Innovation Management, Computational Fluid Dynamics, Turbulence, Data-driven methods, Reduced order models, Uncertainty quantification, Artificial Intelligence, Deep reinforcement learning, Optimisation
Saeed works on design and analysis of turbomachinery, hydraulic turbines and pumps, analysis of fluid flow and heating systems, development and application of numerical, data-driven and machine learning algorithms, robust optimisation of engineering systems. His expertise is in computational fluid dynamics (CFD) and flow analysis. Saeed has mainly focused on the open source CFD software in C++, OpenFOAM, but also in various data-driven and machine learning algorithms including uncertainty quantification, reduced order modelling and optimisation. Saeed is passionate about creating sustainable solutions for hydropower, contributing to fluid dynamics research and using innovative data-driven algorithms in fluid dynamics.
During his PhD studies, Saeed developed and applied various data-driven algorithms for uncertainty quantification of turbulent flows in hydraulic equipment. After his postdoc at Chalmers University of Technology, Saeed developed innovative methods to perform in-depth numerical studies of the turbulent flow characteristics that occur during transient operation of hydraulic turbines. Today, Saeed’s research focuses on leveraging machine learning and artificial intelligence to gain deeper insights and control over flow patterns inside hydraulic turbines.