Optimal design of hydrofoil and marine propeller using micro-genetic algorithm (μGA)

This paper presents results from the application of the genetic algorithm (GA) technique to the design optimization of hydrofoil and marine propeller incorporating potential based boundary element method (BEM). Although, larger population size as implemented by simple genetic algorithm (SGA) could find the optimal individual after a fewer number of generations than smaller population size, it is penalized by a longer amount of time to evaluate fitness in every generation. An investigation is, therefore, conducted in this research to implement micro genetic algorithm (I¼GA) with a very small population, and with simple genetic parameters, in order to achieve faster convergence to better solution from generation to generation. The technique is applied here to optimize hydrofoils of different plan forms, e.g., rectangular, elliptical, trapezoidal etc. Firstly, the hydrofoil design parameters, such as, angle of incidence, maximum thickness and camber ratios, aspect ratio, taper ratio, angle of sweep etc. are initialized randomly and the generated hydrofoil is analyzed by potential based boundary element method. GA then updates the design parameters over generation after generation and finally, finds an improved hydrofoil of maximum lift-drag ratio or minimum drag coefficient satisfying some design constraints. An improved blade or hydrofoil section is also designed by GA satisfying some design constraints. Finally, the technique is applied to the optimum design of marine propeller. In this study, I¼GA is found useful and prospective tool for the design optimization of hydrofoil and marine propeller due to its faster convergence. Keywords: Genetic algorithm, boundary element method, hydrofoil, propeller, design optimization  doi: 10.3329/jname.v1i1.2038 Journal of Naval Architecture and Marine Engineering 1(2004) 47-61

[1]  Sam Kwong,et al.  Genetic Algorithms : Concepts and Designs , 1998 .

[2]  D. Carroll.  GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS , 1996 .

[3]  S A Kinnas,et al.  A NUMERICAL OPTIMIZATION TECHNIQUE APPLIED TO THE DESIGN OF TWO- DIMENSIONAL CAVITATING HYDROFOIL SECTIONS , 1996 .

[4]  J. Hess.  Panel Methods in Computational Fluid Dynamics , 1990 .

[5]  I. H. Abbott,et al.  Theory of Wing Sections , 1959 .

[6]  Masatsugu Maeda,et al.  An experiment of cavitating flow around a finite span hydrofoil , 1992 .

[7]  J. Hess,et al.  Calculation of potential flow about arbitrary bodies , 1967 .

[8]  Koichi Koyama.  COMPARATIVE CALCULATIONS OF PROPELLER BY SURFACE PANEL METHOD - WORKSHOP ORGANIZED BY THE 20TH ITTC PROPULSOR COMMITTEE , 1993 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  David E. Goldberg,et al.  Sizing Populations for Serial and Parallel Genetic Algorithms , 1989, ICGA.

[11]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[12]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[13]  L. Morino,et al.  A nonlinear finite-element analysis of wings in steady incompressible flows with wake roll-up , 1976 .

[14]  Kalmanje Krishnakumar,et al.  Micro-Genetic Algorithms For Stationary And Non-Stationary Function Optimization , 1990, Other Conferences.

[15]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[16]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[17]  Young T. Shen,et al.  WING SECTIONS FOR HYDROFOILS--PART 1: SYMMETRICAL PROFILES , 1979 .

[18]  James Bowen,et al.  Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  Charles E. Taylor.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Complex Adaptive Systems.John H. Holland , 1994 .

[21]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[22]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[23]  D. Carroll.  Chemical laser modeling with genetic algorithms , 1996 .

[24]  Jong Jae Lee,et al.  A SURFACE PANEL METHOD FOR THE HYDRODYNAMIC ANALYSIS OF DUCTED PROPELLERS , 1987 .