State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Integrating continuous differential evolution with discrete local search for meander line RFID antenna design

The automated design of meander line RFID antennas is a discrete self-avoiding walk(SAW) problem for which efficiency is to be maximized while resonant frequency is to beminimized. This work presents a novel exploration of how discrete local search may beincorporated into a continuous solver such as differential evolution (DE). A prior DE algorithmfor this problem that incorporates an adaptive solution encoding and a bias favoringantennas with low resonant frequency is extended by the addition of the backbite localsearch operator and a variety of schemes for reintroducing modified designs into the DEpopulation. The algorithm is extremely competitive with an existing ACO approach and thetechnique is transferable to other SAW problems and other continuous solvers. The findingsindicate that careful reintegration of discrete local search results into the continuous populationis necessary for effective performance

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization

The use of unmanned aerial vehicle (UAV)-based communication in millimeter-wave (mmWave) frequencies to provide on-demand radio access is a promising approach to improve capacity and coverage in beyond-5G (B5G) systems. There are several design aspects to be addressed when optimizing for the deployment of such UAV base stations. As traffic demand of mobile users varies across time and space, dynamic algorithms that correspondingly adjust the UAV locations are essential to maximize performance. In addition to careful tracking of spatio-temporal user/traffic activity, such optimization needs to account for realistic backhaul constraints. In this work, we first review the latest 3GPP activities behind integrated access and backhaul system design, support for UAV base stations, and mmWave radio relaying functionality. We then compare static and mobile UAV-based communication options under practical assumptions on the mmWave system layout, mobility and clusterization of users, antenna array geometry, and dynamic backhauling. We demonstrate that leveraging the UAV mobility to serve moving users may improve the overall system performance even in the presence of backhaul capacity limitations.Comment: 7 pages, 5 figures. This work has been accepted to IEEE Communications Magazine, 201

Optimization of nonlinear function with planar regions using supernova

Nowadays, optimization is begun to be use in different fields, e.g. preference algorithms. These new challenges need a robustness meta heuristics to solve them. Supernova meta heuristic that emules the descent behavior of the gradients and share the same weakness of them. They get stuck planar regions and hardly find the needle minimum. The main objective of this works is to improve the performance of the original version of supernova for the problematic topologies mention above. First, a review of how to these problems are solved in the literature is presented. Second, A criterion to determine planar regions is described . Third, a strategy to choose the parameters agree with the topology of the function is implemented. Supernova 2.0 was tested using the set of benchmarks functions proposed in CEC2013. The new version is significantly better than the original version, no significantly better than SPSO2011 and significantly inferior with SADE. Although, the results are applied to Supernova, most of the strategies can be applied to other methods.Doctorad

Efficient design optimization of high-performance MEMS based on a surrogate-assisted self-adaptive differential evolution

High-performance microelectromechanical systems (MEMS) are playing a critical role in modern engineering systems. Due to computationally expensive numerical analysis and stringent design specifications nowadays, both the optimization efficiency and quality of design solutions become challenges for available MEMS shape optimization methods. In this paper, a new method, called self-adaptive surrogate model-assisted differential evolution for MEMS optimization (ASDEMO), is presented to address these challenges. The main innovation of ASDEMO is a hybrid differential evolution mutation strategy combination and its self-adaptive adoption mechanism, which are proposed for online surrogate model-assisted MEMS optimization. The performance of ASDEMO is demonstrated by a high-performance electro-thermo-elastic micro-actuator, a high-performance corrugated membrane microactuator, and a highly multimodal mathematical benchmark problem. Comparisons with state-of-the-art methods verify the advantages of ASDEMO in terms of efficiency and optimization ability

Computational Design of Colourful and Flexible Photonic-enhanced Solar Cells

The need for coloured solar cells is particularly relevant to the adoption of photovoltaic devices in building-integrated photovoltaics. This thesis describes the process by which perovskite-based thin-film (MAPbI3) solar cells (PSCs) were optimised to tune colour without compromising flexibility whilst increasing short-circuit current density (JSC). Solar cells containing antireflective front coatings of planar, nanopillar and cross-grating geometries are simulated in a three-dimensional space by the Finite Differences in Time Domain (FDTD) numerical method using Ansys Lumerical software. The main outcomes of the numerical simulations are the optical photocurrent and the overall reflection, which is converted into colour using the CIE 1931 Colour-matching functions for standardised observers. Two Figures of Merit were developed, based on the optical photocurrent and the Euclidian distance between the simulated and desired colour. The best of the two FoMs is maximized via particle swarm optimization algorithm, by variation of geometrical properties of the front coverings and intrinsic ITO and Spiro-OMeTAD layer thicknesses. Through analytical elimination of redundant variables and successive restrictions in parameter ranges, the optimal geometrical parameters for flexible thin-film photonically-enhanced PSCs coloured red, green, or pink are obtained for increases in JSC upwards of 10.7%, and a versatile methodology is posited for future optimizations.A necessidade de células solares coloridas é particularmente relevante à adoção de dispositivos fotovoltaicos em equipamentos incorporados em facetas arquitetónicas. Esta dissertação descreve o processo pelo qual células solares de filme-fino à base de Perovskite (MAPbI3) foram otimizadas para ajustar a cor sem prejudicar a flexibilidade e aumentando a densidade de corrente. Células solares contendo revestimentos frontais antirreflexo de geometrias planares, nano-pilares e grelhas cruzadas são simuladas num espaço tridimensional pelo método numérico de Diferenças Finitas no Domínio do Tempo com o software Ansys Lumerical. Os principais resultados das simulações numéricas são a foto-corrente e a reflexão global, que é convertida em cor utilizando as funções de correspondência do CIE 1931 para observadores normalizados. Desenvolveram-se duas figuras de mérito, com base na foto-corrente e na distância Euclidiana entre a cor simulada e a cor desejada. A melhor figura foi maximizada por otimização Particle Swarm ao variar as geometrias das estruturas fotónicas frontais e das camadas intrínsecas de ITO e Spiro-OMeTAD. Após eliminar variáveis redundantes e restringir os intervalos de otimização, obtiveram-se geometrias ótimas para obtenção de estruturas coloridas vermelhas, verdes e rosa acrescendo aumentos de corrente superiores a 10.7% com o desenvolver de uma metodologia versátil aplicável a futuras otimizações