Coursework BENV0191:
Computational Performance-based Design of a Modular Facade
Overview
The main objective of this coursework is to give you practical experience in framing, exploring, and solving a design problem using computational design support methods involving 3D and parametric modelling along with building visual and thermal performance simulation.
Specifically, you are expected to investigate and optimise the performance of a modular facade for the South-West elevation of 22 Gordon Street building at UCL Bloomsbury Campus, and explore design trade-offs towards reaching at least two of the following performance objectives:
• Minimising heating energy demand;
• Minimising cooling energy demand;
• Minimising overheating hours;
• Maximising daylight provision;
• Maximising visual comfort.
Rhino 3D and Grasshopper will be used as a parametric 3D modelling tool. A relatively good knowledge of these software tools is required for this coursework. Ladybug and Honeybee (LB and HB) plug-ins will be used within Grasshopper for energy and daylight simulations. Collibri, Wallacei or other plugins could be used for parametric simulations and optimisation, but this is not compulsory.
The coursework is composed of five sections:
1. Defining the design problem and formulating your design intent
2. Establishing the building’s baseline performance
3. Development of a design concept
4. Parametric iterative simulation of design variants
5. Review of results and discussion
The learning outcome of the coursework is as follows:
• Understanding computational performance-based design aims and strategies;
• Understanding and communicating design synergies and trade-offs between energy efficiency, occupants’ comfort and daylighting design;
• Setting up iterative, parametric, or optimization-based building performance simulations to inform. building design decisions;
• Documentation and communication of building performance simulation and optimisation to support designing sustainable built environments.
Brief
Housing Bartlett School of Architecture, 22 Gordon Street (formerly known as Wates House) was redeveloped in 2016. The concrete frame. of the existing building was retained, and internal layouts were re-arranged. New facades were installed on all elevations. This coursework is based on 22 Gordon Street as a case study building, exploring alternative options for the Southwest façade onto Gower Street. This coursework focuses on a small part at the South-West end of the building, floors 2 to 6 only.
The South-West facade is partly shaded by the tall buildings across the road. However, the highest parts of the facade are not shaded. To enhance the design of this façade in terms of thermal and daylighting performance, a strategy for the apertures and shading elements is to be developed and tested with the aid of parametric building performance simulations. To this end, your coursework must include the following five sections.
Section 1: Defining the problem and formulating your design intent
Depending on your interests and the MSc programme you are studying, you should focus on specific performance challenges of the building and formulate and communicate your design intent to address those. Your design intent should involve at least two of the following objectives:
- Minimising heating energy demand;
- Minimising cooling energy demand;
- Minimising overheating hours;
- Maximising thermal comfort;
- Maximising daylight provision;
- Maximising visual comfort.
Section 2: Establishing the building’s baseline performance
Before starting the iterative computational design process, it is very helpful to make a baseline model and establish a baseline performance for your building. To this end, you need an initial design and specific performance indicators relevant to your design intent and performance objectives.
The initial design is only a starting point in your design process before you optimise its parameters and allows you to make a baseline model. You must use the Rhino model of 22 Gordon Street offered with this coursework to make this baseline model.
The baseline model must be documented with its key relevant characteristics including the parameters that will stay fixed in your design process (fixed design parameters) and the parameters that will be subjected to iterative changes in the subsequent parametric studies (design variables). You will learn more about these two types of parameters in the next section.
You must also simulate the thermal and/or daylight performance of your baseline model and benefit from solar radiation computation and document your baseline performance using a number of relevant performance indicators depending on your performance objectives.
Some possible performance indicators are:
- Solar irradiation [kWh/m2] received by the vertical SW facade over the summer period.
- Annual space heating demand per floor area [kWh/m2.a] for heating energy assessment
- Annual space cooling demand per floor area [kWh/m2.a] for cooling energy assessment
- Hours of Exceedance [Hours or Percentage] following CIBSE TM52 definition for overheating assessment
- Spatial Daylight Autonomy (sDA) [%] for daylight provision assessment
- Useful Daylight Autonomy Exceedance (UDI-x) [%] for visual discomfort assessment
- Spatial Glare Autonomy (sGA) [%] for visual discomfort assessment
Where relevant, use of tables and charts and other forms of visualization to present the baseline performance is highly recommended. Furthermore, it is recommended that you benchmark the baseline performance against relevant standards (such as EN 17037 Method 2 for daylight provision, or CIBSE TM52 for hours of Exceedance).
Section 3: Development of a design concept
You must develop a façade concept with the aim of fulfilling the performance objectives formulated in Section 1. The design concept could respond to the varied access to daylight between floors, and varied levels of solar radiation received across the façade. It is also important that the facade has a consistent aesthetic over the whole elevation. Similar modules could be used over the whole façade (floors 2 to 6 only), but their number and dimensions may vary between floor, to map to the shading requirements.
You are welcome to develop your own concept. Alternatively, you may want to develop a version of the concept illustrated below (Figures 1 and 2). In this concept, each module consists of a frame, within which an opaque panel (left) and a translucent panel (right) are fitted. The frame. acts as an overhang and vertical shading fins. Its depth can be adjusted. The widths of the opaque and translucent panels can also be adjusted.
Unless you are very familiar with Grasshopper, it is recommended to develop a parametric model following the concept below or even a simpler design. In any case, you will need to justify your design proposal as to how it responds your performance objectives.
Furthermore, in preparation of your design concept for its iterative evolution and simulation in the next section, it is essential that you determine the relevant fixed parameters and variables of your performance-based computational design endeavour.
Fixed design parameters: As an example, if you aim to minimize the building heating and/or cooling demand, U-Value of the external wall is a relevant parameter because it affects heat transmission through the walls and thus the heating and cooling energy demand of the building. Similarly, if you want to study daylight provision of the facade, reflectance of the inside face of the walls affects the distribution of light across your interior surfaces and is therefore a relevant parameter. But you may decide to keep these parameters fixed during your study to focus on the design of window and shades. Nonetheless, you should document these relevant fixed parameters of the design; that is, the assumed U-Value for the external walls (in case of a building energy simulation) and the assumed reflectance of the walls (in case of a daylight simulation). This makes it possible to interpret your estimated performance indictors on the basis of clearly documents assumptions.
Design variables: There are the input parameters that you want to iteratively explore to assess their impact on your performance objectives and optimize their values. Examples of design variable that you may decide chose are window-to-wall ratio, thermal and optical properties of the windows, and depth of shading surfaces.
For design variables, you must:
- Allow enough flexibility to ensure the facade layout can map to the shading and daylight requirements;
- Define the range that each variable can take, focusing on appropriately minimising the number of possible solutions. It is recommended to use range of discrete values when appropriate. For example, for a horizonal shading surface, you may consider only three possible values of 0.1m, 0.5m, 0.9m to capture an almost non-existent shade (without setting it to 0, which may cause issues for your automated optimization), a moderately deep overhang, and a deep overhang.
- Use a minimum number of geometric parameters to define the facade layout to control the number of possible solutions. You should make use of mathematical relationships to allow definition of a wide range of possible solutions with minimum number of geometric parameters.
Figure 1: Facade module
Figure 2: Example of facade modules differentiation over two floors
Section 4: Parametric iterative simulation of design variants
The façade design concept should be developed as a parametric model within Rhino and Grasshopper such that you can easily (be it in a manual or automated way) generate the design variants that you want to simulate.
For the parametric simulation of design variants, you have three options:
a) An iterative process of manual simulation of limited number of design variants by assigning the selected values for the study variables in a one-at-a-time approach (for example, simulation of the building with three overhang depth mentioned above, while all other parameters remain fixed);
b) Automated parametric simulation of all combinations of your study variables, the results of which could be communicated in one or multiple parallel coordinates plots.
c) Setting up an automated optimisation, whereby a specific algorithm selects and simulates specific combinations of your study variables with the aim of optimizing your objective function.
In either option, use Grasshopper and HB run the façade configurations with the aim of building a mapping (and potentially graphical relationship) between study variables and performance indicators. Use the relationships developed to identify design solution(s) for fulfilling your performance objectives in a balanced manner.
The focus should be on:
- Clear presentation of the method adopted to explore different solutions and clear presentation of the results;
- Ensuring that the range of solutions explored cover a good enough optimum. This should be justified.
If you purse options b and c, try to limit the number of design variables and their values as much as possible, estimate the total running time and ensure that you will have sufficient time to complete the runs and prepare results and conclusions.
You will not get additional points for using more advanced computational methods. The marking will be primarily based on the rational approach taken to optimise the design, and how this approach is justified and presented.
Refer to Appendix A for guidance on how to set the model parameters.
Section 5: Review of results and discussion
After verifying the validity of your simulation results, document them in a concise and effective manner in your report and discuss your findings. What would be a balanced optimum solution? What would be the solution you recommend? Justify your choice. Also, discuss the value and limitations of using the chosen optimisation method.
Final report
The final report must include the five sections detailed above. The process and main results of the calculations should be presented concisely and effectively. Use of tables and charts to present results is highly recommended. Other types of diagrams may be used where appropriate. The report should also have a conclusion that draws the parts together, and a take a critical look on the merits and limitations of the approach adopted. References to any relevant papers, studies or reports that have been used in the discussions must be indicated at the end of the report.
In total the main body of the report word limit is 3,000 words, and including diagrams, charts, and tables should be no more than 20 PAGES excluding appendices. The report needs to be concise and only include information that is relevant. The detail of your calculations should go in appendix. Other supporting information can also go in appendix, but they should be supplementary, and the main report should be self-contained as the appendix will not be marked.
The report must be submitted in PDF format. The document is to be uploaded to Moodle by the submission deadline specified below. The submission will be run through Turnitin to check for plagiarism. Also, please note that, regarding the use of AI tools, this coursework falls into Category 2. This means AI tools can be ONLY used in an assistive role.
Submission deadline: 9 Jan 2026, 11:00 AM UK time
Guidance on marking
As a guide to the way this coursework will be marked, note that you are likely to receive a B if you include all the sections as mentioned above, using the different tools appropriately. If you have developed and implemented a coherent and well-defined approach, along with a thoughtful understanding of the problem, the process and the results, then you are likely to get an A. If you miss out some of the above sections, if you show poor understanding of the process or its application, you will have marks deducted. Also, as you can see in the marking scheme below, a well-structured report that links the sections well, will be marked higher than a less structured one.