Publicación: Otimização multiobjetivo de ventiladores centrífugos com CFD e NSGA-II integrando a análise fatorial
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2024-10-22
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info:eu-repo/semantics/openAccess
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Universidad Nacional de Educación a Distancia (España), Universidad de Concepción - Chile. Departamento de Ingeniería Mecánica
Resumen
Ventiladores centrífugos desempenham um papel crucial em diversas indústrias. Este estudo investiga a otimização multiobjetivo de ventiladores centrífugos utilizando Computational Fluid Dynamics e Non-dominated Sorting Genetic Algorithm II. Técnicas como Principal Components Analysis e Factor Analysis são aplicadas para lidar com dados de alta dimensionalidade, reduzindo a complexidade e facilitando a otimização multiobjetivo. O algoritmo genético é utilizado para criar soluções ótimas não dominadas. Após a obtenção das soluções, estas são avaliadas utilizando a Technique for Order of Preference by Similarity to Ideal Solution e a Distância de Mahalanobis. O ventilador é feito de aço inoxidável AISI 430 e opera em condições de alta temperatura. Os resultados da otimização multiobjetivo foram replicados e apresentaram alta confiabilidade, com erro inferior a 10%, contribuindo para a fabricação de ventiladores com melhor desempenho e eficiência de recursos, sem a necessidade de prototipagem extensiva.
Ventilators play a crucial role in various industries. This study investigates the multi-objective optimization of centrifugal ventilators using Computational Fluid Dynamics and Non-dominated Sorting Genetic Algorithm II. Techniques such as Principal Components Analysis and Factor Analysis are applied to handle high-dimensional data, reducing complexity and facilitating multi-objective optimization. The genetic algorithm is used to create optimal non-dominated solutions. After obtaining these solutions, they are evaluated using the Technique for Order of Preference by Similarity to Ideal Solution and Mahalanobis Distance. The ventilator is made of AISI 430 stainless steel and operates under high-temperature conditions. The results of multi-objective optimization were replicated and demonstrated high reliability, with errors below 10%, contributing to the manufacture of ventilators with enhanced performance and resource efficiency, without extensive prototyping.
Ventilators play a crucial role in various industries. This study investigates the multi-objective optimization of centrifugal ventilators using Computational Fluid Dynamics and Non-dominated Sorting Genetic Algorithm II. Techniques such as Principal Components Analysis and Factor Analysis are applied to handle high-dimensional data, reducing complexity and facilitating multi-objective optimization. The genetic algorithm is used to create optimal non-dominated solutions. After obtaining these solutions, they are evaluated using the Technique for Order of Preference by Similarity to Ideal Solution and Mahalanobis Distance. The ventilator is made of AISI 430 stainless steel and operates under high-temperature conditions. The results of multi-objective optimization were replicated and demonstrated high reliability, with errors below 10%, contributing to the manufacture of ventilators with enhanced performance and resource efficiency, without extensive prototyping.
Descripción
Organizado y patrocinado por: Federación iberoamericana de Ingeniería Mecánica y Universidad de Concepción - Chile. Departamento de Mecánica, FeIbIm – FeIbEM
Categorías UNESCO
Palabras clave
Computational Fluid Dynamics, Multiobjective Optimization, Factor Analysis, Non-Dominated Sorting Genetic Algorithm, Design of Experiments, Centrifugal Fan
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E.T.S. de Ingenieros Industriales
Departamento
Mecánica