ABSTRACT
Analysis of the onboard data offers and alternative approach to the conventional one described above. In this way providing information of the actual realized power consumption and its origins: wave, wind, and shallow water effects, and
1 INTRODUCTION
The cost of main engine represents around 16% of the total cost of a merchant ship according to Stopford (2009). The cost of the engine and required installations for ship propulsion increase with increased engine power. The Energy Efficiency Design Index (EEDI) (IMO Resolution MEPC 245(66) 2014) also set limitations to installed power aiming to reduce Green House Gas (GHS) emissions. Then again, in terms of safety and the ability of the ship to maintain its required speed, it must have sufficiently installed propulsion power (Papanikolaou et al. 2016). The hull condition also deteriorates over time. The hull fouling increases with the growth of algae and barnacles, which can have significant effects on the resistance of the ship. Moreover, the condition of hull plating gets worse over time contributing to the increased resistance. Thus, the ship need to be equipped with sufficient engine power to be able to overcome these effects over its lifetime, but at the same time limiting the construction costs without installing more engine power than needed. At the phase of estimating the commercial feasibility of a ship project and at the contractual design phase the question of power requirement is essential, among the definition of the main dimensions of the ship. The clean hull resistance in calm water conditions can be quite
hull fouling. In this paper, we describe a statistical method to assess the power consumption based on the onboard measurements. We apply an integrated onboard data acquisition, monitoring and analysis system implemented in NAPA software to study the power consumption. Statistical methods for estimation of ship’s resistance and requirement of propulsion power have a long history. The most common method, still widely used, for estimation of calm water hull resistance was published by Holtrop (1984). Vesterinen (2012) applies statistical regression model to estimate the effect of hull fouling on the power consumption. Petersen et al. (2012) investigate artificial neural networks and Gaussian process approach to estimate ship propulsion efficiency. Perera et al. (2015) applied statistical analysis to study trim effect on consumption. Eide (2015) estimated the sea margin based on noon-reports and concluded that the margin is speed dependent. Mao et al. (2016) establish statistical methods to predict ship speed for given power and sea environment.
