In the heart of Rajasthan, India, Shilpa, a researcher from the Department of Mathematics & Statistics at Manipal University Jaipur, is delving into the intricate world of nanofluids and magnetic fields to revolutionize industrial cooling systems and energy efficiency. Her latest study, published in the International Journal of Thermofluids, explores the behavior of heat and mass transfer in a complex fluid flow scenario, with implications that could reshape how we approach thermal management in various industries.
Imagine a vertical cylinder, stretching and flowing with a non-Newtonian fluid—a fluid that doesn’t follow the typical linear stress-strain relationship. Now, introduce a magnetic field, heat radiation, and a chemical reaction into this scenario. This is the complex system that Shilpa and her team have been studying, using a combination of numerical computations and artificial neural networks to unravel the mysteries of heat and mass transfer.
The key to their investigation lies in the use of ternary hybrid nanofluids—fluids engineered with three different types of nanoparticles: copper, aluminum oxide, and titanium dioxide, suspended in water. “The superior thermo-physical properties of these nanoparticles make them ideal for enhancing heat transfer,” Shilpa explains. By manipulating various parameters such as radiation, Prandtl number, and heat source, the researchers have observed significant changes in temperature, velocity, and concentration of the nanoparticles.
One of the most striking findings is the impact of radiation on the temperature of the modified nanofluids. As the radiation parameter increases, the temperature decreases, a trend that could be crucial for developing more efficient cooling systems. Similarly, the concentration of nanoparticles like aluminum oxide can significantly affect the thermal efficiency of the system.
The implications of this research are vast, particularly for the energy sector. Efficient thermal management is crucial for everything from nuclear waste storage to oil reservoir modeling and industrial cooling systems. By understanding how to manipulate these complex fluid flows, industries can potentially enhance their processes, reduce energy consumption, and minimize environmental impact.
Shilpa’s work doesn’t stop at numerical computations. She has also constructed an artificial neural network model to predict kinetic energy values with greater accuracy. The comparison between numerical values and ANN predicted values shows a remarkable agreement, highlighting the potential of AI in enhancing our understanding of fluid dynamics.
As we look to the future, this research opens up new avenues for exploration. The use of ternary hybrid nanofluids and advanced computational techniques could lead to breakthroughs in various fields, from medicine to engineering. “The potential applications are vast,” Shilpa notes, “and we are just scratching the surface of what’s possible.”
The study, published in the International Journal of Thermofluids, which translates to the International Journal of Heat and Fluid Flow, is a testament to the power of interdisciplinary research. By combining mathematics, physics, and engineering, Shilpa and her team are paving the way for a more efficient and sustainable future. As industries continue to seek ways to optimize their processes, the insights gained from this research could be the key to unlocking new levels of performance and innovation.
