Thermal performance of windows depends on many parameters, such as dimensional characteristics and material properties of the components. The thermal transmittance U can be evaluated by a numerical method based on the CFD approach for the evaluation of the frame U-value (ISO 10077-1, ISO 10077-2) or by experimental campaigns on window prototypes, according to ISO 12657-1; in both cases significant effort and time are required. The paper aims at developing an artificial neural network (ANN) model to predict the U-value of wooden windows, with the same accuracy of the numerical calculation procedure cited above (therefore greater than the one of the simplified method), but in real time and on the basis of a limited number of parameters. In particular, after a preliminary analysis, only 10 main parameters were selected as network inputs: window typology (windows and French windows), wood kind (hardwood and softwood), frame and shutters thickness, glazing spacer, top frame junction characteristics (size and number of small and large non-ventilated air cavities), U-value of the glazing and glazing size; the U-value of the window is the ANN output. Data set for the training and test of the ANN model consist of respectively 256 and 26 wooden window samples (windows and French windows). In such hypothesis, the developed ANN model, based on a multilayer feed-forward architecture, provides in real time the evaluation of the window U-value with an error of about 1% with respect to the results provided by the CFD numerical procedure), obviously when the input parameters vary within appropriate ranges (corresponding to the variation range of the data used for the network training). The ANN model set-up, therefore, allows to easily determine with high accuracy the thermal performance of wooden windows, saving both money and time. A sensitivity analysis of the main design parameters was also carried out.

Application of artificial neural network to predict thermal transmittance of wooden windows

BURATTI, Cinzia;BARELLI, Linda;MORETTI, ELISA
2012

Abstract

Thermal performance of windows depends on many parameters, such as dimensional characteristics and material properties of the components. The thermal transmittance U can be evaluated by a numerical method based on the CFD approach for the evaluation of the frame U-value (ISO 10077-1, ISO 10077-2) or by experimental campaigns on window prototypes, according to ISO 12657-1; in both cases significant effort and time are required. The paper aims at developing an artificial neural network (ANN) model to predict the U-value of wooden windows, with the same accuracy of the numerical calculation procedure cited above (therefore greater than the one of the simplified method), but in real time and on the basis of a limited number of parameters. In particular, after a preliminary analysis, only 10 main parameters were selected as network inputs: window typology (windows and French windows), wood kind (hardwood and softwood), frame and shutters thickness, glazing spacer, top frame junction characteristics (size and number of small and large non-ventilated air cavities), U-value of the glazing and glazing size; the U-value of the window is the ANN output. Data set for the training and test of the ANN model consist of respectively 256 and 26 wooden window samples (windows and French windows). In such hypothesis, the developed ANN model, based on a multilayer feed-forward architecture, provides in real time the evaluation of the window U-value with an error of about 1% with respect to the results provided by the CFD numerical procedure), obviously when the input parameters vary within appropriate ranges (corresponding to the variation range of the data used for the network training). The ANN model set-up, therefore, allows to easily determine with high accuracy the thermal performance of wooden windows, saving both money and time. A sensitivity analysis of the main design parameters was also carried out.
2012
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/903499
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