Browsing by Author "Schaberg, PW"

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    Model-based exploration of the inter-relationships between diesel fuel properties and engine performance and exhaust emissions
    (Stellenbosch : Stellenbosch University, 2023-03) Schaberg, PW; Harms, TM; Groenwold, A; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.
    ENGLISH ABSTRACT: This work utilises artificial neural network models to enable examination of the relationships between diesel fuel properties and engine performance and exhaust emissions in novel ways. The models were trained with experimental test data to accurately predict engine performance and exhaust emission parameters using duty-cycle, engine control, and fuel property parameters as inputs. The training data were collected during an engine test campaign conducted at the Sasol Fuels Application Centre in Cape Town, using a 15 litre, 373 kW heavy-duty diesel engine equipped with a common-rail fuel injection system and variable geometry turbocharger. The test fuels were formulated by blending market diesel fuels, refinery components, and biodiesel, to provide variations in pre-selected fuel properties, namely hydrogen to carbon ratio (H/C), oxygen to carbon ratio (O/C), derived cetane number (DCN), viscosity, and mid- and end-point distillation parameters. Care was taken to ensure that correlation between these fuel properties in the test fuel matrix was minimised, to avoid confounding model input variables. The test engine was exercised over a wide variety of transient test cycles during which fuel injection pressure, injection timing, air flow, and recirculated exhaust gas flow were systematically varied. The transient test data were used for training dynamic, recurrent neural network models so that transient engine operation could be accurately simulated. Modelling was performed in the MATLAB programming environment and the cluster supercomputer facilities at the national Centre for High Performance Computing were used for model training. The resulting models could predict the transient engine torque and fuel consumption, and nitrogen oxide (NOx), soot, carbon monoxide (CO), total hydrocarbon (THC), and carbon dioxide (CO2) exhaust emissions with good accuracy, which provided assurance that the characterisation of the test fuels using the selected fuel property parameters was sufficient to capture the fuel-related effects. The model inputs can be varied independently within the limits of the training dataset, enabling model-based parametric studies to be performed to quantify the relative impacts that the input variables have on engine performance and emissions. The newly-developed tool therefore allowed the effect of fuel properties to be examined through a new lens. NOx emissions were found to be primarily determined by the H/C and O/C ratios of the fuel, while soot was additionally impacted by DCN and viscosity. CO emissions showed the same trends as soot emissions, except that with DCN an opposite trend was observed. THC emissions were impacted by all fuel parameters but showed very little sensitivity to variations in engine control parameters. The models were also incorporated into a numerical optimisation routine which allowed synergies between fuel properties and engine control parameters to be identified to improve engine brake thermal efficiency (BTE). The H/C ratio was found to offer the greatest potential for improving the trade-off between BTE and NOx emissions. Besides providing a powerful new way to examine engine-fuel interactions, the tool can also be very useful for predicting the impact that new fuel formulations or fuel specification changes may have on engine performance and emissions.