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Evaluation of Prediction Model for Compressor Performance Using Artificial Neural Network Models and Reduced-Order Models

Author

Listed:
  • Hosik Jeong

    (Graduate School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Kanghyuk Ko

    (Graduate School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Junsung Kim

    (Graduate School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Jongsoo Kim

    (R&D Center, LG Electronics, Seoul 06763, Republic of Korea)

  • Seongyong Eom

    (Center for Advanced Air-Conditioning Refrigeration and Energy, Pusan National University, Busan 46241, Republic of Korea)

  • Sangkyung Na

    (Center for Advanced Air-Conditioning Refrigeration and Energy, Pusan National University, Busan 46241, Republic of Korea)

  • Gyungmin Choi

    (Department of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea)

Abstract

In order to save the time and material costs associated with refrigeration system performance evaluations, a reduced-order model (ROM) using highly accurate numerical analysis results and some experimental values was developed. To solve the shortcomings of these traditional methods in monitoring complex systems, a simplified reduced-order system model was developed. To evaluate the performance of the refrigeration system compressor, the temperature of several points in the system where the compressor actually operates was measured, and the measured values were used as input values for ROM development. A lot of raw data to develop a highly accurate ROM were acquired from a VRF system installed in a building for one year, and in this study, specific operating conditions were selected and used as input values. In this study, the ROM development process can predict the performance of compressors used in air conditioning systems, and the research results on optimizing input data required for ROM generation were observed. The input data are arranged according to the design of experiments (DOE), and the accuracy of ROM according to data arrangement is compared through the experiment results.

Suggested Citation

  • Hosik Jeong & Kanghyuk Ko & Junsung Kim & Jongsoo Kim & Seongyong Eom & Sangkyung Na & Gyungmin Choi, 2024. "Evaluation of Prediction Model for Compressor Performance Using Artificial Neural Network Models and Reduced-Order Models," Energies, MDPI, vol. 17(15), pages 1-12, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3686-:d:1443491
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    References listed on IDEAS

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    1. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
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