Study on Wind-Blown Snow Hazards and Snow Fence Parameters Based on Different Cutting Depths of Mountain Highways

To address the severe snow accumulation within road cuttings triggered by wind-blown snow on mountainous highways, and to elucidate the influence mechanisms of cutting depth and snow fence parameters on the wind–snow flow field, this study presents a systematic investigation based on typical sections of the G577 Grade I mountain highway in the Xinjiang Uygur Autonomous Region, China. First, indoor wind tunnel experiments were conducted to observe the distribution characteristics of the wind– snow field inside and outside the cuttings and around the snow fences under varying cutting depths and fence parameters. Second, numerical simulations were performed using the Analysis System Fluent software with models identical to those used in the wind tunnel tests to analyze the airflow field and snow particle movement patterns. Finally, experimental results were compared with field observations of winter snow accumulation to validate the reliability of both the numerical simulations and wind tunnel experiments. The results indicate that under small intersection angles (15–30°), deep cuttings significantly exacerbate snowdrift accumulation trends, reducing wind speed within the cutting and increasing snow accumulation at the bottom (an increase of 31–81% per 5 m of depth). Furthermore, a nonlinear relationship regarding the impact of different snow fence parameters on flow field distribution. These findings provide theoretical support and valuable engineering references for optimizing road cutting design and snow fence construction in mountainous regions.

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Publication Details

Journal
Atmosphere
Published
2026-04-30
DOI
https://doi.org/10.3390/atmos17050460
Primary Topic
Smart Materials for Construction
Type
article
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article

Study on Wind-Blown Snow Hazards and Snow Fence Parameters Based on Different Cutting Depths of Mountain Highways

FengLong Wang, Haiwei Xie, Haojie Tang, Ruixin Liu et al.
Atmosphere
Smart Materials for Construction
article

Study on Wind-Blown Snow Hazards and Snow Fence Parameters Based on Different Cutting Depths of Mountain Highways

FengLong Wang, Haiwei Xie, Haojie Tang, Ruixin Liu, Jian Liu, Zhixuan Hu
article en

Abstract

To address the severe snow accumulation within road cuttings triggered by wind-blown snow on mountainous highways, and to elucidate the influence mechanisms of cutting depth and snow fence parameters on the wind–snow flow field, this study presents a systematic investigation based on typical sections of the G577 Grade I mountain highway in the Xinjiang Uygur Autonomous Region, China. First, indoor wind tunnel experiments were conducted to observe the distribution characteristics of the wind– snow field inside and outside the cuttings and around the snow fences under varying cutting depths and fence parameters. Second, numerical simulations were performed using the Analysis System Fluent software with models identical to those used in the wind tunnel tests to analyze the airflow field and snow particle movement patterns. Finally, experimental results were compared with field observations of winter snow accumulation to validate the reliability of both the numerical simulations and wind tunnel experiments. The results indicate that under small intersection angles (15–30°), deep cuttings significantly exacerbate snowdrift accumulation trends, reducing wind speed within the cutting and increasing snow accumulation at the bottom (an increase of 31–81% per 5 m of depth). Furthermore, a nonlinear relationship regarding the impact of different snow fence parameters on flow field distribution. These findings provide theoretical support and valuable engineering references for optimizing road cutting design and snow fence construction in mountainous regions.

AtmosphereVol. 17(5)
Xinjiang Agricultural University (CN), Xinjiang Institute of Engineering (CN), China Railway Construction Corporation (China) (CN), China Railway Group (China) (CN), Xinjiang University (CN)
Openalex Percentile: Top 12%
Smart Materials for Construction
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