WHITE PAPER
Cabin Noise Modeling for Seat Location Variation Using Simulation and AI
Cabin noise impacts passenger comfort and experience. In modern vehicles, sound quality within the cabin plays a pivotal role in the overall user experience, especially for electric and luxury vehicles in which engine noise is less prevalent. The main challenges include resonance effects, inefficient sound absorption, incorrect speaker placements, and the influence of cabin geometry changes such as seat position variations.
This paper investigates a method to address these challenges using Ansys SimAI™ cloud-enabled generative artificial intelligence (AI) platform in combination with simulation techniques. The ultimate goal is to predict and optimize sound levels for improved acoustic performance, thereby enhancing driver and passenger comfort. We explore the possibility of training an AI model to accurately predict sound pressure levels in a vehicle cabin under different speaker locations and seat configurations, setting the first step towards an eventual optimization to improve occupant comfort by utilizing the rapid predictions of the trained AI model. This study represents a unique attempt to address these technological requirements while identifying challenges that can inform future advancements.
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