Optimal design of hydrofoil and marine propeller using micro-genetic algorithm (Î¼GA)

Karim, Md. Mashud; Suzuki, Keiji; Kai, Hisashi

doi:10.3329/jname.v1i1.2038

Cited by 13 publications

(9 citation statements)

References 14 publications

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“…Furthermore, the elongated chord distribution for optimized propeller in comparison with initial one can be seen in Figure 9. A little ripple at the top most of blade's tip is also demonstrated, which is similar to the behavior which was shown by Karim [29]. The time needed for calculation was approximately 10 minutes for 113 generations on a personal computer with a CPU speed of 3.1 GHz.…”

Section: Resultssupporting

confidence: 81%

“…The history of the convergence is shown in Figure 10. Scrutinizing Figure 11, it is obvious that efficiency at the predetermined advanced ratio is higher than those for which calculation was done for the initial and redesigned ones [29]. In order to reach higher efficiency, as shown in both Figure 12 and Figure 13, the thinner camber distribution, whether non-dimensional distribution or just the very distribution, should accommodate with the length of the blade.…”

Section: Resultsmentioning

confidence: 94%

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Hydrodynamic Optimization of Marine Propeller Using Gradient and Non-Gradient-based Algorithms

2013

APH

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Here a propeller design method based on a vortex lattice algorithm is developed, and two gradient-based and non-gradient-based optimization algorithms are implemented to optimize the shape and efficiency of two propellers. For the analysis of the hydrodynamic performance parameters, a vortex lattice method was used by implementing a computer code. In the first problem, one of the Sequential Unconstraint Minimization Techniques (SUMT) is employed to minimize the torque coefficient as an objective function, while keeping the thrust coefficient constant as a constraint. Also, chord distribution is considered as a design variable, namely 11 design variables. In the second problem, a modified Genetic algorithm is used. The objective function is to maximize efficiency by considering the design variables as non-dimensional blade's chord and thickness distribution along the blade, namely 22 design variables. The hydrodynamic performance analyzer code is modified by a higher order Quasi-Newton scheme. Also, a hybrid function is used to improve the accuracy of the convergence. The solution of the optimization problems showed that a nearly 13% improvement in efficiency and a nearly 15% decrease in torque coefficient for the first propeller, as well as nearly 10% improvement for efficiency of the later propeller, is possible.

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Section: Resultssupporting

confidence: 81%

Section: Resultsmentioning

confidence: 94%

Hydrodynamic Optimization of Marine Propeller Using Gradient and Non-Gradient-based Algorithms

2013

APH

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“…ADM, constituting a widely employed analysis/design tool both in its potential and CFD version (Bontempo & Manna, 2013, 2016, 2017Conway, 1998), focuses more on the global effects of the propeller after the hull. Potential flow theory, including Lifting Line Method (LLM) (Çelik & Güner, 2006;Coney, 1989;Mishkevich, 2006), Lifting Surface Method (LSM) (Kawakita & Hoshino, 1998;Kerwin & Lee, 1987;Yamasaki & Ikehata, 1992) and Panel Method (PM) (Hess, 1990;Karim, Suzuki, & Kai, 2004;Kerwin, 1987;Suciu & Morino, 1976), is a commonly utilized tool to analyze or design ship propeller. Although CFD tools based on RANS or LES have been widely applied, the computational cost is too high to be practical for use in propeller optimization, especially when the number of iterations is large.…”

Section: Fluid-structure Interactionmentioning

confidence: 99%

A ship propeller design methodology of multi-objective optimization considering fluid–structure interaction

Jiang¹,

Cai

Ma³

et al. 2017

Engineering Applications of Computational Fluid Mechanics

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This paper presents a multi-objective optimization methodology that applies the Non-dominated Sorting Genetic Algorithm-II(NSGA-II) to propeller design, and realizes Fluid-Structure Interaction (FSI) weak-coupling based on Panel Method (PM) and the Finite Element Method (FEM). The FSI iterative process and the convergent pressure coefficient distribution and pressure fluctuation of HSP (a propeller installed on a Japanese bulk freighter-Seiun-Maru) are numerical calculated. The FSI results turn out to have higher precision than those without FSI. The appropriate optimization parameters are chosen after studying five cases. The Sobol method, a global Sensitivity Analysis (SA) algorithm, is used to quantify the dependence of objectives and constraints on the input parameters. In the multi-objective optimization methodology, efficiency, unsteady force, and mass are chosen as optimum objectives under certain constraints. Effectiveness and robustness of the methodology are validated by running the program starting from four different random values, which all improve the objectives and converge to the similar results. The proposed multi-objective optimization methodology could be a promising tool for propeller design to help improve design efficiency and ability in the future.

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“…A lot of heuristic (or semi heuristic) optimization techniques have been proposed in the literature. A comprehensive but not complete list includes Tabu Search [94], Simulated Annealing [95,96], Ant Colonies Optimization [97,98], Genetic Algorithms [99][100][101][102][103][104][105][106][107][108][109], Differential Evolution [110], Particle Swarm Algorithm [111][112][113][114][115][116], Immune Systems [117,118], Gravity Optimization [119], Imperialist Competitive Algorithm [120], and Intelligent Water Drop [121,122]. In this paper, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) will be used since they are tested, and widespread methods and other techniques could have been applied.…”

Section: Introductionmentioning

confidence: 99%

Mixed Static and Dynamic Optimization of Four-Parameter Functionally Graded Completely Doubly Curved and Degenerate Shells and Panels Using GDQ Method

Tornabene

Ceruti

2013

Mathematical Problems in Engineering

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This study deals with a mixed static and dynamic optimization of four-parameter functionally graded material (FGM) doubly curved shells and panels. The two constituent functionally graded shell consists of ceramic and metal, and the volume fraction profile of each lamina varies through the thickness of the shell according to a generalized power-law distribution. The Generalized Differential Quadrature (GDQ) method is applied to determine the static and dynamic responses for various FGM shell and panel structures. The mechanical model is based on the so-called First-order Shear Deformation Theory (FSDT). Three different optimization schemes and methodologies are implemented. The Particle Swarm Optimization, Monte Carlo and Genetic Algorithm approaches have been applied to define the optimum volume fraction profile for optimizing the first natural frequency and the maximum static deflection of the considered shell structure. The optimization aim is in fact to reach the frequency and the static deflection targets defined by the designer of the structure: the complete four-dimensional search space is considered for the optimization process. The optimized material profile obtained with the three methodologies is presented as a result of the optimization problem solved for each shell or panel structure.

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Optimal design of hydrofoil and marine propeller using micro-genetic algorithm (Î¼GA)

Abstract: 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

Cited by 13 publications

References 14 publications

Hydrodynamic Optimization of Marine Propeller Using Gradient and Non-Gradient-based Algorithms

Hydrodynamic Optimization of Marine Propeller Using Gradient and Non-Gradient-based Algorithms

A ship propeller design methodology of multi-objective optimization considering fluid–structure interaction

Mixed Static and Dynamic Optimization of Four-Parameter Functionally Graded Completely Doubly Curved and Degenerate Shells and Panels Using GDQ Method

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