Airfoil shape optimization using non-traditional optimization technique and its validation

Mukesh, R.; Lingadurai, K.; Selvakumar, U.

doi:10.1016/j.jksues.2013.04.003

Cited by 41 publications

(38 citation statements)

References 12 publications

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“…The objective is to enhance the lift-to-drag ratio as the geometry is altered through an aerodynamic optimization, with the airfoil coordinates as design variables. Usually, the geometry of the airfoils is defined by a number of points around 100 (Mukesh et al, 2014). Any optimization necessitates a long computational time.…”

Section: Preliminary Designmentioning

confidence: 99%

Improvement of aerodynamic performance of a small wind turbine

et al. 2019

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The aerodynamic performance of horizontal-axis wind turbines is strongly dependent on many parameters, among which the airfoil type and the blade geometry (mainly defined by the chord and the twist distributions) are considered the most critical ones. In this article, an approach giving the appropriate airfoil for a small wind turbine design was conducted by performing an aerodynamic improvement of the blade’s airfoil. First, a preliminary design of the rotor blades of a small wind turbine (11 kW) was conducted using the small wind turbine rotor design code. This preliminary approach was done for different airfoils, and it resulted in a maximum power coefficient of 0.40. Then, the aerodynamic efficiency of the wind turbine was improved by modifying the geometry of the airfoils. This technique targets the optimization of the lift-to-drag ratio (Cl/Cd) of the airfoil within a range of angles of attack. Also, a non-uniform rational B-spline approximation of the airfoil was adopted in order to reduce the number of the design variables of the optimization. This methodology determined the best airfoil for the design of a small wind turbine, and it gave an improved power coefficient of 0.42.

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Section: Preliminary Designmentioning

confidence: 99%

Improvement of aerodynamic performance of a small wind turbine

et al. 2019

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“…Ref. [5] discusses the optimization of the airfoil NACA 2411 by using genetic (PANEL) algorithms. The point set of the polygonal form of the airfoil is replaced by the PARSEC parameters for the purposes of lowering the size of the tuning variable set.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Refs. Advantages Disadvantages Evolution from an initial guess, using optimization methods [1,2,3,4,5,6,7,8,12,16,17] (1) Successful to achieve a specific effect.…”

Section: Approachmentioning

confidence: 99%

Wing profile evolution driven by computational fluid dynamics

Rendon-Cardona¹,

Hernandez²,

Ruiz-Salguero³

2019

revuin

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In the domain of fluid dynamics, the problem of shape optimization is relevant because is essential to increase lift and reduce drag forces on a body immersed in a fluid. The current state of the art in this aspect consists of two variants: (1) evolution from an initial guess, using optimization to achieve a very specific effect, (2) creation and genetic breeding of random individuals. These approaches achieve optimal shapes and evidence of response under parameter variation. Their disadvantages are the need of an approximated solution and / or the trial-and-error generation of individuals. In response to this situation, this manuscript presents a method which uses Fluid Mechanics indicators (e.g. streamline curvature, pressure difference, zero velocity neighborhoods) to directly drive the evolution of the individual (in this case a wing profile). This pragmatic strategy mimics what an artisan (knowledgeable in a specific technical domain) effects to improve the shape. Our approach is not general, and it is not fully automated. However, it shows to efficiently reach wing profiles with the desired performance. Our approach shows the advantage of application domain-specific rules to drive the optimization, in contrast with generic administration of the evolution.

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“…Particle Swarm Optimization (PSO) is one of evolutionary method that popular for non-derivative optimization problems, understand easily and simple step when compared with another method. [3] The work used PSO procedure to combine with CFD analysis which used Xfoil program to find out the L/D of the randomized airfoil variables. constructed as the cubic spline which good continuously curve, because of it is the second derivative of the line slope which are drawn between all nodes.…”

Section: Introductionmentioning

confidence: 99%

“…For example for N panels, we must be had The node number is numbered form the trailing edge forward along the lower airfoil surface to the leading edge and turn back to the upper airfoil surface until reach the starting node. The pressure of each panels are calculated by considering the air flow around the midpoint of individual panel [3]. The pressure coefficient Cp for each panel is calculated by Eq.…”

Section: Introductionmentioning

confidence: 99%

Study of airfoil shape optimization by using the evolutionary method

Traisak¹,

Dolwichai²,

Srisertpol³

et al. 2016

Proceedings of the 5th International Conference on Mechanical Engineering, Materials and Energy (5th ICMEME2016)

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Nowadays, Airfoil shape optimization are developed by many researchers. Especially, evolutionary method is taken to find out the shape of airfoil. The main objective work is finding the shape of airfoil that get the aerodynamic properties which good for lift and drag of the aircraft wing. PSO is the evolutionary method that used for this work. The work are combination between the CFD analysis as Reynolds number 550,000 and optimization technique to get the airfoil shape that maximum lift to drag ratio. The result is compared with the standard NACA 2412 airfoil. The optimization airfoil has improved lift to drag ratio compared to the standard NACA 2412 airfoil for 53.22%. Airfoil shape optimization Airfoil shape profile. The airfoil profile can be generated by setting the point variables and make it continuously by using the spline interpolation. The NACA2412 airfoil shape is created by using the seven points as shown in the Fig. 1. The four points at the lower and upper part of airfoil are assigned as the seven of design variables. The leading and tailing edge are fixed in the same y direction that equal to zero and x direction are assigned equal one and zero, respectively. The spline interpolation is

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Airfoil shape optimization using non-traditional optimization technique and its validation

Cited by 41 publications

References 12 publications

Improvement of aerodynamic performance of a small wind turbine

Improvement of aerodynamic performance of a small wind turbine

Wing profile evolution driven by computational fluid dynamics

Study of airfoil shape optimization by using the evolutionary method

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