The current sea margin estimate applied in early ship design, commonly assumed 15-20% extra installed engine power, is not based on calculations, but has nonetheless become an industry standard. These sea margin estimations, applied in early ship design, are insufficiently accurate. This paper evaluates if a data driven approach is suitable to more accurately predict the sea margin in early ship design. Using operational data this method considers the whole operational profile of the vessel not limited to design or calm water conditions. A case study is performed where a data driven model is trained to make power predictions, subsequently this trained model is used to make calm water predictions. This proof of concept illustrates the potential of proposed method to be utilised for sea margin estimations in early ship design.

The digital and energy transition will change our industry. To be prepared for this challenge, NHL Stenden University of Applied Sciences puts quite some effort in developing new innovative courses and new types of digitally enabled education. An example is a new minor concentrated on engineering tools and methods that have emerged in the (construction) industry over the past decades. In addition, the school is also working on a game to educate an old trade: ship stability. In addition to the changes to the existing programs, a new level of education is introduced in the Netherlands, the Professional Doctorate. The Professional Doctorate is comparable to the PhD but focusses on practically applied research. All these changes and innovations to the current maritime education at NHL Stenden are elaborated upon in this paper. The paper concludes with an outlook to the future, based upon the results from a survey held under students and lecturers regarding their view on the future of maritime education. The results of this survey show that especially green and modern propulsion methods are underexposed in the current curriculum.

In courses of ship design and engineering the particulars of the profession are well-taught. However, driven by software and computer advancement, in the industry over the past decades new tools have emerged, such as optimization, geometric modelling, CFD, big data and machine learning. These tools have been considered too complex for an undergraduate program. Yet, some knowledge of this trade is essential on every professional level, and our proposition is that if the material is offered in a first-principle fashion, in combination with practical exercises and oral discussion, the heart of the matter can very well be educated to an undergraduate. Recently, a 30 EC minor was given in this fashion.

In the shipping industry power prediction methods are commonly used. An option is to predict the power based with a theoretical analysis. However, with a purely theoretical approach it is not possible to evaluate all operating conditions. The second, simulation methods, are able to describe all the necessary quantities in detail. Nonetheless, simulation requires relatively high computational power. Thus, the current power prediction methods used in the shipping industry are insufficiently all-encompassing or accessible. Therefore, a machine learning approach is developed to calculate the ships speed over ground using neural network and convolutional neural network techniques. For training and validation of the model operational data from a fall-pipe vessel is used. The developed method combined with ship motion could result in an optimal power usage, and thus leads to reduced fuel consumption and emissions. The method could also be used for optimised routing. Although in this case study applied to one single vessel, the developed model is generally applicable, providing ship management companies the possibility to train the model with operational data from their fleet, therewith, offering the possibility of reduced fuel consumption and thus emissions on a global level.

Most ship management companies base the operational cost calculation on scheduled maintenance jobs. Scheduled maintenance jobs do not take unforeseen maintenance into account. This under-estimates the operational budget. The estimation of the operational costs can be improved by including the unforeseen maintenance costs. The amount of unforeseen maintenance costs depends on the implemented maintenance policy, as well as the failure and maintenance intervals. To study this inter-action a model is required. A Maintenance Cost Model (MCM) is developed and validated to demon-strate the impact of maintenance policies at Anthony Veder. This model focuses on maintenance cost calculations for different maintenance policies, based on failure behaviour. Anthony Veder will be able to save 60% on average on maintenance costs of mechanical equipment by optimising their main-tenance policies. Although applied to Anthony Veder the developed model is generally applicable offering ship management companies’ insight in suitable maintenance options.