LAPSE:2019.0764
Published Article
LAPSE:2019.0764
A Performance Prediction Method for Pumps as Turbines (PAT) Using a Computational Fluid Dynamics (CFD) Modeling Approach
July 26, 2019
Small and micro hydropower systems represent an attractive solution for generating electricity at low cost and with low environmental impact. The pump-as-turbine (PAT) approach has promise in this application due to its low purchase and maintenance costs. In this paper, a new method to predict the inverse characteristic of industrial centrifugal pumps is presented. This method is based on results of simulations performed with commercial three-dimensional Computational Fluid Dynamics (CFD) software. Model results have been first validated in pumping mode using data supplied by pump manufacturers. Then, the results have been compared to experimental data for a pump running in reverse. Experimentation has been performed on a dedicated test bench installed in the Department of Civil Construction and Environmental Engineering of the University of Naples Federico II. Three different pumps, with different specific speeds, have been analyzed. Using the model results, the inverse characteristic and the best efficiency point have been evaluated. Finally, results have been compared to prediction methods available in the literature.
Keywords
energy saving, numerical modeling, PAT, urban hydraulic network
Suggested Citation
Frosina E, Buono D, Senatore A. A Performance Prediction Method for Pumps as Turbines (PAT) Using a Computational Fluid Dynamics (CFD) Modeling Approach. (2019). LAPSE:2019.0764
Author Affiliations
Frosina E: Department of Industrial Engineering, University of Naples Federico II, Via Claudio, 21-80125 Naples, Italy [ORCID]
Buono D: Department of Industrial Engineering, University of Naples Federico II, Via Claudio, 21-80125 Naples, Italy [ORCID]
Senatore A: Department of Industrial Engineering, University of Naples Federico II, Via Claudio, 21-80125 Naples, Italy [ORCID]
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Journal Name
Energies
Volume
10
Issue
1
Article Number
E103
Year
2017
Publication Date
2017-01-16
Published Version
ISSN
1996-1073
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Original Submission
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PII: en10010103, Publication Type: Journal Article
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LAPSE:2019.0764
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doi:10.3390/en10010103
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Jul 26, 2019
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CC BY 4.0
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Jul 26, 2019
 
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Original Submitter
Calvin Tsay
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