Modeling thermal conductivity of hemp insulation material: A multi-scale homogenization approach

Sang, Nguyen Xuan; Tran-Le, A.D.; Vu, Minh‐Ngoc; To, Quy‐Dong; Douzane, Omar; Langlet, Thierry

doi:10.1016/j.buildenv.2016.07.026

Cited by 57 publications

(29 citation statements)

References 20 publications

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“…A recent model, proposed by Nguyen et al [22] is also used to identify the local thermal conductivity of Juncus maritimus fibers. This model is based on the Self-consistent scheme, it was previously applied to model the thermal conductivity of hemp insulation material.…”

Section: Effective Thermal Conductivity Of Porous Mortarmentioning

confidence: 99%

“…Thus, we can retain that the local thermal conductivity of Juncus maritimus fibers identified could be overestimated using this method. Radiation contribution is often neglected in previous works dealing with local solid thermal conductivity determination for hemp insulation materials and other bio based materials [8,22]. However, it can be noted that for traditional porous insulating media used in building application (such as polystyrene foam at ambient temperature), radiation contribution can reach 35% of the total effective thermal conductivity.…”

Section: Effective Thermal Conductivity Of Porous Mortarmentioning

confidence: 99%

“…An approach is proposed to estimate thermal conductivities of mortar matrix and also the local thermal conductivity of crushed Juncus maritimus (JM) fibers (JM local thermal conductivity). Then, based on these data and on the sample microstructure analysis, analytical models currently used in the literature [21][22][23] are tested and compared with effective thermal conductivity measurements. Appropriate analytical models are deduced to predict thermal conductivity of new composites.…”

Section: Introductionmentioning

confidence: 99%

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Thermal Properties of New Insulating Juncus Maritimus Fibrous Mortar Composites/Experimental Results and Analytical Laws

et al. 2019

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This study aims to study the thermal properties and the microstructure of composite materials based on mortar combined with Juncus maritimus fibers. Effective thermophysical properties of the composite materials containing Juncus maritimus fibers are experimentally and theoretically investigated. To better understand the morphology of these new composites, the corresponding microstructures were characterized in 2D by scanning electron microscope and in 3D using micro computed tomography. The local thermal conductivity of the Juncus maritimus fibers was identified using theoretical models and experimental measurement of the effective thermal conductivity of packed bed of crushed fibers. The thermal conductivity of the mortar matrix at given porosity was also determined using experimental measurement data and a theoretical model. The most appropriate analytical laws to predict effective thermal conductivity of mortar composites containing fibers are deduced from experimental thermal conductivity results.

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Section: Effective Thermal Conductivity Of Porous Mortarmentioning

confidence: 99%

Section: Effective Thermal Conductivity Of Porous Mortarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Thermal Properties of New Insulating Juncus Maritimus Fibrous Mortar Composites/Experimental Results and Analytical Laws

et al. 2019

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“…With appropriate proportions, it can cover different uses in the building: roof insulation, wall and ground floor insulating slabs [1]. Many studies concern, on the one hand, the characterization and the behavior of the hemp concrete versus temperature and humidity and, on the other hand, the development of predictive numerical models, which need these data [2]. If the physical properties of the binder can easily be measured, it is not the case for the hemp shives.…”

Section: Introductionmentioning

confidence: 99%

Transient infrared thermography to characterize thermal properties of millimeter-sized low conductivity materials

Pierre¹,

Carin²,

Carré³

2018

Proceedings of the 2018 International Conference on Quantitative InfraRed Thermography

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This work deals with the thermal characterization of a low conductivity material of millimeter-sized used mainly in building insulation, the hemp shiv. Experimentally, the samples are positioned in a stainless steel chamber, in which it is possible to control the environment. They are heated during few seconds on one side and an infrared camera measures the temperature field of the sample surface. The experimental thermograms are compared with a theoretical estimation model derived from the fin model in order to estimate simultaneously two parameters by ordinary least-squares regression: the thermal diffusivity and a modified Biot number.

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“…Historically, the multiscale materials modeling efforts have addressed either homogenization (communication of information from the lower length scale to the higher length scale) [30][31][32][33] or localization (communication of information from the higher length scale to the lower length scale) [8,13,14,[34][35][36]. Although both homogenization and localization have been studied extensively in literature using physically based approaches [31][32][33]37], recent work has identified the tremendous benefits of fusing these approaches with data-driven approaches [8,13,14,30,[34][35][36]. However, most of the prior effort has only addressed a limited number of the multiscale features.…”

Section: Introductionmentioning

confidence: 99%

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

Liu

Yabansu

Yang

et al. 2017

Integr Mater Manuf Innov

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The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.

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Modeling thermal conductivity of hemp insulation material: A multi-scale homogenization approach

Cited by 57 publications

References 20 publications

Thermal Properties of New Insulating Juncus Maritimus Fibrous Mortar Composites/Experimental Results and Analytical Laws

Thermal Properties of New Insulating Juncus Maritimus Fibrous Mortar Composites/Experimental Results and Analytical Laws

Transient infrared thermography to characterize thermal properties of millimeter-sized low conductivity materials

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

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