A neural network approach on forecasting spark duration effect on in-cylinder performance of a large bore compression ignition engine fueled with propane direct injection

IF 7.2 2区 工程技术 Q1 CHEMISTRY, APPLIED
Cahyani Windarto , Ocktaeck Lim
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Abstract

In the current study, we examined the impact of spark duration strategy on a large bore compression ignition engine fueled with propane direct injection. An artificial neural network also was used to forecast engine in-cylinder performance characteristics. A rapid compression and expansion machine (RCEM) with a spark plug was tested with a high-pressure direct injection propane of 200 bar. While the timing of the injection was set to 20 °CA bTDC, the spark duration can range from 0.7 to 5.0 milliseconds. Crank angle degree, pressure, ignition coil number and spark duration were used as input parameters in the ANN model to predict in-cylinder performance, while engine performance parameters such as heat release rate (HRR), turbulent kinetic energy (TKE), tumble ratio, indicated power, and combustion efficiency (ηc) were used as output parameters. The ANN model was created using the neural network toolbox and standard backpropagation with the Levenberg-Marquardt training algorithm was used with the learning rate and training epochs of the ANN model set to 0.001 and 1000, respectively. The accuracy of the model was validated by comparing the predicted datasets with the experimental data. The five projected parameters of heat release rate (HRR), turbulent kinetic energy (TKE), tumble ratio, indicated power, and combustion efficiency (ηc) showed R2 values of 0.9833, 0.9860, 0.9728, 0.9807, 0.9052, and 0.9999, respectively, and MSE values of 0.1419, 0.0023, 0.6428, 0.0106, 0.0050, and 0.0134. The R2 of the validation dataset was nearly 0.98, which is close to that of the training dataset. The coefficients of determination (R2) were greater than 0.9 in the projected results, and the MSE was reasonably low, indicating that a predictive model based on ANN model could predict in-cylinder performance of a large bore compression ignition engine.

Abstract Image

预测火花持续时间对丙烷直喷式大缸径压燃发动机缸内性能影响的神经网络方法
在当前的研究中,我们考察了火花持续时间策略对以丙烷直接喷射为燃料的大缸径压燃式发动机的影响。同时还使用了人工神经网络来预测发动机的缸内性能特征。使用 200 巴的高压直喷丙烷对带有火花塞的快速压缩膨胀机(RCEM)进行了测试。喷射时间设定为 20 °CA bTDC,火花持续时间范围为 0.7 至 5.0 毫秒。曲柄角度、压力、点火线圈数和火花持续时间被用作 ANN 模型的输入参数,以预测缸内性能,而热释放率 (HRR)、湍流动能 (TKE)、翻滚比、指示功率和燃烧效率 (ηc) 等发动机性能参数被用作输出参数。使用神经网络工具箱创建了 ANN 模型,并使用了 Levenberg-Marquardt 训练算法的标准反向传播,ANN 模型的学习率和训练历元分别设置为 0.001 和 1000。通过比较预测数据集和实验数据,验证了模型的准确性。热释放率 (HRR)、湍流动能 (TKE)、翻滚率、指示功率和燃烧效率 (ηc) 这五个预测参数的 R2 值分别为 0.9833、0.9860、0.9728、0.9807、0.9052 和 0.9999,MSE 值分别为 0.1419、0.0023、0.6428、0.0106、0.0050 和 0.0134。验证数据集的 R2 接近 0.98,与训练数据集的 R2 接近。预测结果的决定系数(R2)大于 0.9,MSE 也相当低,表明基于 ANN 模型的预测模型可以预测大缸径压燃式发动机的缸内性能。
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来源期刊
Fuel Processing Technology
Fuel Processing Technology 工程技术-工程:化工
CiteScore
13.20
自引率
9.30%
发文量
398
审稿时长
26 days
期刊介绍: Fuel Processing Technology (FPT) deals with the scientific and technological aspects of converting fossil and renewable resources to clean fuels, value-added chemicals, fuel-related advanced carbon materials and by-products. In addition to the traditional non-nuclear fossil fuels, biomass and wastes, papers on the integration of renewables such as solar and wind energy and energy storage into the fuel processing processes, as well as papers on the production and conversion of non-carbon-containing fuels such as hydrogen and ammonia, are also welcome. While chemical conversion is emphasized, papers on advanced physical conversion processes are also considered for publication in FPT. Papers on the fundamental aspects of fuel structure and properties will also be considered.
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