Full text

Turn on search term navigation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In recent years, the interest towards the use of pumps operating as turbines (PATs) for the generation of electrical energy has increased, due to the low cost of implementation and maintenance. The main issue that inhibits a wider use of PATs is the lack of corresponding characteristic curves, because manufacturers usually provide only the pump-mode performance characteristics. In the PAT selection phase, the lack of turbine-mode characteristic curves forces users to expend expensive and time-consuming efforts in laboratory testing. In the technical literature, numerous methods are available for the prediction of PAT turbine-mode performance based on the pump-mode characteristics, but these models are usually calibrated making use of few devices. To overcome this limit, a performance database called Redawn is presented and the data collected are used to calibrate novel PAT performance models.

Details

Title
A Performance Prediction Model for Pumps as Turbines (PATs)
Author
Fontanella, Stefania 1 ; Fecarotta, Oreste 2   VIAFID ORCID Logo  ; Molino, Bruno 3 ; Cozzolino, Luca 1   VIAFID ORCID Logo  ; Renata Della Morte 1   VIAFID ORCID Logo 

 Department of Engineering of the University of Naples ‘Parthenope’, Centro Direzionale Isola C4, 80125 Naples, Italy; [email protected] (L.C.); [email protected] (R.D.M.) 
 Department of Civil, Architectural and Environmental Engineering, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy; [email protected] 
 Department of Bioscience and Territory, University of Molise, Via Francesco De Sanctis, 1, 86100 Campobasso, Italy; [email protected] 
First page
1175
Publication year
2020
Publication date
2020
Publisher
MDPI AG
e-ISSN
20734441
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2394498790
Copyright
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.