A Real-Time Pressure Wave Model for Predicting Engine Knock

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS
Ruixue C. Li, G. Zhu
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引用次数: 1

Abstract

This paper proposes a control-oriented pressure wave model, utilizing outputs of a reaction-based two-zone engine combustion model developed earlier, to accurately predict the key knock characteristics. The model can be used for model-based knock prediction and control. An in-cylinder pressure wave model of oscillation magnitude decay is proposed and simplified to describe pressure oscillations due to knock combustion, and the boundary and initial conditions of the pressure wave model at knock onset are provided by the two-zone reaction-based combustion model. The proposed pressure wave model is calibrated using experimental data, and the chemical kinetic-based Arrhenius integral (ARI) and maximum amplitude of pressure oscillations (MAPO) are used as the evaluation criteria for predicting knock onset and intensity, and the knock frequency is studied with the fast Fourier transform (FFT). The calibrated model is validated for predicting knock onset timing, knock intensity and frequency. Simulation results are compared with the experimental ones to demonstrate the capability of predicting engine knock characteristics by the proposed model.
预测发动机爆震的实时压力波模型
本文提出了一种面向控制的压力波模型,利用先前开发的基于反应的两区发动机燃烧模型的输出,准确预测关键爆震特性。该模型可用于基于模型的爆震预测和控制。提出并简化了振荡幅度衰减的缸内压力波模型来描述爆震燃烧引起的压力振荡,并利用基于两区反应的燃烧模型给出了爆震开始时压力波模型的边界和初始条件。利用实验数据对所建立的压力波模型进行了标定,采用基于化学动力学的Arrhenius积分(ARI)和压力振荡最大幅值(MAPO)作为预测爆震发生时间和强度的评价标准,并采用快速傅立叶变换(FFT)对爆震频率进行了研究。验证了校正后的模型对爆震发生时间、爆震强度和爆震频率的预测效果。仿真结果与实验结果进行了比较,验证了该模型对发动机爆震特性的预测能力。
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来源期刊
Mechatronic Systems and Control
Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.40
自引率
66.70%
发文量
27
期刊介绍: This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.
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