标题：First and Second-Law Efficiency Analysis and ANN Prediction of a Diesel Cycle with Internal Irreversibility, Variable Specific Heats, Heat Loss, and Friction Considerations
摘要：The variability of specific heats, internal irreversibility, heat and frictional losses are neglected in air-standard analysis for different internal combustion engine cycles. In this paper, the performance of an air-standard Diesel cycle with considerations of internal irreversibility described by using the compression and expansion efficiencies, variable specific heats, and losses due to heat transfer and friction is investigated by using finite-time thermodynamics. Artificial neural network (ANN) is proposed for predicting the thermal efficiency and power output values versus the minimum and the maximum temperatures of the cycle and also the compression ratio. Results show that the first-law efficiency and the output power reach their maximum at a critical compression ratio for specific fixed parameters. The first-law efficiency increases as the heat leakage decreases; however the heat leakage has no direct effect on the output power. The results also show that irreversibilities have depressing effects on the performance of the cycle. Finally, a comparison between the results of the thermodynamic analysis and the ANN prediction shows a maximum difference of 0.181% and 0.194% in estimating the thermal efficiency and the output power. The obtained results in this paper can be useful for evaluating and improving the performance of practical Diesel engines.
