Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/15227
Title: Design of radial turbomachinery for supercritical CO 2 systems using theoretical and numerical CFD methodologies
Authors: Holaind, N
Bianchi, G
De Miol, M
Sayad Saravi, S
Tassou, SA
Leroux, A
Jouhara, H
Keywords: supercritical CO2;waste heat recovery;turbomachinery design;power generation
Issue Date: 2017
Publisher: Elsevier
Citation: Holaind, N. Bianchi, G., De Miol, M., Sayad Saravi, S. Tassou, S.A., Leroux, A. and Jouhara, H. (2017) 'Design of radial turbomachinery for supercritical CO 2 systems using theoretical and numerical CFD methodologies', Energy Procedia, 123: pp. 313 - 320. doi 10.1016/j.egypro.2017.07.256.
Abstract: © 2017 The Authors. In high temperature waste heat to power conversion applications, bottoming thermodynamic cycles using carbon dioxide in supercritical phase (sCO2) have recently become a promising developing technology that could outperform conventional Organic Rankine Cycle systems in terms of efficiency and compactness. Moreover, carbon dioxide is a fluid chemically stable, reliable, low-cost, non-toxic, non-flammable and readily available. Supercritical CO2 power generation systems have been investigated by scientists and engineers mostly for large scale applications. However, when the electrical target output power is lower (50-100 kW), there are additional challenges on the turbomachinery design that need to be addressed. In the current research work, with reference to simple regenerative cycle architecture, the design of small scale sCO2 radial compressor and turbine are firstly addressed through the similarity approach. Further to this study, numerical CFD simulations are performed to optimize the 3D design of the impellers and of the stators. In particular, steady state RANS simulations using the mixing plane approach are carried out taking into account real gas properties for CO2.
URI: https://bura.brunel.ac.uk/handle/2438/15227
DOI: https://doi.org/10.1016/j.egypro.2017.07.256
Appears in Collections:Dept of Computer Science Research Papers

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