Mathematical Model Of Energy Saving Glass Coating Shape Design Using Binary Harmony Search For Better Signal Transmission

In recent years, buildings are designed using a special coated glass window. This glass serves as the outer shell that avoids the exposure of dangerous Ultra-violet rays; hazard light, direct sunlight and heat. Instead of being a normal window, this glass also acts to maintain the internal temperature of a building. Besides, this glass is known with the ability to save energy, which is an extensive technology of low-emissivity glasses that assist in reducing electricity usage. However, the use of this glass has impacted signal transmissions such as electromagnetic signal use in mobile communication by causing attenuation to the useful signals such as mobile phone (GSM, 3G), global positioning system (GPS), wireless network (Wi-Fi) and wireless broadband (LTE) due to the fabricated layer made of metallic-oxide on the window. Thus, engraving approach using a symmetrical shape design on the surface layer has shown improvement in reducing the attenuation problem. A well-designed algorithm is able to generate an optimized irregular shape design that can result in less attenuation on the transmission signal. Therefore, this study was conducted to propose a practical and effective irregular shape design that considers the property of coated layer and transmission signal. This approach is able to provide less attenuation problem and high efficiency of the signals through the coated glass. A model was developed to specify the requirement of coated glass on the energy saving glass. It determines the optimum irregular shape design, which is then integrated with Harmony Search (HS) optimization technique. By applying HS, an optimized shape design was generated, which met the objective of this study. HS generates a binary design representing bit ‘1’ and ‘0’. The obtained result were then simulated into a CST (Microwave) and tested on the S-parameter aspects, which are the return loss (S11) and transmission coefficient (S21). The efficiency of the irregular shape design was attained after the simulation process. Meanwhile, experimental result obtained in this study showed an irregular shape design generated by HS, hence showing an improvement in reducing the attenuation problem by 99.88% efficiency. The coated glass with optimized irregular shape design engraved on it can give a better signal transmission for mobile device, tracking system, wireless network and wireless broadband

Optimised Behaviour of Load-Bearing Tensegrities

Tensegrities are pin-jointed frameworks consisting of discontinuous compressive struts held within a tensioned cable net. Existing in a state of self-equilibrium, tensegrity structures can be found in vary-ing states of rigidity dependent on the initial pretension existing within the cables. The current state of tensegrity understanding appears comprehensive, and yet, tensegrities appear to be relegated to a class of architectural sculpture, due to the complexity associated with understanding their behaviour. This com-plexity is often attributed to the lack of application for tensegrity structures within the built environment. For this reason, this thesis aims to examine the effect of compressive loading on tensegrities, and how different pretensions, geometries and materials aid in resisting this applied loading. A review was conducted to determine a current understanding of load bearing tensegrity structures. Through conducting this review, it was found that stiffness and load bearing capacity directly correlate with the level of applied pretension. A computer model was developed using Rhinoceros 3D and Grasshopper (in conjunction with K2Engineering analysis software) to confirm this result. Iterative form-finding was utilised to determine resultant geometry (dependant on initial pretensions and applied loads), which was subsequently used to obtain axial loads within each member of the tensegrity

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Multiobjective in-core fuel management optimisation for nuclear research reactors

Thesis (PhD)--Stellenbosch University, 2016.ENGLISH SUMMARY : The efficiency and effectiveness of fuel usage in a typical nuclear reactor is influenced by the specific arrangement of available fuel assemblies in the reactor core positions. This arrangement of assemblies is referred to as a fuel reload configuration and usually has to be determined anew for each operational cycle of a reactor. Very often, multiple objectives are pursued simultaneously when designing a reload configuration, especially in the context of nuclear research reactors. In the multiobjective in-core fuel management optimization (MICFMO) problem, the aim is to identify a Pareto optimal set of compromise or trade-off reload configurations. Such a set may then be presented to a decision maker (i.e. a nuclear reactor operator) for consideration so as to select a preferred configuration. In the first part of this dissertation, a secularization-based methodology for MICFMO is pro- posed in order to address several shortcomings associated with the popular weighting method often employed in the literature for solving the MICFMO problem. The proposed methodology has been implemented in a reactor simulation code, called the OSCAR-4 system. In order to demonstrate its practical applicability, the methodology is applied to solve several MICFMO problem instances in the context of two research reactors. In the second part of the dissertation, an extensive investigation is conducted into the suitability of several multiobjective optimization algorithms for solving the constrained MICFMO problem. The computation time required to perform the investigation is reduced through the usage of several artificial neural networks constructed in the dissertation for objective and constraint function evaluations. Eight multiobjective metaheuristics are compared in the context of a test suite of several MICFMO problem instances, based on the SAFARI-1 research reactor in South Africa. The investigation reveals that the NSGA-II, the P-ACO algorithm and the MOOCEM are generally the best-performing metaheuristics across the problem instances in the test suite, while the MOVNS algorithm also performs well in the context of bi-objective problem instances. As part of this investigation, a multiplicative penalty function (MPF) constraint handling technique is also proposed and compared to an existing constraint handling technique, called constrained-domination. The comparison reveals that the MPF technique is a competitive alternative to constrained-domination. In an attempt to raise the level of generality at which MICFMO may be performed and potentially improve the quality of optimization results, a multiobjective hyperheuristic, called the AMALGAM method, is also considered in this dissertation. This hyperheuristic incorporates multiple metaheuristic sub-algorithms simultaneously for optimization. Testing reveals that the AMALGAM method yields superior results in the majority of problem instances in the test suite, thus achieving the dual goal of raising the level of generality and of yielding improved optimization results. The method has also been implemented in the OSCAR-4 system and is applied to solve several MICFMO case study problem instances, based on two research reactors, in order to demonstrate its practical applicability. Finally, in the third part of this dissertation, a conceptual framework is proposed for an optimization-based personal decision support system, dedicated to MICFM. This framework may serve as the basis for developing a computerized tool to aid nuclear reactor operators in designing suitable reload configurations.AFRIKAANSE OPSOMMING : Die doeltreffendheid en doelmatigheid van brandstofverbruik in 'n tipiese kernreaktor word deur die spesieke rangskikking van beskikbare brandstofelemente in die laaiposisies van die reaktor beinvloed. Hierdie rangskikking staan bekend as 'n brandstof herlaaikongurasie en word gewoonlik opnuut bepaal vir elke operasionele siklus van 'n reaktor. Die gelyktydige optimering van veelvuldige doele word dikwels tydens die ontwerp van 'n herlaaikongurasie nagestreef, veral binne die konteks van navorsingsreaktore. Die doelwit van meerdoelige binne-kern brandstofbeheeroptimering (MBKBBO) is om 'n Pareto optimale versameling van herlaaikongurasieafruilings te identiseer. So 'n versameling mag dan vir oorweging (deur byvoorbeeld 'n kernreaktoroperateur) voorgele word sodat 'n voorkeurkongurasie gekies kan word. In die eerste gedeelte van hierdie proefskrif word 'n skalariseringsgebaseerde metodologie vir MBKBBO voorgestel om verskeie tekortkominge in die gewilde gewigverswaringsmetode aan te spreek. Laasgenoemde metode word gereeld in die literatuur gebruik om die MBKBBO probleem op te los. Die voorgestelde metodologie is in 'n reaktorsimulasiestelsel, bekend as die OSCAR-4 stelsel, geimplementeer. Om die praktiese toepasbaarheid daarvan te demonstreer, word die metodologie gebruik om 'n aantal MBKBBO probleemgevalle binne die konteks van twee navorsingsreaktore op te los. In die tweede gedeelte van die proefskrif word 'n uitgebreide ondersoek ingestel om die geskiktheid van verskeie meerdoelige optimeringsalgoritmes vir die oplos van die beperkte MBKBBO probleem te bepaal. Die berekeningstyd wat vir die ondersoek benodig word, word verminder deur die gebruik van kunsmatige neurale netwerke, wat in die proefskrif gekonstrueer word, om doelfunksies en beperkings te evalueer. Agt meerdoelige metaheuristieke word binne die konteks van verskeie MBKBBO toetsprobleemgevalle vergelyk wat op die SAFARI-1 navorsingsreaktor in Suid-Afrika gebaseer is. Toetse dui daarop dat die NSGA-II, die P-ACO algoritme en die MOOCEM oor die algemeen die beste oor al die toetsprobleemgevalle presteer. Die MOVNS algoritme presteer ook goed in die konteks van tweedoelige probleemgevalle. 'n Vermenigvuldigende boetefunksie (VBF) beperkinghanteringstegniek word ook voorgestel en vergelyk met 'n bestaande tegniek bekend as beperkte dominasie. Daar word bevind dat the VBF tegniek 'n mededingende alternatief tot beperkte dominasie is. 'n Poging word aangewend om die vlak van algemeenheid waarmee MBKBBO uitgevoer word, te verhoog, asook om potensieel die kwaliteit van die optimeringsresultate te verbeter. 'n Meerdoelige hiperheuristiek, bekend as die AMALGAM metode, word in die nastreef van hierdie twee doelwitte oorweeg. Die metode funksioneer deur middel van die gelyktydige insluiting van 'n aantal metaheuristieke deel-algoritmes. Toetse dui daarop dat the AMALGAM metode beter resultate vir die meerderheid van toetsprobleme lewer, en dus word die bogenoemde twee doelwitte bereik. Die metode is ook in the OSCAR-4 stelsel ge mplementeer en word gebruik om 'n aantal MBKBBO gevallestudie probleemgevalle (binne die konteks van twee navorsingsreaktore) op te los. Sodoende word die praktiese toepasbaarheid van die metode gedemonstreer. In die derde deel van die proefskrif word 'n konseptuele raamwerk laastens vir 'n optimeringsgebaseerde persoonlike besluitsteunstelsel gemik op MBKBB, voorgestel. Hierdie raamwerk mag as grondslag dien vir die ontwikkeling van 'n gerekenariseerde hulpmiddel vir kernreaktoroperateurs om aanvaarbare herlaaikongurasies te ontwerp.Doctora

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Design and optimization of self-deployable damage tolerant composite structures: A review

Composite deployable structures are becoming increasingly important for the space industry, emerging as an alternative to conventional metallic mechanical systems in space applications. In most cases, the life-cycle of these structures includes a single deployment sequence, once the spacecraft is in orbit. So long as reliability is ensured, this fact opens the possibility of using the materials past their elastic regime and, possibly, beyond the initiation of damage, increasing the efficiency and applicability of the developed designs. This review explores this possibility, surveying the design of deployable structures, as well as the state of the art on the design and damage tolerance in composites. An overview of the developments performed on the topology optimization of composite structures is included for its novelty and potential application in the design of deployable structures. Finally, the possibility of combining these topics into a single efficient design approach is discussed

Automated design optimisation and simulation of stitched antennas for textile devices

This thesis describes a novel approach for designing 7-segment and 5-angle pocket and collar planar antennas (for operation at 900 MHz). The motivation for this work originates from the problem of security of children in rural Nigeria where there is risk of abduction. There is a strong potential benefit to be gained from hidden wireless tracking devices (and hence antennas) that can protect their security. An evolutionary method based on a genetic algorithm was used in conjunction with electromagnetic simulation. This method determines the segment length and angle between segments through several generations. The simulation of the antenna was implemented using heuristic crossover with non-uniform mutation. Antennas obtained from the algorithm were fabricated and measured to validate the proposed method.This first part of this research has been limited to linear wire antennas because of the wide range and flexibility of this class of antennas. Linear wire antennas are used for the design of high or low gain, broad or narrow band antennas. Wire antennas are easy and inexpensive to build. All the optimised linear wire antenna samples exhibit similar performances, most of the power is radiated within the GSM900 frequency band. The reflection coefficient (S11) is generally better than -10dB. The method of moment (MoM-NEC2) and FIT (CST Studio Suite 2015) solvers were used for this design. MATLAB is used to as an interface to control computational electromagnetic solvers for antenna designs and analysis. The genetic algorithm procedures were written in MATLAB. The second part of the work focuses on meshed ground planes for applications at 900 MHz global system for mobile communications (GSM), 2.45 GHz industrial, scientific, and medical (ISM) band and 5 GHz wearable wireless local area networks (WLAN) frequencies. Square ground planes were developed and designed using linear equations in MATLAB. The ground plane was stitched using embroidery machines. To examine the effect of meshing on the antenna performance and to normalise the meshed antenna to a reference, solid patch antenna was designed, fabricated on an FR4 substrate. A finite grid of resistors was created for numerical simulation in MATLAB. The resistance from the centre to any node of a finite grid of resistors are evaluated using nodal analysis. The probability that a node connects to each node in the grid was computed. The circuit model has been validated against the experimental model by measurement of the meshed ground plane. A set of measurement were collected from a meshed and compared with the numerical values, they show good agreement.</div