Project Overview
Project Name: ML-Driven Optimisation of Rocket Fuel Oxidisers
Category: Mechanical Engineering
Sub-category: Energetic Materials, Machine Learning
Professor-in-charge: Neeraj Kumbhakarna
Description: This project aims to use machine learning tools to generate new energetic molecules for rocket fuel oxidisers, with the purpose of enhancing the efficiency of fuel-related processes, particularly in the field of rocket propulsion. The potential of ML in fuels and combustion is still to be fully explored; the project seeks to fill this gap, first through theoretical methods and then through practical implementations.
Work Description
Roles: 2 Students
Project Duration: Not fixed. Registration for R&D project is recommended post summers.
Location: The project can be done remotely.
Key Tasks:
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Research and Exploration: Gain a fundamental understanding of rocket propulsion and delve into the basics of rocket science. Familiarise yourself with the key characteristics of rocket fuels, with a specific focus on oxidizers. Explore existing literature and databases to identify potential candidates for new energetic molecules.
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Data Collection and Analysis: Gather relevant data on known oxidisers, their properties, and performance characteristics. Develop data collection methodologies and ensure the accuracy and reliability of the acquired data. Use machine learning techniques to analyse and interpret the data, identifying patterns and relationships.
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Machine Learning Model Development: Apply Python programming skills to develop machine learning models using tools such as PyTorch. Design and implement models capable of predicting the performance of potential oxidiser molecules. Train the models using the collected data and optimise their performance.
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Experimental Validation: Collaborate with experimental researchers to validate the predictions made by the machine learning models. Participate in laboratory experiments to test and measure the performance of synthesised oxidiser molecules. Compare the experimental results with the model predictions to assess the accuracy and reliability of the ML approach.
Skills Learned
A strong work ethic and project commitment is absolutely necessary. Prior experience with Python programming is highly desirable, and familiarity with AI/ML-specific tools such as PyTorch are appreciated in carrying out the project effectively. The use of PyTorch is mandatory for executing the project's objectives.
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ML tools: Acquire proficiency in Python programming and familiarity with AI/ML-specific tools such as PyTorch
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Applications of ML: Gain practical experience in applying machine learning techniques to real-world problems
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Fuel Science: Fundamental principles of thermal and fluid sciences, including thermodynamics, heat transfer, and fluid mechanics
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Rocket Propulsion: Knowledge of rocket propulsion mechanisms and fuel properties as it pertains to rocket performance
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Research: Experience in critically analyzing scientific literature and databases to identify relevant information
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Data Analysis: Skills in data interpretations, pattern recognition, an drawing insights from complex datasets
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Collaboration and Communication: Work as part of a team, enhance interpersonal skills including effective communication and presenting findings and progress reports
Qualifications Required
Year of Study: Second year and above (at least four semesters completed)
Recommended Courses: ME 209, ME 219 or equivalent courses
Experience: A solid grasp of the fundamental principles of thermal and fluid sciences, including thermodynamics, heat transfer, and fluid mechanics, is essential for the successful completion of this project. Proficiency in Python is highly appreciated. Familiarity with libraries and frameworks such as NumPy, Pandas, and PyTorch will also help.
Enrolling
To enroll in this project, interested students can fill the form below. Please include your name, contact information, and any relevant background or experience. Further instructions regarding the project and enrollment process will be provided.
Submission Link: https://docs.google.com/forms/d/e/1FAIpQLSdSsXCax2xCPkfbyPL1cjU6hhfooe4hElKoar1OLQaxsHtwOg/viewform?usp=sharing
Deadline : 16th May, 2023 (Extended)
Contact Us
For any general queries, join the ProSpace WhatsApp group- https://chat.whatsapp.com/E09qtrcuShp1uf2w82LCsa
Email: neeraj_k@iitb.ac.in
Phone: 25767397
Announcements
ML in Rocket Engineering: Project under Prof. Neeraj K
10-May-2023Attention Mechanical Engineering students! Join Professor Neeraj Kumbhakarna's new project "ML-Driven Optimisation of Rocket Fuel Oxidisers" and explore the potential of machine learning in enhancing fuel efficiency. Registration for this R&D project is recommended post-summer, so don't miss out! Fill out the enrollment form by the deadline to be a part of this exciting opportunity.