Investigation of Wind Pressure Dynamics on Low-Rise Buildings in Sand-Laden Wind Environments
Abstract
1. Introduction
2. Numerical Simulation Methods
2.1. Governing Equations
2.2. Computational Domain, Boundary Conditions, and Meshing
2.3. Turbulence Modeling and Numerical Configuration
2.4. Wind Field and Sand Particle Configuration
2.5. Data Processing
- (1)
- Wind Pressure Coefficients. Wind pressure is characterized by the dimensionless wind pressure coefficient [5], defined as the ratio of the pressure induced by airflow on the building surface to the dynamic pressure of the undisturbed incoming wind speed (Equation (7)). The average wind pressure coefficient Cp,mean (Equation (8)) represents the static effect of wind loads and serves as the basis for the overall structural design; conversely, the fluctuating wind pressure coefficient Cp,rms (Equation (9)) represents the dynamic effects of wind loads, causing structural vibrations and potentially leading to fatigue failure of components. In this study, the eave height is utilized as the normalization height for wind pressure.
- (2)
- Skewness and Kurtosis of Wind Pressure. Existing studies have indicated the presence of large-scale vortices-characteristic turbulence (Building Induced Turbulence) –in areas such as the rooftop, side, and leeward surfaces of structures, influenced by the shape of the structure [53]. In regions affected by characteristic turbulence, the assumption of quasi-stationarity [54] is no longer valid, leading to significant non-Gaussian characteristics in the fluctuating wind pressure. The time series of wind pressure is characterized by asymmetric pressure distribution accompanied by large-magnitude wind pressure pulses. These pulses can generate substantial instantaneous suction forces in localized areas of the structural surface, and their repeated action can easily result in structural damage to the enclosing components and their connections, as illustrated in Figure 1. Therefore, it is crucial to pay special attention to regions of wind pressure that exhibit non-Gaussian characteristics.
2.6. Validation and Grid Independence Testing
3. Results and Discussion
3.1. Wind Field Characteristics
3.2. Wind Pressure Characteristics
3.2.1. Average Wind Pressure and Fluctuating Wind Pressure
3.2.2. Skewness and Kurtosis Results of Wind Pressure
4. Conclusions
- (1)
- The presence of sand particles results in a portion of the wind field’s energy being consumed to drive particle motion, leading to a slight reduction in the mean wind speed in the sand-laden wind compared to the clear wind. Enhanced disturbance of the flow field by particles increases the energy of small-scale vortices, resulting in an increase in the fluctuating wind speed.
- (2)
- In sand-laden wind, the average wind pressure coefficient on low-rise buildings remains comparable to that in clean wind, but the fluctuating wind pressure coefficient significantly increases, particularly in windward regions across various wind directions. Consequently, it is recommended that the design value for fluctuating wind loads on low-rise buildings in wind-sand regions be increased by 10% to improve the safety of envelope structures and their connections.
- (3)
- Under clean and sand-laden wind, the distribution of non-Gaussian regions on building surfaces remains similar. However, sand-laden wind significantly intensifies non-Gaussian wind pressure characteristics, with the area of non-Gaussian regions in the model increasing by up to 1.5 times at its maximum. This suggests a substantially higher risk of localized tearing damage to building envelopes in strong sand-laden wind environments.
5. Limitations and Outlooks
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Saracoglu, B.O.; Ohunakin, O.S.; Adelekan, D.S.; Gill, J.; Atiba, O.E.; Okokpujie, I.P.; Atayero, A.A. A framework for selecting the location of very large photovoltaic solar power plants on a global/supergrid. Energy Rep. 2018, 4, 586–602. [Google Scholar] [CrossRef]
- National Development and Reform Commission, National Energy Administration. Planning and Layout Scheme for Large-scale Wind and Photovoltaic Bases Focusing on Desert, Gobi, and Desert Areas [EB/OL]. Available online: http://www.spic.com.cn/spicm/xwzx/hyqy/202203/t20220307_318670.html (accessed on 7 March 2022).
- Cao, S.Y.; Wang, J. Statistical summary and case studies of strong wind damage in China. J. Disaster Res. 2013, 8, 1096–1102. [Google Scholar] [CrossRef]
- Taleb, R.; Ramanantoa, H.; Reynolds, T.; Beckett, C.T.; Huang, Y.; Rakotoarivony, M.; Gagnon, A.S.; Andriamaro, L. Fragility assessment of traditional wooden houses in Madagascar subjected to extreme wind loads. Eng. Struct. 2023, 289, 116220. [Google Scholar] [CrossRef]
- Levitan, M.L.; Mehta, K.C.; Vann, W.P.; Holmes, J.D. Field measurements of pressures on the Texas Tech building. J. Wind. Eng. Ind. Aerodyn. 1991, 38, 227–234. [Google Scholar] [CrossRef]
- Pita, G.; Pinelli, J.P.; Cocke, S.; Gurley, K.; Mitrani-Reiser, J.; Weekes, J.; Hamid, S. Assessment of hurricane-induced internal damage to low-rise buildings in the Florida Public Hurricane Loss Model. J. Wind. Eng. Ind. Aerodyn. 2012, 104, 76–87. [Google Scholar] [CrossRef]
- Liu, Z.; Ishihara, T. Study of the effects of translation and roughness on tornado-like vortices by large-eddy simulations. J. Wind. Eng. Ind. Aerodyn. 2016, 151, 1–24. [Google Scholar] [CrossRef]
- Razavi, A.; Sarkar, P.P. Tornado-induced wind loads on a low-rise building: Influence of swirl ratio, translation speed and building parameters. Eng. Struct. 2018, 167, 1–12. [Google Scholar] [CrossRef]
- Jesson, M.; Sterling, M.; Letchford, C.; Haines, M. Aerodynamic forces on generic buildings subject to transient, downburst-type winds. J. Wind. Eng. Ind. Aerodyn. 2015, 137, 58–68. [Google Scholar] [CrossRef]
- Haines, M.; Taylor, I. Numerical investigation of the flow field around low-rise buildings due to a downburst event using large eddy simulation. J. Wind. Eng. Ind. Aerodyn. 2018, 172, 12–30. [Google Scholar] [CrossRef]
- Zhang, M. Numerical Simulation of Wind-Blown-Sand Two Phase Flow Field Around the Building Based on Fluent. Master’s thesis, Harbin Institute of Technology, Harbin, China, 2008. [Google Scholar]
- Ma, Q. Numerical Simulation of Wind-Blown Sand Flow Around a Couple of Buildings. Master’s Thesis, Lanzhou University, Lanzhou, China, 2020. [Google Scholar]
- Bai, J. Numerical Simulation of Wind-Blown Sand Flow Around Cylindrical Engineering Structure. Master’s Thesis, Lanzhou University, Lanzhou, China, 2021. [Google Scholar]
- Hu, D.; Zhang, J.; Pakzad, R.; Huang, N. A numerical study on load effects of low-rise buildings in a wind-blown sand environment. J. Build. Eng. 2024, 84, 108610. [Google Scholar] [CrossRef]
- Huang, B.; Li, Z.; Gong, B.; Zhang, Z.; Shan, B.; Pu, O. Study on the sandstorm load of low-rise buildings via wind tunnel testing. J. Build. Eng. 2023, 65, 105821. [Google Scholar] [CrossRef]
- Huang, B.; Li, Z.; Zhao, Z.; Wu, H.; Zhou, H.; Cong, S. Near-ground impurity-free wind and wind-driven sand of photovoltaic power stations in a desert area. J. Wind. Eng. Ind. Aerodyn. 2018, 179, 483–502. [Google Scholar] [CrossRef]
- Xin, G.; Huang, N.; Zhang, J.; Dun, H. Investigations into the design of sand control fence for Gobi buildings. Aeolian Res. 2021, 49, 100662. [Google Scholar] [CrossRef]
- Yao, Z.; Xiao, J.; Jiang, F. Characteristics of daily extreme-wind gusts along the Lanxin Railway in Xinjiang, China. Aeolian Res. 2012, 6, 31–40. [Google Scholar] [CrossRef]
- Deng, E.; Yue, H.; Liu, X.–Y.; Ni, Y.–Q. Aerodynamic impact of wind-sand flow on moving trains in tunnel-embankment transition section: From field testing to CFD modeling. Eng. Appl. Comput. Fluid Mech. 2023, 17, 2279993. [Google Scholar] [CrossRef]
- Cheng, J.–J.; Jiang, F.-Q.; Xue, C.-X.; Xin, G.-W.; Li, K.-C.; Yang, Y.-H. Characteristics of the disastrous wind-sand environment along railways in the Gobi area of Xinjiang, China. Atmos. Environ. 2015, 102, 344–354. [Google Scholar] [CrossRef]
- Alghamdi, A.A.A.; Al-Kahtani, N.S. Sand control measures and sand drift fences. J. Perform. Constr. Facil. 2005, 19, 295–299. [Google Scholar] [CrossRef]
- Li, Z.; Pu, O.; Gong, B.; Zhao, Z.; Huang, B.; Wu, H. A new method of measuring sand impact force using piezoelectric ceramics. Measurement 2021, 179, 109390. [Google Scholar] [CrossRef]
- Zhang, K.; Zhang, H.; Liu, B.; Wang, T.; Wang, Z.; Tian, J. Numerical simulation study on the impact of wind-blown sand action on the loading of photovoltaic systems. Phys. Fluids 2024, 36, 075166. [Google Scholar] [CrossRef]
- Wiesinger, F.; Sutter, F.; Fernández-García, A.; Wette, J.; Hanrieder, N. Sandstorm erosion on solar reflectors: A field study on height and orientation dependence. Energy 2021, 217, 119351. [Google Scholar] [CrossRef]
- Photovoltaic Industry Network. Severe Losses! Sandstorm Hits, Solar Power Plants Fly Away–‘Photovoltaic Desertification Control’ Once Again Becomes a Hot Topic [EB/OL]. Available online: https://mp.weixin.qq.com/s/WETbWQ9R2HGGpsTPSYy41w (accessed on 20 April 2024).
- Qinhan Engineering. “Multiple Solar Power Stations Attacked! ‘Photovoltaic Desertification Control’ Once Again Becomes a Hot Topic” [EB/OL]. Available online: https://www.163.com/dy/article/IRIJU2AE05566LXQ.html (accessed on 22 February 2024).
- Hughes, G.O. Inside the head and tail of a turbulent gravity current. J. Fluid Mech. 2016, 790, 1–4. [Google Scholar] [CrossRef]
- Wang, G.; Gu, H.; Zheng, X. Large scale structures of turbulent flows in the atmospheric surface layer with and without sand. Phys. Fluids 2020, 32, 106604. [Google Scholar] [CrossRef]
- China Central Television News. Mongolia Has Issued a Severe Weather Warning for Intense Blizzards and Sandstorms. [EB/OL]. Available online: http://m.news.cctv.com/2021/05/21/ARTIZYVDvAKX9aIBSSCHPTIM210521.shtml (accessed on 20 May 2021).
- Ong, R.H.; Patruno, L.; Yeo, D.; He, Y.; Kwok, K.C.S. Numerical simulation of wind-induced mean and peak pressures around a low-rise structure. Eng. Struct. 2020, 214, 110583. [Google Scholar] [CrossRef]
- Balachandar, S.; Eaton, J.K. Turbulent dispersed multiphase flow. Annu. Rev. Fluid Mech. 2010, 42, 111–133. [Google Scholar] [CrossRef]
- Zhang, Z.; Chen, Q. Comparison of the Eulerian and Lagrangian methods for predicting particle transport in enclosed spaces. Atmos. Environ. 2007, 41, 5236–5248. [Google Scholar] [CrossRef]
- Shanmugasundaram, J.; Arunachalam, S.; Gomathinayagam, S.; Lakshmanan, N.; Harikrishna, P. Cyclone damage to buildings and structures-A case study. J. Wind. Eng. Ind. Aerodyn. 2000, 84, 369–380. [Google Scholar] [CrossRef]
- Xu, B.; Zhang, J.; Huang, N.; Gong, K.; Liu, Y. Characteristics of turbulent aeolian sand movement over straw checkerboard barriers and formation mechanisms of their internal erosion form. J. Geophys. Res. Atmos. 2018, 123, 6907–6919. [Google Scholar] [CrossRef]
- Yamamoto, Y.; Potthoff, M.; Tanaka, T.; Kajishima, T.; Tsuji, Y. Large-eddy simulation of turbulent gas-particle flow in a vertical channel: Effect of considering inter-particle collisions. J. Fluid Mech. 2001, 442, 303–334. [Google Scholar] [CrossRef]
- Kok, J.F.; Renno, N.O. A comprehensive numerical model of steady state saltation (COMSALT). J. Geophys. Res. Atmos. 2009, 114, 204. [Google Scholar] [CrossRef]
- Clift, R.; Grace, J.R.; Weber, M.E. Bubbles, Drops, and Particles; Academic Press: Cambridge, MA, USA, 1978. [Google Scholar]
- Zheng, X.; Feng, S.; Wang, P. Modulation of turbulence by saltating particles on erodible bed surface. J. Fluid Mech. 2021, 918, A16. [Google Scholar] [CrossRef]
- Tominaga, Y.; Mochida, A.; Yoshie, R.; Kataoka, H.; Nozu, T.; Yoshikawa, M.; Shirasawa, T. AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. J. Wind. Eng. Ind. Aerodyn. 2008, 96, 1749–1761. [Google Scholar] [CrossRef]
- Ricci, M.; Patruno, L.; De Miranda, S. Wind loads and structural response: Benchmarking LES on a low-rise building. Eng. Struct. 2017, 144, 26–42. [Google Scholar] [CrossRef]
- Wang, X.; Cai, C.; Yuan, P.; Xu, G.; Sun, C. An efficient and accurate DSRFG method via nonuniform energy spectra discretization. Eng. Struct. 2024, 298, 117014. [Google Scholar] [CrossRef]
- Cai, Y.; Wan, J.; Kareem, A. A new divergence-free synthetic eddy method for generating homogeneous isotropic turbulence with a prescribed energy spectrum. Comput. Fluids 2023, 253, 105788. [Google Scholar] [CrossRef]
- Melaku, A.F.; Bitsuamlak, G.T. A divergence-free inflow turbulence generator using spectral representation method for large-eddy simulation of ABL flows. J. Wind. Eng. Ind. Aerodyn. 2021, 212, 104580. [Google Scholar] [CrossRef]
- Huang, X.B.; Guo, Q. OpenFOAM: From Introduction to Mastery; Water & Power Press: Beijing, China, 2021. [Google Scholar]
- Cabot, W.; Moin, P. Approximate wall boundary conditions in the large-eddy simulation of high Reynolds number flow. Flow Turbul. Combust. 2000, 63, 269–291. [Google Scholar] [CrossRef]
- Tao, W. Numerical Heat Transfer, 2nd ed.; Xi’an Jiaotong University Press: Xi’an, China, 2001. [Google Scholar]
- Spalart, P.R.; Deck, S.; Shur, M.L.; Squires, K.D.; Strelets, M.K.; Travin, A. A new version of detached-eddy simulation, resistant to ambiguous grid densities. Theor. Comput. Fluid Dyn. 2006, 20, 181–195. [Google Scholar] [CrossRef]
- He, K.; Minelli, G.; Wang, J.; Gao, G.; Krajnović, S. Assessment of LES, IDDES and RANS approaches for prediction of wakes behind notchback road vehicles. J. Wind. Eng. Ind. Aerodyn. 2021, 217, 104737. [Google Scholar] [CrossRef]
- Liu, J.; Niu, J. CFD simulation of the wind environment around an isolated high-rise building: An evaluation of SRANS, LES and DES models. Build. Environ. 2016, 96, 91–106. [Google Scholar] [CrossRef]
- Lateb, M.; Masson, C.; Stathopoulos, T.; Bédard, C. Simulation of near-field dispersion of pollutants using detached-eddy simulation. Comput. Fluids 2014, 100, 308–320. [Google Scholar] [CrossRef]
- Liu, H.; He, X.; Zheng, X. Amplitude modulation in particle-laden atmospheric surface layers. J. Fluid Mech. 2023, 957, A14. [Google Scholar] [CrossRef]
- Blott, S.J.; Pye, K. Particle size distribution analysis of sand-sized particles by laser diffraction: An experimental investigation of instrument sensitivity and the effects of particle shape. Sedimentology 2006, 53, 671–685. [Google Scholar] [CrossRef]
- Jeong, S.H. Simulation of large wind pressures by gusts on a bluff structure. Wind. Struct. 2004, 7, 333–344. [Google Scholar] [CrossRef]
- Davenport, A.G. Note on the Distribution of the Largest Value of a Random Function with Applications to Gust Loading. Proc. Inst. Civ. Eng. 1964, 28, 187–196. [Google Scholar] [CrossRef]
- Cheng, X.X.; Ke, S.T.; Li, P.F.; Ge, Y.J.; Zhao, L. External extreme wind pressure distribution for the structural design of cooling towers. Eng. Struct. 2019, 181, 336–353. [Google Scholar] [CrossRef]
- Yang, X.; Yang, Y.; Li, M.; Wang, P. Effects of free-stream turbulence on non-Gaussian characteristics of fluctuating wind pressures on a 5:1 rectangular cylinder-ScienceDirect. J. Wind. Eng. Ind. Aerodyn. 2021, 217, 104759. [Google Scholar] [CrossRef]
- Kumar, K.S.; Stathopoulos, T. Power spectra of wind pressures on low building roofs. J. Wind. Eng. Ind. Aerodyn. 1998, 74, 665–674. [Google Scholar] [CrossRef]
- Kumar, K.S.; Stathopoulos, T. Wind loads on low building roofs: A stochastic perspective. J. Struct. Eng. 2000, 126, 944–956. [Google Scholar] [CrossRef]
State | Roof (A) | Windward (B) | Right (C) | Lee (D) | Left (E) | |||||
---|---|---|---|---|---|---|---|---|---|---|
NNG | NGA (%) | NNG | NGA (%) | NNG | NGA (%) | NNG | NGA (%) | NNG | NGA (%) | |
0°–I | 78 | 51.8 | 7 | 2.7 | 73 | 68.4 | 52 | 29.9 | 70 | 60.5 |
0°–S | 108 | 69.1 | 39 | 33.1 | 77 | 63.6 | 90 | 69.1 | 77 | 67.1 |
15°–I | 25 | 12.2 | 0 | 0.0 | 39 | 33.6 | 1 | 0.3 | 17 | 7.3 |
15°–S | 27 | 15.3 | 2 | 0.6 | 30 | 30.4 | 3 | 0.9 | 25 | 17.5 |
30°–I | 57 | 41.9 | 22 | 21.4 | 51 | 62.2 | 33 | 16.6 | 62 | 56.8 |
30°–S | 67 | 41.7 | 58 | 53.1 | 34 | 47.8 | 33 | 16.5 | 86 | 83.3 |
45°–I | 32 | 21.7 | 7 | 6.4 | 10 | 4.5 | 13 | 3.8 | 17 | 5.0 |
45°–S | 52 | 32.2 | 31 | 30.7 | 3 | 1.7 | 12 | 3.9 | 49 | 34.0 |
60°–I | 31 | 20.1 | 0 | 0.0 | 1 | 0.4 | 41 | 23.2 | 3 | 1.0 |
60°–S | 69 | 41.9 | 32 | 30.8 | 18 | 13.4 | 50 | 28.2 | 19 | 9.5 |
75°–I | 42 | 28.8 | 31 | 27.7 | 1 | 0.4 | 79 | 56.1 | 3 | 1.8 |
75°–S | 46 | 27.3 | 40 | 34.4 | 32 | 31.9 | 92 | 61.9 | 9 | 4.0 |
90°–I | 74 | 37.6 | 70 | 34.5 | 6 | 2.9 | 95 | 49.6 | 24 | 11.7 |
90°–S | 64 | 30.9 | 77 | 39.3 | 24 | 24.0 | 81 | 41.7 | 23 | 9.2 |
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Hu, D.; Zhang, T.; Jin, Q. Investigation of Wind Pressure Dynamics on Low-Rise Buildings in Sand-Laden Wind Environments. Buildings 2025, 15, 2779. https://doi.org/10.3390/buildings15152779
Hu D, Zhang T, Jin Q. Investigation of Wind Pressure Dynamics on Low-Rise Buildings in Sand-Laden Wind Environments. Buildings. 2025; 15(15):2779. https://doi.org/10.3390/buildings15152779
Chicago/Turabian StyleHu, Di, Teng Zhang, and Qiang Jin. 2025. "Investigation of Wind Pressure Dynamics on Low-Rise Buildings in Sand-Laden Wind Environments" Buildings 15, no. 15: 2779. https://doi.org/10.3390/buildings15152779
APA StyleHu, D., Zhang, T., & Jin, Q. (2025). Investigation of Wind Pressure Dynamics on Low-Rise Buildings in Sand-Laden Wind Environments. Buildings, 15(15), 2779. https://doi.org/10.3390/buildings15152779