In some climates, massive buildings made of stone, masonry, concrete, earth and … can be utilized as one of the simplest and most effective ways of reducing building heating and cooling loads. Very often such savings could be achieved in the design stage of the building and with a relatively low-cost. Such declines in building envelope heat losses combined with optimized material configuration and proper amount of thermal insulation in the building envelope could help to decrease the building's cooling and heating energy demands and building related co2 emission into environment. This paper presents a typical study of thermal mass buildings, especially, a kind of masonry building called YAKHCHAL, where most of the buildings are constructed out of mud or sun-dried bricks. They behave like a thermal mass building types. In this climate, there are great many buildings which have been adapted to their climatic conditions. Such traditional solutions may help to overcome the energy crisis which the mankind faces today and may face in the future.

In this paper, efficiency of defected graphene nano ribbon incorporated with additional nanoparticles on mass detection operations is studied via the Reverse Non Equilibrium Molecular Dynamics (RNEMD) method. Thermal conductivity management of this structure is challenging because of imposed losses in electrical conductivity and any procedure that could manage the thermal conductivity of grapheme will be useful. In this paper it is observed that on the mass detection operation, due to the porosity generation in the nano ribbon surface or even creation of external nanoparticles, thermal properties of graphene change considerably. This should be noted in calibration of graphene based mass sensors. In summary, Results show that the graphene’s thermal conductivity would reduce by increasing the concentration of nanoparticles and thermal conductivity of graphene is higher when porosities and impurities are at the edges. This indicates that the location of vacancies and nanoparticles influences the thermal conductivity. For a better thermal management with the help of nanoparticles, with respect to the porosities, addition of nanoparticles decreases the thermal conductivity more and more. By increasing the cavity’s diameter from 0.5nm to 4.4nm in a specific single layer graphene, thermal conductivity was reduced from 67 W/mK to 1.43 W/mK.

The use of concrete in special structures always faces challenges in implementation. By increasing the amount of cement to increase the compressive strength, the thermal gradient between the surface and the center of the concrete due to hydration heat will lead to an increase in thermal stress. On the other hand, due to the highly congested rebars in massive structural members of reinforced concrete such as columns of high-rise structures, the use of self-compacting concrete will facilitate the implementation and therefore understanding the thermal behavior of concrete and comparing it with ordinary concrete can be a good ground for studying crack risk. In this paper, the evaluation of thermal and mechanical properties affected by the application of high strength self-compacting mass concrete regime with three ratios of water to cement ratio and two cement content has been done in the form of twelve mixed designs. The results show that self-compacting concrete in addition to better mechanical properties on the surface and core of high strength mass concrete had different and more suitable thermal regime compared to ordinary concrete. Its lower strain and higher stress conversion time reduces thermal stress and ultimately reduces the risk of cracking up to thirty-six percent.

Thermal mass is the material's ability to store heat and release it after an amount of time and concrete is considered one of the best thermal mass material. Since concrete has been used widely in many building constructions, by considering the capability of concrete in terms of thermal mass, it is worthwhile to use this ability of concrete in order to build buildings more healthy and comfortable for an increase in the occupants’ performance. Ventilated Hollow Core Slab (VHCS) is one of the efficient ways to provide adequate thermal mass within buildings. The present study aimed to assess the thermal performance of VHCS; and its effect on the occupant's thermal comfort of a college building located in Luton, England, using a VHCS system as the exposed thermal mass. Various techniques have been used over two weeks and the recorded data were analyzed. Based on the findings from the review of existing literature in the field and the integrated approach outlined in this paper, results indicate that the application of VHCS as a thermal mass in university buildings decrease not only the daily temperature fluctuation but also the number of times with extreme heat or colds. Results also show the influence of the system on the level of habitants’ thermal comfort; though, this influence could be varied hinge on physical and psychological factors.

This paper examines the environmental conditions of basements in the hot arid city of Kashan. To this end, basements of traditional houses were chosen with depths ranging from 3 to 14 meters. Environmental data measured for these houses was then compared with the baseline data measured for the roof. A comparison was then made with thermal comfort conditions using Givoni Diagram. The results indicate that while the outdoor summer temperatures vary between 27o to 43 o Celsius, the average indoor temperature of the basement is always in the comfort range of 23 o to 29 o. The temperature fluctuations decrease with the depth of the basement. In deeper basements with little infiltration, the fluctuations are as small as only 1 o. Connecting the wind-catcher shafts to the basement is a clever solution to balance humidity and temperature for further human comfort.

A4 explosive, containing 96.5 wt. % RDX and 3.5 wt. % paraffin wax, is one of the main components in the ammunitions, and warheads of military rockets and missiles. In this work, the thermal behavior and the decomposition kinetics of this explosive has been studied experimentally by non-isothermal differential thermal analysis technique (DTA) and thermal gravimetric (TG) methods, under various heating rates (2.0-8.0 oC/min). Kinetic parameters such as activation energy and frequency factor and critical ignition temperatures for thermal decomposition of these explosives have been evaluated via the fitting-model and free-model methods, proposed by International Confederation for Thermal Analysis and Calorimetry (ICTAC). The results show a single thermal decomposition process for A4, with the integral fitting- model of [-Ln (1-a)] 1/3, indicating an autocatalytic degradation with 3-dimensional diffusion mechanism. The mean kinetic parameters of activation energy (Ea) and A of exothermic decomposition of these explosives, calculated by Kissinger, Ozawa, Friedman and KAS methods, are near to 190.397 kJ/mol and 1.89E+22 min-1. The kinetics results were used for the estimation of mass loss prediction of mentioned explosives and were statistically compared to experimental accelerated ageing consequences. By using this method, it is possible to be predicted the lifetime of explosives in the temperatures near to decompositions regions.

A pressure-based coupled heat and mass transfer model was used to simulate temperature and soil suction in a drying process within a clay soil column. Closed form functions were used for all parameters needed in the governing equations. Model predictions were compared with experimental data using the mean relative percentage deviation method. Thermocouples and mini-gypsum blocks were used to monitor the data collected hourly at different depths of the soil column. The model showed very high sensitivity to the proposed hydraulic conductivity function, while lower sensitivity was found for the proposed thermal conductivity function. This result highlights the importance of a proper hydraulic conductivity estimate while a rough estimate for thermal conductivity would have no significant adverse effect on the predicted values.