Apparatus and method for a real-time-monitoring of a riser and mooring of floating platforms

Patent metadata

Field	Value
Publication number	US-2022081080-A1
Application number	US-202117447501-A
Country	US
Kind code	A1
Filing date	Sep 13, 2021
Priority date	Sep 11, 2020
Publication date	Mar 17, 2022
Grant date	—

Abstract

An apparatus, method and system for real-time monitoring of underwater risers, cables, and mooring lines based on a Kalman filter. In an embodiment, the system is formed with sensors configured to sense an inclination of a riser segment between riser nodes of the riser between the upper end and the lower end. A data processing system is configured to employ a Kalman filter algorithm to produce real-time estimates of a deformed shape and a stress of the riser segment using the sensed inclination between the riser nodes.

First claim

What is claimed is:

1 . A system operable with a riser having an upper end coupled to a platform and a lower end coupled to a seabed, comprising:
sensors configured to sense an inclination of a riser segment between riser nodes of said riser between said upper end and said lower end; and a data processing system configured to employ a Kalman filter algorithm to produce real-time estimates of a deformed shape and a stress of said riser segment using said sensed inclination between said riser nodes.

2 . The system as recited in claim 1 wherein said system is configured to detect a malfunction or a damage of said riser segment.

3 . The system as recited in claim 1 wherein said data processing system is configured to employ a machine-learning algorithm to produce real-time estimates of said deformed shape and said stress of said riser in a portion outside of a sensor location.

4 . The system as recited in claim 1 wherein said Kalman filter algorithm reduces a sensor error and a prediction error of said real-time estimates of said deformed shape and said stress of said riser segment by a recursive calculation process.

5 . The system as recited in claim 1 wherein said upper end and said lower end of said riser are known at time steps of execution of said Kalman filter algorithm.

6 . The system as recited in claim 1 wherein said deformed shape and said stress of said riser segment are induced by environmental loadings and motions of said platform.

7 . The system as recited in claim 1 wherein a global navigation satellite system is employed to estimate a position of said upper end of said riser.

8 . The system as recited in claim 1 wherein said lower end of said riser is located at an anchoring point in said seabed.

9 . The system as recited in claim 1 wherein said Kalman filter algorithm is an extended Kalman filter algorithm.

10 . The system as recited in claim 9 wherein said extended Kalman filter algorithm is a nonlinear version of a Kalman filter constructed by linearization of a nonlinear function.

11 . The system as recited in claim 9 wherein a measured vertical inclination and a measured azimuth of said riser segment are employed by said extended Kalman filter algorithm to produce said estimates of said deformed shape and said stress of said riser segment.

12 . A method operable with a riser having an upper end coupled to a platform and a lower end coupled to a seabed, comprising:
sensing an inclination of a riser segment between riser nodes of said riser between said upper end and said lower end; and producing real-time estimates of a deformed shape and a stress of said riser segment using said sensed inclination between said riser nodes employing a Kalman filter algorithm.

13 . The method as recited in claim 12 further comprising detecting a malfunction or a damage of said riser segment.

14 . The method as recited in claim 12 further comprising producing real-time estimates of said deformed shape and said stress of said riser in a portion outside of a sensor location employ a machine-learning algorithm.

15 . The method as recited in claim 12 wherein said Kalman filter algorithm reduces a sensor error and a prediction error of said real-time estimates of said deformed shape and said stress of said riser segment by a recursive calculation process.

16 . The method as recited in claim 12 wherein said upper end and said lower end of said riser are known at time steps of execution of said Kalman filter algorithm.

17 . The method as recited in claim 12 wherein a global navigation satellite system is employed to estimate a position of said upper end of said riser.

18 . The method as recited in claim 12 wherein said Kalman filter algorithm is an extended Kalman filter algorithm.

19 . The method as recited in claim 18 wherein said extended Kalman filter algorithm is a nonlinear version of a Kalman filter constructed by linearization of a nonlinear function.

20 . The method as recited in claim 18 wherein a measured vertical inclination and a measured azimuth of said riser segment are employed by said extended Kalman filter algorithm to produce said estimates of said deformed shape and said stress of said riser segment.

Assignees

• Texas A & M Univ Sys

Inventors

• Kim Moo-Hyun
• Kim Hansung
• Jin Chungkuk

Classifications

• Y02T70/10

  Measures concerning design or construction of watercraft hulls · CPC title

• B63B21/502

  by means of tension legs · CPC title

• B63B21/507

  with mooring turrets · CPC title

• B63B2021/008

  Load monitors · CPC title

• B63B22/021

  and for transferring fluids, e.g. liquids (container type buoys having means to selectively release contents, not adapted for mooring a vessel B63B22/26) · CPC title