Uncertainty and sensitivity analysis applied to hygrothermal simulation of a brick building in a hot and humid climate

This paper presents a statistical approach for uncertainty and sensitivity analyses applied to 14 inputs whose 10 properties associated with brick material, using the four different EnergyPlus wall models. The variability of inputs has been extracted from several characterization works presented in IEA Annexes 14, 24 and 55, being coherent to the lack of knowledge in the early design stage. Besides the methodology, this paper presents the moisture effects on cooling energy demand and indoor air conditions, using a simple building geometry and the humid climate of Singapore. Results are presented in terms of uncertainty quantification, most uncertain parameters and sensitivity indices for all models, illustrating the impact of moisture and the importance of the need to well define moisture-dependent functions. The methodology is well adapted for use in complex interactive models with a low-cost simulation and can be used to reduce uncertainties in the design stage and promote reliability of retrofitting assessment.

[1]  K. Shuler,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. II Applications , 1973 .

[2]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[3]  Jan Carmeliet,et al.  A Comparison of Different Techniques to Quantify Moisture Content Profiles in Porous Building Materials , 2004 .

[4]  Ilya M. Sobol,et al.  Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .

[5]  Thomas Bednar,et al.  Increasing the indoor humidity levels in buildings with ventilation systems: Simulation aided design in case of passive houses , 2010 .

[6]  Wei Tian,et al.  A review of sensitivity analysis methods in building energy analysis , 2013 .

[7]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[8]  Carsten Rode,et al.  Influence of the Convective Surface Transfer Coefficients on the Heat, Air, and Moisture (HAM) Building Performance , 2009 .

[9]  Sándor Molnár,et al.  Modeling the hygrothermal performance of selected North American and comparable European wood-frame house walls , 2012 .

[10]  Andreas Nicolai,et al.  Stochastic study of hygrothermal performance of a wall assembly—The influence of material properties and boundary coefficients , 2011, HVAC&R Research.

[11]  Yanfeng Liu,et al.  Effect of moisture transfer on internal surface temperature , 2013 .

[12]  Paola Annoni,et al.  Sixth International Conference on Sensitivity Analysis of Model Output How to avoid a perfunctory sensitivity analysis , 2010 .

[13]  M. Lurdes Simões,et al.  Concept for development of stochastic databases for building performance simulation – A material database pilot project , 2015 .

[14]  Jan Carmeliet,et al.  Influence of uncertainty in heat-moisture transport properties on convective drying of porous materials by numerical modelling , 2013 .

[15]  F. E. Satterthwaite Random Balance Experimentation , 1959 .

[16]  Thierry Alex Mara,et al.  Extension of the RBD-FAST method to the computation of global sensitivity indices , 2009, Reliab. Eng. Syst. Saf..

[17]  Monika Woloszyn,et al.  Tools for performance simulation of heat, air and moisture conditions of whole buildings , 2008 .

[18]  Robert Černý,et al.  Exterior thermal insulation systems for AAC building envelopes: Computational analysis aimed at increasing service life , 2012 .

[19]  Jon C. Helton,et al.  Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems , 2002 .

[20]  Stefano Tarantola,et al.  A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output , 1999, Technometrics.

[21]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[22]  Philip Fairey,et al.  Theoretical and computational investigation of algorithms for simultaneous heat and moisture transport in buildings: Task 2 final report , 1988 .

[23]  Arnold Janssens,et al.  Sensitivity analysis of CFD coupled non-isothermal heat and moisture modelling , 2010 .

[24]  M. Barclay,et al.  Methods to determine whole building hygrothermal performance of hemp–lime buildings , 2014 .

[25]  Pappenberger Florian,et al.  Review of Sensitivity Analysis Methods , 2010 .

[26]  Monika Woloszyn,et al.  Whole Building Heat, Air, Moisture Response: Modelling Principles and Common Exercises , 2008 .

[27]  Stefano Tarantola,et al.  Random balance designs for the estimation of first order global sensitivity indices , 2006, Reliab. Eng. Syst. Saf..

[28]  Carey J. Simonson,et al.  Moisture buffering capacity of hygroscopic building materials: Experimental facilities and energy impact , 2006 .

[29]  Elmar Plischke,et al.  An effective algorithm for computing global sensitivity indices (EASI) , 2010, Reliab. Eng. Syst. Saf..

[30]  C. Simonson,et al.  The effect of structures on indoor humidity--possibility to improve comfort and perceived air quality. , 2002, Indoor air.

[31]  Andreas Holm,et al.  Practical application of an uncertainty approach for hygrothermal building simulations—drying of an AAC flat roof , 2002 .

[32]  K. Shuler,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. III. Analysis of the approximations , 1975 .

[33]  Stefano Tarantola,et al.  Sensitivity analysis of spatial models , 2009, Int. J. Geogr. Inf. Sci..

[34]  Prabal Talukdar,et al.  Reliability of material data measurements for hygroscopic buffering , 2010 .

[35]  A. Saltelli,et al.  Update 1 of: Sensitivity analysis for chemical models. , 2012, Chemical reviews.

[36]  Jlm Jan Hensen,et al.  Overview of HVAC system simulation , 2010 .

[37]  Hyeun Jun Moon,et al.  Investigation of Physical Characteristics of Houses and Occupants’ Behavioural Factors for Mould Infestation in Residential Buildings , 2010 .

[38]  C. Fortuin,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory , 1973 .

[39]  Mickael Rabouille Recherche de la performance en simulation thermique dynamique : application à la réhabilitation des bâtiments , 2014 .

[40]  Hans Janssen,et al.  Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence , 2013, Reliab. Eng. Syst. Saf..

[41]  M. E. Johnson,et al.  Minimax and maximin distance designs , 1990 .

[42]  K. Shuler,et al.  A STUDY OF THE SENSITIVITY OF COUPLED REACTION SYSTEMS TO UNCERTAINTIES IN RATE COEFFICIENTS . II , 2014 .

[43]  R. Judkoff,et al.  International Energy Agency building energy simulation test (BESTEST) and diagnostic method , 1995 .

[44]  Fraunhofer-Institut für Bauphysik,et al.  Simultaneous heat and moisture transport in building components: One- and two-dimensional calculation using simple parameters , 1995 .

[45]  Clémentine Prieur,et al.  Bias correction for the estimation of sensitivity indices based on random balance designs , 2012, Reliab. Eng. Syst. Saf..