US 12,280,859 B2
Apparatus and method for a real-time-monitoring of a riser and mooring of floating platforms
Moo-Hyun Kim, College Station, TX (US); Hansung Kim, College Station, TX (US); and Chungkuk Jin, College Station, TX (US)
Assigned to THE TEXAS A&M UNIVERSITY SYSTEM, College Station, TX (US)
Filed by The Texas A&M University System, College Station, TX (US)
Filed on Sep. 13, 2021, as Appl. No. 17/447,501.
Claims priority of provisional application 62/706,827, filed on Sep. 11, 2020.
Prior Publication US 2022/0081080 A1, Mar. 17, 2022
Int. Cl. B63B 79/10 (2020.01); B63B 21/26 (2006.01); B63B 79/30 (2020.01); G01S 19/42 (2010.01)
CPC B63B 79/10 (2020.01) [B63B 21/26 (2013.01); B63B 79/30 (2020.01); G01S 19/42 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system operable with a riser having an upper end coupled to a platform and a lower end coupled to a seabed, wherein said riser comprises a plurality of riser segments collectively interconnected by a plurality of nodes each joining a corresponding pair of said riser segment, said system comprising:
sensors including bi-axial inclinometers each positioned in a middle of a corresponding one of a subset of said riser segments and configured to sense and output a signal indicative of vertical inclination and azimuthal heading measurements at said middle of said corresponding riser segment in real-time; and
a data processing system configured to employ a nonlinear “extended” Kalman filter (“EKF”) algorithm to produce real-time estimates of a deformed shape and a stress of said riser, and of each individual riser segment, using known locations of said upper and lower ends and said vertical inclination and azimuthal heading measurements by reproducing a profile of each riser segment at each of a plurality of time steps, wherein:
said EKF algorithm reduces a sensor error and a prediction error of the real-time estimates of said deformed shape and said stress of each riser segment via a recursive calculation process;
said EKF algorithm produces said real-time estimates of each riser segment, in a portion outside of a location of said corresponding bi-axial inclinometer, via a machine-learning algorithm executing on said data processing system;
said data processing system is further configured to estimate bending, tension, and axial stresses of said riser segments based on said estimated deformed shape of said riser and said riser segments; and
said data processing system is further configured to detect malfunctions, damages, and accumulated fatigue of said riser segments using said real-time estimates of said deformed shape and said stresses of said riser segments.