Adaptability of turbulence models to predict the performance and blade surface pressure prediction of a Francis turbine. Issue 1 (7th March 2016)

• Record Type: Journal Article
• Authors: Zhu, WenRuo; Gao, ZhongXin; Tang, YongJun; Zhang, JianGuang; Lu, Li
• Is Part Of: Engineering computations. Volume 33:Issue 1(2016)
• Page Start: 238
• Page End: 251
• Publication Date: 2016-03-07
• Subjects: Performance -- Pressure distribution -- Numerical simulation -- Francis turbin -- Turbulence model -- Experimental study; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-06-2014-0137
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: – The purpose of this paper is to analyze the ability of turbulence models to model the flow field in the runner of a Francis turbine. Although the complex flow in the turbine can be simulated by CFD models, the prediction accuracy still needs to be improved. The choice of the turbulence model is one key tool that affects the prediction accuracy of numerical simulations. Design/methodology/approach: – This study used the SST k-w and RNG k-e turbulence models, which can both accurately predict complex flow fields in numerical simulations, to simulate the flow in the entire flow passage of a Francis turbine with the results compared against experimental data for the performance and blade pressure distribution in the turbine to evaluate the applicability of the turbulence models. Findings: – The results show that the SST k-w turbulence model more accurately predicts the turbine performance than the RNG turbulence model. However, the blade surface pressures predicted by the SST k-w turbulence model were basically identical to those predicted by the RNG k-e turbulence model, with both accurately predicting the experimental data. Research limitations/implications: – Due to the lack of space, the method used to measure the blade surface pressure distributions is not introduced in this paper. Practical implications: – Turbine performance and flow field pressure in the runner, which are the basis of turbine preliminary performance judgment and optimization through CFD, can be used to judge the rationality of the turbine runner design. The paper provides an evidence for the turbulence selection in numerical simulation to predict turbine performance and flow field pressure in the runner and improves the CFD prediction accuracy. Originality/value: – This paper fulfils a test of the flow field pressure in the runner, which provide an evidence for judge the adaptability of turbulence model on the flow field in runner. And this paper also provides important evaluations of two turbulence models for modeling the flow field pressure distribution in the runner of a Francis turbine to improve the accuracy of CFD models for predicting turbine performance.