Recently, I’ve been digging around in Constructal Theory, mainly because it provides a “predictive” framework for natural growth unifying – in a sense – physics and life. As it is now, constructal law theoretically accounts for all design phenomena in nature. It states that “For a finite-size system to persist in time (to live), it must evolve in such a way that it provides easier access to the imposed currents that flow through it.”.
I’ve got a huge interest in anthropic growth – namely growth enabled by humanity – and the rules behind its development. More importantly, we can now see a new materialist framework being contoured by philosophers like Manuel DeLanda together with Adrian Bejan and Constructal Theory, framework which encompasses both natural and anthropic growth and development under the same paradigm. The world is no longer split into Nature and Culture – Modernity’s dichotomy is being erased by rules and principles which guide both, making the distinction irrelevant.
Anyway, the main reason for this post is not to blabber about modernity and its dissolution (maybe that will come in the future in the writings section), but to share with you a processing implementation of RRT (Rapidly-exploring Random Trees) which is repurposed to grossly simulate growth through the perspective of Constructal Theory.
If you have a given surface (or volume) consisting of a population and the main goal of that population is to reach, or flow towards a specific point in the given surface, the most efficient structure, imposing the least flow resistance, is a dendritic one as shown in the video below.
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In the case of a city (or the built environment), the main difference between it and a tree is that it does not have a single attractor, and flow is not linear in one direction. A city is composed of many attractors of different forces, each generating a different type of traffic flow which can be described as having different velocities, therefore requiring different flow resistances. For example, the average traveling speed in a medieval city was low, therefore the highly tortuous street network. As travel velocity increased, so the need for less flow resistance, which ultimately resulted in the Manhattan Grid. In this way you can differentiate between layers and layers of infrastructure. The video below shows how the networks of five different attractors merge with each other. Flow resistance is slowly decreased as the simulation progresses.
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Finally, here’s a zipped processing sketch which contains a basic implementation of Constructal Theory generating infrastructure (based on this sketch). Of course, this can be enhanced further by limiting growth to certain angle variations and a host of other tricks which would more accurately simulate infrastructural growth. Each infrastructure type has different needs and limitations which can be transformed into parameters for this sketch.
You can play with an online version here!
Enjoy, and share alike. I am sharing out of goodwill, please do the same and do not abuse. Everything here is released under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Licence if not specified otherwise.