Abstract
It is essential for a safe and cost-efficient marine operation to improve the knowledge about the real-time onboard vessel conditions. This paper proposes a novel algorithm for simultaneous tuning of important vessel seakeeping model parameters and sea state characteristics based on onboard vessel motion measurements and available wave data. The proposed algorithm is fundamentally based on the unscented transformation and inspired by the scaled unscented Kalman filter, which is very computationally efficient for large dimensional and nonlinear problems. The algorithm is demonstrated by case studies based on numerical simulations, considering realistic sensor noises and wave data uncertainties. Both long-crested and short-crested wave conditions are considered in the case studies. The system state of the proposed tuning framework consists of a vessel state vector and a sea state vector. The tuning results reasonably approach the true values of the considered uncertain vessel parameters and sea state characteristics, with reduced uncertainties. The quantification of the system state uncertainties helps to close a critical gap towards achieving reliability-based marine operations.
Highlights
For marine operations, operational limit diagrams are normally provided in operating reports or operation manual booklets
Important vessel parameters related to inertia distribution and damping are challenging to measure directly and still expected to be subject to significant uncertainties. Identification of these important vessel hydrodynamic parameters has been mainly studied for maneuvering [21,22,23] and dynamic positioning (DP) [24] scenarios, where the responses at wave frequencies are considered as a disturbance or ignored
To solve the curse of dimensionality, this paper proposes a novel and much more efficient algorithm to tune the vessel seakeeping model parameters by applying a second-order statistical inference algorithm based on the mean and variance of the variables
Summary
Operational limit diagrams are normally provided in operating reports or operation manual booklets. Important vessel parameters related to inertia distribution and damping are challenging to measure directly and still expected to be subject to significant uncertainties Identification of these important vessel hydrodynamic parameters has been mainly studied for maneuvering [21,22,23] and dynamic positioning (DP) [24] scenarios, where the responses at wave frequencies are considered as a disturbance or ignored. The previously developed methodology faces a common challenge with respect to the curse of dimensionality [29] This makes the discrete Bayesian inference based model tuning approach time-consuming, computationally expensive, and unrealistic for practical applications within such an extended system framework.
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