Multi-objective shape optimization of helico-axial multiphase pump impeller based on NSGA-II and ANN

In order to improve the prototype’s performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by combining the artificial neural network (ANN) with non-dominated sorting genetic algorithm-II (NSGA-II). The main geometric parameters influenc...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Energy conversion and management Ročník 52; číslo 1; s. 538 - 546
Hlavní autoři: Zhang, Jinya, Zhu, Hongwu, Yang, Chun, Li, Yan, Wei, Huan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 2011
Elsevier
Témata:
algorithms
Applied sciences
Computer simulation
Crude oil, natural gas and petroleum products
Crude oil, natural gas, oil shales producing equipements and methods
Energy
Energy. Thermal use of fuels
equipment performance
Exact sciences and technology
experimental design
Fuels
Genetic algorithm
Learning theory
Materials and auxiliary equipments used in energy engineering
Mathematical analysis
Mathematical models
Multiphase
Multiphase pump
Neural network
Neural networks
Numerical simulation
Oil extraction from wells. Pumping. Shale oil extraction. Well workover
Optimization design
prediction
Prospecting and production of crude oil, natural gas, oil shales and tar sands
Pump impellers
Pumps
Numerical simulation
Multiphase pump
Neural network
Genetic algorithm
Optimization design
ISSN:0196-8904, 1879-2227
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In order to improve the prototype’s performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by combining the artificial neural network (ANN) with non-dominated sorting genetic algorithm-II (NSGA-II). The main geometric parameters influencing the impeller’s performance were chosen as the optimization variables, and the sample spaces were structured according to the orthogonal experimental design method. Then the pressure rise and efficiency in specific working conditions were obtained about all the elements in the sample space by numerical simulation. With the simulated results as the input specimen, a multiphase pump performance prediction model was designed through BP neural network. With the obtained prediction model as the fitness value evaluation method, the pump impeller was optimized using the NSGA-II multi-objective genetic algorithm, which finally offered an improved impeller structure with enhanced pressure rise and efficiency. Furthermore, five stages of optimized compression cells were manufactured and applied in experiment test. The result shows compared to the original design, the pressure rise of the optimized pump has increased by ∼10% and the efficiency has increased by ∼3%, which is in keeping with our optimal result and confirms our method is feasible.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2010.07.029