Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Onboard tuning of vessel seakeeping model parameters and sea state characteristics
https://orcid.org/0000-0002-5223-2431
https://orcid.org/0000-0001-5950-6186
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 Both the uncertain vessel parameters and wave information are simultaneously tuned. It is computationally efficient considering large dimension and nonlinearity. Uncertainties of vessel and wave conditions are quantitatively updated. Tuning algorithm is fundamentally based on unscented transformation.
Onboard tuning of vessel seakeeping model parameters and sea state characteristics
https://orcid.org/0000-0002-5223-2431
https://orcid.org/0000-0001-5950-6186
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 Both the uncertain vessel parameters and wave information are simultaneously tuned. It is computationally efficient considering large dimension and nonlinearity. Uncertainties of vessel and wave conditions are quantitatively updated. Tuning algorithm is fundamentally based on unscented transformation.
Onboard tuning of vessel seakeeping model parameters and sea state characteristics
https://orcid.org/0000-0002-5223-2431
https://orcid.org/0000-0001-5950-6186
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 Both the uncertain vessel parameters and wave information are simultaneously tuned. It is computationally efficient considering large dimension and nonlinearity. Uncertainties of vessel and wave conditions are quantitatively updated. Tuning algorithm is fundamentally based on unscented transformation.
Onboard tuning of vessel seakeeping model parameters and sea state characteristics
Han, Xu (Autor:in) / Leira, Bernt Johan (Autor:in) / Sævik, Svein (Autor:in) / Ren, Zhengru (Autor:in)
Marine Structures ; 78
08.03.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Vessel takes parallel computer onboard
Online Contents | 1995
Minimum ship size for seakeeping
| Online Contents | 2012
Minimum ship size for seakeeping
| SAGE Publications | 2012