Enhancing Heat Transfer and Thermal Management Efficiency in Micro Heat Sinks through Machine Learning Algorithms

Modern electronics are getting smaller and more powerful, which makes them produce a lot of heat that regular cooling methods, like basic heat sinks, can’t handle well. This research introduces a new way to cool electronics by combining advanced computer simulations, real-world experiments, and smart machine learning, while also considering how to make these solutions practical to produce.

The researchers created unique cooling designs inspired by nature, such as shapes resembling mushrooms, flowers, and fish fins. These designs cool electronics 30-70% better than traditional ones by improving heat transfer and airflow while reducing resistance. This is the first time such complex, nature-inspired shapes have been used in tiny cooling systems, showing they can work well and still be made efficiently.

Machine learning helped speed up the design process by predicting how well these cooling systems would work with over 90% accuracy. This cut down the time needed for calculations by 60-70%, making it easier to test and improve designs quickly. The research also introduced new ways to monitor performance and predict how heat and air or liquid flow behave.

To make production cheaper and greener, the researchers used smart manufacturing techniques, which lowered costs by 43%, saved 29% in energy, and reduced carbon emissions by 19%. These efforts support environmental goals like the UK’s Net Zero and the EU’s Green Deal.

Overall, this research offers a new, practical way to cool powerful electronics used in computers, cars, planes, and renewable energy systems. It sets a high standard for efficient, adaptable, and eco-friendly cooling solutions that can be used worldwide.