Fabrication and characterization of form-stable FA ternary eutectic mixture/GO nanocomposite for thermal energy storage

The thermal conductivity of commonly used phase change materials (PCM) for thermal energy storage (TES), such as, fatty acids, paraffin etc., is relatively poor, which is one of the main drawbacks for limiting their utility. In the recent past, few attempts have been made to enhance the thermal conductivity of PCM by mixing different additives in the appropriate amount. Graphene nanoparticles, having higher thermal conductivity may be a potential candidate for the same, when mixed appropriately with different PCM. In present study, acetic acid , tristearin and stearic acid (CA-PA-SA) ternary eutectic mixture was impregnated into nano-graphene oxide (nano-GO) to prepare a form-stable composite PCM (CAPA-SA/nano-SiO2). The phase change temperature range of the composite PCM is 17.2 ◦C–26 ◦C, which is suitable for indoor thermal environment. The high latent heat value and thermal conductivity of the composite PCM are 169.43 kJ kg-1 and 0.68239 W (m K)-1, which is helpful to stabilize the indoor temperature for a long time and improve human comfort. Furthermore, after 500 heating/cooling cycles, the composite PCM showed good thermal and chemical stability. All the results indicated that the composite PCM is suitable for storing excess solar radiation and reducing the amount and rate of heat loss of buildings in the winter, which will help reduce building energy consumption

Comparison of the Experimental and Predicted Data for Thermal Conductivity of Fe3O4/water Nanofluid Using Artificial Neural Networks

Objective(s): This study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ANN) and correlation using experimental data. Methods: Two-layer perceptron feedforward artificial neural network and backpropagation Levenberg-Marquardt (BP-LM) training algorithm are used to predict the thermal conductivity of the nanofluid. Fe3O4 nanoparticles are prepared by chemical co-precipitation method and thermal conductivity coefficient is measured using 2500TPS apparatus. Results: Fe3O4 nanofluids with particle size of 20-25 nm are used to test the effectiveness of ANN. Thermal conductivity of Fe3O4 /water nanofluid at different temperatures of 25, 30 and 35℃ and volume concentrations, ranging from 0.05% to 5% is employed as training data for ANN. The obtained results show that the thermal conductivity of Fe3O4 nanofluid increases linearly with volume fraction and temperature. Conclusions: the artificial neural network model has a reasonable agreement in predicting experimental data. So it can be concluded the ANN model is an effective method for prediction of the thermal conductivity of nanofluids and has better prediction accuracy and simplicity compared with the other existing theoretical methods

Hamid Mohammadiun Real-Time Evaluation of Severe Heat Load Over Moving Interface of Decomposing Composites

Decomposing composites undergo both surface removal and in-depth decomposition, when they are subjected to severe heating environments. As a result, the gas phase and the chemical species are injected into the boundary layer, resulting in a reduction of the heat flux entering into the solid structure. Under such conditions that geometry changes, the reconstruction of heat flux at the ablating front is quite complicated. Utilizing a procedure based on the sequential function specification method, an inverse problem is solved to anticipate the front-surface heating condition. Temperature measurements as well as measurement of the position of the ablating surface accompanied with additive noises are used for the implementation of the current procedure. Taking into account a complex set of phenomena, a numerical experiment is employed to examine the accuracy and appropriateness of the proposed technique for such problems. The results obtained demonstrate the usefulness and efficiency of the proposed method for the estimation of heat flux at the moving boundary of decomposing materials