Översikt
- Datum:Startar 16 januari 2026, 09:00Slutar 16 januari 2026, 12:00
- Plats:EE lecture hall, EDIT-building
- Opponent:Professor Emeritus Apostolos Papanikolaou, National Technical University of Athens
- AvhandlingLäs avhandlingen (Öppnas i ny flik)
Inland waterway transport offers substantial potential to mitigate greenhouse gas emissions and congestion from road freight transport. The development of advanced inland vessels equipped with clean energy systems and a high degree of automation represents a promising direction for future transport networks. Nevertheless, inland waterways are often restricted by shallow water and limited manoeuvring space. Hence, the deployment of full-scale autonomous vessels requires careful consideration of environmental and operational challenges to ensure safety and energy efficiency. To enhance the automation of inland shipping, this thesis provides an in-depth analysis of how shallow and confined water affects ship resistance, manoeuvring, control implementation, and energy efficiency. The aim of this thesis is to develop a holistic simulation framework for ship performance prediction and operational analysis.
The development of such a comprehensive framework requires modelling of vessel characteristics with hydrodynamic and river hydraulic effects. Firstly, this thesis develops a novel ship energy performance model explicitly tailored for resistance and energy consumption prediction of inland waterway vessels. It aims to generate fast and accurate predictions based on a collection of purely empirical methods. Secondly, this thesis develops a Manoeuvring Modelling Group-based manoeuvring model for motion prediction under shallow water and bank effects. Building upon this, a systematic control design was conducted with consideration of ship hydrodynamic characteristics in confined waterways. A comparison of a conventional controller and an advanced model predictive control was performed to evaluate their performance and robustness in tackling path-following tasks under complex environments with various disturbances. With these models and control methods, an integrated voyage planning framework (VPF) is proposed for analysing vessel operations. It captures a vessel’s dynamic performance with energy consumption analysis under coupled interactions between ship hydrodynamics, river hydraulics, and motion control. Based on the analysis of ship energy performance, this thesis proposes a particle swarm optimisation (PSO) module for fuel optimisation in inland waterways.
Validation studies with full-scale trial measurements revealed that the energy performance prediction model achieved promising accuracy, ensuring a mean absolute error below 10% based on finite input parameters. It was also observed that disturbances in inland waterways, such as currents and bank effects, significantly affect a vessel’s course stability, which should be carefully considered in the control system development of autonomous vessels operating in narrow channels. A series of case studies showcased the VPF’s capabilities for enabling a wide range of applications, such as route assessment, evaluation of control design, and operational energy efficiency analysis. It was shown that the PSO-based optimisation achieved an average of 5.7% fuel savings in shallow water operations with water depth-aware speed initialisation methods.
The development of such a comprehensive framework requires modelling of vessel characteristics with hydrodynamic and river hydraulic effects. Firstly, this thesis develops a novel ship energy performance model explicitly tailored for resistance and energy consumption prediction of inland waterway vessels. It aims to generate fast and accurate predictions based on a collection of purely empirical methods. Secondly, this thesis develops a Manoeuvring Modelling Group-based manoeuvring model for motion prediction under shallow water and bank effects. Building upon this, a systematic control design was conducted with consideration of ship hydrodynamic characteristics in confined waterways. A comparison of a conventional controller and an advanced model predictive control was performed to evaluate their performance and robustness in tackling path-following tasks under complex environments with various disturbances. With these models and control methods, an integrated voyage planning framework (VPF) is proposed for analysing vessel operations. It captures a vessel’s dynamic performance with energy consumption analysis under coupled interactions between ship hydrodynamics, river hydraulics, and motion control. Based on the analysis of ship energy performance, this thesis proposes a particle swarm optimisation (PSO) module for fuel optimisation in inland waterways.
Validation studies with full-scale trial measurements revealed that the energy performance prediction model achieved promising accuracy, ensuring a mean absolute error below 10% based on finite input parameters. It was also observed that disturbances in inland waterways, such as currents and bank effects, significantly affect a vessel’s course stability, which should be carefully considered in the control system development of autonomous vessels operating in narrow channels. A series of case studies showcased the VPF’s capabilities for enabling a wide range of applications, such as route assessment, evaluation of control design, and operational energy efficiency analysis. It was shown that the PSO-based optimisation achieved an average of 5.7% fuel savings in shallow water operations with water depth-aware speed initialisation methods.
