Use of an Innovative Predictive Heat Release Model Combined to a 1D Fluid-Dynamic Model for the Simulation of a Heavy Duty Diesel Engine

Paper #:
  • 2013-24-0012

Published:
  • 2013-09-08
DOI:
  • 10.4271/2013-24-0012
Citation:
Baratta, M., Finesso, R., Kheshtinejad, H., Misul, D. et al., "Use of an Innovative Predictive Heat Release Model Combined to a 1D Fluid-Dynamic Model for the Simulation of a Heavy Duty Diesel Engine," SAE Int. J. Engines 6(3):1566-1579, 2013, https://doi.org/10.4271/2013-24-0012.
Pages:
14
Abstract:
An innovative 0D predictive combustion model for the simulation of the HRR (heat release rate) in DI diesel engines was assessed and implemented in a 1D fluid-dynamic commercial code for the simulation of a Fiat heavy duty diesel engine equipped with a Variable Geometry Turbocharger system, in the frame of the CORE (CO2 reduction for long distance transport) Collaborative Project of the European Community, VII FP.The 0D combustion approach starts from the calculation of the injection rate profile on the basis of the injected fuel quantities and on the injection parameters, such as the start of injection and the energizing time, taking the injector opening and closure delays into account. The injection rate profile in turn allows the released chemical energy to be estimated. The approach assumes that HRR is proportional to the energy associated with the accumulated fuel mass in the combustion chamber. This procedure allows an accurate calculation of different combustion parameters important for engine calibration, such as MFB50 (50% of fuel burned mass fraction crank angle).The 0D HRR model was included in a complete 1D fluid-dynamic model of the heavy duty diesel engine, which was designed with GT-POWER commercial code.The 0D combustion model and the 1D fluid-dynamic model were calibrated in 12 steady-state engine operating conditions acquired over the whole engine map, and the complete model was finally validated on two load transient conditions at 1100 rpm and 1400 rpm, demonstrating a very good predictive capability.
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