Bosch and Zeuch method–based flowmeters use the pressure rise triggered by the injection event in a different way in order to characterize the injected flow-rate time history. These typologies of measurement are the most widely applied to evaluate the injection rate of fuel injection systems. The injection rate of a Common Rail electroinjector has been measured with both Bosch and Zeuch method–based flowmeters. The objective has been to perform a benchmark on these two different flow-rate measuring principles. A slight reduction in the rising slope of the injected flow-rate, an anomalous tail at the end of the injection and a time delay in the flow-rate trace have been observed in the case of the Bosch method measurement. A one-dimensional numerical model of the hydraulic circuit of the flowmeter based on the Bosch method has been developed and validated successfully. This model has then been applied to study the cause and effect relationships between the features of the flowmeter hydraulic circuit and the alteration in the measured injected flow-rate pattern. Design keys for the optimization of the Bosch-based flowmeters are provided, even though the investigation has definitely assessed the superior accuracy of Zeuch method–based flowmeters, at least for the examined working conditions.
Trajectory planning for a heavy-duty mining truck near the loading/dumping sites of an open-pit mine is difficult. As opposed to trajectory planning for a small-sized passenger car in a parking lot, trajectory planning for a heavy-duty mining truck involves complex factors in vehicle kinematics and environment. These factors make the concerned trajectory planning scheme a mixed-integer nonlinear program (MINLP) incorporated with conditional constraints (denoted as C-MINLP). MINLP solvers can neither deal with conditional constraints nor find global optima in real time. Instead of solving the C-MINLP directly, we build a from-coarse-to-fine framework so that the coupled difficulties (the mixed integral variables, conditional constraints, and the demand for global optimality) are divided and conquered. At the coarse search stage, a global-optimality-enhanced hybrid A* search algorithm is proposed to find a near-optimal coarse trajectory with the mixed integral variables, conditional kinematic constraints, and global optimality considered. The coarse trajectory is further polished at the refinement stage, wherein the nominal C-MINLP is simplified as a small-scale NLP. The solution to the NLP is an optimized trajectory, which does not violate the complex constraints in the nominal C-MINLP. This indicates that conversion from the C-MINLP to an NLP is efficient with the help of a high-quality coarse trajectory.
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