Description
Motivations
Installation of OTC3D
Running OTC3D
License
Outdoor Thermal Comfort in 3D is a numerical model for calculating the spatial variability of outdoor thermal comfort (OTC) in urban areas. OTC is currently described as Standard Effective Temperature, which is a comprehensive thermal comfort metric that represents the human response to the thermal environment.
In order to comprehensively and accurately investigates urban microclimate, OTC3D employs a modular approach, such that the model can be used in combination with existing microclimate tools of urban flow and energy analysis with different levels of spatial and temporal resolutions.
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Desribing outdoor thermal comfort with a comrepehsive metric that integrate air temperature and humidity, as well as more complex factors such as solar radiation and wind speed, all interactting with the body’s thermal regulation processes.
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Considering the detailed spatial variability of outdoor thermal comfort in urban areas, which is highly dependent on the urban form and radiative properties of urban areas.
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Accurately describing the radiant exposure of human in outdoor spaces by incorporating a) the visibility of urban surfaces to the pedestrians at any point, b) the spatial distribution of sky view factor, and c) inter-building shadowing and shortwave radiation effects on thermal comfort.
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Streamlining and facilitating the geometry implementation by linking OTC3D with Python Library for Urban Optimisation.
- Install Anaconda for python2.7. Instructions on how to install and use Anaconda here. Alternatively, you can insall Spyder 2.3.8 by following the steps given here.
- Download and insall the following libraries (execute these commands in Anaconda prompt):
* conda install -c https://conda.anaconda.org/dlr-sc pythonocc-core=0.17
* conda install scipy
The following libraries are automatically installed by running thermalcomfort.py**
* Pvlib / pyliburo / pythonocc (GNU LGPL3) / numpy (BSD 3-clause "New" or "Revised" License) / Pandas ("New" or "Revised" License) / scipy (BSD 3-clause "New" or "Revised" License) / matplotlib / pymf / cvxopt (GNU General Public License v3.0) / scikit-learn (BSD 3-clause "New" or "Revised" License) / pyshp (mit license) / pycollada (BSD 3-clause "New" or "Revised" License) /networkx (BSD 3-clause "New" or "Revised" License) / lxml ((BSD) libxml2 and libxslt2 (MIT))
The estimated time required for installation is 1hr (?)
- Run thermalcomfort.py in the home directory of Python/Spyder.
- Run ExtraFunctions.py in the home directory of Python/Spyder.
- Execute the example code in the same directory as thermalcomfort.py and ExtraFunctions.py. An example of running OTC3D for a) an idealized array of building, and b) a complex urban configuration is provided in the "Examples" folder of this repository.
The input files required to run the model are as follows:
- ped_properties.csv
- model_inputs.csv Examples of these files are given in the "Input_Data" directory.
Run the file Building_IdealModel.py. The output is idealized set of buildings.
(i) Input the file path of the input file.
In this module, functions rely on a helper class pdcoord that standardize a pandas Dataframe as one with four columns consisting of {x,y,z} coordinates and the corresponding value v. The pdcoord class is used to pass microclimate data and calculations between functions.
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solar_param(time,latitude,longitude, timezone, groundalbedo) uses PVLib to calculate solar parameters needed in the thermal comfort model. Returns a DataFrame of solar vector, solar view factor, direct solar radiation intensity, and diffuse solar radiation intensities from the sky and the ground
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check_shadow(key, model, solarvector) checks if location key is shaded by the model when solar radiation is incident at the \textit{solarvector} direction, and returns 0 if the location is shaded, and 1 if the location is sunlit.
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skyviewfactor(key, model) calculates SVF at the key location. This function is can be replaced by fourpiradiation, which combines the calculation with groundview and wall visibility.
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fourpiradiation(ped, model) constructs the discretized sphere of directions at the key location, and returns SVF, ground view factor, and a list of points of intercepts on wall surfaces visible to the pedestrian.
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call_values(intercepts, surfpdcoord, gridsize) is given a list of intercepts, a pdcoord of surface values, and the grid size, and returns a list of values at the intercepts.
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calc_radiation_from_values(SurfTemp, SurfReflect, SurfEmissivity) returns the amount of long and shortwave radiation from urban surfaces by calculating the Stefan-Boltzmann law for the values returned by call_values()
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calc_Esky_emis(Ta,RH) calculates longwave radiation from the sky based on the ambient temperature
$T_{a}$ and relative humidity$RH$ . -
meanradtemp(Esky,Esurf, Eground,Ereflect, solarparam, SVF, GVF, pedestrian_albedo, shadow) calculates mean radiant temperature
$T_{mrt}$ from different sources of radiation in the urban environment. -
all_ mrt(key, compound, pdAirTemp, pdReflect, pdSurfTemp, solarparam,model_inputs, ped_constants) is given microclimate data, pedestrian information, and the urban model, and calculates the necessary components for meanradtemp() at location key. Returns
$T_{mrt}$ , longwave and shortwave radiation components, SVF and shading effects. -
calc_ SET(microclimate, ped_constants, ped_properties) returns SET at one location with the given inputs: * ped_properties : Pedestrian properties, including height, skin wetness, mass, ratio of effective radiation area of the body (Fanger 1967), body emissivity, body albedo, metabolic rate, work activity, and clothing levels. * microclimate : Microclimate parameters, including air temperature, wind speed, mean radiant temperature, and relative humidity.
Currently no license is needed. However, following publications should be cited when using this model: