Stian Skjong

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.

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.

Virtual Prototyping of Maritime Systems and Operations

While in aerospace and automotive industry, airplanes and cars are built in quantity, in maritime industry ships and offshore platforms are built uniquely such that even sister ships can be significantly different from each other. Hence, building a full scale prototype to test, verify, and demonstrate effectiveness of new innovative solutions, is not an option in maritime sector. Model testing and simulation of separate modules have been practiced in many applications successfully, however, capturing the complete interaction of different modules in a maritime system is not straight forward. To best of our knowledge, the modeling and simulation of a maritime system to the extent where the complete system, including the mutual interactions, is not accomplished yet. A maritime system incorporates a wide variety of components from different engineering fields and in order to develop a simulation framework for such a complex system, an interdisciplinary effort is needed from different branches of science including but not limited to hydrodynamics, machinery and power systems, structural engineering, navigation and control. This paper aims to introduce a joint effort from different research institutes, universities, and industrial partners to shed a light on the different issues in virtual prototyping in maritime systems and operations. It summarizes some of the available frameworks for virtual prototyping, and ends with a numerical simulation of a generic hull model coupled with propellers, propeller actuators, DP controller, thrust loss calculations, wind, waves and current, performed with the current implementation of our Virtual Prototyping Framework (VPF).© 2016 ASME