Wind and Structures

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Numerical modelling of shelter effect of porous wind fences

  • Janardhan, Prashanth (Department of Civil Engineering, National Institute of Technology Silchar) ;
  • Narayana, Harish (Department of Civil Engineering, M S Ramaiah Institute of Technology)
  • Received : 2018.11.15
  • Accepted : 2019.04.26
  • Published : 2019.11.25
⟨ Previous Next ⟩

Abstract

The wind blowing at high velocity in an open storage yard leads to wind erosion and loss of material. Fence structures can be constructed around the periphery of the storage yard to reduce the erosion. The fence will cause turbulence and recirculation behind it which can be utilized to reduce the wind erosion and loss of material. A properly designed fence system will produce lesser turbulence and longer shelter effect. This paper aims to show the applicability of Support Vector Machine (SVM) to predict the recirculation length. A SVM model was built, trained and tested using the experimental data gathered from the literature. The newly developed model is compared with numerical turbulence model, in particular, modified $k-{\varepsilon}$ model along with the experimental results. From the results, it was observed that the SVM model has a better capability in predicting the recirculation length. The SVM model was able to predict the recirculation length at a lesser time as compared to modified $k-{\varepsilon}$ model. All the results are analyzed in terms of statistical measures, such as root mean square error, correlation coefficient, and scatter index. These examinations demonstrate that SVM has a strong potential as a feasible tool for predicting recirculation length.

Keywords

References

  1. Bitsuamlak, G., Stathopoulos, T. and Bedard, C. (2006), "Effects of upstream two-dimensional hills on design wind loads: a computational approach", Wind Struct., 9(1), 37-58. http://dx.doi.org/10.12989/was.2006.9.1.037.
  2. Bourdin, P. and Wilson, J.D. (2008), "Windbreak aerodynamics: is computational fluid dynamics reliable?", Bound.-Lay. Meteorol., 126(2), 181-208. https://doi.org/10.1007/s10546-007-9229-y.
  3. Burges, C.J. (1998), "A tutorial on support vector machines for pattern recognition", Data Min. Knowl. Disc., 2(2), 121-167. https://doi.org/10.1023/A:1009715923555.
  4. Chen, G., Wang, W., Sun, C. and Li, J. (2012), "3D numerical simulation of wind flow behind a new porous fence", Powder Technol., 230, 118-126. https://doi.org/10.1016/j.powtec.2012.07.017.
  5. Chen, K., Zhu, F. and Niu, Z. (2006), "Evaluation on shelter effect of porous windbreak fence through wind tunnel test", Acta Scientiarum Naturalium-Universitatis Pekinensis, 42(5), 636.
  6. Chen, Q. (1995), "Comparison of different k-${\varepsilon}$ models for indoor air flow computations", Numer. Heat Tr. Part B Fund., 28(3), 353-369. https://doi.org/10.1080/10407799508928838.
  7. Chen, S.T. and Yu, P.S. (2007), "Pruning of support vector networks on flood forecasting", J. Hydrology, 347(1-2), 67-78. https://doi.org/10.1016/j.jhydrol.2007.08.029.
  8. Cheng, J.J., Lei, J.Q., Li, S.Y. and Wang, H.F. (2016), "Effect of hanging-type sand fence on characteristics of wind-sand flow fields", Wind Struct., 22(5), 555-571. https://doi.org/10.12989/was.2016.22.5.555.
  9. Cornelis, W.M. and Gabriels, D. (2005), "Optimal windbreak design for wind-erosion control", J. Arid Environ., 61(2), 315-332. https://doi.org/10.1016/j.jaridenv.2004.10.005.
  10. Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018.
  11. Cristianini, N. and Shawe-Taylor, J. (2000), "An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods", Cambridge university press.
  12. Dong, Z., Luo, W., Qian, G. and Wang, H. (2007), "A wind tunnel simulation of the mean velocity fields behind upright porous fences", Agricultural Forest Meteorol., 146(1-2), 82-93. https://doi.org/10.1016/j.agrformet.2007.05.009.
  13. Dong, Z., Luo, W., Qian, G., Lu, P. and Wang, H. (2010), "A wind tunnel simulation of the turbulence fields behind upright porous wind fences", J. Arid Environ., 74(2), 193-207. https://doi.org/10.1016/j.jaridenv.2009.03.015.
  14. Ferreira, A.D. (2011), "Structural design of a natural windbreak using computational and experimental modeling", Environ. Fluid Mech, 11(5), 517-530. https://doi.org/10.1007/s10652-010-9203-y.
  15. Grantz, D., Vaughn, D., Farber, R., Kim, B.O.N.G., VanCuren, T. and Campbell, R. (1998), "Wind barriers offer short-term solution to fugitive dust", California Agriculture, 52(4), 14-18. https://doi.org/10.3733/ca.v052n04p14.
  16. Gunn, S.R. (1998), "Support vector machines for classification and regression", ISIS Technical Report, 14(1), 5-16.
  17. Hagen, L.J. (1976), "Windbreak design for optimum wind erosion control," Publ Great Plains Agric Counc.
  18. Harish, N., Mandal, S., Rao, S. and Patil, S.G. (2015), "Particle Swarm Optimization based support vector machine for damage level prediction of non-reshaped berm breakwater", Appl. Soft Comput., 27, 313-321. https://doi.org/10.1016/j.asoc.2014.10.041.
  19. Hong, S.W., Lee, I.B. and Seo, I.H. (2015), "Modelling and predicting wind velocity patterns for windbreak fence design", J. Wind Eng. Ind. Aerod., 142, 53-64. https://doi.org/10.1016/j.jweia.2015.03.007.
  20. Janardhan, P., Pruthviraj, U. and Subhash, C.Y., (2011), "Shelter Effect of Porous Wind Fences in Open Coal Storage Yard at Harbour", LAP Lambert Academic Publishing. India.
  21. Jensen, M. (1954), "Shelter Effect: Investigations into Aerodynamics of Shelter and its Effects on Climate and Crops, The Danish Technical Press, Copenhagen.
  22. Kecman, V. (2001), "Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models", MIT press, Cambridge, MA, USA.
  23. Kim, H.B. and Lee, S.J. (2002), "The structure of turbulent shear flow around a two-dimensional porous fence having a bottom gap", J. Fluid. Struct., 16(3), 317-329. https://doi.org/10.1006/jfls.2001.0423.
  24. Kim, H.G., Kim, M.K., Kim, B.E. and Yoon, H.Y. (2005), "Optimization of fugitive dust control system for weather conditions", Research Institute of Industrial Science & Technology, 19(1), 21-31.
  25. Lee, S.J. and Park, C.W. (2000), "The shelter effect of porous wind fences on coal piles in POSCO open storage yard", J. Wind Eng. Ind. Aerod., 84(1), 101-118. https://doi.org/10.1016/S0167-6105(99)00046-X.
  26. Mandal, S., Subba Rao, Harish, N. and Lokesha (2012), "Damage level prediction of non-reshaped berm breakwater using ANN, SVM, and ANFIS models," Int. J. Nav. Archit. Ocean Eng., 4(2), 112-122. https://doi.org/10.2478/IJNAOE-2013-0082.
  27. Ni, Y.Q., Hua, X.G., Fan, K.Q. and Ko, J.M. (2005), "Correlating modal properties with temperature using long-term monitoring data and support vector machine technique", Eng. Struct., 27(12), 1762-1773. https://doi.org/10.1016/j.engstruct.2005.02.020.
  28. Packwood, A.R. (2000), "Flow through porous fences in thick boundary layers: comparisons between laboratory and numerical experiments", J Wind Eng. Ind. Aerod., 88(1), 75-90. https://doi.org/10.1016/S0167-6105(00)00025-8.
  29. Papesch, A.J.G. (1992), "Wind tunnel test to optimize barrier spacing and porosity to reduce wind damage in horticultural shelter systems", J. Wind Eng. Ind. Aerod., 44(1-3), 2631-2642. https://doi.org/10.1016/0167-6105(92)90055-F.
  30. Perera, M.D.A.E.S. (1981), "Shelter behind two-dimensional solid and porous fences", J. Wind Eng. Ind. Aerod., 8(1-2), 93-104. https://doi.org/10.1016/0167-6105(81)90010-6.
  31. Purthviraj, U, Janardhan, P., Yaragal, S.C. and Nagaraj M.K., (2011), "Study on shelter effect of solid wind fences", Int. J. Earth.Sci. Eng., 4(01), 158-164.
  32. Raine, J. K., and Stevenson, D. C. (1977), "Wind protection by model fences in a simulated atmospheric boundary layer", J Wind Eng. Ind. Aerod., 2(2), 159-180. https://doi.org/10.1016/0167-6105(77)90015-0.
  33. Sagrado, A.P.G., van Beeck, J., Rambaud, P. and Olivari, D. (2002), "Numerical and experimental modelling of pollutant dispersion in a street canyon", J. Wnd Eng. Ind. Aerod., 90(4-5), 321-339. https://doi.org/10.1016/S0167-6105(01)00215-X.
  34. Samui, P. (2008), "Predicted ultimate capacity of laterally loaded piles in clay using support vector machine", Geomech. Geoeng., 3(2), 113-120. https://doi.org/10.1080/17486020802050844.
  35. San, B., Wang, Y. and Qiu, Y. (2018), "Numerical simulation and optimization study of the wind flow through a porous fence", Environ. Fluid Mech., 18(5), 1057-1075. https://doi.org/10.1007/s10652-018-9580-1
  36. Santiago, J.L., Martin, F., Cuerva, A., Bezdenejnykh, N. and Sanz-Andres, A. (2007), "Experimental and numerical study of wind flow behind windbreaks", Atmos. Environ., 41(30), 6406-6420. https://doi.org/10.1016/j.atmosenv.2007.01.014.
  37. Song, C.K., Kim, J.J. and Song, D.W. (2007), "The effects of windbreaks on reduction of suspended particles", Atmosphere, 17(4), 315-326.
  38. Stredova, H., Podhrazska, J., Litschmann, T., Středa, T. and Roznovsky, J. (2012), "Aerodynamic parameters of windbreak based on its optical porosity", Contributions to Geophysics and Geodesy, 42(3), 213-226.
  39. Tani, N. (1958), "On the wind tunnel test of the model shelter hedge", Bull. Natl. Inst. Agrid. Sci., Ser. A 6, 75.
  40. Van Maele, K. and Merci, B. (2006), "Application of two buoyancy-modified k-${\varepsilon}$ turbulence models to different types of buoyant plumes", Fire Safety J., 41(2), 122-138. https://doi.org/10.1016/j.firesaf.2005.11.003.
  41. Vapnik, V. (1998), "Statistical Learning Theory", Vol. 3, Wiley, New York.
  42. Vapnik, V. (2013), "The Nature of Statistical Learning Theory", Springer science & business media, New York, NY, USA
  43. Vapnik, V.N. (1999), "An overview of statistical learning theory", IEEE T. Neural Networ., 10(5).
  44. Vapnik, V.N., Golowich, S.E. and Smola, A.J. (1996), "Support vector machine for function approximation, regression estimation, and signal processing", Adv. Neural Inf. Process, 9, 281-287.
  45. Xu, Y. and Mustafa, M.Y. (2015), "Investigation of the structure of airflow behind a porous fence aided by CFD based virtual sensor data", Sensors & Transducers, 185(2), 149.
  46. Yang, M.H., Roth, D. and Ahuja, N. (2002), "A tale of two classifiers: SNoW vs. SVM in visual recognition", European Conference on Computer Vision, Springer, Berlin, Heidelberg, May, 685-699.
  47. You, J., Jeon, J., You, K. and Kim, Y. (2006), "Experiment of the shelter effect of porous wind fences base on the wind tunnel test", Proceedings of the 2006 Architectural Institute of Korea, 26(1), 81-84.