 Experimental and numerical assessment of a methodology for performance prediction of Pumps-as-Turbines (PaTs) operating in off-design conditionsMosè Rossi, Alessandra Nigro and Massimiliano Renzi Applied Energy, 2019, vol. 248, issue C, 555-566 Abstract: In this work, an extensive assessment of a predicting model used to evaluate Pumps-as-Turbines’ (PaTs) characteristic curves is presented, with specific attention to the off-design operating conditions. The novelty of the proposed model consists in the possibility to reconstruct the performance curves of a PaT only by knowing a limited number of operating data in turbine mode at the Best Efficiency Point (BEP), which, in many applications, represents a design constraint. The availability of the off-design performance curves supplies important indications for technical and economic evaluations in those applications where a constant flow rate cannot be granted. The predicting model was derived by re-elaborating a wide experimental data-set based on the most relevant scientific literature related to several PaTs operating in turbine mode. The prediction’s capability of the model was validated with experimental tests and confirmed by numerical simulations. The experimental tests were carried on in both direct and reverse modes by inspecting several flow rates. The model data were compared with the experimental ones in order to validate the Computational Fluid Dynamics (CFD) analyses. Subsequently, the numerical model was used to investigate the performances of other two PaTs operating in turbine mode. The study of the performance obtained with the CFD analyses allowed to evaluate the effectiveness of the predicting model, highlighting its pros together with its possible improvements. In general, it is possible to conclude that the proposed model is able to predict the performances of the studied PaTs with errors included in the range of ±7% with respect to the Best Efficiency Point (BEP) in turbine mode. Keywords: Hydraulic machines; Pumps-as-Turbines; Predicting model; Performances forecast; Laboratory tests; Computational fluid dynamics (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:248:y:2019:i:c:p:555-566 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2019.04.123 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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