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

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...

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Bibliographic Details
Published inEnergies (Basel) Vol. 17; no. 1; p. 26
Main Authors Seo, Jong-Beom, Lee, Hosaeng, Han, Sang-Jo
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2024
Subjects
Artificial intelligence
Boundary conditions
Design of experiments
Design optimization
Efficiency
Electric power systems
Genetic algorithms
Heat
Machine learning
Neural networks
Numerical analysis
Optimization techniques
organic Rankine cycle
R1233zd(E)
radial basis neural network
radial turbine
Turbines
Turbulence models
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17010026