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LMS Virtual.Lab Noise and Vibration

From Component to System-level Noise and Vibration Prediction

 
 
   
 

While it is relatively easy to predict performance at the component level, most noise and vibration problems are only discovered at the full system level. But experience shows that making extensive prediction models is a tedious and difficult process, which does not necessarily produce results that match with reality. How can you quickly and reliably synthesize system-level models? How can you accurately predict the most critical noise and vibration contributors and quickly identify the most appropriate design modifications?

A process approach to Noise and Vibration engineering
A vehicle’s NVH performance largely depends on the various loads the vehicle is exposed to, and on the resulting complex interactions between the numerous components and connections. Therefore, the correct approach to noise and vibration engineering is optimizing the vehicle as a complete system. From the early concept development stages onwards, LMS Virtual.Lab Noise and Vibration supports vibro-acoustic assessment on full-system models. It captures all critical process steps to systematically improve the noise and vibration characteristics of a complete assembly.
 
Delivering the right Noise and Vibration performance
LMS Virtual.Lab Noise and Vibration allows engineering teams to evaluate the vibro-acoustic behavior of multiple vehicle variants. Its unique hybrid simulation approach intelligently combines Finite Element (FE) modeling with test-derived models. Its industry-standard solvers execute fast and accurate predictions, and dedicated post-processing capabilities provide users with immediate feedback on vibro-acoustic responses and transfer paths.
  • Integrate test-derived component information in full-system simulation models
  • Analyze the most critical noise and vibration contributors and identify the most appropriate design modifications
  • Refine noise and vibration performance before building the first prototype

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