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

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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]
Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 119997155
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Characterization of an airflow network model by sensitivity analysis: parameter screening, fixing, prioritizing and mapping.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Building+Performance+Simulation%22">Journal of Building Performance Simulation</searchLink>. Jan2017, Vol. 10 Issue 1, p17-36. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Air+flow%22">Air flow</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+software%22">Simulation software</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink>
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  Label: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=119997155
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        Value: 10.1080/19401493.2015.1110621
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      – Code: eng
        Text: English
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        PageCount: 20
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    Subjects:
      – SubjectFull: Air flow
        Type: general
      – SubjectFull: Big data
        Type: general
      – SubjectFull: Simulation software
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Sensitivity analysis
        Type: general
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      – TitleFull: Characterization of an airflow network model by sensitivity analysis: parameter screening, fixing, prioritizing and mapping.
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              Text: Jan2017
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