Yujin Kim, PhD
Comfort Assessment Using Python
Approach and Methodology
Using NumPy and Pandas in Python (v.3.9) via Anaconda, PLW comfort was assessed (Fig. 4.1). For the assessment, London’s prevailing four directional winds (northwest, northeast, southwest and southeast) were first simulated with the use of OpenFOAM (v.9).
Fig. 4.1. (A) Contours of wind speed ratios at 1.5 m above ground level by four directional winds (NW, NE, SW and SE), (B) PLW comfort representation, using ParaView 5.9.1 and Python coding.
Wind speed ratio datasets on all cell points from the OpenFOAM results at 1.5 m above ground level, were exported into comma-separated values (csv) files, and imported into the Python program. The wind speed ratio (U) represents normalised wind speed (Equation 4.1):
(4.1)
where U1.5m is the local wind speed (m/s) at 1.5 m above ground level, and Uref is the reference wind speed (= 10 m/s) at the reference height (zref = 518 m).
Using the code I wrote with Numpy and Panda, the PLW comfort of each cell point was assessed and categorised as “acceptable for frequent sitting, occasional sitting, standing, walking and uncomfortable for regular pedestrian access [5]” in accordance with the “City Lawson Criteria [5]” based on the “Wind Microclimate Guidelines for Developments in the City of London [5].”
The PLW comfort assessment is based on the Weibull probability density function in accordance with Equation 4.2 [5]:
(4.2)
where f(x) is to calculate the exceeding probability of the wind speed threshold for each activity from each wind direction based on the City Lawson Criteria. k and c represent the shape and scale parameters of a Weibull distribution, respectively. p is the probability of wind rose from a given wind direction. x indicates the wind speed threshold (m/s) for the comfort assessment of each activity [5]. U is the wind speed ratio, as described in Equation 4.1. PLW comfort areas were then represented using ParaView 5.9.1 [6] as previously shown in Fig. 4.1.
The Python code that I wrote is as follows:
Fig. 4.2. Python coding to assess PLW comfort
Note that this is part of my PhD thesis, "Geometry of Tall Buildings Improving Wind Comfort in London":
[1] Kim, Yujin. 2023. “Geometries of Tall Buildings Improving Wind Comfort in London.” Architectural Association School of Architecture and The Open University.
https://doi.org/10.21954/ou.ro.00016330.
[2] Anaconda. “Anaconda.” Accessed October 18, 2022.
https://www.anaconda.com/.
[3] OpenFOAM. “OpenFOAM: Overview.” Accessed April 24, 2021.
https://www.openfoam.com/governance/overview.
[4] Architectural Institute of Japan. “Guidebook for CFD Predictions of Urban Wind Environment.” Accessed October 6, 2021.
https://www.aij.or.jp/jpn/publish/cfdguide/index_e.htm.
[5] City of London and RWDI. “Wind Microclimate Guidelines for Developments in the City of London.” Accessed November 1, 2022.
https://www.cityoflondon.gov.uk/assets/Services-Environment/wind-microclimate-guidelines.pdf.
[6] ParaView. “ParaView (v.5.9.1).” Accessed April 18, 2021.
https://www.paraview.org/.