In an hour, the Earth is impacted with enough solar energy to power the world for an entire year. The best way to expend this renewable source of energy is by storing solar power. Many solar energy harvesting methods only produce power when directly exposed to sunlight. This issue can be resolved by implementing thermal energy storage (TES) systems. This paper presents a novel method for increasing the efficiency of TES systems for building applications. Efficiency is determined by two main factors: heat storage capacity and thermal conductivity. Although latent systems have lower energy storage densities than other types of heat storage technologies, they are an inexpensive and sustainable energy harvesting system. Additionally, the disadvantage associated with lower energy density can be counteracted by improving the charging rate of latent energy storage systems. Therefore, this work focuses on Latent TES systems and how to improve their efficiencies. This paper presents a novel approach for increasing the thermal conductivity of latent heat storage systems using graphene foams. The high thermal conductivity of graphene foam will help counteract the low conductivity of the PCMs with a small sacrifice of the effective latent heat. The expected effect is a doubled charging rate and increased efficiency within the heat storage system.

This study performs numerical modeling for the climate of semi-arid regions by running a high-resolution atmospheric model constrained by large-scale climatic boundary conditions, a practice commonly called climate downscaling. These investigations focus especially on precipitation and temperature, quantities that are critical to life in semi-arid regions. Using the Weather Research and Forecast (WRF) model, a non-hydrostatic geophysical fluid dynamical model with a full suite of physical parameterization, a series of numerical sensitivity experiments are conducted to test how the intensity and spatial/temporal distribution of precipitation change with grid resolution, time step size, the resolution of lower boundary topography and surface characteristics. Two regions, Arizona in U.S. and Aral Sea region in Central Asia, are chosen as the test-beds for the numerical experiments: The former for its complex terrain and the latter for the dramatic man-made changes in its lower boundary conditions (the shrinkage of Aral Sea). Sensitivity tests show that the parameterization schemes for rainfall are not resolution-independent, thus a refinement of resolution is no guarantee of a better result. But, simulations (at all resolutions) do capture the inter-annual variability of rainfall over Arizona. Nevertheless, temperature is simulated more accurately with refinement in resolution. Results show that both seasonal mean rainfall and frequency of extreme rainfall events increase with resolution. For Aral Sea, sensitivity tests indicate that while the shrinkage of Aral Sea has a dramatic impact on the precipitation over the confine of (former) Aral Sea itself, its effect on the precipitation over greater Central Asia is not necessarily greater than the inter-annual variability induced by the lateral boundary conditions in the model and large scale warming in the region. The numerical simulations in the study are cross validated with observations to address the realism of the regional climate model. The findings of this sensitivity study are useful for water resource management in semi-arid regions. Such high spatio-temporal resolution gridded-data can be used as an input for hydrological models for regions such as Arizona with complex terrain and sparse observations. Results from simulations of Aral Sea region are expected to contribute to ecosystems management for Central Asia.