International Journal of Naval Architecture and Ocean Engineering最新文献

In this paper, an experimental validation of a numerical procedure for estimating the structure-borne Underwater Radiated Noise (URN) Transfer Function (TF) of a marine structure based on the SEA theory has been performed. For the purpose, the structure-borne URN TF of a central point-excited one-side fluid-loaded four-edge stiffened plate in a reverberant water tank has been measured and compared with the SEA result. Additionally, the practical applicability of the procedure for a real ship structure has been demonstrated by comparing the URN analysis result based on the transfer function method and the measurement result for a Korean research vessel, ‘Cheong-Hae’. From the results, it is confirmed that the presented procedure can be used to estimate the structure-borne URN level emitted from the vibrating fluid-loaded side shells of a ship structure.

Aircraft on an aircraft carrier deck are taxied or towed using a tractor. As these vehicles move within a limited area and are affected by the aircraft carrier motion, the possibility of aircraft movement in the entire space must be confirmed. The potential aircraft movements can be determined using simulations, wherein the movement characteristics of the aircraft must be analyzed by considering the movement of the carrier. Therefore, an aircraft dynamics simulation model considering the six-degrees-of-freedom movement of the platform is presented in this study to analyze the aircraft movement characteristics on the deck. The proposed model was verified using various reference data and models, and the simulation results of the multi-body dynamics analysis program DAFUL were employed to estimate the tire stiffness coefficient according to the slope. The proposed model facilitated the analysis of the movement characteristics of the aircraft by implementing driving simulations for various platform movements.

Designing a hull form typically involves beginning with a reference hull form based on ship owner requirements, editing the hull form to satisfy the requirements, and determining the most efficient hull form. Numerical analyses using Computational Fluid Dynamics (CFD) were employed to assess the performance of the hull form. However, these analyses require extensive computational resources, making it challenging to perform thorough analyses within the design timeframe. To address this issue, this paper proposes an approach that involves defining a range of hull forms with characteristic curves, predicting their performance using Deep Neural Networks (DNNs), and subsequently determining the optimal hull form based on these predictions. Initially, the hull form of a small ship was defined using four characteristic curves and parameterized using 29 variables. Fairness optimization was performed using these characteristic curves to define the hull form surface. By varying 29 parameters, 896 different hull forms were generated, with CFD analysis conducted for each variant. These data were then used to build a DNN model capable of predicting the performance based on hull form parameters. The accuracy of the DNN model was evaluated, resulting in a Mean Absolute Error (MAE) of 2.835%. Subsequently, the DNN model is combined with a genetic algorithm to identify the optimal set of parameters for the hull form, resulting in an optimal hull form. This optimization process revealed that the optimal hull form reduced the total hydrodynamic resistance by approximately 7% compared to the initial reference design. Consequently, this study demonstrates the effectiveness of the proposed method for deriving the optimal hull form for small ships.

In order to solve the integration problem of lifting point layout, 3D dynamic simulation and safety assessment, a multi-functional integrated design system for ship lifting process is developed. Based on the discrete data extracted from AM model (The three-dimensional ship block created through AVEVA Marine), lifting process information and lifting equipment information, this paper develops an automatic design module with the optimization algorithm of lifting point layout as the core, and retains the interactive design module. To solve the problem of automatic finite element mesh generation, the proposed system uses the highly reliable AFT-Delaunay algorithm as the finite element mesh generation algorithm. In addition, the proposed system combines the control algorithm, unit calculation and constitutive calculation extension interface to build a finite element analysis framework. Taking the deck block of a crude oil tanker as an example, this paper compares the functions of commercial finite element software MSC.NASTRAN and the proposed system in the finite element calculation results, mesh generation and finite element visualization. The comparison results shows that the proposed system has reached a level comparable to MSC.NASTRAN in the safety evaluation of block lifting.

The hydrodynamic performance of a sea-going ship can be analyzed using data from different sources, like onboard recorded in-service data, AIS data, and noon reports. Each of these sources is known to have its inherent problems. The current work highlights the most prominent issues, explained with examples from actual datasets. A streamlined semi-automatic approach to processing the data is finally outlined, which can be used to prepare a dataset for ship performance analysis. Typical data processing steps like interpolating metocean data, deriving additional features, estimating resistance components, data cleaning, and outlier detection are arranged in the best possible manner not only to streamline the data processing but also to obtain reliable results. A semi-automatic implementation of the data processing framework, with limited user intervention, is used to process the datasets here and present the example plots for various data processing steps, proving the effectiveness of the proposed approach.

This study proposes an automatic gain-tuning algorithm for ships. The proposed algorithm is designed to tune the gains of the ship controller automatically, rather than using trial and error. The forward speed and steering models were derived by linearizing and simplifying the 3-degrees of freedom (DOF) nonlinear equation of motion of the ship. The initial control gains were calculated using an error dynamics model constructed by combining the steering and system models of the controller. The maneuvering simulations and sensitivity analysis of the control performance at various control gains were performed for gain-tuning. System identification was conducted based on derived dynamics models and free-running test data. The tests verified that the gain-tuning algorithm corrects the gains more accurately and rapidly than trial and error. In addition, the algorithm reduced overshoot by 85% compared to the initial control gains.

In ship design, the matching of engine, propeller, and hull is an analytical process to establish the optimal operating mode so that the engine works safely with full power and the lowest fuel consumption, and a ship reaches desired speed. In theory, the operating process of the engine, hull, and propeller is usually expressed by their characteristics, and the matching problem is solved by finding the intersection of these characteristics. The problem here is that the above characteristics are determined under design conditions, corresponding to the standard technical conditions of the engine, hull, and propeller, however, all of these will be changed under actual service conditions, leading to a change in the position of the defined design match points and thereby greatly affect the safety and performance of the ships. This paper presents a new approach to determining the actual characteristics of the engine, hull, and propeller and how to use them to solve the matching problem under service conditions. This study was verified and validated on three study ships and was also applied to solve the matching problem of the Glory Star tanker. All the obtained results are in good agreement with the published real experimental data for this ship with a power loss of 21.5% under current service conditions, including 6.5% due to changes in propeller characteristics by changing technical conditions of propeller and hull surface, 6.3% due to engine deterioration, and 8.7% due to the engine - propeller mismatch after 5 years of operation.