Bayesian inference method for in situ thermal conductivity and heat capacity identification: Comparison to ISO standard

Rodler, Auline; Guernouti, Sihem; Musy, Marjorie

doi:10.1016/j.conbuildmat.2018.11.110

Cited by 19 publications

(18 citation statements)

References 21 publications

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“…Huang et al (2017) have derived the heat conductivity in the layer between the atmosphere and the soil surface, Choi et al (2018) have inferred soil thermal conductivity from a thermal response test, and Tran et al (2016) have inferred organic matter content from soil temperature, liquid water, and apparent resistivity data. Bayesian inference has been more widely applied in engineering (Kaipio and Fox, 2011) to estimate thermal properties of fins (Gnanasekaran and Balaji, 2013;Somasundharam and Reddy, 2017) and walls (De Simon et al, 2018;Rodler et al, 2019). While the above studies have shown promise in estimating thermal properties from time series of temperature, several challenges still remain, including the need for an approach that can assess the conditions under which such estimates are reliable.…”

Section: Introductionmentioning

confidence: 99%

Estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series and uncertainty quantification

Brunetti

Lamb

Wielandt

et al. 2021

Preprint

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Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach needs to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time-windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise) and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 °C at 5 cm below the ground surface, temperature gradients > 2 °C m−1, and a sliding time-window of at least 4 days, the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors spaced precisely (with few-millimeter accuracy), with a level of noise ≤ 0.02 °C, and with a bias defined by a standard deviation ≤ 0.01 °C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time-window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in carbon or water content.

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Section: Introductionmentioning

confidence: 99%

Estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series and uncertainty quantification

Brunetti

Lamb

Wielandt

et al. 2021

Preprint

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“…For this case, a good compromise is in three days of monitoring. Note that the influence of the length of the measurement period is investigated empirically in [12]. For their case study, the authors suggest to carry at least three days of monitoring to have an accurate estimation.…”

Section: Determining the Reduced Sequence Of Observations Using The Oed Methodologymentioning

confidence: 99%

Estimation of the thermal properties of an historic building wall by combining modal identification method and optimal experiment design

Berger

Kadoch

2020

Building and Environment

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The estimation of wall thermal properties by in situ measurement enables to increase the reliability of the model predictions for building energy efficiency. Nevertheless, retrieving the unknown parameters has an important computational cost. Indeed, several computations of the heat transfer problem are required to identify these thermal properties. To handle this drawback, an innovative approach is investigated. The first step is to search the optimal experiment design among the sequence of observation of several months. A reduced sequence of observations of three days is identified which guarantees to estimate the parameter with the maximum accuracy. Moreover, the inverse problem is only solved for this short sequence. To decrease further the computational efforts, a reduced order model based on the modal identification method is employed. This a posteriori model reduction method approximates the solution with a lower degree of freedom. The whole methodology is illustrated to estimate the thermal diffusivity of an historical building that has been monitored with temperature sensors for several months. The computational efforts is cut by five. The estimated parameter improves the reliability of the predictions of the wall thermal efficiency.

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“…To converge R c -value using the Average Method given by ISO 9869-1 [56], it has been recommended that the difference between exterior and interior surface temperature should be at least 5-10 °C when using the Average Method [56,61,62]. Therefore, the top and bottom of green roof specimens were subjected to 16.5 °C and 23.5 °C, respectively, resulting in the surface temperature difference of 7 °C.…”

Section: Thermal Heat Transfer (Iso 9869-1)mentioning

confidence: 99%

Modelling hygrothermal conditions of unsaturated substrate and drainage layers for the thermal resistance assessment of green roof: Effect of coarse recycled materials

Kazemi

Courard

2021

Energy and Buildings

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Alternative materials exist for green roof layers: secondary resources like coarse or fine recycled aggregates may be used as a substitute to natural materials. For using these new types of materials, it is needed to assess their heat resistance which is performed according to ISO 9869-1 standard. Moreover, the initial hygrothermal conditions of unsaturated substrate and drainage layers have also to be modelled and assessed for optimizing the layers' thickness. In this study, the green roofs with unsaturated substrate and drainage layers incorporating coarse recycled materials were tested and assessed. The hygrothermal conditions of unsaturated substrate and drainage layers were simulated using WUFI software. A small difference (4.2%) was observed between the Rc-value of the green roofs with and without coarse recycled materials, confirming that these materials provided a sufficient thermal resistance, similar to soil particles for the substrate layer. Considering a constant thickness for the substrate layer (15 cm), a 6-cm drainage layer with coarse aggregates was considered as the optimum design for green roof systems. Besides, 18-cm unsaturated substrate layer was the optimum design when the drainage layer' thickness was considered constant (5 cm). The 6-cm drainage layer and 18-cm unsaturated substrate layer were definitely the best design for the roofing systems with the simultaneous change in the substrate and drainage layers' thickness.

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Bayesian inference method for in situ thermal conductivity and heat capacity identification: Comparison to ISO standard

Cited by 19 publications

References 21 publications

Estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series and uncertainty quantification

Estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series and uncertainty quantification

Estimation of the thermal properties of an historic building wall by combining modal identification method and optimal experiment design

Modelling hygrothermal conditions of unsaturated substrate and drainage layers for the thermal resistance assessment of green roof: Effect of coarse recycled materials

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