An overview of the marine systems simulator (MSS) : a Simulink toolbox for marine control systems

The Marine Systems Simulator (MSS) is an environment which provides the necessary resources for rapid implementation of mathematical models of marine systems with focus on control system design. The simulator targets models¡Xand provides examples ready to simulate¡Xof different floating structures and its systems performing various operations. The platform adopted for the development of MSS is Matlab/Simulink. This allows a modular simulator structure, and the possibility of distributed development. Openness and modularity of software components have been the prioritized design principles, which enables a systematic reuse of knowledge and results in efficient tools for research and education. This paper provides an overview of the structure of the MSS, its features, current accessability, and plans for future development.

Frequency-motivated observer design for the prediction of parametric roll resonance control applications in marine systems

This work deals with estimators for predicting when parametric roll resonance is going to occur in surface vessels. The roll angle of the vessel is modeled as a second-order linear oscillatory system with unknown parameters. Several algorithms are used to estimate the parameters and eigenvalues of the system based on data gathered experimentally on a 1:45 scale model of a tanker. Based on the estimated eigenvalues, the system predicts whether or not parametric roll occurred. A prediction accuracy of 100% is achieved for regular waves, and up to 87.5% for irregular waves.

Report of the sub-regional workshop to validate the scoping study of nutrient pollution on the coastal and marine systems of South Asia, Colombo, Sri Lanka, 21-22 May, 2014

The workshop agenda included: presentations from collaborative institutions, national governments and resource persons; a draft scoping study on nutrient loading; and an ecosystem approach to pollution management was tested.

Does terrestrial epidemiology apply to marine systems?

Most of epidemiological theory has been developed for terrestrial systems, but the significance of disease in the ocean is now being recognized. However, the extent to which terrestrial epidemiology can be directly transferred to marine systems is uncertain. Many broad types of disease-causing organism occur both on land and in the sea, and it is clear that some emergent disease problems in marine environments are caused by pathogens moving from terrestrial to marine systems. However, marine systems are qualitatively different from terrestrial environments, and these differences affect the application of modelling and management approaches that have been developed for terrestrial systems. Phyla and body plans are more diverse in marine environments and marine organisms have different life histories and probably different disease transmission modes than many of their terrestrial counterparts. Marine populations are typically more open than terrestrial ones, with the potential for long-distance dispersal of larvae. Potentially, this might enable unusually rapid propagation of epidemics in marine systems, and there are several examples of this. Taken together, these differences will require the development of new approaches to modelling and control of infectious disease in the ocean.

Adverse feedback sequences in exploited marine systems: Are deliberate interruptive actions warranted?

Several mechanisms for self-enhancing feedback instabilities in marine ecosystems are identified and briefly elaborated. It appears that adverse phases of operation may be abruptly triggered by explosive breakouts in abundance of one or more previously suppressed populations. Moreover, an evident capacity of marine organisms to accomplish extensive geographic habitat expansions may expand and perpetuate a breakout event. This set of conceptual elements provides a framework for interpretation of a sequence of events that has occurred in the Northern Benguela Current Large Marine Ecosystem (off south-western Africa). This history can illustrate how multiple feedback loops might interact with one another in unanticipated and quite malignant ways, leading not only to collapse of customary resource stocks but also to degradation of the ecosystem to such an extent that disruption of customary goods and services may go beyond fisheries alone to adversely affect other major global ecosystem concerns (e.g. proliferations of jellyfish and other slimy, stingy, toxic and/or noxious organisms, perhaps even climate change itself, etc.). The wisdom of management interventions designed to interrupt an adverse mode of feedback operation is pondered. Research pathways are proposed that may lead to improved insights needed: (i) to avoid potential 'triggers' that might set adverse phases of feedback loop operation into motion; and (ii) to diagnose and properly evaluate plausible actions to reverse adverse phases of feedback operation that might already have been set in motion. These pathways include the drawing of inferences from available 'quasi-experiments' produced either by short-term climatic variation or inadvertently in the course of biased exploitation practices, and inter-regional applications of the comparative method of science.

Marine ecosystem models for earth systems applications: the MarQUEST experience

The MarQUEST (Marine Biogeochemistry and Ecosystem Modelling Initiative in QUEST) project was established to develop improved descriptions of marine biogeochemistry, suited for the next generation of Earth system models. We review progress in these areas providing insight on the advances that have been made as well as identifying remaining key outstanding gaps for the development of the marine component of next generation Earth system models. The following issues are discussed and where appropriate results are presented; the choice of model structure, scaling processes from physiology to functional types, the ecosystem model sensitivity to changes in the physical environment, the role of the coastal ocean and new methods for the evaluation and comparison of ecosystem and biogeochemistry models. We make recommendations as to where future investment in marine ecosystem modelling should be focused, highlighting a generic software framework for model development, improved hydrodynamic models, and better parameterisation of new and existing models, reanalysis tools and ensemble simulations. The final challenge is to ensure that experimental/observational scientists are stakeholders in the models and vice versa.

Hybrid frequency–time domain models for dynamic response analysis of marine structures

Time-domain models of marine structures based on frequency domain data are usually built upon the Cummins equation. This type of model is a vector integro-differential equation which involves convolution terms. These convolution terms are not convenient for analysis and design of motion control systems. In addition, these models are not efficient with respect to simulation time, and ease of implementation in standard simulation packages. For these reasons, different methods have been proposed in the literature as approximate alternative representations of the convolutions. Because the convolution is a linear operation, different approaches can be followed to obtain an approximately equivalent linear system in the form of either transfer function or state-space models. This process involves the use of system identification, and several options are available depending on how the identification problem is posed. This raises the question whether one method is better than the others. This paper therefore has three objectives. The first objective is to revisit some of the methods for replacing the convolutions, which have been reported in different areas of analysis of marine systems: hydrodynamics, wave energy conversion, and motion control systems. The second objective is to compare the different methods in terms of complexity and performance. For this purpose, a model for the response in the vertical plane of a modern containership is considered. The third objective is to describe the implementation of the resulting model in the standard simulation environment Matlab/Simulink.