Maruf Md. Ikram

Research Assistant
Macro-to-Micro scale Fluids Engineering Lab (MμFEL)
Department of Mechanical Engineering
Bangladesh University of Engineering & Technology

Hi, I am Maruf Md. Ikram. I have completed my undergraduate study in Mechanical Engineering from Bangladesh University of Engineering and Technology (BUET), the most prestigious engineering institute in Bangladesh. In my undergraduate thesis, I worked on the thrust vectoring analysis of a supersonic micro nozzle in rarefied conditions using the Direct Simulation Monte Carlo (DSMC) approach. Currently, I'm working as a Research assistant in the Macro-to-Micro scale Fluids Engineering Lab (MμFEL) in the Department of Mechanical Engineering, BUET. In addition, I am also mentoring an undergraduate research group that is analyzing the behavior of the supersonic microjet and developing a data-driven deep neural network model for predicting the microscale flow behavior to save significant computational time.

I also have prior experience in both laminar and turbulent modeling for steady as well as transient conditions and have been part of several research projects which are related to Direct Simulation Monte Carlo (DSMC), Finite Element Method (FEM), and deep learning. I collaborated with Dr. Sumon Saha, BUET, Bangladesh, and Dr. Goutam Saha, Dhaka University. I have also worked with Dr. Suvash Saha, University of Technology Sydney, Australia in our research project on modeling a transient thermo-fluid system considering the fluid-solid interaction (FSI). We analyzed the thermal response of the system using Fast Fourier Transform (FFT). Our article was published in the International Journal of Heat and Mass Transfer (IF: 5.584). After my graduation, I collaborated with Dr. Muhammad Rahman from Rice University. We worked on an additive manufacturing review paper with the title "Direct Ink Writing as a Versatile 3D Printing Technique". The article is submitted in Nature Reviews Materials (IF: 66.308). The article was a collaborative work with Ajayan Research Group and was co-authored by A. John Hart from Massachusetts Institute of Technology (MIT).

As a young inquisitive mind, I always focus on learning new ideas, computational methods, software, and programming languages so that I can be a better researcher and have an edge over my peers. Being an enthusiastic young mind, I am also interested in developing efficient computational tools for combining fluid dynamics with deep learning for computationally efficient flow simulation.

About Me

Thermo-Fluid System Design and Optimization:

I have been part of several thermo-fluid system design research projects which are modeled using the Finite Element Method (FEM). These projects conceptualized both the laminar and the turbulent flow modeling from a steady as well as a transient state perspective. The primary focus during designing the systems is given to higher thermal transport efficiency while the optimization process is focused on lower entropy generation.

Direct Simulation Monte Carlo (DSMC)

In my thesis, I have investigated the thrust vectoring phenomenon of a supersonic micro nozzle using Direct Simulation Monte Carlo (DSMC) method. This is a mesoscale approach for modeling rarefied gas flows, in which Knudsen number is greater than 1. In the rarefied flows, the Navier-Stokes equations can be inaccurate and thus, the DSMC method is used .

Flow Predictive Deep Neural Network Modelling

One of my research interests lies in the micro to nanoscale flow analysis and implementation of the deep neural network to predict the rarefied flow behavior. Currently, I'm developing a deep neural network model, compatible with the Direct Simulation Monte Carlo (DSMC) method, to minimize the statistical error using less time averaging data. The project aims to save significant computational time with minimum compromisation of the simulation accuracy.