- Unit:
- SINTEF Ocean AS
- Department:
- Fisheries and New Biomarine Industry
- Office:
- Ålesund
Stian Skjong received the M.Sc. degree in 2014 and the Ph.D. degree in 2017, both in marine technology from the Norwegian University of Science and Technology (NTNU), and is currently working as a senior research scientist in SINTEF Ocean. His research interests include modeling, simulation and control of marine systems, nonlinear control, model based control designs, co-simulation strategies for virtual prototyping of marine systems and operations, and simulation software development.
Education
- M.Sc. degree in 2014 in marine technology from the Norwegian University of Science and Technology (NTNU)
- Ph.D. degree in 2017 in marine technology from the Norwegian University of Science and Technology (NTNU)
Competence and research areas
- Modeling, simulation and control of marine systems
- Nonlinear control
- Model based control designs
- Co-simulation strategies for virtual prototyping of marine systems and operations
- Simulation software development.
- Numerical integration and general numerics
Highlighted publications
- Virtual Prototyping of Maritime Systems and Operations: Applications of Distributed Co-Simulations
- Lumped, constrained cable modeling with explicit state-space formulation using an elastic version of Baumgarte stabilization
- On the numerical stability in dynamical distributed simulations
- Non-angular MPC-based Thrust Allocation Algorithm for Marine Vessels - A Study of Optimal Thruster Commands
- Modeling of Generic Offshore Vessel in Crane Operations With Focus on Strong Rigid Body Connections
Projects
Other publications
- Directional wave spectrum estimation with ship motion responses using adversarial networks
- Dynamic modelling of PEM fuel cell system for simulation and sizing of marine power systems
- Bond Graph Approach for Modelling of Proton Exchange Membrane Fuel Cell System
- Does co-simulation have any value?
- A Framework for Condition Monitoring and Risk-Based Decision Support Involving a Vessel State Observer
- An Uncertainty-aware Hybrid Approach for Sea State Estimation Using Ship Motion Responses
- Data-driven sea state estimation for vessels using multi-domain features from motion responses
- A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion Data
- D1.1 - Monitoring system specification
- On-board decision support framework functionality developed in 2020