Our Research

Research overview: Our research concerns at the understanding, predicting and optimising the mechanical response of composite materials experimental, analytical and computational efforts. We aim at developing high fidelity models that can predict material performance and provide fundamental insight in a wide range of multidisciplinary challenges, from carbon fibre composites to carbon nanotube composites, from macro-scale to nano-scale, from mono-functional to multi-functional applications. Particularly, these research projects include:

(1) proposing novel characterisation method to reveal the microstructures and deformation/failure mechanisms of composites operating over different length scale;

(2) developing novel, robust and efficient models for the mechanical response of composites under impact or crush loading;

(3) promoting a new generation of damage tolerant and multifunctional composites using carbon nanotube materials.

Research interests:

(1) Mechanics of Composite Materials: fracture behaviour and finite element modelling of carbon fibre composites.

(2) Data-driven modelling of composite materials.

(3) Electrochemical Behaviour of Energy-storage Materials: supercapacitors and batteries.

(4) Electrical and Thermal Behaviour of Nanomaterials: carbon nanotube mats/fibres.

Research highlights:

(1) Impact and crush behaviour of carbon fibre composites

Research summary:

Composite structures are susceptible to impact damage, which requires costly and highly inefficient experimental testing to meet safety-critical certification. My project aimed to develop a predictive material model for capturing impact damage and energy absorption capacity of CFRP.

1) A multiscale model was also proposed to take into account the physical mechanisms of deformation at different length scales of composite structures. This efficient strategy enables carrying out multiscale modelling from the properties of the constituents (fibre, matrix and interfaces) and homogenise the results into a constitutive model, followed by the transfer of information to the next length scale, which is both time-saving and economical for industry.

2) A physically-based model based on crystal plasticity has been proposed to accurately capture the inelastic behaviour and strain rate effect of composites subjected to shear or compressive or impact loading.

3) Low-velocity impact damage can drastically reduce the residual strength of a composite structure even when the damage is barely visible. The ability to computationally predict the extent of damage and compression-after-impact (CAI) strength of a composite structure can potentially lead to the exploration of a larger design space without incurring significant time and cost penalties. A high-fidelity three-dimensional composite damage model, to predict both low-velocity impact damage and CAI strength of composite laminates, has been developed and implemented as a user material subroutine in the commercial finite element package, ABAQUS/Explicit. 

4) A crushing model based on a new distorted element deletion strategy was presented to capture the crushing behaviour of composite materials.  This model solves the convergence issue due to element distortion under large deformation via deleting element based on the determinant of deformation gradient.  

Example: Predicting the crushing process of thermoplastic composites

(2) Data-driven modelling of composite materials

Research summary:

The adoption of cellular structures for applications involving crash energy absorption are increasing due to their beneficial mechanical properties including low weight and high specific energy absorption. This project aims to integrate the data-driven approach and finite element modelling for designing energy-absorbing composite materials.

The framework of machine learning in designing cellular composites (credit of Qichen Zhou)

(3) Mechanics of direct-spun carbon nanotube mat and composites

Research summary:

CNT with superior structural, electrical and thermal properties, is of great potential to introduce multi-functionalities to CFPR, such as impact-tolerance, lightning strike protection and de-icing. Cambridge University has first proposed floating catalyst chemical vapour deposition method (FFCVD) to produce macroscopic CNT fibres/mats continuously in large volume (500 m2/day).   However, the properties of macroscopic CNT fibres/mats haven’t yet reach the full potential of individual CNT (only 1% at present). To understand the properties of macroscopic CNT sheet, I have developed various novel characterisation and computational methods.

1) Achieved the first in-situ microscopy that reveals the deformation mechanisms of CNT mat.  We found that CNT bundles form random interlinked bundle network and the network deforms like a foam under tension, with dominate transverse deflection of struts. The lack of stretching on CNT bundles limits the macroscopic mechanical properties of CNT mat. 

2) Elemental mapping of CNT-epoxy composite firstly revealed that epoxy resin does not penetrate CNT bundle. Consequently, the interfacial properties between individual CNTs are not improved. This explains the limitation of epoxy in the enhancement of CNT performance.

3) Proposed a novel micromechanical model to relate macroscopic CNT mat properties to those of CNT bundle network and CNT-epoxy composites. The model was able to describe the degree of elastic and plastic anisotropy of the composite and the dependence of modulus and yield strength upon composition. I also developed a special four-point probe system to measure the electrical conductivity of CNT-epoxy composites, eliminating the contact and wire resistance. A novel steady-state method using infrared camera to measure thermal conductivity of CNT-epoxy composite under vacuum was also presented. These results found that the electrical and thermal conductivities of CNT-epoxy composite is primarily dependent on the CNT volume fraction.

Example: Mass production of direct-spun carbon nanotube mat (Tortech)

(4) Mechanics of carbon nanotube polyaniline composites for energy storage

Research Summary:

Novel direct-spun carbon nanotube polyaniline composite electrodes are developed for supercapacitor applications.

Example: RECHARGEABLE Supercapacitor Tram

Research Projects

2020-2023, Graphene Flagship Core3, QMUL Mini-CDT, £376,501, EU, Co-Investigator.  

2017-2018, Structural supercapacitors using hybrid carbon fibre/carbon nanotube composites, funded by University of Cambridge, CAPE Acorn Blue Sky Research Award, NMZD/256, £20K, UK, Principal Investigator.

2015-2020, Advanced Nanotube Application and Manufacturing Initiative (ANAM). funded by EPSRC, £2.8 millions, University of Cambridge, Researcher Associate.

2012-2016, Modelling the impact and crush behaviour of composite areostructures, funded by Royal Academy of Engineering and Bombardier, Queen’s University Belfast, Research Assistant (PhD)

Research Sponsors