Personal Projects

  1. Recycling Plant Simulator:

    rcplant represents a robust and adaptable open-source Python package tailored for simulating a recycling plant environment. Its primary objective is to streamline the assessment and experimentation of diverse classification methods targeting recycling challenges. This package offers an extensive framework for examining and scrutinizing recycling processes. Notably, it has been downloaded Downloads times, signifying its widespread adoption within the community.

  1. Robust Economic Model Predictive Conytol with Application to Solar Thermal Systems:
    • For my PhD research project, I am focused on developing an innovative smart control system that leverages the power of the Gaussian process machine learning technique in conjunction with model predictive control (MPC). My current work involves developing a novel control system that combines MPC with the Gaussian process, which allows for more precise and adaptable control in the face of quasi-periodic disturbances such as hot water demand. Specifically, I am investigating the potential applications of this control system in domestic solar thermal systems, where it has the potential to significantly improve energy efficiency and reduce energy waste.
  1. Train Monitoring System:
    • With the aim of improving train safety and reliability, I undertook the design and development of a portable data-logger capable of monitoring various key performance metrics. Specifically, the device I designed can accurately capture and record both the vertical and axial acceleration of a train in motion, as well as the temperature of its wheelset. These data can then be analyzed in real-time to provide timely insights into the train’s performance, and in the case of any anomalies or issues, can alert the control center. Overall, the development of this innovative data-logging device represents an important step forward in the field of train monitoring and has the potential to significantly improve the efficiency and safety of railway transportation systems.
  1. Control of Adaptive Optics Systems using Transverse Actuators:
    • As a research project for my master’s degree, I tackled a challenging inverse dynamic problem related to shape control of deformable mirrors, which are crucial components used in modern large telescopes. Employing the powerful PDE-constrained optimization method, I successfully solved this complex problem and generated novel insights that have the potential to revolutionize the field of astrophysics. Through my work, I demonstrated an unwavering dedication to precision and accuracy, as well as a keen ability to utilize advanced mathematical concepts to address real-world challenges.
  1. Design, Fabricate, and Control a Double Pendulum System with ARM microcontroller:
    • For the final project of my Mechatronics course, I undertook the ambitious task of designing and fabricating a fully functional double-pendulum system from the ground up. Drawing upon my knowledge of mechanical design, control theory, and programming, I carefully crafted the system to meet my precise specifications. To further enhance its capabilities, I then implemented a digital PID (Proportional-Integral-Derivative) control system using an advanced ARM-based STM32F3DISCOVERY board.
  1. GM locomotive’s DC traction motor condition monitoring and fault diagnostics using articifial neural network:
    • The focus of my project was the development of an intelligent monitoring system for DC electric motors, with a particular emphasis on General Motors (GM) locomotives. By leveraging advanced techniques such as vibration analysis and the discrete wavelet transform (DWT), I was able to gain valuable insights into the motors’ condition and performance. Specifically, I utilized a Learning Vector Quantization (LVQ) artificial neural network to analyze the data and provide real-time fault diagnostics. This approach allowed for a more efficient and accurate means of monitoring and maintaining GM locomotive’s DC traction motors. By contributing to the development of sophisticated condition monitoring systems, my work has the potential to enhance safety, reduce downtime, and ultimately improve the overall efficiency of locomotive operations.
  1. Design and Fabrication of the Magnetic Electron Lens for a Transmission Electron Microscopy (TEM):
    • Over the course of a three-month project, I undertook the construction and implementation of a magnetic electron lens designed specifically for use within a transmission electron microscope (TEM). This cutting-edge lens was built to the highest specifications, utilizing the latest materials and manufacturing techniques to ensure optimal performance within the TEM environment. The magnetic electron lens is a crucial component within the TEM, responsible for shaping and focusing electron beams to produce high-resolution images of specimens at the atomic scale.
  1. Spectrum analysis of Y25 bogie by SRSS and CQC method:
    • Our project team successfully developed an innovative method to accurately evaluate the forces applied to bogies on the rail, without resorting to computationally-intensive dynamic simulations. Our approach relied on a combination of advanced analytical techniques, such as Finite Element Analysis (FEA), and experimental data, which allowed us to obtain precise force measurements with minimal computational burden. This breakthrough has significant implications for the field of railway engineering, as it enables more efficient and accurate evaluation of bogie behavior and facilitates the development of improved designs for rail systems.
  1. Dynamic analysis of MD523 Bogie with ADAMS/Rail:
    • Using detailed manufacturing documents as reference, I successfully modeled the MD523 bogie with precision in the advanced software program Adams/Rails. Building upon this model, I conducted an extensive dynamic analysis of the bogie’s performance on four distinct rail classes. Throughout my analysis, I utilized a passenger car as a representative wagon to more accurately predict real-world performance. This rigorous investigation not only allowed for a deeper understanding of the MD523 bogie’s behavior under various conditions but also paved the way for potential improvements and optimizations in future designs.
  1. Stress analysis of rolling ball bearing:
  • In this project, I conducted a comprehensive analysis of the rolling ball bearing, utilizing the powerful finite element analysis software ANSYS Workbench. Through this method, I was able to generate detailed and accurate simulations of the bearing’s behavior, allowing for a more thorough understanding of its mechanical properties and performance. Additionally, I compared my ANSYS Workbench results to those obtained through the well-established Hertz theory methodology.
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