<p>This study conducted a comprehensive performance evaluation of the linear motor-driven Rapid Compression Machine (RCM) through experimental and simulation methods. The RCM is designed to simulate the compression process of internal combustion engines; it leverages the advantages of linear motors—such as high precision, repeatability, and controllability—to optimize the boundary conditions of the compression process. A Simulink-based simulation model was developed to analyze the compression process, in which factors including piston motion, gas leakage, heat dissipation, and compression ratio were considered. Experimental verification was performed to evaluate the RCM’s repeatability, gas leakage, and heat dissipation characteristics. The results show that the RCM exhibits excellent repeatability: the maximum deviation of cylinder pressure is 0.005&#xa0;MPa across five compression cycles. A method of adding counterweights to moving components to increase the compression ratio was proposed, and its feasibility was verified via simulations. Based on experimental data, the Simulink model was verified and calibrated for gas leakage and heat dissipation, which enhanced its reliability. The test bench demonstrated good repeatability and sealing performance. The effective gas leakage area was calculated, and the heat dissipation equation was determined. Tests on the calibrated model indicate that the maximum compression ratio of the machine is approximately 11. Simulations show that when the mass of moving components exceeds 18&#xa0;kg, the compression ratio can reach 15. In addition, a mapping relationship between the initial and final parameters of the compression process was established using the Simulink model, which facilitates the precise control of compression end conditions. This study demonstrates the potential of the linear motor-driven RCM in providing a reliable experimental platform for fundamental combustion research.</p>

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Performance evaluation of linear motor-driven rapid compression machine (RCM) based on experiments and simulations

  • Wei Yin,
  • Jie Tian,
  • Qingzhong Yan,
  • Yong Cheng,
  • Zongfa Xie

摘要

This study conducted a comprehensive performance evaluation of the linear motor-driven Rapid Compression Machine (RCM) through experimental and simulation methods. The RCM is designed to simulate the compression process of internal combustion engines; it leverages the advantages of linear motors—such as high precision, repeatability, and controllability—to optimize the boundary conditions of the compression process. A Simulink-based simulation model was developed to analyze the compression process, in which factors including piston motion, gas leakage, heat dissipation, and compression ratio were considered. Experimental verification was performed to evaluate the RCM’s repeatability, gas leakage, and heat dissipation characteristics. The results show that the RCM exhibits excellent repeatability: the maximum deviation of cylinder pressure is 0.005 MPa across five compression cycles. A method of adding counterweights to moving components to increase the compression ratio was proposed, and its feasibility was verified via simulations. Based on experimental data, the Simulink model was verified and calibrated for gas leakage and heat dissipation, which enhanced its reliability. The test bench demonstrated good repeatability and sealing performance. The effective gas leakage area was calculated, and the heat dissipation equation was determined. Tests on the calibrated model indicate that the maximum compression ratio of the machine is approximately 11. Simulations show that when the mass of moving components exceeds 18 kg, the compression ratio can reach 15. In addition, a mapping relationship between the initial and final parameters of the compression process was established using the Simulink model, which facilitates the precise control of compression end conditions. This study demonstrates the potential of the linear motor-driven RCM in providing a reliable experimental platform for fundamental combustion research.