An artificial neural network approach to compressor performance prediction

Ghorbanian, K.; Gholamrezaei, Mohammad

doi:10.1016/j.apenergy.2008.06.006

Cited by 133 publications

(61 citation statements)

References 23 publications

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“…The network is trained to associate the output with trained input patterns and subsequently predict the parameter values for given new maps. This method was applied to a compressor and was reported by [11]. The disadvantage of this method is that the training of the input neurons will have to rely heavily on a great quantity of existing data in order to accurately predict new map.…”

Section: Literature Reviewmentioning

confidence: 99%

Turbocharger matching methodology for improved exhaust energy recovery

Pesiridis

Salim

Martinez-Botas

2012

10th International Conference on Turbochargers and Turbocharging

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Section: Literature Reviewmentioning

confidence: 99%

Turbocharger matching methodology for improved exhaust energy recovery

Pesiridis

Salim

Martinez-Botas

2012

10th International Conference on Turbochargers and Turbocharging

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“…Several other ANN modelling studies have been reported recently, but they focused on modelling isolated system components [41][42][43][44][45][46].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies

et al. 2015

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“…Table 7 compares results obtained using the integrated FIS and ANN with those obtained using ANN in Tsoutsanis et al 12 and Ghorbanian and Gholamrezaei. 13 The same test cases are used for these results. However, as it can be seen, in approach I, the SR of ANN method is not available.…”

Section: Case I: Eight-stage Axial Compressormentioning

confidence: 99%

Application of integrated fuzzy logic and neural networks to the performance prediction of axial compressors

Gholamrezaei

Ghorbanian

2015

Proceedings of the Institution of Mechanical Engineers, Part A:

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An integrated fuzzy logic-neural network methodology is presented as a mean to improve the reconstruction of the performance map of axial compressors and fans. The learning capability of artificial neural network technique is integrated to the knowledge aspect of fuzzy inference system to offer enhanced prediction capabilities compared to using a single methodology independently. The proposed technique incorporates information of experimental data on surge, operating, and choke lines at any arbitrary but fixed rotational speed. A comparison of the predicted results with experimental data reveals a very good agreement. The proposed technique has the capability to model the nonlinear surge line as well as the kink in the performance map. Application of the method for compressor map generation showed that the proposed technique is robust and capable of enhancing any performance simulation tool used for the dynamic simulation and condition monitoring.

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An artificial neural network approach to compressor performance prediction

Cited by 133 publications

References 23 publications

Turbocharger matching methodology for improved exhaust energy recovery

Turbocharger matching methodology for improved exhaust energy recovery

Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies

Application of integrated fuzzy logic and neural networks to the performance prediction of axial compressors

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