CN114861565A - Determination method of air vibration isolation system and establishment method of simulation model thereof - Google Patents
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Abstract
The application relates to a determination method of an air vibration isolation system and an establishment method of a simulation model thereof. The method for establishing the air vibration isolation system simulation model comprises the following steps: the method comprises the steps of obtaining parameters to be calibrated of the air vibration isolation system, calibrating the parameters to be calibrated based on a Markov chain Monte Carlo method, and constructing a simulation model of the air vibration isolation system based on the parameters to be calibrated. According to the method for establishing the air vibration isolation system simulation model, the parameters of the model are optimized through a Markov chain Monte Carlo method, and the model is established according to the optimized parameters of the air vibration isolation system simulation model, so that the uncertainty of the air vibration isolation system simulation model is reduced, the simulation precision of the air vibration isolation system simulation model is improved, and the simulation result of the air vibration isolation system simulation model is closer to the real situation.
Description
Technical Field
The present application relates to the field of air vibration isolation systems for audio drilling machines, and more particularly, to a method and an apparatus for establishing a simulation model of an air vibration isolation system, and a method, an apparatus, a computer device, a computer-readable storage medium, and a computer program product for determining an air vibration isolation system.
Background
The acoustic frequency vibration drilling technology is a high-efficiency vibration drilling technology which is driven by a hydraulic pressure drilling tool to drill without using drilling fluid, does not need water during drilling, and has irreplaceable advantages under working conditions of arid water-deficient areas, unconsolidated strata and the like. The acoustic vibration drilling machine is driven by two (or more) symmetrical eccentric shafts (blocks) to generate high-frequency vibration force with mutually superposed vertical directions under the drive of a hydraulic motor, and the force in the horizontal direction is counteracted by the eccentric shafts (blocks) with opposite motion directions. The vibration force intensity in the vertical direction is high, the periodicity is achieved, the whole vibration of the drilling machine can be caused during drilling, the stability of the equipment is reduced, the service life is shortened, and the long-term stable work of the drilling machine is not facilitated. And the vibration isolator can protect the drilling machine, ensure to provide maximum vibration energy for the drill rod and reduce the damage to the drilling machine.
As a novel vibration isolation structure, the air spring has the advantages of light weight, low noise, low natural vibration frequency, controllable rigidity through initial internal pressure, capability of bearing larger load, capability of isolating high-frequency vibration and the like, and when the air vibration isolation system is applied to an audio frequency drilling machine, a mathematical model which can quantitatively depict vibration isolation response of the air vibration isolation system in the drilling process needs to be established, dynamic mechanical characteristic analysis is carried out on the mathematical model, a simulation model is established, and finally the mathematical model is compared with an indoor test result for verification.
However, the simulation accuracy of the currently established air vibration isolation system simulation model is low.
Disclosure of Invention
In view of the above, it is desirable to provide a method and an apparatus for establishing an air vibration isolation system simulation model, a method and an apparatus for determining an air vibration isolation system, a computer device, a computer-readable storage medium, and a computer program product, which can improve the simulation accuracy of the established air vibration isolation system simulation model.
In a first aspect, a method for establishing a simulation model of an air vibration isolation system is provided, which includes: acquiring parameters to be calibrated of the air vibration isolation system; calibrating the parameter to be calibrated based on a Markov chain Monte Carlo method; and constructing an air vibration isolation system simulation model based on the calibrated parameters to be calibrated.
In one embodiment, the parameter to be calibrated includes an initial air pressure value of an air chamber of the air vibration isolation system and an excitation frequency of the air vibration isolation system.
In one embodiment, the rating the parameter to be rated based on the markov chain monte carlo method includes: determining prior distribution of the parameter to be calibrated; defining the number of Markov chains as N and the length of each chain as H; randomly producing a starting point for each Markov chain from a prior distribution of parameters
Calculating a combined likelihood model of each Markov chain; wherein,
for the ith parameter to be calibrated in the jth chainA sample; and carrying out evolution operation on the to-be-calibrated parameter samples on each Markov chain based on each Markov chain joint likelihood model until the Markov chain reaches the convergence standard, and acquiring calibrated to-be-calibrated parameters based on the converged Markov chain.
In one embodiment, the obtaining the calibrated parameter to be calibrated according to the converged markov chain includes: determining the parameter to be calibrated with the frequency greater than the preset frequency as a target parameter to be calibrated according to the frequency of each group of parameter to be calibrated of the converged Markov chain; and taking a parameter range to be calibrated formed by the target parameter to be calibrated as the parameter to be calibrated after calibration.
In one embodiment, the prior distribution of the initial pressure values is 0.2MPa to 0.8MPa and the prior distribution of the excitation frequencies is
Hertz to
Hertz.
In a second aspect, there is provided a method of determining an air vibration isolation system, the method comprising: carrying out dynamic mechanical characteristic analysis on the air vibration isolation system determined by the air vibration isolation system simulation model; the air vibration isolation system simulation model is established based on the method; correcting the air vibration isolation system simulation model based on the dynamic mechanical characteristic analysis result until the dynamic mechanical characteristic analysis result meets the preset dynamic mechanical property requirement; and determining the parameters of the air vibration isolation system which meet the dynamic mechanical property requirement based on the corrected air vibration isolation system simulation model.
In a third aspect, an apparatus for creating a simulation model of an air vibration isolation system is provided, the apparatus comprising: the acquisition module is used for acquiring parameters to be calibrated of the air vibration isolation system; the calibration module is used for calibrating the parameter to be calibrated based on a Markov chain Monte Carlo method; and the construction module is used for constructing an air vibration isolation system simulation model based on the parameters to be calibrated after calibration.
In a fourth aspect, there is provided a determination apparatus of an air vibration isolation system, the apparatus comprising: the analysis module is used for carrying out dynamic mechanical characteristic analysis on the air vibration isolation system determined by the air vibration isolation system simulation model; the air vibration isolation system simulation model is established based on the method; the correction module is used for correcting the air vibration isolation system simulation model based on the dynamic mechanical characteristic analysis result until the dynamic mechanical characteristic analysis result meets the preset dynamic mechanical property requirement; and the determining module is used for determining the parameters of the air vibration isolation system which meet the dynamic mechanical property requirement based on the corrected air vibration isolation system simulation model.
In a fifth aspect, there is provided a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method for establishing the simulation model of the air vibration isolation system according to any one of the first aspect or implements the method for determining the air vibration isolation system according to any one of the second aspect when executing the computer program.
In a sixth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the method for establishing a simulation model of an air vibration isolation system according to any one of the above first aspects, or which, when being executed by a processor, implements the method for determining an air vibration isolation system according to any one of the above second aspects.
According to the method for establishing the air vibration isolation system simulation model, the to-be-calibrated parameters of the air vibration isolation system are obtained, the to-be-calibrated parameters are calibrated based on the Markov chain Monte Carlo method, and the air vibration isolation system simulation model is established based on the calibrated to-be-calibrated parameters. According to the method for establishing the air vibration isolation system simulation model, the parameters of the model are optimized through a Markov chain Monte Carlo method, and the model is established according to the optimized parameters of the air vibration isolation system simulation model, so that the uncertainty of the air vibration isolation system simulation model is reduced, the simulation precision of the air vibration isolation system simulation model is improved, and the simulation result of the air vibration isolation system simulation model is closer to the real condition.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for creating a simulation model of an air vibration isolation system according to an embodiment;
FIG. 2 is a schematic flow chart diagram of a calibration method in one embodiment;
FIG. 3 is a schematic flow chart of a calibration method in another embodiment;
FIG. 4 is a flowchart illustrating a method for obtaining a parameter to be calibrated according to an embodiment;
FIG. 5 is a schematic flow chart diagram illustrating a method for obtaining an evaluation model according to one embodiment;
FIG. 6 is a schematic illustration of a three-dimensional model of an air vibration isolation system in one embodiment;
FIG. 7 is a schematic flow chart illustrating a method of determining an air vibration isolation system in one embodiment;
FIG. 8 is a block diagram of an apparatus for modeling a simulation model of an air vibration isolation system in an embodiment;
FIG. 9 is a block diagram of a determining device of the air vibration isolation system in one embodiment;
FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The inventor researches and discovers that due to the complexity of the air vibration isolation system and the limitation of observation data, when the air vibration isolation system is described by using a mathematical language, a certain uncertainty exists in an established simulation model and a simulation result thereof, so that a larger error exists between the simulation result and an actual result.
Referring to fig. 1, a method for establishing a simulation model of an air vibration isolation system according to an embodiment of the present application is shown, and as shown in fig. 1, the method may include steps S102 to S106.
And S102, obtaining parameters to be calibrated of the air vibration isolation system.
And S104, calibrating the to-be-calibrated parameters based on the Markov chain Monte Carlo method.
And S106, constructing an air vibration isolation system simulation model based on the calibrated parameters to be calibrated.
Among them, Monte Carlo Simulation (MCS) is a method of performing computer Simulation using random numbers, and is also called random Simulation. The MCS may be classified into a static MC method (uniform sampling) and a dynamic MC method (importance sampling) according to different sampling methods. The importance sampling method makes the occurrence frequency of random variables with high contribution rate (high probability density) larger, wherein Markov Chain Monte Carlo (MCMC) is a representative importance sampling method. A markov chain, a discrete-time stochastic process that mathematically has the markov property, has irreducible, aperiodic, and smoothly distributed properties. The MCMC can better simulate the posterior probability distribution condition of parameters through parameter sampling, the MCMC is utilized to consider the parameter uncertainty in the air vibration isolation system simulation model, the uncertainty of the air vibration isolation system simulation model can be reduced, and the model simulation precision is improved.
It should be noted that the vibration isolation performance of the air vibration isolation system is related to a plurality of parameters of the air vibration isolation system, one or more parameters of the air vibration isolation system are determined to be used as parameters to be calibrated, the parameters to be calibrated are calibrated based on the MCMC method, and an air vibration isolation system simulation model is constructed based on the calibrated parameters to be calibrated, so that the uncertainty of the model parameters can be reduced, the simulation precision of the model is improved, and the guidance accuracy of the air vibration isolation structure of the acoustic frequency drilling machine is improved.
In one embodiment, the parameter to be calibrated may include an initial air pressure value of an air chamber of the air vibration isolation system and an excitation frequency of the air vibration isolation system. It should be noted that the air chamber of the air vibration isolation system includes an upper air chamber and a lower air chamber, and the initial air pressure values of the upper air chamber and the lower air chamber of the air vibration isolation system are equal, so the initial air pressure value of the air chamber described in this embodiment refers to the initial air pressure value of the upper air chamber and also refers to the initial air pressure value of the lower air chamber.
According to the method for establishing the air vibration isolation system simulation model, the parameters of the model are optimized through a Markov chain Monte Carlo method, the model is established according to the optimized parameters of the air vibration isolation system simulation model, the uncertainty of the air vibration isolation system simulation model is reduced, the simulation precision of the air vibration isolation system simulation model is improved, the simulation result of the air vibration isolation system simulation model is closer to the real condition, and the guidance accuracy of designing and improving the air vibration isolation structure of the audio drilling machine is improved.
As can be seen from the above description of the embodiments, the uncertainty of the model parameter can be reduced by using the MCMC method to calibrate the parameter to be calibrated, and the following embodiments provide a calibration method to calibrate the parameter to be calibrated. The following description will be given taking the parameters to be calibrated as the initial air pressure value of the air vibration isolation system and the vibration excitation frequency of the air vibration isolation system as examples.
Referring to fig. 2, which illustrates a calibration method provided in an embodiment of the present application, as shown in fig. 2, the steps of calibrating the calibration parameters based on the markov chain monte carlo method include steps S202 to S208.
S202, determining prior distribution of the parameters to be calibrated.
S204, defining the number of Markov chains as N and the length of each chain as H.
S206, randomly producing the starting point of each Markov chain from the parameter prior distribution
Calculating a combined likelihood model of each Markov chain;wherein,
the parameter sample is determined for the ith to be rated in the jth chain (i 1, 2 …, H, j 1, 2 …, N);
and S208, carrying out evolution operation on the to-be-calibrated parameter samples on each Markov chain based on each Markov chain joint likelihood model until the Markov chain reaches a convergence standard, and acquiring calibrated to-be-calibrated parameters based on the converged Markov chain.
The prior distribution of the parameters to be calibrated is the initial range of the parameters to be calibrated, and can be determined according to the data of the existing air vibration isolation system. In one embodiment, the prior distribution of initial air pressure values for the air chambers of the air vibration isolation system may be 0.2 to 0.8 megapascals and the prior distribution of excitation frequencies for the air vibration isolation system may be
Hertz to
Hertz.
It should be noted that the MCMC method establishes a steadily distributed markov chain based on a bayesian theory framework, then samples the steadily distributed markov chain, fully searches in a space of probability distribution of a target function in the markov chain evolution process, continuously adjusts a search strategy according to previous sampling information, fully samples in a region with high probability density, and finally obtains a posterior probability distribution converging to the target function. It can be understood that the number N of markov chains, and the length H of each chain may be set as needed, and the embodiment of the present invention is not limited herein. Optionally, N is 3 and H is 50000.
In one embodiment, the likelihood model may be a first formula, the first formula being:
n is the number of the observation data, f (theta) is the result of the simulation output of the simulation model under the condition that the parameter to be calibrated is theta, Σ represents the error structure of the observation data, y is the indoor test data of the air vibration isolation system under the condition that the parameter to be calibrated is theta, and L (theta | y) is the likelihood function of the indoor test data and the result of the simulation output.
Please refer to fig. 3, which illustrates a calibration method according to an embodiment of the present application, in which the DREAM is applied zs An algorithmic implementation to obtain a converged markov chain, as shown in fig. 3, may include steps S302 through S314.
S302, the prior distribution of the initial air pressure value is defined to be 0.2MPa to 0.8MPa, and the prior distribution of the excitation frequency is defined to be
To
S304, defining the number of Markov chains as N and the length of each chain as H.
S306, randomly producing the starting point of each Markov chain from the parameter prior distribution
And calculating each Markov chain joint likelihood model.
The description of the likelihood model is detailed in the above embodiments, and is not repeated here.
S308, based on the second formula and
determining
Wherein,
for the ith parameter sample to be calibrated in the jth chain (i 1, 2 …, H, j ═ j1, 2 …, N), the second formula is:
where δ is the number of heterochains generating alternate points, r 1 (m) and r 2 (n) each represent a different Markov chain, I d Is an identity matrix, e d And ε d Is a random number, γ (δ, d ') is a jump scale, and d' represents a value for substituting the parameter dimension in subspace evolution.
S310, replacing each element in each chain with cross probability, and then calculating a joint likelihood function and each sample
The acceptance rate of (c); if the acceptance rate indicates acceptance, then note as
If the acceptance rate indicates not acceptance, then
In one embodiment, the acceptance rate α is:
wherein,
is the posterior distribution density with a parameter theta to be calibrated.
S312, judging whether the Markov chain meets the convergence condition, if so, executing the step S314; if not, the process returns to step S308.
In one embodiment, the convergence condition includes that the Markov chain passes the Gelman-Rubin convergence criterion, i.e., R sta Is less than 1.2, and the iteration number of the Markov chain reaches H. Wherein，R sta Comprises the following steps:
wherein W is the intra-chain variance, B is the inter-chain variance, N is the number of observed data, and H is the length of each chain.
And S314, acquiring the calibrated parameter to be calibrated based on the converged Markov chain.
Based on DREAM zs The algorithm can determine the posterior distribution of the initial air pressure value of the air chamber and the posterior distribution of the excitation frequency, and according to indoor verification, the simulation result of the air vibration isolation system simulation model established according to the posterior distribution of the initial air pressure value and the posterior distribution of the excitation frequency is closer to the test result compared with the simulation result of the air vibration isolation system simulation model established according to the prior distribution of the initial air pressure value and the prior distribution of the excitation frequency.
It should be noted that, whether the simulation accuracy of the air vibration isolation system simulation model constructed based on the calibrated parameters to be calibrated is higher than the simulation accuracy of the air vibration isolation system simulation model constructed based on the parameters that are not calibrated may be determined according to the evaluation model. In one embodiment, the evaluation model may be the vibration isolation transmissibility of an air vibration isolation system. Wherein, the vibration isolation transmissibility is:
wherein xi is the damping ratio of the air vibration isolation system,
wherein ω is the excitation frequency of the air vibration isolation system, ω n Is the natural frequency of the air vibration isolation system, natural frequency omega n Associated with the initial air pressure value of the air chamber. To be explainedIs a natural frequency ω n The stiffness coefficient K is related to the initial air pressure value of the air chamber, so the natural frequency omega n Associated with the initial air pressure value of the air chamber. In particular, the method comprises the following steps of,
k is a stiffness coefficient, A is an effective area of an air vibration isolation system, n is a polytropic exponent, n is 1 in an isothermal process, n is 1.4 in an adiabatic process, the value range of n is 1-1.4 in a dynamic process, and P is 0 Is the initial air pressure value, V, of the air vibration isolation system 0 Is the initial volume of the air chamber of the air vibration isolation system.
Referring to fig. 4, which illustrates a method for obtaining a parameter to be calibrated according to an embodiment of the present application, as shown in fig. 4, the step of obtaining the parameter to be calibrated based on the converged markov chain includes steps S402 to S404.
S402, determining the parameter to be calibrated with the frequency greater than the preset frequency as a target parameter to be calibrated according to the frequency of each group of parameter to be calibrated of the converged Markov chain.
It is understood that the posterior samples of MCMC collectively include group a, where a is:
a＝N*(H-b)，
b is the length of the preheating period, N is the number of Markov chains, and H is the length of each chain. It will be appreciated that the warm-up period is the initial sampling period, which has no posterior distribution results, after which the likelihood functions for each set of samples are calculated. It should be noted that the preset frequency can be set as needed to determine the target calibration parameters from the posterior samples of the MCMC.
And S404, taking a parameter range to be calibrated formed by the target parameter to be calibrated as the parameter to be calibrated after calibration.
It should be noted that the parameter to be calibrated includes an initial air pressure value and an excitation frequency, the initial air pressure value after calibration should be the parameter to be calibrated composed of the target initial air pressure value, and the excitation frequency after calibration should be the parameter to be calibrated composed of the target excitation frequency. Exemplarily, assuming target initial air pressure values of 0.3MPa, 0.6MPa, and 0.4MPa, the calibrated initial air pressure values are 0.3MPa to 0.6 MPa; assuming that the target excitation frequencies are 90Hz, 100Hz, and 80Hz, the calibrated excitation frequency is 80Hz to 100 Hz.
In one embodiment, the target parameter to be calibrated is a parameter to be calibrated with the highest frequency in each set of parameters to be calibrated, and the parameter to be calibrated after calibration is less than or equal to a first value and greater than or equal to a second value. Wherein the first value is the sum of the target parameter to be calibrated and the preset value, and the second value is the difference between the target parameter to be calibrated and the preset value. It should be noted that the preset value may be set as needed, as long as the parameter to be calibrated after calibration is ensured to be a narrow interval in which a peak occurs. Illustratively, assuming that the target initial air pressure value is 0.4MPa and the preset value is 0.1MPa, the calibrated initial air pressure value is 0.3MPa to 0.5 MPa.
In one embodiment, statistics is carried out on a probability density distribution histogram of the parameter to be calibrated after being calibrated through the MCMC algorithm, and a first interval is searched in the probability density distribution histogram of the parameter to be calibrated. Wherein, the first interval is the parameter to be calibrated after calibration. The first interval is a parameter range to be calibrated corresponding to the probability density larger than the preset probability density. The range with obvious frequency peak and narrow convergence interval is determined as the parameter to be calibrated after calibration.
Referring to fig. 5, an obtaining method of an evaluation model provided in an embodiment of the present application is shown, and as shown in fig. 5, the obtaining method may include steps S502 to S508.
And S502, determining a three-dimensional model of the air vibration isolation system.
In one embodiment, the air vibration isolation system is mainly composed of a case 602, a spindle 604, a base 606, end caps (including an upper end cap 608a and a lower end cap), an exciter base 610, a V-shaped combined sealing ring 612, an O-shaped sealing ring 614, a one-way valve 616, and the like. The housing 602 is divided equally into two air chambers 618 (upper and lower air chambers) by the piston, and each air chamber 618 is provided with an opening for inputting compressed air; the piston and the box body 602 adopt straight-through type labyrinth seal to separate the gas of the two gas chambers 618; the case 602 is bolted to the upper end cover 608a and the base 606, and sealed by an O-ring 614; the shaft 604 is sealed with the upper end cover 608a and the base 606 by a V-shaped combined sealing ring 612. As shown in fig. 6, a schematic diagram of a three-dimensional model of the air vibration isolation system is shown.
And S504, generalizing the three-dimensional model based on the air vibration isolation system to obtain a mechanical model of the air vibration isolation system.
It can be understood that the mechanical model of the air vibration isolation system refers to a model obtained by neglecting factors which have little influence on the vibration isolation effect in the air vibration isolation system and factors which cannot be changed practically.
S506, analyzing the parameters of the air vibration isolation system based on the mechanical model of the air vibration isolation system.
The parameters of the air vibration isolation system are analyzed according to the generalized model, so that the difficulty and complexity of analysis can be greatly reduced. Optionally, the parameters of the air vibration isolation system may include, but are not limited to, restoring force, stiffness coefficient, excitation frequency, and amplitude.
And S508, determining an evaluation model based on the analysis result of the parameters of the air vibration isolation system.
Optionally, the evaluation model may be used to evaluate the vibration isolation effect of the air vibration isolation system. In one embodiment, the evaluation model may be the vibration isolation transmissibility of an air vibration isolation system. Wherein, the vibration isolation transmissibility is:
wherein xi is the damping ratio of the air vibration isolation system,
wherein ω is the excitation of said air vibration isolation systemFrequency, omega n Is the natural frequency of the air vibration isolation system, natural frequency omega n Associated with the initial air pressure value of the air chamber. It should be noted that the natural frequency ω n The stiffness coefficient K is related to the initial air pressure value of the air chamber, so the natural frequency omega n Associated with the initial air pressure value of the air chamber. In particular, the method comprises the following steps of,
k is a stiffness coefficient, A is an effective area of an air vibration isolation system, n is a polytropic exponent, n is 1 in an isothermal process, n is 1.4 in an adiabatic process, the value range of n is 1-1.4 in a dynamic process, and P is 0 Is the initial air pressure value, V, of the air vibration isolation system 0 Is the initial volume of the air chamber of the air vibration isolation system.
The air vibration isolation system of the audio frequency drilling machine achieves a vibration effect by inhibiting the vibration response of the vibration isolator to the power head frame, namely vibration energy which is transmitted to the base of the air vibration isolation system all the time, so that the air vibration isolation system of the audio frequency drilling machine is a negative vibration isolation system. When the air vibration isolation system is excited by simple resonance, the amplitude-frequency characteristic (vibration isolation transmission rate) of the displacement of the vibration isolator to the displacement of the base is an index for evaluating the performance of the air vibration isolation system.
In the embodiment, a three-dimensional model of the air vibration isolation system is established firstly, the volume and the pressure of an air chamber of the air vibration isolation system are changed in the vibration process, and the vibration energy is absorbed by utilizing the compression deformation of the air, so that the vibration isolation purpose is achieved. The mechanical model of the air vibration isolation system is obtained after the air vibration isolation system is generalized, a transfer function of the air vibration isolation system is constructed according to the stress analysis of the air vibration isolation system in the drilling process, the base displacement is used as an input quantity, the vibration isolator displacement is used as an output quantity, and the obtained transfer function is as follows:
G(jω)＝R(ω)e jθ(ω) ，
wherein, R (omega) is the amplitude of G (j omega), which is called the amplitude-frequency characteristic of the air vibration isolation system; u (ω) is the phase angle of G (j ω), and is referred to as the phase-frequency characteristic of the system.
The amplitude-frequency characteristic shows that the ratio of the base displacement to the vibration isolator displacement is a function changing along with the frequency, so that the sensitivity of the vibration isolation system to simple resonance exciting force can be accurately described, and the amplitude-frequency characteristic can be used as an index for evaluating the vibration isolation performance of the air vibration isolation system. Ratio of frequencies
The vibration isolation transfer rate T (lambda) can be obtained by substituting the frequency amplitude characteristic expression, so that the vibration isolation transfer rate T (lambda) can be used for evaluating the vibration isolation performance of the air vibration isolation system.
Referring to fig. 7, a method for determining an air vibration isolation system according to an embodiment of the present application is shown. As shown in fig. 7, the determination method may include steps S702 to S706.
And S702, carrying out dynamic mechanical characteristic analysis on the air vibration isolation system determined by the air vibration isolation system simulation model.
And S704, correcting the air vibration isolation system simulation model based on the dynamic mechanical analysis characteristic analysis result until the dynamic mechanical analysis result meets the preset dynamic mechanical property requirement.
And S706, determining parameters of the air vibration isolation system which meet the dynamic mechanical property requirement based on the corrected air vibration isolation system simulation model.
The air vibration isolation system simulation model is established according to the establishing method provided by the embodiment, so that the simulation accuracy of the air vibration isolation system simulation model adopted by the embodiment is high. The dynamic mechanical characteristics of the air vibration isolation system are used for describing the sensitivity of the air vibration isolation system to simple resonance exciting force, the dynamic mechanical property requirements can be set according to requirements, the dynamic mechanical property requirements are not limited by the embodiment of the application, and the ratio of the displacement of the exemplary vibration isolator to the displacement of the base is smaller than a preset threshold value.
It should be noted that the dynamic mechanical characteristics of the air vibration isolation system corresponding to the air vibration isolation system simulation model can be determined according to the air vibration isolation system simulation model, if the dynamic mechanical characteristics do not meet the performance requirement, the parameters of the air vibration isolation system simulation model are corrected until the dynamic mechanical performance requirement is met, at this time, the air vibration isolation system corresponding to the air vibration isolation system simulation model can also be considered to meet the dynamic mechanical performance requirement, and the air vibration isolation system is designed by using the parameters of the air vibration isolation system simulation model, so as to obtain the air vibration isolation system meeting the dynamic mechanical performance requirement. In the embodiment, the design and improvement of the structural parameters of the air vibration isolation system are guided by using the simulation result of the air vibration isolation system simulation model established in the embodiment, and the guiding accuracy is improved.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a device for establishing the air vibration isolation system simulation model, which is used for realizing the method for establishing the air vibration isolation system simulation model. The implementation scheme for solving the problems provided by the device is similar to the implementation scheme recorded in the method, so that specific limitations in the following embodiment of the device for establishing one or more air vibration isolation system simulation models can be referred to the limitations on the method for establishing the air vibration isolation system simulation models, and are not described herein again.
In one embodiment, as shown in fig. 8, there is provided an apparatus 800 for creating a simulation model of an air vibration isolation system, including: an acquisition module 802, a rating module 804, and a construction module 806. The obtaining module 802 is configured to obtain a parameter to be calibrated of the air vibration isolation system. The calibration module 804 is configured to calibrate the parameter to be calibrated based on a markov chain monte carlo method. The building module 806 is configured to build an air vibration isolation system simulation model based on the calibrated parameter to be calibrated.
In one embodiment, the rating module is further configured to determine a prior distribution of the parameter to be rated; defining the number of Markov chains as N and the length of each chain as H; randomly producing a starting point for each Markov chain from a prior distribution of parameters
Calculating a combined likelihood model of each Markov chain; wherein,
determining a parameter sample for the ith to be calibrated in the jth chain; and carrying out evolution operation on the to-be-calibrated parameter samples on each Markov chain based on each Markov chain joint likelihood model until the Markov chain reaches the convergence standard, and acquiring calibrated to-be-calibrated parameters based on the converged Markov chain.
In one embodiment, the calibration module is further configured to determine, according to the frequency of each group of parameters to be calibrated of the converged markov chain, a parameter to be calibrated whose frequency is greater than a preset frequency as a target parameter to be calibrated; and taking a parameter range to be calibrated formed by the target parameter to be calibrated as the parameter to be calibrated after calibration.
All or part of the modules in the device for establishing the air vibration isolation system simulation model can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Based on the same inventive concept, the embodiment of the present application further provides a determination device of an air vibration isolation system for implementing the determination method of the air vibration isolation system. The implementation scheme of the solution provided by the apparatus is similar to the implementation scheme described in the above method, so specific limitations in the following embodiment of the apparatus for determining one or more air vibration isolation systems may refer to the above limitations on the method for determining an air vibration isolation system, and details are not repeated herein.
In one embodiment, as shown in fig. 9, there is provided a determination apparatus 900 of an air vibration isolation system, including: an analysis module 902, a modification module 904, and a determination module 906. The analysis module 902 is configured to perform dynamic mechanical characteristic analysis on the air vibration isolation system determined by the air vibration isolation system simulation model. The correcting module 904 is configured to correct the air vibration isolation system simulation model based on the dynamic mechanical characteristic analysis result until the dynamic mechanical characteristic analysis result meets a preset dynamic mechanical property requirement. The determining module 906 is configured to determine, based on the corrected air vibration isolation system simulation model, a parameter of the air vibration isolation system that meets the requirement of the dynamic mechanical property.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, and a communication interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of establishing a simulation model of an air vibration isolation system and/or a method of determining an air vibration isolation system.
Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
In an embodiment, a computer program product is provided, comprising a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A method for establishing a simulation model of an air vibration isolation system is characterized by comprising the following steps:
acquiring parameters to be calibrated of the air vibration isolation system;
calibrating the parameter to be calibrated based on a Markov chain Monte Carlo method;
and constructing an air vibration isolation system simulation model based on the calibrated parameters to be calibrated.
2. The method for establishing the simulation model of the air vibration isolation system according to claim 1, wherein the parameters to be calibrated comprise an initial air pressure value of an air chamber of the air vibration isolation system and an excitation frequency of the air vibration isolation system.
3. The method for establishing the simulation model of the air vibration isolation system according to claim 2, wherein the calibrating the parameter to be calibrated based on the markov chain monte carlo method comprises:
determining prior distribution of the parameter to be calibrated;
defining the number of Markov chains as N and the length of each chain as H;
randomly producing a starting point for each Markov chain from a prior distribution of parameters
Calculating a combined likelihood model of each Markov chain; wherein,
determining a parameter sample for the ith to be calibrated in the jth chain;
and carrying out evolution operation on the to-be-calibrated parameter samples on each Markov chain based on each Markov chain joint likelihood model until the Markov chain reaches the convergence standard, and acquiring calibrated to-be-calibrated parameters based on the converged Markov chain.
4. The method for establishing the simulation model of the air vibration isolation system according to claim 3, wherein the obtaining the calibrated parameters to be calibrated according to the converged Markov chain comprises:
determining the parameter to be calibrated with the frequency greater than the preset frequency as a target parameter to be calibrated according to the frequency of each group of parameter to be calibrated of the converged Markov chain;
and taking a parameter range to be calibrated formed by the target parameter to be calibrated as the parameter to be calibrated after calibration.
5. The method for establishing the simulation model of the air vibration isolation system according to claim 3, wherein the prior distribution of the initial air pressure values is 0.2MPa to 0.8MPa, and the prior distribution of the excitation frequencies is
Hertz to
Hertz.
6. A method of determining an air vibration isolation system, the method comprising:
carrying out dynamic mechanical characteristic analysis on the air vibration isolation system determined by the air vibration isolation system simulation model; the air vibration isolation system simulation model is established based on the method of any one of claims 1-6;
correcting the simulation model of the air vibration isolation system based on the dynamic mechanical characteristic analysis result until the dynamic mechanical characteristic analysis result meets the preset dynamic mechanical property requirement;
and determining the parameters of the air vibration isolation system which meet the dynamic mechanical property requirement based on the corrected air vibration isolation system simulation model.
7. An apparatus for creating a simulation model of an air vibration isolation system, the apparatus comprising:
the acquisition module is used for acquiring parameters to be calibrated of the air vibration isolation system;
the calibration module is used for calibrating the parameter to be calibrated based on a Markov chain Monte Carlo method;
and the construction module is used for constructing an air vibration isolation system simulation model based on the parameters to be calibrated after calibration.
8. A device for determining an air vibration isolation system, said device comprising:
the analysis module is used for carrying out dynamic mechanical characteristic analysis on the air vibration isolation system determined by the air vibration isolation system simulation model; the air vibration isolation system simulation model is established based on the method of any one of claims 1-6;
the correction module is used for correcting the air vibration isolation system simulation model based on the dynamic mechanical characteristic analysis result until the dynamic mechanical characteristic analysis result meets the preset dynamic mechanical property requirement;
and the determining module is used for determining the parameters of the air vibration isolation system which meet the dynamic mechanical property requirement based on the corrected air vibration isolation system simulation model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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