COURSE CURRICULUM

The MSc Computational Fluid Dynamics course comprises a rigorous set of compulsory modules that build a comprehensive foundation in fluid mechanics, numerical modelling, high performance computing, turbulence modelling, and data analysis. The course begins with Introduction to Fluid Mechanics and Heat Transfer, which covers the thermodynamics of gases and liquids, compressible and incompressible flows, and the mathematical properties of governing equations, laying the groundwork for advanced CFD applications. Numerical Methods and High Performance Computing introduces students to numerical analysis and discretisation approaches such as finite difference, finite volume, and finite element methods, while also exploring parallel computing techniques like MPI and OpenMP, enabling efficient use of modern computing resources. Grid Generation and CAD provides essential skills in structured and unstructured mesh generation, offering hands-on experience with commercial software tools like ANSYS Fluent Meshing and Pointwise. Data Analysis and Uncertainty focuses on visualisation, data mining, model validation, and the evaluation of simulation errors, combining CFD with experimental comparisons, such as wind tunnel testing. Turbulence Modelling explores closure methods for the Navier-Stokes equations, including RANS, LES, and DNS, allowing students to critically assess model performance for transitional and turbulent flows. Numerical Modelling for Compressible Flows covers shock wave formation, non-linear equations, and high-resolution schemes like WENO and MUSCL, providing tools to simulate supersonic and transonic flows. Each module equips students with deep technical knowledge and hands-on skills to analyse and simulate real-world flow problems. Together, they form a solid foundation for careers in CFD and related fields, particularly in aerospace, automotive, biomedical, and energy industries. This curriculum ensures graduates are proficient in applying both foundational theories and advanced computational methods to solve complex engineering challenges, making them highly competitive in a field that continues to grow in industrial relevance and research innovation.