CN109635308A - Pipeline Sensitivity Analysis Method, device, storage medium and electronic equipment - Google Patents
Pipeline Sensitivity Analysis Method, device, storage medium and electronic equipment Download PDFInfo
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Abstract
This disclosure relates to a kind of pipeline Sensitivity Analysis Method, pipeline sensitivity analysis device, computer readable storage medium and electronic equipment.This method comprises: establishing pipeline model according to parameter preset relevant to pipe-line system；One or more parameter in the parameter preset is chosen as target variable；The main index of sensitivity and sensitivity overall performane of the target variable are calculated using power point estimations；Wherein, the main index of sensitivity is used to reflect that the uncertain of target variable itself to export probabilistic influence on pipeline model response, and the sensitivity overall performane is used to reflect the influence of the pipeline model response of mutually opposing of target variable and other parameters.The disclosure, come approximate solution sensitivity index, improves the computational efficiency of sensitivity index, the sensitivity analysis for the system under double stochastic uncertainties provides theory support using the nested loop approach of power point estimations.
Description
Technical field
This disclosure relates to dynamic parameter analysis technical field, and in particular to a kind of pipeline Sensitivity Analysis Method, pipeline spirit
Basis of sensitivity analysis device, computer readable storage medium and electronic equipment.
Background technique
Hydraulic air pipe-line system is one of system mostly important in aviation aircraft, and predominantly aircraft engine is defeated
Fuel oil is sent to guarantee that aircraft has a lasting power output, or for the hydraulic systems such as pressurized strut convey hydraulic oil make Notified body or
System completes specified operation.However, due to the vibration that the vibration of engine generation, the flutter of wing or both coupling generate,
So that hydraulic system is often in complicated random vibration environment.Simultaneously as its span is long, need largely to constrain support
To guarantee its stability.Furthermore due to factors such as rigging errors, Support Position has certain randomness, this is to needing height
It is very unfavorable for the hydraulic plumbing system of reliability.For guarantee fluid pressure line can under complicated random vibration environment energy
Enough trouble free services need to screen out the parameter being affected to hydraulic plumbing system response and do key design, sensitivity to it
Analysis is to realize one of the important means of this task.
Sensitivity analysis includes local sensitivity and global sensitivity.Local sensitivity mainly studies variable at particular value
Output to output response, and global sensitivity mainly studies the whole uncertain uncertain to response output of stochastic variable
Influence.Relative to local sensitivity, global sensitivity reflection is the stochastic uncertainty of design variable itself to system sound
Bring is answered to influence.Currently, Global sensitivity analysis method is more mature.Sobol and Iman proposes the side based on variation decomposition
Poor Sensitivity Analysis Method, and obtained most commonly used application.Borgonovo proposes the square based on output response statistical moment
Independent Sensitivity Analysis Method.Since variance Sensitivity Analysis Method only considers that design variable uncertainty responses to which variance
It influences, and what square independence Sensitivity Analysis Method considered is influence of the uncertainty of design variable to output response square, simultaneously
Square is responded again indirectly comprising the information of variance, so based on the independent importance analysis of square compared with the importance analysis based on variance
Method responses to which probabilistic influence with more persuasion property to variable.But due to the reality of square independence Sensitivity Analysis Method
It applies process complexity and is difficult to realize be widely used without the sensitivity method based on variance.Therefore, how to complicated random
It is current urgent problem to be solved that pipe-line system under environment, which carries out expeditiously sensitivity analysis,.
It should be noted that information is only used for reinforcing the reason to the background of the disclosure disclosed in above-mentioned background technology part
Solution, therefore may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Summary of the invention
The disclosure is designed to provide a kind of pipeline Sensitivity Analysis Method, pipeline sensitivity analysis device, computer
Readable storage medium storing program for executing and electronic equipment, and then overcome the limitation and defect due to the relevant technologies at least to a certain extent and cause
Sensitivity Analysis Method is complicated and technical problem that computational efficiency is low.
According to one aspect of the disclosure, a kind of pipeline Sensitivity Analysis Method is provided, is characterized in that, comprising:
Pipeline model is established according to parameter preset relevant to pipe-line system；
One or more parameter in the parameter preset is chosen as target variable；
The main index of sensitivity and sensitivity overall performane of the target variable are calculated using power point estimations；
Wherein, the main index of the sensitivity is used to reflect the uncertain shadow to the pipeline model of target variable itself
It rings, the sensitivity overall performane is used to reflect influence of the interaction of target variable and other parameters to the pipeline model.
In a kind of exemplary embodiment of the disclosure, pipeline mould is being established according to parameter preset relevant to pipe-line system
After type, the method also includes:
Calculate the reliability index of the pipeline model relevant to the working time；Wherein, the reliability index is used for
Measure the reliability of the pipeline model.
In a kind of exemplary embodiment of the disclosure, it is described calculate relevant to the working time pipeline model can
Include: by property index
Multiple nodes are chosen on the pipeline model；
Calculate the reliability index R at i-th of nodei:
Wherein, t is the working time, and b is given threshold value, σ1For speed responsive standard deviation, σ2For dynamic respond standard deviation, σ3
For stress response standard deviation；
Calculate the overall reliability index R of the pipeline modelt:
Wherein, n is the number for choosing node.
It is described to calculate the sensitive of the target variable using power point estimations in a kind of exemplary embodiment of the disclosure
It spends main index and sensitivity overall performane includes:
Using power point estimations calculate unconditional variance V [R (t, X)], conditional expectation E [V (R (t, X) | X-i)] and condition side
Poor V [E (R (t, X) | Xi)]；
Calculate the main index S of sensitivity of the target variablei:
Calculate the sensitivity overall performane S of the target variableTi:
Wherein, t is the working time, and X is target variable, XiAnd X-iFor the aleatory variable in target variable, R is and t and X phase
The reliability index function of pass, V are variance functions, and E is expectation function.
In a kind of exemplary embodiment of the disclosure, the unconditional variance V of the use power point estimations calculating [R (t,
X)], conditional expectation E [V (and R (t, X) | X-i)] and conditional variance V [E (R (t, X) | Xi)] include:
Select m characteristic point ul, and determine the corresponding weighted value P of each characteristic pointl；
Establish the expectation computation model E (Y) and variance computation model V (Y) of one-variable function Y=g (X):
Wherein, function T-1(ul) it is characteristic point ulOne-variable function, μgIt is the value of Y when X to be fixed on to its mean μ；
Based on the expectation computation model E (Y) and variance computation model V (Y), unconditional side is calculated using nested loop approach
Poor V [R (t, X)], conditional expectation E [V (R (t, X) | X-i)] and conditional variance V [E (R (t, X) | Xi)]。
In a kind of exemplary embodiment of the disclosure, target variable X is normally distributed variable, the function T-1(ul)
Are as follows: T-1(ul)=μ+ulσ；Wherein, μ is the mean value of target variable X, and σ is the standard deviation of target variable X.
In a kind of exemplary embodiment of the disclosure, the target variable include the pipeline model elastic model,
Pipes Density, pipe thickness, the length of oil liquid density and specified supporting point.
According to one aspect of the disclosure, a kind of pipeline sensitivity analysis device is provided, is characterized in that, comprising:
Model building module is configured as establishing pipeline model according to parameter preset relevant to pipe-line system；
Variable chooses module, is configured as choosing one or more parameter in the parameter preset and becomes as target
Amount；
Index computing module is configured as calculating the main index of sensitivity and spirit of the target variable using power point estimations
Sensitivity overall performane；
Wherein, the main index of the sensitivity is used to reflect the sensitivity of pipeline model described in target variable itself affect, institute
Sensitivity overall performane is stated for reflecting that the interaction of target variable and other parameters influences the sensitivity of the pipeline model.
According to one aspect of the disclosure, a kind of computer readable storage medium is provided, computer program is stored thereon with,
It is characterized in that, the pipeline sensitivity analysis side of any description above is realized when the computer program is executed by processor
Method.
According to one aspect of the disclosure, a kind of electronic equipment is provided, is characterized in that, including processor and storage
Device；Wherein, memory is used to store the executable instruction of the processor, the processor is configured to via can described in execution
It executes instruction to execute the pipeline Sensitivity Analysis Method of any description above.
In the pipeline Sensitivity Analysis Method provided by the embodiment of the present disclosure, nested circulation is combined using power point estimations
Method carrys out approximate sensitivity index, improves the computational efficiency of sensitivity index, in the sensitivity of double stochastic uncertainty systems
Analysis provides theory support.In addition, the disclosure completes complicated ring by directly applying arbitrary excitation in finite element software
The simulation of border excitation is combined train reliability principle, is directly counted by combining based on the fatigue resistance formula for passing through method for the first time
The overall reliability for calculating pipe-line system, provides height for sensitivity analysis of the hydraulic air pipeline under double stochastic uncertainties
The analysis approach and theoretical direction of effect.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
The disclosure can be limited.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the disclosure
Example, and together with specification for explaining the principles of this disclosure.It should be evident that the accompanying drawings in the following description is only the disclosure
Some embodiments for those of ordinary skill in the art without creative efforts, can also basis
These attached drawings obtain other attached drawings.
Fig. 1 schematically shows the step flow chart of pipeline Sensitivity Analysis Method in disclosure exemplary embodiment.
Fig. 2A schematically shows the hydraulic air pipeline model established in disclosure exemplary embodiment by ANSYS.
Fig. 2 B schematically shows the local pipeline schematic diagram of aviation fluid pressure line model in Fig. 2A.
Fig. 3 is schematically shown in disclosure exemplary embodiment for motivating the acceleration power of hydraulic air pipeline model
Spectral density function.
Fig. 4 A schematically shows the shift standards difference response cloud of local fluid pressure line model in disclosure exemplary embodiment
Figure.
Fig. 4 B schematically shows the velocity standard difference response cloud of local fluid pressure line model in disclosure exemplary embodiment
Figure.
Fig. 4 C schematically shows the stress standard deviation response cloud of local fluid pressure line model in disclosure exemplary embodiment
Figure.
Fig. 5 A schematically shows the main index of sensitivity of target variable in disclosure exemplary embodiment.
Fig. 5 B schematically shows the sensitivity overall performane of target variable in disclosure exemplary embodiment.
Fig. 6 A schematically shows the variation schematic diagram of the main index of disclosure exemplary embodiment medium sensitivity.
Fig. 6 B schematically shows the variation schematic diagram of disclosure exemplary embodiment medium sensitivity overall performane.
Fig. 7 schematically shows the composition block diagram of pipeline sensitivity analysis device in disclosure exemplary embodiment.
Fig. 8 schematically shows a kind of schematic diagram of program product in disclosure exemplary embodiment.
Fig. 9 schematically shows the module diagram of a kind of electronic equipment in disclosure exemplary embodiment.
Specific embodiment
Example embodiment is described more fully with reference to the drawings.However, example embodiment can be real in a variety of forms
It applies, and is not understood as limited to example set forth herein；On the contrary, these embodiments are provided so that the disclosure will more comprehensively and
Completely, and by the design of example embodiment comprehensively it is communicated to those skilled in the art.Described feature, structure or characteristic
It can be incorporated in any suitable manner in one or more embodiments.
In addition, attached drawing is only the schematic illustrations of the disclosure, it is not necessarily drawn to scale.Identical attached drawing mark in figure
Note indicates same or similar part, thus will omit repetition thereof.Some block diagrams shown in the drawings are function
Energy entity, not necessarily must be corresponding with physically or logically independent entity.These function can be realized using software form
Energy entity, or these functional entitys are realized in one or more hardware modules or integrated circuit, or at heterogeneous networks and/or place
These functional entitys are realized in reason device device and/or microcontroller device.
A kind of pipeline Sensitivity Analysis Method is provided in the exemplary embodiment of the disclosure first, referring to Fig. 1, this method master
It may comprise steps of:
Step S10. establishes pipeline model according to parameter preset relevant to pipe-line system.
Step S20. calculates the reliability index of pipeline model relevant to the working time；Wherein, reliability index is used for
Measure the reliability of the pipeline model.
Step S30. chooses one or more parameter in parameter preset as target variable；
Step S40. calculates the main index of sensitivity and sensitivity overall performane of target variable using power point estimations.Wherein,
The main index of sensitivity is used to reflect the uncertain influence degree to pipeline model response output of target variable itself, spirit
Sensitivity overall performane is used to reflect the influence that the interaction of target variable and other parameters exports the response of the pipeline model
Degree.
Each step is described in detail in example with reference to the accompanying drawing.
Step S10. establishes pipeline model according to parameter preset relevant to pipe-line system.
The present embodiment establishes hydraulic air pipeline as shown in Figure 2 A, the partial enlargement shown in Fig. 2 B in ANSYS
In figure, C-terminal connects hydraulic pump, and it is all fixed constraint that the end A, the end B, which are oil liquid outlet end,.The end D, the end E and the end F are constraint support, and
It is fixed constraint.All supports are by 2.5 × 106The spring of N/m is provided in ANSYS.The material that the pipeline uses is
1Cr18Ni9Ti stainless steel, and the part parameter preset for being used to establish pipeline model is listed in table 1.
1 hydraulic plumbing system relevant parameter of table
Wherein, ρpFor piping material density, D is pipeline outer radius, and d is pipe thickness, ρ0It is for oil liquid density, T in pipeline
Environment temperature, P are pipeline internal pressure, and E is piping material elasticity modulus, and μ is the Poisson's ratio of piping material.
Step S20. calculates the reliability index of the pipeline model relevant to the working time；Wherein, the reliability
Index is used to measure the reliability of the pipeline model.
The arbitrary excitation for the pipeline model established in step S10 show that Fig. 3 shows a kind of acceleration power spectrum by testing
Function is spent, under the excitation, in conjunction with the design parameter in table 1, the reliability of the pipeline model can be by moving based on first super theoretical
Strength formula obtains.For example, the method that reliability index is calculated in this step may comprise steps of:
Step S21. chooses multiple nodes on the pipeline model；
Step S22. calculates the reliability index R at i-th of nodei:
Wherein, t is the working time, and b is given threshold value, σ1For speed responsive standard deviation, σ2For dynamic respond standard deviation, σ3
For stress response standard deviation；
Step S23. calculates the overall reliability index R of the pipeline modelt:
Wherein, n is the number for choosing node.
In the model, as long as the response at a node is more than given threshold value, then it is assumed that the pipeline is failure.
So, then it is assumed that the pipeline is a train, therefore its overall reliability index RtIt can be calculated with formula (2).
Given T and b is 3.6 × 107(s) and 2 × 108(Pa), the reliability at part of nodes is listed in such as the following table 2.
Influence and reliability index at 2 feature node of table
According to formula (2), the reliability index of the part pipeline can be directly calculated are as follows:
Corresponding displacement, speed and stress standard deviation response are as shown in Fig. 4 A, 4B and 4C.
Step S30. chooses one or more parameter in parameter preset as target variable.
In the present embodiment, by elastic model E, Pipes Density ρp, pipe thickness d, oil liquid density p0And three supporting points
D, length l1, l2 and l3 corresponding to E and F are given as target variable, and distribution pattern and parameter are listed in Table 3 below.
The distribution of 3 circuit design variable of table and its distribution parameter
Step S40. calculates the main index of sensitivity and sensitivity overall performane of the target variable using power point estimations.Its
In, the main index of sensitivity is used to reflect the uncertain shadow to pipeline model response output of target variable itself
It rings, the sensitivity overall performane is used to reflect the interaction of target variable and other parameters to the pipeline model output response
Influence.
More preferably, this step may include:
Using power point estimations calculate unconditional variance V [R (t, X)], conditional expectation E [V (R (t, X) | X-i)] and condition side
Poor V [E (R (t, X) | Xi)]；
Calculate the main index S of sensitivity of the target variablei:
Calculate the sensitivity overall performane S of the target variableTi:
Wherein, t is the working time, and X is target variable, XiAnd X-iFor the aleatory variable in target variable, R is and t and X phase
The reliability index function of pass, V are variance functions, and E is expectation function.
Using power point estimations calculate unconditional variance V [R (t, X)], conditional expectation E [V (R (t, X) | X-i)] and condition side
Poor V [E (R (t, X) | Xi)] include:
Select m characteristic point ul, and determine the weighted value P of each characteristic pointl；
Establish the expectation computation model E (Y) and variance computation model V (Y) of one-variable function Y=g (X):
Wherein, function T-1(ul) it is characteristic point ulOne-variable function, μgIt is the value of Y when X to be fixed on to its mean μ；
Based on the expectation computation model E (Y) and variance computation model V (Y), unconditional side is calculated using nested loop approach
Poor V [R (t, X)], conditional expectation E [V (R (t, X) | X-i)] and conditional variance V [E (R (t, X) | Xi)]。
In the present example embodiment, unconditional variance, conditional variance and conditional expectation are calculated using seven point estimations, i.e.,
Characteristic point ulNumber be 7.
For a single argument power function Y=g (X), the expectation and variance for responding Y can be respectively indicated as follows,
More preferably, target variable X is normally distributed variable, function T-1(ul) are as follows:
T-1(ul)=μ+ulσ (8)
Wherein, μ is the mean value of target variable X, and σ is the standard deviation of target variable X.
For ulAnd Pl, the present embodiment is that its assignment is as follows: u1=0, u2=-u3=1.1544054, u4=-u5=
2.3667594 u6=-u7=3.7504397, P1=16/35, P2=P3=0.2401233, P4=P5=3.07571 × 10-2,
P6=P7=5.48269 × 10-4。
For multi-variable function Y=g (X)=g (X1,X2,…,Xn), it can be approximated to be following form,
gμ=g (μ1,μ2,…,μn) (10)
gi=g (μ1,…,μi-1,Xi,μi+1,…,μn) (11)
Obvious giIt is XiOne-variable function.Multi-variable function is converted into a series of group of one-variable functions by this process
It closes, then shown in the mean value and variance of its multi-variable function can be expressed as follows,
HereWithIt is one-variable function giMean value and variance, can be directly obtained by formula (6) and formula (7).
Next estimate unconditional variance V [R (t, X)], conditional expectation E [V (R (t, X) | X-i)] and conditional variance V [E (R
(t,X)|Xi)]。
For unconditional variance V [R (t, X)], a series of combination of one-variable functions can be converted into according to equation (13), and
It is expressed as follows
Here Ri(t, X)=Ri(t, μ1..., μi-1, Xi, μi+1..., μn) it is single argument variance, it can be direct by formula (7)
It obtains, and is expressed as follows,
In this way, the variance V [R (t, X)] of multi-variable function directly can be approximately following form by seven point estimation,
For conditional variance V [E (and R (t, X) | Xi)], it will be assumed that V [E (R (t, X) | Xi)] it is inside and outside two parts, and first
Concern inner part E (R (t, X) | Xi).By XiIt is fixed on its implementation valuePlace simultaneously enables X-iFor a stochastic variable.So, it is believed thatFor a multi-variable function.For simplification, ψ (X is used-i) substitutionI.e.In this way, E (ψ (X-i)) can be directly obtained and be expressed as follows by formula (12)
HereIndicate one-variable function ψj(X-i) mean value, can be directly obtained by formula (6), and be expressed as follows,
ψ (the X in equation (17)-i)μIt indicates:
Equation (18), (19) are brought into equation (17), it can be found that E (ψ (X-i)) beOne-variable function.By E (ψ
(X-i)) useInstead of, then conditional variance V [E (and R (t, X) | Xi)] can be finally expressed as follows by formula (7),
HereIt is one-variable functionMean value.
For conditional expectation E [V (and R (t, X) | X-i)], it inside and outside two parts can equally analyze respectively.For internal layer V (R
(t,X)|X-i), by X-iIt is fixed on its implementation valueKnown toIt is
One one-variable function, is used in combinationIt indicates.Its variance can be obtained directly by formula (7), and be expressed as follows,
HereIndicate one-variable functionMean value.ObviouslyFor n-1
Multi-variable function.It willReplace with ψ ' (X-i), then E [V (and R (t, X) | X-i)] can simultaneously table directly be obtained by formula (12)
Show as follows
HereIt isOne-variable function.ψ′(X-i)μ=
ψ′(μ1..., μj..., μi-1..., T-1(μl) ..., μn) it is multi-variable function ψ ' (X-i) mean value.In this way, then ψj′(X-i) it is equal
ValueDirectly it can be obtained and be expressed as follows by formula (6),
Here, E [V (and R (t, X) | X-i)] can be directly obtained by bringing formula (21) into formula (23).
Finally, main index SiWith overall performane STiIt can directly be obtained by formula (4) and formula (5), when coefficient of variation ζ is
The sensitivity index of design parameter is as fig. 5 a and fig. 5b when 0.005.
When coefficient of variation variation, the variation of two kinds of sensitivity indexs is as shown in Figure 6 A and 6 B, from Fig. 5 A and Fig. 5 B
As can be seen that influence maximum of the design variable l3 to the part hydraulic plumbing system, followed by piping material density p, springform
E, l1 are measured, pipe thickness t and l2 is essentially identical, influences the smallest for oil liquid density p0.Providing importance ranking is l3>ρ>E>l1>
l2≈t>ρ0, illustrate that l3 should cause enough attention in the part circuit design.
From Fig. 6 A and Fig. 6 B as can be seen that with the coefficient of variation increase, the main sensitivity index of all variables and total
Sensitivity index is all increasing.It should be noted, however, that when, design variable sensitive indexes itself are big, and amplitude of variation is big,
Sensitivity index itself is small, and amplitude of variation is small.
It is directed to a pith of certain type complicated hydraulic pipe-line system in disclosure the above exemplary embodiments, establishes it
The Sensitivity Analysis Method of random parameter in random vibration situation drag.Firstly, the fluid pressure line system established in ANSYS
One pith of system, and joined arbitrary excitation wherein.That is, the analytic process of entire model is built upon engineering
On the basis of, and arbitrary excitation caused by random environment, directly by applying in ANSYS, this also more meets practical work and asks
Topic.Secondly, analyzing its reliability on the basis of this model.Pay attention to relating generally to the reliability theory of random process engineering problem
Calculating is all considerably complicated, and most of its power function is provided in the form of display, in the reality for being related to finite element analysis
It is difficult to apply in the engineering of border.Here it binds directly based on the fatigue resistance reliability general character for surmounting method for the first time, divides at random according to ANSYS
Dynamic respond standard deviation, speed responsive standard deviation and the speed responsive standard deviation obtained in analysis, then managed by train probability
By immediately arriving at the reliability of the pipeline.Thereafter, on the basis of this reliability, pipeline model parameter is analyzed to reliability
It influences, has obtained the parametric sensitivity index of the model using efficient seven point estimations and sorted.Mentioned method is
The sensitivity analysis of hydraulic air pipe-line system under complicated random environment, which provides, a kind of meets the efficient of Practical Project demand
Method for solving and theoretical direction.
It should be noted that, although exemplary embodiment above describes each step of method in the disclosure with particular order
Suddenly, still, this does not require that perhaps hint must execute these steps in this particular order or have to carry out whole
Step is just able to achieve desired result.Additionally or alternatively, it is convenient to omit multiple steps are merged into one by certain steps
Step executes, and/or a step is decomposed into execution of multiple steps etc..
In an exemplary embodiment of the disclosure, a kind of pipeline sensitivity analysis device is also provided.As shown in fig. 7, pipeline
Sensitivity analysis device 70 mainly may include model building module 71, variable selection module 72 and index computing module 73.Its
In, model building module 71 is configured as establishing pipeline model according to parameter preset relevant to pipe-line system；Variable chooses mould
Block 72 is configured as choosing one or more parameter in the parameter preset as target variable；73 quilt of index computing module
It is configured to calculate the main index of sensitivity and sensitivity overall performane of the target variable using power point estimations；The sensitivity master
Index is used to reflect the sensitivity of pipeline model described in target variable itself affect, and the sensitivity overall performane is for reflecting target
The interaction of variable and other parameters influences the sensitivity of the pipeline model.
The detail of above-mentioned pipeline sensitivity analysis device carries out in corresponding pipeline Sensitivity Analysis Method
Detailed description, therefore details are not described herein again.
It should be noted that although being referred to several modules or list for acting the equipment executed in the above detailed description
Member, but this division is not enforceable.In fact, according to embodiment of the present disclosure, it is above-described two or more
Module or the feature and function of unit can embody in a module or unit.Conversely, an above-described mould
The feature and function of block or unit can be to be embodied by multiple modules or unit with further division.
In an exemplary embodiment of the disclosure, a kind of computer readable storage medium is also provided, calculating is stored thereon with
Machine program can realize the above-mentioned pipeline Sensitivity Analysis Method of the disclosure when computer program is executed by processor.?
In some possible embodiments, various aspects of the disclosure is also implemented as a kind of form of program product comprising journey
Sequence code；The program product, which can store, (can be CD-ROM, USB flash disk or mobile hard disk in a non-volatile memory medium
Deng) in or network on；When described program product a calculating equipment (can be personal computer, server, terminal installation or
Person's network equipment etc.) on when running, said program code is above-mentioned each exemplary in the calculatings equipment execution disclosure for making
Method and step in embodiment.
It is shown in Figure 8, it, can be with according to the program product 80 for realizing the above method of embodiment of the present disclosure
Using portable compact disc read-only memory (CD-ROM) and including program code, and can be to calculate equipment (such as personal
Computer, server, terminal installation or network equipment etc.) on run.However, the program product of the disclosure is without being limited thereto.?
In the present exemplary embodiment, computer readable storage medium can be any tangible medium for including or store program, the program
Execution system, device or device use or in connection can be commanded.
Described program product can use any combination of one or more readable medium.Readable medium can be readable
Signal media or readable storage medium storing program for executing.
Readable storage medium storing program for executing for example can be but be not limited to the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device
Or device or any above combination.The more specific example (non exhaustive list) of readable storage medium storing program for executing includes: with one
The electrical connection of a or multiple conducting wires, portable disc, hard disk, random access memory (RAM), read-only memory (ROM), erasable type
Programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical memory
Part, magnetic memory device or above-mentioned any appropriate combination.
Readable signal medium may include in a base band or as the data-signal that carrier wave a part is propagated, wherein carrying
Readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetic signal, optical signal
Or above-mentioned any appropriate combination.Readable signal medium can also be any readable medium other than readable storage medium storing program for executing, should
Readable medium can send, propagate or transmit for by instruction execution system, device or device use or it is in connection
The program used.
The program code for including on readable medium can transmit with any suitable medium, including but not limited to wirelessly, have
Line, optical cable, RF etc. or above-mentioned any appropriate combination.
Can with any combination of one or more programming languages come write for execute the disclosure operation program
Code, described program design language include object oriented program language, Java, C++ etc., further include conventional mistake
Formula programming language, such as C language or similar programming language.Program code can be calculated fully in user and be set
Standby upper execution is partly executed on the user computing device, is set as an independent software package execution, partially in user's calculating
Standby upper part executes on a remote computing or executes in remote computing device or server completely.It is being related to remotely
In the situation for calculating equipment, remote computing device can pass through the network of any kind (including local area network (LAN) or wide area network
(WAN) etc.) it is connected to user calculating equipment；Or, it may be connected to external computing device, such as provided using Internet service
Quotient is connected by internet.
In an exemplary embodiment of the disclosure, a kind of electronic equipment is also provided, the electronic equipment includes at least one
Processor and at least one be used for store the processor executable instruction memory；Wherein, the processor is matched
It is set to via the executable instruction is executed and executes the method and step in the disclosure in above-mentioned each exemplary embodiment.
The electronic equipment 900 in the present exemplary embodiment is described below with reference to Fig. 9.Electronic equipment 900 is only
One example, should not function to the embodiment of the present disclosure and use scope bring any restrictions.
Shown in Figure 9, electronic equipment 900 is showed in the form of universal computing device.The component of electronic equipment 900 can be with
Including but not limited to: at least one processing unit 910, at least one storage unit 920, the different system components of connection (including place
Manage unit 910 and storage unit 920) bus 930, display unit 940.
Wherein, storage unit 920 is stored with program code, and said program code can be executed with unit 910 processed, so that
Processing unit 910 executes the method and step in the disclosure in above-mentioned each exemplary embodiment.
Storage unit 920 may include the readable medium of volatile memory cell form, such as Random Access Storage Unit
921 (RAM) and/or cache memory unit 922 can further include read-only memory unit 923 (ROM).
Storage unit 920 can also include program/utility 924 with one group of (at least one) program module 925,
Such program module includes but is not limited to: operating system, one or more application program, other program modules and program
It may include the realization of network environment in data, each of these examples or certain combination.
Bus 930 can be to indicate one of a few class bus structures or a variety of, including storage unit bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use any bus structures in various bus structures
Local bus.
Electronic equipment 900 can also be with one or more external equipments 1000 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, the equipment communication that user can also be allowed to interact with the electronic equipment 900 with one or more, and/or with
The electronic equipment 900 and one or more other are enabled to calculate any equipment that equipment are communicated (such as router, modulation
Demodulator etc.) communication.This communication can be carried out by input/output (I/O) interface 950.Also, electronic equipment 900 may be used also
To pass through network adapter 960 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network
Network, such as internet) communication.As shown in figure 9, network adapter 960 can be by other of bus 930 and electronic equipment 900
Module communication.It should be understood that although not shown in the drawings, other hardware and/or software mould can be used in conjunction with electronic equipment 900
Block, including but not limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape
Driver and data backup storage system etc..
It will be appreciated by those skilled in the art that various aspects of the disclosure can be implemented as system, method or program product.
Therefore, various aspects of the disclosure can be with specific implementation is as follows, it may be assumed that complete hardware embodiment, complete software
The embodiment that embodiment (including firmware, microcode etc.) or hardware and software combine, may be collectively referred to as here " circuit ",
" module " or " system ".
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to its of the disclosure
Its embodiment.This application is intended to cover any variations, uses, or adaptations of the disclosure, these modifications, purposes or
Person's adaptive change follows the general principles of this disclosure and including the undocumented common knowledge in the art of the disclosure
Or conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the disclosure are by appended
Claim is pointed out.
Above-mentioned described feature, structure or characteristic can be incorporated in one or more embodiment party in any suitable manner
In formula, if possible, it is characterized in discussed in each embodiment interchangeable.In the above description, it provides many specific thin
Section fully understands embodiment of the present disclosure to provide.It will be appreciated, however, by one skilled in the art that this can be practiced
Disclosed technical solution, or can be using other methods, component, material without one or more in specific detail
Deng.In other cases, known features, material or operation are not shown in detail or describe to avoid each side of the fuzzy disclosure
Face.
Claims (10)
1. a kind of pipeline Sensitivity Analysis Method characterized by comprising
Pipeline model is established according to parameter preset relevant to pipe-line system；
One or more parameter in the parameter preset is chosen as target variable；
The main index of sensitivity and sensitivity overall performane of the target variable are calculated using power point estimations；
Wherein, the main index of the sensitivity is used to reflect the sensitivity of pipeline model described in target variable itself affect, the spirit
Sensitivity overall performane is used to reflect the sensitivity of the interaction influence pipeline model of target variable and other parameters.
2. pipeline Sensitivity Analysis Method according to claim 1, which is characterized in that according to relevant to pipe-line system
Parameter preset is established after pipeline model, the method also includes:
Calculate the reliability index of the pipeline model relevant to the working time；Wherein, the reliability index is for measuring
The reliability of the pipeline model.
3. pipeline Sensitivity Analysis Method according to claim 2, which is characterized in that the calculating is related to the working time
The reliability index of the pipeline model include:
Multiple nodes are chosen on the pipeline model；
Calculate the reliability index R at i-th of nodei:
Wherein, t is the working time, and b is given threshold value, σ1For speed responsive standard deviation, σ2For dynamic respond standard deviation, σ3To answer
Force-responsive standard deviation；
Calculate the overall reliability index R of the pipeline modelt:
Wherein, n is the number for choosing node.
4. pipeline Sensitivity Analysis Method according to claim 1, which is characterized in that described to be calculated using power point estimations
The main index of the sensitivity of the target variable and sensitivity overall performane include:
Using power point estimations calculate unconditional variance V [R (t, X)], conditional expectation E [V (R (t, X) | X-i)] and conditional variance V
[E(R(t,X)|Xi)]；
Calculate the main index S of sensitivity of the target variablei:
Calculate the sensitivity overall performane S of the target variableTi:
Wherein, t is the working time, and X is target variable, XiAnd X-iFor the aleatory variable in target variable, R is relevant to t and X
Reliability index function, V are variance functions, and E is expectation function.
5. pipeline Sensitivity Analysis Method according to claim 4, which is characterized in that described to be calculated using power point estimations
Unconditional variance V [R (t, X)], conditional expectation E [V (R (t, X) | X-i)] and conditional variance V [E (R (t, X) | Xi)] include:
Select m characteristic point ul, and determine the corresponding weighted value P of each characteristic pointl；
Establish the expectation computation model E (Y) and variance computation model V (Y) of one-variable function Y=g (X):
Wherein, function T-1(ul) it is characteristic point ulOne-variable function, μgIt is the value of Y when X to be fixed on to its mean μ；
Based on the expectation computation model E (Y) and variance computation model V (Y), unconditional variance V is calculated using nested loop approach
[R (t, X)], conditional expectation E [V (R (t, X) | X-i)] and conditional variance V [E (R (t, X) | Xi)]。
6. pipeline Sensitivity Analysis Method according to claim 5, which is characterized in that target variable X is normal distribution change
Amount, the function T-1(ul) are as follows: T-1(ul)=μ+ulσ；Wherein, μ is the mean value of target variable X, and σ is the standard of target variable X
Difference.
7. pipeline Sensitivity Analysis Method described in any one of -6 according to claim 1, which is characterized in that the target becomes
Amount include the elastic model of the pipeline model, Pipes Density, pipe thickness, oil liquid density and specified supporting point position.
8. a kind of pipeline sensitivity analysis device characterized by comprising
Model building module is configured as establishing pipeline model according to parameter preset relevant to pipe-line system；
Variable chooses module, is configured as choosing one or more parameter in the parameter preset as target variable；
Index computing module is configured as calculating the main index of sensitivity and sensitivity of the target variable using power point estimations
Overall performane；
Wherein, the main index of the sensitivity is used to reflect the sensitivity of pipeline model described in target variable itself affect, the spirit
Sensitivity overall performane is used to reflect the sensitivity of the interaction influence pipeline model of target variable and other parameters.
9. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program quilt
Pipeline Sensitivity Analysis Method described in any one of claim 1-7 is realized when processor executes.
10. a kind of electronic equipment characterized by comprising
Processor；
Memory, for storing the executable instruction of the processor；
Wherein, the processor is configured to carrying out any one of perform claim requirement 1-7 via the executable instruction is executed
The pipeline Sensitivity Analysis Method.
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