Enhancement of the Thermal Energy Storage Using Heat-Pipe-Assisted Phase Change Material

Usage of phase change materials' (PCMs) latent heat has been investigated as a promising method for thermal energy storage applications. However, one of the most common disadvantages of using latent heat thermal energy storage (LHTES) is the low thermal conductivity of PCMs. This issue affects the rate of energy storage (charging/discharging) in PCMs. Many researchers have proposed different methods to cope with this problem in thermal energy storage. In this paper, a tubular heat pipe as a super heat conductor to increase the charging/discharging rate was investigated. The temperature of PCM, liquid fraction observations, and charging and discharging rates are reported. Heat pipe effectiveness was defined and used to quantify the relative performance of heat pipe-assisted PCM storage systems. Both experimental and numerical investigations were performed to determine the efficiency of the system in thermal storage enhancement. The proposed system in the charging/discharging process significantly improved the energy transfer between a water bath and the PCM in the working temperature range of 50 & DEG;C to 70 & DEG;C

Modeling of shear wave velocity in limestone by soft computing methods

The main purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters. Because rocks have complicated structure, direct determination of this parameter takes time, spends expenditure and requires accuracy. On the other hand, there are no precise equations for indirect determination of it; most of them are empirical. By using data sets of several dams of Iran and neuro-genetic, adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP) methods, models are rendered for prediction of shear wave velocity in limestone. Totally, 516 sets of data has been used for modeling. From these data sets, 413 ones have been utilized for building the intelligent model, and 103 have been used for their performance evaluation. Compressional wave velocity (Vp), density (γ) and porosity (n), were considered as input parameters. Respectively, the amount of R for neuro-genetic and ANFIS networks was 0.959 and 0.963. In addition, by using GEP, three equations are obtained; the best of them has 0.958R. ANFIS shows the best prediction results, whereas GEP indicates proper equations. Because these equations have accuracy, they could be used for prediction of shear wave velocity for limestone in the future. Keywords: Shear wave velocity, Limestone, Neuro-genetic, Adaptive neuro-fuzzy inference system, Gene expression programmin