In the parametric platform of Grasshopper-Rhino3D was the best

In building science,designers often use dynamic thermal simulation programs to analyze thermal andenergy behaviors of a building and to achieve specific targets, e.g. reducingenergy consumption, environmental impacts or improving indoor thermalenvironment (Nguyen 2014). Today, alarge number of simulation tools are available with user-friendly environmentwhich with their application, design teams after determining the parameters andobjectives, can achieve new solutions that were not previously possible throughconventional methods (Machairas 2014). An approach known as “parametricsimulation method” can be used to improve building performance. It was determinedthat a workflow developed Within theparametric platform of Grasshopper-Rhino3Dwas the best approach to work on this project because parametric tools have thepotential to solve complex design formulations and offer the freedom to adaptto 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. Theability to visualize the environmental analysis data within the design platformallows designers to make a clear connection between the data analysis and thedesign (Roudsari 2013). In that sense Rhino3D offers the interface over whichany kind of modeling solutions can be examined, while Grasshopper offers thefreedom to create a set of capabilities that go beyond the existing optionsoffered by standard energy modeling applications. For instance, some of theexisting gaps in energy simulationtools that can be solved through theimplementation of Grasshopper are the following (Gamas 2014):- The dislocation between daylight and thermalstudies- The inability to iterate through severaldesign options (>15) in short periods of time- The capacity to automate the process ofgenerating shading systems based on user-definedconstraints of peakdays and hoursWesimulated the fenestrations of a specific building to develop optimalperformance algorithms.

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The study was conducted by simulating a floor withvarying façade properties: window to wall ratio, window height, number ofwindows and sill height. The most efficient control principles were chosenbased on the energy performance and daylight quality.