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Design for Improved Cost & Magnetic Efficiency

gasoline injector

Figure 1 - SDI XL2 Injector

The injector is a valve that has to control and adjust the gasoline flow in order to generate the required mixture for the Engine Control Unit (ECU). The Gasoline injectors are mainly divided into two different groups: low pressure and high pressure applications depending on if the injection is performed into the manifold, or directly into the combustion chamber in the pressurizing phase. The most common injector technology is Solenoid activated. A current signal (ECU control) (I(t)) generates a magnetic field into a magnetic components circuit (ø(t)) which generates a force between two components (F(t)). This force generates the movement of a movable component (lift(t)) which opens a nozzle generating the Flow rate (Q(t)) and Spray (Fig. 1). In this work, we studied the current Continental Solenoid injector for Direct Gasoline Injection, named XL2 (Fig. 1), and some of the possible variations.

Project & Risks
With the economic crisis and fallen production numbers in the automotive industry in mind, Continental started a Design-to- Cost Project (DTC) with the primary objective to decrease the material cost of the injector, the XL2 SDI product. Their Project Manager and the three Product Engineers (PE) responsible for the three different component sub-groups (Fig. 2) defined a Project Chart.

It was clear that the design work of all three component subgroups would have a strong impact on the magnetic performances of the injector. Hence, the risk was that if each PE would work on its own, not sharing results with the others, it might be impossible in the end to define guidelines which match the other PE’s strategies. To avoid this, after a preliminary development phase without sufficient reliable results, a Complete Layout Optimization was suggested to solve the problem.

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Altri case studies sullo stesso argomento

Altri case studies sullo stesso argomento

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Articolo completo

gasoline injector

Figure 2 - XL2-DTC Project – Organization & Risks

gasoline injector

Figure 3 - Design To Cost Changes description

gasoline injector

Figure 4 - Parametric Model

gasoline injector

Figure 5 - Simulation Description

gasoline injector

Figure 6 - Simulation Outputs and Objectives

gasoline injector

Figure 7 - Comparison between initial (red) and optimized (green) DTC configurations

Continental could benefit immediately from the advantages of this new approach:

  • low mistake rate due to automated processes,
  • possibility to reach best performances with an optimization algorithm,
  • high number of simulations and low number of samples reducing time and cost of the Concept Definition phase.

Technical Activities A package of changes was defined which is described in Fig.3. Two components with different magnetic characteristics (1 non-magnetic) were merged into one magnetic component. The shape of the merged component was modified from the initial one and new dimensions were defined for another component (Armature). Moreover, the possible impact on coil shape and position was identified. These changes delivered by the DTC also proved to have a considerable impact on the magnetic performances of the injector.

Fig. 7 highlights the performances of the current product (left red bars) and the previous injector. When we look at the initial performances of the DTC Layout compared to the current product (Fig. 7 left red bars), it is obvious that the previous design was not feasible and a magnetic optimization was needed. A Parametric Model was defined using 12 parameters as described in Fig. 4.

The Simulations were done with an axial-symmetric model in ANSYS Emag using different B-h curves (magnetic characteristics) of each material. The input of a generic simulation was a current signal (I(t)), and the output is the force profile at constant lift (F(t) at lift=cost.) (Fig.1 and Fig.5). While, in this way, it is not possible to compute the complete dynamics of the injector, the magnetic performances of the configurations could be compared. For each configuration, two simulations were performed with two different current signals as shown in Fig. 5. The Outputs of the two simulations (F(t)) were characterized by three parameters typical of the force history shape (Fig. 6). The target of the DTC Layout was to reach at least the current product performances and to maximize them. The data was converted into a modeFRONTIER workflow which maximized the three simulation outputs.

It also defined a function, named chi, to identify the distance (for lower performance cases only) of a generic configuration from the current product’s magnetic targets. This chi function is also a way to guide the optimization loop in the design space as well as a quality index of a generic design (Fig. 6).

Managed by the MOGA II algorithm and by modeFRONTIER’s Workflow, 936 simulations were performed on 418 configurations. A design family which delivers higher performances than the current product (chi=0) could be defined as well as the best magnetic design for each target. Fig. 6 highlights the comparison between the best DTC Layout, and the initial DTC Layout compared to the current product.The optimization procedure finally led to a feasible DTC Layout in terms of magnetic behavior. A time saving configuration was identified, some samples were built and tested on experimental benches.

The targets of the magnetic optimization are not directly measurable experimentally, but the results of the sample benches showed many analogies with the simulations performed.

A complete Layout Magnetic Optimization was used as a new methodology in the concept development phase. The completed Layout Magnetic Optimization delivered a more efficient and effective procedure in comparison with the "standard" methodology based on single component development, a trial & error approach and many samples (Design-to-cost method).

Despite different layouts, it was possible to identify a number of configurations with higher performances than the current design, and several DTC technically feasible layouts. The comparative simulation showed results aligned with experimental benches. The validation with Continental’s automotive customer is currently in progress and will implement some beneficial changes in the design and development cycles.









Articolo pubblicato sulla Newsletter EnginSoft Anno 7 n°2

M. Omeri, M. Mechi, A. Agresta
Continental AG

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