Coupling 3D CFD of Air Knife Jets with an Analytical Model for Coating Thickness Prediction and Operating Window Definition in Hot-Dip Galvanizing

A coupled modeling framework is developed to predict coating thickness after air knife wiping in hot-dip galvanizing. A 3D large eddy simulation (LES) using the WALE sub-grid scale (SGS) model is performed to resolve the jet impingement on the moving strip. Time-averaged wall static pressure pωy and wall shear stress τωy along the strip direction are extracted and used as driving inputs for a thin film model. Starting from the continuity and momentum equations, a lubrication-type formulation is derived, leading to a local cubic equation for film thickness h(y) that accounts for both pressure gradient and gravity. A coupling workflow is established to preprocess the LES wall signals and compute the final coating thickness hfinal. Parametric sweeps of inlet total pressure P0 and the knife-to-strip distance H are employed to construct operating window maps. The predicted trends show that increasing P0 or decreasing H intensifies wall loading and reduces hfinal, while the operating window boundary is governed by the balance between the gas-induced shears. Representative results, including peak wall loading and thickness ranges, are reported for industrially relevant operating conditions.

Authors

Institutions

Publication Details

Journal
Eng—Advances in Engineering
Published
2026-04-29
DOI
https://doi.org/10.3390/eng7050206
Primary Topic
Heat Transfer Mechanisms
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Coupling 3D CFD of Air Knife Jets with an Analytical Model for Coating Thickness Prediction and Operating Window Definition in Hot-Dip Galvanizing

Daiyan Zhao, Zhengyi Jiang, Tieling Zhang, Hongwei Cao et al.
Eng—Advances in Engineering
Heat Transfer Mechanisms
article

Coupling 3D CFD of Air Knife Jets with an Analytical Model for Coating Thickness Prediction and Operating Window Definition in Hot-Dip Galvanizing

Daiyan Zhao, Zhengyi Jiang, Tieling Zhang, Hongwei Cao, Muyuan Zhou, Haibo Xie, Di Pan, Hao Liu, Jian Han, Li Sun, Lisong Zhu, Xi Wu, Hongqiang Liu
article en

Abstract

A coupled modeling framework is developed to predict coating thickness after air knife wiping in hot-dip galvanizing. A 3D large eddy simulation (LES) using the WALE sub-grid scale (SGS) model is performed to resolve the jet impingement on the moving strip. Time-averaged wall static pressure pωy and wall shear stress τωy along the strip direction are extracted and used as driving inputs for a thin film model. Starting from the continuity and momentum equations, a lubrication-type formulation is derived, leading to a local cubic equation for film thickness h(y) that accounts for both pressure gradient and gravity. A coupling workflow is established to preprocess the LES wall signals and compute the final coating thickness hfinal. Parametric sweeps of inlet total pressure P0 and the knife-to-strip distance H are employed to construct operating window maps. The predicted trends show that increasing P0 or decreasing H intensifies wall loading and reduces hfinal, while the operating window boundary is governed by the balance between the gas-induced shears. Representative results, including peak wall loading and thickness ranges, are reported for industrially relevant operating conditions.

Eng—Advances in EngineeringVol. 7(5)
Australian Research Council (AU), University of Wollongong (AU), HBIS (China) (CN), ARC Centre of Excellence for Electromaterials Science (AU), Everbright International (China) (CN)
Australian Research Council
Openalex Percentile: Top 11%
Heat Transfer Mechanisms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.