Building Simulation Cairo 2013 ‐ Towards Sustainable & Green Life, Cairo, June 23rd ‐ 24th Topic name: Indoor Environmental Quality

New Parametric workflow based on validated day-lighting simulation Building Simulation Cairo 2013

A. Wagdy Mohamed Ibrahim

Politecnico Di Milano, Milan, Italy

* Corresponding author. Tel: +39 3932694435, Mob: +2 01001036090, E-mail: ayman.wagdy@mail.polimi.it

Abstract: Daylight can reduce the need of artificial lighting only if the architecture design allows it to happen with right quantity, but the problem is that architects use the simulation software at the end of design process. In order to get the real benefits of simulation process, the simulation results should be used as the main driven tool for the design process. This process could not be done just using one type of software, it is a complex workflow which starts from creating a 3D dynamic model that can be modified without regenerating the 3D model each time, followed by connecting it with validated daylight simulation tool to ensure the correct analysis results. These results are evaluated to give us a numerical and visual feedback after being processed by optimization algorithm which automatically adjusts different variables in order to get better simulation results which eventually will prove that we are going towards more optimized solution. The author proposes a new tool which will help the architects to find the best day-lighting solution for any kind of buildings. Because this tool finds the optimal dimensions for each opening in automatic way to ensure the daylight quality inside the space, as well as it becomes much more efficient than if they would try other different random solutions manually. This paper shows a new parametric workflow which runs in automatic mode without the need to export or import the 3D modeling information between each type of software to have totally automatic optimization process.

Keywords: Parametric, Optimization, Daylight, Simulation, Grasshopper, Radiance, Evaluation.

1. Introduction

The proposed approach is developed by using the daylight simulation as the main driven tool for the design development, and it will show the benefits of using the parametric tools in the architecture design process in order to achieve a specific daylight quality, then it explains the logic behind combining a validated analysis tool together with a parametric modeling tool in order to develop a close loop process between design and simulation which optimizes the architecture design based on the daylight requirements.

1.1. Daylighting

Successful daylighting arrangement would enhance the architecture quality, keeps the occupants healthy and reduce the energy consumption of the artificial lighting [1]. Recently, daylighting simulation tools have been developed to help designers evaluating the daylight performance of their proposals on the early design phase, because at this

phase, designers could take major design decisions related to daylight quality and make the required adjustments to receive better feedback of their design proposals, however these tools couldn’t achieve yet a total integration with design platforms [2].

1.2. Parametric environment

Parametric tools are relatively new to architecture design process because it is based on the ideas of exploring design variations, by using this method, the computer will generate many designs variation’s between the predefined ranges which satisfy particular conditions. These rules and constrains usually involving numerical data and mathematical operations in order to control the properties of a generative model which could be manually or automatically based on optimization algorithms to accomplish a precise performance objective. The main benefit of using this type of software is the high ability of making modification on any parameters such as

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geometry shape and size without the need for recreate the entire model each modification, many architects and designers prefer to explorer complex ideas at the early stage of design solution with relative easy way of modeling it.

1.3. Required Software’s

To follow this workflow, some software needs to be installed: Rhino, Grasshopper, and Geco plugins. Grasshopper is a free downloadable plugin for rhino users. Geco is an add-on for Grasshopper by [uto] and can be downloaded from the food4rhino website for free. You will also need a copy of Autodesk Ecotect Analysis. A student version of Ecotect can be downloaded from Autodesk website. Finally, you will need Radiance Desktop software which is also available for download.

1.3.1. Grasshopper

Grasshopper which was developed by David Rutten in 2007 is a parametric modeling plug-in for Rhino. It is based on explicit history concept that recorded the modeling process, allowing users to manipulate graphic node to generate parametric models. Grasshopper lets users manipulate graphic nodes, and allows users to script in VB.net, C#. This scripting capability is used by advanced users to develop new applications for non-programming users to manipulate new functions.

1.3.2. Today digital tools

Before we dive into how the new workflow might enhance the daylight quality inside the space. It’s important to understand what today digital tools offer is?

That way we can clearly identify any advantages and benefits in contrast. As shown in Fig. 1 we cannot use just one type of software in order to explorer and evaluate the best design solution which meets a certain environmental performance in fully automatic optimization process.

Fig. 1 Shows the advantages and disadvantages of each type of software such as Grasshopper, Ecotect, Radiance and Galapagos

The author propose a new parametric workflow which combines the advantages of each type of software and makes one platform that controls the other types of software without the need to import or export the 3D modeling information between the different platforms.

2. Methodology of ParametricWorkflow

This workflow describes the linking of a three dimensional parametric modeler (Grasshopper) plug-in for Rhinoceros with advanced daylight simulations using Radiance and Daysim. This complex algorithm controls all design variables through an optimization process. Here is a simplified diagram of the main four parts of the workflow shown in Fig. 2 which starts with using parametric modeling tool, and setting the numerical and conditional relations between design variables ,then connecting this algorithm with validated daylight simulation tool and ultimately controlling the hall system by multi objective optimization process.

Fig. 2 Shows the workflow sequence

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This new effective workflow is done inside Grasshopper as shown in Fig. 3, it allows the user to work simultaneously with one platform which has the ability to export the 3D modeling information, material properties, and analysis grids into Radiance/Daysim format and calculates a series of daylight illumination analyses. After that the simulation results are automatically loaded back into the Grasshopper with the numeric values of each the analysis points as well as RGB color mappings. In that case the analysis results are evaluated within fitness functions, giving feedback which is processed in a loop action inside the evolutionary solver. This optimizes the algorithm parameters through time to find the best configuration that allows better daylight illumination. In fact Grasshopper has outstanding capabilities for controlling different design parameters such as geometry dimensions, glazing size, material properties, date and time, which can be changed incrementally and the simulation results can be generated in a real-time building performance simulation. The design workflow has been specifically developed with the architectural design process in mind, not only aiming to provide designers with immediate, high quality feedback all the way from schematic design to design development, but also to help them optimize the design process in right direction.

2.1. Parametric Controls

The Parametric Controls contain all Boolean functions for the data flow and control the connection between different types of software. In addition to many numeric sliders which control each parameter for room generation such as width, depth, height, glazing size, shades and reflectors dimensions, as well as the rotations angles shown in Fig. 4.

Fig. 4 Shows the parametric controls

Fig. 3 Shows all different areas of the workflow in one Grasshopper definition

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This workflow is not limited to the linear progression of a dataflow, sometimes it needs critical thinking. Based on the aimed result, some parameters should be limited to certain values, others need to branch out to different conditions and so on. As in model generation, the decision should also be completed through the progression of data. Because the data is not limited to numbers. There are other data types which are useful for different purposes in programming and algorithms. For instance, a conditional function is used for making a Boolean operation to orient the dataflow in a different direction, i.e. changing between horizontal or vertical grid analyses grids.

2.1.1. Parametric modeling

The parametric room model is constructed in very specific ways. Since each geometry model is attached to specific data structures that contains 3D modeling information, they must be constructed in specific ways which can be encapsulated as procedures. These procedures ensure correct material assignments that correspond to different element types.

Fig. 5 Shows the nonlinear modeling algorithm.

The algorithm shown in Fig. 5 is complex but organized, a point is moved in the X direction to make a line (Room Width) then this line is moved in the Z direction, finally a loft operation is made between the two lines to generate a surface. This was the basic logic for creating every surface in the room. This is very useful because it is possible to get a smart room that is controlled by little number of metric sliders. At the end, the surfaces are connected to a special mesh component which controls the

automatic exportation process to the analysis software. The importance of this step is to control and organize 3D modeling information to be exported automatically and correctly to Ecotect

2.1.2. Simulated test model

The test model is a 3D model parametric room with variable numbers of reflectors. The room area can vary form (3m x 3m) to (6m x 12m) and the room height can vary form (3m to 6m). The four facades are oriented towards the four cardinal directions, The model is made of 3 solid walls with only one façade facing south that has a transparent part, and two opaque plans representing the floor and ceiling shown in Fig. 6.

Fig. 6 Shows the tested model configuration.

2.2. Weather data, sun position, date and time

Fig. 7 Shows the environmental controls.

This part of the definition shown in Fig. 7 is responsible for project orientation, geographical location, and weather data file, as well as date and time. The computer simulations run in a clear sky condition on September 21 at 9 a.m. and 3 p.m. This is corresponding to the LEED 8.1 requirement for daylighting. As mentioned on the

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official USGBC website, an architectural space should achieve an illumination level of at least 108 lux and a maximum of 5283 lux, which equals a 10 to 500 foot-candle. In order to get one LEED credit, 75% of the room area should be within the illumination levels, or a minimum of 90% or more to get two LEED credits. A special component is used for displaying the sun ray reflections, which helps provide a better understanding of the reflectors behavior.

2.3. Materials assignment

In this area of the definition, the full geometry is exported in a real-time process to Ecotect, shown in Fig. 8. Every material was set corresponding to each element type, the logic is very intelligent; because it can handle any geometrical change of face counts without any problem assigning the correct material to each building element.

Fig. 8 Shows part of G.H. definition which controls materials properties of the parametric geometries.

The solution for this problem was a critical part of developing the definition, because the script must be very flexible in order to accept the frequent changes made by the evolutionary solver. Ecotect materials are used in this tested model, but the definition also supports any new custom material

which should be imported first into Ecotect materials library.

2.4. Analysis grid

This part of the definition defines the analysis grids based on the width, depth, and height of the room. Some special scripts are used with VB.NET to create a special Boolean function which controls the type of analysis grid. The logic is important because it makes it possible to toggle between the two types of grids in real-time without setting the properties of each analysis grid each time shown in Fig. 9.

Fig. 9 Shows part of G.H definition which controls the analysis grid.

These operations take the form of Boolean logic, which will apply or not depending on the local conditions found. The operations can also take the form of a rule when they are the result of IF-THEN statements. If a particular condition is found and a rule is triggered, the result is a Boolean of the form True/False that will either activate or deactivate a particular analysis grid.

2.5. Radiance analysis

Radiance is a validated lighting analysis tool which can be used for LEED 8.1 Daylight credits. This part of the definition is responsible for the different settings for Radiance Desktop software by using a combination of VB.NET and EcoLua components inside grasshopper, It provides a direct connection from Ecotect to Radiance.

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After the analysis is done, the result appears in an Ecotect analysis grid and is imported back to Grasshopper automatically.

2.6. Analysis feedback

This part of the definition as shown in Fig. 10 collects the results depending on which analysis grid was used. The logic separates each analysis data and organizes it in the required structure for the evaluation process.

Fig. 10 Show part of G.H. definition which organize the daylight analysis data.

2.7. Analysis Evaluation

This part of the definition is dedicated to evaluating the daylight analysis for the current state of design. The evaluation process counts the number of analysis points which meet the target daylight, and divides this by the total number of analysis points to get a percentage shown in Fig. 11

Fig. 11 Show part of G.H. definition which evaluating the daylight quality.

This is very useful for decision making because it indicates the effectiveness of the current shading system and reflectors, and indicates whether they are effective in keeping the illumination levels within the required range.

2.8. Fitness Function and Loop process

This part of the definition is dedicated to taking the evaluation results and optimizing

them over time in a loop process. The function controls the room’s parameter as well as the shades and reflectors. Galapagos (the evolutionary solver) will keep changing all values of each design parameters to achieve the best combination between all variables. The approach developed by decomposing the overall required performance and architecture quality requirements into individual mathematical equations which control different design parameters ,in this way the logic that was set for the fitness function depends on more than one criteria to represent the different weight of each design objectives shown in Fig. 12. Designers must think very carefully when they are setting these functions, because this is the area that allows them to control the direction of the final results.

Fig. 12 Shows the optimization Mathematical equation inside G.H. definition.

The main criteria is to find the best values in all parameters for achieving the highest percentage of analysis points that fall within the required daylight illumination. All other criteria mainly take into consideration architectural quality, some functions were set to force Grasshopper - Evolutionary Solver - to make the room as big as possible, and to make the shades and the reflectors as small as possible, while maintaining the daylight quality. The optimization process starts by activating the evolutionary solver as show in Fig. 13

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2.9. Results summary

This area of the definition represent the most important results such as room dimension ratios, the amount of illumination, solid to void ratios, precise angels and sizes of the reflectors and other information that is useful for designers as shown in Fig. 14.

Fig. 14 Shows the numerical results

Fig. 13 Shows the process of optimizing the room design based on the daylight simulations

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Another condition is added to the fitness function that forces Galapagos to reduce the range between the maximum and minimum illumination values. This makes the program try to configure the distribution of the daylight more evenly inside the space, helping to reduce glare. Controlling results to meet your needs is a form of art.

3. Results

The final result of the simulation shown in Fig. 15 gives the required information about designing a room which allows good daylight within LEED standard. One of the main objectives was to find the maximum allowable room depth while maintains the daylight illuminations values within the useful daylight luminance [5]. The simulation succeed to validate 100% of the total points on the analysis gird even until 8.6 meter room depth within a daylight illumination between 221 and 922 Lux. This final solution shows the ability of lighting the deep rooms with the daylight. It also gives the needed information about the possible number of reflectors as we as the correct rotation angle of each one to ensure

the daylight quality in reality as it shown in the simulation results.

4. Discussion

Without any doubt, this workflow has the ability to control all design parameters for each type of geometer at any level of detail, and it transfers the 3D modeling information data correctly to the analysis software without any problem with mesh typology or materials conflicts. Furthermore the workflow revels the real benefits of using daylight analysis in architecture design. It uses the analysis data and evaluates it, then it changes the design parameter to achieve better analysis results in the next evaluation. This loop process of enhancing the design runs automatically until it reaches the best solution. Finally this technique becomes an effective way to computationally drive the modeling process based on daylight illumination quality.

The main results of this workflow can be discussed in four parts:

- Developing of a new parametric design

Fig. 15 shows the most optimized design solution based on daylighting simulation results.

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methodology based on daylight analysis.

- A parametric form finding system based on daylight analysis, which acts as a decision-making algorithm. This parametric algorithm carries instructions in a systematic order where all geometrical components that represent a design are parameterized.

- This Workflow can work as a design tool to modify one or more elements in any current design to achieve better daylight conditions inside the space.

- Also, the workflow has the ability to create daylighting systems which can respond to sun position and controls the daylighting levels during the day.

5. Conclusion

As a conclusion, the new parametric workflow is offering an extraordinary ways for design exploration and evaluations in totally interactive process. Performance-based design that integrates daylight simulations in the design process has many advantages over traditional design methods, because it allows a many design variations to be evaluated against different daylight solutions.

Ultimately, this workflow could be upgraded In the future to involve different environmental factor such as (solar radiation, wind and ventilation, energy demand, thermal losses and more), by involving it with other validated simulation tools. Of course some factors will conflict with each other, but I think that the new tool will be able to weight the final solution towards the main design objective, while taking into account the overall performance of the building.

References

[1] A Review of the Empirical Literature on the Relationships between Indoor

Environment and Stress in Health Care and Office Settings: Problems and Prospects of Sharing Evidence, Journal of Environment and Behavior, 40, 2008, pp. 151 – 190.

[2] A. Galasiu, C. Reinhart, Current daylighting design practice: a survey, Building Research and Information, 36, 2008, pp. 159-174.

[3] J. Lee, M. Andersen, Y. Sheng, B. Cutler, Goal-based daylighting design using an interactive simulation method, Proceedings of 11th IBPSA Conference, 2009, pp. 936-943.

[4] J. Harding, S. Joyce, P. Shepherd, C.Williams, Thinking Topologically at Early Stage Parametric, Advances in Architectural Geometry, 1, 2012, pp. 67-76.

[5] N. Azza, M. John, Useful daylight illuminances: A replacement for daylight factors, Journal of Energy and building, 38, 2006, pp. 905-913.

[6] D. Rutten, Evolutionary Principles applied to Problem solving http://www.grasshopper3d.com/profiles/blogs/evolutionary-principles, 2010, accessed 5th April 2013.

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