The present work deals with an experimental investigation of charging and discharging processes in thermal storage system using a phase change material PCM. Paraffin wax was used as the PCM which is formed in spherical capsules and packed in a cylindrical packed column which acted as an energy storage system. Air was used as the heat transfer fluid HTF in thermal storage unit. The effect of flow rate and inlet temperature of HTF on the time of charging and discharging process were studied. The results showed that the faster storage of thermal energy can be made by high flow rate of heat transfer fluid HTF and high inlet temperature of heat transfer fluid. It was found that at 65°C HTF inlet temperature, the melting and solidification processes accelerated by 27.9% and 57.14% respectively, when the flow rate was increased from 9 to 24 L/s. Also, when the HTF inlet temperature changed from 65°C to 80°C, the time needed to complete melting process decreased by 38.8%.
Study of the Performance of Paraffin Wax as a Phase Change Material in Packed Bed Thermal Energy Storage System
Phase Change Material PCM
Paraffin wax
Heat transfer fluid HTF
Thermal energy storage TES.
Publication Date
Fri Jun 29 2018
Journal Name
Journal Of The College Of Education For Women
Audio Classification Based on Content Features
Multimedia
Audio classification
Feature extraction
Short time energy
Local Roughness features
First Order Gradient Feature.
Audio classification is the process to classify different audio types according to contents. It is implemented in a large variety of real world problems, all classification applications allowed the target subjects to be viewed as a specific type of audio and hence, there is a variety in the audio types and every type has to be treatedcarefully according to its significant properties.Feature extraction is an important process for audio classification. This workintroduces several sets of features according to the type, two types of audio (datasets) were studied. Two different features sets are proposed: (i) firstorder gradient feature vector, and (ii) Local roughness feature vector, the experimentsshowed that the results are competitive to
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