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Cited 5 time in webofscience Cited 8 time in scopus
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Artificial neural networks modelling for power coefficient of Archimedes screw turbine for hydropower applications

Authors
Paturi, Uma Maheshwera ReddyCheruku, SuryapavanReddy, N. S.
Issue Date
Oct-2022
Publisher
SPRINGER HEIDELBERG
Keywords
Hydropower; Archimedes screw turbine; Power coefficient; Artificial neural networks; Response surface methodology; Quantitative estimation
Citation
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, v.44, no.10
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
Volume
44
Number
10
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/842
DOI
10.1007/s40430-022-03757-8
ISSN
1678-5878
1806-3691
Abstract
Hydrokinetic turbines are the most efficient way to generate energy and electricity in hydropower applications. A hydrokinetic turbine's operational characteristics and physical dimensions affect its efficiency. The relationship between the turbine's geometric configuration and output is complicated and nonlinear. Thus, in the current work, a standalone artificial neural network (ANN) with a graphical user interface (GUI) was used to evaluate the performance of an Archimedes screw turbine (AST). This model used the geometrical configuration of the AST as input variables (axle length, blade stride, blade angle, and diameter ratio) and the power coefficient (C-p) as the only output. Among all the neural network topologies, the ANN model with a 4-3-1 architecture generated the lowest average error and root mean square error (RMSE), respectively, of 0.0211 and 0.0008. The predictions of the ANN model were extremely well congruent with available computational fluid dynamics (CFD) and second-order regression model (SORM) data. Additionally, a virtual hydropower system was developed to quantify the effect of AST factors on hydropower production efficiency. The ANN model projections indicate that the diameter ratio is the most sensitive parameter to AST performance, accounting for 84%, followed by blade stride and other factors. The results revealed that the developed model could accurately evaluate the relationship between the geometric configuration of the AST and its hydropower production efficiency.
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공과대학 (나노신소재공학부금속재료공학전공)
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