Nanofluids at the Point: Exploring Their Behavior Near Thin Needles from Cooling to Medicine
Fiction writers have long imagined a future where tiny robots navigate your bloodstream to deliver medicine precisely where needed.
This isn’t just a sci-fi idea anymore; it has soon become a reality. At the core of the idea lies an understanding of how fluids, especially those containing nanoparticle-sized objects, behave in tight spaces and around tiny objects.
This world is explored further by recent research from the Vellore Institute of Technology and the University of Central Florida, USA. Specifically, the researchers dove deep into the complex dance between a special type of fluid called a power-law nanofluid and a thin, moving needle. Nanofluids are liquids with nanoparticles (1-100nanometers in size) mixed in with the liquid. A power-law nanofluid is a non-newtonian nanofluid whose viscosity changes with applied force, meaning its thickness changes depending on how fast it’s being pushed. Whether the delicate needles used in medical procedures like biopsies or infusion pumps, or the fine wires in a hot wire anemometer used to measure wind speed, understanding fluid flow around such tiny structures is crucial for improving these technologies.
The researchers in this study set out to understand this relationship between a nanofluid and a thin wire by focusing on two classic scenarios of fluid flow, adapted for their specific setup: Blasius flow and Sakiadis flow. Blasius flow involves holding the thin wire stationary while the fluid moves past it. Sakiadis flow, on the other hand, is like pulling that wire through a stationary fluid. They wanted to see how a power-law nanofluid, which doesn’t behave like normal liquids, would act near a thin needle that could either be still (Blasius) or moving (Sakiadis).
To tackle this complex problem, the scientists used a mathematical model that accounts for the base fluid and the nanoparticles mixed within it. A key part of their model was incorporating two effects with tiny particles: Brownian motion and thermophoresis. Brownian motion is the random jiggling of nanoparticles as they get bumped around by the molecules in the fluid or air. Thermophoresis is the movement of nanoparticles caused by temperature differences. Particles tend to drift from hotter areas to colder ones like dust motes scattering away from a hot light bulb. These effects are essential in any modelling as they influence how heat and the nanoparticles spread through the fluid.
They represented the thin needle mathematically, considering its curved shape, which is important when the needle is very slender. They then wrote down the fundamental equations that describe how the fluid’s velocity, temperature, and concentration of nanoparticles change around this needle. These equations are incredibly complicated, so the researchers transformed them into simpler, dimensionless versions, which calculate the simplest interactions.
Solving these scaled-down equations still requires powerful tools. The team used a numerical method called Bvp4c, a computer algorithm designed to solve boundary value problems – essentially, problems where you know the conditions at the edges (like the needle surface and far away from it) and must figure out what happens in between. By plugging in different values for things like the fluid’s power-law index or how non-Newtonian it is, the needle’s thickness, and the strength of the Brownian motion and thermophoresis effects, they could simulate how the fluid would behave in various situations.
They found that the role played by the needle’s thickness differs depending on the flow type. For Blasius flow (static needle), a thicker needle slows the fluid down more near the surface due to increased drag. But for Sakiadis flow (moving needle), a thicker needle actually seems to speed up the fluid near the surface. However, for both flow types, a thinner needle was better at transferring heat and nanoparticles away from the surface.
The power-law nature of the fluid also had opposite effects on velocity for the two flow types, but for both, increasing this index reduced the temperature and nanoparticle concentration away from the needle—the jiggling (Brownian motion) and heat-pushing (thermophoresis) of nanoparticles significantly impacted temperature and concentration. Higher Brownian motion increased temperature but pushed particles away from the hot needle surface, reducing concentration. Higher thermophoresis also increased temperature and pulled particles towards colder areas, making the concentration boundary layer thicker. Notably, these nanoparticle effects play a more dominant role in the Sakiadis flow (moving needle) compared to the Blasius flow (static needle). Comparing the two classic flows directly, the Sakiadis flow generally created thicker layers of affected fluid (boundary layers) around the needle and showed a more intense temperature difference near the surface than the Blasius flow.
Beyond just simulating the flow, the researchers used a statistical technique called regression analysis to build a formula based on their simulation results to predict key engineering quantities. Their formula could be used to calculate the drag force on the needle (skin friction), how well heat is transferred (Nusselt number), and how well particles are transferred (Sherwood number), based on the input parameters.
They found that factors like Brownian motion and thermophoresis hindered heat transfer, while the Lewis number, which compares how fast heat spreads versus how fast particles spread, affected nanoparticle transfer. Their regression models were remarkably accurate, allowing engineers to quickly estimate these crucial values without running complex simulations from scratch.
This work improves on previous studies by being the first to combine the analysis of power-law nanofluids, thin needles, and the direct comparison between Blasius and Sakiadis flow types. Their model also incorporates the effects of Brownian motion and thermophoresis. Using the observations and regression analysis, their model was far better at predicting the behaviour of power-law nanofluids around tiny structures and providing practical engineering insights. The paper points to future research possibilities, suggesting that their current model might be limited by focusing on a specific type of nanoparticle and not accounting for factors like different shapes, sizes, or how nanoparticles might clump together (aggregate). These are areas where the research could be expanded.
Ultimately, this research provides fundamental insights into the complex behaviour of special fluids containing nanoparticles near tiny moving objects. Scientists and engineers can design and improve a wide range of technologies by understanding the subtle interplay between fluid properties, particle dynamics, and surface motion. From developing more effective targeted drug delivery systems and cancer treatments to optimising micro-cooling for electronics and space applications, the understanding gained from this study is invaluable.
