EDP Sciences logo
Access to the submission and peer-review system
Search
Menu
All issues Volume 2 (2017) Sust. Build., 2 (2017) 8 References
Open Access
Issue
Sust. Build.
Volume 2, 2017
Article Number 8
Number of page(s) 19
Section Modelling and Optimisation of Building Performance
DOI https://doi.org/10.1051/sbuild/2017006
Published online 03 October 2017
  1. A. Parr, M. Zaretsky, New directions in sustainable design (Routledge, London, 2011) [Google Scholar]
  2. K. Lomas, H. Eppel, M. Cook, J. Mardaljevic, Ventilation and thermal performance of design options for Stadium Australia, in: The 5th International IBPSA Conference [online] (International Building Performance Simulation Association, Prague, 1997), Available at: https://dspace.lboro.ac.uk/2134/12393 [Google Scholar]
  3. D. Fiala, K.J. Lomas, 1999, Application of a computer model predicting human thermal responses to the design of sports stadia, in: CIBSE'99, Conference Proc., Harrogate, UK, pp. 492–499 [Google Scholar]
  4. J. Bouyer, J. Vinet, P. Delpech, S. Carre, Thermal comfort assessment in semi-outdoor environments: application to comfort study in stadia, J. Wind Eng. Ind. Aerodyn. 95, 963 (2007) [CrossRef] [Google Scholar]
  5. J. Persoon, T. van Hooff, B. Blocken, J. Carmeliet, M. de Wit, On the impact of roof geometry on rain shelter in football stadia, J. Wind Eng. Ind. Aerodyn. 96, 1274 (2008) [CrossRef] [Google Scholar]
  6. A. Szucs, S. Moreau, F. Allard, Aspects of stadium design for warm climates, Build. Environ. 44, 1206 (2009) [CrossRef] [Google Scholar]
  7. T. van Hooff, B. Blocken, M. van Harten, 3D CFD simulations of wind flow and wind-driven rain shelter in sports stadia: influence of stadium geometry, Build. Environ. 46, 22 (2011) [CrossRef] [Google Scholar]
  8. P. Biagini, C. Borri, L. Facchini, Wind response of large roofs of stadions and arena, J. Wind Eng. Ind. Aerodyn. 95, 871 (2007) [CrossRef] [Google Scholar]
  9. T. van Hooff, B. Blocken, Full-scale measurements of indoor environmental conditions and natural ventilation in a large semi-enclosed stadium: possibilities and limitations for CFD validation, J. Wind Eng. Ind. Aerodyn. 104, 330 (2012) [CrossRef] [Google Scholar]
  10. B. Blocken, J. Persoon, Pedestrian wind comfort around a large football stadium in an urban environment: CFD simulation, validation and application of the new Dutch wind nuisance standard, J. Wind Eng. Ind. Aerodyn. 97, 255 (2009) [CrossRef] [Google Scholar]
  11. T. van Hooff, B. Blocken, Coupled urban wind flow and indoor natural ventilation modelling on a high-resolution grid: a case study for the Amsterdam ArenA stadium, Environ. Model. Softw. 25, 51 (2010) [CrossRef] [Google Scholar]
  12. T. van Hooff, B. Blocken, On the effect of wind direction and urban surroundings on natural ventilation of a large semi-enclosed stadium, Comput. Fluids 39, 1146 (2010) [CrossRef] [Google Scholar]
  13. A. Stamou, I. Katsiris, A. Schaelin, Evaluation of thermal comfort in Galatsi Arena of the Olympics “Athens 2004” using a CFD model, Appl. Therm. Eng. 28, 1206 (2008) [CrossRef] [Google Scholar]
  14. M. Ucuncu, D. Woolf, M. Zikri, Thermal comfort of spectators in stadia built in hot climates, paper presented at Adapting to Change: New Thinking on Comfort, Windsor, UK, 9–11April (Network for Comfort and Energy Use in Buildings, London, 2010), p. 8 [Google Scholar]
  15. A. Matzarakis, D. Fröhlich, Sport events and climate for visitors—the case of FIFA World Cup in Qatar 2022, Int. J. Biometeorol. 59, 481 (2014) [CrossRef] [Google Scholar]
  16. T. Nishioka, K. Ohtaka, N. Hashimoto, H. Onojima, Measurement and evaluation of the indoor thermal environment in a large domed stadium, Energy Build. 32, 217 (2000) [CrossRef] [EDP Sciences] [Google Scholar]
  17. P. Sofotasiou, B. Hughes, J. Calautit, Qatar 2022: Facing the FIFA World Cup climatic and legacy challenges, Sustain. Cities Soc. 14, 16 (2015) [CrossRef] [Google Scholar]
  18. B. Blocken, 50 years of Computational Wind Engineering: Past, present and future, J. Wind Eng. Ind. Aerodyn. 129, 69 (2014) [Google Scholar]
  19. P. Sofotasiou, J. Calautit, B. Hughes, D. O'Connor, Towards an integrated computational method to determine internal spaces for optimum environmental conditions, Comput. Fluids 127, 146 (2016) [CrossRef] [Google Scholar]
  20. FIFA, Football Stadiums: Technical recommendations and requirements, 5th edition [e-book] (FIFA, 2011), p. 108, 283 [Google Scholar]
  21. F. Lien, E. Yee, Y. Cheng, Simulation of mean flow and turbulence over a 2D building array using high-resolution CFD and a distributed drag force approach, J. Wind Eng. Ind. Aerodyn. 92, 117 (2004) [CrossRef] [Google Scholar]
  22. X. Xiaomin, H. Zhen, W. Jiasong, The impact of urban street layout on local atmospheric environment, Build. Environ. 41, 1352 (2006) [CrossRef] [Google Scholar]
  23. Y. Huang, X. Hu, N. Zeng, Impact of wedge-shaped roofs on airflow and pollutant dispersion inside urban street canyons, Build. Environ. 44, 2335 (2009) [Google Scholar]
  24. L. Yik, S. Salim, A. Chan, C. Cheong, CFD study of flow over parallel ridges with varying height and spacing, in: World Congress on Engineering, London (2010), Available at: http://www.iaeng.org/publication/WCE2010/WCE2010_pp1206-1211.pdf [Google Scholar]
  25. F. Joerg, Recommendations of the COST action C14 on the use of CFD in predicting pedestrian wind environment, Fourth Int. Symp. Comput. Wind Eng. 2006, 529 (2006) [Google Scholar]
  26. P. Richards, R. Hoxey, Appropriate boundary conditions for computational wind engineering models using the k-ε turbulence model, J. Wind Eng. Ind. Aerodyn. 46–47, 145 (1993) [CrossRef] [Google Scholar]
  27. D. Hargreaves, N. Wright, On the use of the k-ε model in commercial CFD software to model the neutral atmospheric boundary layer, J. Wind Eng. Ind. Aerodyn. 95, 355 (2007) [CrossRef] [Google Scholar]
  28. J. Wieringa, Updating the Davenport roughness classification, J. Wind Eng. Ind. Aerodyn. 41, 357 (1992) [CrossRef] [Google Scholar]
  29. X. Shen, G. Zhang, B. Bjerg, Investigation of response surface methodology for modelling ventilation rate of a naturally ventilated building, Build. Environ. 54, 174 (2012) [CrossRef] [Google Scholar]
  30. A. Szucs, S. Moreau, F. Allard, Spectators' aerothermal comfort assessment method in stadia, Build. Environ. 42, 2227 (2007) [CrossRef] [Google Scholar]
  31. J. Kindangen, G. Krauss, P. Depecker, Effects of roof shapes on wind-induced air motion inside buildings, Build. Environ. 32, 1 (1997) [CrossRef] [Google Scholar]
  32. M. Cavazzuti, Optimization methods (Springer, Berlin, 2013) [CrossRef] [Google Scholar]
  33. ANSYS, Design Exploration Release 12.1, no. November (ANSYS, Inc, South-pointe 275 Technology Drive Canonsburg, PA 15317, USA, 2009) [Google Scholar]
  34. L. Friedman, The simulation metamodel (Springer US, Boston, MA, 1996) [CrossRef] [Google Scholar]
  35. B. Schölkopf, C. Burges, A. Smola, Advances in kernel methods (MIT Press, Cambridge, MA, 1999), p. 11 [Google Scholar]
  36. V. Vapnik, The nature of statistical learning theory (Springer, New York, 2000) [CrossRef] [Google Scholar]
  37. M. Wand, M. Jones, Kernel smoothing (Chapman & Hall, London, 1995) [Google Scholar]
  38. T. Kvålseth, Note on the R2 measure of goodness of fit for nonlinear models, Bull. Psychon. Soc. 21, 79 (1983) [CrossRef] [Google Scholar]
  39. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6, 182 (2002) [Google Scholar]
  40. C. Foncesa, P. Fleming, Genetic algorithms for multiobjective optimization: formulation, discussion and generalization, in: Fifth International Conference on Genetic Algorithms (Morgan Kauffman, San Mateo, CA), 416 (1993) [EDP Sciences] [Google Scholar]
  41. S.F. Fatima, H.N. Chaudhry, Steady-state CFD modelling and experimental analysis of the local microclimate in Dubai (UAE), Sust. Build. 2, 5 (2017) [CrossRef] [EDP Sciences] [Google Scholar]
  42. F. Calcerano, C. Cecchini, L. Martinelli, Numerical analysis of passive strategies for energy retrofit of existing buildings in Mediterranean climate: thermal mass and natural ventilation combination, Sust. Build. 2, 4 (2017) [CrossRef] [EDP Sciences] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.