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Pareto-Driven Multiobjective Design of Axial-Flow Automotive Fan with Response Surface Modeling.

Kai Ren1, Yuxi Chen1, Guoqing Wang1

  • 1School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

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Summary

This study optimized automotive cooling fan designs for better thermal management. The new design significantly improved fan efficiency without compromising flow or pressure, enhancing aerodynamic stability.

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Area of Science:

  • * Engineering and Applied Sciences
  • * Aerodynamics and Fluid Mechanics

Background:

  • * Automotive cooling fans are crucial for thermal management but face challenges in balancing efficiency, pressure, and flow.
  • * Conventional designs often present limitations in achieving optimal performance across these critical parameters.

Purpose of the Study:

  • * To perform a multiobjective optimization of an axial-flow fan's aerodynamic performance.
  • * To enhance volumetric flow rate (Q), static pressure (P), and efficiency (η) through systematic parameter adjustment.

Main Methods:

  • * Utilized response surface methodology and a genetic algorithm for fan design optimization.
  • * Employed Latin Hypercube Sampling to generate 25 data points for building an accurate surrogate model (R² > 0.99).
  • * Conducted sensitivity analysis to identify key parameters influencing fan performance.

Main Results:

  • * Identified critical parameters: root and tip installation angles and sweep angles.
  • * Achieved a significant efficiency improvement from 18.31% to 21.19% via Pareto solutions, maintaining flow and pressure.
  • * Flow-field analysis revealed enhanced aerodynamic stability with smoother velocity profiles and reduced flow separations.

Conclusions:

  • * The multiobjective optimization framework successfully improved axial-flow fan aerodynamic efficiency.
  • * Demonstrated a generalizable strategy for optimizing rotating machinery, enhancing aerodynamic stability.
  • * The optimized fan design offers improved thermal management capabilities for automotive applications.