Environmental Performance Intent & Simulation Data Visualization (Case Study)

By | June 2, 2014

One of the primary drivers behind the development of Mr.Comfy has always been the idea that by making the invisible visible in a spatial format, design performance thinking and its underlying mental models can be enriched to gain greater intuition (or “tacit” knowledge) – which then aids the construction of explicit knowledge, as e.g. argued by Friedman (2003, see discussion in this paper).

Performance representations, especially when distributed over the incomplete scope of what individual calculation engines can resolve, never fully encapsulate that which a building is intended to do; indeed, one can argue that the same is true for a building in other domains, e.g. the way different artefacts represent it never does justice to the totality of experiences created through a structure’s existence. Add aspects of being in-process, prior to completion, and the waters are even murkier- do tools and representations used to shape something in the making influence the possibility of its creation, of the way it is being “thought”?


South Florida Wildlife Center, 2009 – 2010, 2014 (Perspective Rendering, Author)

Positions on this in design research vary, however as far as the interaction of performance simulation, its data expression and influences on geometric optimization mediated through operative design thinking is concerned, it has always been my experience (and conviction, I admit) that yes, there is no escaping the tangible impact of different modes of model manipulation on design cognition.

Coincidentally, that is one of the reasons Mr.C’s inputs are designed to specifically answer question complexes such as “What is happening in a design, when do the behaviours occur, where do they occur, and how do they compare to simultaneous states in other parts of the intended building?” To answer these enables designers to find out why patterns exist, and through contextual cognition to influence them- quoted directly from my upcoming BSO14 paper on Mr.C, which I’ll share here once it’s published in the proceedings.

Performance representations that are intimately linked to the building geometry they investigate (or make possible) are not quite new- think, for a much overused example, of Gaudí’s catenary models (http://en.wikipedia.org/wiki/File:GaudiCatenaryModel.jpg), or even graphic statics in general, which firmly occupy an almost uncanny position between drawing the intended and deriving its shape from rules – or physical laws – while you’re at it. Now do not get me wrong, I in no way intend to compare my small contribution to building analysis to Gaudí’s (and the Catalan engineers he worked with) genius innovations, but merely intend to through this point open up wider the discussion about the interface of design intent, the way it is encoded in models, including their subsequent representations, and “actual” performance as then derived from spatio-mathematical operations.

Indeed the way performance design intent is often encoded in early environmental design, which is a different beast from structural engineering due to its extreme temporal volatility, is through concept drawings that e.g. contain “magic arrows” of ventilation or shading design- such as the one below, taken from one of my own projects for a wildlife sanctuary in South Florida. Coincidentally, the common appearance of such hybrid drawings in the past TU Design/Sim classes had initially motivated to think up first Mr.C concepts (even though there’s plenty more examples of such representations in the literature, but hey, personal experiences always have a greater impact, don’t they?).

small_wccFormPerformance-2South Florida Wildlife Center “Magic” Ventilation & Shading Section (Author)

Thoughts on the representation/multi-domain performance complex first appeared in a completely forgotten paper for eCAADe 2012; in a sense, the Mr.C representations developed since then attempt to link an understanding of geometry with the explicit – that is, the calculated – knowledge of what the actual performance, mediated through geometric factors, really looks like. The resultant representations can be understood as “magic drawings on steroids”, especially if you consider that typically, in early design simulation models, boundary conditions and input data scope may be extremely limited.

Given later design and simulation stages, that limitation might obviously no longer apply, but I nonetheless suspect that the close relationship of “traditional” environmental design intent sketching and spatial sketch simulation data mapping breeds a greater cognitive proximity of early concept and validation levels, without the disconnect of switching representational domains (e.g. from drawing to chart, from space to abstract projection).


South Florida Wildlife Center Conceptual Energy, Ventilation & Daylight Data Visualization (Author)

As the reader might have picked up on, the previous sketch contained multiple performance domains (daylight/shading considerations and natural ventilation). The common occurrence of multi-domain intent sketching (not just my own, but also that of countless others) reveals the tendency of designers to mentally see buildings as totalities of form and sensual impacts, which the ambient environment co-determines. This observation is what in part and to this day drives the development of Mr.C’s features that allow for the simultaneous display of several datasets at once, including daylight, to construct a spatially displayed totality that might come close to what a designer might envision.

Through this and the facilitation of asking spatio-temporal questions through the tool, I hope I’ve made the point that to me, a visualization is not an illustration, but is “the use of interactive visual representations of abstract data to amplify cognition” (Ware, 2004 p. xvii) – and from understanding one state a designer becomes able to envision others. In one of the next posts, I’ll try to write something about how this type of “cognitive” approach might differ from “genetic” or “algorithmic” optimization – but that is for another time.

Ware, C. Information Visualization, Perception for
Design. San Francisco: Morgan Kaufmann,