计算机科学
噪音(视频)
变化(天文学)
模拟
人工智能
物理
天体物理学
图像(数学)
作者
Santosh Kottalgi,Junyan He,Bhaskar Banerjee
摘要
<div class="section abstract"><div class="htmlview paragraph">Analyzing acoustic performance in large and complex assemblies, such as vehicle cabins, can be a time-intensive process, especially when considering the impact of seat location variations on noise levels. This paper explores the use of Ansys simulation and AI tools to streamline this process by predicting the effects of different speaker locations and seat configurations on cabin noise, particularly at the driver’s ear level. The study begins by establishing a baseline simulation of cabin noise and generating training data for various seat location scenarios. This data is then used to train an AI model capable of predicting the noise impact of different design adjustments. These predictions are validated through detailed simulations. The paper discusses the accuracy of these predictions, the challenges encountered and provides insights into the effective use of AI models in acoustic analysis for cabin noise, with a specific emphasis on seat location as a key variable.</div></div>
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