Representative Projects
PhD project - A Mathematical Model for Drug Delivery in the Brain
Motivations:
Brain diseases, such as Parkinson’s disease, Alzheimer’s disease, and malignant brain tumours, have been laying a considerable burden on global health and economics in recent decades. It is estimated by WHO that the death due to brain diseases will reach 12.22% of total death in 2030 worldwide. Although some effective drugs for brain diseases have been clinically approved, it is still challenging to deliver them to the brain properly. The main reason is that the blood–brain barrier (BBB), a highly selective border surrounding most of the blood vessels of the brain, blocks 100% of the macromolecular and over 98% of the small-molecular drugs from entering the brain parenchyma.
Convection-enhanced delivery (CED) (as schematically shown in Fig. 1), a mechanically controlled infusion method that delivers therapeutic agents directly to the target regions through one or more implanted catheters, therefore, is regarded as a promising technique for brain disease treatments. However, making the appropriate preoperative planning, such as the choice of injection location, infusion pressure, and the size of the catheter is very difficult because it is technically challenging to make precise predictions of drug transport in brain tissue. As a result, few desired results have been achieved in clinical trials of CED. The fundamental reason can be, in fact, attributed to the insufficient understanding of (i) brain tissue’s transport properties and (ii) brain tissue’s mechanical response to the infusion pressure, which further alters the intrinsic transport properties of brain tissue.
Applying nano drugs is another emerging technique and has shown great promise to treat brain diseases, as the tunable particle size and surface properties enable the nanoparticles (NPs), serving as drug carriers, to efficiently penetrate the BBB. However, due to the high complexity of brain microstructure and limited visibility of NPs, our understanding of how the particle parameters govern the transport efficiency of these drug carriers in the brain parenchyma after penetrating the BBB is still superficial. This limits the development of nanocarriers for brain disease treatments.
Aims:
This project aims to establish a mathematical model which is able to provide precise suggestions on drug delivery protocols for brain disease treatments and help to improve the development of nanocarriers of drugs for brain diseases.
Fig.1 Convection-enhanced delivery technique for brain diseases treatments
Methodologies & Results:
1. 3D geometrical reconstruction of brain white matter's microstructure by using the MIMICS software package and MATLAB coding based on high-fidelity images contained by Focused Ion Beam Scanning Electron Microscopy (FIB-SEM).
2. Mechanobiology experiments to measure mechanical and hydraulic properties of biological tissues.
3. Computational fluid mechanics (CFD) modelling to characterise the hydraulic properties of the brain white matter (See Fig.2. The contour shows the pressure distribution in a 3D representative geometry of the brain white matter's microstructure).
4. Fluid-Solid Interaction (FSI) modelling to uncover the mechanism of pressure-dependent properties of the brain white matter.
5. Multiscale Modelling to establish the microstructure-informed properties of the brain white matter.
6. Mathematical particle tracing modelling to understand the diffusion behaviours of NPs in the brain white matter (See Fig.3) and characterise the equivalent diffusion coefficient of NPs.
Outputs:
[J1], [J2], [J3], [J4], [J5], [J6], [C1], [C2], [C3]
Fig.2 Characterising fluid transport property of brain white matter by CFD
Fig.3 Mathematical modelling of nano drugs transport in the brain white matter
Master project - Nonlinear Similarity Theory for Buckling Process
Motivations:
Buckling resistance is a very important property for load-bearing structures, especially for thin-walled structures e.g. ships, planes, wind turbines, and offshore platforms, because buckling can significantly reduce the strength (load-bearing capacity) of the structures. However, due to the complexity of buckling phenomena which is governed by the coupling effect of geometric nonlinearity and material nonlinearity, it is challenging to provide reliable predictions on the buckling behaviours and ultimate load-bearing capacity of engineering structures theoretically. Experimental tests are thus still indispensable for the evaluation of the safety of complex engineering structures. Yet, it is impossible to test the giant structures in labs and a huge waste of resources to conduct real structure tests are, so the structural safety evaluations are relying on lab testing of the scaled-down similarity models that have the same/similar buckling behaviours and collapse modes as the real structures. Under this condition, a similarity theory (scaling law) is essential to guide the design of scaled-down models for lab testing but remains an outstanding problem because the existing similarity theories/methods are difficult to guarantee precise similarity, especially at the post-buckling stage.
Aims:
This project aims to put forward a similarity theory (scaling law) and the corresponding design method which is able to ensure the scaled-down models can more precisely reproduce the whole buckling process of structures from real ship structures.
Fig.4 The concept of "Similarity by parts"
Fig.5 Nonlinear buckling simulation to verify the similarity theory. Each column contains one original structure (top) and two scaled-down models (bottom). The scaled-down models are zoomed in to compare the buckling modes and stress distributions against the original models.
Methodologies & Results:
1. The concept of "Similarity by Parts" was put forward, which divides the buckling process into 3 parts: (i) linear stage, (ii) critical point, and (iii) nonlinear stage, as shown in Fig.4.
2. A set of dimensionless parameters that govern the whole buckling process was obtained by mathematical derivation. These form the core of the nonlinear similarity theory. If the scaled-down models have the same dimensionless parameters as their original structures, they can then reproduce the buckling process of the original structure.
3. Non-linear buckling numerical simulations were conducted to verify the nonlinear similarity theory and scaled-down model design method, as shown in Fig.5.
4. An experimental setup including a widefield digital image correlation (DIC) system dedicated to observing the detailed buckling process of stiffened plates was designed, as shown in Fig.6.
5. A group of experiments were conducted to verify the nonlinear similarity theory and scaled-down model design method, as shown in Fig.7.
6. The theory and method have been adopted in the structures in real ships.
Outputs:
Fig.6 Experimental setup for buckling assessment. 1-longitudinal beam; 2-transverse beam; 3-base plate; 4-specimen; 5-square bar; 6-fixture for the lateral boundary condition; 7-MTS hydraulic machine.
Fig.7 Original structure, scaled-down mode I, scaled-down model II
Undergraduate project - A Vector Control System for Water-jet Ships
Motivations:
The manoeuvrability of ships is exceedingly poor because of their tremendous inertia; this makes manoeuvring ships, especially in busy areas, a particularly challenging and dangerous task. Many serious accidents have happened during the processes of crossing narrow waterways and berthing ships into a port. The above video is an example. Increasing the manoeuvrability of ships and making them move as flexibly as cars are always the dream of ship designers.
Strategy:
Inspired by crabs that can move transversely, we proposed the concept of "Vector Control". The idea was to make the ship able to freely perform complex movements, e.g. translational motion and 360° in-situ rotation. Combing translational motion and 360° in-situ rotation, the ship could perform even more complex motions, as exemplified above.
Difficulties and solutions:
This could be realised by making the resultant force generated by the two waterjet propellers exerts at the ship centroid and points to the direction of ship motion. However, this is challenging in operation as it requires the 6 parameters (the rotation rate of the engine, the nozzle direction, and the thrust direction of both waterjet propulsions ) to be controlled simultaneously. I thus built a mathematical model that can automatically calculate i) the resultant force needed for the ship to achieve a targeted movement, and ii) the set of 6 parameters for the two waterjet propulsions to achieve the required resultant force. Based on the mathematical model, the "Vector Control System" was built. It allows us to control the 6 parameters by the simplest operations, as explained by the movie above.
Results:
The above video shows the great manoeuvrability our Vector Control System can provide to ships. Our work provided a new idea and methodology to significantly increase the manoeuvrability of ships and reduce sailing risks.
Outputs:
First Prize in the 14th “Challenge Cup” National Competition of Undergraduate Academic Science and Technology Works (the highest national award for undergraduate research) My Awards
[P2] My Publications
Project List
2024 - present Cancer microfluidic modelling for geometric and therapeutic translation
Category: 3-Year PhD studentship (Miss Saki Okada)
Contributions & Roles:
Proposal: Preliminary data, Computational model of OAC tissue
Role: Associate Supervisor
Collaborators: Dr Stefan Antonowicz, Prof. Daniele Dini
2023 - present LOCATE: Local Oesophageal CAncer Treatment Engineering to advance the understanding and treatment of oesophageal adenocarcinoma. £520K Details
Category: Medical Research Council (UKRI) project
Contributions & Roles:
Proposal: Preliminary data, WP1 (In silico modelling), Figures
Tole: Named Researcher
Collaborators: Dr Sara Valpione, Dr Stefan Antonowicz, Dr Michael Chen, Dr Michela Esposito
2023 - present Poroelasticity of brain-like hydrogels
Category: PhD project of research partner (Mr Manuel Kainz and Dr Michele Terzano) in biomechanics at the Graz University of Technology supervised by Prof. Gerhard Holzapfel.
Role: Participant
Contributions:
1. A new compression method to measure the actual porosity of the hydrogels;
2. Measure the local permebaility/initial permeability of the hydrogels.
2023 - present The fractional Darcy's law for brain tissues and brain-like hydrogels
Category: PhD project of research partner (Mr Gunda Sachin) in Continuum Mechanics at Indian Institute of Technology Madras (Prof. Sundararajan Natarajan and Dr Olga Barrera's Group)
Role: Participant
Contributions:
1. Measure the transient fluid transport satus of sheep brain tissues and the hydrogels;
2019 - 2023 A mathematical model for drug delivery in the brain Details
Category: PhD project
Collaborators: Dr Wenbo Zhan, Dr Asad Jamal, Dr Andrea Bernardini, Dr Marco Vidotto, Prof. Antonella Castellano, Prof. Andrea Falini, Prof. Marco Riva, Dr Li Shen, Dr Shaoli Jiang, Mr Kian Kun Yap.
2021 - present Pore-scale modelling of drug delivery in brain tumour tissue
Category: PhD project of research partner (Mr Yi Yang) in Fluid Mechanics at the University of Aberdeen (Dr Wenbo Zhan's Group)
Role: Participant
Contributions:
1. Helping to shape the project and provide suggestions on developing the mathematical model;
2. Providing training in software operations;
3. Providing experimental support in scanning the glioblastoma's microstructure by SEM.
2020 - 2021 Optimisation of microfluidic Liver-on-a-Chip using mathematical modelling
Category: PhD project of research partner (Dr Foivos Chatzidimitriou) in Bioengineering at IC supervised by Prof. Darryl Overby.
Role: Participant
Contributions:
1. Building a mathematical model to predict concentrations of oxygen and glucose in the liver tissue;
2. Optimising the shape of the micro-channel to obtain desired time-concentration relationships of oxygen and glucose in the liver tissue.
2016 - 2019 Nonlinear similarity theory for buckling process Details
Category: Master project
Collaborators: Mr Yi Yang, Mr Wang Zhuo, Dr Hui Deng, Dr Lei Ao
2016-2018 Experimental measurement of the ultimate strength of a container ship's hull and numerical optimisation
Category: Industry project
Role: Postgraduate student coordinator of the project (A team formed by 1 PhD and 8 master's students)
Contributions:
1. Managing project progress and assigning tasks to team members;
2. Designing the scaled-down model of the real ship's hull and sketching shipbuilding drawings;
3. Designing the experimental protocol and conducting experiments;
4. Developing the numerical protocol to predict the ultimate strength of the scaled-down model and validating its precision by comparing the simulation results against the experimental results;
5. Optimising the ultimate strength of the real ship's hull by the newly established numerical protocol.
2016 Strength measurement of a steel rudder by experiments
Category: Industry project
Role: Principal member of the project
Contributions:
1. Designing rigs to provide the loading boundary condition to the steel rudder;
2. Conducting the experimental measurements.
2016 Stress analysis of a yacht made of composite laminates
Category: Industry project
Role: Principal member of the project
Contributions:
1. Building the finite element model of the yacht using MSC Patran;
2. Calculating the stress distribution on the yacht under different working conditions using MSC Nastran;
3. Drafting the evaluation report.
2013 - 2015 A vector control system for water-jet ships Details
Category: Undergraduate project funded by the Undergraduate Innovation Funding of WUT
Role: Team Leader (A team formed by 11 undergraduate students)
Gourp members: Dr Tian Yuan, Mr Tianqi Zhang, Dr Xudong Wang, Mr Jie Liu, Mr Qi Chen, Miss Mengxue Zhang, Mr Jiabing Jiang, Mr Lizhong Gu, Mr Jiaxi Zhang, Mr Ziqi Ye, Mr Qian Xue
2012 A smart system for turn signals of cars
Category: Undergraduate project funded by the Undergraduate Innovation Funding of WUT
Role: Principal member of the project
Contribution:
Designing and manufacturing the circuit to receive instructions from the central CPU and control the turning on/off of the turn signals.
Tian Yuan
Department of Mechanical Engineering
Imperial College London
South Kensington Campus
Exhibition Road
London SW7 2AZ