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The current Project deals with the prediction of Two-Phase heat transfer coefficients using Artificial Neural Networks (ANN) for natural refrigerant mixtures which are widely used for low-temperature applications such as Cryocoolers, Bio-Freezers, small scale air, and nitrogen liquefaction plants.
About your Instructor
Mr. Venkatesh Dasari is a PhD Scholar at the Refrigeration and Air conditioning laboratory, Mechanical Engineering Department, IIT Madras.
He is currently working on Low Temperature Mixed Refrigerant Joule Thomson(MRJT) Cryocoolers. Familiar with simulation softwares such as Aspen plus and hands on experience in Rating and Design of Helical coil heat exchangers, Capillary tubes, and selection of different Mixed refrigerants using various optimization techniques.
LinkedIn Profile: https://www.linkedin.com/in/venkateshdasari-iitm/
About the Project
Heat Exchangers are used in almost all industrial processes that involve either direct or indirect heating or cooling applications. The most familiar applications that we use daily are refrigerators, Air-Conditioners, Pasteurization of Milk, etc. Design and Rating of heat exchangers are crucial for all the above-mentioned processes and process outcomes may deviate significantly if we chose incorrect heat exchangers. There are different types of heat exchangers that are used for specific applications.
The challenging part in the design of heat exchangers is the prediction of the heat transfer coefficient. Heat transfer coefficient is the function of both flow and geometrical parameters. Flow parameters include mass flow rate, pressure, temperature, etc. and geometrical parameters include orientation of heat exchangers, type of heat exchanger, geometrical details such as length, diameter, etc. Prediction of Two-phase heat transfer coefficient is one of the challenging tasks for thermal engineers and most conventional methods are applicable for particular data groups with limited accuracy.
The current Project deals with the prediction of Two-Phase heat transfer coefficients using Artificial Neural Networks (ANN) for natural refrigerant mixtures which are widely used for low-temperature applications such as Cryocoolers, Bio-Freezers, small scale air, and nitrogen liquefaction plants.
Outcome of the Project
This project will give you insights into the basic difference between the design and rating of heat exchangers. Basics of two-phase heat transfer flow patterns and calculation of flow boiling heat transfer coefficients. Introduction to Machine learning, ANN, and supervised ANN techniques. At the end of the project, you will gain the following skills.
What are you waiting for ?
