Application of GPU processing for Brownian particle simulation

Reports on the anomalous thermal-fluid properties of nanofluids (dilute suspension of nano-particles in a base fluid) have been the subject of attention for 15 years. The underlying physics that govern nanofluid behavior, however, is not fully understood and is a subject of much dispute. The interactions between the suspended particles and the base fluid have been cited as a major contributor to the improvement in heat transfer reported in the literature. Numerical simulations are instrumental in studying the behavior of nanofluids. However, such simulations can be computationally intensive due to the small dimensions and complexity of these problems. In this study, a simplified computational approach for isothermal nanofluid simulations was applied, and simulations were conducted using both conventional CPU and parallel GPU implementations. The GPU implementations significantly improved the computational performance, in terms of the simulation time, by a factor of 1000–2500. The results of this investigation show that, as the computational load increases, the simulation efficiency approaches a constant. At a very high computational load, the amount of improvement may even decrease due to limited system memory.