Journal of ILASS-Korea (한국분무공학회지)

Institute for Liquid Atomization and Spray Systems-Korea (한국분무공학회)
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Machine-Learning Based Prediction of Rate of Injection in High-Pressure Injector

기계학습 기법을 적용한 고압 인젝터의 분사율 예측

  • 윤린 (세종대학교 대학원 우주항공시스템공학과 ) ;
  • 박지호 (세종대학교 대학원 우주항공시스템공학과 ) ;
  • 심형섭 (세종대학교 대학원 우주항공시스템공학과 )
  • Received : 2024.09.03
  • Accepted : 2024.09.20
  • Published : 2024.09.30
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Abstract

This study explores the rate of injection (ROI) and injection quantities of a solenoid-type high-pressure injector under varying conditions by integrating experimental methods with machine learning (ML) techniques. Experimental data for fuel injection were obtained using a Zeuch-based HDA Moehwald injection rate measurement system, which served as the foundation for developing a machine learning model. An artificial neural network (ANN) was employed to predict the ROI, ensuring accurate representation of injection behaviors and patterns. The present study examines the impact of ambient conditions, including chamber temperature, chamber pressure, and injection pressure, on the transient profiles of the ROI, quasi-steady ROI, and injection duration. Results indicate that increasing the injection pressure significantly increases ROI, with chamber pressure affecting its initial rising peak. However, the chamber temperature effect on ROI is minimal. The trained ANN model, incorporating three input conditions, accurately reflected experimental measurements and demonstrated expected trends and patterns. This model facilitates the prediction of various ROI profiles without the need for additional experiments, significantly reducing the cost and time required for developing injection control systems in next-generation aero-engine combustors.

Keywords

Acknowledgement

본 연구는 본 연구는 2022년 정부(방위사업청)의 재원으로 국방과학연구소의 지원을 받아 수행된 미래도전국방기술 연구개발사업(No. 915066201)과 과학기술정보통신부의 재원으로 한국연구재단 미래우주 교육센터(2022M1A3C2074404, 미래우주항법 및 위성기술연구센터)의 지원을 받아 수행된 연구임.

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