An Improved Heat Release Rate (HRR) Model for the Analysis of Combustion Behaviour of Diesel, GTL, and HVO Diesel 2020-01-2060
Heat Release Rate (HRR) analysis is indispensable in engine research. The HRR of Internal Combustion Engines (ICEs) is most sensitive to gamma (γ). The proposed HRR models in literature were largely based on γ expressed as functions of temperature. However, γ is depended on temperature as well as the excess air ratio (λ). In this work, an improved HRR model based on γ(T, λ) was used to investigate the combustion behaviour of standard diesel, Gas-to-Liquid (GTL) diesel and Hydrotreated Vegetable Oil (HVO) diesel in a 96 kW, multiple fuel injection, Euro V, Direct Injection (DI) engine. The improved HRR model (Leeds HRR model) was validated for the alternative fuels by comparing the fuel masses predicted by the model to the measured fuel masses. The fuel masses predicted by the Leeds HRR model were also compared to the predictions from four HRR models that were based on γ(T). No work has been done in the past to investigate the combustion behaviour of GTL and HVO diesel in a multiple fuel injection, Compression Ignition (CI) engine. This work also featured two novel approximation techniques that were used to estimate the rate of evaporation of the injected fuel from the HRR profiles and the actual SoC from the HRR and fuel burn profiles (for the case of significant heat release bTDC). The overall average error in the predictions of the Leeds HRR model was 4.86% with a standard deviation of 2.39 while the typical error in the other models ranged from 14.66% to 19.99%. The accuracy of the HRR model of CI engines for the HRR analysis of GTL and HVO diesel is therefore, improved by using γ(T, λ). The combustion of HVO diesel was found to be the smoothest of the three fuels due to the narrow distillation range of HVO diesel.
Citation: Olanrewaju, F., Wu, Y., Li, H., Andrews, G. et al., "An Improved Heat Release Rate (HRR) Model for the Analysis of Combustion Behaviour of Diesel, GTL, and HVO Diesel," SAE Technical Paper 2020-01-2060, 2020, https://doi.org/10.4271/2020-01-2060. Download Citation
Francis Omotola Olanrewaju, Yanlong Wu, Hu Li, Gordon Andrews, Herodotos Phylaktou