Aerodynamic Influences on Structures with Varied Plan Configurations


Open Access

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 13 | Special Issue 01 | Page : 377-388
    By

    Rahul Kumar Meena,

  • Abhishek Prakash Paswan,

  • Prateek Roshan,

  • Manvendra Verma,

  1. Assistant Professor, Department of Civil Engineering, NIT, Delhi, India
  2. PhD Scholar, Department of Civil Engineering, Delhi Technological University, Delhi, India
  3. PhD Scholar, Department of Civil Engineering, Delhi Technological University, Delhi, India
  4. Assistant Professor, Department of Civil Engineering, GLA University, Uttar Pradesh, India

Abstract

The wind-induced effects on tall buildings demonstrate a level of unpredictability, attributed to the intricate influence of the building’s plan shape on the distribution of wind loads. The oscillations experienced by tall structures, both longitudinally and laterally, are influenced by factors such as wind direction, building configuration, height, and structural characteristics. This investigation is centred around examining the impact of wind on tall buildings featuring diverse plan cross-sectional shapes. The analysis employs Computational Fluid Dynamics (CFD) through ANSYS CFX version 2023 R2, scaled at 1:200. The study places particular emphasis on exploring aerodynamic modifications to building shapes, delving into their effectiveness and acceptance within the wind engineering community. Noteworthy findings reveal the inherent positivity of wind forces on the windward face and negativity on the leeward face. The graphical presentation of results, including pressure contours and streamlines in both plan and elevation, adds a visual dimension to the research. Significantly, the study establishes a direct correlation between the plan layout and size of the model and wind pressure distribution on the windward face. An intriguing observation is the similar pressure distribution patterns on both windward and leeward faces across all models. These discoveries contribute valuable insights for structural designers and architects, offering guidance on incorporating various plan shapes into tall building designs. Recognizing the nuanced influence of plan layouts on wind pressure distribution becomes crucial in crafting tall building designs that are not only structurally robust but also architecturally viable.

Keywords: Effects of Wind; Computational Fluid Dynamics (CFD); Pressure Distribution; Flow Patterns; Tall Buildings.

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

aWQ6MTkwMDM5fGZpbGVuYW1lOmJkZDVkY2MwLTEtcG5nLndlYnB8c2l6ZTp0aHVtYm5haWw=
How to cite this article:
Rahul Kumar Meena, Abhishek Prakash Paswan, Prateek Roshan, Manvendra Verma. Aerodynamic Influences on Structures with Varied Plan Configurations. Journal of Polymer and Composites. 2024; 13(01):377-388.
How to cite this URL:
Rahul Kumar Meena, Abhishek Prakash Paswan, Prateek Roshan, Manvendra Verma. Aerodynamic Influences on Structures with Varied Plan Configurations. Journal of Polymer and Composites. 2024; 13(01):377-388. Available from: https://journals.stmjournals.com/jopc/article=2024/view=190043



Full Text PDF

Browse Figures

References

  1. Jafari M, Alipour A. Methodologies to mitigate wind-induced vibration of tall buildings: A state-of-the-art review. J Build Eng. 2021;33:1–60.
  2. Kayvani K. Design of high-rise buildings : past , present and future. 23rd Australas Conf Mech Struct Mater. 2014;I:15–20.
  3. Baskaran A. Wind engineering studies on tall buildings-transitions in research. Build Environ. 1993;28(1):1–19.
  4. Ankireddi S, Yang HTY. Simple ATMD Control Methodology for Tall Buildings Subject to Wind Loads. J Struct Eng. 122(1).
  5. Mendis P, Ngo T, Haritos N, Hira A, Samali B, Cheung J. Wind loading on tall buildings. Electron J Struct Eng. 2007;7:41–54.
  6. Isyumov N, Case PC. Wind-Induced torsional loads and responses of buildings. Struct Congr 2000 Adv Technol Struct Eng. 2004;103(April 2000).
  7. Tse KT, Kwok KCS, Hitchcock PA, Samali B, Huang MF. Vibration control of a wind-excited benchmark tall building with complex lateral-torsional modes of vibration. Adv Struct Eng. 2007;10(3):283–304.
  8. Kim HS, Kang JW. Semi-active fuzzy control of a wind-excited tall building using multi-objective genetic algorithm. Eng Struct. 2012;41:242–57.
  9. Ni YQ, Chen Y, Ko JM, Cao DQ. Neuro-control of cable vibration using semi-active magneto-rheological dampers. Eng Struct. 2002;24(3):295–307.
  10. Jafari M, Sarkar PP, Alipour AA. A numerical simulation method in time domain to study wind-induced excitation of traffic signal structures and its mitigation. J Wind Eng Ind Aerodyn. 2019;193(July):103965.
  11. Elias S, Matsagar V. Wind response control of tall buildings with a tuned mass damper. J Build Eng. 2018;15(February 2017):51–60.
  12. MEENA RK, Awadhiya GP, Paswan AP, Jayant HK. Effects of Bracing System on Multistoryed Steel Building. 2018;
  13. Pal S, Raj R, Anbukumar S. Comparative study of wind induced mutual interference effects on square and fish-plan shape tall buildings. Sādhanā. 2021;0123456789.
  14. Meena RK, Raj R, Anbukumar S. Effect of wind load on irregular shape tall buildings having different corner configuration. Sadhana - Acad Proc Eng Sci [Internet]. 2022;47(3). Available from: https://doi.org/10.1007/s12046-022-01895-2
  15. Meena RK, Raj R, Anbukumar S. Numerical Investigation of Wind Load on Side Ratio of High-Rise Buildings Numerical Investigation of Wind Load on Side Ratio of High-Rise Buildings. Springer Singapore; 2021.
  16. Meena RK, Raj R, Anbukumar S. Wind Excited Action around Tall Building Having Different Corner Configurations. Adv Civ Eng. 2022;2022.
  17. ASCE: 7-16(2017). Minimum Design Loads and Associated Criteria for Buildings and Other Structures. Structural Engineering Institute of the American Society of Civil Engineering, Reston. AMERICAN SOCIETY OF CIVIL ENGINEERS,Reston. 2017. 1–330 p.
  18. ASCE: 49-12(2012). Wind Tunnel Testing for Buildings and Other Structures.Structural Engineering Institute of the American Society of Civil Engineering, Reston. American Society of Civil Engineers 1801 Alexander Bell Drive Reston, Virginia 20191 www.asce.org/pubs; 2012.
  19. AS/NZS:1170.2(2011). Structural Design Actions - Part 2: Wind actions. Standards Australia/Standards New Zealand, Sydney. 2011. 101 p.
  20. IS: 875 (2015). Indian Standard design loads (other than earthquake) for buildings and structures-code of practice,part 3(wind loads). BIS, New Delhi. 2015. 51 p.
  21. ETHIOPIAN STANDARD. ES ISO 4354 (2012) (English): Wind actions on structures. Vol. 2012, Ethiopian Standards Agency( ESA) Ethiopia, Addis Ababa. 2012. 76 p.
  22. MNBC. MYANMAR NATIONAL BUILDING CODE 2020. International relation and legal Section Department of Building Minis; 2020.
  23. GB 50009-2001. NATIONAL STANDARD OF THE PEOPLE’S REPUBLIC OF CHINA. 2002.
  24. Abu Zidan Y, Mendis P, Gunawardena T. Optimising the computational domain size in CFD simulations of tall buildings. Heliyon.
  25. Sharma U, Gupta N, Verma M. Prediction of compressive strength of GGBFS and Flyash-based geopolymer composite by linear regression, lasso regression, and ridge regression. Asian J Civ Eng [Internet]. 2023;24(8):3399–3411. Available from: https://doi.org/10.1007/s42107-023-00721-2
  26. Sharma U, Gupta N, Verma M. Prediction of Compressive Strength of Geopolymer Concrete using Artificial Neural Network. Asian J Civ Eng [Internet]. 2023;24(8):2837–2850. Available from: https://doi.org/10.1007/s42107-023-00678-2
  27. Verma M. Prediction of compressive strength of geopolymer concrete using random forest machine and deep learning. Asian J Civ Eng [Internet]. 2023;24(7):2659–68. Available from: https://doi.org/10.1007/s42107-023-00670-w
  28. Verma M. Prediction of compressive strength of geopolymer concrete by using ANN and GPR. Asian J Civ Eng [Internet]. 2023;24(8):2815–2823. Available from: https://doi.org/10.1007/s42107-023-00676-4
  29. Verma M, Upreti K, Khan MR, Alam MS, Ghosh S, Singh P. Prediction of Compressive Strength of Geopolymer Concrete by Using Random Forest Algorithm. In: ICACIS 2022. 2023. p. 170–9.
  30. Verma M, Upreti K, Dadhich P, Ghosh S, Khatri V, Singh P, et al. Prediction of Compressive Strength of Green Concrete by Artificial Neural Network. In: ICACIS 2022. 2023. p. 622–32.

 


Special Issue Open Access Original Research
Volume 13
Special Issue 01
Received 23/04/2024
Accepted 08/08/2024
Published 18/12/2024


Loading citations...

Views: 0