Research

Mini Magnetohydrodynamic Thruster: Simulation & Prototype

Magnetohydrodynamic (MHD) propulsion moves conductive fluids without moving parts, using electric and magnetic fields. This project covers theoretical modeling, computational simulation, and construction of a functional prototype. An excellent opportunity to apply electromagnetism, fluid dynamics, and computational simulation.

Mini Self-Balancing Robot: Modeling, Control & Prototype

Development of a mini self-balancing robot covering mathematical modeling, control strategies (PID, optimal control), and construction of a functional prototype. Introduces dynamic systems control, sensor/actuator integration, and mobile robotics.

Performance Prediction Using Machine Learning on Simulated Structures

Investigating ML algorithms to predict performance criteria of structural designs represented as binary meshes. Involves dataset generation from numerical simulations, supervised learning techniques, and predictive model validation. Combines machine learning, structural optimization, and computational analysis.

Fluidic Diode Design

Fluidic diodes allow preferential fluid flow in one direction without moving parts. This project covers CFD simulation, design optimization, 3D printing prototyping, and experimental validation. Applications include biomedical systems, respiratory masks, and microfluidics.

Topology Optimization of Fuel Cells

Applying advanced topology optimization to PEMFC and SOFC design, addressing efficiency, durability, and cost-effectiveness. Key components include multiphysics modeling (electrochemical reactions, mass transport, heat transfer, mechanical stresses), multi-objective optimization, degradation-aware design, manufacturing constraints, and experimental validation.

Topology Optimization of Magnetohydrodynamics

Development of topology optimization methods for MHD systems to maximize energy conversion efficiency and flow control. Includes coupled MHD equation solvers, novel parameterization methods, adjoint sensitivity analysis, and experimental validation. Applications: MHD generators, liquid metal pumps, aerospace flow control, biomedical devices.

Machine Learning Surrogate Models for Topology Optimization

Developing ML surrogate models to replace computationally intensive sensitivity calculations in topology optimization. Includes specialized neural network architectures (CNN, GNN, transformers), physics-informed constraints, hybrid optimization strategies, and uncertainty quantification. Applications: automotive lightweight design, aerospace components, heat exchangers.

Topology Optimization

• Bendsøe, M. P. & Sigmund, O. — Topology Optimization: Theory, Methods, and Applications, Springer, 2003. The foundational textbook for the field.
• Borrvall, T. & Petersson, J. — Topology optimization of fluids in Stokes flow, Int. J. Numer. Methods Fluids, 2003. Seminal paper on fluid topology optimization.
• Sigmund, O. & Maute, K. — Topology optimization approaches, Structural and Multidisciplinary Optimization, 2013. Comprehensive review of methods.

Computational Fluid Dynamics

• Versteeg, H. K. & Malalasekera, W. — An Introduction to Computational Fluid Dynamics: The Finite Volume Method, Pearson, 2007.
• Anderson, J. D. — Computational Fluid Dynamics: The Basics with Applications, McGraw-Hill, 1995.
• Ferziger, J. H. & Perić, M. — Computational Methods for Fluid Dynamics, Springer, 2002.

Machine Learning & Deep Learning

• Goodfellow, I., Bengio, Y. & Courville, A. — Deep Learning, MIT Press, 2016. Available free at deeplearningbook.org.
• Bishop, C. M. — Pattern Recognition and Machine Learning, Springer, 2006.
• Brunton, S. L. & Kutz, J. N. — Data-Driven Science and Engineering, Cambridge University Press, 2022. Bridges ML with physical systems.

Fuel Cells

• O’Hayre, R. et al. — Fuel Cell Fundamentals, Wiley, 2016. Covers PEMFC and SOFC electrochemistry, transport, and design.
• Barbir, F. — PEM Fuel Cells: Theory and Practice, Academic Press, 2012.

Control Systems & Robotics

• Ogata, K. — Modern Control Engineering, Pearson, 2010. Classic reference for PID and state-space control.
• Nise, N. S. — Control Systems Engineering, Wiley, 2019.
• Siciliano, B. et al. — Robotics: Modelling, Planning and Control, Springer, 2010.

Finite Element Method

• Hughes, T. J. R. — The Finite Element Method: Linear Static and Dynamic Finite Element Analysis, Dover, 2000.
• Reddy, J. N. — An Introduction to the Finite Element Method, McGraw-Hill, 2005.
• Zienkiewicz, O. C. & Taylor, R. L. — The Finite Element Method, Butterworth-Heinemann, 2000.