Thermal conductivity and heat transfer coefficient of concrete

Guo, Lixia; Guo, Lei; Zhong, Ling; YueMing, Zhu

doi:10.1007/s11595-011-0312-3

Cited by 41 publications

(26 citation statements)

References 5 publications

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“…The α‐MoO 3 channel conductance can be repeatedly potentiated from 3.5 to 4.75 nA under positive pulses and depressed back to 3.5 nA under negative pulses. Similar potentiation and depression behaviors were also reported in nanogranular SiO 2 and chitosan‐based electrolyte gated oxide neuromorphic transistors . Fu et al also fabricated iontronic neuromorphic transistor based on mixed protonic and electronic conductor hybrid (MPEC) IWO transistor.…”

Section: Ionotronic Transistorssupporting

confidence: 54%

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Zhu

2019

Physica Rapid Research Ltrs

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The von Neumann bottleneck constrains developments of conventional artificial intelligences (AIs) toward portable, energy efficient, and truly brain‐like systems. Biological synapses connecting neurons deliver neural action potentials by regulating neurotransmitters between presynaptic and postsynaptic membranes. In a similar way, ionotronic transistors and memristors transmit electronic signals through the migration of ionic species in dielectric and resistive switching electrolyte under external electrical field. Thus, utilizing ionotronic devices to emulate synapses and building bionic neural networks opens up a brand‐new path to realize hardware‐based AI. Moreover, it would allow the fabrication of an artificial perception learning system to mimic the human perception system with multi‐sensory learning abilities. This review presents ionotronic devices for neuromorphic engineering applications. The operation mechanisms and brain‐inspired synaptic responses and neural functions are discussed. At last, outlooks for ionotronic devices to construct bionic neural networks are provided.

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Section: Ionotronic Transistorssupporting

confidence: 54%

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Zhu

2019

Physica Rapid Research Ltrs

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“…Heat transfer of concrete is greatly affected by mixing proportion, aggregate type and cementitious particles. In contrast to other thermal properties, such as thermal conductivity and the specific heat of the concrete, however, few studies are available [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Mechanical and thermal properties of prepacked aggregate concrete incorporating palm oil fuel ash

2016

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Prepacked aggregate concrete (PAC) is a special type of concrete which is made by placing coarse aggregate in a formwork and injecting a grout either by pump or under the gravity force to fill the voids. Use of pozzolanic materials in conventional concrete has become increasingly extensive, and this trend is expected to continue in PAC as well. Palm oil fuel ash (POFA) is one of these pozzolanic ash, which has been recognized as a good pozzolanic material. This paper presents the experimental results of the performance behaviour of POFA in developing physical and mechanical properties of prepacked aggregate concrete. Four concrete mixes namely, prepacked concrete with 100% OPC as a control, and PAC with 10, 20 and 30% POFA were cast, and the temperature growth due to heat of hydration and heat transfer in all the mixtures was recorded. It has been found that POFA significantly reduces the temperature rise in prepacked aggregate concrete and delay the transfer of heat to the concrete body. The compressive and tensile strengths, however, increased with replacement up to 20% POFA. The results obtained and the observation made in this study suggest that the replacement of OPC by POFA is beneficial, particularly for prepacked mass concrete where thermal cracking due to extreme heat rise is of great concern.

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“…Due to the known influence of materials porosity on insulation performance, the authors offten predictive models for the thermal insulation performance of concrete taking into account the density, which in turn is closely related to porosity. The authors presented a simple exponential relationship between density, porosity and thermal conductivity in works [ 37 , 38 ]. A two-phase theoretical model for the prediction of the thermal conductivity of concrete was developed by [ 39 ] using the mortar and aggregate as the two components of the model.…”

Section: Introductionmentioning

confidence: 99%

Measurements of Thermal Conductivity of LWC Cement Composites Using Simplified Laboratory Scale Method

et al. 2021

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The implementation of low-energy construction includes aspects related to technological and material research regarding thermal insulation. New solutions are sought, firstly, to reduce heat losses and, secondly, to improve the environment conditions in isolated rooms. The effective heat resistance of insulating materials is inversely proportional to temperature and humidity. Cement composites filled with lightweight artificial aggregates may be a suitable material. Selecting a proper method for measuring the thermal conductivity of concrete is important to achieve accurate values for calculating the energy consumption of buildings. The steady state and transient methods are considered the two main thermal conductivity measurement approaches. Steady state is a constant heat transfer, whereby the temperature or heat flow is time independent. In the transient method, temperature changes over time. Most researchers have measured the conductivity of cement-based materials based on transient methods. The availability and cost of equipment, time for experimental measurements and measurement ability for moist specimens may be some of the reasons for using this method. However, considering the accuracy of the measurements, the steady state methods are more reliable, especially for testing dry materials. Four types of composites were investigated that differed in filler: natural aggregate, sintered fly ash filler, sintered clay and granular foam glass aggregate. The method of preparing the samples for testing is especially important for the obtained results. The samples, with a specific surface roughness, will show a lower coefficient of thermal conductivity by 20–30%; therefore, the selection of the type of contact layer between the plate of the measuring device and the sample is of particular importance.

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Thermal conductivity and heat transfer coefficient of concrete

Cited by 41 publications

References 5 publications

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

RETRACTED ARTICLE: Mechanical and thermal properties of prepacked aggregate concrete incorporating palm oil fuel ash

Measurements of Thermal Conductivity of LWC Cement Composites Using Simplified Laboratory Scale Method

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