کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6688352 | 501886 | 2015 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks](/preview/png/6688352.png)
چکیده انگلیسی
This study uses HCCI experimental data to characterize variations in seven engine performance metrics including indicated mean effective pressure (IMEP), thermal efficiency, in-cylinder pressure, net total heat released, nitrogen oxides (NOx), carbon monoxide (CO), and total hydrocarbon (THC) concentrations. Two types of ANNs including radial basis function (RBF) and feedforward (FF) are developed to predict the seven engine performance metrics. The experimental data at 123 HCCI operating points from two different engines are collected to validate the ANN models. The validation results indicate both RBF and FF models can predict HCCI engine performance metrics with less than 4% error for butanol and ethanol fueled engines. The results show that the FF neural network models are advantageous in terms of network simplicity with fewer required neurons but need twice as much training time compared to the RBF models.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 138, 15 January 2015, Pages 460-473
Journal: Applied Energy - Volume 138, 15 January 2015, Pages 460-473
نویسندگان
Javad Rezaei, Mahdi Shahbakhti, Bahram Bahri, Azhar Abdul Aziz,