Project Summary: The project aims to develop a data fusion framework to validate simulation models of multi-physics and multi-scale computational mechanics so that decision making can be more efficient, reliable and autonomous for engineering design, analysis and service maintenance. This framework would be very important for the digital twining for the industrial 4.0
Specific Requirements of the Project
First degree (2:1 or above) in Mechanical Engineering or in Applied Mathematics Good understanding of Computational Mechanics (e.g. finite element methods); Knowledge of full-field experimental mechanics (e.g. digital image correlation);
Project Aims and Objectives
Computer simulation offers powerful tools to assess structural reliability of the new designs. However, computational models are only as good as their assumptions. Comprehensive experimental validation with uncertainty quantification is necessary to assure the model’s credibility. The models’ fidelity is crucial when digital twinning application becomes popular in the modern engineering (a.k.a industrial 4.0). This project will aim for developing a validation framework from multiple sources of measurements so that confident model prediction can be achieved.
Objectives (1) Benchmark models in computational mechanics will be investigated in the fashion of multi-physics and multi-scales (2) Multiple sensing technologies will be applied to capture experimental responses on the real structures corresponding to the benchmark models. (3) Error analysis and reduction between computational and experimental data will be carried out to understand the discrepancy. (4) Data fusion framework on multi-physics and multi-scale models will be developed to assist decision making for design and analysis in engineering mechanics.