Tag: Mechanical Engineering-Engineering Mechanics

Since I began learning the basics of science, the effects of vibrations on environments has always fascinated me. As Nikola Tesla said, “If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.” It was with this aim that I started my Ph.D journey in Fall 2020.

My research has been in regards with “Metastructures”. These are unique structures which absorb vibrations in a system in particular frequency range, often called as ”bandgap”. It is because of this bandgap phenomenon that metastructures are widely used to mitigate vibration effects. Owing to large number of applications, it becomes important to estimate bandgaps in a metastructure to predict the frequency range in which the vibrations will be absorbed so that metastructures can be designed for various applications.

Through my research, I have aimed to developed new methods to estimate these bandgaps. The current methods available in the literature require a physics-based model of the metastructure (analytical model, finite/spectral element model) in order to estimate bandgaps. However, for various anisotropic materials, the material properties are difficult to quantify accurately which makes the physics-based model inaccurate. My research aims to overcome these limitations by developing methods which estimate bandgaps using purely experimental data. We have used the experimental data to study how a vibration wave is propagating through the metastructure (dispersion curve) and estimated bandgaps. Various other techniques such as substructuring, data-driven modeling algorithms were utilized. The developed techniques considerably reduced the design efforts required and made the entire design process much easier.

The funding provided by this fellowship will truly be helpful for me in putting all my energies in finishing my thesis in time and complete my Ph.D.!
I am grateful to the Graduate School for granting me this fellowship.
My advisor, Dr. Sriram Malladi has been more than helpful in guiding me through various ups and downs throughout my Ph.D journey. I am truly thankful for the relentless support he and his family has given me. Last but certainly not the least, I am thankful to my family i.e. my wife and my parents for their support in every aspect of my journey!

Growing up in a township full of scientists and engineers, I have always been curious about how things work. This led me to pursue a bachelor’s in engineering from Nehru Institute of Engineering and Technology affiliated with Anna University, Chennai. I then pursued a master’s from the esteemed Indian Institute of Technology, Madras where I delved into diverse research projects that captivated my interest. Fueled by this newfound interest, I started my journey as a Ph.D. student eager to tackle intriguing and fundamental challenges within the field of engineering.

I started working on my Ph.D. in the Fall of 2019 at the Computational Mechanics and Machine Learning Lab led by Dr. Susanta Ghosh at Michigan Tech. The focus of my research is on understanding the uncertainties associated with the predictions of computational and machine-learning models. Any model, computational or data-driven, is a representation of a physical phenomenon. We develop such models to understand the world around us better. However, predictions of such models are not always reliable due to the uncertainties associated with them. These uncertainties could arise for various reasons such as natural variability in the systems we study, assumptions in developing these models, numerical approximations, lack of data, etc. In order to use these models in real-life scenarios, quantifying these uncertainties is crucial. My research involves developing novel techniques to quantify the uncertainties, use these uncertainties to improve the model’s performance, and explain the reasoning behind the uncertainties. In my first project, we developed a Bayesian neural network-based machine-learning model that can reliably classify breast histopathology images into benign and malignant images. In addition, the model can quantify uncertainties associated with the predictions. We further developed techniques to explain the uncertainties and use them to further improve the model’s performance. In my second project, we developed novel loss functions for Bayesian neural networks and showed their advantages over the state-of-the-art in image classification problems. I am currently working on quantifying uncertainties in computational models that are used to characterize material behavior and extending the first two projects for several other applications.

I would like to thank my advisor Dr. Susanta Ghosh for giving me the opportunity to carry out this exciting research as well as for his immense help and guidance throughout the process. I thank the Graduate Dean Awards Advisory Panel and the dean for recommending me for this award. It is an honor. I thank the graduate school and the Department of Mechanical Engineering-Engineering Mechanics for their constant support.

Growing up as a teenager, I always wanted to become a person who could help people around the world. I got the opportunity to start my academic life in health research, which brought me closer to what I have always wanted. As a mechanical engineer with a background in biomedical engineering and biomaterials, helping people and sharing the multidimensional point of view of these fields would be a fantastic opportunity to develop solutions for health-related problems worldwide.

I started my PhD in Mechanical Engineering in the Spring of 2020 at Michigan Technological University. Being part of the MTU family has been an exceptional experience for me. I am very fortunate to work in Dr. Abadi’s lab, who has guided me through research and several aspects of life. My research includes four different projects related to cardiovascular diseases and disorders (i.e., those related to the heart and blood vessels). Cardiovascular diseases are the leading cause of mortality worldwide. Nanomaterials, with their unique morphologies and properties, have great potential for advancing cardiovascular engineering to treat diseases and disorders. My research is in three main categories, ranging from tissue engineering to robotics and medical devices.

Despite being far from my hometown, I feel at home in Michigan Tech. I have had several opportunities to participate in different organizations and competitions. This incredible journey will always hold a special place in my heart. I am so glad that I am close to my dream of obtaining a PhD and being able to help people around the world on a small scale in health.

I am grateful to the Graduate School for awarding me this doctoral finishing fellowship; this fellowship means a lot to me and motivates me to work harder to finish my PhD journey! I am thankful to the people who have supported me on my journey.

I started working at the APS LABS for my Masters’ thesis in Fall 2017. The research and the people at the APS are so amazing, that it wasn’t a difficult decision to stay for my PhD. I started my PhD in Summer 2018 under the guidance of Dr. Jeremy Worm, who is the associate director of APS LABS.

Majority of my PhD work has been on gasoline engines- how they can be further improved and made more efficient. The projects I have worked on have been industrial projects for major automotive companies like General Motors, Ford, Stellantis and Nostrum Energy. The current automotive climate is all about electrification. But I believe that the internal combustion engine, which is one of the major factors that has brought civilization to the advanced stage it is at right now, still has a massive role to play in the future. But the beauty of working at the APS LABS is that you are not confined to a single subject, and get exposure to all sorts of power systems. In the last five years, I have worked with hybrid electric vehicles as well, and also on a project to consider the environmental and economic impacts of electrifying the Mackinac Island ferry.

The last five years have been memorable and fulfilling, and a lot of that is thanks to my advisor, Dr. Jeremy Worm. With the help of the Finishing Fellowship, I will be able to write the final chapter of doctoral journey (figuratively and literally), and move a step closer to my goal of becoming an academic.

I joined Michigan Technological University as a Fulbright PhD Scholar in 2017. I earned a master’s degree on the fly in Mechanical Engineering-Engineering Mechanics in 2019. My doctoral research focuses on modeling and predictive control of a multi-mode engine. As we know, engine-out emissions increase air pollution and contribute to climate change. The transportation sector is one of the sources of air pollution. My research focuses on improving thermal efficiency and reducing engine-out emissions. 

Low-temperature combustion modes are among the advanced combustion technologies which offer high thermal efficiency and reduced engine-out NOx and soot emissions. A conventional spark ignition (SI) engine is modified to achieve low-temperature combustion modes. The main challenges associated with the low-temperature combustion modes include combustion timing and engine load control, high maximum pressure rise rate, cyclic variability and limited operating range. I have developed linear and nonlinear model predictive controller frameworks to control combustion phasing and engine load while restricting cyclic variability and maximum pressure rise rate for different low-temperature combustion modes. In addition, I have developed a closed-loop model predictive controller which ensures mode switching between conventional spark ignition (SI) engine and a low-temperature combustion mode to achieve better efficiency and reduced emissions. The developed controller performance is validated for the multi-mode engine operation in real-time.

I greatly appreciate the Graduate Dean Awards Advisory Panel for awarding me the finishing fellowship. I would like to extend my gratitude to my co-advisors, Dr. Jeffrey Naber and Dr. Mahdi Shahbakhti, for their guidance, support, and encouragement throughout my research.