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Multiscale design and analysis models promise a robust, multimethod, multidisciplinary approach, but at present have limited application during the architectural design process. To explore the use of multiscale models in architecture, we develop a calibrated modeling and simulation platform for the design and analysis of a prototypical envelope made of phase change materials. The model is mechanistic in nature, incorporates material-scale and precinct scale-attributes, and supports the design of two-and three-dimensional phase change material geometries informed by heat transfer phenomena. Phase change material behavior, in solid and liquid states, dominates the visual and numerical evaluation of the multiscale model. Model calibration is demonstrated using real-time data gathered from the prototype. Model extensibility is demonstrated when it is used by designers to predict the behavior of alternate envelope options. Given the challenges of modeling phase change material behavior in this multiscale model, an additional multiple linear regression model is applied to data collected from the physical prototype in order to demonstrate an alternate method for predicting the melting and solidification of phase change materials.

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This paper presents the prototyping of new methods by which functionally graded materials can be specified and produced. The paper presents a case study exploring how machine learning can be used to train a model in order to predict fabrication files from formalised design requirements. By using knit as a model for material fabrication, the paper outlines the making of new cyclical design methods employing machine learning in which simpler prototypical materials acts as input for more complex graded materials. A case study-Ombre-showcases the implementation of this workflow and results and perspectives are discussed.