The recent advancements in the composite manufacturing processes led to manufacture parts that were inconceivable in the last two decades. This, associated with the ever-increasing requirements to reduce CO2 emission along with the availability of data have increased the use of composite materials in large structures such as, Boeing 787 Dreamliner, Airbus A350, etc. Yet, the structural properties of composite materials are derived from the constituents, i.e., inclusion size, shape, volume fraction, orientation, spatial distribution and the nature of interactions between inclusions and the surrounded matrix.
Designing and developing a new composite material product requires a large investment in testing equipment to generate experimental data. Also, considering the variables mentioned above, any changes to design, properties of the constituents, fibre volume fraction, orientation, etc. will invalidate the experimental data and that requires further costly experimentations. Hence, virtual testing is of high interest to the industry. Virtual testing means the ability to complete the entire design and test it in a workstation without the needs for building a physical prototype and preforming extensive testing protocols. The first step in virtual testing is to estimate the elastic moduli at the macroscopic level based on the microscopic information, such as the elastic moduli of the constituents, constituent volume fraction and void content.
It is computationally infeasible to analyse large structures based on the microscopic structures and therefore, analysis methodologies, such as representative volume elements (RVE), are sought. The RVE is the smallest representative material volume element for which the usual spatially constant "overall modulus" is sufficiently accurate to represent the macroscopic constitutive response.
This PhD opportunity aims to explore various aspects of multiscale virtual testing of composite materials and structures such as: the effective RVE size, rate of convergence, formulation, micro-to-macro scale transition and eventually building a virtual testing framework.
This research will benefit from excellent computing facilities, expertise in computer-aided engineering (CA2M lab), the available experimental facilities including mechanical testing and links with industry and with our Advanced Manufacturing Research Centre (AMRC) through our collaborative work.
1st or 2:1 degree in Engineering, Materials Science, Physics, Chemistry, Applied Mathematics, or other Relevant Discipline.