臺灣博碩士論文加值系統

摘要:
本文係應用智慧型全域基因演算法來搜尋船舶推進軸系排列的一組最佳軸承垂直偏移量(offset) 。其設計目標是傳動軸的正向應力及剪力有最小值。其限制條件是在操作狀態下，軸承的反力及應力與傳動軸止推凸緣的剪力及彎矩力在容許範圍內，而操作狀態主要包含冷態及熱態。由於一個軸系排列的正確與否嚴重的影響到一艘船舶運轉航行的安全性，為了使軸系的軸承及傳動軸的負荷及應力在操作狀態中都在容許值內，必須決定軸承垂直偏移量。本文利用一種結合田口與基因演算法的智慧型全域基因演算法，有系統的來搜尋一組最佳的軸承垂直偏移量，取代目前各船廠慣用且耗時的嘗試錯誤法。該法中，田口法是安插在傳統基因演算法的交配與突變運算之間，用來選擇整體族群中較佳的基因來參與交配，以增強基因演算法，使結果更強健、搜尋更周密及收斂更迅速。其適應函數係用懲罰函數以線性結合設計目標及限制條件之僞目標函數。同時，利用有限元素法求解整個軸系的軸承反力及應力與傳動軸的正向應力、剪力及彎矩力等數據。與兩艘2200TEU姊妹貨櫃船利用嘗試錯誤法設計結果比較，證實智慧型全域基因演算法確可大幅縮短設計時間及提昇設計品質。

ABSTRACT:
In this Thesis, an intelligent hybrid Taguchi-genetic algorithm (IHTGA) approach is proposed to search optimal bearing offsets of shafting alignment for the vessel propulsion system. Its objectives are to minimize the shaft normal stress and shear force. Its constraints include permissible reaction forces and stresses of bearings, and shear forces and bending moments of the shaft thrust flange at operation conditions, which mainly contain cold and hot conditions. As well know, the correct alignment of the shafting system for main propulsion system is important to ensure the safe operation of a vessel. In order to obtain a set of acceptable forces and stresses for bearings and shaft at operation conditions, a set of optimal bearing offsets to be determined. However, instead of usually carried out on a time-consuming trial-and-error procedure in most of shipyard, the IHTGA approach is applied to search for the above bearing offsets. The IHTGA is to combine traditional genetic algorithms (TRGAs) with Taguchi method. Taguchi method is inserted between crossover and mutation operations of TRGAs. Then, the systematic reasoning ability of Taguchi method is incorporated in the crossover operations to intelligently select the better genes to achieve crossover, and consequently enhance the genetic algorithms. Therefore, the IHTGA can be more robust, statistically sound, and quickly convergent. Its fitness function is assigned as a pseudo objective function, which is a linear combination of design objectives and constraints by penalty function method. At the same time, the bearing reaction forces and stresses, and the shaft normal stresses, bending moments and shear forces become determined by using finite element method. The computational experiments show that the proposed IHTGA approach can significant reduce alignment time and improve performance as compared with trial-and-error result for 2200 TEU container vessel.

CONTENTS:
摘要 i
ABSTRACT ii
ACKNOWLEDGEMENTS iii
CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
NOMENCLATURE viii
CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Literature Survey 2
1.3 Brief Sketch of the Contents 4
CHAPTER 2 SYSTEM DESCRIPTIONS 5
2.1 Definition of the Objects and Constraints 5
2.1.1 The Pseudo-Objective Equation 5
2.1.2 The sigamoid function 6
2.1.3 The Triangular function 7
2.1.4 The Piece-Ramp function 7
2.1.5 The Twin-Ramp function 8
2.2 Description of finite element program for shaft alignment Calculations 9
2.2.1 The finite element formulation 11
2.2.2 The finite element program 14
CHAPTER 3 DESIGN APPROACHES AND ALGORITHM 15
3.1 Taguchi Method 16
3.1.1 Taguchi Methods Procedure 16
3.1.2 Orthogonal Arrays (OAs) 17
3.1.3 Signal-to-Noise Ratios (SNR) 18
3.1.4 Analysis of Variance (ANOVA) 20
3.2 Genetic Algorithms (GAs) 21
3.2.1 Genetic Algorithms (GAs) process 22
3.2.1.1 Initialization and Encoding 23
3.2.1.2 Selection and Reproduction 23
3.2.1.3 Crossover 24
3.2.1.4 Mutation 25
3.2.2 Description of Genetic Algorithm for shafting alignment 25
3.3 Intelligent Hybrid Taguchi-Genetic Algorithm (IHTGA) 28
3.3.1 Description of Global Numerical Optimization by IHTGA 28
3.3.1.1 Generation of Initial Population 29
3.3.1.2 Selection for Reproduction 29
3.3.1.3 Generation of Diverse Offspring by Crossover Operation 30
3.3.1.4 Generation of Better Offspring by Taguchi Method 30
3.3.1.5 Mutation Operation for Speeding up Convergence 31
3.3.2 Description of IHTGA for shafting alignment 31
CHAPTER 4 PARAMETER DESIGN AND COMPUTER SIMULATIONS 36
4.1 Illustration of Shafting Alignment Problem 36
4.2 Computational Results 39
CHAPTER 5 CONCLUSIONS AND DISCUSSIONS 49
5.1 Conclusions 49
5.2 Future works 49
REFERENCES 50

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