Building performance simulation can support parametric explorations of design option spaces. Resources available for modelling and computing often require the reduction of the descriptive information of a design solution at the level of design-space model rather than at that of building model. To obtain that reduction, two design-space models of different scope (for example, one full-scope and simplified, the other partial-scope and detailed) can be combined to make their responses usable as surrogates of those produced by a full-scope detailed inquiry. But aligning two building models of different scope can be difficult and sometimes impossible. This article presents a design-space ‘grafting’ technique supported by metamodelling that makes possible to hybridize two non-aligned design-space models so as to obtain a diffusely calibrated conjoint response. The strategy can be integrated into metamodelling and decomposition-based optimization to decrease the information costs entailed by parametric explorations.

Grafting of design-space models onto models of different scope or resolution

Gian Luca Brunetti
2020-01-01

Abstract

Building performance simulation can support parametric explorations of design option spaces. Resources available for modelling and computing often require the reduction of the descriptive information of a design solution at the level of design-space model rather than at that of building model. To obtain that reduction, two design-space models of different scope (for example, one full-scope and simplified, the other partial-scope and detailed) can be combined to make their responses usable as surrogates of those produced by a full-scope detailed inquiry. But aligning two building models of different scope can be difficult and sometimes impossible. This article presents a design-space ‘grafting’ technique supported by metamodelling that makes possible to hybridize two non-aligned design-space models so as to obtain a diffusely calibrated conjoint response. The strategy can be integrated into metamodelling and decomposition-based optimization to decrease the information costs entailed by parametric explorations.
2020
subspace grafting
k-nears neighbour
metamodelling
optimization
model reduction
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1132430
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