Aihong Zou, Jean-Camille Chassaing, Wei Li, YuanTong Gu, Emilie Sauret,
Quantified dense gas conical diffuser performance under uncertainties by flow characteristic analysis,
Applied Thermal Engineering,
Volume 161,
2019,
114158,
ISSN 1359-4311,
https://doi.org/10.1016/j.applthermaleng.2019.114158.
(http://www.sciencedirect.com/science/article/pii/S1359431118367401)
Abstract: The Organic Rankine Cycle (ORC) has become a leading thermodynamic technique to extract more energy by benefiting from the application of dense gas. As the connecting component to the ORC turbine outlet, dense gas diffusers are key components designed to improve the efficiency of ORC. However, investigations in the robust optimal design of dense gas diffusers are lacking, which restricts the improvement of overall ORC efficiency. An advanced and robust framework coupling an Uncertainty Quantification (UQ) approach with Computational Fluid Dynamics (CFD) and NIST REFPROP is proposed to effectively implement sensitivity analysis of dense gas conical diffusers. R143a, a potential dense gas is employed in this analysis. Both operating and geometric parameters have significant impact on the performance of conical diffusers, and thus a performance analysis is conducted using the proposed framework. This paper is the first attempt to quantify the influence of coupled and multiple uncertain parameters on a dense gas conical diffuser. It is shown that the swirl velocity has more impact than inlet axial velocity on pressure recovery under various geometric conditions regarding length and angle of the dense gas conical diffuser. This study highlights the need to achieve a robust optimal dense gas diffuser design in order to improve overall ORC efficiency.
Keywords: Conical diffuser; Generalized Polynomial Chaos (gPC); Uncertainty Quantification (UQ)