Heat transfer mechanism of glazed hollow bead insulation concrete

https://doi.org/10.1016/j.jobe.2021.102629Get rights and content

Highlights

  • Random aggregate adding method was used to develop a microscopic model of glazed hollow bead insulation concrete (GHBIC), and the model's effectiveness was verified through a test.

  • In order to solve the problem of mesh distortion caused by the small size of single particle and the large number of particles per unit volume in the modeling process of glazed hollow beads, an improved background grid method based on digital image processing was proposed.

  • The insulation aggregate could be considered as a “thermal resistor” in the heat flow channel.

  • A second-order heat transfer calculation model was established by considering the effect of fineness modulus of glazed hollow bead on the thermal conductivity of GHBIC.

Abstract

In this study, an improved random aggregate adding method was used to develop a microscopic model of glazed hollow bead insulation concrete (GHBIC), and the model's effectiveness was verified through a test. In addition, the heat transfer performance of GHBIC was also studied using the model. The effect of shape, volume fraction, and gradation of coarse aggregate as well as the volume fraction and gradation of glazed hollow bead on the effective thermal conductivity of this concrete was studied. The results show that the glazed hollow bead effectively blocked the heat flow channel of GHBIC. The smaller the particle size and the higher the volume, the blocking effect is more obvious. Finally, considering the fineness of glazed hollow bead, a mathematical model for the thermal conductivity of GHBIC was established.

Introduction

In recent years, with the rapid development of construction industry, a series of resources, energy consumption, and environmental problems have followed. In response to the call of global energy conservation of buildings and sustainable development of construction industry, extensive studies have been conducted on bearing concrete with insulation function. The thermal conductivity model is an important method to predict the thermal conductivity of concrete. Accurate prediction of thermal conductivity of concrete materials provided a theoretical basis for the development of green buildings and energy-saving buildings. To date, many experimental studies have been performed to evaluate the enhancement effect of hollow products on the insulation performance of building materials. Ceramsite concrete is made by replacing aggregate with ceramsite with a loose porous structure, thus making it a porous building material with excellent fire resistance and insulation performance [1]. Foam concrete is a building material formed by preparing a foaming agent using a foaming machine, uniformly mixing the foam and cement together, and then mixing, pouring, and curing the mixture of foam and cement. This special process produces a large number of closed pores inside, thus resulting in a low thermal conductivity and better thermal insulation performance. The thermal conductivity of foam concrete is usually 10–50% of that of normal concrete [2,3]. In addition, materials such as aerated concrete [4,5] exhibit excellent thermal insulation properties owing to their unique pore structure. In recent years, due to excellent thermal insulation performance and bearing capacity, glazed hollow bead insulation concrete (GHBIC) [6] has attracted much attention. It is a multiphase heterogeneous material. However, because of the addition of insulation aggregate, its heat transfer pattern is more complex, and it is difficult to analyze the heat transfer mechanism of this type of concrete from only macroscopic indicators. As a bridge connecting macroworking performance with microcharacterization of material, the study of concrete at a microscopic scale can be used to analyze the thermal conductivity mechanism from the perspective of composition structure and internal temperature field of multiphase heterogeneous material, thus providing an effective research method for analyzing the heat transfer mechanism of concrete. Therefore, a thermal conductivity model based on microscopic components was developed.

Wang et al. [[7], [8], [9], [10], [11]] conducted many studies on the mechanical and thermal conductivity of GHBIC. However, in terms of microscopic analysis of insulation concrete, there are few studies on insulation property analysis using heterogeneity characteristics.

In recent years, in the studies of heat transfer and thermal conductivity of multiphase heterogeneous materials, e.g., concrete, studies have been conducted at microscopic scale. Based on the homogenization method, Zhang et al. [12] established a thermal conductivity model of glazed hollow bead insulation mortar using the minimum thermal resistance theory and accurately predicted the thermal conductivity of insulation mortar. Referring to the Chen Chun model, Zhang et al. [13] divided the heat flow path using the minimum thermal resistance theory, introduced ratio K between the heat flow transfer method of discontinuous mortar and aggregate and the overall heat transfer area, and established a thermal conductivity model of GHBIC. However, because K needs to use a curve to analyze the minimum value of thermal conductivity, the value is not accurate, indicating that it is relatively difficult to predict the thermal conductivity of concrete. Using the homogenization method, Miled et al. [14] studied the effective thermal conductivity of foam concrete, introduced the effect of porosity into the model, and carried out efficient fitting of 19 types of concrete models, thus achieving better accuracy. Wang et al. [15] introduced fractal dimension into the concrete heat conduction model, which accurately predicted the thermal conductivity of concrete. Yet, it is too complex to obtain data using the mercury injection test; moreover, it manifests a relatively lower representative. Thus, it is difficult to apply this model in projects.

None of the abovementioned models considered the effect of glazed hollow bead gradation on effective thermal conductivity. Several studies indicated that the random aggregate model can more accurately simulate the internal temperature field of concrete. Zhang et al. [16] studied the effect of random distribution of coarse aggregates on the thermal conductivity of concrete, which fully considered the random distribution during concrete mixing. Nevertheless, only the circular aggregate was used, and it did not completely represent the characteristics of real concrete. Shen [17] established two-dimensional (2D) and three-dimensional (3D) random aggregate models using MATLAB@ and APDL language, simulated guarded hot plate method to analyze the temperature field of foam concrete, established a 3D foam concrete microscopic model with a porosity of 50%, and the simulated value of thermal conductivity was consistent with the test value. Using the Tessellation polygon, Zhang et al. [18] established a 3D asphalt mixture random aggregate model. Through the steady-state thermal conductivity simulation test of the established model and comparison with the values of effective thermal conductivity test of asphalt aggregate reported in the literature, the effectiveness of numerical simulation of asphalt concrete was verified.

In addition, to accurately predict the effective thermal conductivity of concrete, the effect of different factors on the effective thermal conductivity of concrete was evaluated. Alexander M. et al. [19] proposed a microscopic model where spherical particles and dispersed phase containing crustiform particles were randomly added to continuous homogeneous materials, confirming that the thermal conductivity of multiphase homogeneous material is related to the volume fraction and thermal conductivity of discrete and continuous phases. Chai [20] found that the gradation of glazed hollow bead significantly affects the effective thermal conductivity of concrete. Zhang et al. [18] used a Tessellation polygon to establish a 3D model of asphalt mixture with random aggregate, analyzed its cross-section by cutting the microscopic model of asphalt mixture, and thus obtained the temperature field of 2D asphalt mixture. The effect of pore on the internal temperature field of asphalt mixture was analyzed through thermal flow and isotherm diagrams, and it was found that the thermal flux around pores was obviously greater than that in the mortar layer. Wei et al. [21] proposed an effective thermal conductivity analysis method for porous concrete, and a 2D microscopic numerical model was used to evaluate the effect of pore shape on the effective thermal conductivity of concrete. Porous concrete is considered as a two-phase heterogeneous material consisting of pore-containing mortar and natural gravel coarse aggregate. This indicates that when the pore has a triangle shape, the effective thermal conductivity of concrete is relatively lower. When the pore has a noncircular shape (e.g., square and pentagon), the effective thermal conductivity varies slightly. Zhao [22] established a steel fiber concrete random aggregate model based on the gradation of 2D aggregate. After verifying its effectiveness, the relationship between the volume fraction of steel fiber, type, and volume fraction of coarse aggregate and the effective thermal conductivity of concrete was analyzed, and a new idea on how to improve the thermal conductivity of concrete was obtained.

In summary, an improved random aggregate adding method was used to build a GHBIC random aggregate model. To simplify the calculation model of heat transfer, we considered that GHBIC is isotropic; therefore, a one-dimensional (1D) steady-state heat transfer simulation of model was carried out to study the effects of shape, volume fraction, and gradation of coarse aggregate and gradation and volume fraction of insulation aggregate on the thermal conductivity of concrete. Compared with 3D simulation, 1D simulation is simple and intuitive, but the simulation result is consistent with the experimental results. The 1D simulation of heat transfer for GHBIC was successful for the research group. In the next phases, we will perform simulation in 3D considering that GHBIC is anisotropic.

Section snippets

Simulation parameters

In this study, the shape, gradation, and volume fraction of coarse aggregate and the fineness and volume fraction of glazed hollow bead were taken as the influencing factors, and their effect on the heat transfer performance of GHBIC was studied. The mixture ratio of GHBIC is shown in Table 1. The coarse aggregate and insulation aggregate were randomly generated and added using the Monte Carlo principle, and an interference analysis was conducted simultaneously. The algorithm process can be

Validation of model effectiveness

In this study, a thermal conductivity meter was used to test the thermal conductivity of GHBIC plate; the mix proportion and thermal conductivity of specimen are shown in Table 1. The numerical simulation result was compared with the test result; the error is 2.91%. Therefore, the improved GHBIC model used in this study has a high accuracy for studying the heat transfer performance of this concrete.

Effect of coarse aggregate shape on thermal conductivity of GHBIC

In this study, three different shapes of aggregates, circular, elliptical, and convex polygonal,

Calculation model of heat transfer for GHBIC

So far, the thermal conductivity models of concrete can be roughly divided into four types: (1) series-parallel model, (2) Maxwell model established using Maxwell far field theory [24] and its extended model [25,28], (3) heat flow path division model [29], and (4) empirical equation [30,31]. Previous studies showed that the effect of particle size of glazed hollow bead on the effective thermal conductivity of GHBIC is significant. However, for the present calculation model of thermal

Conclusions

In this study, a microscopic numerical model of GHBIC was established through mesoscale analysis. Furthermore, the thermal conductivity mechanism of GHBIC and the influencing factors of effective thermal conductivity of GHBIC were analyzed using this model. The conclusions of this study are as follows:

  • (1)

    By conducting an experiment and using the calculation method, the model parameters of GHBIC were obtained. By using Monte Carlo probability and statistics principle, different shapes of coarse

Data availability

The data used to support the findings of this study are included within the article.

Disclosure

However, the opinions expressed in this paper are solely of the authors.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors are grateful for the financial support received from the National Natural Science Foundation of China (Grant No. 51808375), and Scientific Research Project of the Anhui Provincial Education Department (Nos. KJ2018A0414, KJ2018A0415 and KJ2020A0627).

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