4.6 Article

Optimization of motive nozzle position in a modified two-phase ejector expansion household refrigeration cycle using an artificial neural network

Journal

ENERGY REPORTS
Volume 8, Issue -, Pages 1114-1123

Publisher

ELSEVIER
DOI: 10.1016/j.egyr.2021.12.033

Keywords

Two-phase ejector; Nozzle exit position; Dual evaporator ejector cycle; Optimization; Artificial neural network

Categories

Funding

  1. National Research Founda-tion of Korea (NRF) - Korean government (MSIT) [NRF-2019R1G1A1099288, NRF-2021R1I1A3047845]

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This study investigates the influence of the motive nozzle exit position (NXP) on the performance of a dual evaporator ejector cycle (DEEC) in a household refrigeration system. An artificial neural network (ANN) model is used to analyze the pressure lifting ratio and coefficient of performance (COP) of the DEEC under various operating and ejector geometry conditions. The optimal NXP is proposed to achieve maximum performance in the DEEC, resulting in a COP that is 2.3% and 8.4% higher than those of the conventional NXP and the baseline cycle, respectively.
Two-phase ejectors are highly useful devices because they can recover and use the energy lost in the expansion process of the refrigeration cycle. However, in a small-sized household refrigeration cycle, a modified ejector cycle is required due to the low operating pressure and mass flow rate. In this study, a dual evaporator ejector cycle (DEEC), which is advantageous for household refrigeration, was applied. In the DEEC, unlike the conventional ejector cycle, the motive nozzle exit position (NXP) considerably affects the cycle performance. The objective of this study was to optimize the NXP of a DEEC for a household refrigeration cycle with low-pressure refrigerants via an artificial neural network (ANN) model. Using the developed model, the DEEC pressure lifting ratio and coefficient of performance (COP) were analyzed under various operating and ejector geometry conditions. Moreover, the optimal NXP of a small-sized household DEEC was proposed using the developed correlation to achieve maximum performance under different operating conditions. The COP of the DEEC with the optimized NXP is 2.3% and 8.4% higher than those of the DEEC with the conventional NXP and the baseline cycle, respectively. These results validate the ANN model used for optimization and serve as design guidelines for obtaining optimized NXPs and cycle performance with increased energy efficiency.(c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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