Optimization of Fuel Delivery Variation in PFConventional Pump
Pump is the principle segment in the engine. The major problem identified is, the required fuel delivery is not optimized at the different speeds and the fuel delivery variation was more at the constant speed. The experimentation is conducted in a leading automobile industry to optimize this parameter. Utilizing critical thinking methodology, the major fuel delivery variation at 1800 RPM was observed. The fundamental goal of this work to optimize the fuel delivery variation and reduce the spread. The root cause is carried out utilizing advanced Shainin Technique and Design of Experiments (DOE)
PERFORMANCE ANALYSIS ON BOSCH PF DIESEL FUEL INJECTION PUMP
The following paper mainly focuses on the performance analysis of BOSCH PF Diesel Fuel Injection Pump. The performance analysis was done with the objective of increasing the efficiency of the selected pump. By understanding the basic concepts about the BOSCH PF Diesel Fuel Injection Pump, we concluded that the efficiency can be increased by considering the following design modifications in the pump, i.e. increasing the plunger diameter, changing the diameters of the delivery pipe and replacement of normal valve with the adaption valve. In the current paper, we will focus on the effects of the modified plunger diameters on the different pump efficiencies. Also the variations of speed with discharge for two selected plunger diameters are shown.
A COMBINED OPTIMIZATION METHOD FOR COMMON RAIL DIESEL ENGINES
The optimization method proposed in the present study consists of a multi-objective genetic algorithm combined with an experimental investigation carried out on a test bench, by using a DI Diesel engine. The genetic algorithm selects the injection parameters for each operating condition whereas the output measured by the experimental apparatus determines the fitness in the optimization process. The genetic algorithm creates a random population, which evolves combining the genetic code of the most capable individuals of the previous generation. Each individual of the population is represented by a set of parameters codified with a binary string. The evolution is performed using the operators of crossover, mutation and elitist reproduction. This genetic algorithm allows competitive fitness functions to be optimized with a single optimization process. For the determination of the overall fitness function the concept of Pareto optimality has been implemented. In this work, the input variables used for the optimization method are injection parameters like start of pilot and main injection, injection pressure and duration. The engine used is a FIAT 1929 cc DI diesel engine, in which the traditional injection system has been replaced by a common rail high pressure injection system.The competitive fitness functions were determined based on the measured values of fuel consumption, emissions levels (i.e. NOx, soot, CO, CO2, HC); combustion noise and overall engine noise, for each operating conditions. The optimization was performed for different engine speed and torque conditions typical of the EC driving cycles.