Predicting Errosion Pattern on Archimedes Screw Runner Blade of Hydro Power
One of the fundamental studies of Micro hydro power with low head is the Archimedes screw runner. This research focuses on the study of behaviour of fluid flow acting on the Archimedes screw runner blade and prediction of erosion pattern from fluid flow. The CAD model of the Archimedes screw blade w...
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2013
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Online Access: | http://umpir.ump.edu.my/id/eprint/5760/ http://umpir.ump.edu.my/id/eprint/5760/1/MME-009.pdf |
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ump-57602018-02-14T07:26:17Z http://umpir.ump.edu.my/id/eprint/5760/ Predicting Errosion Pattern on Archimedes Screw Runner Blade of Hydro Power N. M. M., Ammar Ahmad Alif, Ahmad Adam Nurul Suraya, Azahari TJ Mechanical engineering and machinery One of the fundamental studies of Micro hydro power with low head is the Archimedes screw runner. This research focuses on the study of behaviour of fluid flow acting on the Archimedes screw runner blade and prediction of erosion pattern from fluid flow. The CAD model of the Archimedes screw blade was re-inversely design from past researcher. In order to determine the flow pattern, computational fluid dynamic, CFD was used to obtain the pressure distribution along blade structure. Analysis of three-dimensional with steady state flow was done with a standard k-ε as the turbulence model. The result obtained from pressure pattern shows that the highest value of coefficient of pressure obtained at the blade tip. This result shows that the prediction of crack may occur mostly at the blade tip. Further studies of blade design can be improved by reducing crack. 2013 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5760/1/MME-009.pdf N. M. M., Ammar and Ahmad Alif, Ahmad Adam and Nurul Suraya, Azahari (2013) Predicting Errosion Pattern on Archimedes Screw Runner Blade of Hydro Power. In: Malaysian Technical Universities Conference on Engineering & Technology (MUCET 2013), 3-4 December 2013 , Kuantan, Pahang. pp. 1-2.. |
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TJ Mechanical engineering and machinery |
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TJ Mechanical engineering and machinery N. M. M., Ammar Ahmad Alif, Ahmad Adam Nurul Suraya, Azahari Predicting Errosion Pattern on Archimedes Screw Runner Blade of Hydro Power |
description |
One of the fundamental studies of Micro hydro power with low head is the Archimedes screw runner. This research focuses on the study of behaviour of fluid flow acting on the Archimedes screw runner blade and prediction of erosion pattern from fluid flow. The CAD model of the Archimedes screw blade was re-inversely design from past researcher. In order to determine the flow pattern, computational fluid dynamic, CFD was used to obtain the pressure distribution along blade structure. Analysis of three-dimensional with steady state flow was done with a standard k-ε as the turbulence model. The result obtained from pressure pattern shows that the highest value of coefficient of pressure obtained at the blade tip. This result shows that the prediction of crack may occur mostly at the blade tip. Further studies of blade design can be improved by reducing crack. |
format |
Conference or Workshop Item |
author |
N. M. M., Ammar Ahmad Alif, Ahmad Adam Nurul Suraya, Azahari |
author_facet |
N. M. M., Ammar Ahmad Alif, Ahmad Adam Nurul Suraya, Azahari |
author_sort |
N. M. M., Ammar |
title |
Predicting Errosion Pattern on Archimedes Screw Runner Blade of Hydro Power |
title_short |
Predicting Errosion Pattern on Archimedes Screw Runner Blade of Hydro Power |
title_full |
Predicting Errosion Pattern on Archimedes Screw Runner Blade of Hydro Power |
title_fullStr |
Predicting Errosion Pattern on Archimedes Screw Runner Blade of Hydro Power |
title_full_unstemmed |
Predicting Errosion Pattern on Archimedes Screw Runner Blade of Hydro Power |
title_sort |
predicting errosion pattern on archimedes screw runner blade of hydro power |
publishDate |
2013 |
url |
http://umpir.ump.edu.my/id/eprint/5760/ http://umpir.ump.edu.my/id/eprint/5760/1/MME-009.pdf |
first_indexed |
2023-09-18T22:01:09Z |
last_indexed |
2023-09-18T22:01:09Z |
_version_ |
1777414406611664896 |