As a Research Assistant at the Centre for Thermofluids, Energy Systems and High Performance Computing (HPC) at the University of Huddersfield, United Kingdom, I specialise in the application of advanced Computational Fluid Dynamics (CFD) methodologies for the analysis and solution of complex fluid mechanics problems. My research encompasses turbulence modelling, aerodynamics, multiphase and particle-laden flows, biological fluid dynamics for health-related applications, renewable energy systems, as well as machine learning methods and surrogate modelling for fluid flow systems.

My current research focuses on the development and integration of data-driven, multi-objective optimisation frameworks, coupled with machine learning and surrogate modelling techniques, to improve the aerodynamic and aeroacoustic performance of wind energy systems. A key objective is the reduction of computational cost while preserving high-fidelity predictive capability within high-dimensional design spaces, thereby enabling efficient and robust exploration of optimal configurations for performance enhancement and noise mitigation.

With a strong foundation in high-fidelity numerical simulation, and data-driven analysis, my research aims to advance predictive and computational modelling capabilities in fluid dynamics. The broader objective is to contribute to the development of robust and scalable methodologies for engineering optimisation across renewable energy and biomedical fluid flow applications.

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Last updated 4th June 2026