This project is only available to UK or EU applicants.
Project outline
Predicting the noise and vibration from complex structures like cars is a common challenge in engineering design. Transfer Path Analysis (TPA) can be used for this NVH work, speeding prototyping, and reducing design costs. In TPA, you break down systems into key components, coupling together models of vibration sources, such as electric motors, to passive receiver structures, like a car body. This PhD is about improving measurements that feed into TPA, to increase the accuracy of the noise and vibration predictions. Improving prediction accuracy, will help industry ensure their products meet noise regulations and increase vibro-acoustic comfort.
Further project information
You will develop approaches that guarantee accurate vibro-acoustic predictions with the least measurement effort. The first objective is exploring the high sensitivity of full-system predictions to measurement errors coming from individual components. The goal is to gain a more complete understanding of how different inconsistencies within a measured component-level frequency response function (FRF) matrix can lead to spurious peaks in the coupled FRF. You will work to create methods to automatically detect and remove spurious peaks via data quality checks on targeted component-level. A second objective is to research which degrees of freedom to include in the source identification using the blocked force method.
You will be working with Siemens, a leader in data acquisition and processing for TPA, and the Acoustics Research Centre at the University of Salford, which pioneered the block force measurement method enshrined in an ISO international standard.
Subject Areas
- Mechanical Engineering
- Dynamics
- Acoustics
Required technical skills/academic qualifications/experience
You should have as a minimum a 2:1 undergraduate degree, an honours degree or a Masters degree in a science or engineering subject. This ideally would include experience of acoustic or vibration measurement, prediction and analysis.
We particularly encourage applications from women and other groups under-represented in research. All appointments will be made on merit.
Mode of attendance
This project is only available on a full-time basis for EU or UK nationals.
Supervisors
Project Partners
This project is hosted at the University of Salford and supported by Siemens Industry Software NV.
