Project Outline
Vibroacoustic modeling is crucial in engineering systems for accurately predicting, monitoring, and controlling noise and vibration, especially in aerospace and automotive applications. Virtual decoupling techniques have been successfully applied to isolate and analyze vibroacoustic systems in low-frequency domain. However, uncertainties often limit the model’s accuracy. Furthermore, vibroacoustic systems pose challenges due to the absence of a single analysis technique suitable for all frequency ranges. Different physical behaviors and uncertainty impacts in low-, mid-, and high-frequency domains necessitate distinct approaches. Stochastic model updating methods combined with uncertainty quantification provide a systematic framework to address these issues and enhance the reliability of vibroacoustic models. This project aims to integrate these methods, developing a comprehensive digital twin framework for the calibration, verification, and validation (V&V) of vibroacoustic models under uncertainty.
Project partners
This project is hosted at the University of Southampton.
Further project information
The objective is to develop a data-driven digital twin framework for vibroacoustic modeling across different frequency domains. This framework will integrate suitable combinations of techniques for low, mid, and high frequencies and incorporate dedicated model calibration and V&V approaches tailored to the specific features and uncertainty challenges of each frequency domain.
Proposed Work Packages:
WP1: Uncertainty Quantification (UQ) for Vibroacoustic Modelling
Tailor UQ techniques for each frequency domain: FEM and Modal Analysis for low frequencies, FEM-SEA and WFEM for mid frequencies, and SEA, Ray Tracing, and Geometrical Acoustics for high frequencies, addressing uncertainties specific to each domain.
WP2: Model Updating for Different Frequency Domains
Develop and apply model updating techniques for vibroacoustic systems across frequency domains, improving accuracy by incorporating experimental data and adjusting for uncertainties in each range.
WP3: Data-driven Approach for Efficiency and Applicability
Use data-driven techniques to enhance computational efficiency, reducing model complexity and making the framework more practical for real-world applications, especially in real-time settings.
WP4: Verification and Validation (V&V)
Implement a rigorous V&V process by comparing model predictions with experimental data, ensuring accuracy across all frequency domains, and addressing specific uncertainty challenges for each range.
Subject Areas
- Probabilistic theory
- Finite element analysis
- Machine learning
Required qualifications/skills
Essential
- You are expected to have or obtain a 2:1 (minimum) BSc/BEng in Aerospace, Mechanical or Civil, Engineering
Student
Commencing in October 2026