Yong Cai

Dr. Yong Cai


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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7 January 2026 in Micromachines
Verified
Journal Article
Open access
PDF file
ZLZhenzhen Liu
Zhenzhen Liu
JWJingrui Wang
Jingrui Wang
YCYong Cai
Yong Cai
YLYan Liu
Yan Liu
XHXiao-lei Hu
Xiao-lei Hu
HYHaoran Yan
Haoran Yan
This study presents a method for enriching oil-suspended particles within a rectangular microfluidic channel using acoustic standing waves. A modified Helmholtz equation is solved to establish the acoustic field model, and the equilibrium between acoustic radiation forces and viscous drag is described by combining Gor’kov potential theory with the Stokes drag model. Based on this force balance, the particle motion equation is derived, enabling the determination of the critical particle size necessary for efficient enrichment in oil-filled microchannels. A two-dimensional standing-wave microchannel model is subsequently developed, and the influences of acoustic, fluidic, and particle parameters on particle migration and aggregation are systematically investigated through theoretical analysis and numerical simulations. The results indicate that when the channel dimension and acoustic wavelength satisfy the half-wavelength resonance condition, a stable standing-wave field forms, effectively focusing suspended particles at the acoustic pressure nodes. Enrichment efficiency is found to be strongly dependent on inlet flow velocity, particle diameter, acoustic frequency, temperature, and particle density. Lower flow velocities and larger particle sizes result in higher enrichment efficiencies, with the most uniform and stable pressure distribution achieved when the acoustic frequency matches the resonant channel width. Increases in temperature and particle density enhance the acoustic radiation force, thereby accelerating the aggregation of particles. These findings offer theoretical foundations and practical insights for acoustically assisted online monitoring of wear particles in lubricating oils, contributing to advanced condition assessment and fault diagnosis in mechanical systems.