Development of a micro gas turbine for concentrated solar power applications

Khader, M. A. (2017). Development of a micro gas turbine for concentrated solar power applications. (Unpublished Doctoral thesis, City, University of London)

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Abstract

The main objective of this research is to enhance the performance of a solar powered Micro Gas Turbine (MGT) by exploring suitable methods to be applied to the turbomachinery components to increase their efficiency and improve the predictability of their performance over the operating range of the MGT.

A novel idea of reducing turbine rotor friction losses through adding riblets to the rotor hub was explored thoroughly. Computational Fluid Dynamics (CFD) has been used to study the effects of those features at design point conditions of the MGT. Riblets with different height and spacing have been examined to determine the riblet geometry where the maximum drag reduction is achieved. To improve the predictability of performance of the turbomachinery components of the MGT over the operating envelope, a prediction methodology was developed during this research which used a combination of CFD and empirical correlations to account for losses that are not included in the CFD model.

It was found that riblets reduce the cross-stream motion of the low momentum fluid flow near the hub surface of the rotor passage, and separate the streamwise vortex from interaction with the hub surface. The maximum drag reduction was found to occur with riblets of a relative height of 2.5% with respect to the rotor inlet blade height.

The performance prediction method was successfully applied to a radial turbine and centrifugal compressor designed for a 6 kWe solar powered MGT. A purpose-built test rig was built and the actual performance map for the turbine was achieved while running it using warm compressed air from an external air supply. The comparison between the actual and the predicted data revealed a good match between both results, which indicates the validity of the demonstrated performance prediction method.

Item Type: Thesis (Doctoral)
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: City University London PhD theses
School of Engineering & Mathematical Sciences > Engineering
URI: http://openaccess.city.ac.uk/id/eprint/19156

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