The literature on liner shipping includes many models on containership speed optimization, fleet deployment, fleet size and mix, network design and other problem variants and combinations. Many of these models, and in fact most models at the tactical planning level, assume a fixed revenue for the ship operator and as a result they typically minimize costs. This treatment does not capture a fundamental characteristic of shipping market behavior, that ships tend to speed up in periods of high freight rates and slow down in depressed market conditions. This paper develops a simple model for a fixed route scenario which, among other things, incorporates the influence of freight rates, along with that of fuel prices and cargo inventory costs into the overall decision process. The objective to be maximized is the line’s average daily profit. Departing from convention, the model is also able to consider flexible service frequencies, to be selected from a broader set than the standard assumption of one call per week. It is shown that this may lead to better solutions and that the cost of forcing a fixed frequency can be significant. Such cost is attributed either to additional fuel cost if the fleet is forced to sail faster to accommodate a frequency that is higher than the optimal one, or to lost income if the opposite is the case. The impact of the line’s decisions on CO2 emissions is also examined and illustrative runs of the model are made on three existing services.
In this article, we develop a deep neural network model to estimate the wave added resistance. The required data to train the model is generated using strip theory calculations over a wide range of hull geometries and operational conditions. The model is efficient as it only requires the ship’s main particulars: length, beam, draft, block coefficient, and slenderness ratio. In addition, we present an application of this model in a vessel performance framework. This will be used for predicting propulsion power and analyzing the degree of biofouling on ships from the company Ultrabulk2. The study shows that the developed deep neural network model produces reliable results in predicting the added wave resistance coefficient in comparison to strip theory calculations. Also, the developed ship propulsion and biofouling analysis display satisfactory output for monitoring hull performance under actual ship operational conditions.
The design of large diameter monopiles (8–10 m) at intermediate to deep waters is largely driven by the fatigue limit state and mainly due to wave loads. The scope of the present paper is to assess the mitigation of wave loads on a monopile by perforation of the shell. The perforation design consists of elliptical holes in the vicinity of the splash zone. Wave loads are estimated for both regular and irregular waves through physical model tests in a wave flume. The test matrix includes waves with Keulegan–Carpenter (KC) numbers in the range 0.25 to 10 and covers both fatigue and ultimate limit states. Load reductions in the order of 6%–20% are found for KC numbers above 1.5. Significantly higher load reductions are found for KC numbers less than 1.5 and thus the potential to reduce fatigue wave loads has been demonstrated.