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Open access Aug 2026

A Genetic algorithm-based optimization of CFD virtual test environments for axial cooling fan characteristic curves

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 españolTítulo en español: Optimización basada en algoritmos genéticos de entornos virtuales de prueba CFD para las curvas características de ventiladores axiales de refrigeraciónResumen: 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.v17i4.1295Dimensions.Open Alex.  

José Gustavo Leyva Retureta, A. G. García Martínez, Fernando Aldana Franco et al. · 0 citations
Open access 2026

Digital twin model and optimization: CFD and genetic algorithm for indoor ventilation systems

To better understand the application of computational fluid dynamics (CFD) and genetic algorithms (GAs) in indoor ventilation systems, the author proposes a study based on digital twin simulation and optimization: CFD and GAs for indoor ventilation systems. The author first analyzes the basic concepts, application prospects, technological connotations, and development trends of digital twin technology in the fields of complex industrial systems and complex equipment. Second, a classroom model is established through CFD, and relevant data are obtained. The BP neural network possesses strong nonlinear mapping capabilities and robustness, thereby meeting the requirements for fitting complex fluid simulation data. A substitute model for the CFD model is established using the BP neural network, and a GA objective function is formulated based on predicted mean vote indicators and air-age data. Different weights are set to optimize the model and then compared with the original CFD model. The results show that the CFD-coupled GA model takes only 1–3 h. The combination of CFD and GA can effectively replace the model optimized by directly calling the CFD program within the GA, reducing computation time and improving indoor air quality.

Yang Wu · 0 citations
Jul 2026

Fluid Force Optimization on Multi-Way Valve Blades via CFD Simulation and Surrogate Modeling

To optimize fluid forces on the multi-way valve blades within thermal management systems of new energy vehicles, this study employs a systematic design methodology integrating parametric modeling, surrogate modeling, and multi-objective optimization. Using the Tesla Model Y 8-way valve as a case study, a parametric model is established. A high-fidelity sample dataset is generated through computational fluid dynamics (CFD) simulations utilizing optimal Latin hypercube sampling (OLHS). A radial basis function-thin plate spline (RBF-TPS) surrogate model is subsequently developed to replace computationally expensive CFD analyses. Global sensitivity analysis is performed using an improved Sobol’s method. Structural optimization of the valve core blades is then conducted via the NSGA-II genetic algorithm. Results indicate that valve core structural parameters significantly influence the fluid force on individual blades, with inner diameter, outer diameter, and blade thickness exhibiting the greatest impact. Multi-objective optimization achieves a substantial reduction in the fluid force acting on each blade. Simulation verification confirms the optimization outcomes with minor discrepancies.

Jia-ming Liu, Xiaoxia Sun, Xiwang He et al. · 0 citations
Conference Open access Jul 2026

Surrogate-assisted CFD optimization of a radial turbine rotor

The design and optimization of radial turbine rotors play a critical role in improving the performance and efficiency of turbomachinery systems. Traditional optimization approaches often require extensive computational resources due to the need for high-fidelity CFD simulations of numerous design configurations. In this study, an integrated workflow combining automated 3D geometry generation, high-fidelity CFD evaluation, and machine-learning-assisted surrogate modeling was developed to efficiently explore the rotor design space. K-means clustering was employed to select representative training cases, and Gaussian Process Regression (GPR) was used to predict performance across untested configurations, guiding the search for optimal designs. The optimization successfully identified rotor geometries that increased isentropic efficiency by 2% relative to the baseline design. Flow visualizations revealed the aerodynamic mechanisms underlying the performance improvement, including improved flow guidance and reduced secondary losses. The study demonstrates that combining automated geometry, CFD simulations, and machine learning provides a powerful and computationally efficient approach for radial turbine rotor optimization, offering a practical pathway for achieving significant performance gains in turbomachinery applications.

Mostafa A Fawaz, Ahmed F. Aiad, Mohammed M Shahin et al. · 0 citations
Open access Aug 2026

CFD-Based Simulation and Optimization of Summer Environmental Conditions in Laying Hen Houses

To address uneven temperature and relative humidity distributions, localized heat accumulation, and insufficient air velocity in an enclosed stacked-cage laying hen house, a three-dimensional computational fluid dynamics (CFD) model of the laying hen house was developed using field-measured structural and environmental data, and a porous-media model was established for the cage zone. Model validation showed that the normalized mean square error (NMSE) values for temperature, relative humidity, and air velocity were all below 0.25, confirming the reliability of the CFD model. Through visualization analysis of the contour maps, the problems of uneven airflow distribution in the original ventilation system and significant heat accumulation at the fan end were identified. On this basis, numerical simulations were conducted for six air-inlet configurations by varying two key parameters: air-inlet spacing and air-inlet number. The simulation results showed that, compared with the original model, the configuration with an air-inlet spacing of 1.14 m and a total of 32 air inlets on the two gable walls improved the uniformity of temperature, air velocity, and relative humidity by 18.00%, 10.54%, and 18.38%, respectively, while reducing the mean effective temperature index (ETI) in the cage zone by 0.5 °C. This configuration effectively alleviated localized heat accumulation and improved air-velocity uniformity. These findings provide a theoretical basis and technical support for the structural optimization and environmental regulation of enclosed stacked-cage laying hen houses.

Lili Zhang, Shan-Jie Zhang, Miaomiao Xie et al. · 0 citations
Conference Open access 2026

CFD-based optimization of a modified split-type reaction water turbine using response surface methodology

This study addresses the need to improve the performance of split-type reaction water turbines (SRWTs) for low-head hydropower applications, where hydraulic losses and flow instability often limit torque output and efficiency. The objective was to optimize a modified SRWT by evaluating the effects of nozzle-edge sharpening angle, guide-pipe length, and guide-pipe diameter on torque and hydraulic efficiency. A CFD-based optimization framework was developed by integrating ANSYS Fluent simulations with Response Surface Methodology using a three-factor, three-level Box–Behnken Design. Fifteen design cases were simulated, and reduced quadratic models were established for torque and hydraulic efficiency, while pressure drop was analyzed as a supporting hydraulic indicator. Results showed that the sharpened nozzle angle had the strongest influence on both responses. The optimum design, consisting of a 52.02 mm guide-pipe length, 112.49 mm guide-pipe diameter, and 64.99° nozzle angle, produced a predicted torque of 31.22 Nꞏm and hydraulic efficiency of 85.31%, which were closely confirmed by CFD. Compared with the baseline design, the optimized turbine improved torque by 41.42% and hydraulic efficiency by 41.44%. These findings demonstrate that CFD coupled with RSM is an effective tool for optimizing SRWT geometry for low-head hydropower applications.

Murshied Aloyod, J. Honra · 0 citations