An efficient method to estimate sorption isotherm curve coefficients

References

• U.S. Energy Information Administration. Annual Energy Outlook 2015, with projections to 2040. Washington: EIA; 2015.
   Google Scholar
• Woloszyn M, Rode C. Tools for performance simulation of heat, air and moisture conditions of whole buildings. Build Simul. 2008;1(1):5–24. doi: 10.1007/s12273-008-8106-z
   Web of Science ®Google Scholar
• Luikov AV. Heat and mass transfer in capillary-porous bodies. London: Pergamon Press; 1966.
   Google Scholar
• Mendes N, Ridley I, Lamberts R, et al. Umidus: a PC program for the prediction of heat and mass transfer in porous building elements. International Conference on Building Performance Simulation, Japan; 1999. p. 277–283.
   Google Scholar
• Mendes N, Philippi PC. A method for predicting heat and moisture transfer through multilayered walls based on temperature and moisture content gradients. Int J Heat Mass Transf. January 2005;48(1):37–51. doi: 10.1016/j.ijheatmasstransfer.2004.08.011
   Web of Science ®Google Scholar
• Fraunhofer IBP. Wufi. http://www.hoki.ibp.fhg.de/wufi/wufi_frame_e.html2005.
   Google Scholar
• Sasic Kalagasidis A, Weitzmann P, Nielsen TR, et al. The International Building Physics Toolbox in Simulink. Energy Build. June 2007;39(6):665–674. doi: 10.1016/j.enbuild.2006.10.007
   Web of Science ®Google Scholar
• Mendes N, Chhay M, Berger J, et al. Numerical methods for diffusion phenomena in building physics. Curitiba: PUC-Press; 2016.
   Google Scholar
• Mendes N, Philippi PC. Multitridiagonal-matrix algorithm for coupled heat transfer in porous media: Stability analysis and computational performance. J Porous Media. 2004;7(3):193–212. doi: 10.1615/JPorMedia.v7.i3.40
   Web of Science ®Google Scholar
• BC Bauklimatik Dresden Simulation program for the calculation of coupled heat, moisture, air, pollutant, and salt transport. http://www.bauklimatik-dresden.de/delphin/index.php?aLa=en2011.
   Google Scholar
• Rouchier S, Woloszyn M, Foray G, et al. Influence of concrete fracture on the rain infiltration and thermal performance of building facades. Int J Heat Mass Transf. 2013;61:340–352. doi: 10.1016/j.ijheatmasstransfer.2013.02.013
   Web of Science ®Google Scholar
• Janssen H, Blocken B, Carmeliet J. Conservative modelling of the moisture and heat transfer in building components under atmospheric excitation. Int J Heat Mass Transf. 2007;50(5–6):1128–1140. doi: 10.1016/j.ijheatmasstransfer.2006.06.048
   Web of Science ®Google Scholar
• Gasparin S, Berger J, Dutykh D, et al.Stable explicit schemes for simulation of nonlinear moisture transfer in porous materials. J Building Performance Simul. 2018;11:129–144. doi: 10.1080/19401493.2017.1298669
   Web of Science ®Google Scholar
• Gasparin S, Berger J, Dutykh D, et al. An improved explicit scheme for whole-building hygrothermal simulation. Building Simul. 2018;11:465–481. doi: 10.1007/s12273-017-0419-3
   Web of Science ®Google Scholar
• Berger J, Gasparin S, Dutykh D, et al. Accurate numerical simulation of moisture front in porous material. Build Environ. 2017;118:211–224. doi: 10.1016/j.buildenv.2017.03.016
   Web of Science ®Google Scholar
• Kabanikhin SI. Definitions and examples of inverse ill-posed problems. J Inverse Probl and Ill -posed problems. 2008;16:317–357.
   Web of Science ®Google Scholar
• Kabanikhin SI. Inverse and ill-posed problems: theory and applications. Berlin: Walter De Gruyter; 2011.
   Google Scholar
• Berger J, Orlande HRB, Mendes N, et al. Bayesian inference for estimating thermal properties of a historic building wall. Build Environ. 2016;106:327–339. doi: 10.1016/j.buildenv.2016.06.037
   Web of Science ®Google Scholar
• Rouchier S, Woloszyn M, Kedowide Y, et al. Identification of the hygrothermal properties of a building envelope material by the covariance matrix adaptation evolution strategy. J Build Perform Simul. 2014;9(1):101–114. doi: 10.1080/19401493.2014.996608
   Web of Science ®Google Scholar
• Nassiopoulos A, Bourquin F. On-site building walls characterization. Numer Heat Transfer Part A – Appl. 2013;63(3):179–200. doi: 10.1080/10407782.2013.730422
   Web of Science ®Google Scholar
• James C, Simonson CJ, Talukdar P, et al. Numerical and experimental data set for benchmarking hygroscopic buffering models. Int J Heat Mass Transf. 2010;53(19–20):3638–3654. doi: 10.1016/j.ijheatmasstransfer.2010.03.039
   Web of Science ®Google Scholar
• Kanevce GH, Kanevce LP, Dulikravich GS, et al. Estimation of thermophysical properties of moist materials under different drying conditions. Inverse Probl Sci Eng. 2005;13(4):341–353. doi: 10.1080/17415970500098485
   Web of Science ®Google Scholar
• Berger J, Busser T, Dutykh D, et al. On the estimation of moisture permeability and advection coefficients of a wood fibre material using the optimal experiment design approach. Experiment Thermal and Fluid Sci. 2018;90.246–259. doi: 10.1016/j.expthermflusci.2017.07.026
   Web of Science ®Google Scholar
• Rouchier S, Busser T, Pailha M, et al. Hygric characterization of wood fiber insulation under uncertainty with dynamic measurements and Markov Chain monte-Carlo algorithm. Build Environ. 2017;114:129–139. doi: 10.1016/j.buildenv.2016.12.012
   Web of Science ®Google Scholar
• Biddulph P, Gori V, Elwell CA, et al. Inferring the thermal resistance and effective thermal mass of a wall using frequent temperature and heat flux measurements. Energy Build. 2014;78:10–16. doi: 10.1016/j.enbuild.2014.04.004
   Web of Science ®Google Scholar
• Dubois S, McGregor F, Evrard A, et al. An inverse modelling approach to estimate the hygric parameters of clay-based masonry during a moisture buffer value test. Build Environ. 2014;81:192–203. doi: 10.1016/j.buildenv.2014.06.018
   Web of Science ®Google Scholar
• Xu X, Wang S. Optimal simplified thermal models of building envelope based on frequency domain regression using genetic algorithm. Energy Build. 2007;39(5):525–536. doi: 10.1016/j.enbuild.2006.06.010
   Web of Science ®Google Scholar
• Czel B, Grof G. Inverse identification of temperature-dependent thermal conductivity via genetic algorithm with cost function-based rearrangement of genes. Int J Heat Mass Transf. 2012;55(15–16):4254–4263. doi: 10.1016/j.ijheatmasstransfer.2012.03.067
   Web of Science ®Google Scholar
• Palomo Del Barrio E. Multidimensional inverse heat conduction problems solution via lagrange theory and model size reduction techniques. Inverse Probl Eng. 2003;11(6):515–539. doi: 10.1080/1068276031000114884
   Google Scholar
• Berger J, Dutykh D, Mendes N. On the optimal experiment design for heat and moisture parameter estimation. Exp Therm Fluid Sci. 2017;81:109–122. doi: 10.1016/j.expthermflusci.2016.10.008
   Web of Science ®Google Scholar
• Belleudy C, Woloszyn M, Chhay M, et al. A 2D model for coupled heat, air, and moisture transfer through porous media in contact with air channels. Int J Heat Mass Transf. 2016;95:453–465. doi: 10.1016/j.ijheatmasstransfer.2015.12.030
   Web of Science ®Google Scholar
• Delleur JW. The handbook of groundwater engineering. 2nd ed. New York: CRC Press; 2006.
   Google Scholar
• Rafidiarison H, Rémond R, Mougel E. Dataset for validating 1-d heat and mass transfer models within building walls with hygroscopic materials. Build Environ. 2015;89:356–368. doi: 10.1016/j.buildenv.2015.03.008
   Web of Science ®Google Scholar
• Vololonirina O, Coutand M, Perrin B. Characterization of hygrothermal properties of wood-based products – impact of moisture content and temperature. Constr Build Mater. 2014;63:223–233. doi: 10.1016/j.conbuildmat.2014.04.014
   Web of Science ®Google Scholar
• Necati Ozisik M, Orlande HRB. Inverse heat transfer: fundamentals and applications. New York: CRC Press; 2000.
   Google Scholar
• Finsterle S. Practical notes on local data-worth analysis. Water Resour Res. 2015;51(12):9904–9924. doi: 10.1002/2015WR017445
   Web of Science ®Google Scholar
• Walter E, Pronzato L. Qualitative and quantitative experiment design for phenomenological models; a survey. Automatica. 1990;26(2):195–213. doi: 10.1016/0005-1098(90)90116-Y
   Web of Science ®Google Scholar
• Nenarokomov AV, Titov DV. Optimal experiment design to estimate the radiative properties of materials. J Quant Spectros Radiat Tran. 2005;93(1–3):313–323. doi: 10.1016/j.jqsrt.2004.07.036
   Web of Science ®Google Scholar
• Artyukhin EA, Budnik SA. Optimal planning of measurements in numerical experiment determination of the characteristics of a heat flux. J Eng Phys. 1985;49(6):1453–1458. doi: 10.1007/BF00871299
   Google Scholar
• Beck JV, Arnold KJ. Parameter estimation in engineering and science. New York: John Wiley and Sons; 1977.
   Google Scholar
• Wouwer AV, Point N, Porteman S, et al. An approach to the selection of optimal sensor locations in distributed parameter systems. J Process Control. 2005;10(4):291–300. doi: 10.1016/S0959-1524(99)00048-7
   Google Scholar
• Fadale TD, Nenarokomov AV, Emery AF. Two approaches to optimal sensor locations. ASME J Heat Trans. 1998;117(2):373–379. doi: 10.1115/1.2822532
   Google Scholar
• Emery AF, Nenarokomov AV. Optimal experiment design. Meas Sci Technol. 1998;9(6):864.876 doi: 10.1088/0957-0233/9/6/003
   Web of Science ®Google Scholar
• Anderson ML, Bangerth W, Carey GF. Analysis of parameter sensitivity and experimental design for a class of nonlinear partial differential equations. Int J Numer Methods Fluids. 2005;48(6):583–605. doi: 10.1002/fld.938
   Web of Science ®Google Scholar
• Alifanov OM, Artyukhin EA, Rumyantsev SV. Extreme methods for solving ill-posed problems with applications to inverse heat transfer problems. New York: Begellhouse; 1995.
   Google Scholar
• Karalashvili M, Marquardt W, Mhamdi A. Optimal experimental design for identification of transport coefficient models in convection–diffusion equations. Comput Chem Eng. 2015;80:101–113. doi: 10.1016/j.compchemeng.2015.04.036
   Web of Science ®Google Scholar
• Ucinski D. Optimal measurement methods for distributed parameter system identification. New York: CRC Press; 2004.
   Google Scholar
• Busser T, Piot A, Pailha M, et al. From materials properties to modelling hygrothermal transfers of highly hygroscopic walls. Dresden: CESBP; September 2016 .
   Google Scholar
• Byrd RH, Gilbert JC, Nocedal J. A trust region method based on interior-point techniques for nonlinear programming. Math Program. 2000;89(1):149–185. doi: 10.1007/PL00011391
   Web of Science ®Google Scholar
• Mualem Y, Beriozkin A. General scaling rules of the hysteretic water retention function based on Mualem's domain theory. Eur J Soil Sci. 2009;60(4):652–661. doi: 10.1111/j.1365-2389.2009.01130.x
   Web of Science ®Google Scholar
• Derluyn H, Derome D, Carmeliet J, et al. Hysteretic moisture behavior of concrete: modeling and analysis. Cement and Concrete Research. 2012;42(10):1379–1388. doi: 10.1016/j.cemconres.2012.06.010
   Web of Science ®Google Scholar
• Berger J, Gasparin S, Dutykh D, et al. On the solution of coupled heat and moisture transport in porous material. Transp Porous Media. 2018;121:665–702. doi: 10.1007/s11242-017-0980-3
   Web of Science ®Google Scholar
• Shampine LF, Reichelt MW. The MATLAB ODE suite. SIAM J Sci Comput. 1997;18(–):1–22. doi: 10.1137/S1064827594276424
   Web of Science ®Google Scholar
• Koptyug IV, Kabanikhin SI, Iskakov KT, et al.A quantitative NMR imaging study of mass transport in porous solids during drying. Chem Eng Sci. 2000;55(9):1559–1571. doi: 10.1016/S0009-2509(99)00404-2
   Web of Science ®Google Scholar
• Kabanikhin SI, Hasanov A, Penenko AV. A gradient descent method for solving an inverse coefficient heat conduction problem. Numer Anal Appl. 2008;1(1):34–45. doi: 10.1134/S1995423908010047
   Google Scholar
• Walter E, Lecourtier Y. Global approaches to identifiability testing for linear and nonlinear state space models. Math Comput Simul. 1982;24(6):472–482. doi: 10.1016/0378-4754(82)90645-0
   Web of Science ®Google Scholar