Design and optimization of the radial inflow turbogenerator for organic Rankine cycle system based on the Genetic Algorithm
•A 50-kW radial inflow turbogenerator for ORC systems with R245fa was designed.•The circumferential efficiency reached 85.29% after genetic algorithm optimization.•The turbogenerator demonstrated a rated bearing load capacity of 2.43 kN. The backdrop of energy-saving and emission-reduction sets a hi...
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Veröffentlicht in: | Applied thermal engineering 2024-09, Vol.253, p.123749, Article 123749 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •A 50-kW radial inflow turbogenerator for ORC systems with R245fa was designed.•The circumferential efficiency reached 85.29% after genetic algorithm optimization.•The turbogenerator demonstrated a rated bearing load capacity of 2.43 kN.
The backdrop of energy-saving and emission-reduction sets a higher demand for low-grade energy utilization which can be realized by Organic Rankine Cycle systems. A 50-kW radial inflow turbogenerator, the crucial heat conversion equipment, with aerostatic bearings for Organic Rankine Cycle system, is designed. It is optimized focusing on the performance of the turbogenerator which uses R245fa as the working fluid. The study employs a one-dimensional thermodynamic model, developed in MATLAB and bolstered by real-time data from Refprop, ensuring the model’s precision and dependability. On this basis, Genetic Algorithm is applied to optimize design parameters and significantly elevate the system’s efficiency, achieving circumferential efficiency of 85.29%. The turbogenerator demonstrates a rated bearing load capacity of 2.43 kN. The research delves into the influence of inlet temperature, inlet pressure, and rotational speed of turbogenerator on its performance. It reveals that an increase in both inlet temperature and inlet pressure lead to a decrease in isentropic efficiency and an increase in output power. The research also shows that an increase in the rotational speed contributes to increases in isentropic efficiency and output power. |
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ISSN: | 1359-4311 |
DOI: | 10.1016/j.applthermaleng.2024.123749 |