Ship Speed Loss Estimation Using Wave Spectrum of Encounter

Nakano, Haruka; Kuroki, Kentaro; Sato, Yoshiko; Koshita, Satoshi; Matsuura, Kuniaki

doi:10.1109/oceanskobe.2018.8559122

Cited by 3 publications

(2 citation statements)

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“…The primary function of a ship shafting system is to carry out energy transfer from marine engine to propeller, transmit axial thrust produced by the rotation of propeller to the hull, and drive the ship ahead [15]. An accurate estimation of ship speed loss is required to verify ship propulsion performance in actual sea conditions [10]. A correct assessment of the ship speed loss in conditions of exploitation is becoming increasingly important for ship owners as well as ship designers [11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Performance Losses Related to Shaft Angle on Fishing Boats Using the Free Running Model Test Method

Wahidin¹,

Soelistijono²,

Hardiyanti³

et al. 2023

IJMEIR

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⎯ Several traditional fishing boats in Sendang Biru, Malang Regency are of the sekoci type using an inboard engine drive system with the position of the propeller shaft installed at an angle to the ship's base line. Based on the theory can reduce the performance of the ship propulsion system up to (2-3)%. In this research, we analyzed how much performance was lost/decreased on the lifeboat type fishing boats installed above. The method we use is the Free Running Model Test by making a model of a traditional fishing boat type of lifeboat with a model size of Loa = 100 cm, Lwl = 80.72 cm, B = 19.7 cm, H = 10.68 cm and T = 5, 08 cm made of aluminum sheet. The Free Running Model Test uses 1 motor and propeller which has a thrust of 60 watts, remote control equipment uses the main component of the NRF24L01 transceiver which works at a frequency of 2.4 Ghz with a range of 240 meters to 1 kilometer with conditions without obstacles and camera equipment drone to record the track. The results of this study show that there is a decrease/loss of performance at a 3-degree angle of 0.24%, at a 5-d. gree angle of 0.49%, at a 10-degree angle of 2.52%, at a 15-degree angle of 4.41% and at an angle of 20 degrees of 6.48%.

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Section: Introductionmentioning

confidence: 99%

Analysis of Performance Losses Related to Shaft Angle on Fishing Boats Using the Free Running Model Test Method

Wahidin¹,

Soelistijono²,

Hardiyanti³

et al. 2023

IJMEIR

View full text Add to dashboard Cite

show abstract

“…W. Renqiang et al utilized the RBF neural network with a minimum parameter learning algorithm to design a single estimated parameter, studied the ship's heading sliding mode tracking control algorithm, and solved the problem of the slow torturous process of network weight optimization [5]. H. Nakano et al utilized ship monitoring data and neural network spectrum to model the velocity of the ship and calculated the velocity loss rate caused by wind and waves to the ship [6]. Thus, we managed to introduce the method of machine learning to achieve our goal.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Forecasting Model for the Velocity of Hybrid Robotic Fish Based on Back-Propagation Neural Network With Genetic Algorithm Optimization

2020

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Marine unmanned vehicle is a novel robot widely used in ocean observation, and its accurate control is of significance to their path planning. We want to find a method to predict the velocity and course of this robot, which can help us realize the accurate control of it. The paper proposed a promising type of hybrid robotic fish (HRF), which can realize two kinds of motion modes on the sea surface. Firstly, the configuration and dynamic model of the HRF were analyzed elaborately. Then, to realize accurate velocity prediction under two kinds of motion modes of HRF, the influence factors are presented in a complex marine environment. Based on the influence factors to its maneuverability, such as wind or wave parameters, a velocity prediction algorithm based on back-propagation neural network (BPNN) was introduced. However, BPNN has the disadvantages of extended learning and training time, easily falling into local optimum. Then we found that genetic algorithm (GA), which is a kind of evolutionary algorithm, is suitable for our problem. Therefore, the accuracy and efficiency of the prediction algorithm were improved by adopting the genetic algorithm to optimize the weight and threshold of BPNN. Taking the experimental data from the pool test, the back-propagation neural network with genetic algorithm (GA-BPNN) forecasting model was established. Besides, the other prediction methods were compared and evaluated under the same assessment criterion to validate the proposed forecasting model. The experimental results demonstrate that the GA-BPNN model has higher accuracy and efficiency compared with other prediction algorithms, which verifies the feasibility of the velocity prediction model for hybrid robotic fish in complex ocean environments.

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A Speed Prediction Model Using A HIL Simulator and Neural Network for Cargo Ships

Huu

2024

2024 International Russian Smart Industry Conference (SmartIndustryCon)

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Ship Speed Loss Estimation Using Wave Spectrum of Encounter

Cited by 3 publications

References 0 publications

Analysis of Performance Losses Related to Shaft Angle on Fishing Boats Using the Free Running Model Test Method

Analysis of Performance Losses Related to Shaft Angle on Fishing Boats Using the Free Running Model Test Method

A Hybrid Forecasting Model for the Velocity of Hybrid Robotic Fish Based on Back-Propagation Neural Network With Genetic Algorithm Optimization

A Speed Prediction Model Using A HIL Simulator and Neural Network for Cargo Ships

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