A divergence-free synthetic inflow turbulence generator for large eddy simulation of compressible turbulent boundary layers

We propose the Compressible Discretizing and Synthesizing Random Flow Generation (C-DSRFG) method, a divergence-free inflow turbulence generator for large eddy simulations (LES) of compressible boundary layers. As an extension of the incompressible DSRFG framework by Huang et al. [“A general inflow turbulence generator for large eddy simulation,” J. Wind Eng. Ind. Aerodyn. 98(10), 600–617 (2010)], C-DSRFG explicitly enforces the divergence-free condition on velocity fluctuations (∇·u′=0), a constraint whose significance is theoretically analyzed herein and has been largely overlooked in conventional compressible inflow generators. Validation through LES of a supersonic (M∞=2.33) flat-plate turbulent boundary layer demonstrates that enforcing this condition reduces the required turbulence adjustment length by over 40% compared to its non-divergence-free counterpart. Furthermore, the method's robustness and applicability are confirmed through simulations of a weakly compressible (M∞=0.5) boundary layer and a typical engineering problem—a shock-wave/turbulent boundary-layer interaction on a compression ramp. For all cases, key turbulence statistics—including velocity fluctuations, mean velocity profiles, friction coefficient, and spectral distribution—are compared with available datasets. The results reveal a consistent agreement with properties established in the literature, which fully validates the proposed method's physical realizability and its applicability to complex compressible flow engineering scenarios.

Authors

Institutions

Publication Details

Journal
Physics of Fluids
Published
2025-12-01
DOI
https://doi.org/10.1063/5.0301178
Primary Topic
Computational Fluid Dynamics and Aerodynamics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A divergence-free synthetic inflow turbulence generator for large eddy simulation of compressible turbulent boundary layers

Shenghong Huang, Guowang Zhu, Y. T. Zhang, Han Wu
Physics of Fluids
Computational Fluid Dynamics and Aerodynamics
article

A divergence-free synthetic inflow turbulence generator for large eddy simulation of compressible turbulent boundary layers

Shenghong Huang, Guowang Zhu, Y. T. Zhang, Han Wu
article en

Abstract

We propose the Compressible Discretizing and Synthesizing Random Flow Generation (C-DSRFG) method, a divergence-free inflow turbulence generator for large eddy simulations (LES) of compressible boundary layers. As an extension of the incompressible DSRFG framework by Huang et al. [“A general inflow turbulence generator for large eddy simulation,” J. Wind Eng. Ind. Aerodyn. 98(10), 600–617 (2010)], C-DSRFG explicitly enforces the divergence-free condition on velocity fluctuations (∇·u′=0), a constraint whose significance is theoretically analyzed herein and has been largely overlooked in conventional compressible inflow generators. Validation through LES of a supersonic (M∞=2.33) flat-plate turbulent boundary layer demonstrates that enforcing this condition reduces the required turbulence adjustment length by over 40% compared to its non-divergence-free counterpart. Furthermore, the method's robustness and applicability are confirmed through simulations of a weakly compressible (M∞=0.5) boundary layer and a typical engineering problem—a shock-wave/turbulent boundary-layer interaction on a compression ramp. For all cases, key turbulence statistics—including velocity fluctuations, mean velocity profiles, friction coefficient, and spectral distribution—are compared with available datasets. The results reveal a consistent agreement with properties established in the literature, which fully validates the proposed method's physical realizability and its applicability to complex compressible flow engineering scenarios.

Physics of FluidsVol. 37(12)
University of Science and Technology of China (CN)
Openalex Percentile: Top 60%
Computational Fluid Dynamics and Aerodynamics
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.