We model the latent heats of crystallization and fusion in phase change materials with a unified latent heat of phase change, ensuring energy conservation by coupling the heat of phase change with amorphous and crystalline specific heats. We demonstrate the model with 2-D finite element simulations of Ge2Sb2Te5 and find that the heat of phase change increases local temperature up to 180 K in 300 nm × 300 nm structures during crystallization, significantly impacting grain distributions. We also show in electrothermal simulations of 45 nm confined and 10 nm mushroom cells that the higher amorphous specific heat predicted by this model increases nucleation probability at the end of reset operations. These nuclei can decrease set time, leading to variability, as demonstrated for the mushroom cell.
Resistance drift in amorphous Ge2Sb2Te5 is experimentally characterized in melt-quenched line cells in the range of 300 K to 125 K and is observed to follow the previously reported power-law behavior with drift coefficients in the range of 0.07 to 0.11 in the dark, linearly decreasing with 1/kT. While these drift coefficients measured in the dark are similar to commonly observed drift coefficients (∼0.1) at and above room temperature, measurements under light show a significantly lower drift coefficient (0.05 under illumination vs 0.09 in the dark at 150 K). Periodic on/off switching of light shows a sudden decrease/increase in resistance, attributed to photo-excited carriers, followed by a very slow response (∼30 min at 150 K) attributed to contribution of electron traps and slow trap-to-trap charge exchanges. A device-level electronic model is used to relate these experimental findings to gradual charging of electron traps in amorphous Ge2Sb2Te5, which gives rise to growth of a potential barrier for holes in time and, hence, resistance drift.
The size optimization and economic evaluation of the solar-wind hybrid renewable energy system (RES) to meet the electricity demand of 276 kWh/day with 40 kW peak load have been determined in this study. The load data has been collected from the motels situated in the coastal areas of Patenga, Chittagong. RES in standalone as well as grid connected mode have been considered. The optimal system configurations have been determined based on systems net present cost (NPC) and cost of per unit energy (COE). A standalone solar-wind-battery hybrid system is feasible and economically comparable to the present cost of diesel based power plant if 8% annual capacity shortage is allowed. Grid tied solar-wind hybrid system, where more than 70% electricity contribution is from RES, is economically comparable to present grid electricity price. Moreover, grid tied RES results in more than 60% reduction in greenhouse gases emission compared to the conventional grid. Sensitivity analysis has been performed in this study to determine the effect of capital cost variation or renewable resources variation on the system economy. Simulation result of sensitivity analysis has showed that 20% reduction of installation cost results in nearly 9%-12% reductions in cost of per unit energy.
This study presents a comprehensive investigation of multiple Artificial Intelligence (AI) techniques—decision tree, random forest, gradient boosting, and neural network—to generate improved precipitation estimates over the Upper Blue Nile Basin. All the AI methods merged multiple satellite and atmospheric reanalysis precipitation datasets to generate error-corrected precipitation estimates. The accuracy of the model predictions was evaluated using 13 years (2000–2012) of ground-based precipitation data derived from local rain gauge networks in the Upper Blue Nile Basin region. The results indicate that merging multiple sources of precipitation substantially reduced the systematic and random error statistics in the Upper Blue Nile Basin. The proposed methods have great potential in predicting precipitation over the complex terrain region.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.