Eilif Pedersen

Marine Vessel and Power Plant System Simulator

Modern marine electric propulsion vessels have many systems. These interactions and integration aspects are essential when studying a system and subsystem behavior. This is especially important when considering fault scenarios,s harsh weather, and complex marine operations. However, many simulators, including a selection presented here, study the positioning system and the power system separately. This paper proposes a simulator combining the two systems, as an extension to the marine systems simulator MATLAB/Simulink library. The intended use cases and the according design choices are presented. New subsystem models include a power-based electrical bus model and a simplified diesel engine model. Both are validated through the simulation against established models. In addition, established models for generators, electrical storage devices, thrusters, and a mean-value diesel engine model are summarized with rich references. Three case studies illustrate the multi-domain use of the simulator: 1) a semi-submersible drilling rig performing station keeping under environmental disturbances; 2) the same vessel subject to an electrical bus reconfiguration; and 3) a supply vessel with a hybrid power plant.

Bond graph modelling of marine power systems

The main motivation for writing this article is to develop a model library for an All-Electric Ship that gives an opportunity to simulate both existing and new machinery systems without having to remodel the entire system each time. The model library should support the process of modelling and reuse, while also emphasizing openness to brace the modeller during the development and refinement phase. The bond graph approach is good when it comes to the physical modelling of systems and is a good tool for combining different energy domains to better help in understanding the system. In addition, a bond graph is a powerful method to find dependencies between various components. Using a causal analysis, any problems in the model, for example, algebraic constrains or dependent system variables, will be detected, and the necessary remodelling may be performed to handle such problems. The bond graph approach is therefore used when developing the component library. The component library consists of selected power producers such as diesel and gas engines, fuel cell and synchronous generator and power consumers such as asynchronous motor with a voltage source converter in addition to a generic load used for hotel and auxiliary loads. The library also consists of a ship model and propeller models.

Flexible Modeling And Simulation Architecture For Haptic Control Of Maritime Cranes And Robotic Arm.

This paper introduces a modular prototyping system architecture that allows for the modeling, simulation and control of different maritime cranes or robotic arms with different kinematic structures and degrees of freedom using the Bond Graph Method. The resulting models are simulated in a virtual environment and controlled using the same input haptic device, which also provides the user with a valuable force feedback. The arm joint angles can be calculated at runtime according to the specific model of the robot to be controlled. The idea is to develop a library of crane beams, joints and actuator models that can be used as modules for simulating different cranes. The base module of this architecture is the crane beam model. Using different joint modules to connect several such models, different crane prototypes can be easily built. The library also includes a simplified model of a vessel to which the crane models can be connected in order to get a complete model. Related simulations were carried out using the so-called 20-sim simulator to validate efficiency and flexibility of the proposed architecture. In particular, a two-beam crane model connected to a simplified vessel model was implemented. To control the arm, an omega.7 from Force Dimension was used as an input haptic device.

Energy conservation and power bonds in co-simulations: non-iterative adaptive step size control and error estimation

Here, we study the flow of energy between coupled simulators in a co-simulation environment using the concept of power bonds. We introduce energy residuals which are a direct expression of the coupling errors and, hence, the accuracy of co-simulation results. We propose a novel energy-conservation-based co-simulation method (ECCO) for adaptive macro step size control to improve accuracy and efficiency. In contrast to most other co-simulation algorithms, this method is non-iterative and only requires knowledge of the current coupling data. Consequently, it allows for significant speed-ups and the protection of sensitive information contained within simulator models. A quarter car model with linear and nonlinear damping serves as a co-simulation benchmark and verifies the capabilities of the energy residual concept: reductions in the errors of up to 93% are achieved at no additional computational cost.

Modeling of Generic Offshore Vessel in Crane Operations With Focus on Strong Rigid Body Connections

Both marine surface vehicles and underwater vehicles are often equipped with cranes, robotic manipulators, or similar equipment. Much attention is given to modeling of both the dynamics of marine vehicles and the dynamics of manipulators, cranes, and other equipment. However, less attention is given to the interconnected behavior of the vehicle and equipment, even though such equipment may have a considerable impact on the vehicle dynamic behavior, and therefore risk, or conversely, the vehicle may have a considerable impact on the equipment dynamic behavior. With main focus on ships equipped with cranes, this paper presents a framework for modeling the interconnected dynamics of rigid body systems, based on Lagrangian dynamics. The resulting equations of motion are implemented as a bond graph template to which any subsystem of interest, such as actuators, hydrodynamics, and controllers, may be interfaced. An example on how this framework can be used to develop a high-fidelity simulator of an offshore installation vessel with a heavy duty crane is presented. This work represents the first bond graph implementation of crane and vessel dynamics where the interconnections are modeled according to true physical rigid body principles without nonphysical limitations such as diagonal mass-inertia matrix.

Rotordynamics and bond graphs: basic models

Basic rotordynamic models such as for the Jeffcott [H. H. Jeffcott, Lateral vibration of laded shafts in the neighborhood of a whirling speed–Thee ffect of want of imbalance, Philos.Mag. 37, 1919, pp. 304–314] and Stodola–Green [A. Stodola, Dampf-und Gasturbinen, Springer-Verlag, Berlin, 1924, R.Green, Gyroscopic effects of the critical speeds of flexible rotors, J Appl Mech, 15 (1948), pp. 369–376] rotors are developed in a bond graph formalism. The equations of motion for a general rotor with an imbalance are derived from Lagranges equations to include most rotordynamic phenomena including gyroscopic effects. The implementation into the bond graph models using both multibond and scalar bonds is given and discussed. An example of bond graph models for the classical Jeffcott rotor is included and the derivation of the complete state equations from the scalar bond graph is shown in detail. A more complex bond graph-modelling example using the Stodola–Green model mounted on a stiff shaft with bearing elasticity and damping is also included. Simulation results for both the models are shown. The purpose of a bond graph implementation of such rotordynamics models is to explore the modular and foundational pieces of the bond graph in more complex rotordynamic or mechatronic models and visualize the rotordynamic phenomena from the energy flow and visual perspectives.

REAL-TIME MARINE VESSEL AND POWER PLANT SIMULATION

In this paper, we present a system simulator of a marine vessel and power plant which contains the mechanical system with diesel engines, propellers, steering gear, and thrusters; the electrical system with generators, switchboards, breakers, and motors; and the plant level controllers with dynamic positioning controller, thrust control, and power management system. Interconnections are possible to simulate by using a multi domain simulator. This is important when evaluating system performance and fault handling. The simulator is implemented in Simulink and is modular, configurable and scalable. It can be extended to run on National Instruments’ cRIO embedded control and acquisition system, for real-time simulation.Copyright

Bond Graph Modeling of Marine Vehicle Dynamics

Abstract The equations of motion for a marine vehicle in 6 DOF are derived using a Lagrangian approach. Utilizing the close relationship between the Lagrange method and the bond graph modelling method, the resulting equations of motion are consistently implemented into a bond graph framework for model representation and simulation. The bond graph implementation of the general rigid body equations of motion are shown and discussed. The added hydrodynamic forces occurring when a body moves in a fluid, forces from waves and current and propulsion and rudder forces are discussed and shown how to include into a bond graph representation. The purpose of a bond graph implementation of marine vehicle dynamics is to establish a selection of building blocks which supports the modelling process setting up marine mechatronic system models including the marine vehicle for complex system simulations.

Distributed Co-Simulation of Maritime Systems and Operations

Here, we present the concept of an open virtual prototyping framework for maritime systems and operations that enables its users to develop re-usable component or subsystem models, and combine them in full-system simulations for prototyping, verification, training, and performance studies. This framework consists of a set of guidelines for model coupling, high-level and low-level coupling interfaces to guarantee interoperability, a full-system simulation software, and example models and demonstrators. We discuss the requirements for such a framework, address the challenges and the possibilities in fulfilling them, and aim to give a list of best practices for modular and efficient virtual prototyping and full-system simulation. The context of our work is within maritime systems and operations, but the issues and solutions we present here are general enough to be of interest to a much broader audience, both industrial and scientific.

Nonangular MPC-Based Thrust Allocation Algorithm for Marine Vessels—A Study of Optimal Thruster Commands

In this paper, a thrust allocation algorithm for marine vessels based on model predictive control (MPC) theory and a nonangular vector formulation is presented and studied. The main objective in this paper is to highlight the potentials of using an optimal thrust allocation algorithm including a time horizon to reduce the power consumption as well as reducing the environmental disturbances in the thruster commands. The proposed thrust allocation algorithm is compared with a one-step optimization algorithm in a benchmarking test. A one-step thrust allocation algorithm is an optimization algorithm with a time horizon that includes only one sample. When using a longer time horizon in the proposed algorithm, the thrust allocation has the potential of optimizing rate limited states in the long run, e.g., whether it would be beneficial to rotate a thruster or to increase or decrease the commanded thrust when thruster biasing is considered as an option. Preliminary case studies are presented where different cost function weights and horizon lengths are compared. The finite time horizon in the MPC thrust allocation algorithm also makes it possible to affect the dynamics of the optimized thruster signals since it can use the entire time horizon to reach its objective. This is very important when considering reducing the thrust rates when controlling a marine vessel in dynamic positioning operations since wave filters never succeed in filtering out all oscillatory environmental effects. Thus, an optimal thrust allocation algorithm with well-chosen cost function weights, along with thruster biasing, would reduce the magnified oscillations in the produced thrust, while keeping the power consumption at a minimum, which has been devoted the main focus of this paper.