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This study highlights the need to achieve a robust optimal dense gas diffuser design in order to improve overall ORC efficiency.Ĭonical diffuser, Generalized Polynomial Chaos (gPC), Uncertainty Quantification (UQ) 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 paper is the first attempt to quantify the influence of coupled and multiple uncertain parameters on a dense gas conical diffuser. 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.
R143a, a potential dense gas is employed in this analysis. 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. However, investigations in the robust optimal design of dense gas diffusers are lacking, which restricts the improvement of overall ORC efficiency. As the connecting component to the ORC turbine outlet, dense gas diffusers are key components designed to improve the efficiency of ORC.
The Organic Rankine Cycle (ORC) has become a leading thermodynamic technique to extract more energy by benefiting from the application of dense gas.
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