Thermal Energy Storage and Heat Transfer of Nano-Enhanced Phase Change Material (NePCM) in a Shell and Tube Thermal Energy Storage (TES) Unit with a Partial Layer of Eccentric Copper Foam.

Thermal energy storage units conventionally have the drawback of slow charging response. Thus, heat transfer enhancement techniques are required to reduce charging time. Using nanoadditives is a promising approach to enhance the heat transfer and energy storage response time of materials that store heat by undergoing a reversible phase change, so-called phase change materials. In the present study, a combination of such materials enhanced with the addition of nanometer-scale graphene oxide particles (called nano-enhanced phase change materials) and a layer of a copper foam is proposed to improve the thermal performance of a shell-and-tube latent heat thermal energy storage (LHTES) unit filled with capric acid. Both graphene oxide and copper nanoparticles were tested as the nanometer-scale additives. A geometrically nonuniform layer of copper foam was placed over the hot tube inside the unit. The metal foam layer can improve heat transfer with an increase of the composite thermal conductivity. However, it suppressed the natural convection flows and could reduce heat transfer in the molten regions. Thus, a metal foam layer with a nonuniform shape can maximize thermal conductivity in conduction-dominant regions and minimize its adverse impacts on natural convection flows. The heat transfer was modeled using partial differential equations for conservations of momentum and heat. The finite element method was used to solve the partial differential equations. A backward differential formula was used to control the accuracy and convergence of the solution automatically. Mesh adaptation was applied to increase the mesh resolution at the interface between phases and improve the quality and stability of the solution. The impact of the eccentricity and porosity of the metal foam layer and the volume fraction of nanoparticles on the energy storage and the thermal performance of the LHTES unit was addressed. The layer of the metal foam notably improves the response time of the LHTES unit, and a 10% eccentricity of the porous layer toward the bottom improved the response time of the LHTES unit by 50%. The presence of nanoadditives could reduce the response time (melting time) of the LHTES unit by 12%, and copper nanoparticles were slightly better than graphene oxide particles in terms of heat transfer enhancement. The design parameters of the eccentricity, porosity, and volume fraction of nanoparticles had minimal impact on the thermal energy storage capacity of the LHTES unit, while their impact on the melting time (response time) was significant. Thus, a combination of the enhancement method could practically reduce the thermal charging time of an LHTES unit without a significant increase in its size.

New neurophysiological human thermal model based on thermoreceptor responses.

A new neurophysiological human thermal model based on thermoreceptor responses, the NHTM model, has been developed to predict regulatory responses and physiological variables in asymmetric transient environments. The passive system is based on Wissler's model, which is more complex and refined. Wissler's model segments the human body into 21 cylindrical parts. Each part is divided into 21 layers, 15 for the tissues and 6 for clothes, and each layer is divided into 12 angular sectors. Thus, we have 3780 nodes for the tissues and 1512 for clothes. The passive system simulates heat exchange within the body and between the body and the surroundings. The active system is composed of the thermoregulatory mechanisms, i.e., skin blood flow, shivering thermogenesis, and sweating. The skin blood flow model and the shivering model are based on thermoreceptor responses. The sweating model is that of Fiala et al. and is based on error signals. The NHTM model was compared with Wissler's model, and the results showed that a calculation based on neurophysiology can improve the performance of the thermoregulation model. The NHTM model was more accurate in the prediction of mean skin temperature, with a mean absolute error of 0.27 °C versus 0.80 °C for the original Wissler model. The prediction accuracy of the NHTM model for local skin temperatures and core temperature could be improved via an optimization method to prove the ability of the new thermoregulation model to fit with the physiological characteristics of different populations.