Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application
This work provides a detailed description of the fluid dynamic design of a low specific-speed industrial pump centrifugal impeller. The main goal is to guarantee a certain value of the specific-speed number at the design flow rate, while satisfying geometrical constraints and industrial feasibility....
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
|
| Series: | International Journal of Rotating Machinery |
| Online Access: | http://dx.doi.org/10.1155/2011/817547 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832564276639825920 |
|---|---|
| author | Matteo Checcucci Federica Sazzini Michele Marconcini Andrea Arnone Mario Coneri Luigi De Franco Matteo Toselli |
| author_facet | Matteo Checcucci Federica Sazzini Michele Marconcini Andrea Arnone Mario Coneri Luigi De Franco Matteo Toselli |
| author_sort | Matteo Checcucci |
| collection | DOAJ |
| description | This work provides a detailed description of the fluid dynamic design of a low specific-speed industrial pump centrifugal impeller. The main goal is to guarantee a certain value of the specific-speed number at
the design flow rate, while satisfying geometrical constraints and industrial feasibility. The design procedure relies on a modern optimization technique such as an Artificial-Neural-Network-based approach (ANN). The impeller geometry is parameterized in order to allow geometrical variations over a large design space. The computational framework suitable
for pump optimization is based on a fully viscous three-dimensional numerical solver, used for the impeller analysis. The performance prediction of the pump has been obtained by coupling the CFD analysis with a 1D correlation tool, which accounts for the losses due to the other components not included in the CFD domain. Due to both manufacturing and geometrical constraints, two different optimized impellers with 3 and 5 blades have been developed, with the
performance required in terms of efficiency and suction capability. The predicted performance of both configurations were compared with the measured head and efficiency characteristics. |
| format | Article |
| id | doaj-art-8e14c100f0ae47aeac561eb8ff7d21d1 |
| institution | Kabale University |
| issn | 1023-621X 1542-3034 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Rotating Machinery |
| spelling | doaj-art-8e14c100f0ae47aeac561eb8ff7d21d12025-02-03T01:11:22ZengWileyInternational Journal of Rotating Machinery1023-621X1542-30342011-01-01201110.1155/2011/817547817547Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller ApplicationMatteo Checcucci0Federica Sazzini1Michele Marconcini2Andrea Arnone3Mario Coneri4Luigi De Franco5Matteo Toselli6“Sergio Stecco” Department of Energy Engineering, University of Florence, Via di Santa Marta 3, 50139 Firenze, Italy“Sergio Stecco” Department of Energy Engineering, University of Florence, Via di Santa Marta 3, 50139 Firenze, Italy“Sergio Stecco” Department of Energy Engineering, University of Florence, Via di Santa Marta 3, 50139 Firenze, Italy“Sergio Stecco” Department of Energy Engineering, University of Florence, Via di Santa Marta 3, 50139 Firenze, ItalyTermomeccanica Pompe S.p.A, Via del Molo 3, 19126 La Spezia, ItalyTermomeccanica Pompe S.p.A, Via del Molo 3, 19126 La Spezia, ItalyTermomeccanica Pompe S.p.A, Via del Molo 3, 19126 La Spezia, ItalyThis work provides a detailed description of the fluid dynamic design of a low specific-speed industrial pump centrifugal impeller. The main goal is to guarantee a certain value of the specific-speed number at the design flow rate, while satisfying geometrical constraints and industrial feasibility. The design procedure relies on a modern optimization technique such as an Artificial-Neural-Network-based approach (ANN). The impeller geometry is parameterized in order to allow geometrical variations over a large design space. The computational framework suitable for pump optimization is based on a fully viscous three-dimensional numerical solver, used for the impeller analysis. The performance prediction of the pump has been obtained by coupling the CFD analysis with a 1D correlation tool, which accounts for the losses due to the other components not included in the CFD domain. Due to both manufacturing and geometrical constraints, two different optimized impellers with 3 and 5 blades have been developed, with the performance required in terms of efficiency and suction capability. The predicted performance of both configurations were compared with the measured head and efficiency characteristics.http://dx.doi.org/10.1155/2011/817547 |
| spellingShingle | Matteo Checcucci Federica Sazzini Michele Marconcini Andrea Arnone Mario Coneri Luigi De Franco Matteo Toselli Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application International Journal of Rotating Machinery |
| title | Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application |
| title_full | Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application |
| title_fullStr | Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application |
| title_full_unstemmed | Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application |
| title_short | Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application |
| title_sort | assessment of a neural network based optimization tool a low specific speed impeller application |
| url | http://dx.doi.org/10.1155/2011/817547 |
| work_keys_str_mv | AT matteocheccucci assessmentofaneuralnetworkbasedoptimizationtoolalowspecificspeedimpellerapplication AT federicasazzini assessmentofaneuralnetworkbasedoptimizationtoolalowspecificspeedimpellerapplication AT michelemarconcini assessmentofaneuralnetworkbasedoptimizationtoolalowspecificspeedimpellerapplication AT andreaarnone assessmentofaneuralnetworkbasedoptimizationtoolalowspecificspeedimpellerapplication AT marioconeri assessmentofaneuralnetworkbasedoptimizationtoolalowspecificspeedimpellerapplication AT luigidefranco assessmentofaneuralnetworkbasedoptimizationtoolalowspecificspeedimpellerapplication AT matteotoselli assessmentofaneuralnetworkbasedoptimizationtoolalowspecificspeedimpellerapplication |