DOI QR코드

DOI QR Code

Effects of upstream two-dimensional hills on design wind loads: A computational approach

  • Bitsuamlak, G. (RWDI Inc.) ;
  • Stathopoulos, T. (Centre for Building Studies, Concordia University) ;
  • Bedard, C. (ETS-Ecole de Technologie Superieure)
  • Received : 2004.06.07
  • Accepted : 2005.12.22
  • Published : 2006.02.25

Abstract

The paper describes a study about effects of upstream hills on design wind loads using two mathematical approaches: Computational Fluid Dynamics (CFD) and Artificial Neural Network (NN for short). For this purpose CFD and NN tools have been developed using an object-oriented approach and C++ programming language. The CFD tool consists of solving the Reynolds time-averaged Navier-Stokes equations and $k-{\varepsilon}$ turbulence model using body-fitted nearly-orthogonal coordinate system. Subsequently, design wind load parameters such as speed-up ratio values have been generated for a wide spectrum of two-dimensional hill geometries that includes isolated and multiple steep and shallow hills. Ground roughness effect has also been considered. Such CFD solutions, however, normally require among other things ample computational time, background knowledge and high-capacity hardware. To assist the enduser, an easier, faster and more inexpensive NN model trained with the CFD-generated data is proposed in this paper. Prior to using the CFD data for training purposes, extensive validation work has been carried out by comparing with boundary layer wind tunnel (BLWT) data. The CFD trained NN (CFD-NN) has produced speed-up ratio values for cases such as multiple hills that are not covered by wind design standards such as the Commentaries of the National Building Code of Canada (1995). The CFD-NN results compare well with BLWT data available in literature and the proposed approach requires fewer resources compared to running BLWT experiments.

Keywords

References

  1. Akcelik, V., Jaramaz, B. and Ghattas G. (2001), 'Nearly orthogonal two-dimensional grid generation with aspect ratio control', J. Comput. Physics, 171, 805-821 https://doi.org/10.1006/jcph.2001.6811
  2. Beljaars, A.C., Walmsley, J.L. and Taylor, P.A. (1987), 'A mixed spectral finite-difference model for neutrally stratified boundary-layer flow over roughness changes and topography', Boundary-Layer Meteorology, 38, 273-303 https://doi.org/10.1007/BF00122448
  3. Bergeles, G.C. (1985), 'Numerical computation of turbulent flow around two-dimensional hills', J. Wind Eng. Ind. Aerodyn., 21, 307-321 https://doi.org/10.1016/0167-6105(85)90042-X
  4. Bitsuamlak, G.T. and Godbole, P.N. (1999), 'Application of cascade-correlation learning network for determination of wind pressure distribution in buildings', Wind Engineering into the 21st Century, Balkema, Rotterdam, 1493-1496
  5. Bitsuamlak, G.T., Stathopoulos, T. and Bedard, C. (2002), 'Neural network predictions of wind flow over complex terrain', 4th Structural Specialty Conference of the Canadian Society for Civil Engineering, Montreal, Quebec, Canada, ST-026, S-13
  6. Bitsuamlak, G.T., Stathopoulos, T. and Bedard, C. (2003), 'Numerical evaluation and neural net predictions of wind flow over complex terrain',11th International Conference on Wind Engineering, June 2-5, Lubbock, Texas, USA, 2, 2673-2679
  7. Bitsuamlak, G.T., Stathopoulos, T. and Bedard, C. (2004), 'Numerical evaluation of wind flow over complex terrain: Review', J. Aerospace Eng., 17(4), 135-145 https://doi.org/10.1061/(ASCE)0893-1321(2004)17:4(135)
  8. Bitsuamlak, G.T. (2004), 'Evaluating the effect of topographic elements on wind flow: A combined numerical simulation-neural network approach', Ph.D. Thesis, Concordia University, Montreal, Canada
  9. Bradshaw, P. (1976), 'Topics in applied physics-turbulence', 12, Springer-Verlag, NewYork, 2nd Ed
  10. Carpenter, P. and Locke, N. (1999), 'Investigation of wind speed over multiple two-dimensional hills', J. Wind Eng. Ind. Aerodyn., 83, 109- 120 https://doi.org/10.1016/S0167-6105(99)00065-3
  11. Chen, Y., Kopp, G.A. and Surry, D. (2003), 'Prediction of pressure coefficients on roofs of low buildings using artificial neural networks', J. Wind Eng. lnd. Aerodyn., 91, 423-441 https://doi.org/10.1016/S0167-6105(02)00381-1
  12. Chung, J. and Bienkiewicz, B. (2004), 'Numerical simulation of flow past 2D hill and valley', Wind and Struct., An Int. J., 7(1), 1-12 https://doi.org/10.12989/was.2004.7.1.001
  13. Eca, L. (1996), '2D orthogonal grid generation with boundary point distribution control', J. Comput. Physics, 125, 440-453 https://doi.org/10.1006/jcph.1996.0106
  14. English, E.C. and Fricke, F.R. (1999), 'The interference index and its prediction using a neural network analysis of wind-tunnel data', J. Wind Eng. Ind. Aerodyn., 83, 567-575 https://doi.org/10.1016/S0167-6105(99)00102-6
  15. Fahlman, S.E. and Lebiere, C. (1990), 'The cascade correlation learning architecture', D.S. Touretzky. Advances in Neural Information Processing Systems II. Morgan Kaufinann, 524-532
  16. Horsfield, J.N., Chan, C.M. and Denoon, R.O. (2002), 'Towards sustainable development through innovative engineering', Housing Conference, Wanchai, Hong Kong
  17. Khanduri, A.C., Bedard, C. and Stathopoulos, T. (1995), 'Neural network modelling of wind-induced interference effects', Proceedings of the Ninth International Conference on Wind Engineering, New Delhi, India, 1341-1352
  18. Khanduri, A.C., Bedard, C. and Stathopoulos, T. (1997), 'Modelling wind-induced interference effects using backpropagation neural networks', J. Wind Eng. Ind. Aerodyn., 72, 71-79 https://doi.org/10.1016/S0167-6105(97)00259-6
  19. Launder, B.E. and Spalding, D.B. (1974), 'The numerical computation of turbulent flows', Comput. Methods Appl. Mech. Eng., 3, 269-289 https://doi.org/10.1016/0045-7825(74)90029-2
  20. Lun, Y.F., Mochida, A., Yoshino, H., Murakami, S. and Kimura, A. (2003), 'Applicability of linear type revised $\kappa-\varepsilon$ models to flow over topographic feature', 11th International Conference on Wind Engineering, June 2-5, Lubbock, Texas, USA, 2, 1149-1156
  21. Lemelin, D.R., Surry, D., and Davenport, A.G. (1988), 'Simple approximations for wind speed-up over hills', J.Wind Eng. Ind. Aerodyn., 28, 117-127 https://doi.org/10.1016/0167-6105(88)90108-0
  22. Maurizi, A., (2000), 'Numerical simulation of turbulent flows over 2D valleys using three versions of the $\kappa-\varepsilon$ closure model', J. Wind Eng. Ind. Aerodyn., 85, 59-73 https://doi.org/10.1016/S0167-6105(99)00121-X
  23. Miller, C.A. (1996), 'Turbulent boundary layers above complex terrain', Ph.D. thesis, University of Western Ontario, London, Ontario, Canada
  24. NBCC, National Building Code User's Guide-Structural Commentaries (Part 4), Canadian Commission on Building and Fire Codes, National Research Council of Canada, Ottawa (1995)
  25. Paterson, D.A. and Holmes, J.D. (1993), 'Computation of wind flow over topography', J. Wind Eng. Ind. Aerodyn., 46&47, 471-478 https://doi.org/10.1016/0167-6105(93)90314-E
  26. Rhie, C.M. and Chow, W.L. (1983), 'Numerical study of the turbulent flows past an airfoil with trailing edge separation', AIAA J., 21, 1525-1532 https://doi.org/10.2514/3.8284
  27. Sandri, P. and Mehta, K.C. (1995), 'Using backpropagation neural network for predicting wind-induced damage to building', Proceedings of the Ninth International Conference on Wind Engineering, New Delhi, India, 1989-1999
  28. Spalding D.B. (1972), 'A novel finite-difference formulation for differential expressions involving both first and second order derivatives', Int. J. Num. Methods Eng., 4, 551-559 https://doi.org/10.1002/nme.1620040409
  29. Taylor, P.A., Walmesly J.L. and Salmon, J.R. (1983), 'A simple model of neutrally stratified boundary-layer flow over real terrain incorporating wave number dependent scaling', Boundary-Layer Meteorol., 26, 169-189 https://doi.org/10.1007/BF00121541
  30. Van Doormal, J.P and Raithby, G.D. (1984), 'Enhancements of the SIMPLE method for predicting incompressible fluid flows', Numer. Heat Transfer, 7, 147-163 https://doi.org/10.1080/01495728408961817
  31. Weng, W, Taylor, P.A. and Walmsley, J.L. (2000), 'Guidelines for airflow over complex terrain: Model developments', J. Wind Eng. Ind. Aerodyn., 86, 169-186 https://doi.org/10.1016/S0167-6105(00)00009-X
  32. Xu, D., Ayotte, A.W. and Taylor, P.A. (1994), 'Development of a non-linear mixed spectral finite difference model for turbulent boundary-layer flow over topography', Boundary-Layer Meteorol., 70, 341-367 https://doi.org/10.1007/BF00713775

Cited by

  1. CFD simulation of the atmospheric boundary layer: wall function problems vol.41, pp.2, 2007, https://doi.org/10.1016/j.atmosenv.2006.08.019
  2. An experimental investigation on the aeromechanics and wake interferences of wind turbines sited over complex terrain vol.172, 2018, https://doi.org/10.1016/j.jweia.2017.11.018
  3. Turbulent Pressure and Velocity Perturbations Induced by Gentle Hills Covered with Sparse and Dense Canopies vol.133, pp.2, 2009, https://doi.org/10.1007/s10546-009-9427-x
  4. Numerical evaluation of the effect of multiple roughness changes vol.19, pp.6, 2014, https://doi.org/10.12989/was.2014.19.6.585
  5. CFD simulation of wind flow over natural complex terrain: Case study with validation by field measurements for Ria de Ferrol, Galicia, Spain vol.147, 2015, https://doi.org/10.1016/j.jweia.2015.09.007
  6. Designing laboratory wind simulations using artificial neural networks vol.120, pp.3-4, 2015, https://doi.org/10.1007/s00704-014-1201-4
  7. Pedestrian wind comfort around a large football stadium in an urban environment: CFD simulation, validation and application of the new Dutch wind nuisance standard vol.97, pp.5-6, 2009, https://doi.org/10.1016/j.jweia.2009.06.007
  8. Prediction of wind properties in urban environments using artificial neural network vol.107, pp.3-4, 2012, https://doi.org/10.1007/s00704-011-0506-9
  9. Wind direction field under the influence of topography: part II: CFD investigations vol.22, pp.4, 2016, https://doi.org/10.12989/was.2016.22.4.477
  10. Turbulent Intensities and Velocity Spectra for Bare and Forested Gentle Hills: Flume Experiments vol.129, pp.1, 2008, https://doi.org/10.1007/s10546-008-9308-8
  11. Modeling the Effect of Topography on Wind Flow Using a Combined Numerical–Neural Network Approach vol.21, pp.6, 2007, https://doi.org/10.1061/(ASCE)0887-3801(2007)21:6(384)
  12. Dynamic along wind response of tall buildings using Artificial Neural Network pp.1573-7543, 2019, https://doi.org/10.1007/s10586-018-2027-0
  13. Computing dynamic across-wind response of tall buildings using artificial neural network pp.1573-0484, 2018, https://doi.org/10.1007/s11227-018-2708-8
  14. Multiobjective Aerodynamic Optimization of Tall Building Openings for Wind-Induced Load Reduction vol.144, pp.10, 2018, https://doi.org/10.1061/(ASCE)ST.1943-541X.0002199
  15. Computational assessment of blockage and wind simulator proximity effects for a new full-scale testing facility vol.13, pp.1, 2006, https://doi.org/10.12989/was.2010.13.1.021
  16. A neural network shelter model for small wind turbine siting near single obstacles vol.15, pp.1, 2012, https://doi.org/10.12989/was.2012.15.1.043
  17. Topographic effects on tornado-like vortex vol.27, pp.2, 2018, https://doi.org/10.12989/was.2018.27.2.123
  18. Simulation and Analysis of a Turbulent Flow Around a Three-Dimensional Obstacle vol.13, pp.3, 2019, https://doi.org/10.2478/ama-2019-0023
  19. Numerical modelling of shelter effect of porous wind fences vol.29, pp.5, 2006, https://doi.org/10.12989/was.2019.29.5.313
  20. Wind field generation for performance-based structural design of transmission lines in a mountainous area vol.31, pp.2, 2020, https://doi.org/10.12989/was.2020.31.2.165