涡轮机
计算流体力学
尾水管
套管
水力发电
人工神经网络
海洋工程
航程(航空)
发电
计算机科学
工程类
机械工程
功率(物理)
模拟
人工智能
电气工程
航空航天工程
物理
量子力学
作者
Kutay Çelebioğlu,Ece Aylı,Huseyin Cetinturk,Yiğit Taşcıoğlu,Selin Aradağ
标识
DOI:10.1177/09544089231224324
摘要
In this study, investigations were conducted using computational fluid dynamics (CFD) to assess the applicability of a Francis-type water turbine within a pipe. The objective of the study is to determine the feasibility of implementing a turbine within a pipe and enhance its performance values within the operating range. The turbine within the pipe occupies significantly less space in hydroelectric power plants since a spiral casing is not used to distribute the flow to stationary vanes. Consequently, production and assembly costs can be reduced. Hence, there is a broad scope for application, particularly in small and medium-scale hydroelectric power plants. According to the results, the efficiency value increases on average by approximately 1.5% compared to conventional design, and it operates with higher efficiencies over a wider flow rate range. In the second part of the study, machine learning was employed for the efficiency prediction of an inline-type turbine. An appropriate Artificial Neural Network (ANN) architecture was initially obtained, with the Bayesian Regularization training algorithm proving to be the best approach for this type of problem. When the suitable ANN architecture was utilized, the prediction was found to be in good agreement with CFD, with an root mean squared error value of 0.194. An R 2 value of 0.99631 was achieved with the appropriate ANN architecture.
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