Examinando por Autor "Arias-Rosales A."

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    Albatros Create: an interactive and generative tool for the design and 3D modeling of wind turbines with wavy leading edge
    (Springer-Verlag France, 2020-01-01) Arias-Rosales A.; Osorio-Gómez G.; Universidad EAFIT. Departamento de Ingeniería de Diseño; Ingeniería de Diseño (GRID)
    The shape of a wind turbine blade plays a critical role in the efficiency and robustness of energy production. In particular, the Wavy Leading Edge is a morphology that can be implemented in the blades to improve the operating range in unsteady conditions. The best performance is achieved by fine-tuning the blade geometry to the specific context. An aerodynamic exploration of these kinds of morphologies implies generating and evaluating design iterations. Accordingly, this work presents the development of the generative tool Albatros Create ®. Through interactive visualization, infographics, and centralized parameterization, its goal is to support the geometrical definition of the aerodynamic surfaces of horizontal-axis turbines with or without a wavy leading edge. New airfoil profiles can be created, and 3D models of the rotors designed can be automatically generated. The software was implemented in the design of two rotors which were then recreated in a benchmarking analysis with four other softwares. None of the four managed to generate the smooth surfaces in fully-editable models that were achieved with Albatros Create. This work aims at empowering the research community with a user-friendly tool for exploring rotor designs through virtual prototypes. This can help to integrate further the design, modeling, and optimization stages, addressing a wider audience and facilitating the implementation of Wavy Leading Edge morphologies. © 2020, Springer-Verlag France SAS, part of Springer Nature.
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    Optimization of V-Trough photovoltaic concentrators through genetic algorithms with heuristics based on Weibull distributions
    (Elsevier Ltd, 2018-02-15) Arias-Rosales A.; Mejía-Gutiérrez R.; Universidad EAFIT. Departamento de Ingeniería de Diseño; Ingeniería de Diseño (GRID)
    Photovoltaic V-Troughs use simple and low-cost non-imaging optics, namely flat mirrors, to increase the solar harvesting area by concentrating the sunlight towards regular solar cells. The geometrical dispositions of the V-Trough's elements, and the way in which they are dynamically adjusted to track the sun, condition the optical performance. In order to improve their harvesting capacity, their geometrical set-up can be tailored to specific conditions and performance priorities. Given the large number of possible configurations and the interdependence of the multiple parameters involved, this work studies genetic algorithms as a heuristic approach for navigating the space of possible solutions. Among the algorithms studied, a new genetic algorithm named “GA-WA” (Genetic Algorithm-Weibull Arias) is proposed. GA-WA uses new heuristic processes based on Weibull distributions. Several V-Trough performance indicators are proposed as objective functions that can be optimized with genetic algorithms: (i) Ce? (average effective concentration); (ii) Cost (cost of materials) and (iii) Tsp (space required). Moreover, from the integration of these indicators, three multi-objective indices are proposed: (a) ICOE (Ce? versus Cost); (b) MICOE (Ce? versus Cost and Ce? versus Tsp combined) and (c) MDICOE (similar to MICOE but with discretization considerations). The heuristic parameters of the studied genetic algorithms are optimized and their capacities are explored in a case study. The results are compared against reported V-Trough set-ups designed with the interactive software VTDesign for the same case study. It was found that genetic algorithms, such as the ones developed in this work, are effective in the performance indicators improvement, as well as efficient and flexible tools in the problem of defining the set-up of solar V-Troughs in personalized scenarios. The intuition and the more holistic exploration of a trained engineer with an interactive software can be complemented with the broader and less biased evolutionary optimization of a tool like GA-WA. © 2017 Elsevier Ltd