This workshop provides you with the basic concepts and terminology you use in Workshop 1 Parametric Modelling. In the first section, you learn about some difference between using parametric modelling, simulations, and optimisation to support design decisions and to generate data to support research investigations. In the second section, you learn about selecting parameters, variables and constraints for form generation for research. Before you proceed with the following section, make sure you refresh the basics with this overview:

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Data Generation workflow link copied

Research methods can be applied to address conventional design problems, which we know as ‘research by design’. Probably all good design solutions are in some way informed by some kind of research. An example is using simulation and optimization methods to make informed design decisions. Simulation and optimization methods can not only be used during the design process but also for research and experiments. The data gathered from simulations and optimizations can be stored in a dataset and used for research purposes and conducting data analysis. Research methods can also be applied to better understand the built environment, outside the design process only. In either case, using parametric modelling, simulations, and optimisation to generate data to support research investigations implies differences compered to using them to directly support immediate design decisions.  

When we use parametric modelling, simulations, and optimisation in design, optimisation techniques are commonly used to search a design space for solutions that meet specified performance goals (Monks et al., 2000). While finding an optimal or, in many cases, a sub-optimal solution to a design problem is valuable, optimisation can also be highly beneficial for exploring design options and gaining insights into the nature of the design problem. The way we set the parametric modelling, simulations, and optimisation often focus on the specificity of the given design brief. When we use parametric modelling, simulations, and optimisation in research, we often need to be broader in the scope of the models – so to be able to extract generalisable information. In other words, we want to obtain information that is valid also beyond the specificities of one design case only. The main difference between using computational methods, such as optimization and simulation, to inform design decision and per research purposes is the aim of the process. In both cases you need a parametric model, define variables and objectives, and process results to draw conclusions. 

Form generation for design

A design objective mainly comes from the design brief, or designer’s intent, or the demand of the client or market. The boundary conditions of a design project are often specific. They result in design parameters that often are valid only for that unique case. Sometimes they may even derive from the non-negotiable constraints of the design project, such as a given budget and the site conditions. Setting design variables in such cases may focus on the design aspects that can be changed and should be investigated, such as the dimensions, materials and layout for example. The constructed parametric model, or form generation, for a design problem is specific to the design brief and the design requirements and site boundaries. If a project site is a very long and narrow piece of land, it may not necessarily be meaningful to investigate design options that require a very wide site. (Vice versa, to generalise knowledge extracted in research investigations, it may be relevant to include broader varieties of options, representative of more general cases).   

Form generation for research purposes

A research objective mainly comes from the knowledge gap which guides the research question. The knowledge gap is determined after gathering existing knowledge related to the research direction. The boundary conditions of a research are the research parameters, and mainly derive from the research scope, scale and context. The research variables are the aspects that are tested for the research, such as for example different material types, or design strategies. The form generation for research is more general so it can be applied to many different design cases.   

Simulation and optimization

The parametric model can be linked to simulation (or other assessment methods) and optimization, which provide results that can support a design decision or a research investigation. When focusing on design decisions for a specific design, the simulations and optimisation objectives can focus on the specificities of the design case. When conducting more general research investigations, the simulations and optimisation objectives may be formulated to include a broader variety of different aspects. Also, in a design process, we are often interested in design solutions that perform rather well – such as optimal or good sub-optimal. Whereas for research purposes, the non-optimal results are also important during the data analysis, as we can learn a lot from analysing very poorly performing options.  

Workflow generating dataset

In this workshop, you will learn the key steps for generating a dataset using simulation and optimization, particularly for research purposes. Figure 1 shows the overall workflow consisting of four steps, with optimization being an optional step. 

The first step is form generation, which is based on the research question and research boundaries. In the second step, simulations or calculations methods are used to the objective fitness of the generated forms. As an optional third step, an optimization model can be used. 

With these first three steps, a dataset will be generated, which then can be used for various ways of data analysis. In this workshop line, we will demonstrate two methods to analyse optimization and simulation results to draw conclusions from the generated data. Additionally, we will be using visualization methods to extract useful information for the data analysis and communicate results. 

Debate A: What to research with simulation and/or optimization methods

If you are following this tutorial because you are attending the course BK3WV4, after you have learned the theory and practiced the exercises, you will attend a workshop in class at TU Delft. The workshop lasts 3 hours. During the workshop, you will work with the tutors. Two hours are contact hours with your tutor and one hour is for activities without tutors. During the first one hour, you will discuss the tutorial with the tutor and your classmates. During the second hour, you will write a first draft of your workshop deliverable. During the third and fourth hour, you will discuss how the content of the workshop relates to your research question. 

• How will the workflow look for your research direction, are you including optimization or not? 
• Which data is needed to test the objective/performance indicator of the research? In other words, what type of simulation are you including?  
  • (what is the overall topic of the research?) Health, noise, materials, structure, etc.  

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Selecting parameters, variables and constraints link copied

After the research questions is formulated and the objective and boundaries of the research are defined, the parameters and variables for the form generation of the parametric model need to be determined. This section gives the basic terminology of parameters, variables and constraints in terms of design and research projects. Additionally, an exercise shows how to determine and use parameters, variables and constraints for the form generation of a research project.

Fixed Parameters

According to the Oxford Dictionary, a parameter is “A numerical or other measurable factor forming one of a set that defines a system or sets the conditions of its operation”; mathematically, a parameter is “A quantity whose value is selected for the particular circumstances and in relation to which other variable quantities may be expressed”. In terms of a design project, fixed parameters may be for example some non-negotiable factors that a design must adhere to (or that are decided fixed in exploring a certain design strategy). Fixed parameters can include side-specific conditions, zoning guidelines, budget limits, and client requirements. In terms of a research project, the fixed parameters may refer to the boundaries of the research within which the design must operate. The boundaries mainly derive from the scope, scale and context of the research. If your research investigates only single-story buildings, the number of floors is a fixed parameter.  

Variables

The variables are adjustable values. A variable is also known as a design variable as it enables the parametric model to produce different design forms. The variables typically are restricted with certain constraints. In terms of design, the variables are adjustable design elements which allows to explore various design options or optimize building performance. In terms of research, the variables are mainly the aspects the research investigates. If your research investigates the effect of the number of floors on the energy performance of a building, the number of floors will be a variable.  

Constraints

Constraints control the range of each variable, and define the boundaries of a project. The constraints are the minimum and maximum values of a variable, it is the set of values that a variable can have. Constrains can also refer to conditions non directly expressed by variables, but that would make unfeasible a certain design configuration. For example, a footprint of a building can have variable width, length, and position but all sides must be contained within a given plot. Not exceeding the plot is a constraint and infringing the constrain makes the option unfeasible. In terms of design, the constrains mainly stem from site conditions and guidelines. In terms of research, the constrains determine the boundary condition of the research and are highly influenced by the research objective.   

Exercise: Selecting parameters, variables, and constraints

To get a better understanding on how to set parameters, variables and constraints for a parametric model with research purposes, you should follow this exercise. In the exercise you explain with a research example how to set the parameters, variables and constraints.  

Debate B: Selecting parameters, variables, constraints

If you are following this tutorial because you are attending the course BK3WV4, after you have learned the theory and practiced the exercises, you will attend a workshop in class at TU Delft. The workshop lasts 3 hours. During the workshop, you will work with the tutors. Two hours are contact hours with your tutor and one hour is for activities without tutors. During the first one hour, you will discuss the tutorial with the tutor and your classmates. During the second hour, you will write a first draft of your workshop deliverable. During the third and fourth hour, you will discuss how the content of the workshop relates to your research question. 

1. What type of parametric model (in terms of form generation) are you creating to generate the data? 
2. Of the parametric model, which aspects are the parameters based on the boundaries of your research?  
3. Of the parametric model, which aspects are the variables based on the aims of your research?  
4. What are the constraints of your variables based on the boundaries of your research?