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

## TL;DR

This paper improves automotive cooling fan design by optimizing efficiency, pressure, and flow using advanced modeling and optimization techniques.

## Contribution

A novel multiobjective optimization framework using response surface modeling and genetic algorithms for axial-flow fan design.

## Key findings

- Efficiency improved from 18.31% to 21.19% without reducing flow or pressure.
- A surrogate model with R² > 0.99 accurately predicted fan performance.
- Tip angle strongly affects flow and pressure, while root angle impacts efficiency.

## Abstract

Automotive cooling
fans play a vital role in thermal management,
yet conventional designs often struggle to balance efficiency, pressure,
and flow requirements. This work presents a multiobjective optimization
of an axial-flow fan using response surface methodology and a genetic
algorithm. Four critical parameters (the root and tip installation
angles and sweep angles) were optimized with respect to volumetric
flow rate (Q), static pressure (P), and efficiency (η). A surrogate model built from 25 Latin
Hypercube Sampling points achieved high accuracy (R
2 > 0.99). Sensitivity analysis showed that the tip
angle
predominantly affects Q and P, while
the root angle strongly influences η. Optimization yielded Pareto
solutions, where the efficiency improved from 18.31% to 21.19% without
reducing the flow or pressure. The flow-field analysis demonstrated
that the enhanced aerodynamic stability is addressed in the enhanced
aerodynamic stability, characterized by smoother velocity profiles
and reduced regions of separation and recirculation. The proposed
framework not only improves fan aerodynamic efficiency but also establishes
a generalizable strategy for systematic multiobjective optimization
of rotating machinery.