Newtonian and non-Newtonian fluids are commonly transported with the piping system, but non-Newtonian fluids with particle-laden are more complicated to be transported due to various factors. A deposition is one of the problems that must be investigated due to affecting flow efficiency. The purpose of this study is to investigate the performance of 3-lobes spiral pipe in relationship with particles effect. The working fluid used had several variations of concentration weight (i.e. Cw 20%, 30% and 40%). Test pipes used had 1550 mm of length which consisted of a spiral pipe with P/Di=7 and a circular pipe with an inner diameter of 25.4 mm; both of the pipes were used to investigate the particles effect where the circular pipe for examining rheological of the working fluid. Slurry particles sized 1-5 μm in this study was obtained from the mud eruption at Semau Island, Kupang-Indonesia. By comparing both working fluids, the critical velocity observed in the spiral pipe was not as high as observed in the circular pipe, and thus the spiral pipe is more efficient to be used in slurry transportation from mud eruption area.

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The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies focused mainly on shorter time-interval predictions ranging from 30 min to 10 h. A longer time-interval ship position prediction is required not only for MSA, but also for efficient allocation of ships by shipping companies in accordance with global freight demand. This study used an end-to-end tracking method that inputs the previous position of a vessel to a trained deep learning model to predict its next position with an average 24-h interval. An AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide was used. In the first experiment, a deep learning model of the Indian Ocean was examined. Subsequently, the model performance was compared for six different oceans and six primary maritime chokepoints to investigate the influence of each area. In the third experiment, a sample location within the Malacca Strait area was selected, and the number of ships was counted daily. The results indicate that the ship position can be predicted accurately with an average time interval of 24 h using deep learning systems with AIS data.

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