Dr. Muhammad F. B. Raihan

Profile

My academic journey in the realm of fluid dynamics and computational engineering commenced during my undergraduate studies in Mechanical Engineering. Fascinated by the intricate complexities of fluid flow, I delved into the world of Computational Fluid Dynamics (CFD) as part of my coursework and research projects. My undergraduate and Master theses was a testament to my early exploration of CFD applications, where I investigated the performance of conjugate heat transfer systems using simulation techniques. This initial exposure ignited a passion for the intersection of numerical methods and fluid mechanics to enhance the efficiency and performance of engineering systems.

The pivotal turning point in my academic trajectory occurred during my Ph.D. studies, where I embarked on a mission to integrate the power of Artificial Intelligence (AI) and Machine Learning (ML) into the realm of CFD-enabled optimization. Recognizing the limitations of traditional optimization techniques, I sought to leverage AI and ML methodologies to augment the capabilities of CFD simulations. My doctoral research focused on the optimisation of fluid flow and heat transfer systems based on the application of AI and ML to perform supervised machine learning.

Throughout my Ph.D. journey, I collaborated with experts in both CFD and machine learning, pushing the boundaries of interdisciplinary research. This fusion of traditional fluid dynamics with cutting-edge AI technologies not only expanded the horizons of my own academic pursuits but also contributed to the broader scientific community's understanding of the symbiotic relationship between CFD and AI.

Research interests

My research interests include Heat Transfer, Computational Fluid Dynamics (CFD), Machine Learning, Artifical Intelligence and Design optimisation for processes and engineering systems. The application of different types of neural network architectures such as Concolutional Neural Network (CNN), Recurrent Neural Network (RNN), Graph Neural Network (GNN) and Self-Organising Maps are of interest especially in CFD applications. Currently, I am working with the Advanced Manufacturing Research Centre (AMRC) under the EPSRC-funded project developing machine learning/deep learning and optimisation applications for achieving Net Zero and sustainability. 

<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>

Qualifications

• BEng, MEng, PhD, Leeds

Student education

Computational Fluid Dynamics, Machine Learning, Artificial Intelligence, Python, TensorFlow and Design Optimisation.