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

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.
JOURNAL OF BUILDING PERFORMANCE SIMULATION
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: http://hdl.handle.net/11311/1132430
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