White paper

Predict system NVH performance early in the design using virtual prototyping

Component-based transfer path analysis (TPA) is a virtual prototyping methodology to characterize noise source components independently from the receiver structure. It allows you to predict system NVH performance before the first prototype is built.

In hybrid and electric vehicles, drivetrain noise is less prominent. This makes noises from auxiliary systems more noticeable. Contrary to traditional TPA, component-based TPA is a noise source identification methodology that pays particular attention to components rather than the assembled product.

This white paper describes how to detect potential component NVH performance issues and optimize your system design early in the development.

Apply component-based TPA to improve system NVH performance

When developing complex products involving many assemblies, noise and vibration problems might be discovered late in the design process. Once integrated into the full system, the different components (mechanical, electrical, etc.) tend to interact with each other, making it particularly difficult to pinpoint which component causes the poor NVH performance.

Process innovations such as virtual prototyping allow building vehicle models from new and existing sub-systems and components to predict NVH behavior earlier in the design cycle. Manufacturers apply component-based TPA as noise source identification technology for two main reasons:

  1. Predict system-level NVH performance based on individual component testing
  2. Set realistic component targets

Download the white paper and understand how to characterize source components independently from the receiver structure by a set of blocked forces and predict its behavior when coupled to different receivers.

Take advantage of virtual prototyping and build your own NVH knowledge base

Simcenter virtual prototyping solutions support building a knowledge base to maximize the usage of available NVH data. This knowledge base is accessible to everyone at OEMs and the entire supply chain:

  • Experts, for handling the complexity of components and subsystems. The data is synthesized in such a way that it can be used for a variety of NVH performance evaluations before physical prototype assessment
  • Analysts, for consuming available expert component data for NVH prediction of virtual vehicle assemblies and different alternatives at any development stage

NVH performance matters across all application domains

High-quality NVH performance is a concern for manufacturers across all industries. Manufacturers dealing with various configurations or complex products involving many subassemblies (such as cars, trucks, excavators, helicopters, aircrafts, satellites, white goods, etc.) can benefit from applying this noise source identification approach.

In this document, the different steps of the component-based TPA process is illustrated on a wiper e-motor. First, the e-motor was characterized independently using different techniques (e.g. blocked force and free velocities). In a second step, assembly predictions were made using substructuring techniques that can help accelerate engineering decisions.

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Component-based transfer path analysis (TPA) is a virtual prototyping methodology to characterize noise source components independently from the receiver structure. It allows you to predict system NVH performance before the first prototype is built.

In hybrid and electric vehicles, drivetrain noise is less prominent. This makes noises from auxiliary systems more noticeable. Contrary to traditional TPA, component-based TPA is a noise source identification methodology that pays particular attention to components rather than the assembled product.

This white paper describes how to detect potential component NVH performance issues and optimize your system design early in the development.

Apply component-based TPA to improve system NVH performance

When developing complex products involving many assemblies, noise and vibration problems might be discovered late in the design process. Once integrated into the full system, the different components (mechanical, electrical, etc.) tend to interact with each other, making it particularly difficult to pinpoint which component causes the poor NVH performance.

Process innovations such as virtual prototyping allow building vehicle models from new and existing sub-systems and components to predict NVH behavior earlier in the design cycle. Manufacturers apply component-based TPA as noise source identification technology for two main reasons:

  1. Predict system-level NVH performance based on individual component testing
  2. Set realistic component targets

Download the white paper and understand how to characterize source components independently from the receiver structure by a set of blocked forces and predict its behavior when coupled to different receivers.

Take advantage of virtual prototyping and build your own NVH knowledge base

Simcenter virtual prototyping solutions support building a knowledge base to maximize the usage of available NVH data. This knowledge base is accessible to everyone at OEMs and the entire supply chain:

  • Experts, for handling the complexity of components and subsystems. The data is synthesized in such a way that it can be used for a variety of NVH performance evaluations before physical prototype assessment
  • Analysts, for consuming available expert component data for NVH prediction of virtual vehicle assemblies and different alternatives at any development stage

NVH performance matters across all application domains

High-quality NVH performance is a concern for manufacturers across all industries. Manufacturers dealing with various configurations or complex products involving many subassemblies (such as cars, trucks, excavators, helicopters, aircrafts, satellites, white goods, etc.) can benefit from applying this noise source identification approach.

In this document, the different steps of the component-based TPA process is illustrated on a wiper e-motor. First, the e-motor was characterized independently using different techniques (e.g. blocked force and free velocities). In a second step, assembly predictions were made using substructuring techniques that can help accelerate engineering decisions.

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