The aim of this paper is to investigate the effect of the change in structural weight due to optimisation experiments on life-cycle cost and earning elements using the life-cycle cost/earning model, which was developed for structure optimisation. The relation between structural variables and relevant cost/earning elements are explored and discussed in detail. The developed model is restricted to the relevant life-cycle cost and earning elements, namely production cost, periodic maintenance cost, fuel oil cost, operational earning and dismantling earning. Therefore it is important to emphasize here that the cost/earning figure calculated through the developed methodology will not be a full life-cycle cost/earning value for a subject vessel, but will be the relevant life-cycle cost/earning value. As one of the main focuses of this paper is the maintenance/repair issue, the data was collected from a number of ship operators and was solely used for the purpose of regression analysis. An illustrative example for a Chemical Tanker is provided to show the applicability of the proposed approach.
A B S T R A C TThe wind industry is facing new challenges due to the planned construction of thousands of offshore wind turbines all around the world. However, with their increasing distance from the shore, greater water depths, and increasing sizes of the plants, the industry has to face the challenge to develop sustainable installation procedures. Important limiting factors for offshore wind farm installation are the weather conditions and installation strategies. In this context, the focus of this research is the investigation of the most effective approach to installing offshore wind farms at sea, including the effects of weather conditions. This target is achieved through the implementation of a discrete-event simulation approach which includes the analysis of the environmental conditions, distance matrix, vessel characteristics, and assembly scenarios. The model maps the logistics chain in the offshore wind industry. A deterministic and a probabilistic metocean data method have been compared and cross validated. The results point to a good agreement between the two considered models, while highlighting the huge risks to the time and cost of the installation due to the stochastic nature of the weather. We suggest that simulations may improve and reduce these risks in the planning process of offshore wind farms.
Increasing environmental demands, alongside the planned penetration of natural gas as marine fuel, have rendered dual-fuel engines as an attractive prime mover alternative. In this context, knowing the specific fuel consumption is essential to selecting the most efficient engine. The specific fuel consumption can be approached by simulation models with varying levels of complexity that are either implemented by basic programming languages or simulated by dedicated packages. This study aims to develop a simplified model to predict the specific fuel consumption of dual-fuel two-stroke marine engines driving fixed or controllable pitch propellers. The model relies on clear trends approachable by polynomials that were revealed by normalizing specific fuel consumption. This model requires only the value of specific fuel consumption at a nominal maximum continuous rating to predict the engine consumption at any specified rating, including at partial engine load. The outcome of the study shows that the maximum deviations regarding the two simulated engines did not exceed −3.6%. In summary, the proposed model is a fast and effective tool for optimizing the selection of dual-fuel, two-stroke Diesel engines regarding fuel consumption.
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