Search results for: Rustan Taraka

2 An Analysis on Thermal Energy Storage in Paraffin-Wax Using Tube Array on a Shell and Tube Heat Exchanger

Authors: Syukri Himran, Rustan Taraka, Anto Duma

Abstract:

The aim of the study is to improve the understanding of latent and sensible thermal energy storage within a paraffin wax media by an array of cylindrical tubes arranged both in in-line and staggered layouts. An analytical and experimental study was carried out in a horizontal shell-and-tube type system during the melting process. Pertamina paraffin-wax was used as a phase change material (PCM), where as the tubes are embedded in the PCM. From analytical study we can obtain the useful information in designing a thermal energy storage such as : the motion of interface, amount of material melted at any time in the process, and the heat storage characteristic during melting. The use of staggered tubes is proposed as superior to in-line layout for thermal storage. The experimental study was used to verify the validity of the analytical predictions. From the comparisons, the analytical and experimental data are in a good agreement.

Keywords: latent, sensible, paraffin-wax, thermal energy storage, conduction, natural convection

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1 Neural Style Transfer Using Deep Learning

Authors: Shaik Jilani Basha, Inavolu Avinash, Alla Venu Sai Reddy, Bitragunta Taraka Ramu

Abstract:

We can use the neural style transfer technique to build a picture with the same "content" as the beginning image but the "style" of the picture we've chosen. Neural style transfer is a technique for merging the style of one image into another while retaining its original information. The only change is how the image is formatted to give it an additional artistic sense. The content image depicts the plan or drawing, as well as the colors of the drawing or paintings used to portray the style. It is a computer vision programme that learns and processes images through deep convolutional neural networks. To implement software, we used to train deep learning models with the train data, and whenever a user takes an image and a styled image, the output will be as the style gets transferred to the original image, and it will be shown as the output.

Keywords: neural networks, computer vision, deep learning, convolutional neural networks

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