BorgWarner (Germany)

Compressor surge has been investigated and predicted since the early days of turbomachinery research. Experimental testing of turbomachinery applications is still needed to determine whether stable compressor operation is possible in the expected application regime. Measuring compressor maps and operating ranges on hot gas test stands is common. The test benches are designed and optimized to ensure ideal inflow and outflow conditions as well as low measurement uncertainty. Compressor maps are used to match turbocharger and application. However, a shift in surge limit, caused by the piping system or application, can only be adequately addressed with full engine tests. Ideal measurements use the corresponding piston engine in the charged-air system. This can only take place in the development process, when surge detection is unfavorable from an economic perspective. The surge model for turbochargers presented here is an extension of the Greitzer’s surge model, which considers the effect of inlet throttling. Application components, such as air filters, pipe elbows and flow straighteners, reduce pressure in front of the compressor and flow conditions might differ from those in laboratory testing. Experimental results gathered from the hot gas test stand at TU Darmstadt indicate strong variation in surge limit, influenced by inlet throttling. An extension to the surge model is developed to explain the observed phenomena. The model was validated using extensive experimental variations and matches the experienced surge limit shift. Additional measurements with a piston engine downstream of the turbocharger demonstrated the validity of the surge model. The results also show that surge is a system-dependent phenomenon, influenced by compressor aerodynamics and boundary conditions.

Dieses Kapitel beinhaltet: Isolation Filtration Bauwesen Landwirtschaft Fahrzeugindustrie Papiermaschinenbespannungen Simulation von Vliesstoffeigenschaften

Designing turbine wheels for automotive turbochargers one is faced with a multidisciplinary design problem with many input and output parameters. Especially in the automotive industry short development cycles for high quality products in a competitive environment are daily routine. For meeting these requirements optimization algorithms can be a powerful tool in the design process. This paper presents the multidisciplinary optimization of an automotive mixed flow turbine wheel used in a 4 cylinder 1.6 l spark ignition engine. Before describing the optimization workflow in detail, the requirements for turbines operating in an automotive environment under pulsating flow conditions and during an engine load step are discussed. From there objectives for a multidisciplinary optimization are derived. The turbine wheel is optimized with respect to maximizing efficiency in two design points and minimizing its moment of inertia. For the optimization process, an algorithm based on evolution theory is used. As constraints, the operating points are fixed and the natural frequencies are limited to ensure the mechanical strength of the turbine. To speed up the optimization process meta models based on neural networks are applied. Three designs of the Pareto frontier are chosen and their characteristics are discussed. Using statistical methods, the interaction of the input variables and their impact on turbine performance are presented.