Relevant bibliographies by topics / Evolutionary structural optimisation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Evolutionary structural optimisation'

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Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Evolutionary structural optimisation"

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Chen, Yu‐Ming, Kuo‐Shuh Fan, and Ban‐Jwu Shih. "2.5D Nodal based evolutionary structural optimisation." Journal of the Chinese Institute of Engineers 33, no. 6 (September 2010): 899–908. http://dx.doi.org/10.1080/02533839.2010.9671678.

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Proos, K. A., G. P. Steven, O. M. Querin, and Y. M. Xie. "Stiffness and inertia multicriteria evolutionary structural optimisation." Engineering Computations 18, no. 7 (November 2001): 1031–54. http://dx.doi.org/10.1108/02644400110404028.

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Querin, O. M., G. P. Steven, and Y. M. Xie. "Evolutionary structural optimisation using an additive algorithm." Finite Elements in Analysis and Design 34, no. 3-4 (February 2000): 291–308. http://dx.doi.org/10.1016/s0168-874x(99)00044-x.

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Kim, H., M. J. Garcia, O. M. Querin, G. P. Steven, and Y. M. Xie. "Introduction of fixed grid in evolutionary structural optimisation." Engineering Computations 17, no. 4 (June 2000): 427–39. http://dx.doi.org/10.1108/02644400010334838.

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Querin, O. M., G. P. Steven, and Y. M. Xie. "Evolutionary structural optimisation (ESO) using a bidirectional algorithm." Engineering Computations 15, no. 8 (December 1998): 1031–48. http://dx.doi.org/10.1108/02644409810244129.

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Guan, Hong, Grant P. Steven, and Yi-Min Xie. "Evolutionary Structural Optimisation Incorporating Tension and Compression Materials." Advances in Structural Engineering 2, no. 4 (August 1999): 273–88. http://dx.doi.org/10.1177/136943329900200403.

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Chen, G., G. J. Pettet, M. Pearcy, and D. L. S. McElwain. "Modelling external bone adaptation using evolutionary structural optimisation." Biomechanics and Modeling in Mechanobiology 6, no. 4 (August 25, 2006): 275–85. http://dx.doi.org/10.1007/s10237-006-0055-9.

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Chiu, Louis N. S., Daniel Stojanov, Bernard Rolfe, and Wen Yi Yan. "Effect of Optimisation Parameters in Topology Optimisation." Key Engineering Materials 725 (December 2016): 529–34. http://dx.doi.org/10.4028/www.scientific.net/kem.725.529.

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The Bi-directional Evolutionary Structural Optimisation (BESO) method had been used by many authors for the optimisation of structures. This work sets out to investigate the effect of key optimisation parameters utilized in the BESO method, the evolution rate and the filter radius, on the outcome of the optimisation. An understanding of the interaction of these factors in the optimisation process enables a more efficient way to produce optimised components that can fully capitalise on the capabilities of additive manufacturing.
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Steven, Grant, Osvaldo Querin, and Mike Xie. "Evolutionary structural optimisation (ESO) for combined topology and size optimisation of discrete structures." Computer Methods in Applied Mechanics and Engineering 188, no. 4 (August 2000): 743–54. http://dx.doi.org/10.1016/s0045-7825(99)00359-x.

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Merten, Errol A. "Application of Evolutionary Structural Optimisation; Reinventing the (Bicycle) Wheel." Applied Mechanics and Materials 553 (May 2014): 830–35. http://dx.doi.org/10.4028/www.scientific.net/amm.553.830.

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This paper reports the application of Evolutionary Structural Optimisation (ESO) to minimise the weight of a bicycle wheel rim. Two finite element models were developed to analyse the rims internal structure. The internal structure of a bicycle wheel rim consists of two walls connected by a rounded spoke bed, and a horizontal spar. One model was constructed with linear plate elements and suggested material should be distributed around the spoke bed and away from the rim walls, however inaccuracies were found in the spar section. ESO of the second model, consisting of linear brick elements presented similar results but modeled the spar section accurately. Results suggested that ESO applied to bicycle wheel rims can improve the performance of the wheel through weight reduction. These findings present a simple example to engineers who wish to use ESO for improvements to the material distribution of any hollow shell structure.
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Dissertations / Theses on the topic "Evolutionary structural optimisation"

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Proos, Kaarel. "Evolutionary structural optimisation as a robust and reliable design tool." Connect to full text, 2002. http://hdl.handle.net/2123/519.

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Thesis (Ph. D.)--University of Sydney, 2002.
Title from title screen (viewed Apr. 28, 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Aeronautical, Mechatronic and Mechanical Engineering. Includes bibliographical references. Also available in print form.
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Proos, Kaarel Andres. "Evolutionary structural optimisation as a robust and reliable design tool." Thesis, The University of Sydney, 2002. http://hdl.handle.net/2123/519.

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Evolutionary Structural Optimisation (ESO) is a relatively new design tool used to improve and optimise the design of structures. It is a heuristic method where a few elements of an initial design domain of finite elements are iteratively removed. Such a process is carried out repeatedly until an optimum design is achieved, or until a desired given area or volume is reached. There have been many contributions to the ESO procedure since its conception back in 1992. For example, a provision known as Bi-Directional ESO (BESO) has now been incorporated where elements may not only be removed, but added. Also, rather than deal with elements where they are either present or not, the designer now has the option to change the element's properties in a progressive fashion. This includes the modulus of elasticity, the density of the material and the thickness of plate elements, and is known as Morphing ESO. In addition to the algorithmic aspects of ESO, a large preference exists to optimise a structure based on a selection of criteria for various physical processes. Such examples include stress minimisation, buckling and electromagnetic problems. In a changing world that demands the enhancement of design tools and methods that incorporate optimisation, the development of methods like ESO to accommodate this demand is called for. It is this demand that this thesis seeks to satisfy. This thesis develops and examines the concept of multicriteria optimisation in the ESO process. Taking into account the optimisation of numerous criteria simultaneously, Multicriteria ESO allows a more realistic and accurate approach to optimising a model in any given environment. Two traditional methods � the Weighting method and the Global Criterion (Min-max) method have been used, as has two unconventional methods � the Logical AND method and the Logical OR method. These four methods have been examined for different combinations of Finite Element Analysis (FEA) solver types. This has included linear static FEA solver, the natural frequency FEA solver and a recently developed inertia FE solver. Mean compliance minimisation (stiffness maximisation), frequency maximisation and moment of inertia maximisation are an assortment of the specific objectives incorporated. Such a study has provided a platform to use many other criteria and multiple combinations of criteria. In extending the features of ESO, and hence its practical capabilities as a design tool, the creation of another optimisation method based on ESO has been ushered in. This method concerns the betterment of the bending and rotational performance of cross-sectional areas and is known as Evolutionary Moment of Inertia Optimisation (EMIO). Again founded upon a domain of finite elements, the EMIO method seeks to either minimise or maximise the rectangular, product and polar moments of inertia. This dissertation then goes one step further to include the EMIO method as one of the objectives considered in Multicriteria ESO as mentioned above. Most structures, (if not all) in reality are not homogenous as assumed by many structural optimisation methods. In fact, many structures (particularly biological ones) are composed of different materials or the same material with continually varying properties. In this thesis, a new feature called Constant Width Layer (CWL) ESO is developed, in which a distinct layer of material evolves with the developing boundary. During the optimisation process, the width of the outer surrounding material remains constant and is defined by the user. Finally, in verifying its usefulness to the practical aspect of design, the work presented herein applies the CWL ESO and the ESO methods to two dental case studies. They concern the optimisation of an anterior (front of the mouth) ceramic dental bridge and the optimisation of a posterior (back of the mouth) ceramic dental bridge. Comparisons of these optimised models are then made to those developed by other methods.
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Proos, Kaarel Andres. "Evolutionary structural optimisation as a robust and reliable design tool." University of Sydney. Aerospace, 2002. http://hdl.handle.net/2123/519.

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Evolutionary Structural Optimisation (ESO) is a relatively new design tool used to improve and optimise the design of structures. It is a heuristic method where a few elements of an initial design domain of finite elements are iteratively removed. Such a process is carried out repeatedly until an optimum design is achieved, or until a desired given area or volume is reached. There have been many contributions to the ESO procedure since its conception back in 1992. For example, a provision known as Bi-Directional ESO (BESO) has now been incorporated where elements may not only be removed, but added. Also, rather than deal with elements where they are either present or not, the designer now has the option to change the element's properties in a progressive fashion. This includes the modulus of elasticity, the density of the material and the thickness of plate elements, and is known as Morphing ESO. In addition to the algorithmic aspects of ESO, a large preference exists to optimise a structure based on a selection of criteria for various physical processes. Such examples include stress minimisation, buckling and electromagnetic problems. In a changing world that demands the enhancement of design tools and methods that incorporate optimisation, the development of methods like ESO to accommodate this demand is called for. It is this demand that this thesis seeks to satisfy. This thesis develops and examines the concept of multicriteria optimisation in the ESO process. Taking into account the optimisation of numerous criteria simultaneously, Multicriteria ESO allows a more realistic and accurate approach to optimising a model in any given environment. Two traditional methods � the Weighting method and the Global Criterion (Min-max) method have been used, as has two unconventional methods � the Logical AND method and the Logical OR method. These four methods have been examined for different combinations of Finite Element Analysis (FEA) solver types. This has included linear static FEA solver, the natural frequency FEA solver and a recently developed inertia FE solver. Mean compliance minimisation (stiffness maximisation), frequency maximisation and moment of inertia maximisation are an assortment of the specific objectives incorporated. Such a study has provided a platform to use many other criteria and multiple combinations of criteria. In extending the features of ESO, and hence its practical capabilities as a design tool, the creation of another optimisation method based on ESO has been ushered in. This method concerns the betterment of the bending and rotational performance of cross-sectional areas and is known as Evolutionary Moment of Inertia Optimisation (EMIO). Again founded upon a domain of finite elements, the EMIO method seeks to either minimise or maximise the rectangular, product and polar moments of inertia. This dissertation then goes one step further to include the EMIO method as one of the objectives considered in Multicriteria ESO as mentioned above. Most structures, (if not all) in reality are not homogenous as assumed by many structural optimisation methods. In fact, many structures (particularly biological ones) are composed of different materials or the same material with continually varying properties. In this thesis, a new feature called Constant Width Layer (CWL) ESO is developed, in which a distinct layer of material evolves with the developing boundary. During the optimisation process, the width of the outer surrounding material remains constant and is defined by the user. Finally, in verifying its usefulness to the practical aspect of design, the work presented herein applies the CWL ESO and the ESO methods to two dental case studies. They concern the optimisation of an anterior (front of the mouth) ceramic dental bridge and the optimisation of a posterior (back of the mouth) ceramic dental bridge. Comparisons of these optimised models are then made to those developed by other methods.
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Wong, Kin Ming. "Evolutionary structural form optimisation for lateral stiffness design of tall buildings /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CIVL%202007%20WONGK.

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Cervera, Eva. "Evolutionary structural optimisation based on boundary element representation of B-spline geometry." Thesis, Durham University, 2003. http://etheses.dur.ac.uk/2004/.

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Evolutionary Structural Optimisation (ESO) has become a well-established technique for determining the optimum shape and topology of a structure given a set of loads and constraints. The basic ESO concept that the optimum topology design evolves by slow removal and addition of material has matured over the last ten years. Nevertheless, the development of the method has almost exclusively considered finite elements (FE) as the approach for providing stress solutions. This thesis presents an ESO approach based on the boundary element method. Non-uniform rational B-splines (NURBS) are used to define the geometry of the component and, since the shape of these splines is governed by a set of control points, use can be made of the locations of these control points as design variables. The developed algorithm creates internal cavities to accomplish topology changes. Cavities are also described by NURBS and so they have similar behaviour to the outside boundary. Therefore, both outside and inside are optimised at the same time. The optimum topologies evolve allowing cavities to merge between each other and to their closest outer boundary. Two-dimensional structural optimisation is investigated in detail exploring multi-load case and multi-criteria optimisation. The algorithm is also extended to three-dimensional optimisation, in which promising preliminary results are obtained. It is shown that this approach overcomes some of the drawbacks inherent in traditional FE-based approaches, and naturally provides accurate stress solutions on smooth boundary representations at each iteration.
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Kelly, Liam. "Reducing design time : the impact of evolutionary structural optimisation on structural trade studies during preliminary design." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/380508/.

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Aircraft design is an inherently multi-disciplinary decision making process. In many design processes, the vehicle configuration is selected based on aerodynamic considerations in the concept design stage, before the structural layout is considered during preliminary design. Selection of the best vehicle configuration relies on an estimate of weight to determine the required lift of the aircraft. Structural topology optimisation is an efficient method for automatically generating a structural design layout that fits within a given design space and meets a given set of design criteria. By comparison to parametric structural optimisation approaches, topology optimisation permits a much greater design freedom. Though often difficult to manufacture using conventional methods, this design freedom can be exploited by using additive manufacture. In this thesis, an integrated concept and preliminary aerostructural design framework is proposed, which incorporates topology optimisation as a means of structural layout generation and weight estimation. The framework is utilised to optimise the wing geometry of an unmanned air vehicle, while generating a fuselage structure, intended for construction using additive manufacture, which satisfies a von Mises stress constraint. By comparison to an equivalent shell thickness optimisation study, the topology optimisation approach is shown to generate much lighter structural designs for the same aerodynamic efficiency.
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Brodie, Robert Neil. "Development of controllability and robustness methodologies for Bi-directional Evolutionary Structural Optimisation (BESO)." Thesis, University of Leeds, 2007. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486156.

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Agyei, Eugene Osei. "Groundwater modeling and management using the finite element method and evolutionary optimisation techniques /." Title page, synopsis and contents only, 1997. http://web4.library.adelaide.edu.au/theses/09PH/09pha284.pdf.

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Ebenhöh, Oliver. "Structural analysis of metabolic networks." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2003. http://dx.doi.org/10.18452/14853.

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In der vorliegenden Arbeit werden zwei Modelle zur strukturellen Analyse von Stoffwechselsystemen vorgestellt. Die Untersuchung basiert auf der Hypothese, dass heutzutage vorzufindende Stoffwechselsysteme als Ergebnis einer evolutionären Entwicklung, bestimmt durch Mutationsmechanismen und natürlicher Selektion, angesehen werden können. Es kann daher angenommen werden, dass kinetische Parameter sowie strukturelle Eigenschaften im Laufe der Evolution solche Werte angenommen haben, die eine gewisse Optimalität bezüglich ihrer biologischen Funktion darstellen. Das erste Modell untersucht das strukturelle Design ATP und NADH produzierender Systeme, so wie die Glykolyse und der Zitratzyklus. Eine Methode wird präsentiert, die die Beschreibung hypothetischer, chemisch denkbarer, alternativer Stoffwechselwege ermöglicht. Diese Wege werden bezüglich ihrer Effizienz, ATP zu produzieren, untersucht. Es stellt sich heraus, dass die meisten möglichen Wege eine niedrige ATP-Produktionsrate aufweisen und dass die effizientesten Wege einige strukturelle Gemeinsamkeiten besitzen. Die Optimierung bezüglich der ATP-Produktionsrate wird mit einem evolutionären Algorithmus durchgeführt. Folgende Resultate stehen mit dem tatsächlichen Design der Glykolyse und des Zitratzyklus in Einklang: (i) In allen effizienten Wegen befinden sich die ATP-verbrauchenden Reaktionen am Anfang. (ii) In allen effizienten Wegen befinden sich die sowohl die NADH- als auch die ATP-produzierenden Reaktionen am Ende. (iii) Die Anzahl der NADH-Moleküle, die aus einem energiereichen Molekül (Glukose) produziert werden, beläuft sich in allen effizienten Wegen auf vier. Im zweiten Modell werden vollständige Mengen metabolischer Netzwerke konstruiert, wobei von Reaktionen ausgegangen wird, die Änderungen des Kohlenstoffskeletts der beteiligten Metabolite beschreiben. Elementare Netzwerke werden dadurch definiert, dass eine bestimmte chemische Umwandlung durchgeführt werden kann und dass diese Fähigkeit verloren geht, wenn eine der beteiligten Reaktionen ausgeschlossen wird. Übergänge zwischen Netzwerken und Mutationen werden durch den Austausch einer einzigen Reaktion definiert. Es existieren verschiedene Mutationen, solche bei denen Funktionen verloren gehen, welche dazugewonnen werden, und neutrale Mutationen. Mutationen definieren Nachbarschaftsrelationen, die graphentheoretisch beschrieben werden. Eigenschaften wie Durchmesser, Konnektivität und die Abstandsverteilung der Vertizes werden berechnet. Ein Konzept zur Quantifizierung der Robustheit von Netzwerken gegenüber stöchiometrischen Veränderungen wird entwickelt, wobei zwischen starker und schwacher Robustheit unterschieden wird. Evolutionäre Algorithmen werden angewandt, um die Entwicklung von Netzwerkpopulationen unter konstanten und zeitlich veränderlichen Umweltbedingungen zu untersuchen. Es wird gezeigt, dass Populationen sich zu Gruppierungen von Netzwerken hinentwickeln, die gemeinsame Funktionen besitzen und nah benachbart sind. Unter zeitlich veränderlichen Umweltbedingungen zeigt sich, dass multifunktionelle Netzwerke optimal sind und sich im Selektionsprozess durchsetzen.
In the present thesis two models are presented which study the structural design of metabolic systems. The investigation is based on the hypothesis that present day metabolic systems are the result of an evolutionary development governed by mutation mechanisms and natural selection principles. Therefore, it can be assumed that these parameters have reached, during the course of their evolution, values which imply certain optimal properties with respect to their biological function. The first model concerns the structural design of ATP and NADH producing systems such as glycolysis and the citric acid cycle. A method is presented to describe hypothetical, chemically feasible, alternative pathways. We analyse these pathways with respect to their capability to efficiently produce ATP. It is shown that most of the possible pathways result in a very low ATP production rate and that the very efficient pathways share common structural properties. Optimisation with respect to the ATP production rate is performed by an evolutionary algorithm. The following results of our analysis are in close correspondence to the real design of glycolysis and the TCA cycle: (i) In all efficient pathways the ATP consuming reactions are located near the beginning. (ii) In all efficient pathways NADH producing reactions as well as ATP producing reactions are located near the end. (iii) The number of NADH molecules produced by the consumption of one energy-rich molecule (glucose) amounts to four in all efficient pathways. In the second model complete sets of metabolic networks are constructed starting from a limited set of reactions describing changes in the carbon skeleton of biochemical compounds. Elementary networks are defined by the condition that a specific chemical conversion can be performed by a set of given reactions and that this ability will be lost by elimination of any of these reactions. Transitions between networks and mutations of networks are defined by exchanges of single reactions. Different mutations exist such as gain or loss of function mutations and neutral mutations. Based on these mutations neighbourhood relations between networks are established which are described in a graph theoretical way. Basic properties of these graphs are determined such as diameter, connectedness, distance distribution of pairs of vertices. A concept is developed to quantify the robustness of networks against changes in their stoichiometry where we distinguish between strong and weak robustness. Evolutionary algorithms are applied to study the development of network populations under constant and time dependent environmental conditions. It is shown that the populations evolve toward clusters of networks performing a common function and which are closely neighboured. Under changing environmental conditions multifunctional networks prove to be optimal and will be selected.
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Damp, Lloyd Hollis. "Multi-Objective and Multidisciplinary Design Optimisation of Unmanned Aerial Vehicle Systems using Hierarchical Asynchronous Parallel Multi-Objective Evolutionary Algorithms." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/1858.

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The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.
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Books on the topic "Evolutionary structural optimisation"

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Genetic algorithms + data structures = evolution programs. 2nd ed. Berlin: Springer-Verlag, 1994.

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Genetic algorithms + data structures = evolution programs. Berlin: Springer-Verlag, 1992.

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Genetic algorithms + data structures = evolution programs. 3rd ed. Berlin: Springer-Verlag, 1996.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures =: Evolution Programs. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994.

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Michalewicz, Zbigniew. Algorytmy genetyczne + struktury danych = programy ewolucyjne. 3rd ed. Warszawa: Wydawnictwa Naukowo-Techniczne, 2003.

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Allaire, Grégoire. Conception optimale de structures (Mathématiques et Applications). Springer, 2006.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs. Springer, 2014.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs. Springer London, Limited, 2013.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs. Springer London, Limited, 2013.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs. Springer, 2011.

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Book chapters on the topic "Evolutionary structural optimisation"

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Querin, O. M., G. P. Steven, and Y. M. Xie. "Advances in Evolutionary Structural Optimisation: 1992-2000." In Topology Optimization of Structures and Composite Continua, 227–36. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-010-0910-2_16.

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Kim, H., O. M. Querin, and G. P. Steven. "Post-Processing of the Two-Dimensional Evolutionary Structural Optimisation Topologies." In Evolutionary Design and Manufacture, 33–44. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0519-0_3.

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González, L. F., L. Damp, J. Périaux, and K. Srinivas. "High-Fidelity Multi-criteria Aero-structural Optimisation using Hierarchical Parallel Evolutionary Algorithms." In Computational Fluid Dynamics 2006, 543–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92779-2_85.

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S. P. da Costa, Mariana, Joss Kesby, and Philip D. Clausen. "Structural Optimisation of 3D Printed Small Diffuser Augmented Wind Turbine Blade Using Bi-directional Evolutionary Layout Optimisation Method." In Wind Energy Exploitation in Urban Environment, 215–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13531-7_13.

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Feng, Z., P. Gu, M. Zheng, X. Yan, and D. W. Bao. "Environmental Data-Driven Performance-Based Topological Optimisation for Morphology Evolution of Artificial Taihu Stone." In Proceedings of the 2021 DigitalFUTURES, 117–28. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_11.

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AbstractTaihu stone is the most famous one among the top four stones in China. It is formed by the water's erosion in Taihu Lake for hundreds or even thousands of years. It has become a common ornamental stone in classical Chinese gardens because of its porous and intricate forms. At the same time, it has become a cultural symbol through thousands of years of history in China; later, people researched its spatial aesthetics; there are also some studies on its structural properties. For example, it has been found that the opening of Taihu stone caves has a steady-state effect which people develop its value in the theory of Poros City, Porosity in Architecture and some cultural symbols based on the original ornamental value of Taihu stone. This paper introduces a hybrid generative design method that integrates the Computational Fluid Dynamics (CFD) and Bi-directional Evolutionary Structural Optimization (BESO) techniques. Computational Fluid Dynamics (CFD) simulation enables architects and engineers to predict and optimise the performance of buildings and environment in the early stage of the design and topology optimisation techniques BESO has been widely used in structural design to evolve a structure from the full design domain towards an optimum by gradually removing inefficient material and adding materials simultaneously. This research aims to design the artificial Taihu stone based on the environmental data-driven performance feedback using the topological optimisation method. As traditional and historical ornament craftwork in China, the new artificial Taihu stone stimulates thinking about the new value and unique significance of the cultural symbol of Taihu stone in modern society. It proposes possibilities and reflections on exploring the related fields of Porosity in Architecture and Poros City from the perspective of structure.
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Hofmann, Arne. "We look for evolutionary optimisation of structures." In What is the Architect Doing in the Jungle? Biornametics, 48–49. Vienna: Springer Vienna, 2013. http://dx.doi.org/10.1007/978-3-7091-1529-9_7.

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Lane, Peter, Na Helian, Muhammad Haad Bodla, Minghua Zheng, and Paul Moggridge. "Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling." In Applications of Evolutionary Computation, 301–16. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02462-7_20.

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Kulikov, Gennady G., and Haydn A. Thompson. "Nonlinear Model Structure Selection Using Evolutionary Optimisation Methods." In Advances in Industrial Control, 159–76. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3796-2_9.

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Brownlee, Alexander E. I., John A. W. McCall, Siddhartha K. Shakya, and Qingfu Zhang. "Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm." In Evolutionary Learning and Optimization, 45–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12834-9_3.

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Hämmerle-Uhl, Jutta, Michael Karnutsch, and Andreas Uhl. "Evolutionary Optimisation of JPEG2000 Part 2 Wavelet Packet Structures for Polar Iris Image Compression." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 391–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41822-8_49.

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Conference papers on the topic "Evolutionary structural optimisation"

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Weis, Lennart, Hardy Koke, and Christian Huhne. "Structural optimisation of a composite aircraft frame applying a particle swarm algorithm." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7256943.

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Vio, Gareth, and Jonathan Cooper. "Optimisation of Composite Structures for Aeroelastic Applications Using Evolutionary Algorithms." In 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
16th AIAA/ASME/AHS Adaptive Structures Conference
10t. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-1972.

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van Wyk, David, and David Jonson. "The Design of Advanced Composite Structures Using Evolutionary Design Methods." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95775.

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The development of an evolutionary optimisation method and its application to the design of an advanced composite structure is discussed. Composite materials are increasingly being used in various fields, and so optimisation of such structures would be advantageous. From among the various methods available, one particular method, known as Evolutionary Structural Optimisation (ESO), is shown here. ESO is an empirical method, based on the concept of removing and adding material from a structure, in order to create an optimum shape. Much work has been done on ESO by various researchers. V. Young, G.P Steven, Y.M. Xie and O.M. Querin extended the basic ESO algorithm to the addition of elements and multiple load cases. S. Savas, M. Ulker, and M.P. Saka utilised ESO to create structures with a uniform stress distribution. Both ESO algorithms were applied to isotropic structures. The basic principle of ESO is to remove material from the model, based on certain criteria, stress being typical. The method can also add material back, where it may become necessary to reinforce the structure, which may happen when excessive material is removed. The model undergoes an iterative process of analysis and modification, the cycle continuing until certain conditions are met, ranging from weight reduction to stress limits. The objective of the current research is to create an ESO method, utilising MSC.Patran/Nastran, to optimise composite structures. The final algorithm created is simple, in order to improve efficiency and reduce total analysis runtimes. The algorithm modifies the properties of the element, rather than removing it from the structure completely. This ensures that the connectivity of the elements remains intact. Elements are selected for removal or addition based on a driving criterion. The composite structures are modelled as a core and shell. The core consists of 3D elements with orthotropic properties, and the skin is represented by non-removable 2D shell elements. These shell elements bear the loads and boundary conditions. They also keep the external shape of the model, which is important for aerodynamic structures. The models were run through the ESO algorithm until the final optimised structure remained. A tailfin of an aircraft was used as an application example. The aim was to reduce weight and create an optimised design for manufacture. The criterion for the analyses undertaken was stress based. The results of this research are presented in the paper.
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Gentile, Lorenzo, Cristian Greco, Edmondo Minisci, Thomas Bartz-Beielstein, and Massimiliano Vasile. "Structured-chromosome GA optimisation for satellite tracking." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3326841.

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Kononova, Anna V., Fabio Caraffini, Hao Wang, and Thomas Back. "Can Single Solution Optimisation Methods Be Structurally Biased?" In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185494.

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Vié, Aymeric. "Population network structure impacts genetic algorithm optimisation performance." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3463134.

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Gentile, Lorenzo, Elisa Morales, Domenico Quagliarella, Edmondo Minisci, Thomas Bartz-Beielstein, and Renato Tognaccini. "High-Lift Devices Topology Optimisation using Structured-Chromosome Genetic Algorithm." In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185603.

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Herbert, Luke T., and Robin Sharp. "Optimisation of BPMN Business Models via Model Checking." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13047.

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We present a framework for the optimisation of business processes modelled in the business process modelling language BPMN, which builds upon earlier work, where we developed a model checking based method for the analysis of BPMN models. We define a structure for expressing optimisation goals for synthesized BPMN components, based on probabilistic computation tree logic and real-valued reward structures of the BPMN model, allowing for the specification of complex quantitative goals. We here present a simple algorithm, inspired by concepts from evolutionary algorithms, which iteratively generates candidate improved processes based on the fittest of the previous generation. The evaluation of the fitness of each candidate in a generation is performed via model checking, detailed in previous work. At each iteration, this allows the determination of the precise numerical evaluation of the performance of a candidate in terms of the specified goals. A discussion of this method’s application, and the degree of optimization which is possible, is illustrated using an example drawn from the healthcare industry.
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Greco, Cristian, Lorenzo Gentile, Gianluca Filippi, Edmondo Minisci, Massimiliano Vasile, and Thomas Bartz-Beielstein. "Autonomous Generation of Observation Schedules for Tracking Satellites with Structured-Chromosome GA Optimisation." In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. http://dx.doi.org/10.1109/cec.2019.8790101.

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Brownlee, Alexander E. I., John A. W. McCall, Siddartha K. Shakya, and Qingfu Zhang. "Structure learning and optimisation in a Markov-network based estimation of distribution algorithm." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4982980.

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