Granular suspensions simulations are essential for comprehending the behavior of particle-laden fluids. This research project focuses on enhancing simulations of granular suspensions using GPUs. The composition of these suspensions typically involves a high volume fraction of particles, presenting challenges for accurate and efficient simulations.
Traditionally, simulations employ the Discrete Element Method (DEM) for particles on the CPU, while fluid dynamics simulation is handled on the GPU using the Lattice Boltzmann Method (LBM). However, the communication between the CPU and GPU in this approach hampers simulation speed. Existing DEM frameworks, like LIGGGHTS, are effective for particulate systems but lack efficient GPU deployment.
To overcome this limitation, our approach involves adopting a data-oriented strategy, creating a portable, GPU-optimized code in standard C++ with parallel algorithms from the C++17 standard. This enables us to fully leverage the computational power and parallel processing capabilities of GPUs, enhancing performance and efficiency in granular suspension simulations.
The code is implemented in standard C++ for platform versatility and can be seamlessly deployed on GPUs using compilers such as nvc++ from Nvidia. Our goal is not immediate superiority but laying a robust foundation for future advancements. Representing particles as simple spheres with interactions described by a spring-dashpot model, we simulate fluid using LBM with the Two Relaxation Time (TRT) model, coupling based on the partially saturated bounce-back method.
This exploration aims to gain insights into GPU parallel computing complexities and develop specific approaches for effective granular suspension simulations using this powerful hardware. By harnessing GPU computational power and adhering to standard C++ practices, our aim is to create an innovative framework for more efficient and comprehensive simulations of granular suspensions. Through the development and validation of our open-source C++ framework, we contribute to advancing simulations in this domain, opening new possibilities for understanding particle-fluid dynamics on a larger scale.
