Parametric design optimization of e-compressor NVH using blocked forces and substructuring. (March 2021) - Haeussler, M.; Kobus, D.C.; Rixen, D.J.
Highlights: First description and application combining substructuring and blocked forces for acoustic design optimization. First substructuring application of an experimental method for obtaining 12 DoF rubber isolator models. Guidelines on how to properly formulate the cost function for finding robust design optima. Auralization of predicted and experimentally validated sound. Abstract: The combination of frequency based substructuring (FBS) and blocked force Transfer Path Analysis (TPA) allows to perform parametric NVH design optimizations. Blocked forces are not dependent on one specific receiver structure, in contrast to interface forces of classical TPA. Blocked forces can therefore be used as a source description in design optimization. For optimizing the assembly, different substructures are virtually coupled to each other, where each substructure is described by the most appropriate modeling approach. Frequency based substructuring (FBS) allows coupling analytical, numerical or experimental models to each other. The transfer functions of the final assembly can thus be simulated by FBS. Numerical models are used for substructures which can be simulated with high accuracy. These are parametrized for optimization. Experimental substructure models are used for substructures that are hard to simulate accurately. The application example is an electric climate compressor. Its excitation is characterized by means of blocked forces. The assembly consists of: a) a FEM model of the receiver, b) experimental models of different rubber isolators, c) a parametrized FEM model for the compressor support, and d) an analytical rigid body model for the compressor itself. The rubber isolator choice and the FEM model of the support, are iteratively optimized for minimal structure borne noise. Virtually coupling the substructures, and applying the compressors blocked forces to the assembly, makes it possible to simulate the resulting loudness for different design parameters. We discuss the formulation of an objective function and the applicability of different optimization algorithms on a minimal example first. Then we apply a genetic optimization algorithm to the objective function for the compressor design. The simulated predictions for the optimal parameters are validated with measurements on the physically built up design, including auralization of the results.