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

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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]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Modeling of deviation angle and performance losses in wet steam turbines using GMDH-type neural networks.
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  Data: <searchLink fieldCode="DE" term="%22Steam-turbines%22">Steam-turbines</searchLink><br /><searchLink fieldCode="DE" term="%22GMDH+algorithms%22">GMDH algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Steam+flow%22">Steam flow</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink>
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  Data: 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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  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00521-016-2389-2
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        Text: English
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      – SubjectFull: Steam-turbines
        Type: general
      – SubjectFull: GMDH algorithms
        Type: general
      – SubjectFull: Steam flow
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Performance evaluation
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              Text: Dec2017 Supplement 1
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