PAR2 : Parallel Random Walk Particle Tracking Method for solute transport in porous media

Computational modeling of solute migration in groundwater systems is a fundamental component in water resources management and risk analysis. Therefore, it is imperative to have fast and reliable computational tools to simulate solute transport in groundwater systems. In this work we present PAR2, a GPU-accelerated solute transport simulator based on the Random Walk Particle Tracking (RWPT) technique, a Lagrangian method particularly suited for parallelization. PAR2 is able to run on any computing platform equipped with an NVIDIA GPU, such as common desktops and High-Performance Computing (HPC) nodes. The program is developed in C++/CUDA. In our illustration, groundwater flow is simulated on a structured grid using MODFLOW, which can be linked to PAR2 using the LMT package. Simulation parameters can be defined through a convenient YAML configuration file. Additionally, we propose an analytical treatment of the dispersion tensor that allows the RWPT to be effectively implemented using GPU parallelization. The speedup gained with the parallelization drastically reduces the total simulation time, allowing the application of computationally expensive algorithms (e.g., Monte-Carlo simulation) on large-scale stochastic hydro-systems.地下水系统中溶质运移的计算模拟，是水资源管理与风险分析的核心组成部分。因此，研发快速且可靠的计算工具以模拟地下水系统中的溶质运移实属必要。本研究推出PAR2：一款基于随机行走粒子追踪（Random Walk Particle Tracking, RWPT）技术的GPU加速溶质运移模拟器，该技术属于尤其适配并行计算的拉格朗日方法。PAR2可在搭载NVIDIA GPU的任意计算平台上运行，包括普通台式机与高性能计算（High-Performance Computing, HPC）节点。该程序采用C++/CUDA语言开发。在本研究的演示案例中，研究团队采用MODFLOW在结构化网格上模拟地下水流场，可通过LMT包将其与PAR2进行关联。模拟参数可通过便捷的YAML配置文件进行定义。此外，本研究提出了一种针对弥散张量的解析处理方法，使得RWPT可借助GPU并行计算得以高效实现。并行计算带来的加速比极大缩短了总模拟时长，使得计算成本高昂的算法（如蒙特卡洛模拟）可应用于大规模随机水文系统。