A Design Optimization of Organic Rankine Cycle Turbine Blades with Radial Basis Neural Network

Jong-Beom Seo, Hosaeng Lee and Sang-Jo Han ()
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Jong-Beom Seo: Korea Research Institute of Ships & Ocean Engineering, Daejeon 34103, Republic of Korea
Hosaeng Lee: Korea Research Institute of Ships & Ocean Engineering, Daejeon 34103, Republic of Korea
Sang-Jo Han: Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea

Energies, 2023, vol. 17, issue 1, 1-18

Abstract: In the present study, a 100 kW organic Rankine cycle is suggested to recover heat energy from commercial ships. A radial-type turbine is employed with R1233zd(E) and back-to-back layout. To improve the performance of an organic Rankine power system, the efficiency of the turbine is significant. With the conventional approach, the optimization of a turbine requires a considerable amount of time and involves substantial costs. By combining design of experiments, an artificial neural network, and Latin hypercube sampling, it becomes possible to reduce costs and achieve rapid optimization. A radial basis neural network with machine learning technique, known for its advantages of being fast and easily applicable, has been implemented. Using such an approach, an increase in efficiency greater than 1% was achieved with minimal design changes at the first and second turbines.

Keywords: radial basis neural network; organic Rankine cycle; radial turbine; R1233zd(E) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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