CN114182770A - Digital twin-based method and system for predicting basic corrosion of jacket of offshore booster station - Google Patents
Digital twin-based method and system for predicting basic corrosion of jacket of offshore booster station Download PDFInfo
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Abstract
The invention discloses a method and a system for predicting basic corrosion of a jacket of an offshore booster station based on digital twinning, wherein a mathematical model of a corrosion state is established by adopting a nonlinear regression algorithm based on data obtained by a metal corrosion experiment; measuring to obtain real-time data of metal resistance at each measuring point on the basis of the jacket of the offshore booster station; substituting the real-time data into the mathematical model of the corrosion state to obtain the metal corrosion amount at each measuring point; establishing a corrosion digital twinning model; and evaluating the basic corrosion grade of the jacket by using a corrosion digital twin model. Grading the corrosion degree of the jacket foundation according to the corrosion amount of the jacket foundation; outputting the corrosion grade of the jacket foundation in time to complete the evaluation and prediction of the corrosion condition of the jacket foundation of the offshore booster station; and finding out a corrosion area according to the positioning of the monitoring point, and finishing maintenance or repair of the area according to an evaluation result.
Description
Technical Field
The invention relates to the technical field of offshore wind power, in particular to a method and a system for predicting the basic corrosion of a jacket of an offshore booster station based on digital twinning.
Background
At present, offshore wind power generation technology is rapidly developed domestically and globally, and offshore wind power generation becomes one of important power generation modes. The offshore booster station is used as a key facility for power transmission and transformation of the offshore wind farm and is a foundation for stable operation of the whole offshore wind farm. Because the jacket foundation structure of the offshore booster station operates in severe corrosion environments such as high temperature, high humidity, high salt spray, long sunshine, wave erosion and the like for a long time, reliable prediction of the corrosion state of the foundation structure is the key of safe and stable operation of an offshore wind farm.
At present, the conventional manual detection method is mostly adopted for the corrosion state of the jacket foundation of the offshore booster station, the cost is high, the danger coefficient is high, and the uncertainty of the offshore environment is difficult to fix the detection period, so that the difficulty is brought to accurately evaluating the corrosion state of the jacket foundation of the offshore booster station.
The digital twins refer to virtual digital models of physical entities constructed by a data fusion method, and are also called as digital twins. The digital twinning technique utilizes historical data, real-time data and a machine learning algorithm model to realize the simulation, prediction and control of a physical entity.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method and a system for predicting corrosion of a jacket foundation of an offshore booster station based on digital twinning. The corrosion digital twin body establishes a virtual entity by collecting data from the jacket foundation and describes, diagnoses and predicts the corrosion state of the physical entity by combining with a mathematical model.
The technical scheme adopted by the invention is as follows:
a marine booster station jacket foundation corrosion prediction method based on digital twinning comprises the following steps:
the method comprises the following steps that (1) a plurality of groups of target metal resistance values and corresponding corrosion amounts of the target metal resistance values are obtained through a metal corrosion experiment, and a mathematical model of a corrosion state is established by adopting a nonlinear regression algorithm based on data obtained through the metal corrosion experiment;
step (2), measuring to obtain real-time data of metal resistance at each measuring point on the basis of the jacket of the offshore booster station;
step (3), substituting the measured real-time data of the metal resistance value into a corrosion state mathematical model to obtain the size of the metal corrosion amount at each measuring point, and importing the size into a database;
step (4), establishing a corrosion digital twin model based on the database and the corrosion state mathematical model;
and (5) evaluating the basic corrosion grade of the jacket by using a corrosion digital twin model. Dividing the corrosion degree of the jacket foundation into four grades of slight corrosion, moderate corrosion, severe corrosion and severe corrosion according to the corrosion amount of the jacket foundation;
step (6), outputting the basic corrosion grade of the jacket in time, and finishing the evaluation and prediction of the basic corrosion condition of the jacket of the offshore booster station; and finding out a corrosion area according to the positioning of the monitoring point, and finishing maintenance or repair of the area according to an evaluation result.
Further, the setting rule of the measurement points on the jacket basis is as follows:
for the part of the jacket foundation in the full immersion area, the distances between two adjacent measuring points from bottom to top are arranged in an arithmetic progression;
and for the positions of the jacket foundation in the ocean atmosphere region, the splash region and the tidal range region, the adjacent measuring points are arranged at equal intervals.
Further, the method for establishing the corrosion state mathematical model by adopting the nonlinear regression algorithm comprises the following steps:
s1, obtaining multiple groups of experimental data through a metal corrosion experiment, wherein the experimental data are a target metal resistance value and a corrosion amount corresponding to the target metal resistance value;
s2, correcting the experimental data obtained in the step S1 to eliminate system errors;
s3, constructing a fitting function model by using a nonlinear regression algorithm based on the data after the correction processing in the S2; and solving by adopting a Newton iteration method to obtain a fitting function curve.
Further, in S3, the solution process using the newton iteration method is as follows:
s3.1, optimizing an initial value of a Newton iteration method based on a genetic algorithm; firstly, a fitness function f (x) is constructedi) Selecting a group of candidate points in the calculation of the Newton iteration method to participate in iteration, and taking the candidate point with the minimum error of the calculation result as an initial value of the Newton iteration method;
s3.2, the nonlinear regression model is expressed as:
yi＝f(xi，r)+εi i＝1，2，...n (1)
wherein, yiIs a dependent variable of the linear regression model, n is the total number of variables i, r is the regression coefficient to be estimated, and r is (r ═ r0，r1，Λ，rp-1)′，r0，r1，rp-1Respectively are elements of the matrix, and Λ is an omission symbol; error term epsiloni～N(0，σ2) σ is the standard deviation; is provided with
For the initial value of r of the regression coefficient to be estimated, the formula f (x)iR) in g0Taylor expansion is carried out near the point, and partial derivative terms of the second order and above of the second order of the nonlinear regression model are omitted, so as to obtain the following formula
Wherein r iskThe k-th regression coefficient to be estimated, k being 0, 1,. and p-1;
is rkThe corresponding initial value.
Substituting formula (2) into formula (1) to obtain:
order:
wherein the content of the first and second substances,
is a residual error; f (x)i，g(0)) Is f (x)iR) in g0Performing Taylor expansion near the point;
then:
expressed in matrix form, the above formula is:
Y(0)≈D(0)B(0)+ε (3)
wherein: y is(0)Is element of
A matrix of (a); d(0)Is element of
A matrix of (a); b is(0)Is element of
A matrix of (a);
is Y(0)In the form of a matrix of
Is D(0)In the form of a matrix of
Is B(0)In the form of a matrix of
S3.3, estimating a correction factor B for the formula (3) by using a least squares method(0)And then:
b(0)＝(D(0)D(0))-1D(0)Y(0) (4)
wherein, b(0)For correction factors, set g(1)For the first iteration value, then: g(1)＝g(0)+b(0)；
S3.4, accuracyLet the sum of the squares of the residuals be:
s is the number of repeated iterations, given an allowable error rate K, when
If so, stopping iteration; otherwise, performing the next iteration on the formula (4);
s3.5, repeating iteration; repeating equation (4), when the iteration is repeated s times, then there are:
correction factor: b(s)＝(D(s)D(s))-1D(s)Y(s)
The (S +1) th iteration value: g(s+1)＝g(s)+b(s)。
A basic corrosion prediction system for a jacket of an offshore booster station comprises a data acquisition unit, a signal collection processing device and a land centralized control center;
the data acquisition unit adopts a direct current resistance tester, and the direct current resistance tester comprises a plurality of sensors, a central control unit and a program-controlled constant current source; applying current to a measuring point by a program-controlled constant current source, fixing a sensor at the measuring point by an installation reinforcing device, and collecting test voltage and corresponding test current; the signal output end of the sensor is connected with the central control unit, and the obtained data is preprocessed in the central control unit to obtain an actual resistance value;
the signal collection processing device is arranged on an offshore booster station platform, and a digital twin-based offshore booster station jacket foundation corrosion prediction method is arranged in the signal collection processing device; the signal collecting and processing device is in communication connection with the data collecting unit and the land centralized control center, and the signal collecting and processing device uploads the measured data to the land centralized control center in real time for subsequent analysis and evaluation.
Furthermore, the signal output end of the sensor is sequentially connected with the program control preamplifier, the A/D converter and the central control unit.
Furthermore, the installation and reinforcement device of the sensor comprises an anchor ear body, wherein the anchor ear body consists of two semi-circular arc anchor ears, and the curvature radius of the anchor ear body is the same as the radius of the jacket foundation of the offshore booster station; one end of each hoop body adjacent to the corresponding hoop body is provided with a hoop body connecting device, so that the adjacent hoop bodies are fixedly connected; the other two adjacent ends are provided with a hoop locking mechanism; the locking device is used for locking the hoop body; the clamping opening of the sensor is arranged on the hoop body, the size of the clamping opening is matched with the size of the clamping block of the sensor base, the clamping block of the sensor base is embedded with the clamping opening, and therefore the sensor and the hoop are fixedly connected.
Further, staple bolt locking mechanical system includes: and the clamping blocks are arranged at the two ends of the hoop and matched with each other, and the bolts are used for locking the clamping blocks. The clamping blocks are placed at the end openings of the hoops, and when the hoops are locked, the hoops can be locked only by enabling the cross sections of the hoops to be close to the hoops and turning bolts for locking the clamping blocks.
Further, the hoop body is made of resin materials.
Furthermore, a signal wire connected with the sensor adopts a comprehensive cable, the comprehensive cable comprises a power wire core and a data transmission wire core, the power wire core is responsible for supplying power to the sensor device, and the data transmission wire core is responsible for transmitting data collected by the sensor to the data collection device; with the power core, the data transmission sinle silk, high resistance water vulcanization natural rubber is filled to the interior extrusion formula, and the corrosion-resistant rubber protective sheath is wrapped up again to the outside.
The invention has the beneficial effects that:
1. the method and the system for predicting the basic corrosion of the jacket of the offshore booster station avoid regular manual detection, reduce the difficulty of corrosion state detection, reduce the cost and improve the corrosion state evaluation effect.
2. The method and the system for predicting the basic corrosion of the jacket of the offshore booster station are simple in principle and high in operability.
3. Because the factors influencing the metal resistance value are single, the corrosion state-metal resistance value fitting curve evaluation standard constructed by the invention can reduce the influencing factors which possibly influence the evaluation accuracy to the minimum.
4. According to the method and the system for predicting the corrosion of the jacket foundation of the offshore booster station based on the digital twinborn, the nonlinear relation between the corrosion state of the jacket foundation and the change degree of the metal resistance value of the jacket foundation is analyzed, the optimal fitting function curve of the corrosion state and the metal resistance value is constructed, the jacket foundation corrosion prediction model based on the digital twinborn is established, and the corrosion state and the change trend of the jacket foundation of the offshore booster station are accurately and comprehensively reflected.
Drawings
FIG. 1 is a schematic overall view of a jacket infrastructure of an offshore booster station;
FIG. 2 is a schematic diagram of a corrosion parameter measurement scheme for a jacket infrastructure of an offshore booster station;
FIG. 3 is a flow chart of a digital twin based marine booster station jacket base corrosion prediction model;
FIG. 4 illustrates a corrosion detection system for a jacket foundation of a marine booster station according to the present application;
FIG. 5 is a circuit diagram of a data acquisition unit;
FIG. 6 is a schematic view of the installation reinforcement device;
fig. 7 is a schematic cross-sectional view of an integrated cable.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The marine booster station jacket foundation corrosion prediction method based on the digital twinning measures the metal resistance value of a jacket foundation through a sensor, establishes a metal corrosion state mathematical model of the jacket foundation, establishes a corrosion digital twinning body based on the real-time data drive of the sensor on the basis, and establishes the relation between the corrosion digital simulation and the actual metal corrosion process. The corrosion digital twin body establishes a virtual entity by collecting data from the jacket foundation and describes, diagnoses and predicts the corrosion state of the physical entity by combining with a mathematical model.
Metal corrosion can result in changes in the resistance of the metal. The method completes the evaluation of the corrosion state of the base metal of the jacket through the constructed mathematical model of the metal corrosion fitting function, and has the premise that accurate data is obtained through measurement. According to different positions of a jacket foundation structure of the offshore booster station in a marine environment, the marine corrosion environment can be divided into five corrosion areas, namely a marine atmosphere area, a splash area, a tidal range area, a full immersion area, a sea mud area and the like. The corrosion states of the jacket base structure of the offshore booster station in different corrosion areas are different. The invention carries out targeted distribution according to the difference of the corrosion states of each region. The present invention is illustrated by way of example of a typical jacket infrastructure.
The invention provides a digital twin-based marine booster station jacket foundation corrosion prediction method which comprises a hardware part for acquiring, transmitting and storing corrosion data and a real-time data-based corrosion digital twin model. The method comprises the following specific steps:
step (1), establishing a corrosion state mathematical model by adopting a nonlinear regression algorithm according to metal corrosion experimental data;
step (2), obtaining real-time data of the basic metal resistance of the jacket of the offshore booster station through sensor measurement, and feeding the real-time data back to a data terminal;
step (3), substituting the measured real-time data of the resistance value of the basic metal of the jacket into a mathematical model of the corrosion state to obtain the size of the metal corrosion amount and importing the size into a database;
step (4), establishing a corrosion digital twinning model according to the database and the corrosion state mathematical model;
and (5) evaluating the basic corrosion grade of the jacket according to the corrosion digital twin model. The method divides the basic corrosion degree of the jacket into four grades of slight corrosion, moderate corrosion, severe corrosion and severe corrosion;
and (6) timely feeding back, and evaluating and predicting the basic corrosion condition of the jacket of the offshore booster station.
The following will explain the given steps in detail.
Firstly, establishing a basic corrosion parameter measurement scheme of a jacket of an offshore booster station.
The jacket foundation typically has a height of between 25 and 50 meters, depending on the application for the jacket foundation of the offshore booster station. Because the corrosion degree of each area of the jacket foundation structure changes along with the position in the vertical direction, the jacket foundation is simplified into a linear model, and the influence of the position in the vertical direction on the corrosion parameter measurement is only considered.
Most of the area of the jacket foundation is located in the full immersion area. Since the corrosion of the total leaching area is affected by dissolved oxygen in seawater, salt concentration, flow rate, water temperature, marine life, pH value and quicksand, the corrosion rate and depth are in inverse proportion, i.e. the larger the depth, the lower the corrosion rate. Therefore, the jacket foundation structure adopts a method of arranging points at equal-difference intervals in the full-immersion area, namely, the distance between two adjacent measuring points from bottom to top in the full-immersion area forms an equal-difference array. The distance between a first monitoring point and a second monitoring point at a silt line is assumed to be X in combination with the attached figure 11The distance between the second monitoring point and the third monitoring point is X2And so on. X1，X2，X3… … are in an arithmetic decreasing series. X1，X2，X3… … is sized according to the depth of the jacket foundation of the offshore booster station in the total immersion area.
In addition, as the corrosion rate of the jacket foundation structure in an ocean atmospheric region, a splash region and a tidal range region is greatly increased, measuring points are added in the ocean atmospheric region, the splash region and the tidal range region, and the measuring points in the regions are arranged according to a fixed distance, so that the distance between adjacent measuring points is h, which is less than the minimum distance X of a full immersion regionmin。
In the present invention, a data acquisition unit is employed to measure the metal resistance of the jacket infrastructure. The data acquisition unit adopts a typical four-wire system measuring method by adopting a direct current resistance tester so as to improve the accuracy of measuring the resistance (especially the low resistance). The DC resistance tester consists of a central control unit, a program-controlled constant current source, a program-controlled preamplifier, an A/D converter, a probe (sensor) and the like. Wherein, the central control unit applies a constant and high-precision current to the load to be measured (namely the position of a corresponding measuring point of a jacket foundation structure) by controlling the constant current source; collecting a metal resistance value by using a probe (sensor) installed at a measuring point; the signal output end of the probe (sensor) is sequentially connected with the program-controlled preamplifier, the A/D converter and the central control unit, and the obtained data (including test voltage, current test current and the like) are preprocessed in the central control unit to obtain an actual resistance value. The signal collecting and processing device is installed on the offshore booster station platform, the signal collecting and processing device is connected with the data acquisition unit through optical fiber communication, and the signal collecting and processing device uploads the measured data to the onshore centralized control center in real time for subsequent analysis and evaluation.
Establishing a mathematical model of the basic corrosion state of the jacket of the offshore booster station:
(1) the nonlinear regression analysis is to establish a regression relationship function expression between a dependent variable and an independent variable by using a mathematical statistic method on the basis of mastering a large amount of observation data. The nonlinear regression analysis is premised on sufficient data, so that multiple sets of data of the resistance value of the target metal and the corresponding corrosion amount of the target metal are obtained through experimental measurement before a corrosion state mathematical model is constructed.
(2) Eliminating system errors: the invention has system error due to the restriction of various environmental factors when measuring data. The system error has the characteristics of reproducibility, unidirectionality, testability and the like, and the error is eliminated by adopting a correction method. The processing method comprises the following steps: as many measurements as possible are made on the same measurand under repeated measurement conditions, and all measurements are averaged. The difference between the average value and the true value is a correction value; both the observed data and the actually measured data need to be corrected. Subtracting or adding a correction value from the observation data and the actually measured data to be used as an independent variable, and substituting the independent variable into the fitted standard function curve to obtain a dependent variable after error removal; more specifically, if the actual measurement value is smaller than the theoretical value in the ideal state, the correction value should be added, and if the comparison is larger, the correction value should be subtracted.
(3) Nonlinear regression analysis constructs a fitting function model: the nonlinear regression algorithm belongs to a supervised regression learning algorithm. The regression algorithm obtains the correlation between the variables and the dependent variables by establishing a regression model between the variables and utilizing a learning (training) process, and regression analysis can be used for predicting the model. Aiming at the basic corrosion of the jacket, a linear model cannot well fit a target data curve, and a nonlinear regression algorithm needs to be introduced.
The nonlinear regression algorithm is to convert nonlinear regression into linear regression and solve the linear regression. Linear regression typically takes the least square sum of the differences between a given function value and the model predicted value as a loss function and uses the least squares method and the gradient descent method to calculate the final fitting parameters.
The method adopts a Newton iteration method to process and solve to obtain a fitting function curve. And inputting the independent variable to obtain a corresponding dependent variable by fitting the function curve. The independent variable is the actual resistance data measured by the offshore booster station jacket foundation structure after error removal, and the dependent variable is the corrosion amount of the offshore booster station jacket foundation structure.
The basic idea of the Newton iteration method is to use a Taylor series expansion to approximately replace a nonlinear regression model, then modify a regression coefficient for multiple times through multiple iterations, so that the regression coefficient continuously approaches to the optimal regression coefficient of the nonlinear regression model, and finally, the sum of squares of residuals of an original model is minimized. The concrete solving steps are as follows:
selection of initial values. The initial value selection is important when solving the nonlinear equation by the Newton iteration method, because the selection is different, the generated iteration sequence may be converged or not converged, and even if the convergence rate is also the problem. The method is based on the thought of a genetic algorithm and optimizes the initial value of the Newton iteration method. The invention firstly constructs a fitness function f (x)i) And selecting a group of candidate points in the calculation of the Newton iteration method to participate in iteration. The candidate points are selected according to past experience, and the candidate point with the minimum error of the calculation result can be used as an initial value of the Newton iteration method.
Expansion of Taylor series. The nonlinear regression model is set as follows:
yi＝f(xi，r)+εii＝1，2，...n (1)
wherein r is the regression coefficient to be estimated, and the error term epsiloni～N(0，σ2) Is provided with
As the regression coefficient to be estimated r ═ r (r)0，r1，Λ，rp-1) ' initial value, will be formula f (x)iR) in g0Taylor expansion is carried out near the point, and partial derivative terms of the second order and above of the second order of the nonlinear regression model are omitted to obtain
Substituting the formula (2) into the formula (1) to obtain
Order:
then:
expressed in matrix form, the above formula is:
γ(0)≈D(0)B(0)+ε (3)
wherein:
estimating the correction factor. Estimating correction factor B for equation (3) by least squares method(0)And then:
b(0)＝(D(0)D(0))-1D(0)Y(0) (4)
let g(1)For the first iteration value, then: g(1)＝g(0)+b(0)
And fourthly, checking the accuracy. Let the sum of the squares of the residuals be:
s is the number of repeated iterations, given an allowable error rate K, when
If so, stopping iteration; otherwise, the next iteration is performed on equation (4).
Repeating iteration. Repeating equation (4), when the iteration is repeated s times, then there are:
correction factor: b(s)＝(D(s)D(s))-1D(s)Y(s)
The (S +1) th iteration value: g(s+1)＝g(s)+b(s)
And thirdly, establishing a corrosion digital twin model based on the database and the corrosion state mathematical model.
The establishment of the digital twinning model is the key to realizing the digital twinning technology. According to the method, a corresponding corrosion digital twin model is constructed according to a jacket basic physical model. Meanwhile, the problem of data real-time transmission between the jacket basic physical model and the corrosion digital twin model is solved through virtual interactive correlation, namely, data acquired by a corrosion monitoring system are led into the virtual digital model, and real-time data updating is carried out on the virtual digital model.
In order to realize the prediction method, the application also designs a basic corrosion prediction system of the jacket of the offshore booster station, and the system comprises a data acquisition unit, a signal collection processing device and a land centralized control center.
The data acquisition unit adopts a direct current resistance tester, and the direct current resistance tester comprises a plurality of probes (namely sensors), a central control unit and a program-controlled constant current source as shown in figures 4 and 5. Wherein the probe is installed at a corresponding measuring point position of the jacket infrastructure. The central control unit is in signal connection with the program-controlled constant current source and applies constant and high-precision current to a load to be measured (namely the position of a measuring point corresponding to a jacket foundation structure) by controlling the constant current source; collecting a metal resistance value by using a probe (sensor) installed at a measuring point; the signal output end of the probe (sensor) is sequentially connected with the program-controlled preamplifier, the A/D converter and the central control unit, and the obtained data (including test voltage, current test current and the like) are preprocessed in the central control unit to obtain an actual resistance value. A signal collection processing device is installed on an offshore booster station platform, and a digital twin-based offshore booster station jacket foundation corrosion prediction method is built in the signal collection processing device; the signal collecting and processing device is connected with the data collecting unit and the onshore centralized control center through wired communication, and then the measured data is uploaded to the onshore centralized control center in real time by the signal collecting and processing device for subsequent analysis and evaluation. In the present application, the measurement points are shown in fig. 2 for a jacket foundation as shown in fig. 1.
The application also contemplates a sensor mounting reinforcement device, as shown in FIG. 6. The hoop device designed by the invention comprises: the hoop body, hoop body connecting device and hoop locking mechanism. The anchor ear body comprises two semicircle anchor ears, and its curvature radius that corresponds is the same with marine booster station jacket basis radius. The semicircular hoops form bayonets specially used for placing the sensors at 1-2 and 3-4 positions, and the bayonets are square. The size of the bayonet is matched with the size of the clamping block of the sensor base, and the clamping block of the sensor base can be embedded with the bayonet, so that the sensor is fixed on the hoop. The cross section of the hoop body can be in various shapes such as circle, square and ellipse. In the invention, the section of the hoop adopts an isosceles trapezoid section, the outer surface is the short side of the trapezoid, and the inner surface is the long side of the trapezoid. The trapezoidal section can improve the connection stability of two sections of anchor ear bodies. The hoop body is made of resin materials. The resin material has strong deformation resistance and can adapt to deformation to a certain degree without fracture. Meanwhile, the resin material is not easy to corrode, and the service life of the hoop device can be greatly prolonged.
The hoop connecting device plays a role in connecting the two hoop bodies. The connecting device consists of two bolts, a resin plate and a telescopic groove. The invention arranges a telescopic groove at the connecting device. The telescopic slots can rotate around the bolts respectively, and the flexibility of the hoop is improved. The semicircular hoop is connected with the telescopic groove. Normally, the semi-circular arc hoops are shrunk in the telescopic slots. When the length of the hoop body is insufficient, the semicircular hoop can be drawn out to increase the overall length of the hoop.
Staple bolt locking mechanical system includes: and the clamping blocks are arranged at the two ends of the hoop and matched with each other, and the bolts are used for locking the clamping blocks. The clamping blocks are placed at the end openings of the hoops, and when the hoops are locked, the hoops can be locked only by enabling the cross sections of the hoops to be close to the hoops and turning bolts for locking the clamping blocks.
The hoop connecting device and the hoop locking mechanism are not limited to the two forms, for example, the hoop connecting device can directly connect parts such as pins and the like to directly hinge the tail ends of two adjacent hoop bodies; the hoop locking mechanism can be directly connected through a connecting piece, and can also adopt the design form of the existing hoop.
The invention adopts a comprehensive cable to be connected with each sensor. As shown in fig. 7, the composite cable is provided with two types of cores: power core and data transmission sinle silk. The power line core is responsible for supplying power to the sensor device, and the data transmission line core is responsible for transmitting the data collected by the sensor to the data collection device. The power cable core, the data transmission cable core and the inner extrusion type filled high-resistance water vulcanized natural rubber are wrapped by the corrosion-resistant rubber protective outer sleeve, and then the composite cable can be formed. The invention adopts the comprehensive cable to greatly reduce the wiring complexity.
The groove is formed in the inner side of the hoop body, and the size of the groove can just accommodate the cable. The anchor ear body is fixed so that the groove and the surface of the jacket form a narrow channel which just can pass through the cable. And connecting the cable with the sensor, connecting the cable with the sensor at the next monitoring point through the channel, then passing through the channel at the next sensor, and so on. The channel space formed by the groove and the surface of the jacket is narrow, and the cable can be well fixed. Data at various monitoring points may be transmitted to a data collector via a cable.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications in the design concept based on the principles disclosed herein are within the scope of the present invention.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (10)
1. A digital twin-based method for predicting the basic corrosion of a jacket of an offshore booster station is characterized by comprising the following steps:
the method comprises the following steps that (1) a plurality of groups of target metal resistance values and corresponding corrosion amounts of the target metal resistance values are obtained through a metal corrosion experiment, and a mathematical model of a corrosion state is established by adopting a nonlinear regression algorithm based on data obtained through the metal corrosion experiment;
step (2), measuring to obtain real-time data of metal resistance at each measuring point on the basis of the jacket of the offshore booster station;
step (3), substituting the measured real-time data of the metal resistance value into a corrosion state mathematical model to obtain the size of the metal corrosion amount at each measuring point, and importing the size into a database;
step (4), establishing a corrosion digital twin model based on the database and the corrosion state mathematical model;
step (5), evaluating the basic corrosion grade of the jacket by using a corrosion digital twin model; dividing the corrosion degree of the jacket foundation into four grades of slight corrosion, moderate corrosion, severe corrosion and severe corrosion according to the corrosion amount of the jacket foundation;
step (6), outputting the basic corrosion grade of the jacket in time, and finishing the evaluation and prediction of the basic corrosion condition of the jacket of the offshore booster station; and finding out a corrosion area according to the positioning of the monitoring point, and finishing maintenance or repair of the area according to an evaluation result.
2. The method for predicting the corrosion of the jacket base of the offshore booster station based on the digital twin as claimed in claim 1, wherein the setting rule of the measurement points on the jacket base is as follows:
for the part of the jacket foundation in the full immersion area, the distances between two adjacent measuring points from bottom to top are arranged in an arithmetic progression;
and for the positions of the jacket foundation in the ocean atmosphere region, the splash region and the tidal range region, the adjacent measuring points are arranged at equal intervals.
3. The method for predicting the basic corrosion of the jacket of the marine booster station based on the digital twin according to claim 1, wherein the method for establishing the mathematical model of the corrosion state by adopting the nonlinear regression algorithm comprises the following steps:
s1, obtaining multiple groups of experimental data through a metal corrosion experiment, wherein the experimental data are a target metal resistance value and a corrosion amount corresponding to the target metal resistance value;
s2, correcting the experimental data obtained in the step S1 to eliminate system errors;
s3, constructing a fitting function model by using a nonlinear regression algorithm based on the data after the correction processing in the S2; and solving by adopting a Newton iteration method to obtain a fitting function curve.
4. The method for predicting the corrosion of the jacket foundation of the marine booster station based on the digital twin according to claim 3, wherein the solution process in the S3 by using a Newton iteration method is as follows:
s3.1, optimizing an initial value of a Newton iteration method based on a genetic algorithm; firstly, a fitness function f (x) is constructedi) Selecting a group of candidate points in the calculation of the Newton iteration method to participate in iteration, and taking the candidate point with the minimum error of the calculation result as an initial value of the Newton iteration method;
s3.2, the nonlinear regression model is expressed as:
yi＝f(xi，r)+εi i＝1，2，...n (1)
wherein, yiIs a dependent variable of the linear regression model, n is the total number of variables i, r is the regression coefficient to be estimated, and r is (r ═ r0，r1，Λ，rp-1)′，r0，r1，rp-1Respectively are elements of the matrix, and Λ is an omission symbol; error term epsiloni～N(0，σ2) σ is the standard deviation; is provided with
For the initial value of r of the regression coefficient to be estimated, the formula f (x)iR) in g0Taylor expansion is carried out near the point, and partial derivative terms of the second order and above of the second order of the nonlinear regression model are omitted, so as to obtain the following formula
Wherein r iskThe k-th regression coefficient to be estimated, k being 0, 1,. and p-1;
is rkCorresponding initial values;
substituting formula (2) into formula (1) to obtain:
order:
wherein the content of the first and second substances,
is a residual error; f (x)i，g(0)) Is f (x)iR) in g0Performing Taylor expansion near the point;
then:
expressed in matrix form, the above formula is:
Y(0)≈D(0)B(0)+ε (3)
wherein: y is(0)Is element of
A matrix of (a); d(0)Is element of
A matrix of (a); b is(0)Is element of
A matrix of (a);
is Y(0)In the form of a matrix of
Is D(0)In the form of a matrix of
Is B(0)In the form of a matrix of
S3.3, estimating a correction factor B for the formula (3) by using a least squares method(0)And then:
b(0)＝(D(0)D(0))-1D(0)Y(0) (4)
wherein, b(0)For correction factors, set g(1)For the first iteration value, then: g(1)＝g(0)+b(0)；
S3.4, checking accuracy, and setting the sum of squares of residual errors as:
s is the number of repeated iterations, given an allowable error rate K, when
If so, stopping iteration; otherwise, it is to(4) Performing next iteration on the formula;
s3.5, repeating iteration; repeating equation (4), when the iteration is repeated s times, then there are:
correction factor: b(s)＝(D(s)D(s))-1D(s)Y(s)
The (S +1) th iteration value: g(s+1)＝g(s)+b(s)。
5. A basic corrosion prediction system for a jacket of an offshore booster station is characterized by comprising a data acquisition unit, a signal collection processing device and a land centralized control center;
the data acquisition unit adopts a direct current resistance tester, and the direct current resistance tester comprises a plurality of sensors, a central control unit and a program-controlled constant current source; applying current to a measuring point by a program-controlled constant current source, fixing a sensor at the measuring point by an installation reinforcing device, and collecting test voltage and corresponding test current; the signal output end of the sensor is connected with the central control unit, and the obtained data is preprocessed in the central control unit to obtain an actual resistance value;
the signal collection processing device is arranged on an offshore booster station platform, and a digital twin-based offshore booster station jacket foundation corrosion prediction method is arranged in the signal collection processing device; the signal collecting and processing device is in communication connection with the data collecting unit and the land centralized control center, and the signal collecting and processing device uploads the measured data to the land centralized control center in real time for subsequent analysis and evaluation.
6. The offshore booster station jacket foundation corrosion prediction system of claim 5, wherein the signal output end of the sensor is connected with a programmable preamplifier, an A/D converter and a central control unit in sequence.
7. The system of claim 5, wherein the means for mounting the sensor comprises an anchor ear body, wherein the anchor ear body comprises two semi-circular anchors, and the radius of curvature of the anchor ear body is the same as the radius of the jacket base of the offshore booster station; one end of each hoop body adjacent to the corresponding hoop body is provided with a hoop body connecting device, so that the adjacent hoop bodies are fixedly connected; the other two adjacent ends are provided with a hoop locking mechanism; the locking device is used for locking the hoop body; the clamping opening of the sensor is arranged on the hoop body, the size of the clamping opening is matched with the size of the clamping block of the sensor base, the clamping block of the sensor base is embedded with the clamping opening, and therefore the sensor and the hoop are fixedly connected.
8. The offshore booster station jacket foundation corrosion prediction system of claim 7, wherein the hoop locking mechanism comprises: and the clamping blocks are arranged at the two ends of the hoop and matched with each other, and the bolts are used for locking the clamping blocks. The clamping blocks are placed at the end openings of the hoops, and when the hoops are locked, the hoops can be locked only by enabling the cross sections of the hoops to be close to the hoops and turning bolts for locking the clamping blocks.
9. The offshore booster station jacket foundation corrosion prediction system of claim 7 or 8, wherein the hoop body is made of a resin material.
10. The system for predicting corrosion of jacket foundation of offshore booster station according to claim 9, wherein a signal line connected with the sensor is a composite cable, the composite cable includes a power line core and a data transmission line core, the power line core is responsible for supplying power to the sensor device, and the data transmission line core is responsible for transmitting data collected by the sensor device to the data collection device; with the power core, the data transmission sinle silk, high resistance water vulcanization natural rubber is filled to the interior extrusion formula, and the corrosion-resistant rubber protective sheath is wrapped up again to the outside.
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