CN105989422B - Sensor position and number optimization method for jacket ocean platform structure - Google Patents

Sensor position and number optimization method for jacket ocean platform structure Download PDF

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CN105989422B
CN105989422B CN201610364556.8A CN201610364556A CN105989422B CN 105989422 B CN105989422 B CN 105989422B CN 201610364556 A CN201610364556 A CN 201610364556A CN 105989422 B CN105989422 B CN 105989422B
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路玲玲
黄晨光
宋宏伟
王曦
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Abstract

The invention discloses a sensor number and position optimization calculation method for a jacket ocean platform structure, which comprises the steps of setting an optimization objective function, determining sensor candidate positions, dividing sub-regions, determining a position search space of each sensor, calculating the number of sensors and the like. According to the invention, the target function of sensor position optimization is established through a model subtraction method, a structure subregion method is utilized to provide a search space for position variables, an optimization calculation process is realized through a genetic algorithm, the integrity and orthogonality of the vibration measurement type of the structure are fully considered, and the optimization of the position and the number of the sensors of the large-scale complex ocean platform structure is realized, so that the judgment of the position and the number of the sensors required by the ocean platform monitoring does not depend on the experience of engineers, the large-scale complex jacket ocean platform monitoring efficiency is greatly improved, the manpower and material resources are saved, and the economic benefit is improved.

Description

Sensor position and number optimization method for jacket ocean platform structure
Technical Field
The invention belongs to the field of ocean platform structure monitoring, and particularly relates to a sensor arrangement method for a jacket ocean platform structure.
Background
In recent years, structural parameter identification plays a very important role in model modification, structural health monitoring and control and the like, and is attracting more and more high attention. In general, the accuracy and precision of the identification of the structural parameters depends on the number and position of the sensors arranged on the structure, but a large number of sensors entails high economic costs and heavy efforts. In the case of large and complicated structure, the number and positions of sensors needed for judgment are often determined according to the experience of engineers, some interesting vibration modes are selected, and the sensors are arranged on measuring points with large structural reaction. This method relies on the abundant experience and knowledge of structural engineers and can achieve good results in a simple structure, but is difficult to implement in complex structures such as jacket platforms. Currently, in a related theoretical sense, determination of the number of sensors is still dependent on the experience of the engineer. Meanwhile, the existing sensor optimization method for the truss structure mostly takes calculation efficiency and convergence as optimization targets, the identifiability (integrity and orthogonality) of the vibration measurement type of the structure is less considered, and the research on the complex jacket platform structure is more limited.
Disclosure of Invention
The purpose of the invention is: the optimal calculation method for the positions and the number of the jacket ocean platform structure sensors based on the model reduction method and the structure subregion method is provided, complete and high-precision structural modal parameters can be obtained by using fewer sensors, the monitoring efficiency of a large-scale complex ocean platform is greatly improved, manpower and material resources are saved, and the economic benefit is improved.
The technical scheme of the invention is as follows: a method for optimizing the position and number of sensors for a jacket platform structure, comprising the steps of:
A. based on the principle that the selection principle of the main degree of freedom in the model reduction method is consistent with the selection principle of the sensor, the sensor position optimization process is equivalent to the optimization selection process of the main degree of freedom in the model reduction method, and the difference between the natural frequency Ft and the standard natural frequency Ft obtained based on the reduction model is set as a target function of the sensor position optimization:
Figure BDA0001001399690000011
wherein T is the order of the modal to be identified, T is the T-th order modal to be identified, Ft is the T-th order natural frequency calculated based on the reduction model, and Ft is the T-th order natural frequency based on the complete model and is set as the standard frequency;
B. a structural subregion method is adopted, and position variable constraint conditions are given; dividing the structure into N non-overlapping sub-regions according to the given number N of sensors, the structure configuration and the vibration characteristics, wherein all the degrees of freedom in the sub-regions are search spaces of corresponding position variables, and the search space of each position variable is limited to the sub-region;
C. obtaining a group of main freedom degree combinations which enable the value of the objective function to be minimum under the search space constraint condition of each position variable through a genetic algorithm, wherein the group of main freedom degrees are the optimal sensor positions under the given sensor number N;
D. calculating the number of sensors, setting the number N of the sensors as a cyclic variable, and obtaining the corresponding optimal objective function value ObjV through the position optimization calculation of the given number NNSetting a calculation convergence condition, and setting the number of sensors which start to meet the convergence condition as the optimal number of sensors;
the number N of sensors is set as a circulation variable, and the number of sensors is respectively N in two adjacent circulationsiAnd Ni+1And satisfies the following conditions:
Ni+1＝Ni+NUM
wherein N isi+1、NiThe number of sensors in the i +1 th and i th cycles, i being 1,2,3 …; NUM is the number increment of the sensors of two adjacent cycles;
the convergence coefficient corresponding to the i +1 th cycle is defined as:
Figure BDA0001001399690000021
wherein, ObjVNi+1And objVNiRespectively, the number of sensors is Ni+1And NiCalculating to obtain an optimal objective function value;
when the average value of the convergence coefficients of the ith time and the (i + 1) th time is less than 5 percent and the number of the sensors is continuously increased, the Cont value is still less than 5 percent, and the target mode is considered to be identified by at least NiA sensor.
Figure BDA0001001399690000022
According to the invention, the target function of sensor position optimization is established through a model subtraction method, a structure subregion method is used for providing a search space for position variables, a genetic algorithm is used for realizing an optimization process, the signal-to-noise ratio of a measuring point and the integrity and orthogonality of a structural vibration-measured type are fully considered, the optimization of the position and the number of the large and complex jacket ocean platform sensors is realized, the position and the number of the ocean platform sensors are judged without depending on the experience of engineers, the monitoring efficiency of the large and complex ocean platform is greatly improved, manpower and material resources are saved, and the economic benefit is improved.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of the position and number optimization calculation of the sensors according to the present invention;
FIG. 3 is a jacket offshore platform configuration according to an embodiment of the present invention;
FIG. 4 is a ConT curve according to an embodiment of the present invention;
FIG. 5 is an optimized arrangement of sensors according to an embodiment of the present invention;
fig. 6 is a sensor layout evaluation in an embodiment of the present invention.
Detailed Description
Example 1: referring to fig. 3 to 6, taking the jacket platform structure shown in fig. 3 as a research object, a method for optimizing the position and number of sensors of the jacket platform structure comprises the following steps:
the model comprises 12 inclined struts, 12 cross struts, 4 longitudinal struts and 1 deck. The height of the model is 1.56m, the longitudinal support is No. 40 equal-side angle steel, the transverse support and the diagonal support are No. 25 equal-side angle steel, the thickness of the deck is 8mm, and the bottom support is 250 multiplied by 14 mm. The whole model is an all-steel structure, the elastic modulus is 195GPa, the material density is 7850kg/m3, and the Poisson ratio is 0.3. The boundary condition is that four ends at the bottom are fixedly supported, and ANSYS13.0 software is adopted to simulate and calculate the platform model. The first 5 natural frequencies and modes of vibration of the structure are obtained by finite element analysis and are shown in table 1. The 1 st, 2 nd, 3 rd and 5 th order vibration modes are overall vibration of the structure, the 4 th order is local vibration of the structure, and the 1 st, 2 nd, 3 th and 5 th order modes are selected as target modes to be identified.
TABLE 1 first five natural frequencies
Figure BDA0001001399690000031
A. The sensor position optimization process is equivalent to the optimization selection process of the main degree of freedom in model reduction, and the difference between the natural frequency Ft and the standard natural frequency Ft obtained based on the model reduction is set as an objective function of sensor position optimization:
Figure BDA0001001399690000032
the to-be-identified mode T comprises 1,2,3 and 5 orders, T is a T-th order to-be-identified mode, Ft is a T-th order natural frequency calculated based on a reduction model, and Ft is a T-th order natural frequency based on a complete model;
B. and (4) setting a position variable constraint condition by adopting a structure subregion method. Dividing the structure into N non-overlapping sub-regions according to the given number N of sensors, the structure configuration and the vibration characteristics, wherein all the degrees of freedom in the sub-regions are search spaces of corresponding position variables, and the search space of each position variable is limited to the sub-region;
for the jacket platform in the embodiment, the longitudinal support is the main control factor of the overall vibration of the structure, and the degree of freedom on the longitudinal support is set as a candidate measuring point of the sensor arrangement. The number M of the longitudinal supports of the jacket platform is 4, and the number N of the sensors satisfies the condition that N is S multiplied by M is S multiplied by 4, wherein S is an integer not less than 1. Based on the structural characteristics of the jacket platform, the structural division follows the following principle: and uniformly dividing all the longitudinal supports into S areas along the axial direction of the longitudinal supports, wherein each area only contains one sensor candidate position, and all the degrees of freedom in each area are the search space of the corresponding sensor.
C. Through a genetic algorithm, under the search space constraint condition of each variable, a group of main freedom degree combinations which enable the value of the objective function to be minimum are found, and the main freedom degrees are the optimal sensor positions under the given sensor number N;
in this embodiment, a simple genetic algorithm is used to implement an optimized calculation process, and the parameters are set as follows: the initial population NIND is 20, the surrogate GAP is 0.85, and the genetic generation Gene is 30.
D. Calculating the number of sensors, setting the number of sensors N as a cyclic variable, optimizing the calculation by giving the number N of positions,obtaining the corresponding optimal objective function value ObjVNSetting a calculation convergence condition, and setting the number of sensors which start to meet the convergence condition as the optimal number of sensors;
the number N of sensors is set as a circulation variable, and the number of sensors is respectively N in two adjacent circulationsiAnd Ni+1And satisfies the following conditions:
Ni+1＝Ni+NUM(2)
wherein N isi+1、NiThe number of sensors in the i +1 th and i th cycles, i being 1,2,3 …; NUM is the number increment of the sensors of two adjacent cycles;
the convergence coefficient corresponding to the i +1 th cycle is defined as:
Figure BDA0001001399690000041
wherein, ObjVNi+1And objVNiRespectively, the number of sensors is Ni+1And NiAn optimal objective function value of time;
when the average value of the convergence coefficients of the ith time and the (i + 1) th time is less than 5 percent and the number of the sensors is continuously increased, the Cont value is still less than 5 percent, and the target mode is considered to be identified by at least NiA sensor.
Figure BDA0001001399690000042
In the present embodiment, the initial number of sensors N1The number of sensors is incremented by 4 (NUM 4) per cycle until the calculation meets the above convergence condition.
As can be seen from fig. 4, Cont when the number of sensors N is 2020The value is less than 5%; and the calculations still converge steadily as sensors continue to be added. Therefore, it is considered that the number of sensors required to identify the target modality (1,2,3,5 order modality) is at least 20. The optimal arrangement for a sensor count of 20 is shown in FIG. 5 with purple dots. As can be seen from fig. 5, the optimized 20 sensor positionsThe whole longitudinal section of the structure is covered, and the key connecting parts are distributed with measuring points, so that the guarantee is provided for obtaining the structural complete modal parameters.
The optimization schemes obtained in the examples were evaluated, and the results are shown in fig. 6. As can be seen from fig. 6, under different N conditions,1always greater than 1, this means that the modal kinetic energy of the sensor position obtained by model subtraction optimization is higher than the average value of the degrees of freedom on the longitudinal support, which means that the signal-to-noise ratio of the measurement point selected by this method is higher. Meanwhile, under the condition of different N, the parameters2The value of (A) is always around 0.04, which shows that each order mode measured by the optimization scheme has better orthogonality.

Claims (1)

1. A method for optimizing and calculating the position and the number of sensors of a jacket ocean platform structure is characterized by comprising the following steps:
A. based on the principle that the selection principle of the main degree of freedom in the model reduction method is consistent with the selection criterion of the sensor of the jacket ocean platform structure, the sensor position optimization process is equivalent to the optimization selection process of the main degree of freedom in the model reduction method, and the difference between the natural frequency Ft and the standard natural frequency Ft obtained based on the reduction model is set as the objective function of sensor position optimization:
Figure FDA0002551730900000011
wherein T is the order of the modal to be identified, T is the T-th order modal to be identified, Ft is the T-th order natural frequency calculated based on the reduction model, and Ft is the T-th order natural frequency based on the complete model and is set as the standard frequency;
B. a structural subregion method is adopted, and position variable constraint conditions are given; dividing the structure into N non-overlapping sub-regions according to the given number N of sensors, the structure configuration and the vibration characteristics, wherein all the degrees of freedom in the sub-regions are search spaces of corresponding position variables, and the search space of each position variable is limited to the sub-region;
C. through a genetic algorithm, under the search space constraint of each position variable, a group of main freedom degree combinations which enable the value of the objective function to be minimum are found, and the main freedom degrees are the optimal sensor positions under the given sensor number N;
D. calculating the number of sensors, setting the number N of the sensors as a cyclic variable, and obtaining the corresponding optimal objective function value ObjV through the position optimization calculation of the given number NNSetting a calculation convergence condition, and setting the number of sensors which start to meet the convergence condition as the optimal number of sensors;
the number N of sensors is set as a circulation variable, and the number of sensors is respectively N in two adjacent circulationsiAnd Ni+1And satisfies the following conditions:
Ni+1＝Ni+NUM
wherein N isi+1、NiThe number of sensors in the i +1 th and i th cycles, i being 1,2,3 …; NUM is the number increment of the sensors of two adjacent cycles;
the convergence coefficient corresponding to the i +1 th cycle is defined as:
Figure FDA0002551730900000012
wherein, ObjVNi+1And objVNiRespectively, the number of sensors is Ni+1And NiCalculating to obtain an optimal objective function value;
when the average value Cont of the convergence coefficients of the ith time and the (i + 1) th time is less than 5 percent and the number of the sensors is continuously increased, the Cont value is still less than 5 percent, and the condition that at least N is needed for identifying the target mode is considerediA sensor;
Figure FDA0002551730900000013
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