Modeling the performance and emission characteristics of diesel engine and petrol-driven engine by ANN
作者： Kemal Tütüncü , Novruz Allahverdi
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摘要: In this study, performance and emission characteristics of an internal combustion (IC) diesel engine petrol-driven were modeled by Artificial Neural Network (ANN). Diesel input parameters are air flow rate (Aflr), boost pressure (Pb), fuel (Frt), cycle (Cy) load (L) whereas the advance (A) (Cy). Engine torque (Tq), power (P), specific consumption (Sfc), values such as HC, CO2 NOx (Sfc) HC have been investigated. R square Tq, P, Sfc, %99.9, %99.45, %99.32, %99.84, %99.71 %99.26 respectively when ANN was used for modeling. Sfc Hc %97.24, %99.56, %98.19 %97.19 respectively. The back-propagation learning algorithm with Hyperbolic tangent activation functions (for hidden layer neurons output neuron) 5:12:1 combination in topology network engine. Logistic-Hyperbolic (hidden 2:6:1 After having statistical t-test outputs both ANN, it has seen that obtained results approximately %99.5 %98.5 consisted (matched) experimental data Main contribution work includes; 1) Dynamic value so modeling characteristic determination done regarding changing load, 2) highest prediction reached type to previous studies 3) None include
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