The effects of the application of a stern hydrofoil on ship resistance were studied numerically using computational fluid dynamics (CFD) and were verified using data from model tests. A 40 m planing-hull Orela crew boat, with target top speed of 28 knots (Froude number, Fr = 0.73), was considered. The stern foil (NACA 64(1)212) was installed with the leading edge positioned precisely below the transom with angle of attack of 2 degrees at elevation 0.853 T below the water surface (where T is the boat's draft). At relatively low speed (Fr  0.45) the application of a stern foil results in an increase in ship resistance (of up to 13.9%), while at relatively high speed (Fr  0.55) it results in a decrease in ship resistance (of up to 10.0%). As the Froude number increases, the resistance coefficient (C T) first increases, reaches a maximum value, and then decreases. Its maximum value occurs at Fr ≈ 0.5, which is consistent with the prediction of a resistance barrier at approximately this Froude number.

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Nowadays, the issue of energy efficiency in the maritime transportation sector has been strongly associated with the decreasing use of fossil energy and greenhouse gas emissions. Crew boats are one of the ship modes which consumes a lot of fuel in maritime transportation. This affects the number of exhaust gases released into the atmosphere. A study into the estimation of crew boat resistance was carried out experimentally using a towing tank, numerically using a CFD methodology, and then compared with Savitsky's method. Measurements were taken in calm waters under even keel and trim scenarios, considering load variation had been adjusted for. Determining the correct load position affected the LCG (Longitudinal Center of Gravity) and VCG (Vertical Center of Gravity) parameters, affected trim, and decreased crew boat resistance. Overall results showed that the experimental test, CFD method, and the empirical estimation from Savitsky were in good agreement with average errors up to 3%. The calculation results demonstrated that trim had a greater influence on decreasing resistance up to 3.062% than even keel position. Furthermore, the shifting of LCG had a more significant effect than that of VCG in the context of resistance changes.

Determination of service speed has always become a primary concern when designing a ship. However, a ship designer may not consider its effect on the use of energy and seakeeping performance of the vessel. The current paper discusses the determination of the actual and proper speed of a ro-ro ferry, operated between Tanjung Perak (Surabaya) and Lembar (Lombok), based on resistance estimation and seakeeping performance. The investigation is carried out using commercial software MaxsurfTM and further compared with computational fluid dynamics (CFD) approach to capture the effect of detailed wave drag, which is not quantified by the design software. The discussion and analysis include the determination of optimum speed based on the size of main engine and the seakeeping performance of the ship covering the aspects of heave, pitch, and roll.

Estimating shaft power of a crew boat is very important to be analysed because it has high-speed operational characteristics along with limited routes. To understand the phenomena, 3 sister crew-boats with operational distance about 40-60 nautical miles every day are investigated. The daily operational time is 8 hours and the configurations are: 4.04% full speed, 13.63% economical speed, 1.81% slow speed, 7.65% snatching, 1.25% manoeuvring, 5.29% idle, and the remaining time is in standby condition. The crew boats are fitted with a monitoring system namely SHIMOS®, in which data is sent to a server in the centre office every 2 minutes. The data consists of time capture, boat position (latitude and longitude), speed, left and right RPM engine, left and right flow-meter data engine, and average of fuel consumption data in everyday operation. Three years of data has been collected for the vessel. The present study proposed characteristics of crew-boat shaft power, which affected by external factors using Artificial Neural Network (ANN) back propagation method and optimisation in 4 hidden layers and 40 neurons with relative error 6.2%. The results demonstrates good agreement with previous popular method that using statistical models.