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Research Article

Combined Experimental and Machine Learning Study of Injection Characteristics of High-Pressure Spray Injector
Lin Yuna
,
Jiho Parka
,
Hyung Sub Simb*
aDepartment of Aerospace System Engineering, Graduate School of Sejong University, Seoul 05006, Korea
bDepartment of Aerospace Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
*Corresponding Author
30 November 2025. pp. 39-51
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Abstract

This study investigates the high-pressure injection characteristics of the Engine Combustion Network (ECN) Spray C injector using a combined experimental and machine learning approach. Injection rate measurements were performed using a Zeuch-based Moehwald hydraulic discharge analyzer (HDA) under various operating conditions, including different injection pressures, chamber pressures, temperatures, and energizing durations. Building upon a previously validated artificial neural network (ANN) framework originally developed for the piezoelectric Spray A-3 injector, the same modeling strategy was applied to the solenoid-type Spray C injector to assess its cross-applicability. The ANN model, trained using Bayesian regularization backpropagation, accurately reproduced the transient and quasi-steady rate of injection (ROI) profiles of Spray C, including complex nonlinear behaviors arising from cavitation-prone nozzle geometries and short energizing durations. The model achieved a coefficient of determination (R2) exceeding 0.99, showing excellent agreement with experimental measurements. Parametric analyses revealed that injection pressure was the dominant factor influencing ROI and injected mass, whereas chamber pressure and temperature had relatively minor effects within the tested ranges. The model’s capability to capture key hydraulic and acoustic characteristics was additionally confirmed through discharge coefficient (Cd) evaluation and spectral (frequency-domain) analysis. These findings demonstrate the reliability and generalizability of the ANN-based modeling strategy for complex fuel injection phenomena, providing a computationally efficient substitute for traditional techniques and facilitating advancements in injector design and combustion system optimization.

Keywords
Injection rate
High-pressure spray injector
Artificial neural networks
Machine learning
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Information
  • Publisher :The Korean Society of Propulsion Engineers
  • Publisher(Ko) :한국추진공학회
  • Journal Title :Journal of Propulsion and Energy
  • Journal Title(Ko) :Free Open Access International Journal on Propulsion and Energy Science and Technology
  • Volume : 5
  • No :2
  • Pages :39-51
  • Received Date : 2025-07-27
  • Revised Date : 2025-09-22
  • Accepted Date : 2025-09-23
  • DOI :https://doi.org/10.6108/JPNE.2025.5.2.039
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