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Technical Paper

Efficient Prediction and Analysis of the Noise Radiated by an Electric Powertrain

2022-06-15
2022-01-0931
Reducing the emitted noise from vehicles is a primary issue for automotive OEMs due to the constant evolution of the noise regulations. In the context of electric powertrains, virtual prototyping has proven to be a cost-efficient alternative to the build-test process, especially in early design stage and/or if optimization is targeted. Due to the multiphysics nature of the model, the full simulation chain involves multiple components, each having its own specific modelling attributes. The difficulty then resides in the parts assembly, solving issues like mesh-to-mesh projections, time to frequency-domain transformation, 2d-axisymmetric to 3d mapping, data formatting and management, unit and local coordinate systems… This paper presents an environment that allows for the prediction and analysis of the noise radiated by electric automotive powertrains. The stator-rotor electro-magnetic behavior is represented by time-dependent forces applied on stator teeth.
Technical Paper

Optimization of Trim Component and Reduction of the Road Noise Transmission Based on Finite Element Methods

2018-06-13
2018-01-1547
The acoustic trim components play an essential role in NVH behavior by reducing both the structure borne and airborne noise transmission while participating to the absorption inside the car. Over the past years, the interest for numerical solutions to predict the noise transmission through trim packages has grown, leading to the development of dedicated CAE tools. The incrementally restrictive weight and space constraints force today CAE engineers to seek for optimized trim package solution. This paper presents a two-steps process which aims to reduce the structure borne road noise due to floor panel using a coupled simulation with MSC NASTRAN and Actran. The embossment of the supporting steel structure, the material properties of porous layers and the thickness of visco-elastic patches are the design variables of the optimization process.
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