Abstract
Axial-flow turbines represent a well-established technology for a wide variety of power generation systems. Compactness, flexibility, reliability and high efficiency have been key factors for the extensive use of axial turbines in conventional power plants and, in the last decades, in organic Rankine cycle power systems. In this two-part paper, an overall cycle model and a model of an axial turbine were combined in order to provide a comprehensive preliminary design of the organic Rankine cycle unit, taking into account both cycle and turbine optimal designs. Part A presents the preliminary turbine design model, the details of the validation and a sensitivity analysis on the main parameters, in order to minimize the number of decision variables in the subsequent turbine design optimization. Part B analyzes the application of the combined turbine and cycle designs on a selected case study, which was performed in order to show the advantages of the adopted methodology. Part A presents a one-dimensional turbine model and the results of the validation using two experimental test cases from literature. The first case is a subsonic turbine operated with air and investigated at the University of Hannover. The second case is a small, supersonic turbine operated with an organic fluid and investigated by Verneau. In the first case, the results of the turbine model are also compared to those obtained using computational fluid dynamics simulations. The results of the validation suggest that the model can predict values of efficiency within ± 1.3%-points, which is in agreement with the reliability of classic turbine loss models such as the Craig and Cox correlations used in the present study. Values similar to computational fluid dynamics simulations at the midspan were obtained in the first case of validation. Discrepancy below 12 % was obtained in the estimation of the flow velocities and turbine geometry. The values are considered to be within a reasonable range for a preliminary design tool. The sensitivity analysis on the turbine model suggests that two of twelve decision variables of the model can be disregarded, thus further reducing the computational requirements of the optimization.
Highlights
The effective exploitation of medium-to-low temperature heat sources demands power generation technologies which are efficient, flexible and cost-competitive
computational fluid dynamics (CFD) and experimental values are reported as mass-weighted averages except for pressure, which is provided as area-weighted average
The validation of the axial turbine model, TURAX, considering two very different test cases confirms that the design performance can be predicted within 1.3%-points, which is in agreement with the commonly accepted prediction accuracy of the empirical loss model used in the code, namely the Craig and Cox correlation
Summary
The effective exploitation of medium-to-low temperature heat sources demands power generation technologies which are efficient, flexible and cost-competitive. The model presented named TURAX, has been conceived to (i) simulate axial-flow turbines operating with organic fluids and to (ii) perform a coupled optimization with the design of the ORC system. In this respect, the use of a reliable, accurate and computationally efficient model is of paramount importance. The model is validated using the data of an ORC axial turbine, for which no other well-documented references are found in the literature Another novel approach is that a global sensitivity analysis is carried out in order to reduce the number of decision variables and, the computational efforts required by the model optimization.
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