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Knock Detection in a Turbocharged S.I. Engine Based on ARMA Technique and Chemical Kinetics

机译:基于ARMA技术和化学动力学的涡轮增压S.I.引擎爆震检测

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During the last years, a number of techniques aimed at the experimental identification of the knocking onset in Spark-Ignition (SI) Internal Combustion Engines have been proposed. Besides the traditional procedures based on the processing of in-cylinder pressure data in the frequency domain, in the present paper two innovative methods are developed and compared. The first one is based on the use of statistical analysis by applying an Auto Regressive Moving Average (ARMA) technique, coupled to a prediction algorithm. It is shown that such parametric model, applied to the instantaneous in-cylinder pressure measurements, is highly sensitive to knock occurrence and is able to identify soft or heavy knock presence in different engine operating conditions. An alternative, more expensive procedure is developed and compared to the previous one. The latter is based on the solution of a kinetic scheme in the end-gas zone, whose thermodynamic conditions are reconstructed by means of a two-zone inverse heat release analysis. Trains of consecutive experimental pressure cycles are acquired on a "downsized" turbocharged SI engine at full load and for different engine speeds. The above data are processed by the two techniques, and knock occurrence and intensity is estimated through suitably defined indices. The presented results demonstrate that the proposed methods give similar, although not coincident, results. While the kinetic procedure is able to furnish a more detailed insight of the thermo-kinetic conditions inside the cylinder, the ARMA technique is indeed characterized by a lower computational effort. In addition it may be applied to vibrational signals acquired by low-cost accelerometers, too. For this reason, it can be more easily implemented within a on board real time control system, aiming to adjust the spark advance and avoid abnormal combustion phenomena.
机译:在过去几年中,已经提出了许多旨在在火花点火(Si)内燃机中敲击爆震发动机的实验识别的技术。除了基于频域内缸内压力数据加工的传统程序之外,本文在本文中开发了两种创新方法。第一个通过应用自动回归移动平均(ARMA)技术来基于使用统计分析,耦合到预测算法。结果表明,应用于瞬时缸内压力测量的这种参数模型对爆震产生高度敏感,并且能够在不同的发动机操作条件下识别软或重击。替代,更昂贵的程序是开发的,并与前一个相比。后者基于最终气体区中的动力学方案的溶液,其热力学条件通过双区逆热释放分析重建。在全载荷的“缩小的”涡轮增压SI发动机上获取连续实验压力循环的火车,并且用于不同的发动机速度。上述数据由两种技术处理,通过适当定义的指标估计爆震发生和强度。所提出的结果表明,所提出的方法具有相似,虽然不一致,结果。虽然动力学程序能够提供更详细的汽缸内的热动力学条件的洞察力,但是,ARMA技术确实具有较低的计算工作。此外,也可以应用于低成本加速度计获得的振动信号。因此,它可以更容易地在船上实时控制系统内实现,旨在调整火花提前并避免异常燃烧现象。

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