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My research focuses on developing computational modelling methods that balance computational efficiency with physical realism for complex engineering and multi-physics problems. Rather than always pursuing the highest model fidelity, the objective is to identify the modelling approach that provides the required level of accuracy while remaining practical for engineering design, optimization and industrial decision-making.  Computational Fluid Dynamics (CFD), Computational Heat Transfer (CHT), engineering correlations and experimental validation are combined to achieve this balance.

 Throughout my academic career, I have worked across several application areas including turbomachinery, bioenergy, additive manufacturing and sustainable energy systems. Although these fields differ in their engineering challenges, they share a common research philosophy: simplifying geometry, reducing physical complexity or developing engineering correlations whenever appropriate, while maintaining sufficient physical realism to answer the engineering question. 

 Close collaboration with industry has been an essential part of this work, helping bridge fundamental research with practical engineering applications. Future research aims to integrate data analysis, uncertainty quantification and machine learning with physics-based modelling to accelerate simulation workflows and support the development of predictive digital twins for industrial processes such as additive manufacturing and biogas production.