Uncertainty analysis for measurement of added resistance in short regular waves: Its application and evaluation

Sogihara, Naoto; Tsujimoto, Masaru; Fukasawa, Ryohei; Hamada, Tatsuya

doi:10.1016/j.oceaneng.2020.107823

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…In this study, model parameter uncertainty includes the coefficients of added resistance in winds and waves and self-propulsion factors and propeller open characteristics. In this regard, the author has investigated the effect of uncertainty in the added resistance in waves estimated by model tests and reported the relationship between the uncertainty of the added resistance and a decrease of ship speed in actual seas 9) . Physical parameters include the ship principal particulars and physical constants.…”

Section: Uncertainties In Ship Performance Evaluationmentioning

confidence: 99%

Uncertainty Assessment in Ship Performance Evaluation by Monte Carlo Simulation Using Onboard Monitoring Data

Sogihara

2021

J.JASNAOE

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Onboard monitoring for ship performance evaluation in actual seas is widely conducted in the shipping sector. Onboard monitoring usually involves a large number of instruments for measuring ship performance and the sea conditions in which the ship navigates, and such instruments are subject to some amount of error, which means that the accuracy of the instruments influences the evaluation. In this study, an uncertainty assessment was carried out to investigate the influence of instrument accuracy on two subjects by Monte Carlo Simulation. One subject is ship performance in calm seas evaluated by correction of performance for the effects of waves and winds, and the other is voyage simulation. As a result, this study clarified the accuracy of the instruments used in onboard monitoring required for achieving a reliable evaluation of ship performance in actual seas.

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Section: Uncertainties In Ship Performance Evaluationmentioning

confidence: 99%

Uncertainty Assessment in Ship Performance Evaluation by Monte Carlo Simulation Using Onboard Monitoring Data

Sogihara

2021

J.JASNAOE

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“…More recently, computational fluid dynamics (CFD) methods have been used to determine ship resistance, which allows a detailed assessment of the effect of hull form, but also requires that the uncertainty in the predictions be properly accounted for [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Ship Fuel Consumption for Different Hull Roughness in Realistic Weather Conditions

Tadros

Vettor

Ventura

et al. 2022

JMSE

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This paper presents the effect of hull roughness over 10 years of operation on ship performance. The numerical model is developed by coupling NavCad and Matlab to perform the computation and the data processing. On the basis of a given hull, an engine, and an optimized propeller, the performance of the ship is computed for eight cases of hull roughness according to the ITTC recommendations in both calm waters and different weather conditions along the ship route. The effect of both wind and waves is considered for computing the added ship resistance along the route, thus requiring more power than just only the added resistance in waves. This provides a more accurate estimation of the ship’s performance along the different sea states. Lastly, a weighted average of the main ship parameters is estimated to evaluate better the ship’s performance. According to this study, the fuel consumption in calm water can be increased by around 20% after 10 years of ship operation based on the level of hull roughness. However, in the same weather conditions along the ship route, the ship’s fuel consumption can be increased by 10% compared to the same trip with a clean hull.

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“…Several researchers conducted this analysis for economic purposes (to reduce additional ship resistance). An added resistance study was carried out on the container ship (KRISO)fl ], catamaran [2], and trimaran [3], Some of them use the Artificial Neural Network (ANN) method [4], 2.5 D Method [5], and Rankine-Panel Method [6], Some researchers also discuss the uncertainty in calculating the added resistance in short regular waves [7],…”

Section: Introductionmentioning

confidence: 99%

Vertical Motion Optimization of Series 60 Hull Forms Using Response Surface Methods

Utomo

Iqbal

2020

Kapal

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There are many aspects to analyze seakeeping performance, one of which is the ship's vertical motion. As well-known, vertical motion and its derivatives, vertical velocity and acceleration, will be related to other aspects of seakeeping performance, such as slamming, deck wetness, and MSI. This study discusses optimizing the hull shape with small vertical motion using the Response Surface Methods (RSM). This research aims to minimize the ship's vertical motion so that the ship's performance is better than the initial one. Besides, this research was conducted to apply the RSM in the naval architecture field. The hull's shape used in this study is Series 60 hull form with a length of 31 m. The variables used for the optimization process are the ratio of L/B (X1) and B/T (X2) in the range of ± 10% with fixed displacement. Seakeeping analysis was carried out at a speed of 6.78 knots (Fr 0.2), a heading angle of 180°, and a significant wave height of 0.77 meters. The results show that the optimum model is found in Model 9 where the value of X1 = -2.94 or L/B = 6.71 and X2 = 5 or B/T = 2.75. Model 9 can reduce the vertical motion of the ship by 16.38%.

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Uncertainty analysis for measurement of added resistance in short regular waves: Its application and evaluation

Cited by 9 publications

References 3 publications

Uncertainty Assessment in Ship Performance Evaluation by Monte Carlo Simulation Using Onboard Monitoring Data

Uncertainty Assessment in Ship Performance Evaluation by Monte Carlo Simulation Using Onboard Monitoring Data

Assessment of Ship Fuel Consumption for Different Hull Roughness in Realistic Weather Conditions

Vertical Motion Optimization of Series 60 Hull Forms Using Response Surface Methods

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