Browse Topic: Simulation and modeling
Electrification in the automotive industry has been steadily rising in popularity for many years, and with any technology there is always a desire to reduce development cost by efficiently iterating designs using accurate simulation models. In the case of rotating machinery and other devices that produce vibrations, an important physical behavior to simulate is Noise Vibration and Harshness (NVH). Efficient workflow to account for NVH was established at Schaeffler for eMotor design. Quantitative prediction is difficult to achieve and is occasionally intended only for faster iterations and trend prediction. A good validated qualitative simulation model would help achieve early NVH risk assessment based on the specified requirement and provide design direction and feasibility guidance across the design process to mitigate NVH concerns. This paper seeks to provide a general approach to validate the simulation model. The correlation methods used in this paper consist of a combination of
This study introduces a computational approach to evaluate potential noise issues arising from liftgate gaps and their contribution to cabin noise early in the design process. This computational approach uses an extensively-validated Lattice Boltzmann method (LBM) based computational fluid dynamics (CFD) solver to predict the transient flow field and exterior noise sources. Transmission of these noise sources through glass panels and seals were done by a well-validated statistical energy analysis (SEA) solver. Various sealing strategies were investigated to reduce interior noise levels attributed to these gaps, aiming to enhance wind noise performance. The findings emphasize the importance of integrating computational tools in the early design stages to mitigate wind noise issues and optimize sealing strategies effectively.
The ported shroud casing treatment for turbocharger compressors is desirable for mitigating broadband/whoosh noise and enhancing boost pressures at low to mid flow rates. Yet, it is accompanied by elevated narrowband noise at the blade-pass frequency (BPF). Compressor BPF noise occurs at high frequencies where wave propagation is often multi-dimensional, rendering traditional planar wave silencers invalid. An earlier work introduced a novel reflective high-frequency silencer (baseline) targeting BPF noise in the 8-12 kHz range using an “acoustic straightener” that promoted planar wave propagation along arrays of quarter-wave resonators (QWRs). The design, however, faced challenges with high-amplitude tonal noise generation at specific flow conditions due to flow-acoustic coupling at the opening of the QWRs, thereby compromising the noise attenuation. The current study explores two QWR interface geometries that weaken the coupling, including linear and saw-tooth ramps on the upstream
Wind noise is one of the largest sources to interior noise of modern vehicles. This noise is encountered when driving on roads and freeways from medium speed and generates considerable fatigue for passengers on long journeys. Aero-acoustic noise is the result of turbulent and acoustic pressure fluctuations created within the flow. They are transmitted to the passenger compartment via the vibro-acoustic excitation of vehicle surfaces and underbody cavities. Generally, this is the dominant flow-induced source at low frequencies. The transmission mechanism through the vehicle floor and underbody is a complex phenomenon as the paths to the cavity can be both airborne and structure-borne. This study is focused on the simulation of the floor contribution to wind noise of two types of vehicles (SUV and Sports car), whose underbody structure are largely different. Aero-Vibro-acoustic simulations are performed to identify the transmission mechanism of the underbody wind noise and contribution
Large eddy simulations (LES) of two HVAC duct configurations at different vent blade angles are performed with the GPU-accelerated low-Mach (Helmholtz) solver for comparison with aeroacoustics measurements conducted at Toyota Motor Europe facilities. The sound pressure level (SPL) at four near-field experimental microphones are predicted both directly in the simulation by recording the LES pressure time history at the microphone locations, and through the use of a frequency-domain Ffowcs Williams-Hawking (FW-H) formulation. The A-weighted 1/3 octave band delta SPL between the two vent blades angle configurations is also computed and compared to experimental data. Overall, the simulations capture the experimental trend of increased radiated noise with the rotated vent blades, and both LES and FW-H spectra show good agreement with the measurements over most of the frequency range of interest, up to 5,000Hz. For the present O(30) million cell mesh and relatively long noise data collection
In the highly competitive automotive industry, optimizing vehicle components for superior performance and customer satisfaction is paramount. Hydrobushes play an integral role within vehicle suspension systems by absorbing vibrations and improving ride comfort. However, the traditional methods for tuning these components are time-consuming and heavily reliant on extensive empirical testing. This paper explores the advancing field of artificial intelligence (AI) and machine learning (ML) in the hydrobush tuning process, utilizing algorithms such as random forest, artificial neural networks, and logistic regression to efficiently analyze large datasets, uncover patterns, and predict optimal configurations. The study focuses on comparing these three AI/ML-based approaches to assess their effectiveness in improving the tuning process. A case study is presented, evaluating their performance and validating the most effective method through physical application, highlighting the potential
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