Optimization of heat transfer performance for different industrial cooling systems (Heat exchanger and Heat sinks) through CFD simulation with Machine learning algorithms

Project ID: ARP/2023/MPE/04/04

Funding Source: AUST Internal Research Grant

Project Awarded: 13^th September, 2023

 

Principal Investigator:

Dr. Mohammad Rejaul Haque

Assistant Professor, MPE Department, AUST

 

Co-principal Investigator/s:

Nabil Mohammad Chowdhury

Lecturer, MPE Department, AUST

 

Project Duration: 1 year 6 months

Budget approved: 7,50,000/-

 

Expected Research Outcomes:

• Development of economically viable and optimized design, which will aid in less material cost, less pumping cost and high efficiency in terms of 1st law (thermalhydraulic) and 2nd law of thermodynamics (Entropy minimization, exergy analysis).
• Increase in the overall thermal performance of the heat exchanger tube and heat sink, focusing in reduction of energy consumption and a rise in efficiency in a variety of production and engineering fields.
• An improved understanding of the fluid flow and heat transfer which can be applied to enhance the design of various thermal systems for future research.
• Development of a methodology for incorporating machine learning into the design process of mentioned thermal systems which are rare in existing literature. The machine learning can enable faster design iterations and reduce the need for expensive and timeconsuming physical experiments.