Abstract
This paper presents an approach to a new engine calibration method that takes the engine’s operational profile into account. This method has two main steps: modeling and optimization. The Design of Experiments method is first conducted to model the engine’s responses such as Brake Specific Fuel Consumption (BSFC) and Nitrogen Oxide ( N O x ) emissions as the functions of fuel injection timing, common rail pressure and charged air pressure. These response surface models are then used to minimize the fuel consumption during a year, according to a typical load profile of a ferry, and to fulfill the N O x limits set by International Maritime Organization (IMO) regulations, Tier II, test cycle E2. The Sequential Quadratic Programming algorithm is used to solve this minimization problem. The results showed that the fuel consumption can be effectively reduced with the flexibility to trade it off with the N O x emissions while still fulfilling the IMO regulations. In general, this method can decrease the manual calibration effort and improve the engine’s performance with a tailored setting for individual operational profiles.
Highlights
Introduction and MotivationEngine calibration is a process consisting of a large effort to optimize a large number of parameters in order to achieve the desired engine performance
The aim of this study is to prove that by using operational profile based optimization method, the fuel consumption over the whole engine’s working cycle can be effectively reduced without exceeding the International Maritime Organization (IMO) Nitrogen Oxide (NOx) limits
An operational profile based optimization method, targeting large bore, medium-speed maritime diesel engines, is demonstrated with promising results. This method aims for fuel efficiency and fulfilling the IMO emission regulations
Summary
Introduction and MotivationEngine calibration is a process consisting of a large effort to optimize a large number of parameters in order to achieve the desired engine performance. The research in [1] proposed an optimization method using univariate search in which each input factor is varied at a time until the search does not provide any significant reduction in the objective function [2] It is an exhaustive and inefficient method because only one factor is varied at a time and it requires that all varied factors are independent, which might lead to finding the local minimum not the global minimum. Advanced methods such as neural networks or genetic algorithms have been applied earlier in [3,4,5,6]. It takes time for the measurement to be stable before recording
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