Development of a simulation model for compression ignition engine running with ignition improved blend

Department of Automobile Engineering, Anna University, Chennai, India. The present work describes the thermodynamic and heat transfer models used in a computer program which simulates the diesel fuel and ignition improver blend to predict the combustion and emission characteristics of a direct injection compression ignition engine fuelled with ignition improver blend using classical two zone approach. One zone consists of pure air called non burning zone and other zone consist of fuel and combustion products called burning zone. First law of thermodynamics and state equations are applied in each of the two zones to yield cylinder temperatures and cylinder pressure histories. Using the two zone combustion model the combustion parameters and the chemical equilibrium composition were determined. To validate the model an experimental investigation has been conducted on a single cylinder direct injection diesel engine fuelled with 12% by volume of 2- ethoxy ethanol blend with diesel fuel. Addition of ignition improver blend to diesel fuel decreases the exhaust smoke and increases the thermal efficiency for the power outputs. It was observed that there is a good agreement between simulated and experimental results and the proposed model requires low computational time for a complete run

Modelling and Simulation of Machining Attributes in dry Turning of Aircraft Materials Nimonic C263 using CBN

In the current scenario, machinability of the super alloys is of greater importance in an aircraft turbine engine and land-based turbine applications owing to its superior properties. However, the machinability of these alloys is found to be poor owing to its inherent properties. Hence, a predictive model has been developed based on DEFORM 3D to forecast the machining attributes such as cutting force and insert's cutting edge temperature in turning of Nimonic C263 super alloy. The dry turning trials on Nimonic C263 material were carried out based on L27 orthogonal array using CBN insert. Linear regression models were developed to predict the machining attributes. Further, multi response optimization was carried out based on desirability approach for optimizing the machining attributes. The validation test was carried out for optimal parameter values such as cutting speed: 117 m/min, feed rate: 0.055 mm/rev and depth of cut: 0.25 mm. The minimum cutting force of 304N and insert's cutting edge temperature of 468 °C were obtained at optimum level of parameters.The predicted values by FEA and linear regression model were compared with experimental results and found to be closer with minimum percentage error.The minimum percentage error obtained by FEA and linear regression model for the machining attributes (cutting force, temperature) as compared with experimental values were (0.32%, 0.23%) and (2.34%, 1.63%) respectively