A ship production system can be represented by various production factors and production management indicators. To manage such a system efficiently, a theory is required to analyze and predict its behavior. The theory should be able to fully express the relationships between production factors and production management indicators. For this purpose, computational shipyard dynamics (CSD) was proposed by Kim et al. in the Journal of Ship Production and Design (2017). This methodology includes input variables, output variables, and key performance indicator (KPI) functionals, implemented as a simulation model. In this study, a rigorous approach to realize a model of a complex ship production system is presented by embodying previous research. Model-based ship production system theory is defined, which consists of production factors, production management indicators, and the relationships between them, called the simulation-based KPI functionals. The production factors are defined using a six-factor model, and the production management indicators are defined using KPIs that are already used in shipyards so that they are quantitatively measurable. To verify of the proposed theory, it was applied to fabrication shop, panel block assembly line, and ship block logistics simulation cases, which are examples that are similar to the job shop, flow shop, and project shop cases, respectively.
While operating on the Arctic route, ships may face various issues. The ice environment, such as level ice, pre-sawn, pack ice, ice ridge and brash ice, is one of the sources of those issues. Prediction of ship resistance in brash ice is very important for safe operation. There are three ways to estimate the ice resistance: using a mathematical model, numerical simulation, and using empirical formula. In this paper, empirical formulas are used. The main aim of the study is to develop a computer program (I-RES) for prediction of attainable speed in brash ice and for ice resistance estimation. To achieve this goal, first, the brash ice environmental characteristics were analyzed. The results of I-RES were evaluated by comparing with the model test results of brash ice. The accuracy of I-RES calculations was found to be around 5%.
As global warming reduces Arctic sea ice, Russia's Arctic resource development is taking place in earnest. In recent years, Russia has successfully built up Yamal LNG vessels. Interest in the Arctic route has been increasing as a result of the use of the Arctic Sea as a means of shipping and transportation, which saves time and money compared to the existing Suez Canal. Ships operating on the Arctic route are exposed to various ice conditions such as collision with ice and friction. For this reason, it is important to determine the engine power at the initial stage of the ship design, because the ship operating at the Arctic route has a larger hull resistance. For this purpose, research is being conducted in various ways including analytical methods and model tests. The Arctic sea routes have various types of sea ice such as brash ice, which is formed by overlapping small ice, level ice which is frozen flat in a large area, pack ice where ice pieces of different sizes float, ice ridge which is formed by overlapping ice and flat ice. Since ice resistance has a very different value depending on the type of ice, it is important to establish a method for estimating the ice resistance accordingly. The method of estimating the ice resistance includes a mathematical model, a method using a simulation, and use of empirical formulas. The method of using a mathematical model and the method using simulation has an advantage that relatively accurate results can be obtained and the result analysis is also easy. However, these methods are time-consuming to define the shape and characteristics of ships and ice. Therefore, in this study, an empirical formula that can estimate ice resistance in a short time (Kim et al., 2015) was used to estimate ice resistance. The purpose of this study is to expand the application range of ice resistance and to adopt the safe speed estimation program for the brash ice, which was first developed for the level ice. The process of determination of the attainable speed from the estimated ice resistance and calculated engine power was summarized. Engine power was determined from the characteristic curve derived from the relationship between the engine and the propeller. To verify the accuracy and validity of the results of previous studies, we compared the model test results in level ice, pre-sawn, pack ice environment with the I-RES program results. The ice resistance estimation of brash ice developed ice resistance estimation algorithm through the environmental characteristics analysis. Also, the velocity estimation algorithm of brash ice using empirical equation is verified by comparing with the model test. The I-RES program, which has been supplemented with the proven algorithm, can be used to determine the maximum engine power of a ship working at the Arctic route.
Kim, Woo Jin. (Daewoo Shipbuilding & Marine Engineering) | Kim, Sung Pyo. (Daewoo Shipbuilding & Marine Engineering) | Sim, In Hwan. (Daewoo Shipbuilding & Marine Engineering) | Hur, Jae Wook. (Daewoo Shipbuilding & Marine Engineering) | Kim, Hyun Soo. (Inha Technical College) | Ryu, Cheol Ho. (Inha Technical College)
The purpose of this research is to find practical methods for prediction of ice resistance in design stage for ice class vessel. Two methods are performed in this research for prediction. Empirical formula calculation for ice breaking vessel and computational fluid dynamics (CFD) analysis for Baltic ice class vessel are used in this research. Both prediction results are compared with ice model test results. Empirical formula calculation and CFD analysis both still have a limit in some matter, however this research is useful as a beginning step for ice calculation and comparison with model test.
Gas bubbles in LNG sloshing may affect the magnitude of the impact pressure due to the density and compressibility. Flow of the entrapped gas bubbles in the LNG sloshing impacting is modeled by the multiphase flow with a continuous liquid flow and a dispersed gas. A multiphase CFD model has been used to understand the effect of the gas bubble on the magnitude of sloshing impact. The CFD model is introduced based on an Eulerian-Eulerian model that employs the Control Volume-Based Finite Element Method (CVFEM). Sloshing pressure obtained by the CFD is imposed on a rectangular insulation panel to investigate the difference of pressures induced by the sloshing pressure with and without gas bubble. The study is focused on the qualitative simulation of the impact velocities and impact pressures induced by the LNG carrying gas bubble. Also, the Fluid-Structure interaction for the bubble flow is discussed.
It has been well known the sloshing pressure has complex shape and various patterns. The pattern of sloshing pressure is variously characterized by the pressure amplitude, duration time and skewness. The structural response induced by the sloshing pressure is also affected by the pattern of sloshing pressure and the type of structural members. In order to understand the structural response by the perspective view of categorized pattern, it is more efficient to make simple sloshing pressure pattern than to reflect the complex pressure history. In this study, the sloshing pressures obtained by the small scale model test are simplified with respect to their duration and skewness. Dynamic analyses of Mark-III LNG CCS are then parametrically performed with the consideration of various types of sloshing impact. Meanwhile, the failure pressures given the duration and skewness are investigated after parametric calculations are conducted to investigate the effect of pressure parameters on the structural response. INTRODUCTION Liquefied natural gas (LNG) is effectively transported by insulated cargo containment outfitted with membrane tanks. To keep the LNG at cryogenic temperature of -163°C, a containment system is applied to the membrane tanks. In order to provide tightness against the LNG and gas, the insulation is assembled by a thin metal membrane, plywood and foam or perlite. The containment system needs also to withstand the various loads. A critical load on the LNG tank structure is caused by sloshing introduced by a violent motion of LNG at low filling state (Graczyk and Moan, 2011). As sloshing induced pressures may be extremely high, the structural strength of the containment system needs to be assessed during the design stage. There were many attempts made to develop a strength assessment procedure with the consideration of structural response to varying spatial and temporal characteristics of sloshing impact.