Development of probabilistic assessment framework for pedestrian wind environment using Bayesian technique
暂无分享,去创建一个
[1] Theodore Stathopoulos,et al. Pedestrian Level Winds and Outdoor Human Comfort , 2006 .
[2] W. H. Melbourne,et al. Criteria for environmental wind conditions , 1978 .
[3] Y. Tominaga,et al. Recommendation of Gust Factor for Assessment of Pedestrian Wind Environment Based on Probability of Exceedance of Daily Maximum Gust Wind Speed , 2014 .
[4] J. G. Bartzis,et al. A Statistical Model for the Prediction of Wind-Speed Probabilities in the Atmospheric Surface Layer , 2017, Boundary-Layer Meteorology.
[5] Alan G. Davenport,et al. Rationale for Determining Design Wind Velocities , 1960 .
[6] David Huard,et al. PyMC: Bayesian Stochastic Modelling in Python. , 2010, Journal of statistical software.
[7] Fue-Sang Lien,et al. Bayesian inversion of concentration data: Source reconstruction in the adjoint representation of atmospheric diffusion , 2008 .
[8] Marcel Bottema,et al. A method for optimisation of wind discomfort criteria , 2000 .
[9] Jean Palutikof,et al. A review of methods to calculate extreme wind speeds , 1999 .
[10] A.L.S. Chan,et al. Pedestrian level wind environment assessment around group of high-rise cross-shaped buildings: Effect of building shape, separation and orientation , 2016, Building and Environment.
[11] Yoshihide Tominaga,et al. AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings , 2008 .
[12] Yukio Tamura,et al. Characteristics of pedestrian-level wind around super-tall buildings with various configurations , 2017 .
[13] Kenny C. S Kwok,et al. Effects of twisted wind flows on wind conditions in passages between buildings , 2017 .
[14] J. Niu,et al. LES for pedestrian level wind around an idealized building array—Assessment of sensitivity to influencing parameters , 2019, Sustainable Cities and Society.
[15] Hyondong Oh,et al. A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors , 2017, Inf. Fusion.
[16] Zhang Lin,et al. Evaluation of pedestrian wind comfort near ‘lift-up’ buildings with different aspect ratios and central core modifications , 2017, Building and Environment.
[17] Thomas V. Wiecki,et al. Probabilistic Programming in Python using PyMC , 2015, 1507.08050.
[18] Fei Xue,et al. Turbulent Schmidt number for source term estimation using Bayesian inference , 2017 .
[19] Y. Tominaga,et al. Effect of the numerical viscosity on reproduction of mean and turbulent flow fields in the case of a 1:1:2 single block model , 2019, Journal of Wind Engineering and Industrial Aerodynamics.
[20] 日本建築学会. AIJ recommendations for loads on buildings , 1996 .
[21] Ashutosh Sharma,et al. A review on the study of urban wind at the pedestrian level around buildings , 2018, Journal of Building Engineering.
[22] Yasunori Akashi,et al. Hierarchical Bayesian modeling for predicting ordinal responses of personalized thermal sensation: Application to outdoor thermal sensation data , 2018, Building and Environment.
[23] Bje Bert Blocken,et al. Pedestrian-level wind conditions around buildings: Review of wind-tunnel and CFD techniques and their accuracy for wind comfort assessment , 2016 .
[24] Girma Bitsuamlak,et al. Pedestrian level wind assessment through city development: A study of the financial district in Toronto , 2017 .
[25] A. Mochida,et al. Wind tunnel tests on the relationship between building density and pedestrian-level wind velocity: Development of guidelines for realizing acceptable wind environment in residential neighborhoods , 2008 .
[26] A. Hagishima,et al. Evaluation of exceeding wind speed at a pedestrian level around a 1:1:2 isolated block model , 2020, Journal of Wind Engineering and Industrial Aerodynamics.
[27] J. Counihan. Adiabatic atmospheric boundary layers: A review and analysis of data from the period 1880–1972 , 1975 .
[28] 義江 龍一郎,et al. LES による 1:1:2 単体建物周辺流れのベンチマークテスト: 各種計算条件が計算結果に及ぼす影響 , 2020 .
[29] Ryozo Ooka,et al. Bayesian source term estimation of atmospheric releases in urban areas using LES approach. , 2018, Journal of hazardous materials.
[30] Andrew Gelman,et al. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..
[31] H.P.A.H. Irwin,et al. A simple omnidirectional sensor for wind-tunnel studies of pedestrian-level winds , 1981 .
[32] Serge Guillas,et al. Bayesian calibration of the constants of the k–ε turbulence model for a CFD model of street canyon flow , 2014 .
[33] T. Stathopoulos,et al. Computer simulation of wind environmental conditions around buildings , 1996 .
[34] J. Kruschke. Doing Bayesian Data Analysis , 2010 .
[35] Ryozo Ooka,et al. Observational study of power-law approximation of wind profiles within an urban boundary layer for various wind conditions , 2017 .
[36] Yukio Tamura,et al. Wind speed profiles measured over ground using Doppler sodars , 1999 .
[37] Bje Bert Blocken,et al. Pedestrian wind comfort around a large football stadium in an urban environment: CFD simulation, validation and application of the new Dutch wind nuisance standard , 2009 .
[38] J. Tanimoto,et al. Evaluation of rare velocity at a pedestrian level due to turbulence in a neutrally stable shear flow over simplified urban arrays , 2017 .
[39] Jorge Sousa,et al. Computational urban flow predictions with Bayesian inference: Validation with field data , 2019 .
[40] Raghu N. Kacker,et al. Evolution of modern approaches to express uncertainty in measurement , 2007 .
[41] Yeonsook Heo,et al. Calibration of building energy models for retrofit analysis under uncertainty , 2012 .
[42] Fue-Sang Lien,et al. Bayesian inference for source determination with applications to a complex urban environment , 2007 .
[43] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[44] Bje Bert Blocken,et al. Pedestrian wind comfort around buildings : comparison of wind comfort criteria based on whole-flow field data for a complex case study , 2013 .
[45] Bert Blocken,et al. CFD simulation for pedestrian wind comfort and wind safety in urban areas: General decision framework and case study for the Eindhoven University campus , 2012, Environ. Model. Softw..
[46] Marvin D. Troutt,et al. Estimation of Wind Speed Distribution Using Markov Chain Monte Carlo Techniques , 2001 .
[47] Tianzhen Hong,et al. Integrating physics-based models with sensor data: An inverse modeling approach , 2019, Building and Environment.
[48] E. C. Poulton,et al. The effects of wind on people; New criteria based on wind tunnel experiments , 1976 .
[49] R. Ooka,et al. Consistency of mean wind speed in pedestrian wind environment analyses: Mathematical consideration and a case study using large-eddy simulation , 2018 .
[50] John Salvatier,et al. Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..
[51] Jan Carmeliet,et al. Pedestrian Wind Environment around Buildings: Literature Review and Practical Examples , 2004 .
[52] V.V.N. Kishore,et al. Multi-peak Gaussian fit applicability to wind speed distribution , 2014 .
[53] Ryozo Ooka,et al. Bayesian inference for thermal response test parameter estimation and uncertainty assessment , 2018 .
[54] Shuzo Murakami,et al. New criteria for wind effects on pedestrians , 1981 .
[55] Yoshihide Tominaga,et al. Cross Comparisons of CFD Results of Wind Environment at Pedestrian Level around a High-rise Building and within a Building Complex , 2004 .
[56] Ruchi Choudhary,et al. Energy analysis of the non-domestic building stock of Greater London , 2012 .
[57] Shuzo Murakami,et al. Study on acceptable criteria for assessing wind environment at ground level based on residents' diaries , 1986 .
[58] Hans A. Panofsky,et al. Adiabatic atmospheric boundary layers: A review and analysis of data from the period 1880–1972☆ , 1976 .
[59] H. Montazeri,et al. CFD evaluation of building geometry modifications to reduce pedestrian-level wind speed , 2019, Building and Environment.