Volume 106
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A drag model containing compressibility, rarefaction and temperature ratio effects based on genetic algorithm fitting
Lite Zhang a *, Sifan Wu a, Yang Feng a, Xiangbo Meng a, Heng Zhang a, Haozhe Jin a *, Genfu Xu b *
a School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, China
b Hangzhou Special Equipment Emergency Treatment Center, Hangzhou Special Equipment Inspection Scientific Research Institute, Hangzhou, 310022, China
10.1016/j.partic.2025.09.007
Volume 106, November 2025, Pages 248-260
Received 8 April 2025, Revised 17 July 2025, Accepted 11 September 2025, Available online 16 September 2025, Version of Record 23 September 2025.
E-mail: langzichsh@zstu.edu.cn; haozhejin@zstu.edu.cn; xugenfu2001@163.com

Highlights

• Develop a general drag model with compressibility, rarefaction and temperature-ratio corrections.

• The proposed drag model is suitable for all flow regimes by genetic algorithm fitting.

• The benchmark dataset is generated based on extensive experimental, DNS, and DSMC results.

• Conduct model validations and comparisons against two previous experiments.


Abstract

This study presents a semi-empirical, comprehensive drag coefficient formulation for spherical particles moving in a gaseous medium. Leveraging a substantial body of experimental data, Direct Numerical Simulation (DNS), and Direct Simulation Monte Carlo (DSMC) results, the formulation incorporates compressibility, rarefaction, temperature ratio, shock wave physics, drag crisis and recovery effects. This comprehensive approach accurately models particle drag across a wide range of particle Mach and Reynolds numbers. Specifically, a genetic algorithm is employed to fit the formulation to the aforementioned data, resulting in a concrete expression. Compared to two latest universal drag models, the proposed formulation demonstrates a significantly lower relative error. Furthermore, three-dimensional numerical simulations using Ansys Fluent validate the accuracy of the developed model in applications, by contrasting its performance with the two state-of-the-art universal drag models.

Graphical abstract
Keywords
General drag model; Temperature-ratio corrections; Critical Reynolds number; Genetic algorithm