WO2022097466A1 - 線状構造物の挙動を算出する装置及び方法 - Google Patents

線状構造物の挙動を算出する装置及び方法 Download PDF

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Publication number
WO2022097466A1
WO2022097466A1 PCT/JP2021/038481 JP2021038481W WO2022097466A1 WO 2022097466 A1 WO2022097466 A1 WO 2022097466A1 JP 2021038481 W JP2021038481 W JP 2021038481W WO 2022097466 A1 WO2022097466 A1 WO 2022097466A1
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WIPO (PCT)
Prior art keywords
linear structure
behavior
optical fiber
discrete values
likelihood
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Ceased
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PCT/JP2021/038481
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English (en)
French (fr)
Japanese (ja)
Inventor
隆 松井
和秀 中島
信智 半澤
良太 和田
英晶 村山
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University of Tokyo NUC
NTT Inc
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Nippon Telegraph and Telephone Corp
University of Tokyo NUC
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Priority to JP2022560701A priority Critical patent/JP7627912B2/ja
Priority to US18/034,899 priority patent/US12467805B2/en
Priority to EP21889019.2A priority patent/EP4242582B8/en
Publication of WO2022097466A1 publication Critical patent/WO2022097466A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
    • G01L1/00—Measuring force or stress, in general
    • G01L1/24—Measuring force or stress, in general by measuring variations of optical properties of material when it is stressed, e.g. by photoelastic stress analysis using infrared, visible light, ultraviolet
    • G01L1/242—Measuring force or stress, in general by measuring variations of optical properties of material when it is stressed, e.g. by photoelastic stress analysis using infrared, visible light, ultraviolet the material being an optical fibre
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0041—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining deflection or stress
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
    • G01B11/18—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge using photoelastic elements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
    • G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
    • G01L5/04—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes for measuring tension in flexible members, e.g. ropes, cables, wires, threads, belts or bands
    • G01L5/10—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes for measuring tension in flexible members, e.g. ropes, cables, wires, threads, belts or bands using electrical means
    • G01L5/105—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes for measuring tension in flexible members, e.g. ropes, cables, wires, threads, belts or bands using electrical means using electro-optical means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0091—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by using electromagnetic excitation or detection

Definitions

  • the present disclosure relates to an apparatus and a method for estimating the behavior of an underwater linear structure such as a submarine cable by a reflectometry technique using an optical fiber.
  • the optical fiber sensor is a technique for measuring and analyzing the reflection spectrum of an optical fiber by OFDR (Optical Frequency Domain Reflectory) to derive a three-dimensional shape of an object to be measured.
  • Fiber optic sensors are capable of dynamic measurements over long distances.
  • the error accumulating due to the integral calculation along the optical fiber occurs.
  • Non-Patent Document 1 As a method suitable for estimating the phenomenon in real time, there is an example of behavior estimation by sequential data assimilation in which data assimilation is performed every time observation data is obtained (see, for example, Non-Patent Document 1).
  • this method uses discrete position information of a linear structure by GPS (Global Positioning System) or the like as observation data. It is necessary to receive radio waves underwater in order to acquire location information. For this reason, as the depth in water increases, there is a problem that it becomes difficult to acquire position information and it is difficult to obtain continuous data in the length direction.
  • GPS Global Positioning System
  • the purpose of this disclosure is to make it possible to estimate the behavior of linear structures in water.
  • the devices and methods pertaining to this disclosure are At least one parameter is represented by a plurality of discrete values, and each of the discrete values is used to predict the behavior of the linear structure.
  • the behavior of the linear structure detected by using an optical fiber is acquired, and the behavior is obtained.
  • the likelihood of the predicted behavior of the linear structure and the acquired behavior of the linear structure was calculated.
  • the behavior of the linear structure is calculated using the updated plurality of discrete values.
  • the present disclosure makes it possible to estimate the behavior of linear structures in water.
  • An example of the system configuration of the present disclosure is shown.
  • An example of the hardware configuration of the apparatus of the present disclosure is shown.
  • An example of a deformation image of two cores is shown.
  • An example of displacement in the rotation direction is shown.
  • An example of a flow for explaining the method of the present disclosure is shown.
  • the behavior estimation result of the linear structure without this disclosure is shown.
  • the behavior estimation result of the linear structure when this disclosure is used is shown.
  • An example of the system configuration of the present disclosure is shown.
  • FIG. 1 shows an example of a system configuration of the present embodiment.
  • the device 10 is mounted on the ship 100, and the device 10 is connected to the linear structure 20.
  • the device 10 of the present embodiment is a device for calculating the behavior of the linear structure of the present disclosure, and is a linear structure existing in water by parameter estimation by data assimilation based on a distributed optical fiber sensor and Bayesian inference. The behavior of the object 20 is estimated.
  • the behavior of the linear structure 20 is, for example, the three-dimensional coordinates of the linear structure 20.
  • the three-dimensional coordinates of the linear structure 20 can be calculated by using the displacement of the linear structure 20 in the rotational direction with respect to the position of at least one point of the linear structure 20 from which the position information can be acquired. Therefore, in the present embodiment, an example in which the behavior of the linear structure 20 is a displacement in the rotation direction for each length L of the linear structure 20 will be described.
  • FIG. 2 shows an example of the hardware configuration of the device 10.
  • the device 10 of the present embodiment includes an information processing unit 11, a storage unit 12, an optical fiber sensor 13, and an input unit 14.
  • the input unit 14 is an arbitrary means capable of inputting parameters, such as a keyboard and a touch panel.
  • the optical fiber sensor 13 is connected to the optical fibers 21 and 22 arranged along the linear structure 20.
  • the optical fiber sensor 13 detects the strains ⁇ 1 and ⁇ 2 in the two optical fibers 21 and 22.
  • an optical fiber sensor using a BOTDR can be exemplified.
  • BOTDR Boillouin Optical Time Domain Reflectometer
  • the information processing unit 11 calculates the behavior of the linear structure 20 using the distortion detected by the optical fiber sensor 13.
  • the displacement of the linear structure 20 in the rotational direction can be obtained by using the deflection angle ⁇ [rad] at the length L of the linear structure 20 and the length L of the linear structure 20. Therefore, the information processing unit 11 calculates the deflection angle ⁇ [rad] for each length L of the linear structure 20 by using the strains ⁇ 1 and ⁇ 2 of the linear structure 20.
  • the information processing unit 11 calculates the deflection angle ⁇ [rad] for each length L of the linear structure 20 as the behavior of the linear structure 20 will be described.
  • the information processing unit 11 predicts the behavior of the linear structure 20 by using the simulator. For example, the information processing unit 11 simulates the behavior of the linear structure 20 using arbitrary parameters and calculates the state quantity U (t). The information processing unit 11 calculates the displacement of the linear structure 20 in the rotation direction for each length L using the state quantity U (t).
  • the storage unit 12 stores the strains ⁇ 1 and ⁇ 2 detected by the optical fiber sensor 13, arbitrary data used for processing of the information processing unit 11, and the processing result of the information processing unit 11.
  • the data used for the processing of the information processing unit 11 includes a simulation program for predicting the behavior of the linear structure 20 and parameters used in the simulation program.
  • the device 10 of the present embodiment can also be realized by a computer and a program, and the program can be recorded on a recording medium or provided through a network. Hereinafter, it will be described in detail.
  • Two optical fibers 21 and 22 are placed along the underwater linear structure 20, and as shown in FIG. 3, the cores of the optical fibers 21 and 22 are deformed according to the shape of the linear structure 20 to form an optical fiber sensor. At 13, strains of ⁇ 1 and ⁇ 2 are observed, respectively.
  • the deflection angle ⁇ [rad] obtained by integrating the curvature of the linear structure 20 in water over the length L [m] is the equation (1) using the distance d [m] between the cores of the optical fibers 21 and 22. It is represented by.
  • the deflection angle ⁇ in the equation (1) corresponds to the displacement in the rotation direction obtained as the simulation predicted value.
  • the measured deflection angles ⁇ 1 and ⁇ 2 correspond to the deflection angles ⁇ 1 and ⁇ 2 in the simulation predicted values.
  • H is an observation matrix that extracts the displacement of the linear structure 20 in the rotational direction from the state quantity U (t) obtained by the simulation.
  • w (t) is an observation error.
  • the observation error w (t) can be treated on the assumption that it follows an arbitrary probability distribution.
  • the observation matrix H is expressed by the following equation.
  • the observation matrix H is a matrix of (N + 1) ⁇ 9 (N + 1).
  • the observation data is the deflection angle data y and the simulation predicted value u taken out from the state quantity U is the displacement in the rotation direction, but it is appropriately changed according to the combination of the observed data and the simulation predicted value. be able to.
  • a fusion particle filter is used, multiple particles are duplicated according to the likelihood of observation data, and the weighted sum is taken to update the ensemble, which is a set of particles.
  • the absolute likelihood of each particle is the sum of the observed data likelihoods for each element, and the relative likelihood of each element is assumed to be a normal distribution defined by the mean value of rotational displacement and the standard deviation of the observation error. , Derived as a probability density function of its normal distribution.
  • the formulas for calculating the absolute likelihood and the relative likelihood are formulas (4) and (5), respectively.
  • ⁇ (i) (t) and ⁇ (i) (t) are the absolute and relative likelihoods of the i-th particle, respectively, and P n (i) (t) is the n-th particle in the i-th particle.
  • t ⁇ t) is the displacement of the nth element in the i-th particle in the rotational direction, and y n (t) is the deflection angle of the observation data of the corresponding part, W.
  • n (t) represents the observation error of the corresponding portion.
  • FIG. 5 shows an example of the processing flow of the information processing unit 11.
  • the processing flow of the information processing unit 11 includes steps S1 to S4.
  • the information processing unit 11 executes step S1, and when the observation data is acquired, the information processing unit 11 executes steps S2 to S4.
  • the information processing unit 11 executes behavior estimation by the sequential data assimilation method in steps S2 to S4.
  • Step S1 Uncertain parameters in the simulation model are expressed by a plurality of discrete values, and the behavior of the linear structure 20 is predicted using each of the discrete values. As a result, a plurality of simulation predicted values are generated.
  • the simulation predicted value is referred to as a particle.
  • Step S2 When the observation data is acquired, the likelihood of each particle is calculated using the acquired observation data. At this time, as shown in the equation (4), the observation error may be taken into consideration.
  • Step S3 At least one of the plurality of discrete values is updated based on the likelihood, and the particles weighted by the likelihood are resampled.
  • Step S4 Time integration of each particle is performed by a simulation model. At this time, system noise may be taken into consideration.
  • each step will be described in detail.
  • Step S1 In considering the dynamic simulation of the linear structure 20, the range of uncertain parameters (for example, deflection angle ⁇ ) is represented by discrete values ( ⁇ 1 , ⁇ 2 , ..., ⁇ N ). , Obtain simulation prediction values for each case (called particles). Discrete values within the assumed range are input from, for example, the input unit 14.
  • a linear structure made of elastic rubber Youngng's modulus: 5.0 ⁇ 105 Pa
  • a length of 40 m, a diameter of 0.5 m, and a density of 1000 kg / m 3 is hung straight in still water, and the upper end is hung.
  • the steps S2 to S4 are sequentially performed. repeat.
  • Step S2 The information processing unit 11 compares the obtained observation data with the assumed observation error and each simulation predicted value, and obtains the likelihood of each particle. For example, the absolute likelihood and the relative likelihood for each length L are obtained by using the equations (3) and (4).
  • Step S3 Particle resampling is performed with the relative likelihood obtained in step S2 as a weight.
  • the parameters used in the particles k', k'+ 1, k'+ 2 Increase the discrete value of the range, decrease the discrete value of the range of parameters other than particles k', k'+ 1, k'+ 2, and generate the discrete value of the new parameter ( ⁇ 1 , ⁇ 2 , ..., ⁇ N ). do.
  • the simulation predicted value is obtained using each parameter. As a result, particles using parameters with high relative likelihood are calculated.
  • Step S4 Time integration using dynamic simulation is performed on the resampled particles. As a result, the simulation predicted value at the time of acquiring the next observation data is obtained for each element length L.
  • the dynamic simulation is performed using, for example, the model used in step S1. Then, the process returns to step S2.
  • a weighted average of a plurality of particles is taken for each element length L.
  • the number of times of repeating steps S2 to S4 may be a fixed number of times, but a weighted average of a plurality of particles may be taken when the relative likelihood falls below a certain value. Further, as the number of particles for which the weighted average is taken, any number having a high relative likelihood can be adopted.
  • the information processing unit 11 can estimate the behavior with high accuracy. Further, in the present embodiment, in order to measure the overall strain of the vertically long linear structure 20 having a large amount of information and to calculate the likelihood considering the entire linear structure 20 using the equation (4). , The effect that the prediction accuracy of the linear structure 20 can be improved can be obtained.
  • the uncertain parameter can be any parameter used in the simulator for predicting the behavior of the linear structure 20 in water.
  • the parameters used in the simulator for estimating the behavior of the linear structure 20 in water include the tension T of the linear structure 20, the speed of the ship 100, the elasticity E of the linear structure 20, and the linear structure 20.
  • step S1 When there are two or more uncertain parameters, particles are generated for each combination of uncertain parameters in step S1, and in step S3, discrete values in the parameter range are generated for the combination of parameters with high likelihood. Dense. Thereby, the present disclosure has an effect that the prediction accuracy of the linear structure 20 can be improved by using a combination of parameters having a high likelihood even when the number of uncertain parameters is 2 or more. ..
  • the present embodiment relates to a simulation result of behavior estimation to which the sequential data assimilation method shown in the first embodiment is applied.
  • a linear structure 20 made of elastic rubber (Young's modulus: 5.0 ⁇ 105 Pa) with a length of 40 m, a diameter of 0.5 m, and a density of 1000 kg / m 3 is suspended straight in still water, and the upper end is horizontally hung.
  • the effect of behavior estimation was verified by a model in which a simple vibration was performed for 10 seconds with an amplitude of 2 m and a period of about 1.5 s.
  • the time division is 0.001 seconds
  • the length L per element is 1 m
  • the fluid density is 997 kg / m 3
  • the drag coefficient Ct in the tangential direction the resistance coefficient Cn in the normal direction
  • the additional mass coefficient Cmb Each coefficient of is 1.5, 0.03, and 1, respectively.
  • the observation data was a deflection angle ⁇ for each 1 m in length obtained from the strain distribution of the linear structure, and the sampling rate was 25 Hz.
  • the number to take the weighted average is 3, and the weighting coefficient is Was set.
  • ⁇ 1 is the weight of the particle having the highest relative likelihood
  • ⁇ 2 is the weight of the particle having the second highest relative likelihood
  • ⁇ 3 is the weight of the particle having the third highest relative likelihood.
  • the start of sequential data assimilation was 2 seconds after the start of the simulation, and the estimated unknown parameter was the mode attenuation ratio. It was assumed that the damping ratios of the primary mode and the secondary mode were equal.
  • FIG. 6 shows the shape estimation result of the linear structure after 10 seconds when the simulation was performed with the mode attenuation ratio having the uncertainty of the prior distribution without assimilating the data sequentially.
  • the vertical axis is the distance in the depth direction
  • the horizontal axis is the distance in the horizontal direction.
  • the coordinates (0,0) indicate the position of the linear structure 20 fixed to the ship 100 shown in FIG. From FIG. 6, it can be confirmed that a particularly large uncertain factor (estimation error) exists in the region where the horizontal amplitude is maximum.
  • FIG. 7 shows the time change of the estimation error when the sequential data assimilation is applied. It can be seen that the estimation error is remarkably reduced with the application of the sequential data assimilation (after 2 seconds), and the estimation converges to almost the true value 8 seconds after the application.
  • a multi-core optical fiber 23 may be used in which a plurality of cores 231 and 232 are arranged at intervals d in the clad of one optical fiber.
  • the multi-core optical fiber 23 having two cores is shown as an example, but the number of cores may be two or more, and any form can be used for the arrangement of the cores.
  • it may be a 4-core fiber having one in the center and three cores on the outer periphery thereof.
  • This disclosure can be applied to the information and communication industry.

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Electromagnetism (AREA)
  • Optical Transform (AREA)
  • Length Measuring Devices By Optical Means (AREA)
PCT/JP2021/038481 2020-11-05 2021-10-18 線状構造物の挙動を算出する装置及び方法 Ceased WO2022097466A1 (ja)

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JP2022560701A JP7627912B2 (ja) 2020-11-05 2021-10-18 線状構造物の挙動を算出する装置及び方法
US18/034,899 US12467805B2 (en) 2020-11-05 2021-10-18 Apparatus and methods for calculating the behavior of linear structures
EP21889019.2A EP4242582B8 (en) 2020-11-05 2021-10-18 Device and method for calculating behavior of linear structure

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US20240019323A1 (en) 2024-01-18
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US12467805B2 (en) 2025-11-11
JP7627912B2 (ja) 2025-02-07
EP4242582A4 (en) 2024-09-25
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