Prediction of Performance and Geometrical Parameters of Single-Phase Ejectors Using Artificial Neural Networks
نویسندگان
چکیده
Ejectors have gained renewed interest in the last decades, especially heat-driven refrigeration systems, to reduce load of compressor. Their performance is usually influenced by many factors, including working fluid, operating conditions and basic geometrical parameters. Determining relationships between these factors accurately predicting ejector over a wide range remain challenging. The objective this study develop fast efficient models for design operation ejectors using artificial neural networks. To end, two are built. first one predicts entrainment limiting compression ratio given 12 input parameters, geometry. second model optimal geometry desired conditions. An experimental database five fluids (R134a, R245fa, R141b, R1234ze(E), R1233zd(E)) has been built training validation. accuracy ANN assessed terms linear coefficient correlation (R) mean squared error (MSE). obtained results after both cases show maximum MSE less than 10% regression of, respectively, 0.99 0.96 when tested on new data. then good generalization capacity can be used purposes future systems.
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ژورنال
عنوان ژورنال: Thermo
سال: 2022
ISSN: ['2673-7264']
DOI: https://doi.org/10.3390/thermo3010001