Modeling of deviation angle and performance losses in wet steam turbines using GMDH-type neural networks.

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Bibliographic Details
Title:	Modeling of deviation angle and performance losses in wet steam turbines using GMDH-type neural networks.
Authors:	Bagheri-Esfe, Hamed¹ h.bagheri@aut.ac.ir, Safikhani, Hamed²
Source:	Neural Computing & Applications. Dec2017 Supplement 1, Vol. 28, p489-501. 13p.
Subjects:	Steam-turbines, GMDH algorithms, Steam flow, Artificial neural networks, Performance evaluation
Abstract:	In the present study group method of data handling (GMDH) type of artificial neural networks are used to model deviation angle ( θ), total pressure loss coefficient ( ω), and performance loss coefficient ( ξ) in wet steam turbines. These parameters are modeled with respect to four input variables, i.e., stagnation pressure ( P ), stagnation temperature ( T ), back pressure ( P ), and inflow angle ( β). The required input and output data to train the neural networks has been taken from numerical simulations. An AUSM-Van Leer hybrid scheme is used to solve two-phase transonic steam flow numerically. Based on results of the paper, GMDH-type neural networks can successfully model and predict deviation angle, total pressure loss coefficient, and performance loss coefficient in wet steam turbines. Absolute fraction of variance ( R ) and root-mean-squared error related to total pressure loss coefficient ( ω) are equal to 0.992 and 0.002, respectively. Thus GMDH models have enough accuracy for turbomachinery applications. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	In the present study group method of data handling (GMDH) type of artificial neural networks are used to model deviation angle ( θ), total pressure loss coefficient ( ω), and performance loss coefficient ( ξ) in wet steam turbines. These parameters are modeled with respect to four input variables, i.e., stagnation pressure ( P ), stagnation temperature ( T ), back pressure ( P ), and inflow angle ( β). The required input and output data to train the neural networks has been taken from numerical simulations. An AUSM-Van Leer hybrid scheme is used to solve two-phase transonic steam flow numerically. Based on results of the paper, GMDH-type neural networks can successfully model and predict deviation angle, total pressure loss coefficient, and performance loss coefficient in wet steam turbines. Absolute fraction of variance ( R ) and root-mean-squared error related to total pressure loss coefficient ( ω) are equal to 0.992 and 0.002, respectively. Thus GMDH models have enough accuracy for turbomachinery applications. [ABSTRACT FROM AUTHOR]
ISSN:	09410643
DOI:	10.1007/s00521-016-2389-2