Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 31, No. 4 (April 2018) 666-672    Article in Press

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  PARETO OPTIMIZATION OF TWO-ELEMENT WING MODELS WITH MORPHING FLAP USING COMPUTATIONAL FLUID DYNAMICS, GROUPED METHOD OF DATA HANDLING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS
 
H. Safikhani and M. Jamalinasab
 
( Received: August 20, 2017 – Accepted in Revised Form: January 04, 2018 )
 
 

Abstract    A multi-objective optimization (MOO) of two-element wing models with morphing flap by using computational fluid dynamics (CFD) techniques, artificial neural networks (ANN), and non-dominated sorting genetic algorithms (NSGA II), is performed in this paper. At first, the domain is solved numerically in various two-element wing models with morphing flap using CFD techniques and lift (L) and drag (D) coefficients in wings are calculated. Afterward, for modeling L and D using grouped method of data handling (GMDH) type artificial neural networks, numerical data of the preceding step will be applied. Eventually, for Pareto based multi-objective optimization of two-element wing models with morphing flap using NSGA II algorithm, the modeling, which is accomplished by GMDH will be applied. It is shown that the achieved Pareto solution includes important design information on such wings.

 

Keywords    Two-element Wings; Morphing Flap; Multi-objective Optimization; Grouped Method of data Handling; NSGA II; Quadrature Phase Shift Keying

 

چکیده   

در این مقاله با استفاده از الگوریتم ژنتیک چندهدفی، فرآیند بهینه سازی چندهدفی بال های دو المانی انجام شده است. در ابتدا ناحیۀ محاسباتی با استفاده از دینامیک سیالات محاسباتی حل شده است و در تمامی محاسبات ضرایب برا و پسا محاسبه شده اند. در مرحله بعد از داده های مرحله قبل جهت مدلسازی توابع هدف با استفاده از شبکه عصبی انجام شده است. در پایان نمدار پارتو که شامل اطلاعات بسیار مفیدی در مورد طراحی بال های دو المانی می باشد، ارائه شده است.

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