A Genetic algorithm-based optimization of CFD virtual test environments for axial cooling fan characteristic curves
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i4.1295Keywords:
Genetic Algorithm, comcomputational fluid dynamics, axial fan performance, virtual wind tunnel, parameter tuning, algoritmo genético, dinámica de fluidos computacional, túnel de viento virtualAbstract
This paper presents an evolutionary optimization approach aimed at improving the numerical characterization of axial fan performance in computer cooling applications. The proposed framework integrates Computational Fluid Dynamics (CFD) simulations with Genetic Algorithms to refine the geometric configuration of a virtual test environment. A digital wind tunnel was developed in SolidWorks Flow Simulation, employing the Lam-Bremhorst k–ε turbulence model to reproduce operating conditions. The optimization process focused on adjusting key geometric variables, including tunnel diameter, overall length, and measurement point distribution, with the objective of reducing deviations from reference performance data provided by manufacturers. A case study conducted on a ROG STRIX XF120 axial fan showed that the optimized configuration yielded a closer approximation of both static pressure and airflow rates, achieving root mean square errors of 1.7% and 9.2%, respectively. The results indicate that the proposed methodology is adaptable to different fan models and underscore the relevance of blade aerodynamic design in enhancing the reliability of numerically derived characteristic curves.
Spanish-language metadata / Metadatos en español
Título en español:
Optimización basada en algoritmos genéticos de entornos virtuales de prueba CFD para las curvas características de ventiladores axiales de refrigeración
Resumen:
Este artículo presenta un enfoque de optimización evolutiva destinado a mejorar la caracterización numérica del desempeño de ventiladores axiales en aplicaciones de refrigeración de sistemas informáticos. El marco propuesto integra simulaciones de dinámica de fluidos computacional (CFD) con algoritmos genéticos para perfeccionar la configuración geométrica de un entorno virtual de pruebas. Se desarrolló un túnel de viento digital en SolidWorks Flow Simulation, utilizando el modelo de turbulencia k–ε de Lam-Bremhorst para reproducir las condiciones de operación. El proceso de optimización se centró en ajustar variables geométricas clave, entre ellas el diámetro del túnel, su longitud total y la distribución de los puntos de medición, con el objetivo de reducir las desviaciones respecto de los datos de desempeño de referencia proporcionados por los fabricantes. Un estudio de caso realizado con un ventilador axial ROG STRIX XF120 mostró que la configuración optimizada proporcionó una aproximación más cercana tanto de la presión estática como del caudal de aire, con errores de raíz cuadrática media del 1.7 % y 9.2 %, respectivamente. Los resultados indican que la metodología propuesta puede adaptarse a diferentes modelos de ventiladores y subrayan la importancia del diseño aerodinámico de las aspas para mejorar la fiabilidad de las curvas características obtenidas numéricamente.
Palabras Claves:
algoritmo genético; dinámica de fluidos computacional; desempeño de ventiladores axiales; túnel de viento virtual; ajuste de parámetros.
Smart citations:
https://scite.ai/reports/10.61467/2007.1558.2026.v17i4.1295
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