Optimal Experimental Design for the Assessment of Thermophysical Properties in Existing Building Walls

References

• L. Laurenti, F. Marcotullio, and F. de Monte, “Determination of the thermal resistance of walls through a dynamic analysis of in-situ data,” Int. J. Therm. Sci., vol. 43, no. 3, pp. 297–306, 2004. DOI: 10.1016/j.ijthermalsci.2003.08.007.
   Web of Science ®Google Scholar
• J. V. Beck and K. J. Arnold, Parameter Estimation in Engineering and Science. New York, NY: John Wiley and Sons edition, 1977.
   Google Scholar
• L. Pronzato and A. Pázman, Design of Experiments in Nonlinear Models. Number 0930-0325 in Lecture Notes in Statistics. New York, NY: Springer edition, 2019. 10.1007/978-1-4614-6363-4.
   Google Scholar
• A. Nenarokomov and D. V. Titov, “Optimal experiment design to estimate the radiative properties of materials,” J. Quant. Spectrosc. Radiat. Transf., vol. 93, no. 1-3, pp. 313–323, 2005. DOI: 10.1016/j.jqsrt.2004.07.036.
   Web of Science ®Google Scholar
• G. D’Alessandro and F. de Monte, “On the optimum experiment and heating times when estimating thermal properties through the plane source method,” Heat Transf. Eng., vol. 43, no. 3–5, pp. 257–269, 2022. DOI: 10.1080/01457632.2021.1874655.
   Web of Science ®Google Scholar
• G. D'Alessandro and F. de Monte, “Optimal experiment design for thermal property estimation using a boundary condition of the fourth kind with a time-limited heating period,” Int. J. Heat Mass Transf., vol. 134, pp. 1268–1282, 2019. DOI: 10.1016/j.ijheatmasstransfer.2019.02.035.
   Web of Science ®Google Scholar
• E. A. Artyukhin and S. A. Budnik, “Optimal planning of measurements in numerical experiment determination of the characteristics of a heat flux,” J. Eng. Phys., vol. 49, no. 6, pp. 1453–1458, 1985. DOI: 10.1007/BF00871299.
   Google Scholar
• A. Jumabekova, J. Berger, A. Foucquier, and G. S. Dulikravich, “Searching an optimal experiment observation sequence to estimate the thermal properties of a multilayer wall under real climate conditions,” Int. J. Heat Mass Transf., vol. 155, pp. 119810, 2020. DOI: 10.1016/j.ijheatmasstransfer.2020.119810.
   Web of Science ®Google Scholar
• M. Bazargani and F. Kowsary, “Methodology for estimation of local convective heat transfer coefficient for vapor condensation,” Heat Transf. Eng., vol. 36, no. 9, pp. 820–828, 2015. 963420. DOI: 10.1080/01457632.2015.
   Web of Science ®Google Scholar
• J. R. Howell, M. P. Menguc, and R. Siegel, Thermal Radiation Heat Transfer, 5th ed. Boca Raton, FL: CRC Press, 2010, URL: https://books.google.fr/books?id=FBjSBQAAQBAJ.
   Google Scholar
• C. Canuto, M. Y. Hussaini, A. Quarteroni, and T. A. Zang, Spectral Methods in Fluid Dynamics. Springer Series in Computational Physics. Springer Berlin, Heidelberg: Springer-Verlag, 1988, DOI: 10.1007/978-3-642-84108-8.
   Google Scholar
• S. Gasparin, D. Dutykh, and N. Mendes, “A spectral method for solving heat and moisture transfer through consolidated porous media,” Int. J. Numer. Methods Eng., vol. 117, no. 11, pp. 1143–1170, 2019. DOI: 10.1002/nme.5994.
   Web of Science ®Google Scholar
• R. Peyret, Spectral Methods for Incompressible Viscous Flow. New York: Springer, 2002.
   Google Scholar
• B. Fornberg, A Pratical Guide to Pseudospectral Methods. New York, USA: Cambridge University Press, 1996.
   Google Scholar
• F. Liu, X. Ye, and X. Wang, “Efficient Chebyshev spectral method for solving linear elliptic PDEs using quasi-inverse technique,” NMTMA, vol. 4, no. 2, pp. 197–215, 2011. DOI: 10.1017/S100489790000060X.
   Google Scholar
• T. Kolda and B. Bader, “Tensor decompositions and applications,” SIAM Rev., vol. 51, no. 3, pp. 455–500, 2009. DOI: 10.1137/07070111X.
   Web of Science ®Google Scholar
• J. P. Boyd, Chebyshev and Fourier Spectral Methods, 2nd ed. New York: Dover Publications, 2000.
   Google Scholar
• ASME V&V and Committee Members, ASME V&V 20-2009 Standard for Verification and Validation in Computational Fluid Dynamics and Heat Transfer (V&V20 Committee Chair and principal author), vol. 20. New York: American Society of Mechanical Engineering Edition, 2009.
   Google Scholar
• P. J. Roache, “Code verification by the method of manufactured solutions,” J. Fluids Eng., vol. 124, no. 1, pp. 4–10, 2002. DOI: 10.1115/1.1436090.
   Web of Science ®Google Scholar
• R. L. McMasters, F. de Monte, G. D’Alessandro, and J. V. Beck, “Verification of ansys and matlab heat conduction results using an “intrinsically” Verified Exact Analytical Solution,” J. Verification, Validation Uncertain. Quant., vol. 6, no. 2, pp. 21005, 2021. DOI: 10.1115/1.4050610.
   Google Scholar
• C. A. A. Mota, H. R. B. Orlande, M. M. De Carvalho, V. Kolehmainen, and J. P. Kaipio, “Bayesian estimation of temperature-dependent thermophysical properties and transient boundary heat flux,” Heat Transf. Eng., vol. 31, no. 7, pp. 570–580, Jul. 2010. DOI: 10.1080/01457630903425635.
   Web of Science ®Google Scholar
• V. Tahmasbi and S. Noori, “Application of Levenberg–Marquardt method for estimation of the thermophysical properties and thermal boundary conditions of decomposing materials,” Heat Transf. Eng, vol. 41, no. 5, pp. 449–475, 2020. DOI: 10.1080/01457632.2018.1558010.
   Web of Science ®Google Scholar
• D. Ucinski, Optimal Measurement Methods for Distributed Parameter System Identification. Boca Raton, FL: CRC Press, 2004.
   Google Scholar
• J. Berger and B. Kadoch, “Estimation of the thermal properties of an historic building wall by combining modal identification method and optimal experiment design,” Build. Environ., vol. 185, pp. 107065, 2020. DOI: 10.1016/j.buildenv.2020.107065.
   Web of Science ®Google Scholar
• J. Berger, T. Busser, D. Dutykh, and N. Mendes, “An efficient method to estimate sorption isotherm curve coefficients,” Inverse Probl. Sci. Eng., vol. 27, no. 6, pp. 735–772, 2019. 1495720. DOI: 10.1080/17415977.2018.
   Web of Science ®Google Scholar
• J. Berger, S. Gapasrin, and D. Uciński, “Optimal sensor location for inverse heat conduction problem in multilayered building walls,” presented at the 2022 Société Française de Thermique (SFT) Conference Velenciennes, France, 2022, pp. 1–8.
   Google Scholar
• A. Atkinson, A. Donev, and R. Tobias, Optimum Experimental Designs, with SAS. Oxford Statistical Science Series. Oxford, New York: Oxford University Press, 2007.
   Google Scholar
• D. Ucinski, “E-optimum sensor selection for estimation of subsets of parameters,” Measurement, vol. 187, pp. 110286, 2022. DOI: 10.1016/j.measurement.2021.110286.
   Web of Science ®Google Scholar
• J. Berger, S. Gasparin, W. Mazuroski, and N. Mendes, “An efficient two-dimensional heat transfer model for building envelopes,” Numeric. Heat Transf. A: Appl., vol. 79, no. 3, pp. 163–194, Nov. 2020. DOI: 10.1080/10407782.2020.1836936.
   Web of Science ®Google Scholar