Development of advanced validation techniques for computational models

Description

This project is part of a 4 year Dual PhD degree programme between the National Tsing Hua University (NTHU) in Taiwan and the University of Liverpool (UoL) in England. As part of the NTHU-UoL Dual PhD Award students are in the unique position of being able to gain 2 PhD awards at the end of their degree from two internationally recognised world leading Universities. As well as benefiting from a rich cultural experience, students can draw on large scale national facilities of both countries and create a worldwide network of contacts across 2 continents.

Project overview:

This project will develop novel validation methods to build confidence in computational models that predict performance of engineering structures.

Computational models are vital for exploring and developing future infrastructure, such as zero-emission aircraft or supplying clean energy. Before a virtual design is brought to life, its computational models must be validated to demonstrate confidence in the safety and performance of the design. The confidence in the models is particularly important when they are used to inform decisions that could have socio-economic consequences.

Simulations based on the computational models produce complicated 3D maps, such as displacement maps and modal shapes, which contain gigabytes of data. Advances in digital cameras have allowed similar 3D maps to be obtained from experiments with the aid of optical measurement systems, such as digital image correlation (DIC) and electron speckle pattern interferometry (ESPI). At the core of the validation process is a quantitative comparison of model predictions with physical measurements. However, the quantitative comparison between 3D maps is very challenging, and thus industry often limits validation studies to qualitative evaluation or comparison of sparse values at selected locations. The student will aim to address the challenges by developing novel methods to process and quantitatively compare measured and predicted 3D maps.

Project objectives:

This project will aim to develop novel validation tools to support the credibility of virtual testing. This will be achieved by working towards the following objectives:

1) Investigate uncertainty quantification methodologies for optical measurement systems used in mechanical testing to obtain maps of uncertainty across the entire surface of a specimen,

2) Integrate maps of uncertainty with validation metrics to improve comparison between full-field measurements and model predictions;

3) Extend existing validation tools to allow comparisons of data from a range of different sources.

During the project, the student will investigate structural performance of engineering components of different size scales and subject to a range of loading conditions. The novel data processing and validation methods developed in this project will have the potential to benefit a range of industries by providing a much more realistic assessment of computational models and increasing confidence in the virtual testing. Ultimately, these technological advances will lead to production of more sustainable engineering designs, which will produce less CO2 throughout their lifecycle, and shorter development times (UN-SDG 12). 

Training and study pattern:

The student will be working with leaders in model validation and measurement techniques, and will develop skills in both validation and experimental mechanics. The project work will encompass numerical, experimental and statistical approaches, and is anticipated to lead to several publications. The student will become part of two dynamic research groups and will participate in regular research group meetings, which include a variety of activities to develop additional transferable skills, e.g. presentations and lab demonstrations.

It is anticipated that the student will spend 2 years at the UoL, followed by 2 years at NTHU. The student will start by investigating existing approaches of calibration and uncertainty quantification. The student will use DIC for experiments while at the UoL, and ESPI while at the NTHU, and will identify suitable approaches to capture maps of uncertainty (objective 1). Next, student will explore advanced decomposition techniques to transform minimum measurement uncertainty so that it can be used as a threshold with the validation metrics (objective 2). Objective 3) will be achieved by selecting a diverse set of case studies, based on the findings at the earlier stages of the project, and then refining the uncertainty quantification and validation tools at the later stages of the project.

Further Information:

For academic enquires please contact Dr Ksenija Dvurecenska ( k.dvurecenska[at]liverpool.ac.uk ) and Prof Wei-Chung Wang ( wcwang[at]pme.nthu.edu.tw )

For enquires on the application process or to find out more about the Dual programme please contact PGENG@liverpool.ac.uk.