| dc.description.abstract | The Digital Twin (DT) is cited as one of the key concepts associated with Industry 4.0 waves. In fact, the concept of DT is not new and has lasted for over 10 years. In recent years, the clarity of improved technologies and applied case has accelerated technology adoption. DT is defined as a virtual copy-model or concept that is represented on a computer as the same with a physical thing. By building a virtual digital twin on software instead of actual physical assets and simulating them, we can get accurate information about the characteristics of the real assets, such as their current state and behavior. In many industries like energy, transportation, and defense, digital twins is being used to improve the efficiency of all process from design, installation, and services. It includes asset optimization, accident minimization, and increased productivity. Oil and gas companies are now starting to realize the benefits of DT, which can provide unprecedented real-time operational insights and take operational efficiency to the next level.
The DT is mainly used for monitoring, diagnostics and prediction to enhance asset performance and utilization. It aims to provide an amply integrated solution for structural health monitoring to increase the reliability of real assets. In this regard, the monitoring technology is an important part of the DT. In this study, the real-time monitoring algorithm, which estimate ocean wave directional spectrum and subsea riser’s deformed shape, has been developed based on the Kalman filter.
The first application is to estimate ocean wave form vessel motion. The real-time inverse estimation of the ocean wave spectrum and elevation from a vessel-motion sensor is of significant practical importance, but it is still in the developing stage. The Kalman-filter method has the advantages of real-time estimation, cost reduction, and easy installation than other methods. Reasonable estimation of high-frequency waves is important in view of covering various sea states. However, if the vessel is less responsive for high-frequency waves, amplified noise may occur and cause overestimation problem there. In this paper, a configuration of Kalman filter with applying the principle of Wiener filter is proposed to suppress those over-estimations. Over-estimation is significantly reduced at high frequencies when the method is applied, and reliable real-time wave spectra and elevations can be obtained. The simulated sensor data was used, but the proposed algorithm has been proved to perform well for various sea states and different vessels. In addition, the proposed Kalman-filter technique is robust when it is applied to time-varying sea states. Finally, the proposed method was also tested for the case of the directional wave spectrum and the ship's speed inclusion.
The second application is to monitor subsea riser’s deformed shape in real time using Extended Kalman Filter (EKF). The real-time monitoring of underwater risers, cables, and mooring lines by multiple sensors is in great demand but still very challenging. In this study, a new real-time riser monitoring method based on an EKF is proposed. It estimates the overall shape of riser in real-time utilizing the measured signals from multiple bi-axial (inclination and heading) inclinometers along the riser. The novel EKF algorithm is shown to be robust against sensor noises and successfully reproduces the actual riser profiles at each time step, which has been verified by multiple tests through numerical simulations. For verification, a turret-moored FPSO (Floating Production Storage and Offloading) with a SCR (Steel Catenary Riser) is employed in four different random waves and currents. Subsequent algorithms are also developed so that the corresponding bending and axial stresses along the riser can also be estimated in real time from the obtained riser shape, which can further be used for the real-time estimation of fatigue-damage accumulation. | en |
