Optimization of Design and Operational Parameters Using Genetic Algorithm for a Spark Ignition Engine With CNG/H2 Mixtures

Abstract

The present contribution describes the potential of using gaseous fuels like Hythane (CNG/H2 mixtures) as a spark ignition (SI) engine fuel. Genetic Algorithm (GA) is used to optimize the design and operational parameters of a CNG/H2 fueled spark ignition engine for maximizing the engine efficiency subjected to NOx emission constraint. This research deals with quasi-dimensional, two-zone thermodynamic simulation of four-stroke SI engine fueled with CNG/H2 blended fuel for the prediction of the combustion and emission characteristics. The validity of the model has been carried out by comparing the computed results with experimental data obtained under same engine setup and operating conditions. A wide range of engine parameters were optimized using a simple GA regarding both engine efficiency and NOx emissions. The five parameters chosen were compression ratio, engine speed, equivalence ratio, H2 fraction in the fuel, and spark plug position in cylinder head. The amount of NOx emissions was being kept under the constrained value of 750 ppm (< 5 g/kWh), which is less than permissible limit for heavy-duty engines.