Graphite Foam Infused with Pentaglycerine for Solid-State Thermal Energy Storage

The use of a solid-state phase change material, pentaglycerine, in thermal energy storage was investigated. The motivation for exploring a thermal energy storage system that relies on a solid-state phase transition is to eliminate phase change material leakage and sealing issues. Pentaglycerine was effectively injected into graphite foam, and this combination was studied for potential use in a thermal energy storage device. Graphite foam samples that contained pentaglycerine demonstrated a storage capacity that was close to the theoretical capacity. The graphite foam infused with pentaglycerine retained 100% of its storage capacity after 59 separate thermal cycles under various conditions, with many of those cycles contiguous. It was subjected to a 28% duty cycle of applied heat flux under active cooling conditions, and the duty cycle of the sample was not adversely affected by subcooling of the pentaglycerine. The one-dimensional model developed for this study assumed a homogeneous mixture of pentaglycerine and foam which were in local thermal equilibrium with each other. The numerical results reasonably represented the effects of phase change as reflected by the temperature histories for several locations within a graphite foam–pentaglycerine sample. The current study showed that the graphite foam–pentaglycerine combination has potential for use in thermal energy storage devices

Flow Pattern Identification of Horizontal Two-Phase Refrigerant Flow Using Neural Networks

In this work, electrical capacitance tomography (ECT) and neural networks were used to automatically identify two-phase flow patterns for refrigerant R-134a flowing in a horizontal tube. In laboratory experiments, high- speed images were recorded for human classification of liquid–vapor flow patterns. The corresponding permittivity data obtained from tomograms was then used to train feedforward neural networks to recognize flow patterns. An objective was to determine which subsets of data derived from tomograms could be used as input data by a neural network to classify nine liquid–vapor flow patterns. Another objective was to determine which subsets of input data provide high identification success when analyzed by a neural network. Transitional flow patterns associated with common horizontal flow patterns were considered. A unique feature of the current work was the use of the vertical center of mass coordinate in pattern classification. The highest classification success rates occurred using neural network input which included the probability density functions (in time) for both spatially averaged permittivity and center of mass location in addition to the four statistical moments (in time) for spatially averaged permittivity data. The combination of these input data resulted in an average success rate of 98.1% for nine flow patterns. In addition, 99% of the experimental runs were either correctly classified or misclassified by only one flow pattern