Computational efficiency and validation of bi-directional evolutionary structural optimisation

Querin, Osvaldo M.; Young, V.; Steven, Grant P.; Xie, Yi Min

doi:10.1016/s0045-7825(99)00309-6

Cited by 196 publications

(85 citation statements)

References 11 publications

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“…4 and 8 in Ref. (16)). The BESO procedure is stopped when the objective volume is reached for the first time or the second time.…”

Section: Introductionmentioning

confidence: 92%

“…By combining the AESO and ESO methods, bidirectional evolutionary structural optimization (BESO) method has been developed by Yang et al and Querin et al (15), (16) In the BESO method, material of the structure can be either be added or removed. The amount of added and removal material in each iteration is determined by the rejection ratio (RR) and inclusion ratio (IR) respectively.…”

Section: Introductionmentioning

confidence: 99%

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A New Algorithm for Bi-Directional Evolutionary Structural Optimization

Huang

Xie

Burry

2006

JSME Int. J., Ser. C

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In this paper, a new algorithm for bi-directional evolutionary structural optimization (BESO) is proposed. In the new BESO method, the adding and removing of material is controlled by a single parameter, i.e. the removal ratio of volume (or weight). The convergence of the iteration is determined by a performance index of the structure. It is found that the new BESO algorithm has many advantages over existing ESO and BESO methods in terms of efficiency and robustness. Several 2D and 3D examples of stiffness optimization problems are presented and discussed.

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“…4 and 8 in Ref. (16)). The BESO procedure is stopped when the objective volume is reached for the first time or the second time.…”

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Bi-Directional Evolutionary Structural Optimization

Huang

Xie

Burry

2006

JSME Int. J., Ser. C

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“…[46]). The current model is based on what we believe are the most critical features of our hypothesis.…”

Section: Matlab Code Explanationmentioning

confidence: 99%

A New Approach to Modeling Morphogenesis Using Control Theory

Chyba

Kobayashi

Kwon

et al. 2011

São Paulo J. Math. Sci.

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A NEW APPROACH TO MODELING MORPHOGENESIS USING CONTROL THEORYRingrazio la mia famiglia, in ogni sua colorata declinazione, per il supporto incessante in tutti gli anni di studi e per il costante incoraggiamento che mi ha fornito anche nei momenti più difficili; Azzurra per avermi accompagnato in un'altra importantissima tratta di questo straordinario percorso e tutti gli amici, even the Hawaiian ones of course, senza i quali non sarei potuto essere ciò che sono. A NEW APPROACH TO MODELING MORPHOGENESIS USING CONTROL SOMMARIORecenti studi hanno evidenziato l'esistenza di strutture biologiche, chiamate frattoni, in grado di controllare la morfogenesi delle cellule. I frattoni quindi, assorbendo i fattori di crescita prodotti dalle cellule nelle loro immediate vicinanze, danno l'input affinché le cellule procedano alla mitosi. In base a queste ipotesi, proponiamo un modello di crescita cellulare che include i frattoni, i fattori di crescita liberi di diffondersi nello spazio, le interazioni che ne scaturiscono e il conseguente effetto sulla duplicazione cellulare. Possiamo dunque formulare in termini matematici e di controllo il problema della formazione di una struttura biologica complessa composta dalle cellule per la quale l'attivazione e la disattivazione dei frattoni determina lo sviluppo della struttura.Definendo il problema in questi termini, sorgono molte domande nel campo della teoria del controllo poiché lo spazio di operativo è in continua evoluzione (a causa della creazione di nuove cellule) e quindi non può essere condotta un'analisi mediante le tradizionali tecniche della teoria del controllo. Attraverso questo studio, presentiamo una nuova classe di problemi unitamente ad una prima analisi delle domande che ne scaturiscono. Infine, è mostrata una possibile estensione del modello all'ottimizzazione di layout.GIULIO TELLESCHI 6

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“…Bidirectional evolutionary structural optimisation uses heuristic criteria to progressively remove or add material from the design by changing elements from solid to void, or void to solid respectively. 21 Element based methods often required further techniques to avoid numerical issues such as the formation of checkerboard patterns and mesh dependent solutions. 22 Element based methods have been successful in solving a wide range of engineering topology optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous optimisation of structural topology and material grading using level set method

Dunning

Brampton

Kim

2015

Materials Science and Technology

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The present paper introduces a new technique for simultaneously optimising the topology and continuous material distribution of a structure. Topology optimisation offers great potential for novel, improved structural designs and is an ideal design tool for additive manufacturing (AM) techniques. Level set based topology optimisation produces solutions with clear, smooth boundaries that can be directly fabricated using AM. Further benefits of AM may be realised by also optimising the material distribution within the structure. The sequential linear programming level set method is used to include material distribution design variables in the topology optimisation problem. This allows the topology and continuous material distribution to be optimised simultaneously. Several compliance minimisation problems are used to demonstrate the proposed approach.

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Computational efficiency and validation of bi-directional evolutionary structural optimisation

Cited by 196 publications

References 11 publications

A New Algorithm for Bi-Directional Evolutionary Structural Optimization

A New Algorithm for Bi-Directional Evolutionary Structural Optimization

A New Approach to Modeling Morphogenesis Using Control Theory

Simultaneous optimisation of structural topology and material grading using level set method

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