Design Space

DESIGN SPACE Navigating the ocean of possibilities

COMPUTATIONAL DESIGN

 Experience it first : http://digitaldesignacademy.com/processing/applet_design_space/index.html

COMPUTATIONAL DESIGN

Design is about the exploration of possibilities. But these possibilities run into trillions. Designers do not see it that way, because they make decisions incrementally, one at a time. A good designer, like a good chess player, can see a couple of possible steps ahead. Kasprov (Chess Grandmaster) could see about six steps ahead while Deep Blue (the computer that beat him) could see about eight. Humans and computers can explore billions of possibilities. Design is certainly about the exploration of possibility.

Computational approaches to design, in a way, make us come to terms with this in computational terms. For this, we need to develop the ability to look at trillions of designs at a time. We do this through design space.

Design space is a mathematical space that is beyond our geometric imagination. It allows us to imagine designs to be a point in n‐dimensional space where n represents n‐number of parameters.

This is the only way in which we can explore unlimited possibilities. We need to get used to this.

It is important to remember that when we make this switch to looking at designs as computational concept, not only do we lose much of its complexity, but we also limit the discussion to a particular representation. For now, let this representation be a parametric CAD model (A CAD model, whose geometries we can tweak by altering parameters).

Let us explore how this may be done. If we have a CAD model with three driving parameters, we can see how it varies for different x, y & z values, as illustrated here by an exhibition on “Solution Space”

by Magda Jurkowska and Michal Piasecki .

You can see that these design are driven by x,y & z parameters . They are displayed according to their x,y,z parameters in 3D space.

In this case we can visualize the design space because it is limited to three dimensions. Once it exceeds it, we cannot imagine it.

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Parametric space Let us start with a simple example. Let's take the design of cereal box. We can define its geometry with three designer parameters: x, y and z. In most designs, there are many more parameters.

Now let us look at this design in parametric design space where the design of this box can be represented as a point in parametric space: the x axis represents the width, y axis represents the breadth and the z axis represents the height of the box.

The important concept here is, the design can be represented by a single point in the design space.

By varying the design parameters (in this case w, h and b) we can create many different box designs. Hence the entire range of design possibilities can be represented as cloud of points in design space. Since only three parameters are used here, we can imagine this cloud of points, but in most designs we cannot. We need to carry this concept into n‐dimensional space.

You can see that the geometry of the box varies as we change the breadth width and the height.

You can also select the range of designs that you like to explore.

We call such regions as the parametric design space.

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z PARAMETRIC DESIGN SPACE

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We may then evaluate various aspects of this box design within this imaginary space using the attributes that are of interest to us. We call this performance space.

Performance space Let us assume that all the important desirable aspects of the design can be measured. In language of computational design, we refer to it as performance parameters. In the case of the box, the volume of the box, the surface area of the box that is displayed on the shelf (for marketing) and the tipping angle (angle after which the box will fall) of the box are some of the aspects that could be considered as its performance parameters. If we choose these three aspects, we would be able to map them in 3D performance space with each axis corresponding to one particular performative aspect. In this example, the relationship between the parametric space and the performance space is relatively straightforward.

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You can see the boxes now placed in locations that correspond to the chosen performance parameters.

By adjusting the limits of performance ranges, you can help narrow down the selection to a set of viable designs.

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Mapping A good part of computational design is the activity of mapping between design and performance spaces.

A particular point in parametric design space will relate to a particular point in performance space.

Thanks to the concept of “space”, the designs now are represented as “points” in “space”.

Each point in the Design Parameter space refers to an instance of a design – which has a corresponding location in performance space. In other words, each design has a particular performance. All this is, of course, possible only when the performance is measurable. Now, while every point in the design space corresponds to a point in the design space, the reverse is not always true. That is, there could be two dissimilar designs with the same performance.

Design as a solvable problem Once we simplify design by representing it in parametric and performance spaces, any design problem becomes a ‘solvable problem’. All we need to do is to identify the solution within the performance space and identify the corresponding design parameters in the parametric design space. Most routine engineering work involves solving these types of problems. .

I hope you are beginning to see that design is turned into a “problem” – in order for it to be solved using engineering methods. Lets us believe it to be a valid approach for now. In this approach of design as a ‘problem solving’ exercise, all we need to do is to understand the relationship between parametric design space and performance space as they are directly related. The assumptions here is that the performances are computable – which is largely true in most routine engineering problems.

Simulation now even makes more complex engineering designs computable.

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The key difference between routine engineering design problems and creative design problems is that in routine engineering design problems, the design representation and the mapping between the design parameters and performance parameters are known. In creative design problems, this mapping is non‐computable. Apart from that the design representation is also not available. To make matters worse, it is also not known which performance parameters are more important. To cap it all, many of the performance parameters, e.g how the buildings look, are non computable.

Computational approaches to creative design problems therefore are concerned with structuring the design representation and navigating vast design spaces, whereas computational approaches to routine engineering problems are concerned mainly with navigating much smaller design spaces in search of “optimal solutions” (with the underlying assumption that they exist).

Many aspects of creative design activities remain computable. Eg. The cost and thermal performance of buildings can be calculated. Hence, even in creative design, the concept of performance spaces can be gainfully employed to manage design and performance parameters in very useful ways.

Specifications and performance For the sake of clarity, we will explain next how we can interpret conventions of design practice in the context of design spaces. In conventional design practice, we tend to differentiate the desired expectation of the design as specifications and performance requirements. Specifications are often a set at the beginning of the design process: It is a way of setting the limits of acceptable performance.

So this is more about telling the designer what is acceptable and not acceptable. Performance, on the other hand, is seen with an eye towards the merits of the design. Though it is possible to set limits on performance, it is usually used to measure the goodness of the design often at the end of the design process.

In computational design, we work with the concept of limits. We can set the limits of exploration either in the parametric design space or in the performance space. For example, we could say that the cereal box should not exceed 30 centimetres. This would be typically set as a specification set in the parametric space. But suppose we say that the cereal box should not exceed 30 cm in height for the design to be acceptable, it could then be interpreted as a performance criterion limiting the exploration through the performance space. If we take this approach, we have the choice of trashing all the designs that are higher than 30 cm after expending computational resources in generating them. Hence it is better for us to state it as a specification as it is directly related to a design parameter and therefore it can be used to set the limits of that parameter.

The whole purpose of setting up specifications and performance criteria is to indicate to the designer the acceptability of the design. What is often very difficult to do in real life design is to provide the relative importance of the various performance criteria. This is where it gets complicated, as design is a compromise between various criteria. For now let us believe that it is not so.

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Summary The concept of design space allows us to imagine a large number of designs as points in design and performance spaces. Design parameters are used to locate the design in design space and performance parameters define its location in performance space. The assumption here is that all parameters are measurable and that performances measures can be derived out of design parameters through a suitable dosing representation (very often, a parametric CAD file). Design in this frame work is about narrowing the design and performance spaces to a point where satisfactory design solutions are found.

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