Experimental works with new prototype for measuring thermal resistance of building walls

Thanh-Tung Ha¹, Laurent Ibos¹, Vincent Feuillet¹, Yann Garcia², Véronique Le Sant², Alain Koenen², Laurent Peiffer³, Remi Bouchie⁴, Kamel Zibouche⁵, Julien Waeytens^6
⋆ : thanh-tung.ha@u-pec.fr
¹ CERTES-UPEC, ² LNE Trappes, ³ CEREMA Est, ⁴ CSTB Marne-la-Vallée, ⁵ CSTB, ⁶ IFSTTAR
Mots clés : thermal resistance, inverse problem, building, active, measurement
Résumé :

The thermal resistance is considered as a quantitative value to qualify a building envelop element. In order to reach the global thermal performance of building envelope, imposed by the RT2012 in France [1], a minimum resistance value of about 4 .K.W-1 for an opaque building wall is needed. This requirement will be surely moved upward in the next regulations. Therefore, the need for a robust measurement method of the thermal resistance becomes more necessary.

There are several methods and techniques [2,3,4,5] which allow to estimate this resistance. However, they require particular conditions, which may not be available in some cases. For this reason, an ANR project named RESBATI was launched to develop a measurement device based on active method for determining the thermal resistance of an opaque wall without specific required conditions as existing methods.

As a specific task of the RESBATI project, some proposed estimation models were validated theoretically by using numerical simulated data [6]. Then, several measurement campaigns on real walls under steady and transient conditions were performed. Internal Wall Insulation or IWI was tested (the most popular wall in France, medium thermal resistance). Two climatic chambers at LNE and CEREMA were used during these measurements for controlling environment temperatures.

For post-processing step, a 1D thermal quadrupole model is chosen as direct model into estimation process and a Bayesian inference is used as main algorithm to estimate the thermal resistance and its uncertainty. In order to improve estimation quality of Bayesian method, the Robust Adaptive Metropolis (or RAM) algorithm [7] which varies the scale of the proposal distribution via a given acceptance rate without extra cost is integrated into the identification process. For each test, an hourly evolution of estimated resistance and its uncertainty is established.

According to IWI results, it requires at least 6 hours to reach the expected value of resistance in most of the considered test cases. These results are around 5% to a reference value measured with a Guarded hot box at CSTB [5]. These results are promising and shows that the method is suitable for the thermal resistance measurement of IWI. For the next step, another type of wall named Wood Frame Wall or WFW is going to be studied.

 [1] Réglementation thermique RT 2012, Ed. CSTB, 2012.

 [2] ISO 9869-1:2014, "Thermal insulation - Building elements - In-situ measurement of thermal resistance and thermal transmittance - Part 1: Heat flow meter method", ISO Standard, 2014.

 [3] ISO 9869-2:2018, "Thermal insulation - Building elements - In-situ measurement of thermal resistance and thermal transmittance - Part 2: Infrared method for frame structure dwelling", ISO standard, 2018.

 [4] R. Albatici, A. M. Tonelli, M. Chiogna, "A comprehensive experimental approach for the validation of quantitative infrared thermography in the evaluation of building thermal transmittance", Applied Energy, 141, p. 218-228, 2015.

 [5] ISO 8990:1994, "Thermal insulation - Determination of steady-state thermal transmission properties - Calibrated and guarded hot box", ISO standard, 1994.

 [6] T-T. Ha, V. Feuillet, L. Ibos, J. Waeytens, K. Zibouche, S. Thébault, R. Bouchié, V. Le Sant, "Benchmark de méthodes d’identification de paramètres sur données simulées: application à la mesure sur site de la résistance thermique de parois de bâtiments par méthode active", Congrès SFT, 2019.

 [7] M. Vihola, "Robust adaptive Metropolis algorithm with coerced acceptance rate", Statistics and Computing, 22, p. 997-1008, 2012.

doi : https://doi.org/10.25855/SFT2020-096

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