In the parametric platform of Grasshopper-Rhino3D was

In building science,
designers often use dynamic thermal simulation programs to analyze thermal and
energy behaviors of a building and to achieve specific targets, e.g. reducing
energy consumption, environmental impacts or improving indoor thermal
environment (Nguyen 2014). Today, a
large number of simulation tools are available with user-friendly environment
which with their application, design teams after determining the parameters and
objectives, can achieve new solutions that were not previously possible through
conventional methods (Machairas 2014). An approach known as “parametric
simulation method” can be used to improve building performance. It was determined
that a workflow developed Within the
parametric platform of Grasshopper-Rhino3D
was the best approach to work on this project because parametric tools have the
potential to solve complex design formulations and offer the freedom to adapt
to the particular needs of the user through algorithmic modeling. Parametric modeling, by nature, is based on data;
connections and changes between different levels of data are instantaneous.
This system provides multiple benefits for integrating design and analysis. The
ability to visualize the environmental analysis data within the design platform
allows designers to make a clear connection between the data analysis and the
design (Roudsari 2013). In that sense Rhino3D offers the interface over which
any kind of modeling solutions can be examined, while Grasshopper offers the
freedom to create a set of capabilities that go beyond the existing options
offered by standard energy modeling applications. For instance, some of the
existing gaps in energy simulation

tools that can be solved through the
implementation of Grasshopper are the following (Gamas 2014):

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– The dislocation between daylight and thermal
studies

– The inability to iterate through several
design options (>15) in short periods of time

– The capacity to automate the process of
generating shading systems based on user-defined

constraints of peak
days and hours

We simulated the fenestrations of a specific building to develop optimal
performance algorithms. The study was conducted by simulating a floor with
varying façade properties: window to wall ratio, window height, number of
windows and sill height. The most efficient control principles were chosen
based on the energy performance and daylight quality

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