Ship Trim Optimization: Assessment of Influence of Trim on Resistance of MOERI Container Ship

Sherbaz, Salma; Duan, Wenyang

doi:10.1155/2014/603695

Cited by 33 publications

(24 citation statements)

References 5 publications

(5 reference statements)

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“…Trim Trim Trim (15) where f(Trim, Fn, Diss) is the response surface, Dis0 is present volume displacement of the hull, Fn0 is the Froude number based on given speed, and Trim1 and Trim2 define the variation range of the trim condition. The overall optimization procedure is demonstrated in Fig. 14.…”

Section: Fn Fnmentioning

confidence: 99%

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2018

brod

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The paper aims at conducting trim optimization for a hull to reveal the influence of trim on wave resistance by a potential-based panel method coupled with a response surface method. First, a numerical program for solving the linear free-surface flow problem of a hull moving with a uniform speed in calm water is built by the panel method. The S60 hull model is used to validate the numerical procedure. Next, calculation for hull is performed with two different trims at a wide range of Froude number; resistance test is conducted to validate the numerical method in demonstrating the influence of trim on wave resistance. Finally, a response surface of wave resistance is constructed with respect to variations of trim and Froude number, using the database of wave resistance calculated by the surface method. In this way, a framework is developed to perform trim optimization. The optimum trim point for the present hull shows a significant improvement in both wave resistance and total resistance, compared with that of even keel and the worst trim point. The optimization framework is proved to be effective in energy saving due to resistance reduction.

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Section: Fn Fnmentioning

confidence: 99%

“…Iakovatos et al [14] experimentally studied the impact of trim angles on resistance. In addition, Sherbaz and Duan assessed the influence of trim on ship resistance with CFD as well [15]. More recently, Sun et al [16] developed a response surface based program with database from experiment and CFD to perform trim optimization.…”

Section: Introductionmentioning

confidence: 99%

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2018

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“…These days ships are one of the most ordinary vehicles in worldwide business as it transports 90 % of the world cargo and it is commonly one of the most economical method contrasted with different method of transportation [1].…”

Section: Introductionmentioning

confidence: 99%

Numerical investigations of micro bubble drag reduction effect for container ships

Gamal

Kotb

Naguib

et al. 2021

Mar Syst Ocean Technol

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In the course of the most recent thirty years reduction of ship resistance and saving fuel consumption to accomplish higher speed with reduction of pollutants has been the significant subject for researchers. Micro bubble drag reduction technique is the one of the most interesting thoughts in this field owing to its great advantages, such as considerable potential drag reduction, easy operations, environmental friendliness and low costs. In this study a 3-D numerical investigation into frictional drag reduction by air micro bubbles is applied on KRISO container ship model. The objective is to understand the mechanism of resistance reduction through micro bubbles injection under model ship at different Froude numbers, injection rate and of course volume fractions. The numerical simulations are performed using a commercial CFD code solving Reynolds averaged Navier-Stokes (RANS) equations. For more than 36 simulations has performed to investigate the effect of injection of micro bubble under ship model hull and to estimate the local Coefficient of friction values along ship hull model. The results show that at all of the examined Froude number, frictional resistance reduction attained but at different rates and a maximum drag reduction of 27.6 % obtained at 0.282 Froude number with 4.8 % air volume fraction. Keywords ship resistance • KRISO ship model • container ship • micro bubble • drag reduction • numerical simulation • ANSYS FLUENT Marine systems and ocean technology j o u r n a l manuscript No.

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“…a short time. At present, the optimal method of ship fuel consumption is mainly by changing the ship's speed and trim accordingly, to achieve the energy-saving and emission reduction effect of the ships [2]- [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning

Jin

et al. 2019

IEEE Access

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Due to the hike in fuel price and environmental awareness by the International Maritime Organization, more attention has been given in order to optimize the fuel consumption of ships. The capability to predict the fuel consumption of ships plays a significant role in the optimization process. To date, most research on predicting ship fuel consumption did not consider marine environmental factors such as wind, wave, current, and etc. Furthermore, traditional statistical methods on predicting ship fuel consumption have low accuracy. In this paper, two different sets of data showing the fuel consumption of a voyage ship with and without the influence of marine environmental factors were obtained. The Back-Propagation Neural Network (BPNN) and Gaussian Process Regression (GPR) techniques in machine learning were used to train and predict the two datasets. Thereafter, the predictive performance of these two techniques was compared and analyzed. Results showed that both techniques were able to accurately predict the ship fuel consumption, especially on the dataset with the influence of marine environmental factors. Quantitatively, the mean prediction accuracy for GPR (mean R 2 = 0.9887) is slightly higher than BPNN (mean R 2 = 0.9817). However, GPR requires longer runtime (mean T = 2236.4 s) compared to BPNN (mean T = 14.7 s). Due to the longer runtime, GPR is less preferable for online and real-time prediction of enroute ship fuel consumption. The ship real-time fuel consumption data can be accurately predicted by machine learning, which will be beneficial to achieve the goal of ship fuel consumption optimization and greenhouse gas emission reduction in the future. INDEX TERMS Machine learning, Gaussian process regression, back-propagation neural network, enroute ship, fuel consumption prediction.

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Ship Trim Optimization: Assessment of Influence of Trim on Resistance of MOERI Container Ship

Cited by 33 publications

References 5 publications

Untitled

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Numerical investigations of micro bubble drag reduction effect for container ships

Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning

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