scholarly journals Multi-Point Shape Optimization of a Horizontal Axis Tidal Stream Turbine

2021 ◽  
Author(s):  
Hassan El Sheshtawy ◽  
Ould el Moctar ◽  
Satish Natarajan
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Pressure Coefficient ◽  
Three Dimensional ◽  
Panel Method ◽  
Two Dimensional ◽  
Nsga Ii ◽  
Multi Objective Genetic Algorithm ◽  
Tidal Stream ◽  
Tidal Stream Turbine

A method was developed to perform shape optimization of a tidal stream turbine hydrofoil using a multi-objective genetic algorithm. A bezier curve parameterized the refrence hydrofoil profoil NACA 63815. Shape optimization of this hydrofoil maximized its lift-to-darg ratio and minimized its pressure coefficient, thereby increasing the turbines power output power and improving its cavitation characteristics. The Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was employed to perform the shape optimization. A comparative study of two-and three-dimensional optimizations was carried out. The effect of varing the angle of attack on the quality of optimized results was also studied. predictions based on two-dimensional panel method results was also studied. Preditions based on a two-dimensional panel method and on a computational fluid dynamics code were compared to experimental measurments.

scholarly journals Multi-Point Shape Optimization of a Horizontal Axis Tidal Stream Turbine

Eng ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 340-355
Author(s):  
Hassan el Sheshtawy ◽  
Ould el Moctar ◽  
Satish Natarajan
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Pressure Coefficient ◽  
Three Dimensional ◽  
Panel Method ◽  
Two Dimensional ◽  
Nsga Ii ◽  
Multi Objective Genetic Algorithm ◽  
Tidal Stream ◽  
Tidal Stream Turbine

A method was developed to perform shape optimization of a tidal stream turbine hydrofoil using a multi-objective genetic algorithm. A bezier curve parameterized the reference hydrofoil profile NACA 63815. Shape optimization of this hydrofoil maximized its lift-to-drag ratio and minimized its pressure coefficient, thereby increasing the turbines power output power and improving its cavitation characteristics. The Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was employed to perform the shape optimization. A comparative study of two- and three-dimensional optimizations was carried out. The effect of varying the angle of attack on the quality of optimized results was also studied. Predictions based on two-dimensional panel method results were also studied. Predictions based on a two-dimensional panel method and on a computational fluid dynamics code were compared to experimental measurements.

Multi-Objective Shape Optimization of Convective Wavy Channels

2005 ◽  
Author(s):  
Enrico Nobile ◽  
Francesco Pinto ◽  
Gino Rizzetto
Keyword(s):  
Shape Optimization ◽  
Three Dimensional ◽  
Temperature Fields ◽  
Two Dimensional ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Wavy Channels ◽  
Steady Laminar ◽  
Objective Shape

In this paper we describe a procedure for the multi-objective shape optimization of periodic wavy channels, representative of the repeating module of an ample variety of heat exchangers. The two objectives considered are the maximization of heat transfer rate and minimization of friction factor. Since there is no a single optimum to be found, we use a Multi-Objective Genetic Algorithm and the so-called Pareto’s dominance concept. The optimization of the periodic channel is obtained, by means of an unstructured Finite Element solver, for a fluid of Prandtl number Pr = 0.7, assuming fully developed velocity and temperature fields, and steady laminar conditions. For the two-dimensional case, the geometry is parameterized either by means of linear-piecewise profiles, or NURBS, and their control points represent the design variables. The three-dimensional channels are obtained by simple extrusion of the two-dimensional geometries. The results obtained are very encouraging, and the procedure described can be applied, in principle, to even more complex problems.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II)

2022 ◽  
Vol 204 ◽  
pp. 111999
Author(s):  
Hanting Wu ◽  
Yangrui Huang ◽  
Lei Chen ◽  
Yingjie Zhu ◽  
Huaizheng Li
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Nsga Ii ◽  
Sewer Pipes ◽  
Nondominated Sorting

A Novel Combination of Adaptive Tools for Turbomachinery Airfoil Shape Optimization Using a Real-Coded Genetic Algorithm

2013 ◽  
Author(s):  
A. Safari ◽  
H. G. Lemu ◽  
M. Assadi
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Performance Enhancement ◽  
Flow Analysis ◽  
Three Dimensional ◽  
Pressure Rise ◽  
Computation Time ◽  
Point Of View ◽  
Compressor Rotor ◽  
Real Coded Genetic Algorithm

An automated shape optimization methodology for a typical heavy-duty gas turbine (GT) compressor rotor blade section is presented in this paper. The approach combines a Non-Uniform Rational B-Spline (NURBS) driven parametric geometry description, a two-dimensional flow analysis, and a Genetic Algorithm (GA)-based optimization route. The objective is minimizing the total pressure losses for design condition as well as maximizing the airfoils operating range which is an assessment of the off-design behavior. To achieve the goal, design optimization process is carried out by coupling an established MATLAB code for the Differential Evolution (DE)-based optimum parameterized curve fitting of the measured point cloud of the airfoils’ shape, a blade-to-blade flow analysis in COMSOL Multiphysics, and a developed real-coded GA in MATLAB script. Using the combination of these adaptive tools and methods, the first results are considerably promising in terms of computation time, ability to extend the methodology for three-dimensional and multidisciplinary approach, and last but not least airfoil shape performance enhancement from efficiency and pressure rise point of view.

Significance of Delta Winglets on the Air Side Performance of Flat Finned Versus Wavy Finned-Tube Heat Exchangers

2020 ◽  
Author(s):  
Tariq Amin Khan ◽  
Nasir Mehdi Gardezi ◽  
Wei Li ◽  
Yang Zhou ◽  
Zahid Ayub
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Heat Exchangers ◽  
Three Dimensional ◽  
Finned Tube ◽  
Maximum Heat ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Maximum Heat Transfer ◽  
Side Flow

Abstract The performance on the air side flow is often limited due to its lower heat transfer coefficient. This work is related to numerical simulation to study the significance of employing delta winglets in flat finned and wavy finned-tube heat exchangers. For this purpose, three-dimensional simulation data and a multi-objective genetic algorithm are employed. The angle of attack (α) of delta winglets and Reynolds number varied from 15° to 75° and 500 to 1300, respectively. Employing delta winglets has increased the heat transfer per unit temperature and per unit volume (Z) and the fan power per unit core volume (E) for both flat finned and wavy finned-tube heat exchangers. To achieve a maximum heat transfer enhancement and a minimum friction factor, the optimal values of these parameters (Re and α) are calculated using the Pareto optimal strategy. For this purpose, CFD data, a surrogate model (neural network) and a multi-objective optimization genetic algorithm are combined. Results show that the performance of wavy finned-tube heat exchangers is higher than flat-finned tube heat exchangers which signify the importance of delta winglets in the wavy finned-tube heat exchangers.

scholarly journals A comparative study of AumRhn (4 ≤ m + n ≤ 6) clusters in the gas phase versus those deposited on (100) MgO

2016 ◽  
Vol 18 (32) ◽  
pp. 22122-22128 ◽  
Author(s):  
Fernando Buendía ◽  
Jorge A. Vargas ◽  
Marcela R. Beltrán ◽  
Jack B. A. Davis ◽  
Roy L. Johnston
Keyword(s):  
Genetic Algorithm ◽  
Density Functional Theory ◽  
Gas Phase ◽  
Density Functional ◽  
Energy Surface ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Functional Theory ◽  
Phase Versus ◽  
Combined Use

The combined use of a genetic algorithm and Density Functional Theory (DFT) calculations allows us to explore the potential energy surface. Our results show interesting effects on the geometries of the clusters on deposition. Two-dimensional clusters in the gas phase become three-dimensional and vice versa.

Four-Dimensional Printing for Freeform Surfaces: Design Optimization of Origami and Kirigami Structures

2015 ◽  
Vol 137 (11) ◽  
Author(s):  
Tsz-Ho Kwok ◽  
Charlie C. L. Wang ◽  
Dongping Deng ◽  
Yunbo Zhang ◽  
Yong Chen
Keyword(s):  
Shape Optimization ◽  
Three Dimensional ◽  
Optimization Method ◽  
Two Dimensional ◽  
3D Shape ◽  
Optimization Framework ◽  
Interior Boundary ◽  
Folding Structure ◽  
Dimensional Printing ◽  
3D Surfaces

A self-folding structure fabricated by additive manufacturing (AM) can be automatically folded into a demanding three-dimensional (3D) shape by actuation mechanisms such as heating. However, 3D surfaces can only be fabricated by self-folding structures when they are flattenable. Most generally, designed parts are not flattenable. To address the problem, we develop a shape optimization method to modify a nonflattenable surface into flattenable. The shape optimization framework is equipped with topological operators for adding interior/boundary cuts to further improve the flattenability. When inserting cuts, self-intersection is locally prevented on the flattened two-dimensional (2D) pieces. The total length of inserted cuts is also minimized to reduce artifacts on the finally folded 3D shape.

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