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A computational model has been developed to support investigations of temperature, heat flow and friction characteristics, particularly in connection with warm-up behaviour. A lumped capacity model of the engine block and head, empirically derived correlations for local heat transfer and friction losses, and oil and coolant circuit descriptions form the core of the model. Validation of the model and illustrative results are reported.

Two methods of determining the rate of heat transfer from the combustion chamber have been investigated. A First Law analysis is shown to be ill-conditioned because of sensitivity to heat release and gas property calculations. An alternative approach equates cycle-averaged chamber heat transfer to the difference between heat rejected to the coolant and gas heat transfer to the exhaust port. This has been examined as a basis for calibrating the Woschni correlation.

The cycle-by-cycle variation of heat transferred per cycle (q) to the combustion chamber surfaces of spark ignition engines has been investigated for quasi-steady and transient conditions produced by throttle movements. The heat transfer calculation is by integration of the instantaneous value over the cycle, using the Woschni correlation for the heat transfer coefficient. By examination of the results obtained, a relatively simple correlation has been identified: This holds both for quasi-steady and transient conditions and is on a per cylinder basis. The analysis has been extended to define a heat flux distribution over the surface of the chamber. This is given by: where F(x/L) is a polynomial function, q″ is the heat transfer per cycle per unit area to head and piston crown surfaces and gives the distribution along the liner

The rate of heat rejection to the coolant system of an internal combustion engine depends upon coolant composition, among other factors, because this influences the coolant side heat transfer coefficient. The correlation developed by Taylor and Toong for heat transfer rate has been modified to account for this effect. The modification retains the gas-to-coolant passage thermal resistance implicit in the original correlation. The modified correlation gives predictions in agreement with experimental data. Compared to 100% water, mixtures of 50% ethylene glycol/50% water lower heat rejection rates by typically 5% and up to 25% in the extreme. This depends upon local conditions in the coolant circuit, which can give rise to different heat transfer regimes. Application of the modified correlation is outlined and illustrated.

A correlation for total gas-side heat transfer rate has been derived from the analysis of engine data for measured heat rejection rate, frictional dissipation, and published data on exhaust port heat transfer. The correlation is related to the form developed by Taylor and Toong, and the analysis draws on this. However, cylinder and exhaust port contributions are separated. Two empirical constants are fixed to best match predicted to measured results for heat rejection to coolant and oil cooler under steady-state conditions, and also for exhaust port heat transfer rates. The separated contributions also defined a correlation for exhaust port heat transfer rate. The description of gas-side heat transfer is suited to needs for the analysis of global thermal behaviour of engines.

A method of predicting HC, CO and NOx emissions and fuel-used over drive cycles has been developed. This has been applied to FTP-75 and ECE+EUDC drive cycles amended to include cold-start and warm-up. The method requires only fully-warm steady state indicated performance data to be available for the engine. This is used in conjunction with a model of engine thermal behaviour and friction characteristics, and vehicle/drive cycle specifications enabling engine brake load/speed variations to be defined. A time marching prediction of engine-out emissions and fuel consumption is carried out taking into account factors which include high engine friction and poor mixture preparation after cold-start. Comparisons with experimental data indicate that fuel consumption and emissions can be predicted to quantitative accuracy. The method has been applied to compare and contrast the importance of various operating regimes during the two cycles.