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Improved_GA_Solai - S.Nagendra D.Jestin Z.Gurdal R.T.Haftka...

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S.Nagendra, D.Jestin, Z.Gurdal, R.T.Haftka and L.T.Watson Computers & Structures, Vol. 58, No. 3, pp. 543-555, 1996. Presented by Vignesh Solai Rameshbabu
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Introduction Simple programming technique which mimics natural genetics is known as l h genetic algorithm. It consists of reproduction, crossover, mutation, permutation, ply addition and deletion Strengths: Laminate stacking sequence design Close to global minimum Weakness: High computational costs Improved GA : Reduced computational costs Improve the reliability Lighter design – weight reduced by 4%
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High Performance composites High tensile strength Impact resistant Thickness is less – Buckling critical Tuning of Flexural properties – Stacking sequence tailoring Stacking sequence optimization – GA Improved GA - tailoring the genetic operators and thereby improve the reliability and reduce the computational cost. Composite Panels Layered composite laminate(skin) supported by stiffeners – Layered composite laminate(skin) supported by stiffeners which is also made up of composite laminates(blade).
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Problem Description Length – 30 in ; Breadth – 32 in Length 30 in ; Breadth 32 in N x = 20,000 lb/in; N xy = 5000 lb/in Balanced and Symmetric –0 0 , ±45 0 , 90 0 Blade and Flange – Identical Laminates Number of Identical Plies Adjacent 4 – to prevent matrix cracking No zero ply in skin laminate Outer Plies - ±45 0 – To avoid damage due t i l di to compressive loading Program used to run the genetic algorithm - PASCO
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O F l Optimization Formulation Fitness Function: Where Where, W Panel Weight - - - Critical failure load factor. - Critical Buckling load factor = / 1 - Strength Failure load factor= / 1
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Optimization Formulation (contd..) q – Penalty parameter Ɛ Bonus parameter P Pl cont - Ply contiguity parameter => where and are defined as the number of 0 0 or 90 0 stacks in excess to the constraint value in skin and blade laminates resp the constraint value in skin and blade laminates resp.
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