School of Mechanical Engineering Seminar
Wednesday, April 17, 2019 at 14:00
Wolfson Building of Mechanical Engineering, Room 206

Wind Effect on an Induced Flow Air Cooled Condenser

Dvir Mendler

 M.Sc. student of Prof. Alexander Gelfgat

Air Cooled Condensers (ACC) are the largest and most expensive part of the thermoelectric power plant. The ACC role in the Organic Rankine Cycle (ORC) is crucial to the power plant power output since its performance sets the turbine back pressure. In this work an "induced flow ACC" will be investigated, which means the fans are located above the tubes bundle and intake air from it, then create a jet upwards. The ACC size can reach hundreds of meters length and ~10 meters width with height of 5-12 meters. This geometry specification demands the use of large ground areas which exposed to ambient conditions.

During normal measures of the power plant performance, a sudden decrease of power output appeared, this power output reduction was directly linked to wind velocity at the site. In addition to that, ACC fans showed large oscillations in power consumption and a rise of back pressure was noticed during winds. All the above connect the ACC performance to the reduction of power output, hence a computational experiment using CFD was planned to produce a complete picture of the physical phenomena.

Once the computational model was produced from ~10 million cells, a case was built with the main components characteristics as elaborated in this work. The analysis pointed to a decrease in fans flow rate during winds, that leads to a decrease of the effective heat exchanging area of the ACC bundle. This observation was validated by actual measurements of the fans flow rate. The analysis also showed unique flow pattern that was validated by smoke tests. Calculating the decrease of fans flow rate with the effective heat exchange area explains the decrease of power output.

A comprehensive study of the flow field using many analyzes to eliminate different theories, produced an explanation for the flow behavior causing this decrease in performance. The full theory behind this flow phenomena is given in the results and conclusion chapter of this work. In addition, a simple cost-effective solution is offered to reduce wind effect on ACC with minimal impact on normal operation.

You are invited to attend a lecture

Light Field Refocusing based on Sparse
Angular Information

 By:

Shachar Ben Dayan
M.Sc student under supervision of Prof. David Mendlovic and Dr. Raja Giryes
Abstract
Light-field photography has attracted significant academic attention in recent years due to its unique capability to extract depth without active components. While 2D cameras only capture the total amount of light at each pixel on the sensor, namely, the projection of the light in the scene, light-field cameras also record the direction and intensity of each ray intersecting with the sensor in a single capture. Thus, light field images contain spatial and angular information of a given scene.
This property of light-field images is the anchor for many useful applications, such as depth estimation, super-resolution and post-capture refocusing. The later application, at its current implementations, is based on a dense light-field, meaning high angular resolution. However, since in light-field cameras the same sensor records the spatial as well as the angular resolution, there is a trade-off between them, known as the angular-spatial trade-off. Therefore, it is of great interest to be able to perform the various light field applications relying only on sparse angular data.
In this work, we propose a technique for post-capture refocusing that uses a very low angular resolution: only 2x2 sub-aperture views. We exploit the power of convolutional neural networks (CNNs) for this task. The four input views are fed into a CNN, which outputs a refocused image according to a given focus plane. This algorithm shows improved performance in terms of PSNR, SSIM and computational time, compared to the current state-of-the-art that synthesizes from a small number of views a full light-field view in order to refocus one image. Our algorithm is applicable on clean data as well as on noisy data, and is not dependent of the capturing device of the light-field images.
 

On Wednesday, March 27, 2019 at 9:00
Room 011, Kitot Building