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Characterization of an airflow network model by sensitivity analysis: parameter screening, fixing, prioritizing and mapping.

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Bibliographic Details
Title: Characterization of an airflow network model by sensitivity analysis: parameter screening, fixing, prioritizing and mapping.
Authors: Monari, F.1, Strachan, P.1
Source: Journal of Building Performance Simulation. Jan2017, Vol. 10 Issue 1, p17-36. 20p.
Subjects: Air flow, Big data, Simulation software, Monte Carlo method, Sensitivity analysis
Abstract: Due to the over-parameterized models in detailed thermal simulation programs, modellers undertaking validation or calibration studies, where the model output is compared against field measurements, face difficulties in determining those parameters which are primarily responsible for observed differences. Where sensitivity studies are undertaken, the Morris method is commonly applied to identify the most influential parameters. They are often accompanied by uncertainty analysis using Monte Carlo simulations to generate confidence bounds around the predictions. This paper sets out a more rigorous approach to sensitivity analysis (SA) based on a global SA method with three stages: factor screening, factor prioritizing and fixing, and factor mapping. The method is applied to a detailed empirical validation data set obtained within IEA ECB Annex 58, with the focus of the study on the airflow network, a simulation program sub-model which is subject to large uncertainties in its inputs. [ABSTRACT FROM PUBLISHER]
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Database: Engineering Source
Description
Abstract:Due to the over-parameterized models in detailed thermal simulation programs, modellers undertaking validation or calibration studies, where the model output is compared against field measurements, face difficulties in determining those parameters which are primarily responsible for observed differences. Where sensitivity studies are undertaken, the Morris method is commonly applied to identify the most influential parameters. They are often accompanied by uncertainty analysis using Monte Carlo simulations to generate confidence bounds around the predictions. This paper sets out a more rigorous approach to sensitivity analysis (SA) based on a global SA method with three stages: factor screening, factor prioritizing and fixing, and factor mapping. The method is applied to a detailed empirical validation data set obtained within IEA ECB Annex 58, with the focus of the study on the airflow network, a simulation program sub-model which is subject to large uncertainties in its inputs. [ABSTRACT FROM PUBLISHER]
ISSN:19401493
DOI:10.1080/19401493.2015.1110621