Assignment 10
Q1. Explore Example- Diesel_4cyl_DIpulse_injratemap and write a detailed report
- Parameters-SOI, Rail Pressure
- Response- NO and BSFC minimum
- Run all cases and find the optimum values
Ans:
Diesel_4cyl_DIpulse_injratemap:
The example 'Diesel_4cyl_DIPulse_InjRateMap' demonstrates several modeling concepts important for automotive diesel engines, including variable geometry turbine, boost control, intercooler, EGR circuit with cooler and EGR rate control, injection limiting for smoke control, and exhaust after-treatment device modeling (geometry only, no chemistry).
Engine: The engine is a 4-cylinder, four-stroke, direct injection, 2L diesel engine. The engine runs in "speed" mode, where the engine speed is entered by the user and the engine torque is calculated. Combustion is modeled by the DIPulse method, a predictive combustion model using the detailed injection profiles. In-cylinder heat transfer is modeled using the Woschni model. An FE model is used to predict the cylinder, head and piston temperatures. This simulation will run until it reaches steady-state convergence for six different engine speeds at full load, three-part load operating points, and an additional idle point (total of 10 cases).
Intake System: The intake system consists of the air piping upstream of the compressor inlet, and contains a simple model of an airbox. The airbox is modeled as a group of interconnected flow splits that represent the total volume of the airbox. At the inlet and outlet of the air box, "bellmouth" orifice connections are used to model smooth transitions.
The Intercooler: The intercooler is found downstream of the compressor outlet. This intercooler is an air-to-air intercooler that is modeled using a "semi-predictive" method based on heat exchanger effectiveness. Note that if heat exchanger performance data is available, it is generally preferred to model the heat exchanger using 'HxMaster' and 'HxSlave' parts (which will use the available data to create non-dimensional Nusselt correlations defining heat exchanger performance). This approach will generally extrapolate better than assumed effectiveness to conditions beyond those present in the heat exchanger data.
Design Optimizer:
The above is the design optimizer. It has four options which are Main, Factors, Responses, Constraints.
In this model, the Integrated Design Optimizer is selected because it shows the result in a graphic manner which helps in understanding the results. According to the question, two entities have to be optimized so the option Multi-Objective is selected.
Genetic Algorithm is used because it is the best option when the user has to optimize two or more responses.
Factors:
In this option, those factors are to be filled which decides the cases or the output is dependent on these factors. In this example, Rail pressure and Start Of Ignition(SOI) are selected in their respective ranges.
Responses:
According to the question, BSFC and NO are to be optimized simultaneously. Both of the Responses have to minimize in order to develop a better engine model.
Constraints:
In this example, no constraints are used.
Case setup:
In this example, a total of 10 cases are present.
After Optimization:
Case 1: RPM-4000
The 2D Pareto showing all the optimized results or those results which give the best output.
The total elapsed time for one case is 152:39 mins.
NO vs BSFC graph:
BSFC Contours: SOI vs P_Rail graph
The blue zone represents the best option for the BSFC i.e the engine will consume less amount of fuel to generate 1KW of power.
NO contours: SOI vs P_Rail graph
Similar to the BSFC contour, in this map also the blue zone shows the least amount of the NO formation.
It is seen in both the Contour that BSFC and NO are inversely Proportional to each other.
So to get the best-optimized option, it greatly depends on the requirement that either it should be more fuel-efficient or to be less emissive. If the user requires both the quantities minimized, the best optimum result is where NO is 138.19ppm and the BSFC is 294.04g/kW-h.