Izvorni znanstveni članak

https://doi.org/10.31217/p.32.2.1

Mathematical Modelling of Diesel Engine Operational Performance Parameters in Transient Modes

Paulius Rapalis ; Marine Research Institute, Waterborne Transport and Air Pollution Laboratory, Klaipeda University, Klaipeda, Lithuania
Sergejus Lebedevas ; Faculty of Marine Technologies and Natural Sciences, Department of Marine Engineering, Klaipeda University, Klaipeda, Lithuania
Rima Mickevičienė ; Faculty of Marine Technologies and Natural Sciences, Department of Marine Engineering, Klaipeda University, Klaipeda, Lithuania

Sažetak

This study presents innovative methods for solving practical challenges that occur during the operation of heavy diesel engines (DEs). The novelty of the method arises from the combined use of an artificial neural network, a single-zone DE combustion mathematical model, and data from real operation conditions. Using the proposed method, DE transient mode energy efficiency has been analysed, and the primary influencing factors have been identified: qcycl and dqcycl/dt. The adequacy of the method has been tested for CAT3512B-HD series engines installed on freight locomotives. The difference between the model results and experimental data has been 3–4%. CAT3512B-HD series DE transient operation studies have shown that at the low-load range of qcycl (up to 36% of nominal), the fuel consumption during transient operation increases by 10% compared to steady state operation. Transient operation efficiency is not influenced by the operation rate (dqcycl/dt) in the analysed -0.016 to 0.016 g/s range. Near the nominal power (qcycl increases up to 0.5 g/cycle), it is necessary to limit the dqcycl/dt range to 0.006 g/s to avoid overexploitation of the fuel by more than 100%. The proposed method has been recommended for practical use in optimizing vehicle operation load cycle structure by adapting the engine control to the concrete operation conditions, as well as for overall efficiency improvement.

Ključne riječi

Single zone mathematical models; Artificial neural networks; Diesel engine energy parameters; Dynamic factors

Hrčak ID:

213304

URI

https://hrcak.srce.hr/213304

Datum izdavanja:

20.12.2018.

Posjeta: 2.199 *