Experimental and phenomenological investigation of dynamic positioning in managed ice
Introduction
According to the International Maritime Organization (1994), a dynamically positioned vessel automatically maintains its position (fixed location or predetermined track) exclusively through the use of thrusters. The existing control systems for dynamic positioning in open water have an established well-known structure (Fossen, 2011, Sørensen, 2005), and robust commercial solutions are available for different operational scenarios in the presence of wind, waves, and currents (see, e.g., Kongsberg Maritime, 2006, Rolls-Royce Marine, 2009; Sirehna, 2014). However, when the vessel interacts with sea ice, the environmental forces are substantially different, and conventional open-water systems are known to be insufficient (Gürtner et al., 2012, Hals and Jenssen, 2012, Kerkeni et al., 2013a; Kerkeni et al., 2013a). Nevertheless, full-scale, model-scale, and numerical experiments have demonstrated that high-uptime positioning is possible with ice management (IM) support (Hals and Jenssen, 2012, Keinonen and Martin, 2012; Liferov, 2014, Metrikin et al., 2013; Moran et al., 2006, Rohlén, 2009). IM involves all aspects of removing or reducing ice actions on the protected vessel (Eik, 2010). Actual IM activities are operation specific, but the main objective is to either transform the natural ice environment into an acceptable managed ice condition or to suspend the operation if that is not possible. Fig. 1 illustrates a physical IM operation: the operational fleet, the ice cover, and the ice drift direction. More details on stationkeeping operations in ice with an emphasis on IM are given in, e.g., Liferov (2014) and Riska and Coche (2013). Because IM is essential for successful operations, the scope of this paper is limited to the assessment of dynamic positioning (DP) in a channel of managed sea ice.
To investigate a DP operation from the control engineering perspective, this paper examines global managed ice load data from a model-scale dataset of the DYPIC project (DYnamic Positioning in ICe), which was performed in 2010–2012 (Kerkeni et al., 2014). The project consisted of an extensive set of experiments performed at the large ice tank of the Hamburg Ship Model Basin (HSVA) in 2011 and 2012. Details and the setup of those model tests are provided in Section 2 of this paper. Then, Section 3 investigates the resulting ice load signals and their characteristics in various managed ice conditions, accentuating important aspects of control system development. Next, Section 4 discusses the possible physical mechanisms responsible for the ice load signal dynamics, in which a phenomenological analysis of the ice–structure interaction is performed that identifies and investigates key processes in the managed ice cover. Finally, Section 5 utilizes the ice load signal and phenomenological findings to identify the main weaknesses of conventional DP control systems and to propose specific improvements of control algorithms for operations in managed ice. The paper ends with a summary and conclusions in Section 6.
Section snippets
Model tests of the DYPIC project
The European research and development project DYPIC was a 3 year initiative (2010–2012) led by HSVA and financed by the national research agencies of Germany, France, and Norway. The program focused on various aspects of DP technology for offshore operations in ice-infested waters. Comprehensive project overviews can be found in Jenssen et al. (2012) and Kerkeni et al. (2014). This paper builds on the model testing data of DYPIC, in which almost 250 different scenarios were tested in broken ice
Ice load signal analysis
This section investigates the ice load measurements of the DYPIC project to determine signal characteristics that can influence DP control system performance. The towing experiments specified in Table 3 are analyzed, in which the following notation is used: ψr is the oblique angle between the vessel and the ice, vr is the relative velocity between the vessel and the ice in model-scale, N is the ice field number from Table 2; Fig. 5, IC is the ice concentration, h is the ice thickness in
Phenomenological investigation of the managed ice–vessel interaction process
The primary managed ice load signal characteristics, which were identified in the previous section, indicate significant threats posed by managed ice actions on a stationary DP vessel. However, a pure signal analysis does not allow an efficient control system to be designed to overcome these challenges because it lacks a physical understanding of the managed ice interactions. Therefore, this section offers a phenomenological interpretation of the ice load signal characteristics and their
Managed ice implications on DP control systems
The generalized equations of motion of a DP vessel can be written in the following form:where η is the position and orientation vector expressed in an inertial frame, J(η) is the transformation matrix between the inertial frame and the body frame, ν is the body frame velocity vector, M is the vessel rigid body mass matrix, τcontrol is the vessel actuation output, τhydro is the hydrodynamic and hydrostatic loads acting on the vessel (including current
Conclusions
This paper investigated the dynamic positioning of offshore vessels in managed ice conditions using a model-scale dataset from the large ice tank of HSVA. The analysis of signals from 25 towing experiment segments indicated the following:
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Global ice loads contain rapid and significant transients that may abruptly inject energy into the stationkeeping system.
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Mean ice loads depend on the oblique angle between the vessel and the ice but not on the relative ice drift velocity (in the range of
Acknowledgments
The authors would like to acknowledge the Research Council of Norway (RCN) for their financial support of the Arctic DP project at the Norwegian University of Science and Technology (RCN project no. 199567) and the MARTEC ERA-NET project: DYPIC — dynamic positioning in ice covered waters (RCN project no. 196897), which supplied experimental data from the Hamburg Ship Model Basin. The authors would also like to thank the Ministry of Ecology, Sustainable Development, Transport and Housing
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