저항추진성능 예측 기반을 구축하기 위한 데이터베이스의 개발

Abstract

Several methods to predict the ship powering performance has been developed such as the Computational Fluid Dynamics(CFD) that discretize and solve the government equation of the fluid dynamics, empirical method which finds the value of objectives from the model basin database. And more advanced method using the neural network is accessed to predict the model test results. In this paper, the concept of the KRISO ship database is introduced. The database which possesses the model test results of the several hundred ships is constructing to reduce the deviation of the data, and to fit the trend of the recent development. Diagnosis and prognosis functions which can present and predict the performance level of the hull form are applied.