Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Hybrid ANN approach for prediction of lubricant retention in compressor suction lines based on analytical model and parametric study
A modeling study for prediction of oil retention in compressor suction lines is presented in this article. A new flow-pattern-based analytical model called the double-circle model (DCM) was developed to evaluate the oil retention amount in suction lines. A new consolidated oil retention database was established to develop interfacial friction factor correlation. Parametric study was conducted by means of force balance analysis to determine the influence factors of oil transport. The DCM and parametric study were validated by the database, and the results showed that they display reliable accuracy. Finally, a hybrid artificial neural network (ANN) approach was proposed, which used the DCM and parametric study to produce extrapolation data and determine the input parameters, respectively. The hybrid ANN was optimized for the 6-13-1 configuration with logarithmic sigmoid (logsig) transfer function and the Lavenberg–Marquardt (L-M) algorithm. The hybrid ANN model prediction yields a mean relative error (MRE) and R 2 of 6.25% and 96.86%, respectively.
Hybrid ANN approach for prediction of lubricant retention in compressor suction lines based on analytical model and parametric study
A modeling study for prediction of oil retention in compressor suction lines is presented in this article. A new flow-pattern-based analytical model called the double-circle model (DCM) was developed to evaluate the oil retention amount in suction lines. A new consolidated oil retention database was established to develop interfacial friction factor correlation. Parametric study was conducted by means of force balance analysis to determine the influence factors of oil transport. The DCM and parametric study were validated by the database, and the results showed that they display reliable accuracy. Finally, a hybrid artificial neural network (ANN) approach was proposed, which used the DCM and parametric study to produce extrapolation data and determine the input parameters, respectively. The hybrid ANN was optimized for the 6-13-1 configuration with logarithmic sigmoid (logsig) transfer function and the Lavenberg–Marquardt (L-M) algorithm. The hybrid ANN model prediction yields a mean relative error (MRE) and R 2 of 6.25% and 96.86%, respectively.
Hybrid ANN approach for prediction of lubricant retention in compressor suction lines based on analytical model and parametric study
Zeng, Weijie (Autor:in) / Gu, Bo (Autor:in) / Zhang, Zhiting (Autor:in) / Hu, Jinting (Autor:in) / Sha, Yuxiong (Autor:in)
Science and Technology for the Built Environment ; 28 ; 999-1011
22.08.2022
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
| Taylor & Francis Verlag | 2022
Influence of Oil on Pressure Drop in Refrigerant Compressor Suction Lines (RP-336)
| British Library Conference Proceedings | 1990
Oil retention and pressure drop of R1234yf and R134a with POE ISO 32 in suction lines
| Taylor & Francis Verlag | 2014
Analytical Performance Study of Suction Piles in Clay
| British Library Conference Proceedings | 1999
Parametric finite element analyses of suction anchors
| British Library Conference Proceedings | 2005