out a test. According to the piston driving parameter decision-making method, the operation fault-tolerant capability of the driver is improved, and the safety risk caused by random factors in the operation process is reduced.

Description

Technical Field

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

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

[0003] Traditional piston drive parameter methods often make parameter budgets based on experimentally modified theoretical models before the test, which cannot fully consider the randomness of various factors in actual operation. Since the actual driving characteristics of the piston driver are affected by multiple factors such as filling parameters, diaphragm rupture pressure, sealing effect, friction state, etc., it has obvious random characteristics, which may cause the actual piston drive parameters to deviate from the theoretical model, resulting in test results that are not as expected, test 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 method based on risk assessment to overcome the defects of the prior art.

[0006] The risk assessment-based piston drive parameter decision method described in the present invention constructs a random parameter matrix that affects the piston drive characteristics, then conducts simulated random experiments, and analyzes and evaluates the random experiment results to determine whether the piston drive parameters are feasible.

[0007] The piston driving parameter decision-making method based on risk assessment of the present invention comprises the following steps: 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 the piston driver include the risk of piston damage, the risk of misfire, the risk of piston over-speed movement and the risk of insufficient compressed gas; the risk type and corresponding risk probability of the driving parameter combination are evaluated; if the risk probability is lower than the pre-set risk probability, it is evaluated as low risk; 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 returns to S10 for iteration; 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.

[0008] Furthermore, 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.

[0009] Furthermore, the specific method of constructing the driving parameter matrix 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.

[0010] Furthermore, the method of testing the distribution characteristics of the simulated sample points constructed by 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.

[0011] Furthermore, 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 corrected in combination with experiments.

[0012] Furthermore, the random test results described in S40 are: the random test results obtained 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.

[0013] Furthermore, the criteria for determining the risk type described in S50 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.

[0014] Furthermore, the calculation method for evaluating the risk probability of the driving parameter combination described in S50 includes 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.

[0015] The piston drive parameter decision-making method based on risk assessment described in the present invention takes into account the random characteristics of relevant parameters that affect the actual driving characteristics of the piston driver. By simulating random experiments, the risk types and risk probabilities that may be caused by relevant parameter combinations are evaluated, providing quantitative data support for piston drive parameter decision-making, improving the operational fault tolerance of the piston driver, and reducing the safety risks brought by random characteristics during operation, which has practical engineering value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the piston driving parameter decision method based on risk assessment according to the present invention; Figure 2a is the initial pressure P of the gas tank gas in Example 1 tank The distribution frequency histogram of ; Figure 2b is the initial temperature T of the gas in the gas storage tank of Example 1 tank The distribution frequency histogram of ; Figure 2c is the initial pressure P of the compressed gas in Example 1 tube The distribution frequency histogram of ; Figure 2d is the initial temperature T of the compressed gas in Example 1 tube The distribution frequency histogram of ; Figure 2e is the specific heat ratio GAMA of the compressed gas in Example 1 tube The distribution frequency histogram of ; Figure 2f is the molecular weight M of the compressed tube gas in Example 1 tube The distribution frequency histogram of ; Figure 2g is a distribution frequency histogram of the membrane rupture pressure Popen of Example 1; Figure 2h is a distribution frequency histogram of the friction force Friction during the piston motion process of Example 1; Figure 3a The statistical result of the compressed gas pressure obtained in Example 1; Figure 3b The statistical results of the compressed gas temperature obtained in Example 1; Figure 3c The statistical result of the maximum speed of the piston during the compression process obtained in Example 1; Figure 3d The statistical results of the piston impact velocity obtained in Example 1; Figure 4 The probability of a typical risk occurring in Example 1. DETAILED DESCRIPTION

[0017] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0018] Example: Figure 1 As shown, the piston driving parameter decision method based on risk assessment described in this embodiment includes the following steps: 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; By consulting previous test data and instrument calibration data, obtaining the operating experience of operators and making reasonable assumptions, the driving parameter combination that affects the driving characteristics of the piston driver is determined, and the independent basic random variables are extracted and the distribution type and parameter distribution of each basic random variable are determined; The basic random variables of this embodiment and the distribution types of each basic random variable are as follows: Initial gas pressure in the gas tank (MPa), uniformly distributed: P tank ~U(2.0,2.2); The initial temperature of the gas in the gas tank (K) is uniformly distributed: T tank ~U (292,308); Initial pressure of the compressed gas in the tube (MPa), uniformly distributed: P tube ~U(0.4915,0.505); The initial temperature of the gas in the compression tube (K) is uniformly distributed: T tube ~U (292,308); The specific heat ratio of the compressed gas in the tube follows the standard normal distribution: GAMA tube ~N(1.667, 0.003); The molecular weight of the compressed tube gas follows a standard normal distribution: M tube ~N(4,0.2); Diaphragm rupture pressure (MPa), subject to standard normal distribution: Popen~N(40,3); The friction force during piston movement (N) follows the standard normal distribution: Friction~N (6600,500); S20. construct a driving parameter matrix and test the distribution characteristics of the simulated sample points constructed by each basic random variable; Construct a random parameter matrix that affects the driving characteristics of the piston driver, and check the distribution characteristics of the simulated sample points constructed by each random variable to confirm whether the distribution characteristics meet expectations; if they meet expectations, proceed to the next step; otherwise, reconstruct the random parameter matrix and re-check the distribution characteristics; The basic random variables of this embodiment and the distribution characteristics of the simulated sample points of each basic random variable are shown in Figure 2a~Figure 2h , in line with expectations, proceed to the next step; S30. Establish a theoretical model for calculating piston drive characteristics and conduct random experiments using the Monte Carlo method; This example conducts 100,000 random experiments; S40. Conduct statistics and analysis on the results of randomized trials; The random test results obtained by statistics and analysis 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; The statistical results of the distribution of the compressed gas pressure, compressed gas temperature, maximum piston speed during compression, and piston impact speed obtained in this embodiment are shown in Figure 3a~Figure 3d ; S50. Assess the risk type and risk probability of the driving parameter combination; The risk types of the piston driver include the risk of piston damage, the risk of misfire, the risk of piston over-speed movement and the risk of insufficient compressed gas; the risk type and corresponding risk probability of the driving parameter combination are evaluated; if the risk probability is lower than the pre-set risk probability, it is evaluated as low risk; 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 returns to S10 for iteration; The probability of typical risks occurring in this example is shown in Figure 4 ; 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.

[0019] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. For those familiar with the art, all features disclosed in the present invention, or steps in all methods or processes disclosed, except for mutually exclusive features and / or steps, can be combined in any way without departing from the principles of the present invention. The present invention is not limited to the specific details and illustrations shown and described herein.

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; S60. Determine the acceptability of the risk type and the corresponding risk probability, and determine whether to use the group drive parameter combination to conduct the test; If the test is carried out using the group drive parameter combination, a wind tunnel test is carried out; 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 described in S22 for testing the distribution characteristics of the simulated sample points constructed from each basic random variable 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.

7. The risk assessment-based piston drive parameter decision method according to claim 6, characterized in that: The criteria for determining the risk types described in S50 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.

8. The risk assessment-based piston drive parameter decision method according to claim 7, characterized in that: The calculation method for evaluating the risk probability of the driving parameter combination described in S50 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.

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