• Journal of the Society of Naval Architects of Korea
• /
• v.31 no.4
• /
• pp.157-166
• /
• 1994

Recently. the excessive diesel engine torsional excitation of typical energy saving ships has resulted in severe damages of the propeller shaft. Up to now the design and torsional vibration analysis of the marine diesel shafting system has been performed on the assumption that excitations are deterministic. But a diesel engine excitation varies randomly from cylinder to cylinder and from cycle to cycle, due to the imperfect operation of the engine components due to engine misfiring. consequently, a more rational analysis method for the propulsion shafting torsional vibration is required. In this paper probabilistic analysis method of the marine diesel engine shafting system under torsional vibration is presented. First a response surface representing maximum shear stresses in a shafting system is built. Then Monte Carlo simulation with subsequent approximation of the results by one of Pearson's curves, is performed. Some numerical results based on the proposed method are compared with t도 some numerical data available. They show acceptable agreements with the data.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.5
• /
• pp.85-95
• /
• 2021

Structure design sensitivity was evaluated using the orthogonal array experimental method for passive-type deck support frame (DSF) developed for float-over installation of the offshore plant. Moreover, approximation characteristics were also reviewed based on various meta-models. The minimum weight design of the DSF is significantly important for securing both maneuvering performance and buoyancy of a ship equipped with the DSF and guaranteeing structural design safety. The performance strength of the passive type DSF was evaluated through structure analysis based on the finite element method. The thickness of main structure members was applied to design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experimental method and analysis of variance. The optimum design case was also identified from the orthogonal array experiment results. Various meta-models, such as Chebyshev orthogonal polynomial, Kriging, response surface method, and radial basis function-based neural network, were generated from the orthogonal array experiment results. The results of the orthogonal array experiment were validated using the meta-modeling results. It was found that the radial basis function-based neural network among the meta-models could approximate the design space of the passive type DSF with the highest accuracy.

• Journal of the Korea Convergence Society
• /
• v.12 no.3
• /
• pp.187-196
• /
• 2021

The paper deals with comparative study for characteristics of approximation of design space according to various approximate models and sensitivity analysis using orthogonal array experiments in structure design of active type DSF which was developed for float-over installation of offshore plant. This study aims to propose the orthogonal array experiments based design methodology which is able to efficiently explore an optimum design case and to generate the accurate approximate model. Thickness sizes of main structure member were applied to the design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experiment. Best design case was also identified to improve the structure design with weight minimization. From the orthogonal array experiment results, various approximate models such as response surface model, Kriging model, Chebyshev orthogonal polynomial model, and radial basis function based neural network model were generated. The experiment results from orthogonal array method were validated by the approximate modeling results. It was found that the radial basis function based neural network model among the approximate models was able to approximate the design space of the active type DSF with the highest accuracy.