Thermodynamic modelling of a stratified charge spark ignition engine

Smith, Jamie Karl; Roberts, Phil; Kountouriotis, Alexandros; Richardson, David; Aleiferis, P.G.; Ruprecht, Daniel

doi:10.1177/1468087418784845

Cited by 7 publications

(9 citation statements)

References 43 publications

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“…A full description of the engine, ancillaries, sub-systems and experimental methodology is available in the published literature [23,24].…”

Section: Experimental Set-upmentioning

confidence: 99%

“…As engine pressure trace data is used to derive the EGR correction factor correlation, the accuracy of the pressure data is important. A description of the pressure acquisition and associated errors is given by Smith et al [23], stating that "High speed, crank angle resolved data was recorded using AVL Indicom v2.6 as part of an AVL Indiset Advanced Gigabit unit utilising a 14-bit analogue to digital convertor (maximum error of ±0.95 kPa, ±0.061 kPa and ±0.122 kPa for the in-cylinder, intake and exhaust pressure channels). A watercooled Kistler 6041B piezo-electric sensor (accurate to <1% of full scale), mounted flush with the combustion chamber surface, in combination with a Kistler 5064 charge amplifier were used to measure in-cylinder pressure.…”

Section: Experimental Set-upmentioning

confidence: 99%

“…where τ t is the turbulence time scale, τ c is the chemical time scale, L is the integral length scale of turbulence and κ is the thermal diffusivity. The parameters L and u ′ for the studied engine were determined using CFD simulations [23] and were parsed into the code from an ASCII file. The turbulence parameters are crank resolved and the corresponding values are called at each time step.…”

Section: Turbulent Burning Velocity Modelmentioning

confidence: 99%

“…where C τ b is a constant. LUSIE has been introduced in detail and validated for numerous engines under an array of running conditions in previous publications [23,30,40,41,42,43].…”

Section: Forward Modelmentioning

confidence: 99%

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A Comparison of EGR Correction Factor Models Based on SI Engine Data

Smith¹,

Ruprecht²,

Roberts³

et al. 2019

SAE Int. J. Engines

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The article compares the accuracy of different exhaust gas recirculation (EGR) correction factor models under engine conditions. The effect of EGR on the laminar burning velocity of a EURO VI E10 specification gasoline (10% Ethanol content by volume) has been back calculated from engine pressure trace data, using the Leeds University Spark Ignition Engine Data Analysis (LUSIEDA) reverse thermodynamic code. The engine pressure data ranges from 5% to 25% EGR (by mass) with the running conditions, such as spark advance and pressure at intake valve closure, changed to maintain a constant engine load of 0.79 MPa gross mean effective pressure (GMEP). Based on the experimental data, a correlation is suggested on how the laminar burning velocity reduces with increasing EGR mass fraction. This correlation, together with existing models, was then implemented into the quasi-dimensional Leeds University Spark Ignition Engine (LUSIE) predictive engine code and resulting predictions are compared against measurements. It was found that the new correlation is in good agreement with experimental data for a diluent range of 5%-25%, providing the best fit for both engine loads investigated, whereas existing models tend to overpredict the reduction of burning velocity due to EGR.

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“…A full description of the engine, ancillaries, sub-systems and experimental methodology is available in the published literature [23,24].…”

Section: Experimental Set-upmentioning

confidence: 99%

Section: Experimental Set-upmentioning

confidence: 99%

Section: Turbulent Burning Velocity Modelmentioning

confidence: 99%

“…where C τ b is a constant. LUSIE has been introduced in detail and validated for numerous engines under an array of running conditions in previous publications [23,30,40,41,42,43].…”

Section: Forward Modelmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of EGR Correction Factor Models Based on SI Engine Data

Smith¹,

Ruprecht²,

Roberts³

et al. 2019

SAE Int. J. Engines

Self Cite

View full text Add to dashboard Cite

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“…These properties of methanol could reduce the local fuel-lean and fuel-rich zones in the cylinder to decrease fuel consumption and emissions caused by frequent acceleration and deceleration. Since the composition of the combustion products and flame propagation speed are significantly different for local fuel-lean and fuel-rich zones [39][40][41]. Moreover, the industrial technology for preparing methanol is very mature, which has low production cost [42], and technology equipment is relatively complete and easy to popularize [43].…”

Section: Introductionmentioning

confidence: 99%

Effects of Methanol Application on Carbon Emissions and Pollutant Emissions Using a Passenger Vehicle

et al. 2022

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Methanol, as a promising carbon-neutral fuel, has become a research hotspot worldwide. In this study, pure gasoline and gasoline blended with five different volume ratios of methanol (10%, 20%, 30%, 50%, and 75%) were selected as test fuels, which were referred to as M0, M10, M20, M30, M50, and M75. The experiments on carbon and pollutant emissions and performance were carried out on a passenger vehicle with gasoline direct injection (GDI) turbocharged engine using the steady-state, new European driving cycle (NEDC), and acceleration approaches. The results show that under steady-state conditions, as the methanol blending ratio increases, the volume of fuel consumption increases. Compared with pure gasoline, the equivalent fuel consumption and the CO2 emissions are reduced by 0.95 L/100 km (10.6%) and 18.95 g/km (9.6%) in maximum extent by fueling M75, respectively. In the NEDC, the CO2 emissions of M30 are reduced by 5.46 g/km (3.7%) compared with pure gasoline. After blending methanol in gasoline, CO emissions increase, and the emissions of NOx, THC, and PM decrease. The acceleration time is shortened with the increase of blending ratio of methanol. The application of methanol reduces the combustion CO2 emissions by 10% and improves the pollutant emissions.

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