PFLOTRAN

PFLOTRAN User manual: A Massively Parallel Reactive Flow and Transport Model for Describing Surface and Subsurface Processes. PFLOTRAN is an open source, state-of-the-art massively parallel subsurface flow and reactive transport code. PFLOTRAN solves a system of generally nonlinear partial differential equations describing multiphase, multicomponent and multiscale reactive flow and transport in porous materials. The code is designed to run on massively parallel computing architectures as well as workstations and laptops. Parallelization is achieved through domain decomposition using the PETSc (Portable Extensible Toolkit for Scientific Computation) libraries. PFLOTRAN has been developed from the ground up for parallel scalability and has been run on up to 2^18 processor cores with problem sizes up to 2 billion degrees of freedom. PFLOTRAN is written in object oriented, free formatted Fortran 2003. The choice of Fortran over C/C++ was based primarily on the need to enlist and preserve tight collaboration with experienced domain scientists, without which PFLOTRAN’s sophisticated process models would not exist.The reactive transport equations can be solved using either a fully implicit Newton-Raphson algorithm or the less robust operator splitting method.


References in zbMATH (referenced in 22 articles )

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  1. Damiani, Leonardo Hax; Kosakowski, Georg; Glaus, Martin A.; Churakov, Sergey V.: A framework for reactive transport modeling using FEniCS-Reaktoro: governing equations and benchmarking results (2020)
  2. Guérillot, Dominique; Bruyelle, Jérémie: Geochemical equilibrium determination using an artificial neural network in compositional reservoir flow simulation (2020)
  3. Osthus, Dave; Hyman, Jeffrey D.; Karra, Satish; Panda, Nishant; Srinivasan, Gowri: A probabilistic clustering approach for identifying primary subnetworks of discrete fracture networks with quantified uncertainty (2020)
  4. Srinivasan, Shriram; Cawi, Eric; Hyman, Jeffrey; Osthus, Dave; Hagberg, Aric; Viswanathan, Hari; Srinivasan, Gowri: Physics-informed machine learning for backbone identification in discrete fracture networks (2020)
  5. Sweeney, Matthew R.; Gable, Carl W.; Karra, Satish; Stauffer, Philip H.; Pawar, Rajesh J.; Hyman, Jeffrey D.: Upscaled discrete fracture matrix model (UDFM): an octree-refined continuum representation of fractured porous media (2020)
  6. Fourno, André; Ngo, Tri-Dat; Noetinger, Benoit; La Borderie, Christian: FraC: a new conforming mesh method for discrete fracture networks (2019)
  7. Srinivasan, Shriram; Karra, Satish; Hyman, Jeffrey; Viswanathan, Hari; Srinivasan, Gowri: Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways (2019)
  8. Burstedde, Carsten; Fonseca, Jose A.; Kollet, Stefan: Enhancing speed and scalability of the ParFlow simulation code (2018)
  9. Hyman, Jeffrey D.; Hagberg, Aric; Osthus, Dave; Srinivasan, Shriram; Viswanathan, Hari; Srinivasan, Gowri: Identifying backbones in three-dimensional discrete fracture networks: a bipartite graph-based approach (2018)
  10. Jan, Ahmad; Coon, Ethan T.; Painter, Scott L.; Garimella, Rao; Moulton, J. David: An intermediate-scale model for thermal hydrology in low-relief permafrost-affected landscapes (2018)
  11. Reisner, Andrew; Olson, Luke N.; Moulton, J. David: Scaling structured multigrid to 500K+ cores through coarse-grid redistribution (2018)
  12. Srinivasan, Shriram; Hyman, Jeffrey; Karra, Satish; O’Malley, Daniel; Viswanathan, Hari; Srinivasan, Gowri: Robust system size reduction of discrete fracture networks: a multi-fidelity method that preserves transport characteristics (2018)
  13. Valera, Manuel; Guo, Zhengyang; Kelly, Priscilla; Matz, Sean; Cantu, Vito Adrian; Percus, Allon G.; Hyman, Jeffrey D.; Srinivasan, Gowri; Viswanathan, Hari S.: Machine learning for graph-based representations of three-dimensional discrete fracture networks (2018)
  14. Chang, J.; Karra, S.; Nakshatrala, K. B.: Large-scale optimization-based non-negative computational framework for diffusion equations: parallel implementation and performance studies (2017)
  15. Karimi, S.; Nakshatrala, K. B.: A hybrid multi-time-step framework for pore-scale and continuum-scale modeling of solute transport in porous media (2017)
  16. Chen, Jie; McInnes, Lois C.; Zhang, Hong: Analysis and practical use of flexible biCGStab (2016)
  17. Ahusborde, Etienne; Kern, Michel; Vostrikov, Viatcheslav: Numerical simulation of two-phase multicomponent flow with reactive transport in porous media: application to geological sequestration of CO(_2) (2015)
  18. Beisman, James J.; Maxwell, Reed M.; Navarre-Sitchler, Alexis K.; Steefel, Carl I.; Molins, Sergi: ParCrunchFlow: an efficient, parallel reactive transport simulation tool for physically and chemically heterogeneous saturated subsurface environments (2015)
  19. Horgue, P.; Soulaine, C.; Franc, J.; Guibert, R.; Debenest, G.: An open-source toolbox for multiphase flow in porous media (2015)
  20. Makedonska, Nataliia; Painter, Scott L.; Bui, Quan M.; Gable, Carl W.; Karra, Satish: Particle tracking approach for transport in three-dimensional discrete fracture networks. Particle tracking in 3-D DFNs (2015)

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