On the optimal experimental design for heat and moisture parameter estimation

In the context of estimating material properties of porous walls based on in-site measurements and identification method, this paper presents the concept of Optimal Experiment Design (OED). It aims at searching the best experimental conditions in terms of quantity and position of sensors and boundary conditions imposed to the material. These optimal conditions ensure to provide the maximum accuracy of the identification method and thus the estimated parameters. The search of the OED is done by using the Fisher information matrix and a priori knowledge of the parameters. The methodology is applied for two case studies. The first one deals with purely conductive heat transfer. The concept of optimal experiment design is detailed and verified with 100 inverse problems for different experiment designs. The second case study combines a strong coupling between heat and moisture transfer through a porous building material. The methodology presented is based on a scientific formalism for efficient planning of experimental work that can be extended to the optimal design of experiments related to other problems in thermal and fluid sciences.

[1]  Frédéric Bourquin,et al.  On-Site Building Walls Characterization , 2013 .

[2]  O. Alifanov,et al.  Extreme Methods for Solving Ill-Posed Problems with Applications to Inverse Heat Transfer Problems , 1995 .

[3]  Romain Rémond,et al.  Dataset for validating 1-D heat and mass transfer models within building walls with hygroscopic materials , 2015 .

[4]  Prabal Talukdar,et al.  Numerical and experimental data set for benchmarking hygroscopic buffering models , 2010 .

[5]  Aleksey V. Nenarokomov,et al.  Two Approaches to Optimal Sensor Locations , 1995 .

[6]  James V. Beck,et al.  Parameter Estimation in Engineering and Science , 1977 .

[7]  Tadiwos Zerihun Desta,et al.  Experimental data set for validation of heat, air and moisture transport models of building envelope , 2011 .

[8]  W. J. Whiten,et al.  Computational investigations of low-discrepancy sequences , 1997, TOMS.

[9]  Monika Woloszyn,et al.  Identification of the hygrothermal properties of a building envelope material by the covariance matrix adaptation evolution strategy , 2016 .

[10]  Aleksey V. Nenarokomov,et al.  Optimal experiment design to estimate the radiative properties of materials , 2005 .

[11]  Alain Vande Wouwer,et al.  An approach to the selection of optimal sensor locations in distributed parameter systems , 2000 .

[12]  A. Emery,et al.  Optimal experiment design , 1998 .

[13]  M. N. Özişik,et al.  Inverse Heat Transfer: Fundamentals and Applications , 2000 .

[14]  S. A. Budnik,et al.  Optimal planning of measurements in numerical experiment determination of the characteristics of a heat flux , 1985 .

[15]  Wolfgang Marquardt,et al.  Optimal experimental design for identification of transport coefficient models in convection-diffusion equations , 2015, Comput. Chem. Eng..

[16]  Targo Kalamees,et al.  Hygrothermal calculations and laboratory tests on timber-framed wall structures , 2003 .

[17]  Paul Fazio,et al.  Transient model for coupled heat, air and moisture transfer through multilayered porous media , 2010 .

[18]  Gabriel Terejanu,et al.  Bayesian experimental design for the active nitridation of graphite by atomic nitrogen , 2011, ArXiv.

[19]  Prabal Talukdar,et al.  An experimental data set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials. Part I: Experimental facility and material property data , 2007 .

[20]  Arnold Janssens,et al.  Coupled simulation of heat and moisture transport in air and porous materials for the assessment of moisture related damage , 2009 .

[21]  Ching-yu Yang,et al.  Estimation of the temperature-dependent thermal conductivity in inverse heat conduction problems , 1999 .

[22]  P. Glouannec,et al.  Experimental and numerical analysis of the transient hygrothermal behavior of multilayered hemp concrete wall , 2016 .

[23]  P. Salagnac,et al.  Estimation of moisture transport coefficients in porous materials using experimental drying kinetics , 2012 .

[24]  Nathan Mendes,et al.  Estimation of temperature-dependent thermal conductivity using proper generalised decomposition for building energy management , 2016 .

[25]  Stefan Finsterle,et al.  Practical notes on local data‐worth analysis , 2015 .

[26]  N. Kharoua,et al.  Large eddy simulation of a slot jet impinging on a convex surface , 2012 .

[27]  Patrick Glouannec,et al.  Hygrothermal behavior of bio-based building materials including hysteresis effects: Experimental and numerical analyses , 2014 .

[28]  D. Ucinski Optimal measurement methods for distributed parameter system identification , 2004 .

[29]  G. Carey,et al.  Analysis of parameter sensitivity and experimental design for a class of nonlinear partial differential equations , 2005 .

[30]  Richard Cantin,et al.  Field assessment of thermal behaviour of historical dwellings in France , 2010 .

[31]  Ihtesham H. Chowdhury,et al.  Numerical Heat Transfer, Part A: Applications , 2007 .

[32]  Pierre Michel,et al.  Experimental assessment of thermal inertia in insulated and non-insulated old limestone buildings , 2014 .

[33]  Monika Woloszyn,et al.  Dynamic coupling between vapour and heat transfer in wall assemblies: Analysis of measurements achieved under real climate , 2015 .

[34]  Monika Woloszyn,et al.  Proper generalized decomposition for solving coupled heat and moisture transfer , 2015 .

[35]  N. Sun Structure reduction and robust experimental design for distributed parameter identification , 2005 .