CN117034659B - Optimized design method for damping hole of impact waveform generating device - Google Patents
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Abstract
The invention discloses an optimal design method for damping holes of an impact waveform generating device, which is used for establishing a simulation model of a system according to flow characteristics of an orifice of the device and stress states of a piston; and then, establishing an uncertain quantity of damping hole optimization models on the basis of the simulation models, selecting the quantity of each row of damping holes as a design variable, taking the maximum error of the simulation waveform and the standard waveform as an objective function, utilizing the constraint condition of the initial combination setting algorithm of the quantity of the damping holes close to the global optimal point, solving the optimization models by using a subset simulation algorithm, and selecting the combination with the minimum objective function value as the optimal scheme when the optimization algorithm meets the termination convergence criterion. The invention fully utilizes the advantages of the subset simulation algorithm in solving the global optimization problem, and avoids the defects of convergence to a local optimal point, low calculation efficiency and the like in optimizing and solving; the optimized waveform curve meets the standard requirement of the impact test, and has important significance for guiding the design of damping holes of the impact waveform generating device.
Description
Technical Field
The invention relates to the field of optimal design of impact waveform generating devices, in particular to an optimal design method for damping holes of an impact waveform generating device.
Background
The impact waveform generator is an impact pulse waveform device for generating various shapes, and is one of important components of an impact and collision test stand. For some high magnitude, large pulse width, long stroke shock waveforms, a dedicated shock waveform generator is required. At present, an oil damping type porous impact waveform generator is mainly used in China. However, the equipment has theoretical errors on a mechanical model, and the generated impulse waveform is difficult to meet the requirements of standard regulations. How to generate impact pulses meeting the requirements of standard regulation and improve the confidence level of test results becomes a very concerned problem of researchers engaged in vibration impact environment tests.
In practice, means of analysis in combination with experimentation are generally employed to produce a prescribed pulse shape. Parameters such as maximum stroke and maximum speed of a tested object are calculated according to standard acceleration waveforms, initial pressure, piston area, oil hole form, size and the like of an inner cylinder of the oil damping waveform generator are estimated according to the parameters, and finally the opening and closing quantity of each row of damping holes is adjusted through drop tests, so that the generated pulse waveforms meet the tolerance specified by impact test standards. However, this method has various limitations, and cannot fully consider the influence of various design parameters on the waveform shape, so that the generated waveform mostly exceeds the tolerance specified by the impact test standard on the falling edge, and the confidence of the impact test result is seriously affected. Therefore, how to generate a pulse waveform meeting the requirements of the standard regulation and to improve the confidence of the test result becomes a very interesting problem for researchers engaged in the vibration impact environment test.
Disclosure of Invention
In order to overcome the problems of large waveform distortion degree, serious negative peak value out-of-tolerance and the like of an impact waveform generating device in the prior art, the invention provides an optimal design method for a damping hole of the waveform generating device, which adopts the following technical scheme:
and step 1, establishing an impact process system simulation model according to model parameters of the impact waveform generation device. Taking a test table as a research object, and according to Newton's second law and the orifice damping throttling principle, a dynamic mathematical model of an impact process system is described as follows:
（1）
（2）
equation (1) is the differential equation of motion of the object, where M is the total mass of the object, x is the displacement of motion of the object, t is the time of motion of the object,the damping force is small hole throttling damping force, g is gravity acceleration; the formula (2) is the damping force to which the tested object is subjected at the position corresponding to the impact loading displacement, wherein +.>Is oil density->The cross-sectional area of the internal cylinder of the device, u is the movement speed of the object,is the small hole overcurrent coefficient +.>Is a small hole area. />Is the effective throttle orifice number; and carrying out numerical solution on the mathematical model by adopting a fourth-order Dragon-Gregory tower method and a cubic interpolation method.
And step 2, obtaining priori information of subset simulation, namely initial combination of the number of damping holes close to the global optimal point, according to the waveform to be loaded. The standard pulse impact test defines three waveforms, namely half sine wave, triangular wave and trapezoidal wave, and the test part needs to select different types of waveforms according to different purposes and use environments.
Step 3, setting the variation range of the number of the damping holes in each row by using the number of the damping holes in each row as a design variable and utilizing the initial combination given constraint condition of the number of the damping holes to simulate the absolute error of the waveform and the standard waveformMinimum, for optimizing objective function, establishing uncertain quantity damping hole optimizing model:
（3）
wherein,for the number of damping holes of each row->And->Respectively the minimum value and the maximum value of the initial value of the damping hole, < >>Is absolute error->For solving the obtained simulated acceleration values +.>And s is the number of rows of damping holes, which is the standard acceleration value corresponding to the time node.
And 4, selecting the distribution parameters of the input variables. In the original optimization problem, each design variable is a deterministic parameter, and in the subset simulation optimization algorithm, a Probability Density Function (PDF) is artificially selected for each design variable. The problem contains s design variablesThe probability density function of the corresponding design variable isThen the joint probability density function of all design variables is +.>。
Step 5, generating N independent samples with the same distribution by using a direct Monte Carlo method. Each sample isThere are s elements, i.e.)>Wherein->From->J=1, …, s. Then the objective function value W of each random sample is calculated and arranged in ascending order, i.e. there is +.>。The subscript "1" in (a) indicates that the objective function value is obtained when the first-layer simulation is performed. Given the conditional probability of the first simulation is +.>The total number of samples is N, choose +.>Sample->Corresponding->Define a first set of intermediate events +.>. Determine->After that, there is->The objective function value of each sample is less than +.>The set may provide a "seed" sample for the next layer of simulation.
And 6, generating a conditional sample of the intermediate event by using an MMH algorithm. In the k-th layer iteration, the simulation is performed from the upper layerEach sample in an event starts to generate a Markov chain of the same distribution. Since the initial "seed" samples follow the desired distribution, all Markov chains are smooth and the samples on the chains follow the desired distribution. Each Markov chain has a length ofWherein->The conditional probability for the simulation of the previous layer is equal to the probability of the simulation of the first layer +.>The same applies. And merging the generated new sample with the seed sample provided by the simulation of the previous layer, and sequencing in ascending order according to the objective function value corresponding to the sample. From the ascending sequence->Determine->Samples and corresponding objective function values->The objective function value is then less than->Is selected as the "seed" sample for the next layer of simulation.
And 7, judging whether the current layer simulation state meets the termination criterion, and if not, jumping to the step 6.
And 8, selecting a scheme with the smallest objective function value corresponding to the current simulation layer as an optimal combination scheme of the number of damping holes.
Preferably, in step 3, the artificially selected random distribution is a truncated normal distribution, and the truncated normal distribution can directly consider the simple geometric boundary constraint of the design variable, and the probability density function of the distribution is given by the following formula:
（4）
wherein:probability density function of standard normal distribution; />Cumulative distribution function being standard normal distribution and defining domain as +.>. Mean->As close as possible to the global optimum point, i.e. the center of the defined domain is chosen as the mean or based on available a priori information. Standard deviation->1/6 of the domain interval length is defined for the design variables.
Preferably, the conditional probability of the first simulation in step 5And conditional probability of the k-th layer iteration in step 6 +.>The values are the same, and the optimal interval is +.>。
Preferably, the algorithm termination convergence criteria in step 7 are two. The specific expression is given by the following formula:
（5）
in the middle ofIs the k layer before simulation>Estimate of the standard deviation of the samples obtained, +.>Is the threshold for algorithm convergence.
The optimized design method for the damping hole of the waveform generator has the following advantages:
1. according to the method, the subset simulation algorithm is utilized to solve the established optimization model, so that an optimal combination scheme of the number of damping holes is obtained, the generated waveform is more approximate to an ideal curve, and the confidence coefficient of an impact test result is improved;
2. compared with other random optimization algorithms, the subset simulation algorithm can avoid the defects of convergence to a local optimal point, low calculation efficiency and the like when solving, and has higher convergence speed and better accuracy.
Drawings
FIG. 1 is a flow chart of one embodiment of the present invention;
FIG. 2 is a diagram of a waveform generator according to one embodiment of the present invention;
FIG. 3 is a half-sine waveform tolerance discrimination diagram of one embodiment of the present invention;
FIG. 4 is a diagram of an iterative convergence process of an optimization algorithm according to one embodiment of the present invention.
Reference numerals: 1-inner cylinder, 2-outer cylinder, 3-piston rod and 4-damping hole.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Subset simulation is an efficient Monte Carlo simulation method, which was originally used to solve the high-dimensional small failure probability structure reliability problem, and in the later study, in order to solve the optimization problem under the framework of the reliability problem, an extremum event (optimization problem) is regarded as a special case of a rare event (reliability problem). The basic concept and method principles of subset simulation are presented below.
Consider the following unconstrained global optimization problem:
（6）。
artificially letting design variablesThe random variable is assigned a probability distribution type, and the resulting objective function is therefore also a random variable and has its own probability density function and cumulative distribution function. Let->Is the global minimum of W, in this case +.>. The nature of the cumulative distribution function determines that the cumulative distribution function curve must be right continuous and monotonically non-decreasing. "probability of failure" found in reliability problem>The definition is as follows:
（7），
failure event in middle
（8）。
To investigate the point or region where the objective function reaches a minimum, equation (7) is expressed as the product of a series of conditional probabilities. Conditional probabilityFor defining intermediate failure event, demarcation value corresponding to intermediate failure eventSpecifically given by the following formula:
（9），
wherein:for an intermediate event determined by the objective function value in the simulation. According to the multiplication theorem there are:
（10），
during the optimization process, searchThe procedure of (1) is equivalent to->A series of intermediate objective function values are generated +.>Gradually trend towards the global optimization point->. While the sample distribution gradually tapers from a broad area defined by the initial probability density function to contain the global optimization scheme +.>In a narrow areaDomain.
The invention adopts a subset simulation optimization method to optimize the structural parameters of the impact waveform generator, and obtains the optimal combination scheme of the number of the damping holes by solving the established damping hole optimization model with uncertain number.
As shown in fig. 1, the optimized design method for the damping hole of the impact waveform generating device provided in the embodiment is applied to an impact waveform generator with 30×45 adjustable holes designed based on the orifice throttling damping principle.
As shown in fig. 2, the device mainly comprises an inner cylinder 1, an outer cylinder 2, a piston rod 3, a damping hole 4 and the like. During impact test, oil with a certain height is injected into the waveform generator, the number and distribution of the holes of the damping holes on the inner cylinder are adjusted according to the waveform required to be loaded in the test, the piston is pressed into the inner cylinder at a certain speed, and the oil flows through the holes to generate damping force to apply reverse acting force to the piston, so that the purpose of loading an impact test object is achieved. The device-related parameters are compiled in table 1:
TABLE 1 model numerical parameter table
。
And step 1, establishing a dynamic mathematical model of the system according to model parameters of the impact waveform generating device. Taking a test table as a research object, and establishing a dynamic mathematical model of the system in the impact process according to Newton's second law and the orifice damping throttling principle:
（16），
（17），
equation (16) is the differential equation of motion of the object, where M is the total mass of the object, x is the displacement of motion of the object, t is the time of motion of the object,the damping force is small hole throttling damping force, g is gravity acceleration; equation (17) is a damping force equation generated by oil flowing through the small hole corresponding to the impact loading displacement, wherein +.>Is oil density->For the cross-sectional area of the internal cylinder of the device u is the speed of movement of the object,/->Is the small hole overcurrent coefficient +.>Is a small hole area. n is the number of effective throttle apertures. And carrying out numerical solution on the mathematical model by adopting a fourth-order Dragon-Gerdostane method and a Lagrange interpolation function.
And step 2, obtaining priori information of subset simulation, namely initial combination of the number of damping holes close to the global optimal point, according to the waveform to be loaded. Taking half sine wave a=30×g and dt=0.08 ms as an example, the acceleration, speed and displacement of the object to be tested should conform to the following formula:
（18），
（19），
（20），
wherein A is peak acceleration; d is the pulse duration.
Further, a functional relationship between the number of small holes and the corresponding impact loading displacement is obtained by an established mathematical model.
Further, the time t is defined by the formulas (18) - (20)And->The strict corresponding relation between the total number of small holes n and the piston moving downwards to +.>The corresponding relation of the positions obtains the initial combination of the numbers of the damping holes near the global optimal point, and aa= [30,30,27,18,13,10,9,8,7,7,6,6,6,7,8,10,16,0,0, … …,0 is expressed by a vector aa]Wherein aa [ i ]]Representing the number of active apertures in the ith row of waveform generators.
Step 3, setting the variation range of the number of the damping holes in each row by using the number of the damping holes in each row as a design variable and utilizing the initial combination given constraint condition of the number of the damping holes to simulate the absolute error of the waveform and the standard waveformMinimum, for optimizing objective function, establishing uncertain quantity damping hole optimizing model:
（22），
wherein,for the number of damping holes of each row->And->Respectively the minimum value and the maximum value of the initial value of the damping hole, < >>In order to solve the mathematical model in the step 1 by using a fourth-order Dragon-Gregory tower method and cubic interpolation to obtain a simulation acceleration value,and s is the number of rows of damping holes, which is the standard acceleration value corresponding to the time node.
And 4, selecting the distribution parameters of the input variables. In the original optimization problem, each design variable is a deterministic parameter, and in the subset simulation optimization algorithm, a Probability Density Function (PDF) is artificially selected for each design variable. The problem contains s design variablesThe probability density function of the corresponding design variable isThen the joint probability density function of all design variables is +.>。
Step 5, generating N independent samples with the same distribution by using a direct Monte Carlo method. Each sample isThere are s elements, i.e.)>Wherein->From the slaveObtained by the method. Then the objective function value W of each random sample is calculated and arranged in ascending order, namely。/>The subscript "1" in (a) indicates that the objective function value is obtained when the first-layer simulation is performed. Given the conditional probability of the first simulation is +.>The total number of samples is N, choose +.>Sample->Corresponding->Define a first set of intermediate events +.>. Determine->After that, there is->The objective function value of each sample is smaller thanThe set may provide a "seed" sample for the next layer of simulation.
And 6, generating a condition sample in the intermediate event by using an MMH algorithm. In the k-th layer iteration, the simulation is performed from the upper layerEach sample in an event starts to generate a Markov chain of the same distribution. Since the initial "seed" samples follow the desired distribution, all Markov chains are smooth and the samples on the chains follow the desired distribution. Each Markov chain has a length ofWherein->The conditional probability for the simulation of the previous layer is equal to the probability of the simulation of the first layer +.>The same applies. And merging the generated new sample with the seed sample provided by the simulation of the previous layer, and sequencing in ascending order according to the objective function value corresponding to the sample. From the ascending sequence->Determine->Samples and corresponding objective function valuesThe objective function value is then less than->Is selected as the "seed" sample for the next layer of simulation.
And 7, judging whether the current layer simulation state meets the termination criterion, and if not, jumping to the step 6.
Step 8, selecting the scheme with the smallest objective function value corresponding to the current simulation layer as the optimal combination scheme of the number of damping holes, wherein the obtained optimization scheme is given in the following table 2:
table 2 optimization calculation results based on subset simulation algorithm
。
Fig. 3 compares the difference between the simulated acceleration curve of the test object and the standard curve. The subset simulation algorithm can be seen to have good optimizing accuracy, the maximum error of the simulation waveform is controlled within 10% of the acceleration peak value, the requirement of the impact test standard is met, and the confidence coefficient of the impact test result is greatly improved. The simulation result of the waveform obtained by simulation is better in the first 1/4 section, and the larger error exists between the waveform of the last 3/4 section and the standard waveform, because the maximum number of holes and the hole row spacing of each row are limited by the structural strength and the processing cost, and the device of the last 3/4 stroke cannot generate enough damping force on the piston.
As can be seen from the comparison of the optimized iteration curves of fig. 4, the subset simulation optimization algorithm converges in the optimization process at the 3 rd iteration. Compared with a genetic algorithm, the optimal scheme of the opening and closing quantity of the damping holes can be obtained through fewer circulation times by subset simulation, and the method has higher calculation efficiency. And the maximum error of the waveform curve obtained based on each iteration result is smaller than that of a genetic algorithm, and the optimizing convergence is better.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (8)
1. The optimal design method for the damping hole of the impact waveform generating device is characterized by comprising the following steps of:
step 1, establishing a simulation model of an impact process system, which specifically comprises the following steps: taking a tested object as a research object, and according to Newton's second law and the orifice damping throttling principle, the kinetic equation of an impact process system is as follows:
equation (1) is the differential equation of motion of the object, where M is the total mass of the object, x is the displacement of motion of the object, t is the time of motion of the object, F c The damping force is small hole throttling damping force, g is gravity acceleration; the damping force applied to the tested object at the position corresponding to the impact loading displacement is represented by (2), wherein rho is the oil density, A s The cross section area of the inner cylinder of the device is u is the movement speed of the object, C d For the small hole flow coefficient, A d Is the area of the small hole; n is the effective throttle aperture number; carrying out numerical solution on the mathematical model by adopting a fourth-order Dragon-Gerdostat method and a Lagrange interpolation function;
step 2, obtaining prior information of subset simulation, namely initial combination of the number of damping holes close to the global optimal point according to standard pulse waveforms used during testing;
step 3, selecting the number of each row of damping holes as a design variable, initially combining given constraint conditions based on the number of the damping holes, setting the variation range of the number of each row of damping holes, taking the minimum absolute error of a simulation waveform and a standard waveform as an optimization objective function, and establishing an uncertain number of damping hole optimization models;
step 4, selecting distribution parameters of input variables: selecting a probability density function PDF for each design variable; the optimization problem contains s design variablesThe probability density function of the corresponding design variable is f 1 (x 1 )The joint probability density function of all design variables is +.>
Step 5, generating seed samples by using a direct Monte Carlo method, calculating objective function values of all samples, dividing all samples by giving self-adaptive determination events, and providing seed samples for the next-layer simulation;
step 6, using MMH algorithm to generate condition sample of intermediate event from seed sample obtained in step 5, combining with seed sample obtained in step 5, and providing seed sample for next layer simulation;
and 7, judging whether the simulation state of the current layer meets a termination criterion, if not, jumping to the step 6, otherwise, selecting a scheme with the minimum objective function value corresponding to the current simulation layer as an optimal combination scheme of the number of damping holes.
2. The method for optimizing design of damping hole for impact waveform generator as claimed in claim 1, wherein in step 3, the damping hole optimizing model is specifically:
wherein x is i For the number of damping holes of each row, x min And x max Respectively the minimum value and the maximum value of the initial value of the damping hole, G error Is absolute error, G sm To solve the obtained simulation acceleration value, G st And s is the number of rows of damping holes, which is the standard acceleration value corresponding to the time node.
3. The method for optimally designing a damping hole for an impact waveform generating device according to claim 1, wherein in step 4, the selected random distribution is a truncated normal distribution, the truncated normal distribution can directly consider a simple geometric boundary constraint of a design variable, and a probability density function of the distribution is given by the following formula:
wherein: phi (·) is the probability density function of a standard normal distribution; Φ (·) is a cumulative distribution function of a standard normal distribution, and the domain is Ω= { x; x is x l ≤x≤x u μ is the mean and σ is the standard deviation.
4. The method for optimizing design of damping hole for impact waveform generator according to claim 3, wherein the center of the definition domain is used as the mean μ or μ is selected according to available prior information.
5. The method for optimizing design of damping hole for impact waveform generating apparatus according to any one of claims 1 to 4, wherein step 5 specifically comprises: generating N independent co-distributed samples by using direct Monte Carlo methodEvery sample->There are s elements, i.e.)>i=1, …, N, where ∈>From f j (x j ) J=1, …, s; then the samples are ordered according to the objective function value of each random sample, namely { W } 1,k :k＝1,…,N}，W 1,k The subscript "1" in (a) indicates that the objective function value is obtained at the time of the first-layer simulation; given the conditional probability of the first simulation as P 1 The total number of samples is N, and the NP is selected 1 Sample->Corresponding->Define a first set of intermediate events +.>Determine->Thereafter, there is NP 1 The objective function value of each sample is less than +.>The set provides seed samples for the next layer of simulation.
6. The method for optimally designing a damping hole for an impact waveform generating device according to claim 5, wherein step 6 is specifically: in the k-th layer iteration, F is simulated from the previous layer k-1 Each sample in an event begins to generate equally distributed Markov chains, each Markov chainLength of 1/P k-1 Wherein P is k-1 For the conditional probability of the upper layer simulation, k=2, 3,4 …; combining the new samples on the chain with seed samples provided by the last layer of simulation, and sequencing according to the objective function values corresponding to the samples again to obtain a sequence { W } j,k Determination of the NP in k=1,.. j Samples and corresponding objective function valuesThe objective function value is less than->Is selected as the seed sample for the next layer simulation.
7. The method for optimizing design of damping hole for impact waveform generator as claimed in claim 6, wherein the conditional probability P of the first simulation in step 5 is 1 Conditional probability P of iteration with k layers in step 6 k-1 The values are the same, and the selected interval is [ 0.1-0.3 ]]。
8. The method for optimizing design of damping hole for impact waveform generating apparatus as claimed in claim 1, wherein the algorithm termination convergence criterion in step 7 is two, and the specific expression is given by the following formula:
in the middle ofIs the k-th layer pre-analog NP k The estimated value of the standard deviation of each obtained sample, epsilon, is the threshold for algorithm convergence.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2212513C1 (en) * | 2002-04-09 | 2003-09-20 | Открытое акционерное общество "Акционерная нефтяная компания "Башнефть" | Hydrodynamic pulsed-pressure generator |
| CN108509754A (en) * | 2018-05-15 | 2018-09-07 | 北京航空航天大学 | A kind of high-density packages integrated circuit linkage silk touching methods of risk assessment |
| CN113033093A (en) * | 2021-03-24 | 2021-06-25 | 南京航空航天大学 | Simulation model-based system design parameter multi-objective optimization method |
| CN114048547A (en) * | 2021-11-18 | 2022-02-15 | 汉思科特(盐城)减震技术有限公司 | Vehicle air spring engineering optimization design method based on self-adaptive proxy model |
| CN114928099A (en) * | 2022-04-29 | 2022-08-19 | 东北大学 | Virtual synchronous generator self-adaptive control device and method facing network attack |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040211631A1 (en) * | 2003-04-24 | 2004-10-28 | Hsu William W. | Hydraulic damper |
-
2023
- 2023-10-07 CN CN202311277579.1A patent/CN117034659B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2212513C1 (en) * | 2002-04-09 | 2003-09-20 | Открытое акционерное общество "Акционерная нефтяная компания "Башнефть" | Hydrodynamic pulsed-pressure generator |
| CN108509754A (en) * | 2018-05-15 | 2018-09-07 | 北京航空航天大学 | A kind of high-density packages integrated circuit linkage silk touching methods of risk assessment |
| CN113033093A (en) * | 2021-03-24 | 2021-06-25 | 南京航空航天大学 | Simulation model-based system design parameter multi-objective optimization method |
| CN114048547A (en) * | 2021-11-18 | 2022-02-15 | 汉思科特(盐城)减震技术有限公司 | Vehicle air spring engineering optimization design method based on self-adaptive proxy model |
| CN114928099A (en) * | 2022-04-29 | 2022-08-19 | 东北大学 | Virtual synchronous generator self-adaptive control device and method facing network attack |
Non-Patent Citations (3)
| Title |
|---|
| Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process;Yanjun Liu;《Journal of Marine Science and Engineering》;第9卷(第2期);1-9 * |
| 立柱液控单向阀的仿真分析与流场可视化研究;贺志凯;《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》(第9期);B021-29 * |
| 航行于碎冰区船舶冰载荷研究;李凡;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》(第2期);C036-27 * |
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