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An Efficient Thermodynamic Cycle Analysis for the Performance Prediction of Fuel Inducted Spark Ignition Engines
Technical Paper
2008-01-0289
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
A zero dimensional computer code was developed to predict the performance of an spark ignition internal combustion fuel inducted engine. The code can be used to predict engine performance for automotive and racing applications. Expressions for turbulent flame speed were developed based on turbulent flame intensity and cylinder geometry. This turbulent flame intensity varies across the RPM span and was formulated based on a semi-empirical correlation study in a joint experimental/computational effort by the investigator which utilizes extensive dynamometer experimental results for automotive engine applications. In-cylinder wall temperatures are determined based on a newly developed empirical correlation which accounts for the influences of air-fuel ratio, compression ratio, spark timing and coolant temperature. Auto-ignition or knock is also predicted. The analysis predicts turbulent flame speed, burn duration, knock pressure, knock temperature, exhaust temperatures, engine indicated and brake horsepower, mean effective pressure, pumping power loss, total heat loss to the cooling system and thermal efficiencies.
To optimize engine performance of engines for on-road and racing applications and provide an experimental data base for the numerical code validation, a test program was developed and numerous engines were run under various operating conditions. When compared against the experimental data, predicted engine power output and brake engine thermal efficiency were predicted with good agreementfor all configurations tested. The results presented here are for a 7.4 L eight cylinder engine to be used for a drag racing application
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Micklow, G., Murphy, B., and Abdel-Salam, T., "An Efficient Thermodynamic Cycle Analysis for the Performance Prediction of Fuel Inducted Spark Ignition Engines," SAE Technical Paper 2008-01-0289, 2008, https://doi.org/10.4271/2008-01-0289.Data Sets - Support Documents
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References
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