scholarly journals Fundamental Properties and Thermal Transferability of Masonry Built by Autoclaved Aerated Concrete Self-Insulation Blocks

Materials ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1680 ◽  
Author(s):  
Fenglan Li ◽  
Gonglian Chen ◽  
Yunyun Zhang ◽  
Yongchang Hao ◽  
Zhengkai Si
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Shear Strength ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Residential Buildings ◽  
Fundamental Properties ◽  
Autoclaved Aerated Concrete ◽  
Aerated Concrete ◽  
Insulation Block

This paper performed a detailed study on the fundamental properties and thermal conductivity of autoclaved aerated concrete (AAC) self-insulation block, and the mechanical properties and heat transfer resistance of the AAC self-insulation block masonry. Different kinds of joints and the plastering surface were used to build the masonry specimens. The distinctive feature of the blocks and mortars is the lower thermal conductivity with expected strength. Compared to those with larger thickness of insulation mortar joints, the masonry with thin-layer mortar joints had better compressive performance and lower shear strength. The compressive strength of masonry was related with the block and mortar strengths, the shear strength of masonry along mortar joints was related with the mortar strength. The stress–strain relationship of masonry in compression could be predicted by the similar expression of conventional block masonry. The tested heat transfer coefficient of AAC self-insulation block masonry with thickness of 250 mm without plastering surfaces was (0.558 ± 0.003) W/(m2·K). With the plastering surfaces, the heat transfer coefficient reduced by 4.4% to 8.9%. Good agreements in values of heat transfer coefficient existed by using the test, theoretical computation and ANSYS (ANSYS Inc. Canonsburg, PA, USA) analytical methods. Based on the extensibility analyses, the heat transfer coefficients of AAC self-insultation block masonry with different thickness are proposed. The best thickness is proposed for the outer walls of residential buildings in different cold zone to meet the design requirement of energy conservation.

Fly Ash Autoclaved Aerated Concrete Self Thermal Insulation Building Envelope Energy Saving Research

2013 ◽  
Vol 448-453 ◽  
pp. 1243-1247 ◽  
Author(s):  
Li Bai ◽  
Ying Li ◽  
Shu Ming He
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Fly Ash ◽  
Transfer Coefficient ◽  
Energy Saving ◽  
Thermal Insulation ◽  
Building Envelope ◽  
Exterior Wall ◽  
Autoclaved Aerated Concrete ◽  
Aerated Concrete

This article takes an actual public building in Changchun City as an example to field test the heat transfer coefficient of fly ash autoclaved aerated concrete exterior wall. By analysis the field test value, theory calculation value, and the maximum value of the design standard for energy efficiency of public building (GB50189-2005), I concluded that fly ash autoclaved aerated concrete self-thermal insulation building envelope exterior wall fully meet the energy-saving standard requirements, which does not need additional insulation material. And further as heat transfer coefficient of wall test and energy saving standard maximum heat transfer coefficient as the main parameter, I used DeST-c software simulation to calculate the building energy consumption. The simulation results showed that the heating energy consumption of fly ash autoclaved aerated concrete self-thermal insulation building envelope is 16.32% lower than the standards required value, which has the advantage of energy saving property.

Development of a New Correlation and Post Processing of Heat Transfer Coefficient and Pressure Drop of Functionalized COOH MWCNT Nanofluid by Artificial Neural Network

2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Regression Coefficient ◽  
New Correlation ◽  
The Neural Network

Background: Because of nanofluids applications in improvement of heat transfer rate in heating and cooling systems, many researchers have conducted various experiments to investigate nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat transfer are examples of these characteristics. Method: This paper presents a modeling and validation method of heat transfer coefficient and pressure drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and proposing a new correlation. In the current experiment, the ANN input data has included the volume fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN outputs. Results: Comparing modeling results with proposed correlation proves that the empirical correlation is not able to accurately predict the experimental output results, and this is performed with a lot more accuracy by the neural network. The regression coefficient of neural network outputs was equal to 99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure drop. The regression coefficient for the provided equation was also equal to 97.02% and 77.90%, respectively, for these two parameters, which indicates this equation operates much less precisely than the neural network. Conclusion: So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity and viscosity executed by different scholars via neural networks.

Experimental Convective Heat Transfer With Nanofluids

2008 ◽  
Author(s):  
S. Kabelac ◽  
K. B. Anoop
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Turbulent Regime ◽  
Heat Transfer Characteristics ◽  
Forced Convective Heat Transfer ◽  
Transfer Characteristics

Nanofluids are colloidal suspensions with nano-sized particles (<100nm) dispersed in a base fluid. From literature it is seen that these fluids exhibit better heat transfer characteristics. In our present work, thermal conductivity and the forced convective heat transfer coefficient of an alumina-water nanofluid is investigated. Thermal conductivity is measured by a steady state method using a Guarded Hot Plate apparatus customized for liquids. Forced convective heat transfer characteristics are evaluated with help of a test loop under constant heat flux condition. Controlled experiments under turbulent flow regime are carried out using two particle concentrations (0.5vol% and 1vol %). Experimental results show that, thermal conductivity of nanofluids increases with concentration, but the heat transfer coefficient in the turbulent regime does not exhibit any remarkable increase above measurement uncertainty.

Investigations on Transient Natural Convection in Boron Nitride-Mineral Oil Nanofluid Systems

2012 ◽  
Author(s):  
Shijo Thomas ◽  
C. B. Sobhan ◽  
Jaime Taha-Tijerina ◽  
T. N. Narayanan ◽  
P. M. Ajayan
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Natural Convection ◽  
Boron Nitride ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Convective Heat Transfer Coefficient ◽  
Mineral Oil

Nanofluids are suspensions or colloids produced by dispersing nanoparticles in base fluids like water, oil or organic fluids, so as to improve their thermo-physical properties. Investigations reported in recent times have shown that the addition of nanoparticles significantly influence the thermophysical properties, such as the thermal conductivity, viscosity, specific heat and density of base fluids. The convective heat transfer coefficient also has shown anomalous variations, compared to those encountered in the base fluids. By careful selection of the parameters such as the concentration and the particle size, it has been possible to produce nanofluids with various properties engineered depending on the requirement. A mineral oil–boron nitride nanofluid system, where an increased thermal conductivity and a reduced electrical conductivity has been observed, is investigated in the present work to evaluate its heat transfer performance under natural convection. The modified mineral oil is produced by chemically dispersing boron nitride nanoparticles utilizing a one step method to obtain a stable suspension. The mineral oil based nanofluid is investigated under transient free convection heat transfer, by observing the temperature-time response of a lumped parameter system. The experimental study is used to estimate the time-dependent convective heat transfer coefficient. Comparisons are made with the base fluid, so that the enhancement in the heat transfer coefficient under natural convection situation can be estimated.

Forced Convection Heat Transfer of Nanofluids: A Review

2017 ◽  
Author(s):  
Aditya Kuchibhotla ◽  
Debjyoti Banerjee
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Heat Capacity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Specific Heat ◽  
Forced Convection ◽  
Convection Heat Transfer ◽  
Convection Heat

Stable homogeneous colloidal suspensions of nanoparticles in a liquid solvents are termed as nanofluids. In this review the results for the forced convection heat transfer of nanofluids are gleaned from the literature reports. This study attempts to evaluate the experimental data in the literature for the efficacy of employing nanofluids as heat transfer fluids (HTF) and for Thermal Energy Storage (TES). The efficacy of nanofluids for improving the performance of compact heat exchangers were also explored. In addition to thermal conductivity and specific heat capacity the rheological behavior of nanofluids also play a significant role for various applications. The material properties of nanofluids are highly sensitive to small variations in synthesis protocols. Hence the scope of this review encompassed various sub-topics including: synthesis protocols for nanofluids, materials characterization, thermo-physical properties (thermal conductivity, viscosity, specific heat capacity), pressure drop and heat transfer coefficients under forced convection conditions. The measured values of heat transfer coefficient of the nanofluids varies with testing configuration i.e. flow regime, boundary condition and geometry. Furthermore, a review of the reported results on the effects of particle concentration, size, temperature is presented in this study. A brief discussion on the pros and cons of various models in the literature is also performed — especially pertaining to the reports on the anomalous enhancement in heat transfer coefficient of nanofluids. Furthermore, the experimental data in the literature indicate that the enhancement observed in heat transfer coefficient is incongruous compared to the level of thermal conductivity enhancement obtained in these studies. Plausible explanations for this incongruous behavior is explored in this review. A brief discussion on the applicability of conventional single phase convection correlations based on Newtonian rheological models for predicting the heat transfer characteristics of the nanofluids is also explored in this review (especially considering that nanofluids often display non-Newtonian rheology). Validity of various correlations reported in the literature that were developed from experiments, is also explored in this review. These comparisons were performed as a function of various parameters, such as, for the same mass flow rate, Reynolds number, mass averaged velocity and pumping power.

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