No abstract

No abstract

No abstract

In Diesel engines, where fuel is pumped into highly compressed air towards the end of the compression cycle, knocking is more or less unavoidable. By this time there is already a quantity of fuel in the combustion chamber which will first burn in areas of higher oxygen density before the full charge is combusted. The sudden rise in pressure and temperature produces the distinctive 'knock' or 'clatter' diesel, some of which must be allowed in engine design. The aim of knock control strategies is to try to maximize the trade-off between protecting the engine from damaging knock incidents, and optimizing the output torque of the engine. Knock events are a random process and independent. Knock controllers can't be programmed in a deterministic model. Due to the random nature of arriving knock events, a single time history simulation or experiment of knock control methods cannot provide a repeatable measurement of the controller efficiency. The desired trade-off must therefore be achieved in a stochastic context that could provide an appropriate environment for designing and evaluating the output of various knock control strategies with rigorous statistical properties. Clutching characteristics of a dual fuel diesel engine with direct injection of diesel and a liquid petroleum product in dual fuel mode. The engine is tested for knock reduction by adding Diethyl ether in to the diesel along with Liquid petroleum product. Variation of knocking was plotted with respect to different parameters and the result booted as knocking is minimized by the addition of diethyl ether.

This paper presents a neural network predictive controller for three phase inverter fed induction machine speed control. Thus the three phase inverter fed induction motor drive has been simulated with and without step change using NNP controller and the results are compared with PID controller. The performance comparisons of conventional PID and Neural network predictive controller are achieved with the help of IAE (Integral absolute error) and ITAE (Integral time-weighted absolute error).