Dr. Olabode Thomas Olakoyejo

Dr. Olakoyejo is currently an associate Professor in the Department of Mechanical Engineering, University of Lagos, where he teaches, researches and practices Mechanical Engineering especially in the area of Thermal Engineering since 2004. He was once a Visiting Fellow, (Spring, 2016) on MIT-Empowering The Teachers (MIT-ETT) Fellowship, Massachusetts Institute of Technology (MIT), United States. Dr. Olakoyejo has expertise in Computational thermofluids, with a specialised focus on thermal management in heat-generating devices at macro, micro, and nanoscales. His work leverages Constructal Theory, Computational Fluid Dynamics (CFD), and Artificial Intelligence (AI)-driven techniques to optimize energy systems critical to modern technological advancements. He is also at the forefront of single and two-phase flow and heat transfer research, using numerical modeling (CFD – ANSYS FLUENT) to study phase-change phenomena in heat exchangers, microchannels, and industrial cooling systems. His simulations provide insights into bubble dynamics, heat flux distribution, and flow instabilities, aiding in the design of more efficient thermal management systems for energy and electronics applications. Also, Dr. Olakoyejo is in carbon sequestration modeling to address air pollution challenges associated with industrial emissions and gas flaring integrating condition monitoring and predictive models. Using AI-driven predictive models and CFD simulations, he evaluates carbon capture techniques, optimizing sequestration processes for enhanced environmental sustainability, enhancing system reliability, and maximizing energy output. He is also into Renewable energy such as solar and biomass due to the challenge of the electricity problem in Nigeria despite the abundance of Sun in Nigeria. Also, his research extends to wind where he integrates advanced condition monitoring and predictive models using data analysis and machine learning in wind turbine to detect potential failure of wind turbine before it happens to ensure maximize energy output, reliability, and efficiency, and reduce operational costs of the wind turbine.