WO2019085145A1 - 超深井提升容器多失效模式的可靠性稳健设计方法 - Google Patents

超深井提升容器多失效模式的可靠性稳健设计方法 Download PDF

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WO2019085145A1
WO2019085145A1 PCT/CN2017/114932 CN2017114932W WO2019085145A1 WO 2019085145 A1 WO2019085145 A1 WO 2019085145A1 CN 2017114932 W CN2017114932 W CN 2017114932W WO 2019085145 A1 WO2019085145 A1 WO 2019085145A1
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failure
reliability
lifting container
model
container
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French (fr)
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卢昊
朱真才
周公博
彭玉兴
曹国华
李伟
沈刚
王大刚
江帆
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China University of Mining and Technology CUMT
China University of Mining and Technology Beijing CUMTB
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China University of Mining and Technology CUMT
China University of Mining and Technology Beijing CUMTB
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Priority to US16/333,218 priority patent/US10824781B2/en
Priority to CA3037323A priority patent/CA3037323C/en
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
    • E21F13/00—Transport specially adapted to underground conditions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]

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  • the invention relates to a system reliability robust design method for mechanical products in the case of ultra-deep well lifting containers and considering failure mode probability correlation, and belongs to the field of mechanical structure reliability technology research.
  • the lifting container is responsible for loading and unloading the mined coal resources, and undergoes a large vertical load during the lifting and lowering process, and due to the complicated underground lifting environment, the lifting container has various failure modes under the dynamic load. Considering the uncertainty of the structure of the lifting container itself and the uncertainty of various dynamic loads during the lifting process, the lifting container becomes a structural system with uncertain parameters during the operation, and also causes the system to fail.
  • the object of the present invention is to provide a feasible probabilistic modeling and analysis method for system reliability evaluation and structural optimization design in a multi-failure mode combined failure state of an ultra-deep well lifting vessel.
  • a reliable and robust design method for multi-failure mode of ultra-deep well lifting container Firstly, a parametric model of the lifting container is established according to the structural size of the ultra-deep well lifting container. Secondly, a random variable is established according to the probability characteristics of the lifting container random variable. Sampling matrix, and using finite element method to analyze the strength response and stiffness response of the lifting container under the sampling matrix; again, using Kriging method to establish the mapping relationship between random response and random sampling matrix, respectively, establish explicit in the failure mode of strength and stiffness Functional function; Then, the saddle point approximation method is used to calculate the failure probability in each failure mode. Finally, the joint probability model between failure modes is constructed by Clayton copula function, and the system reliability method is used to solve the system reliability under joint failure.
  • Step 1 Determine the average size, material property parameters and working load of the ultra-deep well lifting vessel And the variance, determine the distribution type of each parameter, and establish a finite element model of the lifting container;
  • Step 2 According to the mean and variance of each basic parameter of the lifting container determined in step 1, combined with the Latin hypercube sampling test design method, obtain random response samples of structural failure under each group of driving parameters;
  • Step 3 Using the Kriging method to fit the input and output samples in step 2, obtain the mapping relationship between the failure response of the lifting container and the structural performance parameters, and establish the reliability function function in each failure mode according to the design criteria of the lifting container failure. ;
  • Step 4 Solving the failure probability of each failure mode by using a moment-based saddle point approximation method according to the probability information of the basic parameters;
  • Step 5 The Clayton copula function is used to establish the joint failure distribution of each failure mode probability correlation, and then the system reliability model is established based on the system reliability theory to solve the system failure probability.
  • Step 6 Using a partial derivative method to establish a sensitivity model for improving the reliability of the container system with respect to random parameters;
  • Step 7 Based on the reliability optimization model, the parameter sensitivity and system failure probability of the lifting container obtained in steps 5 and 6 are used as constraint functions to establish a robust design model for the lifting container.
  • Step 1 is specifically as follows:
  • Step 2 is specifically as follows:
  • the structural parameters of the lifting container include the overall size of the lifting container and the size of the chassis; the material performance parameters include elastic modulus, Poisson's ratio and density;
  • the Latin hypercube sampling test design method was applied to drive the parameters of the lifting vessel for random finite element analysis to obtain random response samples under random input.
  • Step 3 is specifically as follows:
  • the reliability function function under each failure mode is established, which is different from other parts of the lifting system.
  • the ultra-deep well lifting container is a large welded structural part, and the fracture mechanics analysis should be adopted when investigating the strength reliability. Its fracture resistance is used as a criterion for strength design.
  • Step 4 is specifically as follows:
  • the first three moments of each functional function namely mean, variance and skewness, are calculated by random perturbation technique.
  • the saddle point approximation method based on the first three moments is used to solve the failure probability of each failure mode.
  • Step 5 is specifically as follows:
  • the random sampling method is used to perform random sampling to obtain discrete sample values of each random variable
  • the failure probability of each failure mode and the joint failure probability are substituted to calculate the system failure probability of the lifting container.
  • Step 6 is specifically as follows:
  • the mean, standard deviation and skewness of the random variables are derived by matrix differential technique to establish the reliability of the container system.
  • the mean and standard deviation of the random variables and Parametric sensitivity model for skewness are derived by matrix differential technique to establish the reliability of the container system.
  • Step 7 is specifically as follows:
  • the system reliability and parameter reliability sensitivity model based on copula function obtained above are introduced into the optimization design model as constraints, and the reliability robust design model of the lifting container is established.
  • the parameter sensitivity characterizes the degree of influence of random variables on the reliability of the lifting container system.
  • FIG. 1 is a flow chart showing the implementation of a robust design method for a multi-failure mode of an ultra-deep well lifting vessel according to the present invention.
  • Figure 2 is a schematic view of the structure of the lifting container.
  • Figure 3 is a probability density plot of the Claytoncopula function.
  • Figure 4 is a scatter plot of the Claytoncopula function.
  • the reliability robust design method for the multi-failure mode of the ultra-deep well lifting container proposed by the present invention comprises the following steps:
  • Step 1 According to the original design drawing of the lifting container or through on-site mapping, obtain the mean and variance of the parameters such as the structural dimensions, material properties and dynamic loads of the lifting container;
  • Step 2 According to the structural parameters of the lifting container, establish a three-dimensional parametric model of the lifting container, and perform finite element static analysis on the established virtual prototype model;
  • Step 3 Through the test design method, according to the mean and variance of each basic parameter of the lifting container determined in step 1, combined with the Latin hypercube sampling method, a random sampling matrix of each basic parameter is established;
  • Step 4 Combining the experimental design matrix, through model reconstruction and finite element reanalysis, obtain a random response sample of the stress intensity factor and strain of the lifting container;
  • Step 5 Using the Kriging method to fit the experimental design matrix and the random response sample, thereby establishing a mapping relationship between the random response of the container and the random parameter;
  • Step 6 According to the damage tolerance criterion and the stiffness design criterion of the crack propagation of the lifting container, respectively establish the reliability function function under the fracture failure and the stiffness failure; calculate the first three moments of the random parameter according to the mean and variance of the random parameters, and then According to the established function function, the mean value and variance of each function function are obtained. Third-order moments, using the saddle point approximation method based on third-order moments to calculate the failure probability under two failure modes;
  • Step 7 Obtain the correlation coefficient between the two failure modes by statistical method, establish the joint probability model by Clayton copula, and then combine the system reliability method to solve the system failure probability when the failure correlation occurs;
  • Step 8 Establish a parameter sensitivity model of system reliability with respect to mean, standard deviation and skewness of random variables by matrix differentiation technique;
  • Step 9 Introduce the system reliability and parameter sensitivity model into the optimization design model, and establish a robust design model for the reliability of the container.
  • the present invention is directed to the ultra-deep well lifting container structure as proposed in Fig. 2, and performs system reliability analysis and structural design related to the failure.
  • the lifting container is subjected to vertical load and bending and torsion coupling.
  • the experimental design sample matrix of the container random variable is established, and the random response of the stress intensity factor and strain of the lifting container is calculated by finite element analysis to obtain the response sample matrix.
  • the Kriging method is used to establish the fitting function of the random response and the experimental design sample matrix, and then the explicit functional functions of the two failure modes are established according to the damage tolerance criterion and the stiffness criterion, namely the function function of the failure strength failure and the function function of the stiffness failure.
  • Table 1 gives the probability information of the lifting container random variable in this embodiment. Where L 1 is the length of the lifting container, L 2 is the height of the lifting container, L 3 is the width of the lifting container, L 4 is the length of the lower tray, and L 5 is the width of the lower tray.
  • the failure probability is solved by the method for solving the failure probability proposed by the present invention.
  • n random samples of the random variables of the container are generated, and the function functions of the two failure modes are substituted and n response values are calculated.
  • the correlation coefficient between the random response samples in the two failure modes is calculated by MATLAB, and the undetermined parameters of the Clayton copula function are estimated, so as to establish the joint probability model when the container failure correlation is improved.
  • the resulting failure probabilities Pf 1 and Pf 2 are substituted into the system reliability analysis model.
  • P f1 represents the maximum failure probability in the failure mode of the lifting container
  • P fi represents the failure probability of the i-th failure mode
  • P fij represents the i-th and j-th
  • the joint failure probability between failure modes, P fs represents the probability of system failure associated with lifting container failure.
  • the calculated system failure probability is about the parameter sensitivity of the random variable mean, standard deviation and skewness.
  • the optimized structural parameters can be obtained through the nonlinear optimization method:
  • L 1 3817.41 mm
  • L 2 3119.55 mm
  • L 3 2112.93 mm
  • L 4 3817.41 mm
  • L 5 1850.01 mm
  • the total volume before the optimization of the lifting container is 15.99m 3 .
  • the total volume of the container structure is 15.16m 3 under the condition of satisfying the reliability constraint in the optimization model.
  • the optimized total volume of the structure obtained using the conventional optimization design method was 15.37 m 3 .
  • the method proposes a robust design method for system reliability considering ultra-deep well lifting vessels considering failure correlation.
  • the parameterized three-dimensional model is established according to the structural size of the lifting container.
  • the experimental design sample matrix of the random variable is established, and the finite element method is used to solve the breaking strength of the lifting container under the sample matrix.
  • Stiffness response again, using Kriging method to establish an explicit function between the random response and the random variable matrix, according to the damage tolerance criterion and the stiffness design criterion, respectively establish the explicit functional function under two failure modes;
  • the saddle point approximation method of moments is used to calculate the failure probability of two failure modes, and the joint failure probability model between two failure modes is constructed by Clayton copula function.
  • the system reliability method is used to solve the system reliability under joint failure. Finally, the calculation is obtained.
  • the system reliability is related to the parameter sensitivity of random variables, and the optimal parameter combination is obtained through the reliability robust design model.

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Abstract

一种超深井提升容器多失效模式的可靠性稳健设计方法。该方法包括以下步骤：1)定义提升容器结构参数、材料属性以及动载荷的随机性，采用试验设计方法求解随机参数下结构失效的随机响应；2)依据提升容器的失效准则建立各失效模式下的可靠性功能函数；3)考虑失效模式间的概率相关性，采用copula理论建立相关失效的联合概率模型；4)结合联合概率模型建立提升机容器失效相关的系统可靠性模型；5)建立提升容器系统可靠性关于各随机参数的灵敏度模型；6)结合优化设计模型建立以联合失效概率与参数灵敏度为约束的提升容器可靠性稳健优化设计模型。

Description

超深井提升容器多失效模式的可靠性稳健设计方法 技术领域
本发明是一种针对超深井提升容器并考虑失效模式概率相关时的机械产品的系统可靠性稳健设计方法，属于机械结构可靠性技术研究领域。
背景技术
我国目前大多数煤井都是浅井，深至地面500～800m，而煤炭资源埋藏深度在1000～2000m的约占总储量的53％，必须采用千米深井提升系统(包括提升机、提升容器、提升钢丝绳等)。提升容器负责装卸载所开采的煤炭资源，在提升及下放过程中承受了较大的垂向载荷，且由于井下提升环境复杂，导致提升容器在动载荷作用下出现多种失效形式。考虑到提升容器结构本身的不确定性，以及提升过程中各种动载荷的不确定性，使得提升容器在运行过程中成为一种具有不确定参数的结构系统，同时也使得该系统在发生失效时，往往伴随着多种不同的失效形式，且各失效形式间存在一定的概率相关性。因此，仅从单一失效模式定义提升容器的可靠性将降低提升容器可靠性评估的精度，导致在设计和后期维护中存在较大的偏差。采用基于可靠性的稳健设计方法，能够降低结构不确定性对提升容器可靠性的影响程度，从而提高提升容器的安全性和可靠性。
发明内容
发明目的：本发明的目的是为超深井提升容器多失效模式联合失效状态下的系统可靠性评估与结构优化设计提供一种可行的概率建模与分析方法。
为了实现上述目的，本发明采用了如下的技术方案：
一种超深井提升容器多失效模式的可靠性稳健设计方法，首先，根据超深井提升容器的结构尺寸建立提升容器的参数化模型；其次，根据提升容器随机变量的概率特性，建立随机变量的随机抽样矩阵，并采用有限元法分析抽样矩阵下提升容器的强度响应与刚度响应；再次，使用Kriging方法建立随机响应与随机抽样矩阵之间的映射关系，分别建立强度和刚度失效模式下的显式功能函数；然后，使用鞍点逼近方法计算各失效模式下的失效概率；最后，通过Clayton copula函数构造失效模式间的联合概率模型，使用系统可靠性方法求解联合失效下的系统可靠性。
其实现步骤具体如下：
步骤1、确定超深井提升容器的尺寸参数、材料属性参数及工况载荷的均值 和方差，确定各参数的分布类型，建立提升容器的有限元模型；
步骤2、根据步骤1所确定的提升容器各基本参数的均值和方差，结合拉丁超立方抽样试验设计方法，获得各组驱动参数下结构失效的随机响应样本；
步骤3、使用Kriging方法对步骤2中的输入输出样本进行拟合，得到提升容器失效响应与结构性能参数的映射关系，依据提升容器失效的设计准则，分别建立各失效模式下的可靠性功能函数；
步骤4、根据基本参数的概率信息，使用基于矩的鞍点逼近方法分别求解各失效模式的失效概率；
步骤5、采用Clayton copula函数建立各失效模式概率相关时的联合失效分布，进而结合系统可靠性理论建立联合概率失效时的系统可靠性模型，求解系统失效概率；
步骤6、采用偏导方法建立提升容器系统可靠性关于随机参数的灵敏度模型；
步骤7、在可靠性优化模型的基础上，将步骤5及步骤6中得到的提升容器的参数灵敏度和系统失效概率作为约束函数，建立提升容器的可靠性稳健设计模型。
步骤1具体为：
确定提升容器结构尺寸和材料属性的分布类型及均值、方差；
确定提升容器的载荷工况，以确定各工况下提升容器所承担的静载荷、弯矩和扭矩等载荷的分布类型及均值、方差；
基于以上信息建立提升容器的有限元分析模型。
步骤2具体为：
通过提升容器的参数化建模，形成建模的过程文件；
通过提升容器的有限元分析，形成有限元分析的过程文件；
其中，提升容器的结构参数包括提升容器的总体尺寸及底盘的尺寸；材料性能参数包括弹性模量、泊松比和密度；
应用拉丁超立方抽样试验设计方法，驱动提升容器的参数进行随机有限元分析，获得随机输入下的随机响应样本。
步骤3具体为：
使用Kriging方法对步骤2获得的输入输出样本进行拟合，建立随机响应与 随机参数间的显式函数关系；
依据失效模式的设计准则，建立各失效模式下的可靠性功能函数，区别于提升系统的其它零部件，超深井提升容器为大型焊接结构件，在考察其强度可靠性时应当采用断裂力学分析，以其抗断裂性能作为强度设计的准则。
步骤4具体为：
采用随机摄动技术计算各功能函数的前三阶矩，即均值、方差和偏度，采用基于前三阶矩的鞍点逼近方法求解各失效模式的失效概率。
步骤5具体为：
依据提升容器随机变量的分布类型，采用均匀抽样方法进行随机抽样，获得各随机变量的离散样本值；
将上述离散样本值代入步骤3所建立的可靠性功能函数，获得相应的功能函数样本值；
使用概率统计方法计算得到两失效模式间的秩相关系数，代入Clayton copula函数模型，计算Claytoncopula的待定参数，建立描述概率相关的联合概率模型；
使用建立的Clayton copula联合概率模型，计算提升容器多失效模式相关时的联合失效概率；
使用系统可靠性理论，代入各失效模式的失效概率以及联合失效概率，计算提升容器的系统失效概率。
步骤6具体为：
在建立提升容器系统可靠性模型的基础上，基于偏导方法，采用矩阵微分技术对随机变量的均值、标准差及偏度进行求导，建立提升容器系统可靠性关于随机变量均值、标准差及偏度的参数灵敏度模型。
步骤7具体为：
将上述所得到的基于copula函数的系统可靠性和参数可靠性灵敏度模型作为约束引入优化设计模型，建立提升容器的可靠性稳健设计模型。
本发明方法的优点和积极效果在于：
1)提升容器不同失效模式间表现为较强的正相关性，区别于失效独立假设，采用Claytoncopula能够更加准确地描述这种正相关性，提高提升容器系统可靠 性评估的建模效率和计算精度；
2)参数灵敏度表征了随机变量对提升容器系统可靠性的影响程度，通过建立随机变量均值、标准差及偏度的灵敏度模型，并将其作为约束条件引入优化设计模型，可以降低随机变量的不确定性对系统可靠性的影响，保证提升容器系统可靠性的稳健性。
附图说明
图1为本发明的超深井提升容器多失效模式的可靠性稳健设计方法的实现流程图。
图2为提升容器的结构示意图。
图3为Claytoncopula函数的概率密度图。
图4为Claytoncopula函数的散点图。
其中，1为上盘；2为高强度螺栓组；3为中盘；4为外立柱；5为罐壁；6为内立柱；7为下盘。
具体实施方式：
下面结合附图和实施例对本发明做进一步的说明。
如图1所示，本发明所提出的超深井提升容器多失效模式的可靠性稳健设计方法，包含如下步骤：
步骤1、根据提升容器的原始设计图纸或者通过现场测绘，获取提升容器结构尺寸、材料属性及动载荷等参数的均值和方差；
步骤2、根据提升容器的结构参数，建立提升容器的三维参数化模型，对所建立的虚拟样机模型进行有限元静力学分析；
步骤3、通过试验设计方法，根据步骤1所确定的提升容器各基本参数的均值和方差，结合拉丁超立方抽样方法，建立各基本参数的随机抽样矩阵；
步骤4、结合试验设计矩阵，通过模型重构和有限元重分析，获得提升容器的应力强度因子与应变的随机响应样本；
步骤5、使用Kriging方法拟合试验设计矩阵和随机响应样本，从而建立提升容器随机响应与随机参数的映射关系；
步骤6、根据提升容器裂纹扩展的损伤容限准则和刚度设计准则，分别建立断裂失效与刚度失效下的可靠性功能函数；根据随机参数的均值和方差，计算随机参数的前三阶矩，进而根据已建立的功能函数，求得各功能函数的均值、方差、 三阶矩，采用基于三阶矩的鞍点逼近方法分别计算两种失效模式下的失效概率；
步骤7、通过统计方法获得两种失效模式间的关联系数，通过Clayton copula建立其联合概率模型，进而结合系统可靠性方法求解失效相关时的系统失效概率；
步骤8、通过矩阵微分技术建立系统可靠性关于随机变量均值、标准差及偏度的参数灵敏度模型；
步骤9、将系统可靠性和参数灵敏度模型引入优化设计模型，建立提升容器的可靠性稳健设计模型。
实施例:
为了更充分地了解该发明的特点及工程适用性，本发明针对如图2所示拟建的超深井提升容器结构，进行其失效相关的系统可靠性分析与结构设计。
该提升容器承受垂向载荷及弯扭耦合作用。根据提升容器的结构尺寸和动载荷，建立提升容器随机变量的试验设计样本矩阵，并通过有限元分析计算提升容器应力强度因子和应变的随机响应，获得响应样本矩阵。通过Kriging方法建立随机响应与试验设计样本矩阵的拟合函数，进而依据损伤容限准则和刚度准则建立两种失效模式的显式功能函数，即断裂强度失效的功能函数及刚度失效的功能函数。表1给出了本实施例中提升容器随机变量的概率信息。其中，L1为提升容器的长度，L2为提升容器的高度，L3为提升容器的宽度，L4为下盘的长度，L5为下盘的宽度。
表1提升容器随机变量的概率统计特性
Figure PCTCN2017114932-appb-000001
该实施例中利用本发明所提出的失效概率的求解方法得到断裂强度失效模式下的失效概率为Pf1＝0.004698，刚度失效概率为Pf2＝0.007344。根据各随机变量的分布类型生成提升容器随机变量的n个随机样本，并分别代入两种失效模式的功能函数并计算得到n个响应值。采用MATLAB计算得到两种失效模式下的 随机响应样本之间的相关系数，并由此估计Clayton copula函数的待定参数，从而建立提升容器失效相关时的联合概率模型。将所得的失效概率Pf1和Pf2代入系统可靠性分析模型。
Figure PCTCN2017114932-appb-000002
式中，m＝2表示提升容器失效模式的个数，Pf1表示提升容器失效模式中的最大失效概率，Pfi表示第i个失效模式的失效概率，Pfij表示第i个和第j个失效模式间的联合失效概率，Pfs表示提升容器失效相关的系统失效概率。
基于Clayton copula的联合概率模型，计算得到提升容器断裂强度失效和刚度失效相关时提升容器的系统失效概率为Pfs＝0.01319。通过1000次仿真方法计算得到的提升容器系统失效概率为Pfsm＝0.01142。
计算得到的系统失效概率关于随机变量均值、标准差以及偏度的参数灵敏度为
Figure PCTCN2017114932-appb-000003
Figure PCTCN2017114932-appb-000004
Figure PCTCN2017114932-appb-000005
Figure PCTCN2017114932-appb-000006
Figure PCTCN2017114932-appb-000007
将提升容器的总体积作为优化目标，将结构系统的系统可靠度及参数灵敏度作为约束条件，通过非线性优化方法可以得到优化后的结构参数组合为：
L1＝3817.41mm，L2＝3119.55mm，L3＝2112.93mm，L4＝3817.41mm，L5＝1850.01mm
该提升容器优化前的总体积为15.99m3，使用可靠性稳健优化后，在满足优化模型中可靠度约束的条件下，提升容器结构的总体积为15.16m3。
使用传统优化设计方法所得到的优化后的结构总体积为15.37m3。
通过比较上述结果可知，本发明中所提出的可靠性稳健设计方法能够获得更佳的优化结果。
综上所述，本方法提出了一种针对超深井提升容器，考虑失效相关性的系统可靠性稳健设计方法。首先，根据提升容器的结构尺寸建立其参数化三维模型；其次，根据提升容器随机变量的概率属性，建立随机变量的试验设计样本矩阵，并使用有限元方法求解样本矩阵下提升容器的断裂强度和刚度响应；再次，使用Kriging方法建立随机响应与随机变量矩阵之间的显式函数，根据损伤容限准则和刚度设计准则，分别建立两种失效模式下的显式功能函数；然后，使用基于三阶矩的鞍点逼近方法计算两种失效模式的失效概率，并通过Clayton copula函数构建两种失效模式间的联合失效概率模型，使用系统可靠性方法求解联合失效下的系统可靠性；最后，计算得到了系统可靠性关于随机变量的参数灵敏度，并通过可靠性稳健设计模型得到了最优的参数组合。
本发明未详细阐述的部分属于本领域研究人员的公知技术。

Claims (8)

  1. 一种超深井提升容器多失效模式的可靠性稳健设计方法，其特征在于：该方法具体步骤如下：
    步骤1、确定超深井提升容器的尺寸参数、材料属性参数及工况载荷的均值和方差，确定各参数的分布类型，建立提升容器的有限元模型；
    步骤2、根据步骤1所确定的提升容器各基本参数的均值和方差，结合拉丁超立方抽样试验设计方法，获得各组驱动参数下结构失效的随机响应样本；
    步骤3、使用Kriging方法对步骤2中的输入输出样本进行拟合，得到提升容器失效响应与结构性能参数的映射关系，依据提升容器失效的设计准则，分别建立各失效模式下的可靠性功能函数；
    步骤4、根据基本参数的概率信息，使用基于矩的鞍点逼近方法分别求解各失效模式的失效概率；
    步骤5、采用Clayton copula函数建立各失效模式概率相关时的联合失效分布，进而结合系统可靠性理论建立联合概率失效时的系统可靠性模型，求解系统失效概率；
    步骤6、采用偏导方法建立提升容器系统可靠性关于随机参数的灵敏度模型；
    步骤7、在系统可靠性模型的基础上，将步骤5及步骤6中得到的提升容器的参数灵敏度和系统失效概率作为约束函数，建立提升容器的可靠性稳健设计模型。
  2. 根据权利要求1所述的一种超深井提升容器多失效模式的可靠性稳健设计方法，其特征在于，步骤1具体为：
    确定提升容器结构尺寸和材料属性的分布类型及均值、方差；
    确定提升容器的载荷工况，以确定各工况下提升容器所承担的静载荷、弯矩和扭矩等载荷的分布类型及均值、方差；
    基于以上信息建立提升容器的有限元分析模型。
  3. 根据权利要求1所述的一种超深井提升容器多失效模式的可靠性稳健设计方法，其特征在于，步骤2具体为：
    通过提升容器的参数化建模，形成建模的过程文件；
    通过提升容器的有限元分析，形成有限元分析的过程文件；
    其中，提升容器的结构参数包括提升容器的总体尺寸及底盘的尺寸；材料性 能参数包括弹性模量、泊松比和密度；
    应用拉丁超立方抽样试验设计方法，驱动提升容器的参数进行随机有限元分析，获得随机输入下的随机响应样本。
  4. 根据权利要求1所述的一种超深井提升容器多失效模式的可靠性稳健设计方法，其特征在于，步骤3具体为：
    使用Kriging方法对步骤2获得的输入输出样本进行拟合，建立随机响应与随机参数间的显式函数关系；
    依据失效模式的设计准则，建立各失效模式下的可靠性功能函数，区别于提升系统的其它零部件，超深井提升容器为大型焊接结构件，在考察其强度可靠性时应当采用断裂力学分析，以其抗断裂性能作为强度设计的准则。
  5. 根据权利要求1所述的一种超深井提升容器多失效模式的可靠性稳健设计方法，其特征在于，步骤4具体为：
    采用随机摄动技术计算各功能函数的前三阶矩，即均值、方差和偏度，采用基于前三阶矩的鞍点逼近方法求解各失效模式的失效概率。
  6. 根据权利要求1所述的一种超深井提升容器多失效模式的可靠性稳健设计方法，其特征在于，步骤5具体为：
    依据提升容器随机变量的分布类型，采用均匀抽样方法进行随机抽样，获得各随机变量的离散样本值；
    将上述离散样本值代入步骤3所建立的可靠性功能函数，获得相应的功能函数样本值；
    使用概率统计方法计算得到两失效模式间的秩相关系数，代入Clayton copula函数模型，计算Claytoncopula的待定参数，建立描述概率相关的联合概率模型；
    使用建立的Clayton copula联合概率模型，计算提升容器多失效模式相关时的联合失效概率；
    使用系统可靠性理论，代入各失效模式的失效概率以及联合失效概率，计算提升容器的系统失效概率。
  7. 根据权利要求1所述的一种超深井提升容器多失效模式的可靠性稳健设计方法，其特征在于，步骤6具体为：
    在建立提升容器系统可靠性模型的基础上，基于偏导方法，采用矩阵微分技术对随机变量的均值、标准差及偏度进行求导，建立提升容器系统可靠性关于随机变量均值、标准差及偏度的参数灵敏度模型。
  8. 根据权利要求1所述的一种超深井提升容器多失效模式的可靠性稳健设计方法，其特征在于，步骤7具体为：
    将上述所得到的基于copula函数的系统可靠性和参数可靠性灵敏度模型作为约束引入优化设计模型，建立提升容器的可靠性稳健设计模型。
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